The effect of five prosthetic feet on the gait and loading of the sound limb in dysvascular below-knee amputees.

The purpose of this study was to determine the effects of prosthetic foot design on the vertical ground reaction forces experienced by the sound and amputated limbs in a group of persons with dysvascular below-knee amputations. Stride characteristics, joint motion, and ground reaction forces were recorded simultaneously during a self-selected free walking velocity in seven subjects wearing five different prosthetic feet (SACH, Flex-Foot, Carbon Copy II, Seattle, Quantum). Subjects used each foot for one month prior to testing. Results indicated that the sound limb was exposed to higher vertical ground reaction forces than normal despite a reduced walking velocity. Use of the Flex-Foot resulted in the lowest sound limb vertical forces, which appears to be related to its large arc of dorsiflexion motion. In addition, there was increased loading response knee flexion of the sound limb indicating an attempt by these subjects to modulate floor impact. These results suggest that the intact lower extremity is susceptible to excessive floor impact, and that prosthetic foot design can have an effect on the magnitude of the vertical forces experienced by the limb.


Non-Local Patch-Based Image Inpainting

Image inpainting is the process of filling in missing regions in an image in a plausible way. In this contribution, we propose and describe an implementation of a patch-based image inpainting algorithm. The method is actually a two-dimensional version of our video inpainting algorithm proposed in [16]. The algorithm attempts to minimise a highly non-convex functional, first introducted by Wexler et al. in [18]. The functional specifies that a good solution to the inpainting problem should be an image where each patch is very similar to its nearest neighbour in the unoccluded area. Iterations are performed in a multi-scale framework which yields globally coherent results. In this manner two of the major goals of image inpainting, the correct reconstruction of textures and structures, are addressed. We address a series of important practical issues which arise when using such an approach. In particular, we reduce execution times by using the PatchMatch [3] algorithm for nearest neighbour searches, and we propose a modified patch distance which improves the comparison of textured patches. We address the crucial issue of initialisation and the choice of the number of pyramid levels, two points which are rarely discussed in such approaches. We provide several examples which illustrate the advantages of our algorithm, and compare our results with those of state-of-the-art methods.


CaffePresso: An optimized library for Deep Learning on embedded accelerator-based platforms

Off-the-shelf accelerator-based embedded platforms offer a competitive energy-efficient solution for lightweight deep learning computations over CPU-based systems. Low-complexity classifiers used in power-constrained and performance-limited scenarios are characterized by operations on small image maps with 2-3 deep layers and few class labels. For these use cases, we consider a range of embedded systems with 5--20 W power budgets such as the Xilinx ZC706 board (with MXP soft vector processor), NVIDIA Jetson TX1 (GPU), TI Keystone II (DSP) as well as the Adapteva Parallella board (custom multi-core with NoC). Deep Learning computations push the capabilities of these platforms to the limit through compute-intensive evaluations of multiple 2D convolution filters per layer, and high communication requirements arising from the movement of intermediate maps across layers. We present CaffePresso, a Caffe-compatible framework for generating optimized mappings of user-supplied ConvNet specifications to target various accelerators such as FPGAs, DSPs, GPUs, RISC-multicores. We use an automated code generation and auto-tuning approach based on knowledge of the ConvNet requirements, as well as platform-specific constraints such as on-chip memory capacity, bandwidth and ALU potential. While one may expect the Jetson TX1 + cuDNN to deliver high performance for ConvNet configurations, (1) we observe a flipped result with slower GPU processing compared to most other systems for smaller embedded-friendly datasets such as MNIST and CIFAR10, and (2) faster and more energy efficient implementation on the older 28nm TI Keystone II DSP over the newer 20nm NVIDIA TX1 SoC in all cases.


The Normalization of Occurrence and Co-occurrence Matrices in Bibliometrics using Cosine Similarities and Ochiai Coefficients

We prove that Ochiai similarity of the co-occurrence matrix is equal to cosine similarity in the underlying occurrence matrix. Neither the cosine nor the Pearson correlation should be used for the normalization of co-occurrence matrices because the similarity is then normalized twice, and therefore over-estimated; the Ochiai coefficient can be used instead. Results are shown using a small matrix (5 cases, 4 variables) for didactic reasons, and also Ahlgren et al.’s (2003) co-occurrence matrix of 24 authors in library and information sciences. The over-estimation is shown numerically and will be illustrated using multidimensional scaling and cluster dendograms. If the occurrence matrix is not available (such as in internet research or author co-citation analysis) using Ochiai for the normalization is preferable to using the cosine.


Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing

In this paper, we apply reinforcement learning for automatically learning cooperative persuasive dialogue system policies using framing, the use of emotionally charged statements common in persuasive dialogue between humans. In order to apply reinforcement learning, we describe a method to construct user simulators and reward functions specifically tailored to persuasive dialogue based on a corpus of persuasive dialogues between human interlocutors. Then, we evaluate the learned policy and the effect of framing through experiments both with a user simulator and with real users. The experimental evaluation indicates that applying reinforcement learning is effective for construction of cooperative persuasive dialogue systems which use framing.


Inference in Probabilistic Logic Programs using Weighted CNF's

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we develop efficient inference algorithms for these tasks. This is based on a conversion of the probabilistic logic program and the query and evidence to a weighted CNF formula. This allows us to reduce the inference tasks to wellstudied tasks such as weighted model counting. To solve such tasks, we employ state-ofthe-art methods. We consider multiple methods for the conversion of the programs as well as for inference on the weighted CNF. The resulting approach is evaluated experimentally and shown to improve upon the state-of-theart in probabilistic logic programming.


Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context

Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.


How does communication heal? Pathways linking clinician-patient communication to health outcomes.

OBJECTIVE
Although prior research indicates that features of clinician-patient communication can predict health outcomes weeks and months after the consultation, the mechanisms accounting for these findings are poorly understood. While talk itself can be therapeutic (e.g., lessening the patient's anxiety, providing comfort), more often clinician-patient communication influences health outcomes via a more indirect route. Proximal outcomes of the interaction include patient understanding, trust, and clinician-patient agreement. These affect intermediate outcomes (e.g., increased adherence, better self-care skills) which, in turn, affect health and well-being. Seven pathways through which communication can lead to better health include increased access to care, greater patient knowledge and shared understanding, higher quality medical decisions, enhanced therapeutic alliances, increased social support, patient agency and empowerment, and better management of emotions.

CONCLUSION
Future research should hypothesize pathways connecting communication to health outcomes and select measures specific to that pathway.

PRACTICE IMPLICATIONS
Clinicians and patients should maximize the therapeutic effects of communication by explicitly orienting communication to achieve intermediate outcomes (e.g., trust, mutual understanding, adherence, social support, self-efficacy) associated with improved health.


Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]

Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of `jumping curves' from the field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curveswhich will eventually lead NLP research to evolve into natural language understanding.


Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations

Knowledge graphs are typical large-scale multi-relational structures, which comprise a large amount of fact triplets. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is widely used to embed fact triplets into low-dimensional spaces. Many existing knowledge graph embedding models either focus on learning rich features from entities but fail to extract good features of relations, or employ sophisticated models that have rather high time and memory-space complexities. In this paper, we propose a novel knowledge graph embedding model, CombinE. It exploits entity features from two complementary perspectives via the plus and minus combinations. We start with the plus combination, where we use shared features of entity pairs participating in a relation to convey its relation features. To also allow differences of each pairs of entities participating in a relation, we also use the minus combination, where we concentrate on individual entity features, and regard relations as a channel to offset the divergence and preserve the prominence between head and tail entities. Compared with the state-of-the-art models, our experimental results demonstrate that CombinE outperforms existing ones and has low time and memory-space complexities.


Automatic classification of irregularly sampled time series with unequal lengths: A case study on estimated glomerular filtration rate

A patient's estimated glomerular filtration rate (eGFR) can provide important information about disease progression and kidney function. Traditionally, an eGFR time series is interpreted by a human expert labelling it as stable or unstable. While this approach works for individual patients, the time consuming nature of it precludes the quick evaluation of risk in large numbers of patients. However, automating this process poses significant challenges as eGFR measurements are usually recorded at irregular intervals and the series of measurements differs in length between patients. Here we present a two-tier system to automatically classify an eGFR trend. First, we model the time series using Gaussian process regression (GPR) to fill in `gaps' by resampling a fixed size vector of fifty time-dependent observations. Second, we classify the resampled eGFR time series using a K-NN/SVM classifier, and evaluate its performance via 5-fold cross validation. Using this approach we achieved an F-score of 0.90, compared to 0.96 for 5 human experts when scored amongst themselves.


DG Placement and Sizing in Radial Distribution Network Using PSO & HBMO Algorithms

Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. Many researchers have used evolutionary methods for finding the optimal DG placement and sizing. This paper proposes a hybrid algorithm PSO&HBMO for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total power loss and improve the voltage profile. The proposed method is tested on a standard 13 bus radial distribution system and simulation results carried out using MATLAB software. The simulation results indicate that PSO&HBMO method can obtain better results than the simple heuristic search method and PSO algorithm. The method has a potential to be a tool for identifying the best location and rating of a DG to be installed for improving voltage profile and line losses reduction in an electrical power system. Moreover, current reduction is obtained in distribution system.


Content delivery over TLS: a cryptographic analysis of keyless SSL

The Transport Layer Security (TLS) protocol is designed to allow two parties, a client and a server, to communicate securely over an insecure network. However, when TLS connections are proxied through an intermediate middlebox, like a Content Delivery Network (CDN), the standard endto- end security guarantees of the protocol no longer apply. In this paper, we investigate the security guarantees provided by Keyless SSL, a CDN architecture currently deployed by CloudFlare that composes two TLS 1.2 handshakes to obtain a proxied TLS connection. We demonstrate new attacks that show that Keyless SSL does not meet its intended security goals. These attacks have been reported to CloudFlare and we are in the process of discussing fixes. We argue that proxied TLS handshakes require a new, stronger, 3-party security definition. We present 3(S)ACCEsecurity, a generalization of the 2-party ACCE security definition that has been used in several previous proofs for TLS. We modify Keyless SSL and prove that our modifications guarantee 3(S)ACCE-security, assuming ACCE-security for the individual TLS 1.2 connections. We also propose a new design for Keyless TLS 1.3 and prove that it achieves 3(S)ACCEsecurity, assuming that the TLS 1.3 handshake implements an authenticated 2-party key exchange. Notably, we show that secure proxying in Keyless TLS 1.3 is computationally lighter and requires simpler assumptions on the certificate infrastructure than our proposed fix for Keyless SSL. Our results indicate that proxied TLS architectures, as currently used by a number of CDNs, may be vulnerable to subtle attacks and deserve close attention.


Reconstruction-Based Pairwise Depth Dataset for Depth Image Enhancement Using CNN

Raw depth images captured by consumer depth cameras suffer from noisy and missing values. Despite the success of CNN-based image processing on color image restoration, similar approaches for depth enhancement have not been much addressed yet because of the lack of raw-clean pairwise dataset. In this paper, we propose a pairwise depth image dataset generation method using dense 3D surface reconstruction with a filtering method to remove low quality pairs. We also present a multi-scale Laplacian pyramid based neural network and structure preserving loss functions to progressively reduce the noise and holes from coarse to fine scales. Experimental results show that our network trained with our pairwise dataset can enhance the input depth images to become comparable with 3D reconstructions obtained from depth streams, and can accelerate the convergence of dense 3D reconstruction results.


Towards Optimal Binary Code Learning via Ordinal Embedding

Binary code learning, a.k.a., hashing, has been recently popular due to its high efficiency in large-scale similarity search and recognition. It typically maps high-dimensional data points to binary codes, where data similarity can be efficiently computed via rapid Hamming distance. Most existing unsupervised hashing schemes pursue binary codes by reducing the quantization error from an original real-valued data space to a resulting Hamming space. On the other hand, most existing supervised hashing schemes constrain binary code learning to correlate with pairwise similarity labels. However, few methods consider ordinal relations in the binary code learning process, which serve as a very significant cue to learn the optimal binary codes for similarity search. In this paper, we propose a novel hashing scheme, dubbed Ordinal Embedding Hashing (OEH), which embeds given ordinal relations among data points to learn the ranking-preserving binary codes. The core idea is to construct a directed unweighted graph to capture the ordinal relations, and then train the hash functions using this ordinal graph to preserve the permutation relations in the Hamming space. To learn such hash functions effectively, we further relax the discrete constraints and design a stochastic gradient decent algorithm to obtain the optimal solution. Experimental results on two large-scale benchmark datasets demonstrate that the proposed OEH method can achieve superior performance over the state-of-the-arts approaches. At last, the evaluation on query by humming dataset demonstrates the OEH also has good performance for music retrieval by using user’s humming or singing.


Symmetric-Aperture Antenna for Broadband Circular Polarization

A novel symmetric-aperture antenna is proposed for broadband circularly polarized (CP) radiation using a coplanar waveguide (CPW) feed. The proposed antenna consists of a CPW-feed and a wide symmetric aperture along the diagonal axis. A symmetric-aperture consists of two diamond-shaped sections. The symmetric-aperture and CPW-feeding structure are fabricated on the same plane of a dielectric substrate. Wideband CP radiation is achieved by changing the arms of symmetric-aperture and stripline length. The measured bandwidths of 3-dB axial ratio (AR) and 10-dB return loss are around 68% (2.4-4.85 GHz) and 107% (1.6-5.25 GHz), respectively. The measured boresight gain is more than 3.0 dBic over the CP bandwidth. Overall antenna size is around 0.7λo × 0.7λo × 0.019λo at 3.5 GHz.


A Novelty Detection Approach to Classification

Novelty Detection techniques are concept-learning methods that proceed by recognizing positive instances of a concept rather than diierentiating between its positive and negative instances. Novelty Detection approaches consequently require very few, if any, negative training instances. This paper presents a particular Novelty Detection approach to classiica-tion that uses a Redundancy Compression and Non-Redundancy Diierentiation technique based on the (Gluck & Myers 1993) model of the hippocampus, a part of the brain critically involved in learning and memory. In particular, this approach consists of training an autoencoder to reconstruct positive input instances at the output layer and then using this au-toencoder to recognize novel instances. Classiication is possible, after training, because positive instances are expected to be reconstructed accurately while negative instances are not. The purpose of this paper is to compare Hippo, the system that implements this technique , to C4.5 and feedforward neural network classi-cation on several applications.


Concept drift detection for streaming data

Common statistical prediction models often require and assume stationarity in the data. However, in many practical applications, changes in the relationship of the response and predictor variables are regularly observed over time, resulting in the deterioration of the predictive performance of these models. This paper presents Linear Four Rates (LFR), a framework for detecting these concept drifts and subsequently identifying the data points that belong to the new concept (for relearning the model). Unlike conventional concept drift detection approaches, LFR can be applied to both batch and stream data; is not limited by the distribution properties of the response variable (e.g., datasets with imbalanced labels); is independent of the underlying statistical-model; and uses user-specified parameters that are intuitively comprehensible. The performance of LFR is compared to benchmark approaches using both simulated and commonly used public datasets that span the gamut of concept drift types. The results show LFR significantly outperforms benchmark approaches in terms of recall, accuracy and delay in detection of concept drifts across datasets.


Cost-Sensitive Online Classification

Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification". In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of the cost-sensitive measures made by the proposed algorithms, and extensively examine their empirical performance on a variety of cost-sensitive online classification tasks. Finally, we demonstrate the application of the proposed technique for solving several online anomaly detection tasks, showing that the proposed technique could be a highly efficient and effective tool to tackle cost-sensitive online classification tasks in various application domains.


Learning from Imbalanced Data

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.


Classifying Data Streams with Skewed Class Distributions and Concept Drifts

Classification is an important data analysis tool that uses a model built from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a challenge for traditional classification algorithms. Concept drifts and skewed distributions, two common properties of data stream applications, make the task of learning in streams difficult. The authors aim to develop a new approach to classify skewed data streams that uses an ensemble of models to match the distribution over under-samples of negatives and repeated samples of positives.


Novel Radiation-Hardened-by-Design (RHBD) 12T Memory Cell for Aerospace Applications in Nanoscale CMOS Technology

In this paper, a novel radiation-hardened-by-design (RHBD) 12T memory cell is proposed to tolerate single node upset and multiple-node upset based on upset physical mechanism behind soft errors together with reasonable layout-topology. The verification results obtained confirm that the proposed 12T cell can provide a good radiation robustness. Compared with 13T cell, the increased area, power, read/write access time overheads of the proposed 12T cell are −18.9%, −23.8%, and 171.6%/−50.0%, respectively. Moreover, its hold static noise margin is 986.2 mV which is higher than that of 13T cell. This means that the proposed 12T cell also has higher stability when it provides fault tolerance capability.


Smart Electric Motorcycle Security System Based on GSM and ZigBee Communication

The storage of natural fuel is decreasing day by day and burning much of it can cause a massive environmental disaster. In order to save the nature from the disaster people need to think about some alternatives. The concept of making the battery operated vehicles comes as a rescue. The battery operated electric vehicle help in saving fuel and also help in lowering air pollution. Hybrid vehicles are already brought by several automobile manufacturers in market. This proposed system is going to offer an electric motorcycle combined with some security features based on ARM microcontroller, 8051 microcontroller, ZigBee and GSM communication which can be utilized both in Hybrid motorcycles and Electric motorcycles. When the proposed security features will be merged with those vehicles it will be an evolutionary concept in the world of next generation two-wheeler industry because besides lowering the level of air pollution and helping in controlling the use of fuel it mainly helps people to drive motorcycles safely.


Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling

We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large number of candidate features and then selecting from them using fast correlation-based filtering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classifier.


Automatic Acquisition of Linguistic Patterns for Conceptual Modeling

Conceptual modeling aims to capture the knowledge of a domain as perceived by the modeler and represent it in a way that will help further system analysis and design. Object-oriented methodology has been taken as a common practice for software system analysis and design because of its obvious advantages of handling the “software crisis” (Booch, 1994). Object-oriented analysis (OOA) is “a method of analysis that examines requirements from the perspectives of the classes and objects” (Booch, 1994). Object-oriented design (OOD) is a method of design “encompassing the process of OO decomposition and a notation for depicting both logical and physical as well as static and dynamic models of the system” (Booch, 1994).


Focus on Formative Feedback

This paper reviews the corpus of research on feedback, with a particular focus on formative feedback—defined as information communicated to the learner that is intended to modify the learner’s thinking or behavior for the purpose of improving learning. According to researchers in the area, formative feedback should be multidimensional, nonevaluative, supportive, timely, specific, credible, infrequent, and genuine (e.g., Brophy, 1981; Schwartz & White, 2000). Formative feedback is usually presented as information to a learner in response to some action on the learner’s part. It comes in a variety of types (e.g., verification of response accuracy, explanation of the correct answer, hints, worked examples) and can be administered at various times during the learning process (e.g., immediately following an answer, after some period of time has elapsed). Finally, there are a number of variables that have been shown to interact with formative feedback’s success at promoting learning (e.g., individual characteristics of the learner and aspects of the task). All of these issues will be discussed in this paper. This review concludes with a set of guidelines for generating formative feedback.


X Goes First: Teaching Simple Games through Multimodal Interaction

What would it take to teach a computer to play a game entirely through language and sketching? In this paper, we present an implemented program through which an instructor can teach the rules of simple board games using such input. We describe the architecture, information flow, and vocabulary of instructional events and walk through an annotated example. In our approach, the instructional and communication events guide abductive reasoning for language interpretation and help to integrate information from sketching and language. Having a general target representation enables the learning process to be viewed more as translation and problem solving than as induction. Lastly, learning by demonstration complements and extends instruction, resulting in concrete, operational rules.


Performance Analysis and Application of Mobile Blockchain

Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. In this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.


Indicator selection for daily equity trading with recurrent reinforcement learning

Recurrent reinforcement learning (RRL), a machine learning technique, is very successful in training high frequency trading systems. When trading analysis of RRL is done with lower frequency financial data, e.g. daily stock prices, the decrease of autocorrelation in prices may lead to a decrease in trading profit. In this paper, we propose a RRL trading system which utilizes the price information, jointly with the indicators from technical analysis, fundamental analysis and econometric analysis, to produce long/short signals for daily trading. In the proposed trading system, we use a genetic algorithm as a pre-screening tool to search suitable indicators for RRL trading. Moreover, we modify the original RRL parameter update scheme in the literature for out-of-sample trading. Empirical studies are conducted based on data sets of 238 S&P stocks. It is found that the trading performance concerning the out-of sample daily Sharpe ratios turns better: the number of companies with a positive and significant Sharpe ratio increases after feeding the selected indicators jointly with prices information into the RRL system.


Self-stabilizing Systems in Spite of Distributed Control

The synchronization task between loosely coupled cyclic sequential processes (as can be distinguished in, for instance, operating systems) can be viewed as keeping the relation “the system is in a legitimate state” invariant. As a result, each individual process step that could possibly cause violation of that relation has to be preceded by a test deciding whether the process in question is allowed to proceed or has to be delayed. The resulting design is readily—and quite systematically—implemented if the different processes can be granted mutually exclusive access to a common store in which “the current system state” is recorded.


Optimisation of the mover kinetic energy of a miniature linear actuator

The linear actuator presented in this paper is a core component of an electronically-controlled linear escapement mechanism. Its role is to unlock mechanical parts, its mover being used as a hammer. In this situation, the kinetic energy acquired by the mover at the position of the impact is a defining factor. The multiobjective optimisation presented in this paper targets the maximisation of said kinetic energy and the minimisation of the overall size of the actuator. In addition to the objective, the originality of this paper is that the control strategy as well as the behaviour of the guide of the mover are directly taken into account in the optimisation process.


Smart Contract Templates: foundations, design landscape and research directions

In this position paper, we consider some foundational topics regarding smart contracts (such as terminology, automation, enforceability, and semantics) and define a smart contract as an agreement whose execution is both automatable and enforceable. We explore a simple semantic framework for smart contracts, covering both operational and non-operational aspects. We describe templates and agreements for legally-enforceable smart contracts, based on legal documents. Building upon the Ricardian Contract triple, we identify operational parameters in the legal documents and use these to connect legal agreements to standardised code. We also explore the design landscape, including increasing sophistication of parameters, increasing use of common standardised code, and long-term academic research. We conclude by identifying further work and sketching an initial set of requirements for a common language to support Smart Contract Templates.


Natural language question answering over RDF: a graph data driven approach

RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a national language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.


A capability-based security approach to manage access control in the Internet of Things

Resource and information protection plays a relevant role in distributed systems like the ones present in the Internet of Things (IoT). Authorization frameworks like RBAC and ABAC do not provide scalable, manageable, effective, and efficient mechanisms to support distributed systems with many interacting services and are not able to effectively support the dynamicity and scaling needs of IoT contexts that envisage a potentially unbound number of sensors, actuators and related resources, services and subjects, as well as a more relevance of short-lived, unplanned and dynamic interaction patterns. Furthermore, as more end-users start using smart devices (e.g. smart phones, smart home appliances, etc.) the need to have more scalable, manageable, understandable and easy to use access control mechanisms increases. This paper describes a capability based access control system that enterprises, or even individuals, can use to manage their own access control processes to services and information. The proposed mechanism supports rights delegation and a more sophisticated access control customization. The proposed approach is being developed within the European FP7 IoT@Work project to manage access control to some of the project’s services deployed in the shop floor.


Automated video looping with progressive dynamism

Given a short video we create a representation that captures a spectrum of looping videos with varying levels of dynamism, ranging from a static image to a highly animated loop. In such a progressively dynamic video, scene liveliness can be adjusted interactively using a slider control. Applications include background images and slideshows, where the desired level of activity may depend on personal taste or mood. The representation also provides a segmentation of the scene into independently looping regions, enabling interactive local adjustment over dynamism. For a landscape scene, this control might correspond to selective animation and deanimation of grass motion, water ripples, and swaying trees. Converting arbitrary video to looping content is a challenging research problem. Unlike prior work, we explore an optimization in which each pixel automatically determines its own looping period. The resulting nested segmentation of static and dynamic scene regions forms an extremely compact representation.


Dwarf germplasm: the key to giant Cannabis hempseed and cannabinoid crops

After a century of banishment, both euphoric (“marijuana”) and non-euphoric (“industrial hemp”) classes of Cannabis sativa are attracting billions of dollars of investment as new legitimate crops. Most domesticated C. sativa is very tall, a phenotype that is desirable only for hemp fibre obtained from the stems. However, because the principal demands today are for chemicals from the inflorescence and oilseeds from the infructescence, an architecture maximizing reproductive tissues while minimizing stems is appropriate. Such a design was the basis of the greatest short-term increases in crop productivity in the history of agriculture: the creation of short-stature (“semi-dwarf”), high-harvest-index grain cultivars, especially by ideotype breeding, as demonstrated during the “Green Revolution.” This paradigm has considerable promise for C. sativa. The most critical dwarfing character for breeding such productivity into C. sativa is contraction of internodes. This reduces stem tissues (essentially a waste product except for fibre hemp) and results in compact inflorescences (which, on an area basis, maximize cannabinoid chemicals) and infructescences (which maximize oilseed production), as well as contributing to ease of harvesting and efficiency of production on an area basis. Four sources of germplasm useful for breeding semi-dwarf biotypes deserve special attention: (1) Naturally short northern Eurasian wild plants (often photoperiodically day-neutral, unlike like most biotypes) adapted to the stress of very short seasons by maximizing relative development of reproductive tissues. (2) Short, high-harvest-index, oilseed plants selected in northern regions of Eurasia. (3) “Indica type” marijuana, an ancient semi-dwarf cultigen tracing to the Afghanistan-Pakistan area. (4) Semi-dwarf strains of marijuana bred illegally in recent decades to avoid detection when grown clandestinely indoors for the black market. Although the high THC content in marijuana strains limits their usage as germplasm for low-THC cultivars, modern breeding techniques can control this variable. The current elimination of all marijuana germplasm from breeding of hemp cultivars is short-sighted because marijuana biotypes possess a particularly wide range of genes. There is an urgent need to develop public gene bank collections of Cannabis.


A 3.5-GHz PLL for fast low-IF/zero-IF LO switching in an 802.11 transceiver

A PLL technique is introduced that enables fast and accurate frequency switching, independent of the loop bandwidth. It uses separate tuning paths, each driving a separate VCO tune port. Different frequencies are produced by letting the VCO make different weighted combinations of the stable tuning voltages. The PLL converges to the stable tuning voltages by switching it a few times between the desired frequencies and tuning paths. Once the stabilized tuning voltages are found, one can switch between frequencies as fast as one can switch between K/sub VCO/s. The technique is applied to a 3.5-GHz integer-N PLL to enable fast jumping of the local oscillator (LO) frequency when an 802.11 transceiver is switched between a low and a zero intermediate frequency (LIF/ZIF). It uses dual phase/frequency detectors (PFD), charge pumps (CPs), and on-chip loop filters to control two separate low-leakage VCO tune ports. Each PFD/tune port combination can be (de)activated separately, without disturbing the loop filters' charge. The 50-kHz bandwidth PLL achieves a measured 7-MHz jump with /spl plusmn/20 kHz accuracy within 6 /spl mu/s. The measured phase noise is -123 dBc/Hz at 1-MHz offset.


Imaging of conjoined twins

The incidence of conjoined twins is estimated to be around 1 in 250,000 live births. There is a distinct female predominance. In this paper the imaging of conjoined twins both antenatally and postnatally is reviewed, in particular taking into consideration recent advances with multidetector CT. Accurate counselling of parents regarding the likely outcome of the pregnancy and the likelihood of successful separation is dependent on good prenatal imaging with ultrasound and MRI. Planning of postnatal surgical separation is aided by accurate preoperative imaging which, depending on the conjoined area, will encompass many imaging modalities, but often relies heavily on CT scanning.


Sentiment Analysis of Twitter Data

We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity features. (2) We explore the use of a tree kernel to obviate the need for tedious feature engineering. The new features (in conjunction with previously proposed features) and the tree kernel perform approximately at the same level, both outperforming the state-of-the-art baseline.


Understanding Judgment of Information Quality and Cognitive Authority in the WWW

In the WWW, people are engaging in interaction with more, and more diverse information than ever before, so there is an increasing need for information “filtering.” But because of the diversity of information resources, and the lack of traditional quality control on the WWW, the criteria of authority and quality of information that people have used for this purpose in past contexts may no longer be relevant. This paper reports on a study of people’s decision making with respect to quality and authority in the WWW. Seven facets of judgment of information quality were identified: source, content, format, presentation, currency, accuracy, and speed of loading. People mentioned source credibility with two levels: institutional level and individual level. Authority was identified as a underlying theme in source credibility. Institutional authority involved: institutional domain identified by URL; institution type; and institution reputation recognized by names. Individual authority involved: identification of creator/author; creator/author affiliation; and creator/author’s name. People were more or less concerned with evaluating information quality depending upon: the consequence of use of information; act or commitment based on information; and the focus of inquiry. It was also found that people believed that the web, as an institution, was less authoritative and less credible than other types of information systems.


Containment of misinformation spread in online social networks

With their blistering expansions in recent years, popular on-line social sites such as Twitter, Facebook and Bebo, have become some of the major news sources as well as the most effective channels for viral marketing nowadays. However, alongside these promising features comes the threat of misinformation propagation which can lead to undesirable effects, such as the widespread panic in the general public due to faulty swine flu tweets on Twitter in 2009. Due to the huge magnitude of online social network (OSN) users and the highly clustered structures commonly observed in these kinds of networks, it poses a substantial challenge to efficiently contain viral spread of misinformation in large-scale social networks.
 In this paper, we focus on how to limit viral propagation of misinformation in OSNs. Particularly, we study a set of problems, namely the β1T -- Node Protectors, which aims to find the smallest set of highly influential nodes whose decontamination with good information helps to contain the viral spread of misinformation, initiated from the set I, to a desired ratio (1 − β) in T time steps. In this family set, we analyze and present solutions including inapproximability result, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based heuristic method for the Node Protector problems. To verify our suggested solutions, we conduct experiments on real world traces including NetHEPT, NetHEPT_WC and Facebook networks. Empirical results indicate that our methods are among the best ones for hinting out those important nodes in comparison with other available methods.


Side Channels in Deduplication: Trade-offs between Leakage and Efficiency

Deduplication removes redundant copies of files or data blocks stored on the cloud. Client-side deduplication, where the client only uploads the file upon the request of the server, provides major storage and bandwidth savings, but introduces a number of security concerns. Harnik et al. (2010) showed how cross-user client-side deduplication inherently gives the adversary access to a (noisy) side-channel that may divulge whether or not a particular file is stored on the server, leading to leakage of user information. We provide formal definitions for deduplication strategies and their security in terms of adversarial advantage. Using these definitions, we provide a criterion for designing good strategies and then prove a bound characterizing the necessary trade-off between security and efficiency.


Using speakers' referential intentions to model early cross-situational word learning.

Word learning is a "chicken and egg" problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.


The Ideal Elf: Identity Exploration in World of Warcraft

In this study, we examine the identity exploration possibilities presented by online multiplayer games in which players use graphics tools and character-creation software to construct an avatar, or character. We predicted World of Warcraft players would create their main character more similar to their ideal self than the players themselves were. Our results support this idea; a sample of players rated their character as having more favorable attributes that were more favorable than their own self-rated attributes. This trend was stronger among those with lower psychological well-being, who rated themselves comparatively lower than they rated their character. Our results suggest that the game world allows players the freedom to create successful virtual selves regardless of the constraints of their actual situation.


Detection of Atrial Fibrillation using model-based ECG analysis

Atrial fibrillation (AF) is an arrhythmia that can lead to several patient risks. This kind of arrhythmia affects mostly elderly people, in particular those who suffer from heart failure (one of the main causes of hospitalization). Thus, detection of AF becomes decisive in the prevention of cardiac threats. In this paper an algorithm for AF detection based on a novel algorithm architecture and feature extraction methods is proposed. The aforementioned architecture is based on the analysis of the three main physiological characteristics of AF: i) P wave absence ii) heart rate irregularity and iii) atrial activity (AA). Discriminative features are extracted using model-based statistic and frequency based approaches. Sensitivity and specificity results (respectively, 93.80% and 96.09% using the MIT-BIH AF database) show that the proposed algorithm is able to outperform state-of-the-art methods.


Index Financial Time Series Based on Zigzag-Perceptually Important Points

Problem statement: Financial time series were usually large in size, unstructured and of high dimensionality. Since, the illustration of fin a cial time series shape was typically characterize d by a few number of important points. These important p oints moved in zigzag directions which could form technical patterns. However, these important p oints exhibited in different resolutions and diffic ult to determine. Approach: In this study, we proposed novel methods of financ i l time series indexing by considering their zigzag movement. The methods c onsist of two major algorithms: first, the identification of important points, namely the Zigz ag-Perceptually Important Points (ZIPs) identification method and next, the indexing method namely Zigzag based M-ary Tree (ZM-Tree) to structure and organize the important points. Results: The errors of the tree building and retrieving compared to the original time series increased when t important points increased. The dimensionality reduction using ZM-Tree based on tree pruning and n umber of retrieved points techniques performed better when the number of important points increase d. Conclusion: Our proposed techniques illustrated mostly acceptable performance in tree op rations and dimensionality reduction comparing to existing similar technique like Specialize Binar y Tree (SB-Tree).


Towards High Performance Video Object Detection

There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Built upon the recent works [37, 36], this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection.


Fusion of Stereo Vision for Pedestrian Recognition using Convolutional Neural Networks

Pedestrian detection is a highly debated issue in the scientific community due to its outstanding importance for a large number of applications, especially in the fields of automotive safety, robotics and surveillance. In spite of the widely varying methods developed in recent years, pedestrian detection is still an open challenge whose accuracy and robustness has to be improved. Therefore, in this paper, we focus on improving the classification component in the pedestrian detection task on the Daimler stereo vision data set by adopting two approaches: 1) by combining three image modalities (intensity, depth and flow) to feed a unique convolutional neural network (CNN) and 2) by fusing the results of three independent CNNs.


Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band

The market for driver assistance systems based on millimeter-wave radar sensor technology is gaining momentum. In the near future, the full range of newly introduced car models will be equipped with radar based systems which leads to high volume production with low cost potential. This paper provides background and an overview of the state of the art of millimeter-wave technology for automotive radar applications, including two actual silicon based fully integrated radar chips. Several advanced packaging concepts and antenna systems are presented and discussed in detail. Finally measurement results of the fully integrated radar front ends are shown.


Science-based views of drug addiction and its treatment.


Massively parallel Monte Carlo for many-particle simulations on GPUs

Current trends in parallel processors call for the design of efficient massively parallel algorithms for scientific computi ng. Parallel algorithms for Monte Carlo simulations of thermodynamic en s mbles of particles have received little attention becaus e of the inherent serial nature of the statistical sampling. In this paper, we present a massively parallel method that obeys det ailed balance and implement it for a system of hard disks on the GPU. We repro duce results of serial high-precision Monte Carlo runs to ve rify the method. This is a good test case because the hard disk equa tion of state over the range where the liquid transforms into the solid is particularly sensitive to small deviations away from the balance conditions. On a Tesla K20, our GPU implementation e xecutes over one billion trial moves per second, which is 148 times fa ster than on a single Intel Xeon E5540 CPU core, enables 27 tim es better performance per dollar, and cuts energy usage by a fac tor of 13. With this improved performance we are able to calcu l te the equation of state for systems of up to one million hard dis ks. These large system sizes are required in order to probe th e nature of the melting transition, which has been debated for the las t forty years. In this paper we present the details of our comp utational method, and discuss the thermodynamics of hard disks separa t ly in a companion paper.


CNN-based ranking for biomedical entity normalization

Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.


Trending topic prediction on social network

The fast information sharing on social network services generates more than thousands of topics every day. It is extremely important for business organizations and administrative decision makers to learn the popularity of these topics as quickly as possible. In this paper, we propose a prediction mode based on SVM with features of three subsets: quantity specific features, quality and user specific features which supplement each other. Furthermore, we divide topic data into time slices which is used as a unit of feature construction. Our findings suggest that the capability of our prediction model outperforms previous methods and also reveals that subsets of features play different role in the prediction of trending topics.


NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison

Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as NoSQL, NewSQL and Search-based systems. This paper motivation is to provide classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.


Towards parameter-free data mining

Most data mining algorithms require the setting of many input parameters. Two main dangers of working with parameter-laden algorithms are the following. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process.Data mining algorithms should have as few parameters as possible, ideally none. A parameter-free algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics and computational theory hold great promise for a parameter-free data-mining paradigm. The results are motivated by observations in Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen or so lines of code. We will show that this approach is competitive or superior to the state-of-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/video datasets.


Do Differences in Password Policies Prevent Password Reuse?

Password policies were originally designed to make users pick stronger passwords. However, research has shown that they often fail to achieve this goal. In a systematic audit of the top 100 web sites in Germany, we explore if diversity in current real-world password policies prevents password reuse. We found that this is not the case: we are the first to show that a single password could hypothetically fulfill 99% of the policies under consideration. This is especially problematic because password reuse exposes users to similar risks as weak passwords. We thus propose a new approach for policies that focuses on password reuse and respects other websites to determine if a password should be accepted. This re-design takes current user behavior into account and potentially boosts the usability and security of password-based authentication.


Review on Machine Learning Techniques for Automatic Segmentation of Liver Images

The segmentation of liver from abdominal images has gained huge importance in medical imaging field since it is fundamental step to diagnose liver tumor and other liver diseases, liver volume measurement and 3D liver volume rendering. In this paper, recent automatic methods for liver image segmentation proposed in literature are reviewed. Generally, Liver image segmentation techniques can be semi-automatic and fully automatic. Under fully automatic segmentation category, various methods, approaches, recent advances and related problems has been explained. Brief summary of all discussed methods has been provided. Finally it is concluded that liver image segmentation is still an open problem since various drawbacks of proposed methods can be addressed. Keywords— Liver image Segmentation, Automatic Segmentation, Neural Network, Support Vector Machine, Clustering, Hybrid, Review.


Vacuum-assisted thermal annealing of CH3NH3PbI3 for highly stable and efficient perovskite solar cells.

Solar cells incorporating lead halide-based perovskite absorbers can exhibit impressive power conversion efficiencies (PCEs), recently surpassing 15%. Despite rapid developments, achieving precise control over the morphologies of the perovskite films (minimizing pore formation) and enhanced stability and reproducibility of the devices remain challenging, both of which are necessary for further advancements. Here we demonstrate vacuum-assisted thermal annealing as an effective means for controlling the composition and morphology of the CH(3)NH(3)PbI(3) films formed from the precursors of PbCl(2) and CH(3)NH(3)I. We identify the critical role played by the byproduct of CH(3)NH(3)Cl on the formation and the photovoltaic performance of the perovskite film. By completely removing the byproduct through our vacuum-assisted thermal annealing approach, we are able to produce pure, pore-free planar CH(3)NH(3)PbI(3) films with high PCE reaching 14.5% in solar cell device. Importantly, the removal of CH(3)NH(3)Cl significantly improves the device stability and reproducibility with a standard deviation of only 0.92% in PCE as well as strongly reducing the photocurrent hysteresis.


Interpreting BLEU/NIST Scores: How Much Improvement do We Need to Have a Better System?

Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU and the related NIST metric, are becoming increasingly important in MT. Yet, their behaviors are not fully understood. In this paper, we analyze some flaws in the BLEU/NIST metrics. With a better understanding of these problems, we can better interpret the reported BLEU/NIST scores. In addition, this paper reports a novel method of calculating the confidence intervals for BLEU/NIST scores using bootstrapping. With this method, we can determine whether two MT systems are significantly different from each other.


Low-power flyback converter with synchronous rectification for a system with AC power distribution

IEC 1000-3-2 regulations impose a reduced harmonic content on any converter with an input power higher than 75 W. However, if the power architecture of the system is based on small on-board converters, and the total power is higher than 75 W, IEC regulations must be fulfilled although each individual converter need not comply with the regulations. In this paper, one of the different possible solutions is presented. Each on-board converter has an active input current shaper (AICS) in order to reduce the input current harmonic content of each converter and, hence, to comply with IEC 1000-3-2 regulations. Moreover, two different types of AICSs were compared: the conventional one and a new type of AICS based on a full-wave rectifier.


An analysis of 3D point cloud reconstruction from light field images

Current methodologies for the generation of 3D point cloud from real world scenes rely upon a set of 2D images capturing the scene from several points of view. Novel plenoptic cameras sample the light field crossing the main camera lens creating a light field image. The information available in a plenoptic image must be processed in order to render a view or create the depth map of the scene. This paper analyses a method for the reconstruction of 3D models. The reconstruction of the model is obtained from a single image shot. Exploiting the properties of plenoptic images, a point cloud is generated and compared with a point cloud of the same object but generated with a different plenoptic camera.


Multi-robot long-term persistent coverage with fuel constrained robots

In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.


Toward Scalable Verification for Safety-Critical Deep Networks

The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state-of-the-art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification. ACM Reference Format: Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, and Mykel J. Kochenderfer. 2018. Toward Scalable Verification for SafetyCritical Deep Networks. In Proceedings of SysML Conference (SysML). ACM, New York, NY, USA, 3 pages.


Identifying careless responses in survey data.

When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been suggested for identifying careless respondents via indices computed from the data, yet almost no prior work has examined the relationships among these indicators or the types of data patterns identified by each. In 2 studies, we examined several methods for identifying careless responses, including (a) special items designed to detect careless response, (b) response consistency indices formed from responses to typical survey items, (c) multivariate outlier analysis, (d) response time, and (e) self-reported diligence. Results indicated that there are two distinct patterns of careless response (random and nonrandom) and that different indices are needed to identify these different response patterns. We also found that approximately 10%-12% of undergraduates completing a lengthy survey for course credit were identified as careless responders. In Study 2, we simulated data with known random response patterns to determine the efficacy of several indicators of careless response. We found that the nature of the data strongly influenced the efficacy of the indices to identify careless responses. Recommendations include using identified rather than anonymous responses, incorporating instructed response items before data collection, as well as computing consistency indices and multivariate outlier analysis to ensure high-quality data.


A new scale of social desirability independent of psychopathology.

It has long been recognized that personality test scores are influenced by non-test-relevant response determinants. Wiggins and Rumrill (1959) distinguish three approaches to this problem. Briefly, interest in the problem of response distortion has been concerned with attempts at statistical correction for "faking good" or "faking bad" (Meehl & Hathaway, 1946), the analysis of response sets (Cronbach, 1946,1950), and ratings of the social desirability of personality test items (Edwards, 19 5 7). A further distinction can be made, however, which results in a somewhat different division of approaches to the question of response distortion. Common to both the Meehl and Hathaway corrections for faking good and faking bad and Cronbach's notion of response sets is an interest in the test behavior of the subject(S). By social desirability, on the other hand, Edwards primarily means the "scale value for any personality statement such that the scale value indicates the position of the statement on the social desirability continuum . . ." (1957, p. 3). Social desirability, thus, has been used to refer to a characteristic of test items, i.e., their scale position on a social desirability scale. Whether the test behavior of 5s or the social desirability properties of items are the focus of interest, however, it now seems clear that underlying both these approaches is the concept of statistical deviance. In the construction of the MMPI K scale, for example, items were selected which differentiated between clinically normal persons producing abnormal te¥Tpfpfiles~snd^cTinically abnormal individuals with abnormal test profiles, and between clinically abnormal persons with normal test profiles and abnormal 5s whose test records were abnormal. Keyed responses to the K scale items tend to be statistically deviant in the parent populations. Similarly, the development of the Edwards Social Desirability Scale (SDS) illustrates this procedure. Items were drawn from various MMPI scales (F, L, K, and the Manifest Anxiety Scale [Taylor, 1953]) and submitted to judges who categorized them as either socially desirable or socially undesirable. Only items on which there was unanimous agreement among the 10 judges were included in the SDS. It seems clear that the items in Edwards SDS would, of necessity, have extreme social desirability scale positions or, in other words, be statistically deviant. Some unfortunate consequences follow from the strict use of the statistical deviance model in the development of-sOcialTtesirSbTBty scales. With items drawn from the MMPI, it is apparent that in addition to their scalability for social desirability the items may also be characterized by their content which,^n a general sense, has pathological implications. When a social desrrabtltty^scale constructed according to this procedure is then applied to a college student population, the meaning of high social desirability scores is not at all clear. When 5s given the Edwards SDS deny, for example, that their sleep is fitful and disturbed (Item 6) or that they worry quite a bit over possible misfortunes (Item 35), it cannot be determined whether these responses are attributable to social desirability or to a genuine absence of such symptoms. The probability of occurrence of the symptoms represented in MMPI items (and incorportated in the SDS)


Simultaneous administration of the Rosenberg Self-Esteem Scale in 53 nations: exploring the universal and culture-specific features of global self-esteem.

The Rosenberg Self-Esteem Scale (RSES) was translated into 28 languages and administered to 16,998 participants across 53 nations. The RSES factor structure was largely invariant across nations. RSES scores correlated with neuroticism, extraversion, and romantic attachment styles within nearly all nations, providing additional support for cross-cultural equivalence of the RSES. All nations scored above the theoretical midpoint of the RSES, indicating generally positive self-evaluation may be culturally universal. Individual differences in self-esteem were variable across cultures, with a neutral response bias prevalent in more collectivist cultures. Self-competence and self-liking subscales of the RSES varied with cultural individualism. Although positively and negatively worded items of the RSES were correlated within cultures and were uniformly related to external personality variables, differences between aggregates of positive and negative items were smaller in developed nations. Because negatively worded items were interpreted differently across nations, direct cross-cultural comparisons using the RSES may have limited value.


Assessing psychopathic attributes in a noninstitutionalized population.

The present study examined antisocial dispositions in 487 university students. Primary and secondary psychopathy scales were developed to assess a protopsychopathic interpersonal philosophy. An antisocial action scale also was developed for purposes of validation. The primary, secondary, and antisocial action scales were correlated with each other and with boredom susceptibility and disinhibition but not with experience seeking and thrill and adventure seeking. Secondary psychopathy was associated with trait anxiety. Multiple regression analysis revealed that the strongest predictors of antisocial action were disinhibition, primary psychopathy, secondary psychopathy, and sex, whereas thrill and adventure seeking was a negative predictor. This argues against a singular behavioral inhibition system mediating both antisocial and risk-taking behavior. These findings are also consistent with the view that psychopathy is a continuous dimension.


Wound healing activity of the fruit skin of Punica granatum.

The skin of the fruit and the bark of Punica granatum are used as a traditional remedy against diarrhea, dysentery, and intestinal parasites. The fruit skin extract of P. granatum was tested for its wound healing activity in rats using an excision wound model. The animals were divided into three groups of six each. The experimental group of animals was topically treated with P. granatum at a dose of 100 mg/kg every day for 15 days, while the controls and standard group animals were treated with petroleum jelly and mupirocin ointment, respectively. Phytochemical analysis of the extract revealed the presence of saponins, triterpenes, tannins, alkaloids, flavonoids, and cardiac glycosides. Extract-treated animals exhibited 95% reduction in the wound area when compared with controls (84%), which was statistically significant (P<.01). The extract-treated wounds were found to epithelize faster compared with controls. The hydroxyproline content of extract-treated animals was significantly higher than controls (P<.05). The fruit skin extract did not show any antimicrobial activity against the microrganisms tested. P. granatum promotes significant wound healing in rats and further evaluation of this activity in humans is suggested.


Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders.

Functional near-infrared spectroscopy (fNIRS) is an emerging functional neuroimaging technology offering a relatively non-invasive, safe, portable, and low-cost method of indirect and direct monitoring of brain activity. Most exciting is its potential to allow more ecologically valid investigations that can translate laboratory work into more realistic everyday settings and clinical environments. Our aim is to acquaint clinicians and researchers with the unique and beneficial characteristics of fNIRS by reviewing its relative merits and limitations vis-à-vis other brain-imaging technologies such as functional magnetic resonance imaging (fMRI). We review cross-validation work between fMRI and fNIRS, and discuss possible reservations about its deployment in clinical research and practice. Finally, because there is no comprehensive review of applications of fNIRS to brain disorders, we also review findings from the few studies utilizing fNIRS to investigate neurocognitive processes associated with neurological (Alzheimer's disease, Parkinson's disease, epilepsy, traumatic brain injury) and psychiatric disorders (schizophrenia, mood disorders, anxiety disorders).


Dose the Use of Big Data Analytics Guarantee a High Firm Performance? An Empirical Study Bases on the Dynamic Capabilities Theory

For the sake of enriching relevant research field, this study focuses on two main questions: (1) What is the effect of DBA usage on firm performance (including market performance and operational performance)? and (2) What factors that drive organizations to use big data analytics (BDA) are key drivers, based on dynamic capabilities theory? And we also identified the moderating effect of environmental dynamism between BDA use and firm performance. Furthermore, we introduce the need pull/technology push framework to identify and theorize paths via which factors influence the use of BDA. Purely from the need pull/technology push theory perspective, we propose that perceived usefulness of BDA should belong to need pull factor. Thus, we select perceived usefulness and satisfaction level with existing IT technology as need pull factors, BDA use of competitors and technology compatibility as technology push factors for big data analytics (BDA) use, and examines if these factors have a positive effect on big data analytics (BDA).


Performance Comparison between Five NoSQL Databases

Recently NoSQL databases and their related technologies are developing rapidly and are widely applied in many scenarios with their BASE (Basic Availability, Soft state, Eventual consistency) features. At present, there are more than 225 kinds of NoSQL databases. However, the overwhelming amount and constantly updated versions of databases make it challenging for people to compare their performance and choose an appropriate one. This paper is trying to evaluate the performance of five NoSQL clusters (Redis, MongoDB, Couchbase, Cassandra, HBase) by using a measurement tool – YCSB (Yahoo! Cloud Serving Benchmark), explain the experimental results by analyzing each database's data model and mechanism, and provide advice to NoSQL developers and users.


Cathodic protection by zinc sacrificial anodes: impact on marine sediment metallic contamination.

Cathodic protection by sacrificial zinc anodes is often applied to prevent immerged metallic structures from corrosion. But this technique induces the zinc anodes dissolution, which can induce marine sediments and seawater contamination. A large scale experiment, in natural seawater, was conducted during 12 months, in order to evaluate the potential environmental impact of this continuous zinc dissolution, and of some necessary cleaning operations of the anodes surfaces. The heavy metal (Cr, Cu, Pb and Zn) concentration in water and sediment samples was monitored. A sequential extraction procedure was applied on sediment samples to differentiate the zinc mobile fractions from the residual one. A significant increase of zinc concentration was observed in water as well as in the surface sediments under the specific operating conditions. Sediments then become a secondary pollution source, as the sorbed labile zinc can be remobilized to seawater.


Larger-Context Language Modelling with Recurrent Neural Network

In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank), we demonstrate that the proposed model improves perplexity significantly. In the experiments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained largercontext language model, we discover that content words, including nouns, adjectives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily.


Title Parental Expectations and Children ' s Academic Performance in Sociocultural Context

In this paper, we review research on parental expectations and their effects on student achievement within and across diverse racial and ethnic groups. Our review suggests that the level of parental expectations varies by racial/ethnic group, and that students' previous academic performance is a less influential determinant of parental expectations among racial/ethnic minority parents than among European American parents. To explain this pattern, we identify three processes associated with race/ethnicity that moderate the relation between students' previous performance and parental expectations. Our review also indicates that the relation of parental expectations to concurrent or future student achievement outcomes is weaker for racial/ethnic minority families than for European American families. We describe four mediating processes by which high parental expectations may influence children's academic trajectories and show how these processes are associated with racial/ethnic status. The article concludes with a discussion of educational implications as well as suggestions for future research.


Frequency Enhancement in Miller Divider with Injection-Locking Portrait

In this paper, we present a methodology to enhance the operating range of a Miller frequency divider. Conventionally Miller frequency dividers are visualized as mixers. We present how injection locking portrait of Miller dividers helps us to understand the dynamics of Miller frequency dividers. Further, we discuss how to enhance the operating range while optimizing the divider between power dissipation and operating range. The Miller divider with the proposed methodology has been designed in a standard CMOS 65 nm (low leakage) process. Post layout simulation results show that Miller divider with optimization techniques presented in this paper can operate over the frequency range of 13 GHz for 0dBm input power at center frequency of 61.5 GHz.


Does Attendance Matter ? An Examination of Student Attitudes , Participation , Performance and Attendance

Non attendance of lectures and tutorials appears to be a growing trend. The literature suggests many possible reasons including students’ changing lifestyle, attitudes, teaching and technology. This paper looks at the reasons for non attendance of students in the Faculty of Commerce at the University of Wollongong and identifies relationships between attendance, participation and performance. The results indicate that there are valid reasons for non attendance that are both in the control of learners and teachers. There are also clear benefits for students to be gained in attendance; however, changes in the way we learn, teach, assess and use technology are recommended if we wish to reverse the trend. This journal article is available in Journal of University Teaching & Learning Practice: http://ro.uow.edu.au/jutlp/vol3/iss2/3 Jour na l o f Un ive rs i t y Teach ing and Lear n ing Pr ac t i ce Does Attendance Matter? An Examination of Student Attitudes, Participation, Performance and Attendance


Applied machine vision of plants: a review with implications for field deployment in automated farming operations

Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.


SmartPaste: Learning to Adapt Source Code

Deep Neural Networks have been shown to succeed at a range of natural language tasks such as machine translation and text summarization. While tasks on source code (ie, formal languages) have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure. In this work, we introduce SmartPaste, a first task that requires to use such information. The task is a variant of the program repair problem that requires to adapt a given (pasted) snippet of code to surrounding, existing source code. As first solutions, we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way. Our evaluation suggests that our models can learn to solve the SmartPaste task in many cases, achieving 58.6% accuracy, while learning meaningful representation of variable usages.


Analysis and reduction of quadrature errors in the material point method ( MPM )

The material point method (MPM) has demonstrated itself as a computationally effective particle method for solving solid mechanics problems involving large deformations and/or fragmentation of structures, which are sometimes problematic for finite element methods (FEMs). However, similar to most methods that employ mixed Lagrangian (particle) and Eulerian strategies, analysis of the method is not straightforward. The lack of an analysis framework for MPM, as is found in FEMs, makes it challenging to explain anomalies found in its employment and makes it difficult to propose methodology improvements with predictable outcomes. In this paper we present an analysis of the quadrature errors found in the computation of (material) internal force in MPM and use this analysis to direct proposed improvements. In particular, we demonstrate that lack of regularity in the grid functions used for representing the solution to the equations of motion can hamper spatial convergence of the method. We propose the use of a quadratic B-spline basis for representing solutions on the grid, and we demonstrate computationally and explain theoretically why such a small change can have a significant impact on the reduction in the internal force quadrature error (and corresponding ‘grid crossing error’) often experienced when using MPM. Copyright q 2008 John Wiley & Sons, Ltd.


Handbook of Natural Language Processing

Handbook of Natural Language Processing and Machine Translation · Learning Natural Language Processing With Python and NLTK p.1 Tokenizing words and By far, the most popular toolkit or API to do natural language processing... Nitin Indurkhya, Fred J. Damerau, Handbook of Natural Language Processing, Second Edition. Chapman & Hall/CRC Machine Learning & Pattern Recognition. Arabic Natural Language Processing, Machine Translation Handbook of Natural Language Processing and Machine Translation, 164-175, 2011. 2011.


A construction of cryptography system based on quantum neural network

Quantum neural networks (QNNs) have been explored as one of the best approach for improving the computational efficiency of neural networks. Because of the powerful and fantastic performance of quantum computation, some researchers have begun considering the implications of quantum computation on the field of artificial neural networks (ANNs).The purpose of this paper is to introduce an application of QNNs in construction of cryptography system in which two networks exchange their outputs (in qubits) and the key to be synchronized between two communicating parties. This system is based on multilayer qubit QNNs trained with back-propagation algorithm.


A Multi-View Fusion Neural Network for Answer Selection

Community question answering aims at choosing the most appropriate answer for a given question, which is important in many NLP applications. Previous neural network-based methods consider several different aspects of information through calculating attentions. These different kinds of attentions are always simply summed up and can be seen as a “single view”, causing severe information loss. To overcome this problem, we propose a Multi-View Fusion Neural Network, where each attention component generates a “view” of the QA pair and a fusion RNN integrates the generated views to form a more holistic representation. In this fusion RNN method, a filter gate collects important information of input and directly adds it to the output, which borrows the idea of residual networks. Experimental results on the WikiQA and SemEval-2016 CQA datasets demonstrate that our proposed model outperforms the state-of-the-art methods.


Calming effects of deep touch pressure in patients with autistic disorder, college students, and animals.

ABSTRACT Many people with autistic disorder have problems with oversensitivity to both touch and sound. The author (an autistic person) developed a device that delivers deep touch pressure to help her learn to tolerate touching and to reduce anxiety and nervousness. The "squeeze machine" applies lateral, inwardly directed pressure to both lateral aspects of a person's entire body, by compressing the user between two foam-padded panels. Clinical observations and several studies suggest that deep touch pressure is therapeutically beneficial for both children with autistic disorder and probably children with attention-deficit hyperactivity disorder. Only minor and occasional adverse effects have been noted. Data are reported that show a similar calming effect in nonreferred college students. A review of the animal literature reveals that animals have similar calming reactions, and also suggests possible additional physiological effects of deep touch pressure. At present, there are increasing anecdotal reports of the clinical value of the squeeze machine, including suggestions that it can be used to reduce required doses of psychostimulant medications. More clinical studies are needed to evaluate the potential role of this seemingly beneficial form of "physiological" stimulation.


"Oops, I Did It Again" - Security of One-Time Signatures Under Two-Message Attacks

One-time signatures (OTS) are called one-time, because the accompanying security reductions only guarantee security under single-message attacks. However, this does not imply that efficient attacks are possible under two-message attacks. Especially in the context of hash-based OTS (which are basic building blocks of recent standardization proposals) this leads to the question if accidental reuse of a one-time key pair leads to immediate loss of security or to graceful degradation. In this work we analyze the security of the most prominent hash-based OTS, Lamport’s scheme, its optimized variant, and WOTS, under different kinds of two-message attacks. Interestingly, it turns out that the schemes are still secure under two message attacks, asymptotically. However, this does not imply anything for typical parameters. Our results show that for Lamport’s scheme, security only slowly degrades in the relevant attack scenarios and typical parameters are still somewhat secure, even in case of a two-message attack. As we move on to optimized Lamport and its generalization WOTS, security degrades faster and faster, and typical parameters do not provide any reasonable level of security under two-message attacks.


Multi-granularity Generator for Temporal Action Proposal

Temporal action proposal generation is an important task, aiming to localize the video segments containing human actions in an untrimmed video. In this paper, we propose a multi-granularity generator (MGG) to perform the temporal action proposal from different granularity perspectives, relying on the video visual features equipped with the position embedding information. First, we propose to use a bilinear matching model to exploit the rich local information within the video sequence. Afterwards, two components, namely segment proposal generator (SPG) and frame actionness generator (FAG), are combined to perform the task of temporal action proposal at two distinct granularities. SPG considers the whole video in the form of feature pyramid and generates segment proposals from one coarse perspective, while FAG carries out a finer actionness evaluation for each video frame. Our proposed MGG can be trained in an end-to-end fashion. Through temporally adjusting the segment proposals with fine-grained information based on frame actionness, MGG achieves the superior performance over state-of-the-art methods on the public THUMOS-14 and ActivityNet-1.3 datasets. Moreover, we employ existing action classifiers to perform the classification of the proposals generated by MGG, leading to significant improvements compared against the competing methods for the video detection task.


The human amygdala and the emotional evaluation of sensory stimuli

A wealth of animal data implicates the amygdala in aspects of emotional processing. In recent years, functional neuroimaging and neuropsychological studies have begun to refine our understanding of the functions of the amygdala in humans. This literature offers insights into the types of stimuli that engage the amygdala and the functional consequences that result from this engagement. Specific conclusions and hypotheses include: (1) the amygdala activates during exposure to aversive stimuli from multiple sensory modalities; (2) the amygdala responds to positively valenced stimuli, but these responses are less consistent than those induced by aversive stimuli; (3) amygdala responses are modulated by the arousal level, hedonic strength or current motivational value of stimuli; (4) amygdala responses are subject to rapid habituation; (5) the temporal characteristics of amygdala responses vary across stimulus categories and subject populations; (6) emotionally valenced stimuli need not reach conscious awareness to engage amygdala processing; (7) conscious hedonic appraisals do not require amygdala activation; (8) activation of the amygdala is associated with modulation of motor readiness, autonomic functions, and cognitive processes including attention and memory; (9) amygdala activations do not conform to traditional models of the lateralization of emotion; and (10) the extent and laterality of amygdala activations are related to factors including psychiatric status, gender and personality. The strengths and weakness of these hypotheses and conclusions are discussed with reference to the animal literature.


Economic dispatch for a microgrid considering renewable energy cost functions

Microgrids are operated by a customer or a group of customers for having a reliable, clean and economic mode of power supply to meet their demand. Understanding the economics of system is a prime factor which really depends on the cost/kWh of electricity supplied. This paper presents an easy and simple method for analyzing the dispatch rate of power. An isolated microgrid with solar and wind is considered in this paper. Generation cost functions are modeled with the inclusion of investment cost and maintenance cost of resources. Economic dispatch problem is solved using the reduced gradient method. The effects on total generation cost, with the inclusion of wind energy and solar energy into a microgrid is studied and found the most profitable solution by considering different practical scenarios. The paper gives a detailed correlation between the cost function, investment cost, lifetime and the fluctuant energy forecasting of wind and solar resources. It also discusses the advantages of including the renewable energy credits for the solar panel.