IR sensitivity enhancement of CMOS Image Sensor with diffractive light trapping pixels

We report on the IR sensitivity enhancement of back-illuminated CMOS Image Sensor (BI-CIS) with 2-dimensional diffractive inverted pyramid array structure (IPA) on crystalline silicon (c-Si) and deep trench isolation (DTI). FDTD simulations of semi-infinite thick c-Si having 2D IPAs on its surface whose pitches over 400 nm shows more than 30% improvement of light absorption at λ = 850 nm and the maximum enhancement of 43% with the 540 nm pitch at the wavelength is confirmed. A prototype BI-CIS sample with pixel size of 1.2 μm square containing 400 nm pitch IPAs shows 80% sensitivity enhancement at λ = 850 nm compared to the reference sample with flat surface. This is due to diffraction with the IPA and total reflection at the pixel boundary. The NIR images taken by the demo camera equip with a C-mount lens show 75% sensitivity enhancement in the λ = 700–1200 nm wavelength range with negligible spatial resolution degradation. Light trapping CIS pixel technology promises to improve NIR sensitivity and appears to be applicable to many different image sensor applications including security camera, personal authentication, and range finding Time-of-Flight camera with IR illuminations.


The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations

This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAs and one-dimensional random walks. Using the solution to a classic random walk problemthe gambler's ruinthe model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its competitors. The result is an equation that relates the size of the population with the desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.


Deep Learning Approaches to Semantic Relevance Modeling for Chinese Question-Answer Pairs

The human-generated question-answer pairs in the Web social communities are of great value for the research of automatic question-answering technique. Due to the large amount of noise information involved in such corpora, it is still a problem to detect the answers even though the questions are exactly located. Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. Since both the questions and their answers usually contain a small number of sentences, the relevance modeling methods have to overcome the problem of word feature sparsity. In this article, the deep learning principle is introduced to address the semantic relevance modeling task. Two deep belief networks with different architectures are proposed by us to model the semantic relevance for the question-answer pairs. According to the investigation of the textual similarity between the community-driven question-answering (cQA) dataset and the forum dataset, a learning strategy is adopted to promote our models’ performance on the social community corpora without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.


JINS MEME algorithm for estimation and tracking of concentration of users

Activity tracking using a wearable device is an emerging research field. Large-scale studies on activity tracking performed with eyewear-type wearable devices remains a challenging area owing to the negative effect such devices have on users' looks. To cope with this challenge, JINS Inc., an eyewear retailer in Japan, has developed a state-of-the-art smart eyewear called JINS MEME. The design of JINS MEME is almost the same as that of Wellington-type eyeglasses so that people can use it in their daily lives. JINS MEME is equipped with sensors to detect a user's eye movement and head motion. In addition to these functions of JINS MEME, JINS developed an application to measure concentration levels of users. In this demonstration, users will experience wearing the JINS MEME glasses and their concentration will be measured while they perform a certain task at our booth.


A 5GS/s voltage-to-time converter in 90nm CMOS

A voltage-to-time converter (VTC) is presented for use in a time-based analog-to-digital converter (ADC). The converter runs with a 5 GHz input clock to provide a maximum conversion rate of 5 GS/s. A novel architecture enables the VTC to provide an adjustable linear delay versus input voltage characteristic. The circuit is realized in a 90nm CMOS process. After calibration, the converter achieves better than 3.7 effective bits for input frequencies up to 1.75 GHz, making it suitable for use in a time-based ADC with up to 4-bit resolution.


A Client-Driven Approach for Channel Management in Wireless LANs

We propose an efficient client-based approach for channel management (channel assignment and load balancing) in 802.11-based WLANs that lead to better usage of the wireless spectrum. This approach is based on a “conflict set coloring” formulation that jointly performs load balancing along with channel assignment. Such a formulation has a number of advantages. First, it explicitly captures interference effects at clients. Next, it intrinsically exposes opportunities for better channel re-use. Finally, algorithms based on this formulation do not depend on specific physical RF models and hence can be applied efficiently to a wide-range of inbuilding as well as outdoor scenarios. We have performed extensive packet-level simulations and measurements on a deployed wireless testbed of 70 APs to validate the performance of our proposed algorithms. We show that in addition to single network scenarios, the conflict set coloring formulation is well suited for channel assignment where multiple wireless networks share and contend for spectrum in the same physical space. Our results over a wide range of both simulated topologies and in-building testbed experiments indicate that our approach improves application level performance at the clients by upto three times (and atleast 50%) in comparison to current best-known techniques.


Meta-Learning with Hessian Free Approach in Deep Neural Nets Training

Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years. But most of the current methods are still not capable to train complex neuron net model with long-time training process. In this paper, a novel second-order meta-optimizer, named Meta-learning with Hessian-Free(MLHF) approach, is proposed based on the Hessian-Free approach. Two recurrent neural networks are established to generate the damping and the precondition matrix of this Hessian-Free framework. A series of techniques to meta-train the MLHF towards stable and reinforce the meta-training of this optimizer, including the gradient calculation of H . Numerical experiments on deep convolution neural nets, including CUDA-convnet and ResNet18(v2), with datasets of CIFAR10 and ILSVRC2012, indicate that the MLHF shows good and continuous training performance during the whole long-time training process, i.e., both the rapiddecreasing early stage and the steadily-deceasing later stage, and so is a promising meta-learning framework towards elevating the training efficiency in real-world deep neural nets.


Thoth: Comprehensive Policy Compliance in Data Retrieval Systems

Data retrieval systems process data from many sources, each subject to its own data use policy. Ensuring compliance with these policies despite bugs, misconfiguration, or operator error in a large, complex, and fast evolving system is a major challenge. Thoth provides an efficient, kernel-level compliance layer for data use policies. Declarative policies are attached to the systems’ input and output files, key-value tuples, and network connections, and specify the data’s integrity and confidentiality requirements. Thoth tracks the flow of data through the system, and enforces policy regardless of bugs, misconfigurations, compromises in application code, or actions by unprivileged operators. Thoth requires minimal changes to an existing system and has modest overhead, as we show using a prototype Thoth-enabled data retrieval system based on the popular Apache Lucene.


A multiparadigm intelligent tutoring system for robotic arm training

To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.


Intellectual Capital (IC) Measurement in the Mass Media Context

Mass media is the key in°uencer of public opinion. The in°uence is not only limited to political and social, but also relates to organisational and economical reputation and brands. Within public opinion, organisations must manage how they are represented competitively within mass media so that they can develop their brand strategically to grow and compete in the current global knowledge economy. This is where the link to Intellectual Capital (IC) Measurement is signi ̄cant. IC, as the sum of all an organisation's intangible assets drives a company's presence and value within the media, albeit related to human, structural or relational capital attributes. The measurement, therefore, of IC in the mass media context is invaluable to understand how a company is placed strategically and competitively in the external space, and how this links to internal activities, goals and outcomes. This paper is an attempt to address some of the issues related to IC measurements in the mass media context by suggesting a framework that provides a multidisciplinary and holistic approach to the understanding and contextualising of the organisation's presence in the public space.


Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on Stiefel manifolds

Matrix factorization-based methods become popular in dyadic data analysis, where a fundamental problem, for example, is to perform document clustering or co-clustering words and documents given a term-document matrix. Nonnegative matrix tri-factorization (NMTF) emerges as a promising tool for co-clustering, seeking a 3-factor decomposition X USV with all factor matrices restricted to be nonnegative, i.e., U P 0; S P 0;V P 0: In this paper we develop multiplicative updates for orthogonal NMTF where X USV is pursued with orthogonality constraints, UU 1⁄4 I; and VV 1⁄4 I, exploiting true gradients on Stiefel manifolds. Experiments on various document data sets demonstrate that our method works well for document clustering and is useful in revealing polysemous words via co-clustering words and documents. 2010 Elsevier Ltd. All rights reserved.


Metal Additive Manufacturing : A Review

This paper reviews the state-of-the-art of an important, rapidly emerging, manufacturing technology that is alternatively called additive manufacturing (AM), direct digital manufacturing, free form fabrication, or 3D printing, etc. A broad contextual overview of metallic AM is provided. AM has the potential to revolutionize the global parts manufacturing and logistics landscape. It enables distributed manufacturing and the productions of parts-on-demand while offering the potential to reduce cost, energy consumption, and carbon footprint. This paper explores the material science, processes, and business consideration associated with achieving these performance gains. It is concluded that a paradigm shift is required in order to fully exploit AM potential.


Measurement Science Needs for Real-time Control of Additive Manufacturing Powder Bed Fusion Processes

Additive Manufacturing is increasingly used in the development of new products: from conceptual design to functional parts and tooling. However, today, variability in part quality due to inadequate dimensional tolerances, surface roughness, and defects, limits its broader acceptance for high-value or mission-critical applications. While process control in general can limit this variability, it is impeded by a lack of adequate process measurement methods. Process control today is based on heuristics and experimental data, yielding limited improvement in part quality. The overall goal is to develop the measurement science 1 necessary to make in-process measurement and real-time control possible in additive manufacturing. Traceable dimensional and thermal metrology methods must be developed for real-time closed-loop control of additive manufacturing processes. As a precursor, this report presents a review on the additive manufacturing control schemes, process measurements, and modeling and simulation methods as it applies to the powder bed fusion process, though results from other processes are reviewed where applicable. The aim of the review is to identify and summarize the measurement science needs that are critical to real-time process control. We organize our research findings to identify the correlations between process parameters, process signatures, and product quality. The intention of this report is to serve as a background reference and a go-to place for our work to identify the most suitable measurement methods and corresponding measurands for real-time control. 1 Measurement science broadly includes: development of performance metrics, measurement and testing methods, predictive modeling and simulation tools, knowledge modeling, protocols, technical data, and reference materials and artifacts; conduct of inter-comparison studies and calibrations; evaluation of technologies, systems, and practices, including uncertainty analysis; development of the technical basis for standards, codes, and practices in many instances via test-beds, consortia, standards and codes development organizations, and/or other partnerships with industry and academia.


A Tlreshold Selection Method from Gray-Level Histograms

A nonparametric and unsupervised method ofautomatic threshold selection for picture segmentation is presented. An optimal threshold is selected by the discriminant criterion, namely, so as to maximize the separability of the resultant classes in gray levels. The procedure is very simple, utilizing only the zerothand the first-order cumulative moments of the gray-level histogram. It is straightforward to extend the method to multithreshold problems. Several experimental results are also presented to support the validity of the method.


Additive manufacturing of polymer-derived ceramics

The extremely high melting point of many ceramics adds challenges to additive manufacturing as compared with metals and polymers. Because ceramics cannot be cast or machined easily, three-dimensional (3D) printing enables a big leap in geometrical flexibility. We report preceramic monomers that are cured with ultraviolet light in a stereolithography 3D printer or through a patterned mask, forming 3D polymer structures that can have complex shape and cellular architecture. These polymer structures can be pyrolyzed to a ceramic with uniform shrinkage and virtually no porosity. Silicon oxycarbide microlattice and honeycomb cellular materials fabricated with this approach exhibit higher strength than ceramic foams of similar density. Additive manufacturing of such materials is of interest for propulsion components, thermal protection systems, porous burners, microelectromechanical systems, and electronic device packaging.


Additive Manufacturing of Metals: A Review

Over the past 20 years, additive manufacturing (AM) has evolved from 3D printers used for rapid prototyping to sophisticated rapid manufacturing that can create parts directly without the use of tooling. AM technologies build near net shape components layer by layer using 3D model data. AM could revolutionize many sectors of manufacturing by reducing component lead time, material waste, energy usage, and carbon footprint. Furthermore, AM has the potential to enable novel product designs that could not be fabricated using conventional processes. This proceedings is a review that assesses available AM technologies for direct metal fabrication. Included is an outline of the definition of AM, a review of the commercially available and under development technologies for direct metal fabrication, possibilities, and an overall assessment of the state of the art. Perspective on future research opportunities is also be presented.


Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription

We investigate the potential of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, from a feature-engineering perspective. Recently, we had shown that for speaker-independent transcription of phone calls (NIST RT03S Fisher data), CD-DNN-HMMs reduced the word error rate by as much as one third—from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs with HLDA features, to 18.5%—using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers.


An Exploratory Review of Design Principles in Constructivist Gaming Learning Environments.

Creating a design theory for Constructivist Gaming Learning Environment necessitates, among other things, the establishment of design principles. These principles have the potential to help designers produce games, where users achieve higher levels of learning. This paper focuses on twelve design principles: Probing, Distributed, Multiple Routes, Practice, Psychosocial Moratorium, Regime of Competence, Self-Knowledge, Collective Knowledge, Engaging, User Interface Ease of Use, On Demand and Just-in-Time Tutorial, and Achievement. We report on two pilot studies of a qualitative nature in which we test our design principles. Game play testing and observations were carried out on five Massively Multiplayer Online Games (MMOGs): RuneScape, GuildWars, Ragnarok, World of WarCraft, and Final Fantasy XI. Two educational games, Carabella Goes to College and Outbreak at WatersEdge were also observed. Our findings indicate that not all of the popular MMOGs and educational games support all of these principles.


Evaluation of machine learning techniques for network intrusion detection

Network traffic anomaly may indicate a possible intrusion in the network and therefore anomaly detection is important to detect and prevent the security attacks. The early research work in this area and commercially available Intrusion Detection Systems (IDS) are mostly signature-based. The problem of signature based method is that the database signature needs to be updated as new attack signatures become available and therefore it is not suitable for the real-time network anomaly detection. The recent trend in anomaly detection is based on machine learning classification techniques. We apply seven different machine learning techniques with information entropy calculation to Kyoto 2006+ data set and evaluate the performance of these techniques. Our findings show that, for this particular data set, most machine learning techniques provide higher than 90% precision, recall and accuracy. However, using area under the Receiver Operating Curve (ROC) metric, we find that Radial Basis Function (RBF) performs the best among the seven algorithms studied in this work.


Visible light communications: Challenges and possibilities

Solid-state lighting is a rapidly developing field. White-light and other visible LEDs are becoming more efficient, have high reliability and can be incorporated into many lighting applications. Recent examples include car head-lights based on white LEDs, and LED illumination as an architectural feature. The prediction that general illumination will use white LEDs in the future has been made, due to the increased energy efficiency that such an approach may have. Such sources can also be modulated at high-speed, offering the possibility of using sources for simultaneous illumination and data communications. Such visible light communications (VLC) was pioneered in Japan, and there is now growing interest worldwide, including within bodies such as the Visible Light Communications Consortium (VLCC) and the Wireless World Research Forum (WWRF). In this paper we outline the basic components in these systems, review the state of the art and discuss some of the challenges and possibilities for this new wireless transmission technique.


A conceptual framework to develop Green IT – going beyond the idea of environmental sustainability

This paper presents a conceptual framework aiming to better research, understand and develop Green IT within organizations. Based on a literature review on Green IT, regarding the concepts of responsibility and sustainability, we propose an initial framework with five dimensions: ethical, technological, economical, social and environmental. These dimensions compose Green IT strategies and practices. Additionally, the framework considers that environmental changing requirements, strategic requirements and dynamic capabilities are the forces which move organizations toward green practices and foster innovation. This framework is part of a five-year horizon project that began in 2009 and aims to contribute to the theory in the field, having as initial goal to identify the constructs associated with Green IT.


Epidermal electronics: Skin sweat patch

An ultrathin, stretchable, and conformal sensor system for skin-mounted sweat measurement is characterized and demonstrated in this paper. As an epidermal device, the sweat sensor is mechanically designed for comfortable wear on the skin by employing interdigitated electrodes connected via stretchable serpentine-shaped conductors. Experimental results show that the sensor is sensitive to measuring frequency, sweat level and stretching deformation. It was found that 20kHz signals provide the most sensitive performance: electrical impedance changes 50% while sweat level increases from 20 to 80. In addition, sensor elongation from 15 up to 50% affected the measurement sensitivity of both electrical impedance and capacitance.


A Survey of Utility-Oriented Pattern Mining

The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques/constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.


Revising the Newman-Girvan Algorithm

Abstract: One of the common approaches for the community detection in complex networks is the Girvan-Newman algorithm [5] which is based on repeated deletion of edges having the maximum edge betweenness centrality. Although widely used, it may result in different dendrograms of hierarchies of communities if there are several edges eligible for removal (see [6]) thus leaving an ambiguous formation of network subgroups. We will present possible ways to overcome these issues using, instead of edge betweenness computation for single edges, the group edge betweenness for subsets of edges to be subjects of removal.


Ontology-based deep learning for human behavior prediction with explanations in health social networks

Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems actually will be adopted, thus driving engagement and loyalty. However, most prediction models do not provide explanations for the behaviors they predict. In this paper, we study the research problem, human behavior prediction with explanations, for healthcare intervention systems in health social networks. We propose an ontology-based deep learning model (ORBM+) for human behavior prediction over undirected and nodes-attributed graphs. We first propose a bottom-up algorithm to learn the user representation from health ontologies. Then the user representation is utilized to incorporate self-motivation, social influences, and environmental events together in a human behavior prediction model, which extends a well-known deep learning method, the Restricted Boltzmann Machine. ORBM+ not only predicts human behaviors accurately, but also, it generates explanations for each predicted behavior. Experiments conducted on both real and synthetic health social networks have shown the tremendous effectiveness of our approach compared with conventional methods.


Edge Agreement: Graph-Theoretic Performance Bounds and Passivity Analysis

This work explores the properties of the edge variant of the graph Laplacian in the context of the edge agreement problem. We show that the edge Laplacian, and its corresponding agreement protocol, provides a useful perspective on the well-known node agreement, or the consensus algorithm. Specifically, the dynamics induced by the edge Laplacian facilitates a better understanding of the role of certain subgraphs, e.g., cycles and spanning trees, in the original agreement problem. Using the edge Laplacian, we proceed to examine graph-theoretic characterizations of the H2 and H∞ performance for the agreement protocol. These results are subsequently applied in the contexts of optimal sensor placement for consensus-based applications. Finally, the edge Laplacian is employed to provide new insights into the nonlinear extension of linear agreement to agents with passive dynamics.


Tracking-based interaction for object creation in mobile augmented reality

In this work, we evaluate the feasibility of tracking-based interaction using a mobile phone's or tablet's camera in order to create and edit 3D objects in augmented reality applications. We present a feasibility study investigating if and how gestures made with your finger can be used to create such objects. A revised interface design is evaluated in a user study with 24 subjects that reveals a high usability and entertainment value, but also identifies issues such as ergonomic discomfort and imprecise input for complex tasks. Hence, our results suggest huge potential for this type of interaction in the entertainment, edutainment, and leisure domain, but limited usefulness for serious applications.


LAN security: problems and solutions for Ethernet networks

Despite many research and development efforts in the area of data communications security, the importance of internal Ž . local area network LAN security is still underestimated. This paper discusses why many traditional approaches to network Ž . security e.g. firewalls, the modern IPSec or various application level protocols are not so effective in local networks and proposes a prospective solution for building of secure encrypted Ethernet LANs. The architecture presented allows for employment of the existing office network infrastructure, does not require changes to workstations’ software, and provides high level of protection. The core idea is to apply security measures in the network interface devices connecting individual Ž Ž .. computers to the network i.e. network interface cards NICs . This eliminates the physical possibility of sending unprotected information through the network. Implementation details are given for data and key exchange, network management, and interoperability issues. An in-depth security analysis of the proposed architecture is presented and some conclusions are drawn. q 2000 Elsevier Science B.V. All rights reserved.


Fault tolerant three-phase AC motor drive topologies: a comparison of features, cost, and limitations

This paper compares the many fault tolerant three-phase ac motor drive topologies that have been proposed to provide output capacity for the inverter faults of switch short or open-circuits, phase-leg short-circuits, and single-phase open-circuits. Also included is a review of the respective control methods for fault tolerant inverters including two-phase and unipolar control methods. The output voltage and current space in terms of dq components is identified for each topology and fault. These quantities are then used to normalize the power capacity of each system during a fault to a standard inverter during normal operation. A silicon overrating cost factor is adopted as a metric to compare the relative switching device costs of the topologies compared to a standard three-phase inverter.


Interactive Plan Explicability in Human-Robot Teaming

Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status, but also be aware of its human teammates' expectation of itself. Being aware of the human teammates' expectation leads to robot behaviors that better align with the human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot interaction with the consideration of such teammate models in sequential domains by leveraging the concept of plan explicability. In plan explicability, however, the human is considered solely as an observer. In this paper, we extend plan explicability to consider interactive settings where the human and robot's behaviors can influence each other. We term this new measure Interactive Plan Explicability (IPE). We compare the joint plan generated by our approach with the consideration of this measure using the fast forward (FF) planner, with the plan generated by FF without such consideration, as well as with the plan created with human subjects interacting with a robot running an FF planner. Since the human subject is expected to adapt to the robot's behavior dynamically when it deviates from her expectation, the plan created with human subjects is expected to be more explicable than the FF plan, and comparable to the explicable plan generated by our approach. Results indicate that the explicability score of plans generated by our algorithm is indeed closer to the human interactive plan than the plan generated by FF, implying that the plans generated by our algorithms align better with the expected plans of the human during execution. This can lead to more efficient collaboration in practice.


Neighboring gray level dependence matrix for texture classification

A new approach, neighboring gray level dependence matrix (NGLDM), for texture classification is presented. The major properties of this approach are as follows: (a) texture features can be easily computed; (b) they are essentially invariant under spatial rotation; (c) they are invariant under linear gray level transformation and can be made insensitive to monotonic gray level transformation. These properties have enhanced the practical applications of the texture features. The accuracies of the classification are comparable with those found in the literature.


A Fast Calibration Method for Triaxial Magnetometers

This paper presents a novel iterative calibration algorithm for triaxial magnetometers. The proposed algorithm estimates and compensates the effects of deterministic interference parameters using only nine distinct measurements. The results of our extensive simulations and empirical evaluations confirm that the proposed method outperforms conventional ellipsoid fitting based models both in terms of accuracy and reliability even in the presence of a moderate wideband noise. The algorithm also achieves considerably faster convergence, which makes it suitable for real-time applications. The algorithm performance is also shown to be independent of the initial guesses of the interference parameters.


An integrated approach for warehouse analysis and optimization: A case study

The paper focuses on the analysis and optimization of production warehouses, proposing a novel approach to reduce inefficiencies which employs three lean manufacturing tools in an integrated and iterative framework. The proposed approach integrates the Unified Modeling Language (UML) – providing a detailed description of the warehouse logistics – the Value Stream Mapping (VSM) tool – identifying non-value adding activities – and a mathematical formulation of the so-called Genba Shikumi philosophy – ranking such system anomalies and assessing how they affect the warehouse. The subsequent reapplication of the VSM produces a complete picture of the reengineered warehouse, and using the UML tool allows describing in detail the updated system. By applying the presented methodology to the warehouse of an Italian interior design producer, we show that it represents a useful tool to systematically and dynamically improve the warehouse management. Indeed, the application of the approach to the company leads to an innovative proposal for the warehouse analysis and optimization: a warehouse management system that leads to increased profitability and quality as well as to reduced errors. 2014 Elsevier B.V. All rights reserved.


Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles

Image content analysis is an important surround perception modality of intelligent vehicles. In order to efficiently recognize the on-road environment based on image content analysis from the large-scale scene database, relevant images retrieval becomes one of the fundamental problems. To improve the efficiency of calculating similarities between images, hashing techniques have received increasing attentions. For most existing hash methods, the suboptimal binary codes are generated, as the hand-crafted feature representation is not optimally compatible with the binary codes. In this paper, a one-stage supervised deep hashing framework (SDHP) is proposed to learn high-quality binary codes. A deep convolutional neural network is implemented, and we enforce the learned codes to meet the following criterions: 1) similar images should be encoded into similar binary codes, and vice versa; 2) the quantization loss from Euclidean space to Hamming space should be minimized; and 3) the learned codes should be evenly distributed. The method is further extended into SDHP+ to improve the discriminative power of binary codes. Extensive experimental comparisons with state-of-the-art hashing algorithms are conducted on CIFAR-10 and NUS-WIDE, the MAP of SDHP reaches to 87.67% and 77.48% with 48 b, respectively, and the MAP of SDHP+ reaches to 91.16%, 81.08% with 12 b, 48 b on CIFAR-10 and NUS-WIDE, respectively. It illustrates that the proposed method can obviously improve the search accuracy.


Evidence and solution of over-RESET problem for HfOX based resistive memory with sub-ns switching speed and high endurance

The memory performances of the HfOX based bipolar resistive memory, including switching speed and memory reliability, are greatly improved in this work. Record high switching speed down to 300 ps is achieved. The cycling test shed a clear light on the wearing behavior of resistance states, and the correlation between over-RESET phenomenon and the worn low resistance state in the devices is discussed. The modified bottom electrode is proposed for the memory device to maintain the memory window and to endure resistive switching up to 1010 cycles.


Media and children's aggression, fear, and altruism.

Noting that the social and emotional experiences of American children today often heavily involve electronic media, Barbara Wilson takes a close look at how exposure to screen media affects children's well-being and development. She concludes that media influence on children depends more on the type of content that children find attractive than on the sheer amount of time they spend in front of the screen. Wilson begins by reviewing evidence on the link between media and children's emotions. She points out that children can learn about the nature and causes of different emotions from watching the emotional experiences of media characters and that they often experience empathy with those characters. Although research on the long-term effects of media exposure on children's emotional skill development is limited, a good deal of evidence shows that media exposure can contribute to children's fears and anxieties. Both fictional and news programming can cause lasting emotional upset, though the themes that upset children differ according to a child's age. Wilson also explores how media exposure affects children's social development. Strong evidence shows that violent television programming contributes to children's aggressive behavior. And a growing body of work indicates that playing violent video games can have the same harmful effect. Yet if children spend time with educational programs and situation comedies targeted to youth, media exposure can have more prosocial effects by increasing children's altruism, cooperation, and even tolerance for others. Wilson also shows that children's susceptibility to media influence can vary according to their gender, their age, how realistic they perceive the media to be, and how much they identify with characters and people on the screen. She concludes with guidelines to help parents enhance the positive effects of the media while minimizing the risks associated with certain types of content.


Curvature preserving fingerprint ridge orientation smoothing using Legendre polynomials

Smoothing fingerprint ridge orientation involves a principal discrepancy. Too little smoothing can result in noisy orientation fields (OF), too much smoothing will harm high curvature areas, especially singular points (SP). In this paper we present a fingerprint ridge orientation model based on Legendre polynomials. The motivation for the proposed method can be found by analysing the almost exclusively used method in literature for orientation smoothing, proposed by Witkin and Kass (1987) more than two decades ago. The main contribution of this paper argues that the vectorial data (sine and cosine data) should be smoothed in a coupled way and the approximation error should not be evaluated employing vectorial data. For evaluating the proposed method we use a Poincare-Index based SP detection algorithm. The experiments show, that in comparison to competing methods the proposed method has improved orientation smoothing capabilities, especially in high curvature areas.


On the Generalization Ability of Online Strongly Convex Programming Algorithms

This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound, that holds with high probability, on the excess risk of the output of an online algorithm in terms of the average regret. This allows one to use recent algorithms with logarithmic cumulative regret guarantees to achieve fast convergence rates for the excess risk with high probability. As a corollary, we characterize the convergence rate of P EGASOS(with high probability), a recently proposed method for solving the SVM optimization problem.


A Tri-Space Visualization Interface for Analyzing Time-Varying Multivariate Volume Data

The dataset generated by a large-scale numerical simulation may include thousands of timesteps and hundreds of variables describing different aspects of the modeled physical phenomena. In order to analyze and understand such data, scientists need the capability to explore simultaneously in the temporal, spatial, and variable domains of the data. Such capability, however, is not generally provided by conventional visualization tools. This paper presents a new visualization interface addressing this problem. The interface consists of three components which abstracts the complexity of exploring in temporal, variable, and spatial domain, respectively. The first component displays time histograms of the data, helps the user identify timesteps of interest, and also helps specify time-varying features. The second component displays correlations between variables in parallel coordinates and enables the user to verify those correlations and possibly identity unanticipated ones. The third component allows the user to more closely explore and validate the data in spatial domain while rendering multiple variables into a single visualization in a user controllable fashion. Each of these three components is not only an interface but is also the visualization itself, thus enabling efficient screen-space usage. The three components are tightly linked to facilitate tri-space data exploration, which offers scientists new power to study their time-varying, multivariate volume data.


Real-time rigid-body visual tracking in a scanning electron microscope

Robotics continues to provide researchers with an increasing ability to interact with objects at the nano scale. As micro- and nanorobotic technologies mature, more interest is given to computer-assisted or automated approaches to manipulation at these scales. Although actuators are currently available that enable displacements resolutions in the subnanometer range, improvements in feedback technologies have not kept pace. Thus, many actuators that are capable of performing nanometer displacements are limited in automated tasks by the lack of suitable feedback mechanisms. This paper proposes the use of a rigid-model-based method for end effector tracking in a scanning electron microscope to aid in enabling more precise automated manipulations and measurements. These models allow the system to leverage domain-specific knowledge to increase performance in a challenging tracking environment.


Extension of phase correlation to subpixel registration

In this paper, we have derived analytic expressions for the phase correlation of downsampled images. We have shown that for downsampled images the signal power in the phase correlation is not concentrated in a single peak, but rather in several coherent peaks mostly adjacent to each other. These coherent peaks correspond to the polyphase transform of a filtered unit impulse centered at the point of registration. The analytic results provide a closed-form solution to subpixel translation estimation, and are used for detailed error analysis. Excellent results have been obtained for subpixel translation estimation of images of different nature and across different spectral bands.


Improving resolution by image registration

Image resolution can be improved when the relative displacements in image sequences are known accurately, and some knowledge of the imaging process is available. The proposed approach is similar to back-projection used in tomography. Examples of improved image resolution are given for gray-level and color images, when the unknown image displacements are computed from the image sequence.


A Comparison of Affine Region Detectors

The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris  (Mikolajczyk and  Schmid, 2002; Schaffalitzky and  Zisserman, 2002) and Hessian points  (Mikolajczyk and  Schmid, 2002), a detector of ‘maximally stable extremal regions', proposed by Matas et al. (2002); an edge-based region detector  (Tuytelaars and Van Gool, 1999) and a detector based on intensity extrema (Tuytelaars and Van Gool, 2000), and a detector of ‘salient regions', proposed by Kadir, Zisserman and Brady (2004). The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression. The objective of this paper is also to establish a reference test set of images and performance software, so that future detectors can be evaluated in the same framework.


PROCEDURAL GENERATION OF IMAGINATIVE TREES USING A SPACE COLONIZATION ALGORITHM

The modeling of trees is challenging due to their complex branching structures. Three different ways to generate trees are using real world data for reconstruction, interactive modeling methods and modeling with procedural or rule-based systems. Procedural content generation is the idea of using algorithms to automate content creation processes, and it is useful in plant modeling since it can generate a wide variety of plants that can adapt and react to the environment and changing conditions. This thesis focuses on and extends a procedural tree generation technique that uses a space colonization algorithm to model the tree branches’ competition for space, and shifts the previous works’ focus from realism to fantasy. The technique satisfied the idea of using interaction between the tree’s internal and external factors to determine its final shape, by letting the designer control the where and the how of the tree’s growth process. The implementation resulted in a tree generation application where the user’s imagination decides the limit of what can be produced, and if that limit is reached can the application be used to randomly generate a wide variety of trees and tree-like structures. A motivation for many researchers in the procedural content generation area is how it can be used to augment human imagination. The result of this thesis can be used for that, by stepping away from the restrictions of realism, and with ease let the user generate widely diverse trees, that are not necessarily realistic but, in most cases, adapts to the idea of a tree.


Thin dual-resonant stacked shorted patch antenna for mobile communications

Introduction: Short-circuited microstrip patch antennas, or PIFAs, can be used as directive internal handset antennas in various communication systems. They provide advantages over traditional external whip and helix antennas in terms of increased total efficiency (when the handset is near the user’s head), decreased radiation towards the user, and increased mechanical reliability. The main disadvantage is their narrow impedance bandwidth compared to the volume occupied by the antenna structure. Typical requirements for directive internal antennas designed for cellular handsets (with a thickness ≤ 20mm) are antenna thickness ≤ 5mm and bandwidth ≥ 10%. It is difficult to design such a small, directive, low-profile handset antenna with high radiation efficiency and an impedance bandwidth greater than 10%. Small antenna design is always a compromise between size, bandwidth, and efficiency. Often, the price for improved performance is increased complexity. If the total dimensions of a patch antenna element are fixed, there are two effective ways to enhance its bandwidth: either dissipative loading [1], which reduces the radiation efficiency, or the addition of more resonators into the antenna structure (matching networks or parasitic elements). The latter method may increase the manufacturing complexity of the antenna. In this Letter, a thin dual-resonant stacked shorted patch antenna is presented. Previously, stacked short-circuited patch antenna elements have been reported in [2, 3]. More recently, similar elements have also been discussed in [4, 5]. The presented antenna is dual-resonant and has a very low profile. The thickness is only one fifth of that of the antenna reported in [5], whereas the surface area reserved for the patches is approximately equal. The small size of the antenna presented in this Letter makes it relatively easy to fit inside a cellular handset. The bandwidth is sufficient for many communication systems. With a common short circuit element which connects both patches to the ground plane, the presented antenna is the simplest example of a stacked shorted microstrip patch antenna. No extra tuning or shorting posts inside the patch area are needed, which makes its manufacturing easier.


Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation.

Recent in vivo structural imaging studies have shown spatial and temporal patterns of brain maturation between childhood, adolescence, and young adulthood that are generally consistent with postmortem studies of cellular maturational events such as increased myelination and synaptic pruning. In this study, we conducted detailed spatial and temporal analyses of growth and gray matter density at the cortical surface of the brain in a group of 35 normally developing children, adolescents, and young adults. To accomplish this, we used high-resolution magnetic resonance imaging and novel computational image analysis techniques. For the first time, in this report we have mapped the continued postadolescent brain growth that occurs primarily in the dorsal aspects of the frontal lobe bilaterally and in the posterior temporo-occipital junction bilaterally. Notably, maps of the spatial distribution of postadolescent cortical gray matter density reduction are highly consistent with maps of the spatial distribution of postadolescent brain growth, showing an inverse relationship between cortical gray matter density reduction and brain growth primarily in the superior frontal regions that control executive cognitive functioning. Inverse relationships are not as robust in the posterior temporo-occipital junction where gray matter density reduction is much less prominent despite late brain growth in these regions between adolescence and adulthood. Overall brain growth is not significant between childhood and adolescence, but close spatial relationships between gray matter density reduction and brain growth are observed in the dorsal parietal and frontal cortex. These results suggest that progressive cellular maturational events, such as increased myelination, may play as prominent a role during the postadolescent years as regressive events, such as synaptic pruning, in determining the ultimate density of mature frontal lobe cortical gray matter.


Broadband antenna design using different 3D printing technologies and metallization processes

The purpose of this paper is to provide a comprehensive evaluation of how plastic materials, different metallization process, and thickness of the metal can influence the performance of 3D printed broadband antenna structures. A set of antennas were manufactured using Fused Deposition Modeling technology and Polyjet technology. Three different plastic materials ABS, PLA and Vero were employed in the tests. The antenna structures were metallized using three common metallization processes: vacuum metallization, electroplating, and conductive paint. In this project, the broadband performances of the metallized plastic antennas are compared to an original metal antenna by measuring VSWR, radiation patterns (H- and E-Planes), and gain. Measurements show that the performances of these plastic structures, regardless of production method, are very similar to the original metal antenna.


Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences

We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains ∼6k questions for +800 paragraphs across 7 different domains (elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings. On a subset of our dataset, we found human solvers to achieve an F1-score of 86.4%. We analyze a range of baselines, including a recent state-of-art reading comprehension system, and demonstrate the difficulty of this challenge, despite a high human performance. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills.


Kd-Jump: a Path-Preserving Stackless Traversal for Faster Isosurface Raytracing on GPUs

Stackless traversal techniques are often used to circumvent memory bottlenecks by avoiding a stack and replacing return traversal with extra computation. This paper addresses whether the stackless traversal approaches are useful on newer hardware and technology (such as CUDA). To this end, we present a novel stackless approach for implicit kd-trees, which exploits the benefits of index-based node traversal, without incurring extra node visitation. This approach, which we term Kd-Jump, enables the traversal to immediately return to the next valid node, like a stack, without incurring extra node visitation (kd-restart). Also, Kd-Jump does not require global memory (stack) at all and only requires a small matrix in fast constant-memory. We report that Kd-Jump outperforms a stack by 10 to 20% and kd-restar t by 100%. We also present a Hybrid Kd-Jump, which utilizes a volume stepper for leaf testing and a run-time depth threshold to define where kd-tree traversal stops and volume-stepping occurs. By using both methods, we gain the benefits of empty space removal, fast texture-caching and realtime ability to determine the best threshold for current isosurface and view direction.


Focused Trajectory Planning for autonomous on-road driving

On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework. Reference Trajectory Planning (I) makes use of dense lattice sampling and optimization techniques to generate an easy-to-tune and human-like reference trajectory accounting for road geometry, obstacles and high-level directives. By focused sampling around the reference trajectory, Tracking Trajectory Planning (II) generates, evaluates and selects parametric trajectories that further satisfy kinodynamic constraints for execution. The described method retains most of the performance advantages of an exhaustive spatiotemporal planner while significantly reducing computation.


Pattern Recognition for Downhole Dynamometer Card in Oil Rod Pump System using Artificial Neural Networks

This paper presents the development of an Artificial Neural Network system for Dynamometer Card pattern recognition in oil well rod pump systems. It covers the establishment of pattern classes and a set of standards for training and validation, the study of descriptors which allow the design and the implementation of features extractor, training, analysis and finally the validation and performance test with


Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment

We consider the task of predicting lexical entailment using distributional vectors. We focus experiments on one previous classifier which was shown to only learn to detect prototypicality of a word pair. Analysis shows that the model single-mindedly learns to detect Hearst Patterns, which are well known to be predictive of lexical relations. We present a new model which exploits this Hearst Detector functionality, matching or outperforming prior work on multiple data sets.


A Subsequence Interleaving Model for Sequential Pattern Mining

Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database. We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, which allows us to search for the most compressing set of patterns without designing a specific encoding scheme. Our proposed algorithm is able to efficiently mine the most relevant sequential patterns and rank them using an associated measure of interestingness. The efficient inference in our model is a direct result of our use of a structural expectation-maximization framework, in which the expectation-step takes the form of a submodular optimization problem subject to a coverage constraint. We show on both synthetic and real world datasets that our model mines a set of sequential patterns with low spuriousness and redundancy, high interpretability and usefulness in real-world applications. Furthermore, we demonstrate that the quality of the patterns from our approach is comparable to, if not better than, existing state of the art sequential pattern mining algorithms.


Towards a theory of supply chain management : the constructs and measurements

Rising international cooperation, vertical disintegration, along with a focus on core activities have led to the notion that firms are links in a networked supply chain. This novel perspective has created the challenge of designing and managing a network of interdependent relationships developed and fostered through strategic collaboration. Although research interests in supply chain management (SCM) are growing, no research has been directed towards a systematic development of SCM instruments. This study identifies and consolidates various supply chain initiatives and factors to develop key SCM constructs conducive to advancing the field. To this end, we analyzed over 400 articles and synthesized the large, fragmented body of work dispersed across many disciplines. The result of this study, through successive stages of measurement analysis and refinement, is a set of reliable, valid, and unidimensional measurements that can be subsequently used in different contexts to refine or extend conceptualization and measurements or to test various theoretical models, paving the way for theory building in SCM. © 2004 Elsevier B.V. All rights reserved.


Sieve: Cryptographically Enforced Access Control for User Data in Untrusted Clouds

Modern web services rob users of low-level control over cloud storage; a user’s single logical data set is scattered across multiple storage silos whose access controls are set by the web services, not users. The result is that users lack the ultimate authority to determine how their data is shared with other web services. In this thesis, we introduce Sieve, a new architecture for selectively exposing user data to third party web services in a provably secure manner. Sieve starts with a user-centric storage model: each user uploads encrypted data to a single cloud store, and by default, only the user knows the decryption keys. Given this storage model, Sieve defines an infrastructure to support rich, legacy web applications. Using attribute-based encryption, Sieve allows users to define intuitive, understandable access policies that are cryptographically enforceable. Using key homomorphism, Sieve can re-encrypt user data on storage providers in situ, revoking decryption keys from web services without revealing new ones to the storage provider. Using secret sharing and two-factor authentication, Sieve protects against the loss of user devices like smartphones and laptops. The result is that users can enjoy rich, legacy web applications, while benefitting from cryptographically strong controls over what data the services can access. Thesis Supervisor: Nickolai Zeldovich Title: Associate Professor Thesis Supervisor: James Mickens Title: Associate Professor


Segmentation of Urban Areas Using Road Networks

Region-based analysis is fundamental and crucial in many geospatialrelated applications and research themes, such as trajectory analysis, human mobility study and urban planning. In this paper, we report on an image-processing-based approach to segment urban areas into regions by road networks. Here, each segmented region is bounded by the high-level road segments, covering some neighborhoods and low-level streets. Typically, road segments are classified into different levels (e.g., highways and expressways are usually high-level roads), providing us with a more natural and semantic segmentation of urban spaces than the grid-based partition method. We show that through simple morphological operators, an urban road network can be efficiently segmented into regions. In addition, we present a case study in trajectory mining to demonstrate the usability of the proposed segmentation method.


Collaborative Deep Learning Across Multiple Data Centers

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method.


An improved box-counting method for image fractal dimension estimation

Article history: Received 6 September 2007 Received in revised form 14 January 2009 Accepted 2 March 2009


BanditSum: Extractive Summarization as a Contextual Bandit

In this work, we propose a novel method for training neural networks to perform singledocument extractive summarization without heuristically-generated extractive labels. We call our approach BANDITSUM as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BANDITSUM is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BANDITSUM performs significantly better than competing approaches when good summary sentences appear late in the source document.


Introduction to the special section on educational data mining

Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to the growing availability of educational data. EDM contributes to the study of how students learn, and the settings in which they learn. It enables data-driven decision making for improving the current educational practice and learning material. We present a brief overview of EDM and introduce four selected EDM papers representing a crosscut of different application areas for data mining in education.


Polynomial method for Procedural Terrain Generation

A systematic fractal brownian motion approach is proposed for generating coherent noise, aiming at procedurally generating realistic terrain and textures. Two models are tested and compared to Perlin noise method for two-dimensional height map generation. A fractal analysis is performed in order to compare fractal behaviour of generated data to real terrain coastlines from the point of view of fractal dimension. Performance analysis show that one of the described schemes requires half as many primitive operations than Perlin noise while producing data of equivalent quality.


Fully Convolutional Attention Networks for Fine-Grained Recognition

Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses. A key to address this problem is to localize discriminative parts to extract pose-invariant features. However, ground-truth part annotations can be expensive to acquire. Moreover, it is hard to define parts for many fine-grained classes. This work introduces Fully Convolutional Attention Networks (FCANs), a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different fine-grained domains. Compared to previous methods, our approach enjoys three advantages: 1) the weakly-supervised reinforcement learning procedure requires no expensive part annotations; 2) the fully-convolutional architecture speeds up both training and testing; 3) the greedy reward strategy accelerates the convergence of the learning. We demonstrate the effectiveness of our method with extensive experiments on four challenging fine-grained benchmark datasets, including CUB-200-2011, Stanford Dogs, Stanford Cars and Food-101.


Consumption of ultra-processed foods and likely impact on human health. Evidence from Canada.

OBJECTIVE
To investigate consumption of ultra-processed products in Canada and to assess their association with dietary quality.

DESIGN
Application of a classification of foodstuffs based on the nature, extent and purpose of food processing to data from a national household food budget survey. Foods are classified as unprocessed/minimally processed foods (Group 1), processed culinary ingredients (Group 2) or ultra-processed products (Group 3).

SETTING
All provinces and territories of Canada, 2001.

SUBJECTS
Households (n 5643).

RESULTS
Food purchases provided a mean per capita energy availability of 8908 (se 81) kJ/d (2129 (se 19) kcal/d). Over 61·7 % of dietary energy came from ultra-processed products (Group 3), 25·6 % from Group 1 and 12·7 % from Group 2. The overall diet exceeded WHO upper limits for fat, saturated fat, free sugars and Na density, with less fibre than recommended. It also exceeded the average energy density target of the World Cancer Research Fund/American Institute for Cancer Research. Group 3 products taken together are more fatty, sugary, salty and energy-dense than a combination of Group 1 and Group 2 items. Only the 20 % lowest consumers of ultra-processed products (who consumed 33·2 % of energy from these products) were anywhere near reaching all nutrient goals for the prevention of obesity and chronic non-communicable diseases.

CONCLUSIONS
The 2001 Canadian diet was dominated by ultra-processed products. As a group, these products are unhealthy. The present analysis indicates that any substantial improvement of the diet would involve much lower consumption of ultra-processed products and much higher consumption of meals and dishes prepared from minimally processed foods and processed culinary ingredients.


Diagnostically relevant facial gestalt information from ordinary photos

Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons.DOI: http://dx.doi.org/10.7554/eLife.02020.001.


Effect of Control-Loops Interactions on Power Stability Limits of VSC Integrated to AC System

This paper investigates the effect of control-loops interactions on power stability limits of the voltage-source converter (VSC) as connected to an ac system. The focus is put on the physical mechanism of the control-loops interactions in the VSC, revealing that interactions among the control loops result in the production of an additional loop. The open-loop gain of the additional loop is employed to quantify the severity of the control-loop interactions. Furthermore, the power current sensitivity, closely related to control-loops interactions, is applied to estimate the maximum transferrable power of the VSC connected to an ac grid. On that basis, stability analysis results show that interactions between dc-link voltage control and phase-locked loop restrict the power angle to about 51° for stable operation with no dynamic reactive power supported. Conversely, the system is capable of approaching the ac-side maximum power transfer limits with alternating voltage control included. Simulations in MATLAB/Simulink are conducted to validate the stability analysis.


Social network sites: definition, history, and scholarship

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.


Facebook in higher education promotes social but not academic engagement

Although there is evidence that academically successful students are engaged with their studies, it has proved difficult to define student engagement clearly. Student engagement is commonly construed as having two dimensions, social and academic. The rapid adoption of social media and digital technologies has ensured increasing interest in using them for improving student engagement. This paper examines Facebook usage among a first year psychology student cohort and reports that although the majority of students (94%) had Facebook accounts and spent an average of one hour per day on Facebook, usage was found to be predominantly social. Personality factors influenced usage patterns, with more conscientious students tending to use Facebook less than less conscientious students. This paper argues that, rather than promoting social engagement in a way that might increase academic engagement, it appears that Facebook is more likely to operate as a distracting influence.


Self-Presentation 2.0: Narcissism and Self-Esteem on Facebook

Online social networking sites have revealed an entirely new method of self-presentation. This cyber social tool provides a new site of analysis to examine personality and identity. The current study examines how narcissism and self-esteem are manifested on the social networking Web site Facebook.com . Self-esteem and narcissistic personality self-reports were collected from 100 Facebook users at York University. Participant Web pages were also coded based on self-promotional content features. Correlation analyses revealed that individuals higher in narcissism and lower in self-esteem were related to greater online activity as well as some self-promotional content. Gender differences were found to influence the type of self-promotional content presented by individual Facebook users. Implications and future research directions of narcissism and self-esteem on social networking Web sites are discussed.


A privacy paradox: Social networking in the United States

Teenagers will freely give up personal information to join social networks on the Internet. Afterwards, they are surprised when their parents read their journals. Communities are outraged by the personal information posted by young people online and colleges keep track of student activities on and off campus. The posting of personal information by teens and students has consequences. This article will discuss the uproar over privacy issues in social networks by describing a privacy paradox; private versus public space; and, social networking privacy issues. It will finally discuss proposed privacy solutions and steps that can be taken to help resolve the privacy paradox.


The effects of modern mathematics computer games on mathematics achievement and class motivation

This study examined the effects of a computer game on students' mathematics achievement and motivation, and the role of prior mathematics knowledge, computer skill, and English language skill on their achievement and motivation as they played the game. A total of 193 students and 10 teachers participated in this study. The teachers were randomly assigned to experimental and control groups. A mixed method of quantitative and interviews were used with Multivariate Analysis of Co-Variance to analyze the data. The results indicated significant improvement of the achievement of the experimental versus control group. No significant improvement was found in the motivation of the groups. Students who played the games in their classrooms and school labs reported greater motivation compared to the ones who played the games only in the school labs. Prior knowledge, computer and English language skill did not play significant roles in achievement and motivation of the experimental group. 2010 Elsevier Ltd. All rights reserved.


Improving Matching Models with Contextualized Word Representations for Multi-turn Response Selection in Retrieval-based Chatbots

We consider matching with pre-trained contextualized word vectors for multi-turn response selection in retrieval-based chatbots. When directly applied to the task, state-ofthe-art models, such as CoVe and ELMo, do not work as well as they do on other tasks, due to the hierarchical nature, casual language, and domain-specific word use of conversations. To tackle the challenges, we propose pre-training a sentence-level and a sessionlevel contextualized word vectors by learning a dialogue generation model from large-scale human-human conversations with a hierarchical encoder-decoder architecture. The two levels of vectors are then integrated into the input layer and the output layer of a matching model respectively. Experimental results on two benchmark datasets indicate that the proposed contextualized word vectors can significantly and consistently improve the performance of existing matching models for response selection.


Low-rank matrix completion using alternating minimization

Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem of low-rank matrix completion, this method is believed to be one of the most accurate and efficient, and formed a major component of the winning entry in the Netflix Challenge [17].
 In the alternating minimization approach, the low-rank target matrix is written in a bi-linear form, i.e. X = UV†; the algorithm then alternates between finding the best U and the best V. Typically, each alternating step in isolation is convex and tractable. However the overall problem becomes non-convex and is prone to local minima. In fact, there has been almost no theoretical understanding of when this approach yields a good result.
 In this paper we present one of the first theoretical analyses of the performance of alternating minimization for matrix completion, and the related problem of matrix sensing. For both these problems, celebrated recent results have shown that they become well-posed and tractable once certain (now standard) conditions are imposed on the problem. We show that alternating minimization also succeeds under similar conditions. Moreover, compared to existing results, our paper shows that alternating minimization guarantees faster (in particular, geometric) convergence to the true matrix, while allowing a significantly simpler analysis.


A general design formula of multi-section power divider based on singly terminated filter design theory

A novel design formula of multi-section power divider is derived to obtain wide isolation performance. The derived design formula is based on the singly terminated filter design theory. This paper presents several simulation and experimental results of multi section power divider to show validity of the proposed design formula. Experiments show excellent performance of multi section power divider with multi-octave isolation characteristic.


ASAP: Prioritizing Attention via Time Series Smoothing

Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users’ attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning, pixel-aware preaggregation, and ondemand refresh. We demonstrate that ASAP can improve users’ accuracy in identifying long-term deviations in time series by up to 38.4% while reducing response times by up to 44.3%. Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.


LD-BSCA: A local-density based spatial clustering algorithm

Density-based clustering algorithms are very powerful to discover arbitrary-shaped clusters in large spatial databases. However, in many cases, varied local-density clusters exist in different regions of data space. In this paper, a new algorithm LD-BSCA is proposed with introducing the concept of local MinPts (a minimum number of points) and the new cluster expanding condition: ExpandConClId (Expanding Condition of ClId-th Cluster). We minimize the algorithm input down to only one parameter and let the local MinPts diversified as clusters change from one to another simultaneously. Experiments show LD-BSCA algorithm is powerful to discover all clusters in gradient distributing databases. In addition, we introduce an efficient searching method to reduce the runtime of our algorithm. Using several databases, we demonstrate the high quality of the proposed algorithm in clustering the implicit knowledge in asymmetric distribution databases.


An Optimal Algorithm for Finding the Kernel of a Polygon

The kernel K(P) of a simple polygon P wah n verUces is the locus of the points internal to P from which all verUces of P are wslble Equwalently, K(P) is the mtersectmn of appropriate half-planes determined by the polygon's edges Although it is known that to find the intersection of n generic half-planes requires time O(n log n), we show that one can exploit the ordering of the half-planes corresponding to the sequence of the polygon's edges to obtain a kernel finding algorithm which runs m time O(n) and is therefore optimal


Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development

This case study examines 18 elementary school teachers’ perceptions of the barriers to technology integration (access, vision, professional development, time, and beliefs) and instructional practices with technology after two years of situated professional development. Months after transitioning from mentoring to teacher-led communities of practice, teachers continued to report positive perceptions of several barriers and were observed engaging in desirable instructional practices. Interviews suggest that the situated professional development activities helped create an environment that supported teachers’ decisions to integrate technology. Implications for teacher professional development and technology integration are discussed in conjunction with the strengths and limitations of the study. 2012 Elsevier Ltd. All rights reserved.


Looking at Vehicles in the Night : Detection & Dynamics of Rear Lights

Existing nighttime vehicle detection methods use color as the primary cue for detecting vehicles. However, complex road and ambient lighting conditions, and camera configurations can influence the effectiveness of such explicit rule and threshold based methods. In this paper, there are three main contributions. First, we present a novel method to detect vehicles during nighttime involving both learned classifiers and explicit rules, which can operate in the presence of varying ambient lighting conditions. The proposed method which is titled as VeDANt (Vehicle Detection using Active-learning during Nighttime) employs a modified form of active learning for training Adaboost classifiers with Haar-like features using gray-level input images. The hypothesis windows are then further verified using the proposed techniques involving perspective geometries and color information of the taillights. Second, VeDANt is extended to analyze the dynamics of the vehicles during nighttime by detecting three taillight activities braking, turning left and turning right. Third, we release three new and fully annotated LISA-Night datasets with over 5000 frames for evaluation and benchmarking, which capture a variety of complex traffic and lighting conditions. Such comprehensively annotated and complex public datasets is a first in the area of nighttime vehicle detection. We show that VeDANt is able to detect vehicles during nighttime with over 98% accuracy and less than 1% false detections.


RASSH - Reinforced adaptive SSH honeypot

The wide spread of cyber-attacks made the need of gathering as much information as possible about them, a real demand in nowadays global context. The honeypot systems have become a powerful tool on the way to accomplish that. Researchers have already focused on the development of various honeypot systems but the fact that their administration is time consuming made clear the need of self-adaptive honeypot system capable of learning from their interaction with attackers. This paper presents a self-adaptive honeypot system we are developing that tries to overlap some of the disadvantaged that existing systems have. The proposed honeypot is a medium interaction system developed using Python and it emulates a SSH (Secure Shell) server. The system is capable of interacting with the attackers by means of reinforcement learning algorithms.


Digital Facial Engraving

This contribution introduces the basic techniques for digital facial engraving, which imitates traditional copperplate engraving. Inspired by traditional techniques, we first establish a set of basic rules thanks to which separate engraving layers are built on the top of the original photo. Separate layers are merged according to simple merging rules and according to range shift/scale masks specially introduced for this purpose. We illustrate the introduced technique by a set of black/white and color engravings, showing different features such as engraving-specific image enhancements, mixing different regular engraving lines with mezzotint, irregular perturbations of engraving lines etc. We introduce the notion of engraving style which comprises a set of separate engraving layers together with a set of associated range shift/scale masks. The engraving style helps to port the look and feel of one engraving to another. Once different libraries of pre-defined mappable engraving styles and an appropriate user interface are added to the basic system, producing a decent gravure starting from a simple digital photo will be a matter of seconds. The engraving technique described in this contribution opens new perspectives for digital art, adding unprecedented power and precision to the engraver's work. 1. Rationale Engraving is among the most important traditional graphical techniques. It first appeared in the fifteenth century as an illustrative support for budding book-printing, but very quickly became an art in its own right, thanks to its specific expressive power. Actually, four main classes of engraving are used by artists: letterpress or relief printing, intaglio or in-hollow printing, silk screen process and lithography, with several different techniques in each class. The history of printmaking was punctuated by prosperous periods of techniques which later declined for various reasons. Facial engraving is one such example. Extremely popular in seventeenth and eighteenth centuries, when photography did not exist, this wonderful art became almost unused, due to the extreme technical demands that it made on the engraver. Professional copperplate engravers are rare today, and the cost of true engravings is simply too prohibitive to be used in everyday printing. At the same time, traditional facial engraving has no doubt very specific appeal: its neat, sharp appearance distinguishes it advantageously from photos. To appreciate the graphical impact of engravings it's enough to compare the engraved portraits in the Wall Street Journal with portraits in other newspapers produced with traditional impersonal screening. Does it mean that this enjoyable art is condemned to disappear …


Correcting a metacognitive error: feedback increases retention of low-confidence correct responses.

Previous studies investigating posttest feedback have generally conceptualized feedback as a method for correcting erroneous responses, giving virtually no consideration to how feedback might promote learning of correct responses. Here, the authors show that when correct responses are made with low confidence, feedback serves to correct this initial metacognitive error, enhancing retention of low-confidence correct responses. In 2 experiments, subjects took an initial multiple-choice test on general knowledge facts and made a confidence judgment after each response. Feedback was provided for half of the questions, and retention was assessed by a final cued-recall test. Taking the initial test improved retention relative to not testing, and feedback further enhanced performance. Consistent with prior research, feedback improved retention by allowing subjects to correct initially erroneous responses. Of more importance, feedback also doubled the retention of correct low-confidence responses, relative to providing no feedback. The function of feedback is to correct both memory errors and metacognitive errors.


A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations

A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multi-structural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a " one-way " partial optimization to the feedback-based, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management.


We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence of GPS points, and diverse associated meta-information, such as the departure time, the driver id and client information. Contrary to most published competitor approaches, we used an almost fully automated approach based on neural networks and we ranked first out of 381 teams. The architectures we tried use multi-layer perceptrons, bidirectional recurrent neural networks and models inspired from recently introduced memory networks. Our approach could easily be adapted to other applications in which the goal is to predict a fixed-length output from a variable-length sequence. Random order, this does not reflect the weights of contributions. ar X iv :1 50 8. 00 02 1v 2 [ cs .L G ] 2 1 Se p 20 15 2 Artificial Neural Networks Applied to Taxi Destination Prediction


Data Compression Techniques in Wireless Sensor Networks

Wireless sensor networks (WSNs) open a new research field for pervasive computing and context-aware monitoring of the physical environments. Many WSN applications aim at long-term environmental monitoring. In these applications, energy consumption is a principal concern because sensor nodes have to regularly report their sensing data to the remote sink(s) for a very long time. Since data transmission is one primary factor of the energy consumption of sensor nodes, many research efforts focus on reducing the amount of data transmissions through data compression techniques. In this chapter, we discuss the data compression techniques in WSNs, which can be classified into five categories: 1) The string-based compression techniques treat sensing data as a sequence of characters and then adopt the text data compression schemes to compress them. 2) The image-based compression techniques hierarchically organize WSNs and then borrow the idea from the image compression solutions to handle sensing data. 3) The distributed source coding techniques extend the Slepian-Wolf theorem to encode multiple correlated data streams independently at sensor nodes and then jointly decode them at the sink. 4) The compressed sensing techniques adopt a small number of nonadaptive and randomized linear projection samples to compress sensing data. 5) The data aggregation techniques select a subset of sensor nodes in the network to be responsible for fusing the sensing data from other sensor nodes to reduce the amount of data transmissions. A comparison of these data compression techniques is also given in this chapter.


A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management.

A meta-analysis was conducted to systematically evaluate the current state of research on human-system interfaces for users controlling semi-autonomous swarms composed of groups of drones or unmanned aerial vehicles (UAVs). UAV swarms pose several human factors challenges, such as high cognitive demands, non-intuitive behavior, and serious consequences for errors. This article presents findings from a meta-analysis of 27 UAV swarm management papers focused on the human-system interface and human factors concerns, providing an overview of the advantages, challenges, and limitations of current UAV management interfaces, as well as information on how these interfaces are currently evaluated. In general allowing user and mission-specific customization to user interfaces and raising the swarm's level of autonomy to reduce operator cognitive workload are beneficial and improve situation awareness (SA). It is clear more research is needed in this rapidly evolving field.


On HMM static hand gesture recognition

A real time static isolated gesture recognition application using a Hidden Markov Model approach with features extracted from gestures' silhouettes is presented. Nine different hand poses with various degrees of rotation are considered. The system, both simple and effective, uses color images of the hands to be recognized directly from the camera and is capable of processing 23 frames per second on a Quad Core Intel Processor.


Teaching Programming in Secondary Education Through Embodied Computing Platforms: Robotics and Wearables

Pedagogy has emphasized that physical representations and tangible interactive objects benefit learning especially for young students. There are many tangible hardware platforms for introducing computer programming to children, but there is limited comparative evaluation of them in the context of a formal classroom. In this work, we explore the benefits of learning to code for tangible computers, such as robots and wearable computers, in comparison to programming for the desktop computer. For this purpose, 36 students participated in a within-groups study that involved three types of target computer platform tangibility: (1) desktop, (2) wearable, and (3) robotic. We employed similar blocks-based visual programming environments, and we measured emotional engagement, attitudes, and computer programming performance. We found that students were more engaged by and had a higher intention of learning programming with the robotic rather than the desktop computer. Furthermore, tangible computing platforms, either robot or wearable, did not affect the students’ performance in learning basic computational concepts (e.g., sequence, repeat, and decision). Our findings suggest that computer programming should be introduced through multiple target platforms (e.g., robots, smartphones, wearables) to engage children.


A Dynamic Programming Algorithm for Computing N-gram Posteriors from Lattices

Efficient computation of n-gram posterior probabilities from lattices has applications in lattice-based minimum Bayes-risk decoding in statistical machine translation and the estimation of expected document frequencies from spoken corpora. In this paper, we present an algorithm for computing the posterior probabilities of all ngrams in a lattice and constructing a minimal deterministic weighted finite-state automaton associating each n-gram with its posterior for efficient storage and retrieval. Our algorithm builds upon the best known algorithm in literature for computing ngram posteriors from lattices and leverages the following observations to significantly improve the time and space requirements: i) the n-grams for which the posteriors will be computed typically comprises all n-grams in the lattice up to a certain length, ii) posterior is equivalent to expected count for an n-gram that do not repeat on any path, iii) there are efficient algorithms for computing n-gram expected counts from lattices. We present experimental results comparing our algorithm with the best known algorithm in literature as well as a baseline algorithm based on weighted finite-state automata operations.


Magnet design based on transient behavior of an IPMSM in event of malfunction

This paper deals with the approach of analyzing the magnet design based on transient behavior of an IPMSM during switching processes in the event of malfunction for automotive applications. Depending on the maximal current increase during the transient process the needed percentage of Dysprosium for the magnet design is conducted. Switching off strategy is introduced for both Voltage-Source- and Current-Source-Inverter for automotive application. Both inverters are compared concerning the transient current increase and respectively the Dy-content for the magnet design.