Towards a Business Process Management Maturity Model

Business Process Management (BPM) has been identified as the number one business priority by a recent Gartner study (Gartner, 2005). However, BPM has a plethora of facets as its origins are in Business Process Reengineering, Process Innovation, Process Modelling, and Workflow Management to name a few. Organisations increasingly recognize the requirement for an increased process orientation and require appropriate comprehensive frameworks, which help to scope and evaluate their BPM initiative. This research project aims toward the development of a holistic and widely accepted BPM maturity model, which facilitates the assessment of BPM capabilities. This paper provides an overview about the current model with a focus on the actual model development utilizing a series of Delphi studies. The development process includes separate studies that focus on further defining and expanding the six core factors within the model, i.e. strategic alignment, governance, method, Information Technology, people and culture.


Guidelines for conducting and reporting case study research in software engineering

Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors’ own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.


Capability maturity model, version 1.1

The capability maturity model (CMM), developed to present sets of recommended practices in a number of key process areas that have been shown to enhance software-development and maintenance capability, is discussed. The CMM was designed to help developers select process-improvement strategies by determining their current process maturity and identifying the issues most critical to improving their software quality and process. The initial release of the CMM, version 1.0, was reviewed and used by the software community during 1991 and 1992. A workshop on CMM 1.0, held in April 1992, was attended by about 200 software professionals. The current version of the CMM is the result of the feedback from that workshop and ongoing feedback from the software community. The technical report that describes version 1.1. is summarised.<<ETX>>


Innovative Contactless Energy Transfer Accessory for Rotary Ultrasonic Machining and Its Circuit Compensation Based on Coil Turns

An innovative concentric rotary ultrasonic machining (RUM) device based on local induction is proposed. The device is used as a machine tool accessory that can add the RUM to any machining center. Rather than using a complete ring, a portion of a ring is used as the primary core in the proposed rotary transformer, which eliminates spindle speed limits. The simplified configuration provides increased safety and is convenient for material processing applications. Additionally, a circuit compensation method based on coil turns is proposed. It takes the whole circuit, reliability of electronic components, transmission efficiency, and output power into consideration. We use this method to choose the optimal number of coil turns and compensation elements required to achieve increased and safe power transmission performance. This paper also demonstrates the performance of the device through experimentation. Finally, practicability is discussed.


What Actually Wins Soccer Matches : Prediction of the 2011-2012 Premier League for Fun and Profit

Sports analytics is a fascinating problem area in which to apply statistical learning techniques. This thesis brings new data to bear on the problem of predicting the outcome of a soccer match. We use frequency counts of in-game events, sourced from the Manchester City Analytics program, to predict the 380 matches of the 2011-2012 Premier League season. We generate prediction models with multinomial regression and rigorously test them with betting simulations. An extensive review of prior efforts is presented, as well as a novel theoretically optimal betting strategy. We measure performance different feature sets and betting strategies. Accuracy and simulated profit far exceeding those of all earlier efforts are achieved.


The Acquisition of Reading Comprehension Skill

How do people acquire skill at comprehending what they read? That is the simple question to which we shall try to make a tentative answer. To begin, we have to acknowledge some complexities about the concept of reading comprehension and what it means to develop it.


Column-store support for RDF data management: not all swans are white

This paper reports on the results of an independent evaluation of the techniques presented in the VLDB 2007 paper “Scalable Semantic Web Data Management Using Vertical Partitioning”, authored by D. Abadi, A. Marcus, S. R. Madden, and K. Hollenbach [1]. We revisit the proposed benchmark and examine both the data and query space coverage. The benchmark is extended to cover a larger portion of the query space in a canonical way. Repeatability of the experiments is assessed using the code base obtained from the authors. Inspired by the proposed vertically-partitioned storage solution for RDF data and the performance figures using a column-store, we conduct a complementary analysis of state-of-the-art RDF storage solutions. To this end, we employ MonetDB/SQL, a fully-functional open source column-store, and a well-known – for its performance – commercial row-store DBMS. We implement two relational RDF storage solutions – triple-store and vertically-partitioned – in both systems. This allows us to expand the scope of [1] with the performance characterization along both dimensions – triple-store vs. vertically-partitioned and row-store vs. column-store – individually, before analyzing their combined effects. A detailed report of the experimental test-bed, as well as an in-depth analysis of the parameters involved, clarify the scope of the solution originally presented and position the results in a broader context by covering more systems.


Machine Learning for Intelligent Systems

Recent research in machine learning has focused on supervised induction for simple classification and reinforcement learning for simple reactive behaviors. In the process, the field has become disconnected from AI’s original goal of creating complete intelligent agents. In this paper, I review recent work on machine learning for planning, language, vision, and other topics that runs counter to this trend and thus holds interest for the broader AI research community. I also suggest some steps to encourage further research along these lines.


Learning narrative structure from annotated folktales

Narrative structure is an ubiquitous and intriguing phenomenon. By virtue of structure we recognize the presence of Villainy or Revenge in a story, even if that word is not actually present in the text. Narrative structure is an anvil for forging new artificial intelligence and machine learning techniques, and is a window into abstraction and conceptual learning as well as into culture and its influence on cognition. I advance our understanding of narrative structure by describing Analogical Story Merging (ASM), a new machine learning algorithm that can extract culturally-relevant plot patterns from sets of folktales. I demonstrate that ASM can learn a substantive portion of Vladimir Propp’s influential theory of the structure of folktale plots. The challenge was to take descriptions at one semantic level, namely, an event timeline as described in folktales, and abstract to the next higher level: structures such as Villainy, StuggleVictory, and Reward. ASM is based on Bayesian Model Merging, a technique for learning regular grammars. I demonstrate that, despite ASM’s large search space, a carefully-tuned prior allows the algorithm to converge, and furthermore it reproduces Propp’s categories with a chance-adjusted Rand index of 0.511 to 0.714. Three important categories are identified with F-measures above 0.8. The data are 15 Russian folktales, comprising 18,862 words, a subset of Propp’s original tales. This subset was annotated for 18 aspects of meaning by 12 annotators using the Story Workbench, a general text-annotation tool I developed for this work. Each aspect was doubly-annotated and adjudicated at inter-annotator F-measures that cluster around 0.7 to 0.8. It is the largest, most deeply-annotated narrative corpus assembled to date. The work has significance far beyond folktales. First, it points the way toward important applications in many domains, including information retrieval, persuasion and negotiation, natural language understanding and generation, and computational creativity. Second, abstraction from natural language semantics is a skill that underlies many cognitive tasks, and so this work provides insight into those processes. Finally, the work opens the door to a computational understanding of cultural influences on cognition and understanding cultural differences as captured in stories. Dissertation Supervisor: Patrick H. Winston Professor, Electrical Engineering and Computer Science Dissertation Committee: Whitman A. Richards Professor, Brain & Cognitive Sciences Peter Szolovits Professor, Electrical Engineering and Computer Science & Harvard-MIT Division of Health Sciences and Technology Joshua B. Tenenbaum Professor, Brain & Cognitive Sciences


Neural computation and self-organizing maps - an introduction

In this age of modern era, the use of internet must be maximized. Yeah, internet will help us very much not only for important thing but also for daily activities. Many people now, from any level can use internet. The sources of internet connection can also be enjoyed in many places. As one of the benefits is to get the on-line neural computation and self organizing maps an introduction computation neural systems series book, as the world window, as many people suggest.


Comparing NoSQL MongoDB to an SQL DB

NoSQL database solutions are becoming more and more prevalent in a world currently dominated by SQL relational databases. NoSQL databases were designed to provide database solutions for large volumes of data that is not structured. However, the advantages (or disadvantages) of using a NoSQL database for data that is structured, and not necessarily "Big," is not clear. There are not many studies that compare the performance of processing a modest amount of structured data in a NoSQL database with a traditional relational database. In this paper, we compare one of the NoSQL solutions, MongoDB, to the standard SQL relational database, SQL Server. We compare the performance, in terms of runtime, of these two databases for a modest-sized structured database. Results show that MongoDB performs equally as well or better than the relational database, except when aggregate functions are utilized.


Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention

In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a firststage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence’s first-stage representation to attend words appeared in itself, which is called ”Inner-Attention” in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of ”Inner-Attention” mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.


VeriCon: towards verifying controller programs in software-defined networks

Software-defined networking (SDN) is a new paradigm for operating and managing computer networks. SDN enables logically-centralized control over network devices through a "controller" software that operates independently from the network hardware, and can be viewed as the network operating system. Network operators can run both inhouse and third-party SDN programs (often called applications) on top of the controller, e.g., to specify routing and access control policies. SDN opens up the possibility of applying formal methods to prove the correctness of computer networks. Indeed, recently much effort has been invested in applying finite state model checking to check that SDN programs behave correctly. However, in general, scaling these methods to large networks is challenging and, moreover, they cannot guarantee the absence of errors.
 We present VeriCon, the first system for verifying that an SDN program is correct on all admissible topologies and for all possible (infinite) sequences of network events. VeriCon either confirms the correctness of the controller program on all admissible network topologies or outputs a concrete counterexample. VeriCon uses first-order logic to specify admissible network topologies and desired network-wide invariants, and then implements classical Floyd-Hoare-Dijkstra deductive verification using Z3. Our preliminary experience indicates that VeriCon is able to rapidly verify correctness, or identify bugs, for a large repertoire of simple core SDN programs. VeriCon is compositional, in the sense that it verifies the correctness of execution of any single network event w.r.t. the specified invariant, and can thus scale to handle large programs. To relieve the burden of specifying inductive invariants from the programmer, VeriCon includes a separate procedure for inferring invariants, which is shown to be effective on simple controller programs. We view VeriCon as a first step en route to practical mechanisms for verifying network-wide invariants of SDN programs.


Diversifying trending topic discovery via Semidefinite Programming

Discovering trending topics from the Web has attracted much attention in recent years, due to users' increasing need for time-sensitive information. A large body of the existing research focuses on detecting trends by examining search traffic fluctuations, and the queries with large traffic increments would be detected as trends. The weakness of this approach is that the trends are dominated by popular fields such as celebrities, as those related queries have large search traffic. Consequently, other topics such as travel and shopping are rarely regarded as trends. In this paper, we present a scalable diversified trending topic discovery system with a MapReduce implementation. The trending topics are discovered based on three criteria: diversity, representativeness, and popularity. We explicitly model these three factors in our objective function and propose an efficient Semidefinite Programming algorithm to solve the corresponding optimization problem. To the best of our knowledge, no prior work in the literature tackles trending topic diversification. We conduct a comprehensive set of experiments with case studies to demonstrate the effectiveness of our approach. The proposed system has been successfully tested in the real-world operational environment, yielding significant improvement in traffic over the existing production system.


Deep Learning for Detection of Object-Based Forgery in Advanced Video

Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.


Sample size estimation in diagnostic test studies of biomedical informatics

OBJECTIVES
This review provided a conceptual framework of sample size calculations in the studies of diagnostic test accuracy in various conditions and test outcomes.

METHODS
The formulae of sample size calculations for estimation of adequate sensitivity/specificity, likelihood ratio and AUC as an overall index of accuracy and also for testing in single modality and comparing two diagnostic tasks have been presented for desired confidence interval.

RESULTS
The required sample sizes were calculated and tabulated with different levels of accuracies and marginal errors with 95% confidence level for estimating and for various effect sizes with 80% power for purpose of testing as well. The results show how sample size is varied with accuracy index and effect size of interest.

CONCLUSION
This would help the clinicians when designing diagnostic test studies that an adequate sample size is chosen based on statistical principles in order to guarantee the reliability of study.


An ant colony algorithm for the multi-compartment vehicle routing problem

We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem associated with collection of recycling waste from households, treated as nodes in a spatial network. For networks where the nodes are concentrated in separate clusters, the use of k-means clustering can greatly improve the efficiency of the solution. The ACS algorithm is extended to model the use of multi-compartment vehicles with kerbside sorting of waste into separate compartments for glass, paper, etc. The algorithm produces high-quality solutions for two-compartment test problems.


Stopping GAN Violence: Generative Unadversarial Networks

While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs). Under this framework, we simultaneously train two models: a generator G that does its best to capture whichever data distribution it feels it can manage, and a motivator M that helps G to achieve its dream. Fighting is strictly verboten and both models evolve by learning to respect their differences. The framework is both theoretically and electrically grounded in game theory, and can be viewed as a winner-shares-all two-player game in which both players work as a team to achieve the best score. Experiments show that by working in harmony, the proposed model is able to claim both the moral and log-likelihood high ground. Our work builds on a rich history of carefully argued position-papers, published as anonymous YouTube comments, which prove that the optimal solution to NoN violence is more GUNs. Takes skill to be real, time to heal each other Tupac Shakur, Changes, 1998


Discontinuous-Current Mode Operation of a Two-Phase Interleaved Boost DC–DC Converter With Coupled Inductor

A two-phase interleaved boost dc–dc converter with an inversely coupled inductor in a discontinuous-current mode (DCM) is analyzed by the equivalent inductance method. Coupling effects on the circuit statuses are described, and a forced conduction of the power diode or reverse-paralleled diode of MOSFET is caused by coupling. As a result, three major circuit statuses are figured out according to the physical relationship between the input–output voltage ratio and the coupling coefficient, and their condition boundaries are used to classify the DCM operation modes. Then, considering the load (duty cycle) variation, ten DCM operation modes are comprehensively analyzed. The analysis can be used to make an easy prediction of operation modes, and extended to the analysis of an interleaved buck converter or buck/boost bidirectional converter with a coupled inductor in DCM. At last, a 300-W prototype is built and tested in the lab to verify the analysis.


Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.


The Democratization of Personal Consumer Loans? Determinants of Success in Online Peer-to-peer Loan Auctions

Online peer-to-peer (P2P) loan auctions enable individual consumers to borrow from, and lend money to, one another directly. We study the borrowerand auction-related determinants of funding success in these auctions by conceptualizing auction decision variables (loan amount, starting interest rate, duration) as mediators between borrower characteristics such as their demographic attributes, financial strength, and effort prior to the auction, and the likelihood of funding success. Borrower characteristics are also treated as moderators of the effects of auction decision variables on funding success. The results of our empirical study, conducted using a database of 5,370 completed P2P loan auctions, provide support for the proposed conceptual framework, and reveal a strong democratization of lending practices in P2P loan auctions. Although demographic attributes such as race and gender affect likelihood of the auction’s funding success, their effects are small when compared to those of borrowers’ financial strength and effort when listing and publicizing the auction. These results are substantially different from the documented discriminatory practices of regulated financial institutions in the US, suggesting that individual lenders bid more rationally when their own investment money is at stake. The paper concludes with specific suggestions to borrowers to increase their chances of receiving funding in P2P loan auctions, and a discussion of future research opportunities.


A review and analysis of churn prediction methods for customer retention in telecom industries

Customer churn prediction has gathered greater interest in business especially in telecommunications industries. Many authors have presented different versions of the churn prediction models greatly based on the data mining concepts employing the machine learning and meta-heuristic algorithms. This aim of this paper is to study some of the most important churn prediction techniques developed over the recent years. The primary objective is on the churn in telecom industries to accurately estimate the customer survival and customer hazard functions to gain the complete knowledge of churn over the customer tenure. Another objective is the identification of the customers who are at the blink of churn and approximating the time they will churn. This paper focuses on analyzing the churn prediction techniques to identify the churn behavior and validate the reasons for customer churn. This paper summarizes the churn prediction techniques in order to have a deeper understanding of the customer churn and it shows that most accurate churn prediction is given by the hybrid models rather than single algorithms so that telecom industries become aware of the needs of high risk customers and enhance their services to overturn the churn decision.


Anomaly Detection Algorithms in Business Process Logs

In some domains of application, like software development and health care processes, a normative business process system (e.g. workflow management system) is not appropriate because a flexible support is needed to the participants. On the other hand, while it is important to support flexibility of execution in these domains, security requirements can not be met whether these systems do not offer extra control, which characterizes a trade off between flexibility and security in such domains. This work presents and assesses a set of anomaly detection algorithms in logs of Process Aware Systems (PAS). The detection of an anomalous instance is based on the “noise” which an instance makes in a process model discovered by a process mining algorithm. As a result, a trace that is an anomaly for a discovered model will require more structural changes for this model fit it than a trace that is not an anomaly. Hence, when aggregated to PAS, these methods can support the coexistence of security and flexibility.


Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing

Spoken dialogue systems typically use predefined semantic slots to parse users' natural language inputs into unified semantic representations. To define the slots, domain experts and professional annotators are often involved, and the cost can be expensive. In this paper, we ask the following question: given a collection of unlabeled raw audios, can we use the frame semantics theory to automatically induce and fill the semantic slots in an unsupervised fashion? To do this, we propose the use of a state-of-the-art frame-semantic parser, and a spectral clustering based slot ranking model that adapts the generic output of the parser to the target semantic space. Empirical experiments on a real-world spoken dialogue dataset show that the automatically induced semantic slots are in line with the reference slots created by domain experts: we observe a mean averaged precision of 69.36% using ASR-transcribed data. Our slot filling evaluations also indicate the promising future of this proposed approach.


Existential consistency: measuring and understanding consistency at Facebook

Replicated storage for large Web services faces a trade-off between stronger forms of consistency and higher performance properties. Stronger consistency prevents anomalies, i.e., unexpected behavior visible to users, and reduces programming complexity. There is much recent work on improving the performance properties of systems with stronger consistency, yet the flip-side of this trade-off remains elusively hard to quantify. To the best of our knowledge, no prior work does so for a large, production Web service.
 We use measurement and analysis of requests to Facebook's TAO system to quantify how often anomalies happen in practice, i.e., when results returned by eventually consistent TAO differ from what is allowed by stronger consistency models. For instance, our analysis shows that 0.0004% of reads to vertices would return different results in a linearizable system. This in turn gives insight into the benefits of stronger consistency; 0.0004% of reads are potential anomalies that a linearizable system would prevent. We directly study local consistency models---i.e., those we can analyze using requests to a sample of objects---and use the relationships between models to infer bounds on the others.
 We also describe a practical consistency monitoring system that tracks &phis;-consistency, a new consistency metric ideally suited for health monitoring. In addition, we give insight into the increased programming complexity of weaker consistency by discussing bugs our monitoring uncovered, and anti-patterns we teach developers to avoid.


Syntactic Clustering of the Web

We have developed an efficient way to determine the syntactic similarity of files and have applied it to every document on the World Wide Web. Using this mechanism, we built a clustering of all the documents that are syntactically similar. Possible applications include a “Lost and Found” service, filtering the results of Web searches, updating widely distributed web-pages, and identifying violations of intellectual property rights.


Introduction to the Special Issue on the Web as Corpus

The Web, teeming as it is with language data, of all manner of varieties and languages, in vast quantity and freely available, is a fabulous linguists' playground. This special issue of Computational Linguistics explores ways in which this dream is being explored.


Finding Similar Files in a Large File System

We present a tool, called sif, for finding all similar files in a large file system. Files are considered similar if they have significant number of common pieces, even if they are very different otherwise. For example, one file may be contained, possibly with some changes, in another file, or a file may be a reorganization of another file. The running time for finding all groups of similar files, even for as little as 25% similarity, is on the order of 500MB to 1GB an hour. The amount of similarity and several other customized parameters can be determined by the user at a post-processing stage, which is very fast. Sif can also be used to very quickly identify all similar files to a query file using a preprocessed index. Application of sif can be found in file management, information collecting (to remove duplicates), program reuse, file synchronization, data compression, and maybe even plagiarism detection.


Scaling to Very Very Large Corpora for Natural Language Disambiguation

The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. In this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. We are fortunate that for this particular application, correctly labeled training data is free. Since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.


Application of Fuzzy Graph Coloring in Traffic Light Problem

Let G= (V,E,σ,μ) be a simple connected undirected fuzzy graph. In this paper, we use a fuzzy graph model to represent a traffic network of a city and discuss a method to find the different type of accidental zones in a traffic flows. This paper is based on fuzzy coloring of fuzzy graphs and fuzziness of vertices. Using this approach, we can minimize the total waiting time of a traffic flow. It will help to reduce the traffic jam of a road.


Embodiment enables the spinal engine in quadruped robot locomotion

The biological hypothesis of spinal engine states that locomotion is mainly achieved by the spine, while the legs may serve as assistance. Inspired by this hypothesis, a compliant, multiple degree-of-freedom, biologically-inspired spine has been embedded into a quadruped robot, named Kitty, which has no actuation on the legs. In this paper, we demonstrate how versatile behaviors (bounding, trotting, and turning) can be generated exclusively by the spine's movements through dynamical interaction between the controller, the body, and the environment, known as embodiment. Moreover, we introduce information theoretic approach to quantitatively study the spine internal dynamics and its effect on the bounding gait based on three spinal morphologies. These three morphologies differ in the position of virtual spinal joint where the spine is easier to get bent. The experimental results reveal that locomotion can be enhanced by using the spine featuring a rear virtual spinal joint, which offers more freedom for the rear legs to move forward. In addition, the information theoretic analysis shows that, according to the morphological differences of the spine, the information structure changes. The relationship between the observed behavior of the robot and the corresponding information structure is discussed in detail.


PURE-LET Image Deconvolution

We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson or mixed Poisson-Gaussian noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parameterize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds. This parameterization is then optimized by minimizing a robust estimate of the true mean squared error, the Poisson unbiased risk estimate. Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients. In contrast to existing approaches, the proposed algorithm merely amounts to solving a linear system of equations, which has a fast and exact solution. Simulation experiments over different types of convolution kernels and various noise levels indicate that the proposed method outperforms the state-of-the-art techniques, in terms of both restoration quality and computational complexity. Finally, we present some results on real confocal fluorescence microscopy images and demonstrate the potential applicability of the proposed method for improving the quality of these images.


Motion Mode Recognition for Indoor Pedestrian Navigation Using Portable Devices

While indoor portable navigation using portable devices is becoming increasingly required, it faces many challenges in obtaining accurate positioning performance. One of the methods to improve navigation results is to recognize the mode of motion of the user carrying the portable device containing low-cost microelectromechanical systems (MEMS) motion sensors. Pattern recognition methodology has been employed to detect a group of motion modes that are common indoors, namely, walking, stationary, going up/down stairs, standing on an escalator, walking on an escalator, standing on a moving walkway, and walking on a moving walkway. The performance of the motion mode recognition module was examined on different types of mobile computing devices, including various brands of smartphones, tablets, smartwatches, and smartglasses, and the results obtained showed the capability of enhancing positioning performance. The module does not require satellite or wireless positioning signals, and depends only on MEMS sensors.


Stippling by Example

In this work, we focus on stippling as an artistic style and discuss our technique for capturing and reproducing stipple features unique to an individual artist. We employ a texture synthesis algorithm based on the gray-level co-occurrence matrix (GLCM) of a texture field. This algorithm uses a texture similarity metric to generate stipple textures that are perceptually similar to input samples, allowing us to better capture and reproduce stipple distributions. First, we extract example stipple textures representing various tones in order to create an approximate tone map used by the artist. Second, we extract the stipple marks and distributions from the extracted example textures, generating both a lookup table of stipple marks and a texture representing the stipple distribution. Third, we use the distribution of stipples to synthesize similar distributions with slight variations using a numerical measure of the error between the synthesized texture and the example texture as the basis for replication. Finally, we apply the synthesized stipple distribution to a 2D grayscale image and place stipple marks onto the distribution, thereby creating a stippled image that is statistically similar to images created by the example artist.


Ballistocardiography and Seismocardiography: A Review of Recent Advances

In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.


Stochastic Video Prediction with Conditional Density Estimation

Frame-to-frame stochasticity is a major challenge in video prediction. The use of standard feedforward and recurrent networks for video prediction leads to averaging of future states, which can in part be attributed to the networks’ limited ability to model stochasticity. We propose the use of conditional variational autoencoders (CVAE) for video prediction. To model multi-modal densities in frame-to-frame transitions, we extend the CVAE framework by modeling the latent variable with a mixture of Gaussians in the generative network. We tested our proposed Gaussian mixture CVAE (GM-CVAE) on a simple video-prediction task involving a stochastically moving object. Our architecture demonstrates improved performance, achieving noticeably lower rates of blurring/averaging compared to a feedforward network and a Gaussian CVAE. We also describe how the CVAE framework can be applied to improve existing deterministic video prediction models.1


Body mass index reference curves for the UK, 1990.

Reference curves for stature and weight in British children have been available for the past 30 years, and have recently been updated. However weight by itself is a poor indicator of fatness or obesity, and there has never been a corresponding set of reference curves to assess weight for height. Body mass index (BMI) or weight/height has been popular for assessing obesity in adults for many years, but its use in children has developed only recently. Here centile curves for BMI in British children are presented, from birth to 23 years, based on the same large representative sample as used to update the stature and weight references. The charts were derived using Cole's LMS method, which adjusts the BMI distribution for skewness and allows BMI in individual subjects to be expressed as an exact centile or SD score. Use of the charts in clinical practice is aided by the provision of nine centiles, where the two extremes identify the fattest and thinnest four per 1000 of the population.


Microcomputer Playfulness: Developing a Measure With Workplace Implications


Degradation of mechanical properties of class-H winding insulation

Thermal ageing is one of the key factors that result in insulation degradation leading to lower dielectric strength and thereafter occurrence of partial discharge in high voltage machines. However, in low voltage high current machines, the occurrence of partial discharge is minimal and degradation of dielectric strength alone will not cause the breakdown of the insulation. This paper hypothesizes that the breakdown of insulation in low voltage/high current machines occur as a result of insulation cracking among other factors. In this paper, it is shown that thermal degradation leads to degradation of mechanical properties of Polyamide-imide insulation, particularly reduction of the ultimate tensile strength and leads to enhanced brittleness. It follows that once a crack in the insulation is formed, the breakdown strength at the point of cracking will be lower. Multiple such cracks may lead to the formation of a breakdown path thereafter leading to aggravated heating and further accelerated deterioration of the insulation. In this paper, the methodology for testing the mechanical properties of insulation material is presented. A model to account for the variation of such properties due to thermal degradation is developed.


Automated Training of Deep Convolutional Neural Networks for Cell Segmentation

Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.


Parsing Algebraic Word Problems into Equations

This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as Alges. We compare Alges with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, Alges overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15% to 50% reduction in error.


Accurate Language Identification of Twitter Messages

We present an evaluation of “off-theshelf” language identification systems as applied to microblog messages from Twitter. A key challenge is the lack of an adequate corpus of messages annotated for language that reflects the linguistic diversity present on Twitter. We overcome this through a “mostly-automated” approach to gathering language-labeled Twitter messages for evaluating language identification. We present the method to construct this dataset, as well as empirical results over existing datasets and off-theshelf language identifiers. We also test techniques that have been proposed in the literature to boost language identification performance over Twitter messages. We find that simple voting over three specific systems consistently outperforms any specific system, and achieves state-of-the-art accuracy on the task.


Freiburger Fragebogen zur körperlichen Aktivität-Entwicklung, Prüfung und Anwendung

Ziel vorliegender Untersuchung war die Entwicklung und Validierung eines Fragebogens zur Erfassung gesundheitswirksamer körperlicher Aktivität, sowie die Andwendung des Instruments in einer Stichprobe der erwachsenen Freiburger Wohnbevölkerung. Die Reliabilität des Fragebogens wurde anhand von Test-Retest-Untersuchungen, die im Abstand von 2 Wochen und 6 Monaten durchgeführt wurden, überprüft. Die hohen Korrelationen zwischen den Untersuchungen weisen auf eine hohe Reliabilität unseres Instruments hin. Trends im Sinne von saisonal bedingten Schwankungen fanden wir nur beim Rad fahren und der Gartenarbeit, sowie bei der abgeleiteten Basis-und Gesamtaktivität. Die Validierung erfolgte indirekt über die maximale Sauerstoffaufnahme. Es bestand eine enge Korrelation zwischen der maximalen Sauerstoffaufnahme und dem Umfang der Sportaktivitäten (partieller Korrelationskoeffizient: r=0.422, p<0.01). Die mit dem Fragebogen erhobenen Daten waren in sich konsistent. Personen, die sich selbst als “aktiver als ihre Altersgenossen” eingeschätzt hatten, waren, bezogen auf die Sport-(r=0.334, p<0.01) und Gesamtaktivität (r=0.282, p<0.05), tatsächlich aktiver als ihre Altersgenossen. Die Befragung einer (systematischen) Stichprobe der Freiburger Wohnbevölkerung (n-612, 20–98 Jahre) ergab mittlere Gesamtaktivitätsumfänge von 9,2 h/Woche. Aktivitäten mit leichten bis moderaten Belastungsintensitäten machten den grössten Teil der Gesamtaktivität aus. Die Aktivitätsmuster waren abhängig von Alter und Geschlecht. In Anlehnung an die Empfehlungen zum Bewegungssoll waren, nach Paffenbarger (2000 kcal/Woche Gesamtaktivität), 40% der Befragten zu wenig aktiv, nach den Empfehlungen des American College of Sports Medicine (1000 kcal/Woche durch Training) waren dies 63% der Befragten. Aim of the present study was to design a questionnaire to assess health related physical activity, to validate the instrument and to apply it to a population sample. Reliability of the questionnaire was evaluated by testretest investigations with intervals of two weeks and six months. High correlations between the repeated administrations reflect a good reliability of our instrument. Only gardening and cycling, as well as the depending basic and total activity, showed typically seasonal variations. Validity was established by correlating physical activity data with maximum oxygen uptake. Maximum oxygen uptake correlated with sport activities (partial correlation coefficient: r=0.422, p<0.01). Evaluated data were consistent. People rating themselves as “more active than their coevals” were indeed more active in sport (r=0.334, p<0.01) and total activity (r=0.282, p<0.05). Studying activity patterns of a population sample of adult residents of Freiburg (systematic random sampling, n=612, 20–98 years) we found total physical activity of 9,2 hours per week (median), with activities of low to moderate intensities dominating. Age and gender are important determinants of the activity patterns. According to the recommendation of Paffenbarger (2000 kcal/week total physical activity) 40% of the residents of Freiburg did not reach the recommended energy expenditure. Compared to the recommendation of the American College of Sports Medicine (1000 kcal/week by training) 63% of the population sample were not active enough. L'objectif de la présente enquête était le développement et la validation d'un questionnaire visant a estimer les effets d'activités physiques sur la santé ainsi que l'utilisation de cet outil d'analyse sur un échantillon pris au hasard parmi la population adulte Fribourg (F.R.A.). La fiabilité du questionnaire a été verifiée sur la base de testes réeffectués à deux semaines et à six mois d'intervalle. Les hautes corrélations entre les diverses enquêtes ont indiqué une forte fiabilité de notre outil d'analyse. Les tendences dans le sens des variations saisonnières n'ont été trouvées qu'au niveau de l'activité cycliste et du jardinage ainsi qu'au niveau des activités de base et globales qui sont dérivées. La validation s'est effectuée de façon indirecte par le biais de la capacité maximale d'absorption d'oxygène. On a constaté un lien étroit entre la capacité maximale d'absorption d'oxygène et le volume d'activités sportives (coéfficient partielle de corrélation: r=0.422, p<0.01). Les données révélées par le questionnaire étaient en soi consistentes. Les personnes qui s'estiment être plus actives que leurs congénères du même âge en ce qui concerne le sport et l'activité globale étaient effectivement plus actives. Les résultats du questionnaire ont fait apparaître une quantité moyenne d'activité globale de 9,2h par semaine. Pour la plus grande partie des activités globales, il s'agissait d'activités avec un niveau d'intensité physique allant de léger à modéré. Les schemas d'activité variaient suivant l'âge et le sexe. Selon les recommendations formulées par Paffenbarger concernant le minimum d'activité physique requis (2000 kcal par semaine d'activité globale), 40% des personnes interogées etaient en dessous de ce seuil. Selon les recommandations du “American College of Sports Medicine” (1000 kcal par semaine d'activité sportive) 63% des personnes interogées se situaient en dessous de ce seuil.


Enabling ∼100fps detection on a landing unmanned aircraft for its on-ground vision-based recovery

In this paper, a deep learning inspired solution is proposed and developed to enable timeliness and practicality of the pre-existing ground stereo vision guidance system for flxed-wing UAVs' safe landing. Since the ground guidance prototype was restricted within applications due to its untimeliness, eventually the vision-based detection less than 15 fps (frame per second). Under such circumstances, we employ a regression based deep learning algorithm into automatic detection on the flying aircraft in the landing sequential images. The system architecture is upgraded so as to be compatible with the novel deep learning requests, and furthermore, annotated datasets are conducted to support training and testing of the regression-based learning detection algorithm. Experimental results validate that the detection attaches 100 fps or more while the localization accuracy is kept in the same level.


Computer-aided design of analog and mixed-signal integrated circuits

This survey presents an overview of recent advances in the state of the art for computer-aided design (CAD) tools for analog and mixed-signal integrated circuits (ICs). Analog blocks typically constitute only a small fraction of the components on mixed-signal ICs and emerging systems-on-a-chip (SoC) designs. But due to the increasing levels of integration available in silicon technology and the growing requirement for digital systems to communicate with the continuous-valued external world, there is a growing need for CAD tools that increase the design productivity and improve the quality of analog integrated circuits. This paper describes the motivation and evolution of these tools and outlines progress on the various design problems involved: simulation and modeling, symbolic analysis, synthesis and optimization, layout generation, yield analysis and design centering, and test. This paper summarizes the problems for which viable solutions are emerging and those which are still unsolved.


Effects of Small-Group Learning on Undergraduates in Science , Mathematics , Engineering , and Technology : A Meta-Analysis

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..I................ vii Conceptual Framework ........................................................................................................... 2 Motivational Perspective ............................................................................................. 3 Affective Perspective .................................................................................................. 3 Cognitive Perspective .................................................................................................. 3 Forms of Small-Group Learning ................................................................................. 4 Research Questions ..................................................................................................... 4 Meta-Analysis Method ............................................................................................................ 4 Literature Search Procedures ....................................................................................... 4 Inclusion Criteria ......................................................................................................... 5 Metric for Expressing Effect Sizes .............................................................................. 5 Calculations of Average Effect Sizes .......................................................................... 5 Tests for Conditional Effects ....................................................................................... 6 Study Coding ............................................................................................................... 7 Meta Analysis Results ............................................................................................................. 7 Main Effect of Small-Group Learning ........................................................................ 7 Distribution of Effect Sizes ......................................................................................... 7 Conditional Effects of Small-Group Learning .......................................................... 1 0 Discussion and Conclusions .................................................................................................. 1 7 Robust Main Effects .................................................................................................. 1 7 Conditional Effects of Small-Group Learning .......................................................... 1 8 Limitations of the Study ............................................................................................ 2 0 Implications for Theory, Research, Policy, and Practice .......................................... 2 1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 9


IoT smart parking system for reducing green house gas emission

Rapid climate change results natural calamity and severe economic impact and threats to the life. Burning fossil fuels by the medium of transportation contributes 1/3 of portion in increasing greenhouse emission and leading to raise surface temperate. Commuters in and around the developed cities faces difficulties in finding parking lot due to lack of notification process and autonomous parking systems. This causes commuters to take multiple rounds trips to get the parking slot which causes burning additional fuel and ultimately producing excessive CO2 emission. This paper describes solution to smart parking system using Internet of Things (IoT) to override parking hazards and explains how does it helps to minimize emitting greenhouse gases. IoT enables smart parking system using the system of interconnected Raspberry Pi, Distance Sensor, Pi Camera devices together. This hardware reacts to one another collects data and transmits to cloud storage.


A more realistic error distance calculation for indoor positioning systems accuracy evaluation

The accuracy of indoor positioning systems is commonly computed as a metric based on the Euclidean distance from estimated locations to actual locations. This paper suggests that positioning error distances should be computed as the lengths of the paths that a person may follow when going from wrongly estimated positions to the real positions. The paper proposes a method that calculates the paths from floor plan and obstacles information using the visibility graphs and offsetting techniques, which are commonly used in robotics and CAD/CAM for navigation and manufacturing, respectively. Demonstration of the proposed method was done using a WiFi fingerprinting method based on kNN for pedestrian navigation. Comparisons between our proposed distance and the simple Euclidean distance have shown that the error distances are underestimated and that the differences between the two distances cannot be accurately represented by a fixed quantity in the context of an Indoor Positioning System (IPS) deployed in a library building. We consider that our proposed positioning error distance is more in line with the subjective error perceived by IPS users.


There is a blind spot in AI research

advances in the technical domains of AI. Alongside such efforts, designers and researchers from a range of disciplines need to conduct what we call social-systems analyses of AI. They need to assess the impact of technologies on their social, cultural and political settings. A social-systems approach could investigate, for instance, how the app AiCure — which tracks patients’ adherence to taking prescribed medication and transmits records to physicians — is changing the doctor– patient relationship. Such an approach to perform a range of complex tasks in everyday life. These ranged from the identification of skin alterations that are indicative of earlystage cancer to the reduction of energy costs for data centres. The workshops also highlighted a major blind spot in thinking about AI. Autonomous systems are already deployed in our most crucial social institutions, from hospitals to courtrooms. Yet there are no agreed methods to assess the sustained effects of such applications on human populations. Recent years have brought extraordinary There is a blind spot in AI research


Real-time visual odometry from dense RGB-D images

We present an energy-based approach to visual odometry from RGB-D images of a Microsoft Kinect camera. To this end we propose an energy function which aims at finding the best rigid body motion to map one RGB-D image into another one, assuming a static scene filmed by a moving camera. We then propose a linearization of the energy function which leads to a 6×6 normal equation for the twist coordinates representing the rigid body motion. To allow for larger motions, we solve this equation in a coarse-to-fine scheme. Extensive quantitative analysis on recently proposed benchmark datasets shows that the proposed solution is faster than a state-of-the-art implementation of the iterative closest point (ICP) algorithm by two orders of magnitude. While ICP is more robust to large camera motion, the proposed method gives better results in the regime of small displacements which are often the case in camera tracking applications.


On unifying key-frame and voxel-based dense visual SLAM at large scales

This paper proposes an approach to real-time dense localisation and mapping that aims at unifying two different representations commonly used to define dense models. On one hand, much research has looked at 3D dense model representations using voxel grids in 3D. On the other hand, image-based key-frame representations for dense environment mapping have been developed. Both techniques have their relative advantages and disadvantages which will be analysed in this paper. In particular each representation's space-size requirements, their effective resolution, the computation efficiency, their accuracy and robustness will be compared. This paper then proposes a new model which unifies various concepts and exhibits the main advantages of each approach within a common framework. One of the main results of the proposed approach is its ability to perform large scale reconstruction accurately at the scale of mapping a building.


gSLICr: SLIC superpixels at over 250Hz

We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to 83× from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.


IoT-based occupancy monitoring techniques for energy-efficient smart buildings

With the proliferation of Internet of Things (IoT) devices such as smartphones, sensors, cameras, and RFIDs, it is possible to collect massive amount of data for localization and tracking of people within commercial buildings. Enabled by such occupancy monitoring capabilities, there are extensive opportunities for improving the energy consumption of buildings via smart HVAC control. In this respect, the major challenges we envision are 1) to achieve occupancy monitoring in a minimally intrusive way, e.g., using the existing infrastructure in the buildings and not requiring installation of any apps in the users' smart devices, and 2) to develop effective data fusion techniques for improving occupancy monitoring accuracy using a multitude of sources. This paper surveys the existing works on occupancy monitoring and multi-modal data fusion techniques for smart commercial buildings. The goal is to lay down a framework for future research to exploit the spatio-temporal data obtained from one or more of various IoT devices such as temperature sensors, surveillance cameras, and RFID tags that may be already in use in the buildings. A comparative analysis of existing approaches and future predictions for research challenges are also provided.


Quality evaluation of product reviews using an information quality framework

Available online 27 August 2010


Distributed fair scheduling in a wireless LAN

Fairness is an important issue when accessing a shared wireless channel. With fair scheduling, it is possible to allocate bandwidth in proportion to weightsof the packet flows sharing the channel. This paper presents a fully distributed algorithm for fair scheduling in a wireless LAN. The algorithm can be implemented without using a centralized coordinator to arbitrate medium access. The proposed protocol is derived from the Distributed Coordination Function in the IEEE 802.11 standard. Simulation results show that the proposed algorithm is able to schedule transmission such that the bandwidth allocated to different flows is proportional to their weights. An attractive feature of the proposed approach is that it can be implemented with simple modifications to the IEEE 802.11 standard.


Educational Data Mining: A Review of the State of the Art

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.


Artificial Vision in Road Vehicles

The last few decades witnessed the birth and growth of a new sensibility to transportation efficiency. In particular, the need for efficient and improved people and goods mobility pushed researchers to address the problem of intelligent transportation systems. This paper surveys the most advanced approaches to the (partial) customization of road following task, using on-board systems based on artificial vision. The functionalities of lane detection, obstacle detection and pedestrian detection are described and classified, and their possible application on future road vehicles is discussed.


A Log-Logistic Model-Based Interpretation of TF Normalization of BM25

The effectiveness of BM25 retrieval function is mainly due to its sub-linear term frequency (TF) normalization component, which is controlled by a parameter k1. Although BM25 was derived based on the classic probabilistic retrieval model, it has been so far unclear how to interpret its parameter k1 probabilistically, making it hard to optimize the setting of this parameter. In this paper, we provide a novel probabilistic interpretation of the BM25 TF normalization and its parameter k1 based on a log-logistic model for the probability of seeing a document in the collection with a given level of TF. The proposed interpretation allows us to derive different approaches to estimation of parameter k1 based solely on the current collection without requiring any training data, thus effectively eliminating one free parameter from BM25. Our experiment results show that the proposed approaches can accurately predict the optimal k1 without requiring training data and achieve better or comparable retrieval performance to a well-tuned BM25 where k1 is optimized based on training data.


CINCO: a simplicity-driven approach to full generation of domain-specific graphical modeling tools

Even with the help of powerful metamodeling frameworks, the development of domain-specific graphical modeling tools is usually a complex, repetitive, and tedious task, which introduces substantial upfront costs often prohibiting such approaches in practice. In order to reduce these costs, the presented Cinco meta tooling suite is designed to provide a holistic approach that greatly simplifies the development of such domain-specific tools. Our solution is based on the idea to apply the concept of domain specialization also to the (meta-)domain of “domain-specific modeling tools”. Important here is our focus on complex graph-based models, comprising various kinds of nodes and edges together with their individual representation, correlations, and interpretation. This focus allows for high-level specifications of the model structures and functionalities as the prerequisite for push-button tool generation.


Microgrid power electronic converters: State of the art and future challenges

This paper presents a review of the state of the art of power electric converters used in microgrids. The paper focuses primarily on grid connected converters. Different topologies and control and modulation strategies for these specific converters are critically reviewed. Moreover, future challenges in respect of these converters are identified along with their potential solutions.


Parental stress, family quality of life, and family-teacher partnerships: Families of children with autism spectrum disorder.

BACKGROUND
Reducing parental stress and improving family quality of Life (FQOL) are continuing concerns for families of children with autism spectrum disorder (ASD). Family-teacher partnerships have been identified as a positive factor to help parents reduce their stress and improve their FQOL. However, the interrelations among parental stress, FQOL, and family-teacher partnerships need to be further examined so as to identify the possible paths to help parents reduce their stress and improve their FQOL. The purpose of this study was to examine the interrelations among these three variables.

METHOD
A total of 236 parents of school children with ASD completed questionnaires, which included three measures: (a) the Beach Center Family Quality of Life Scale, (b) the Parental Stress Scale, and (c) the Beach Center Family-Professional Partnerships Scale. The structural equation modeling was used to analyze the interrelations among these three variables.

RESULTS
Perceived parental stress had a direct effect on parental satisfaction concerning FQOL and vice versa. Perceived family-teacher partnerships had a direct effect on FQOL, but did not have a direct effect on parental stress. However, family-teacher partnerships had an indirect effect on parental stress through FQOL.

CONCLUSIONS AND IMPLICATIONS
Reducing parental stress could improve FQOL for families of children with ASD and vice versa. Strong family-teacher partnerships could help parents of children with ASD improve their FQOL and indirectly reduce their stress.


A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering

In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.


Advances in High-Voltage Modulators for Applications in Pulsed Power and Plasma-Based Ion Implantation

Modern pulsed power technology has its roots in the late 1950s and early 1960s, and it was driven overwhelmingly by applications in national defense carried out by several countries, especially the U.S., U.K., Russia, and China. The following decades, particularly the early 1990s, witnessed an increased interest in compact systems with pulse repetition rate that could be used in nondefense applications such as treatment of material surfaces by plasma and beam interactions, treatment of pollutants, food sterilization, medical applications, etc. This spawned a new generation of pulsed power components (solid-state switches) that led to completely solid-state modulators. This paper describes how the pulsed power technology used originally in beam sources and cathodic arcs has converged to produce power sources for plasma-based ion implantation (PBII) and related technologies. The present state of the art is reviewed, and prospects for future advances are described, especially for PBII.


Norepinephrine transporter occupancy by antidepressant in human brain using positron emission tomography with (S,S)-[18F]FMeNER-D2

Central norepinephrine transporter (NET) is one of the main targets of antidepressants. Although the measurement of NET occupancy has been attempted in humans, the outcomes have been inconclusive. In this study, the occupancy of NET by different doses of an antidepressant, nortriptyline, was measured using positron emission tomography (PET) with (S,S)-[18F]FMeNER-D2. PET scans using (S,S)-[18F]FMeNER-D2 were performed on six healthy men before and after oral administration of a single oral dose of nortriptyline (10–75 mg). After a bolus i.v. injection of (S,S)-[18F]FMeNER-D2, dynamic scanning was performed for 0–90 min, followed by scanning for 120–180 min. The ratio of the thalamus-to-caudate areas under the curve (120–180 min) minus 1 was used as the binding potential (BPND) for NET. NET occupancy was calculated as the percentage reduction of BPND. Venous blood samples were taken to measure the concentrations of nortriptyline just before injection of the tracer and at 180 min after the injection. Mean NET occupancies by nortriptyline were 16.4% at 10 mg, 33.2% at 25 mg, and 41.1% at 75 mg. The mean plasma concentration of nortriptyline was less than the lower limit of detection at 10 mg, 23.7 ng/mL at 25 mg, and 50.5 ng/mL at 75 mg. Estimated ED50 was 76.8 mg of administration dose and 59.8 ng/mL of plasma concentration. NET occupancy by nortriptyline corresponding to the administration dose of 10–75 mg or plasma concentration was observed from 16% to 41%.


A neuromorphic architecture for anomaly detection in autonomous large-area traffic monitoring

The advanced sensing and imaging capability of today's sensor networks enables real time monitoring in a large area. In order to provide continuous monitoring and prompt situational awareness, an abstract-level autonomous information processing framework is developed that is able to detect various categories of abnormal traffic events with unsupervised learning. The framework is based on cogent confabulation model, which performs statistical inference in a manner inspired by human neocortex system. It enables detection and recognition of abnormal target vehicles within the context of surrounding traffic activities and previous events using likelihood-ratio test. A neuromorphic architecture is proposed which accelerates the computation for real-time detection by leveraging memristor crossbar arrays.


Ontogenesis of Agency in Machines : A Multidisciplinary Review

How does agency arise in biocognitive systems? Can the emergence of agency be explained by physical laws? We review these and related questions in philosophy, psychology, biology, and physics. Based on the review we ask the questions: i) Can machines have agency? and, if so, ii) How can we build machines with agency? We examine existing work in artificial intelligence and machine learning on selfmotivated, self-teaching, and self-developing systems with respect to the ontogenesis (“coming into being”) of agency in computational systems. The impact of these “autogenic” systems or machine agency on science, technology, and humanity will be discussed.


CODRA: A Novel Discriminative Framework for Rhetorical Analysis

Clauses and sentences rarely stand on their own in an actual discourse; rather, the relationship between them carries important information that allows the discourse to express a meaning as a whole beyond the sum of its individual parts. Rhetorical analysis seeks to uncover this coherence structure. In this article, we present CODRA— a COmplete probabilistic Discriminative framework for performing Rhetorical Analysis in accordance with Rhetorical Structure Theory, which posits a tree representation of a discourse.CODRA comprises a discourse segmenter and a discourse parser. First, the discourse segmenter, which is based on a binary classifier, identifies the elementary discourse units in a given text. Then the discourse parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intra-sentential parsing and the other for multi-sentential parsing. We present two approaches to combine these two stages of parsing effectively. By conducting a series of empirical evaluations over two different data sets, we demonstrate that CODRA significantly outperforms the state-of-the-art, often by a wide margin. We also show that a reranking of the k-best parse hypotheses generated by CODRA can potentially improve the accuracy even further.


An Access Control scheme for Big Data processing

Access Control (AC) systems are among the most critical of network security components. A system's privacy and security controls are more likely to be compromised due to the misconfiguration of access control policies rather than the failure of cryptographic primitives or protocols. This problem becomes increasingly severe as software systems become more and more complex, such as Big Data (BD) processing systems, which are deployed to manage a large amount of sensitive information and resources organized into a sophisticated BD processing cluster. Basically, BD access control requires the collaboration among cooperating processing domains to be protected as computing environments that consist of computing units under distributed AC managements. Many BD architecture designs were proposed to address BD challenges; however, most of them were focused on the processing capabilities of the “three Vs” (Velocity, Volume, and Variety). Considerations for security in protecting BD are mostly ad hoc and patch efforts. Even with some inclusion of security in recent BD systems, a critical security component, AC (Authorization), for protecting BD processing components and their users from the insider attacks, remains elusive. This paper proposes a general purpose AC scheme for distributed BD processing clusters.


Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with one dimensional Gaussian filter, and then partitions the smoothed histogram based on its local maximums. Next, each partition will be assigned to a new dynamic range. After that, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will alter the mean brightness of the image. Therefore, the last step in this method is to normalize the output image to the input mean brightness. Our results from 80 test images shows that this method outperforms other present mean brightness preserving histogram equalization methods. In most cases, BPDHE successfully enhance the image without severe side effects, and at the same time, maintain the mean input brightness1.


Language, music, syntax and the brain

The comparative study of music and language is drawing an increasing amount of research interest. Like language, music is a human universal involving perceptually discrete elements organized into hierarchically structured sequences. Music and language can thus serve as foils for each other in the study of brain mechanisms underlying complex sound processing, and comparative research can provide novel insights into the functional and neural architecture of both domains. This review focuses on syntax, using recent neuroimaging data and cognitive theory to propose a specific point of convergence between syntactic processing in language and music. This leads to testable predictions, including the prediction that that syntactic comprehension problems in Broca's aphasia are not selective to language but influence music perception as well.


Long-term effect of high-intensity laser therapy in the treatment of patients with chronic low back pain: a randomized blinded placebo-controlled trial

The aim of this study was to compare the effect of high-intensity laser therapy (HILT), alone or combined with exercise, in the treatment of chronic low back pain (CLBP). A total of 72 male patients participated in this study, with a mean (SD) age of 32.81 (4.48) years. Patients were randomly assigned into three groups and treated with HILT plus exercise (HILT + EX), placebo laser plus exercise (PL + EX), and HILT alone in groups 1, 2, and 3, respectively. The outcomes measured were lumbar range of motion (ROM), pain level by visual analog scale (VAS), and functional disability by both the Roland Disability Questionnaire (RDQ) and the Modified Oswestry Disability Questionnaire (MODQ). Statistical analyses were performed to compare the differences between baseline and post-treatment measurements. The level of statistical significance was set as P < 0.05. ROM significantly increased after 4 weeks of treatment in all groups, then significantly decreased after 12 weeks of follow-up, but was still significantly more than the baseline value in groups 1 and 2. VAS, RDQ, and MODQ results showed significant decrease post-treatment in all groups, although the RDQ and MODQ results were not significantly different between groups 2 and 3. HILT combined with exercise appears to be more effective in patients with CLBP than either HLLT alone or placebo laser with exercise.


A Lightweight Multicast Authentication Mechanism for Small Scale IoT Applications

Security is very important for Internet of Things (IoTs). As a main communication mode, the security mechanism for multicast is not only the measure to ensure secured communications, but also the precondition for other security services. With the analysis of Nyberg's fast one-way accumulator and its security, we discover that it has the property of absorbency in addition to the one-way and quasi-communicative property that makes it very suitable for applications in which accumulated items are dynamic. In this paper, we revise the original Nyberg's fast one-way accumulator and construct a lightweight multicast authentication mechanism for small scale IoT applications. We analyze the security of the mechanism in detail. In addition, we evaluate seven performance aspects of the mechanism.


Efficient set intersection for inverted indexing

Conjunctive Boolean queries are a key component of modern information retrieval systems, especially when Web-scale repositories are being searched. A conjunctive query q is equivalent to a |q|-way intersection over ordered sets of integers, where each set represents the documents containing one of the terms, and each integer in each set is an ordinal document identifier. As is the case with many computing applications, there is tension between the way in which the data is represented, and the ways in which it is to be manipulated. In particular, the sets representing index data for typical document collections are highly compressible, but are processed using random access techniques, meaning that methods for carrying out set intersections must be alert to issues to do with access patterns and data representation. Our purpose in this article is to explore these trade-offs, by investigating intersection techniques that make use of both uncompressed “integer” representations, as well as compressed arrangements. We also propose a simple hybrid method that provides both compact storage, and also faster intersection computations for conjunctive querying than is possible even with uncompressed representations.


Inverted index compression and query processing with optimized document ordering

Web search engines use highly optimized compression schemes to decrease inverted index size and improve query throughput, and many index compression techniques have been studied in the literature. One approach taken by several recent studies first performs a renumbering of the document IDs in the collection that groups similar documents together, and then applies standard compression techniques. It is known that this can significantly improve index compression compared to a random document ordering. We study index compression and query processing techniques for such reordered indexes. Previous work has focused on determining the best possible ordering of documents. In contrast, we assume that such an ordering is already given, and focus on how to optimize compression methods and query processing for this case. We perform an extensive study of compression techniques for document IDs and present new optimizations of existing techniques which can achieve significant improvement in both compression and decompression performances. We also propose and evaluate techniques for compressing frequency values for this case. Finally, we study the effect of this approach on query processing performance. Our experiments show very significant improvements in index size and query processing speed on the TREC GOV2 collection of 25.2 million web pages.


Super-Scalar RAM-CPU Cache Compression

High-performance data-intensive query processing tasks like OLAP, data mining or scientific data analysis can be severely I/O bound, even when high-end RAID storage systems are used. Compression can alleviate this bottleneck only if encoding and decoding speeds significantly exceed RAID I/O bandwidth. For this purpose, we propose three new versatile compression schemes (PDICT, PFOR, and PFOR-DELTA) that are specifically designed to extract maximum IPC from modern CPUs. We compare these algorithms with compression techniques used in (commercial) database and information retrieval systems. Our experiments on the MonetDB/X100 database system, using both DSM and PAX disk storage, show that these techniques strongly accelerate TPC-H performance to the point that the I/O bottleneck is eliminated.


Inverted files for text search engines

The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.


Compressed Inverted Indexes for In-Memory Search Engines

We present the algorithmic core of a full text data base that allows fast Boolean queries, phrase queries, and document reporting using less space than the input text. The system uses a carefully choreographed combination of classical data compression techniques and inverted index based search data structures. It outperforms suffix array based techniques for all the above operations for real world (natural language) texts.


Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian

Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.


Deep Learning for the Classification of Lung Nodules

Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature extractor, and has shown a superior performance in many visual object recognition applications. In this study, we develop a deep convolutional neural network (CNN) and apply it to thoracic CT images for the classification of lung nodules. We present the CNN architecture and classification accuracy for the original images of lung nodules. In order to understand the features of lung nodules, we further construct new datasets, based on the combination of artificial geometric nodules and some transformations of the original images, as well as a stochastic nodule shape model. It is found that simplistic geometric nodules cannot capture the important features of lung nodules.


FUNCTIONAL AND LONGITUDINAL DATA ANALYSIS: PERSPECTIVES ON SMOOTHING

The perspectives and methods of functional data analysis and longitudinal data analysis are contrasted and compared. Topics include kernel methods and random effects models for smoothing, basis function methods, and examination of the relation of covariates to curve shapes. Some directions in which methodology might advance are identified.


Ethical issues raised by the treatment of gender-variant prepubescent children.

Transgender issues and transgender rights have become increasingly a matter of media attention and public policy debates. Reflecting changes in psychiatric perspectives, the diagnosis of "trans-sexualism" first appeared in the International Statistical Classification of Diseases and Related Health Problems in 1975 and shortly thereafter, in 1980, in the Diagnostic and Statistical Manual of Mental Disorders. Since that time, international standards of care have been developed, and today those standards are followed by clinicians across diverse cultures. In many instances, treatment of older adolescents and adults is covered by national health care systems and, in some cases, by private health insurance. Most recently, the Medicare ban on coverage for gender reassignment surgery was lifted in 2014. In contrast to the relative lack of controversy about treating adolescents and adults, there is no expert clinical consensus regarding the treatment of prepubescent children who meet diagnostic criteria for what was referred to in both DSM-IV-TR and ICD-10 as gender identity disorder in children and now in DSM-5 as gender dysphoria. One reason for the differing attitudes has to do with the pervasive nature of gender dysphoria in older adolescents and adults: it rarely desists, and so the treatment of choice is gender or sex reassignment. On the subject of treating children, however, as the World Professional Association for Transgender Health notes in their latest Standards of Care, gender dysphoria in childhood does not inevitably continue into adulthood, and only 6 to 23 percent of boys and 12 to 27 percent of girls treated in gender clinics showed persistence of their gender dysphoria into adulthood. Further, most of the boys' gender dysphoria desisted, and in adulthood, they identified as gay rather than as transgender. In an effort to clarify best treatment practices for transgender individuals, a recent American Psychiatric Association Task Force on the Treatment of Gender Identity outlined three differing approaches to treating prepubescent gender dysphoric children.


Revisiting The American Voter on Twitter

The American Voter - a seminal work in political science - uncovered the multifaceted nature of voting behavior which has been corroborated in electoral research for decades since. In this paper, we leverage The American Voter as an analysis framework in the realm of computational political science, employing the factors of party, personality, and policy to structure the analysis of public discourse on online social media during the 2016 U.S. presidential primaries. Our analysis of 50 million tweets reveals the continuing importance of these three factors; our understanding is also enriched by the application of sentiment analysis techniques. The overwhelmingly negative sentiment of conversations surrounding 10 major presidential candidates reveals more "crosstalk" from Democratic leaning users towards Republican candidates, and less vice-versa. We uncover the lack of moderation as the most discussed personality dimension during this campaign season, as the political field becomes more extreme - Clinton and Rubio are perceived as moderate, while Trump, Sanders, and Cruz are not. While the most discussed issues are foreign policy and immigration, Republicans tweet more about abortion than Democrats who tweet more about gay rights than Republicans. Finally, we illustrate the importance of multifaceted political discourse analysis by applying regression to quantify the impact of party, personality, and policy on national polls.


Event Correlation: Language and Semantics

300.twolf 3000 102 2945 102 2945 Hardware CPU: Intel(R) Xeon(R) CPU 5150 @ 2.66GHz CPU MHz: 2660 FPU: Integrated CPU(s) enabled: 4 cores, 2 chips, 2 cores/chip CPU(s) orderable: 1,2 chip(s) Parallel: No Primary Cache: 32KB(I) + 32KB(D) on chip, per core Secondary Cache: 4096KB(I+D) on chip, per chip, shared L3 Cache: N/A Other Cache: N/A Memory: 4 x 1024MB ECC FB-DIMM DDR2-667MHz Disk Subsystem: 1 x 250GB SATA HDD Other Hardware: Software Operating System: Red Hat Enterprise Linux 4 Update 3 EM64T Compiler: Intel C++ Compiler 9.1.042 for EM64T File System: ext3 System State: Runlevel 3


There is no 16-Clue Sudoku: Solving the Sudoku Minimum Number of Clues Problem

We apply our new hitting set enumeration algorithm to solve the sudoku minimum number of clues problem, which is the following question: What is the smallest number of clues (givens) that a sudoku puzzle may have? It was conjectured that the answer is 17. We have performed an exhaustive search for a 16-clue sudoku puzzle, and we did not find one, thereby proving that the answer is indeed 17. This article describes our method and the actual search. The hitting set problem is computationally hard; it is one of Karp’s twenty-one classic NP-complete problems. We have designed a new algorithm that allows us to efficiently enumerate hitting sets of a suitable size. Hitting set problems have applications in many areas of science, such as bioinformatics and software testing. ∗School of Mathematical Sciences, University College Dublin, Ireland. E-mail: gary.mcguire@ucd.ie


On spatial smoothing for direction-of-arrival estimation of coherent signals


Collaborative detection of cyberbullying behavior in Twitter data

As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One of such undesirable behavior is cyberbullying, which may even lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, if possible in realtime. Prevalent approaches to identify cyberbullying are mainly stand-alone and thus, are time-consuming. This research improves detection task using the principles of collaborative computing. Different collaborative paradigms are suggested and discussed in this paper. Preliminary results indicate an improvement in time and accuracy of the detection mechanism over the stand-alone paradigm.


Orthogonal and Idempotent Transformations for Learning Deep Neural Networks

Identity transformations, used as skip-connections in residual networks, directly connect convolutional layers close to the input and those close to the output in deep neural networks, improving information flow and thus easing the training. In this paper, we introduce two alternative linear transforms, orthogonal transformation and idempotent transformation. According to the definition and property of orthogonal and idempotent matrices, the product of multiple orthogonal (same idempotent) matrices, used to form linear transformations, is equal to a single orthogonal (idempotent) matrix, resulting in that information flow is improved and the training is eased. One interesting point is that the success essentially stems from feature reuse and gradient reuse in forward and backward propagation for maintaining the information during flow and eliminating the gradient vanishing problem because of the express way through skip-connections. We empirically demonstrate the effectiveness of the proposed two transformations: similar performance in single-branch networks and even superior in multi-branch networks in comparison to identity transformations.


Country-wide rainfall maps from cellular communication networks.

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km(2)), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.


Smooth assembled mappings for large-scale real walking

Virtual reality applications prefer real walking to provide highly immersive presence than other locomotive methods. Mapping-based techniques are very effective for supporting real walking in small physical workspaces while exploring large virtual scenes. However, the existing methods for computing real walking maps suffer from poor quality due to distortion. In this paper, we present a novel divide-and-conquer method, called Smooth Assembly Mapping (SAM), to compute real walking maps with low isometric distortion for large-scale virtual scenes. First, the input virtual scene is decomposed into a set of smaller local patches. Then, a group of local patches is mapped together into a real workspace by minimizing a low isometric distortion energy with smoothness constraints between the adjacent patches. All local patches are mapped and assembled one by one to obtain a complete map. Finally, a global optimization is adopted to further reduce the distortion throughout the entire map. Our method easily handles teleportation technique by computing maps of individual regions and assembling them with teleporter conformity constraints. A large number of experiments, including formative user studies and comparisons, have shown that our method succeeds in generating high-quality real walking maps from large-scale virtual scenes to small real workspaces and is demonstrably superior to state-of-the-art methods.