title: Real-Time Salient Object Detection with a Minimum Spanning Tree

abstract: In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connectivity efficiently is a challenging problem. Existing methods either rely on superpixel representation to reduce the processing units or approximate the distance transform. Instead, we propose an exact and iteration free solution on a minimum spanning tree. The minimum spanning tree representation of an image inherently reveals the object geometry information in a scene. Meanwhile, it largely reduces the search space of shortest paths, resulting an efficient and high quality distance transform algorithm. We further introduce a boundary dissimilarity measure to compliment the shortage of distance transform for salient object detection. Extensive evaluations show that the proposed algorithm achieves the leading performance compared to the state-of-the-art methods in terms of efficiency and accuracy.


title: A Soft Strain Sensor Based on Ionic and Metal Liquids

abstract: A novel soft strain sensor capable of withstanding strains of up to 100% is described. The sensor is made of a hyperelastic silicone elastomer that contains embedded microchannels filled with conductive liquids. This is an effort of improving the previously reported soft sensors that uses a single liquid conductor. The proposed sensor employs a hybrid approach involving two liquid conductors: an ionic solution and an eutectic gallium-indium alloy. This hybrid method reduces the sensitivity to noise that may be caused by variations in electrical resistance of the wire interface and undesired stress applied to signal routing areas. The bridge between these two liquids is made conductive by doping the elastomer locally with nickel nanoparticles. The design, fabrication, and characterization of the sensor are presented.


title: The M5 Simulator: Modeling Networked Systems

abstract: The M5 simulator is developed specifically to enable research in TCP/IP networking. The M5 simulator provides features necessary for simulating networked hosts, including full-system capability, a detailed I/O subsystem, and the ability to simulate multiple networked systems deterministically. M5's usefulness as a general-purpose architecture simulator and its liberal open-source license has led to its adoption by several academic and commercial groups


title: Generalized principal component analysis (GPCA)

abstract: We propose an algebraic geometric approach to the problem of estimating a mixture of linear subspaces from sample data points, the so-called generalized principal component analysis (GPCA) problem. In the absence of noise, we show that GPCA is equivalent to factoring a homogeneous polynomial whose degree is the number of subspaces and whose factors (roots) represent normal vectors to each subspace. We derive a formula for the number of subspaces n and provide an analytic solution to the factorization problem using linear algebraic techniques. The solution is closed form if and only if n /spl les/ 4. In the presence of noise, we cast GPCA as a constrained nonlinear least squares problem and derive an optimal function from which the subspaces can be directly recovered using standard nonlinear optimization techniques. We apply GPCA to the motion segmentation problem in computer vision, i.e. the problem of estimating a mixture of motion models from 2D imagery.


title: Neuro-Cognitive Intervention for Working Memory: Preliminary Results and Future Directions

abstract: Definitions of working memory identify it as a function of the executive function system in which an individual maintains two or more pieces of information in mind and uses that information simultaneously for some purpose. In academics, working memory is necessary for a variety of functions, including attending to the information one's teacher presents and then using that information simultaneously for problem solving. Research indicates difficulties with working memory are observed in children with mathematics learning disorder (MLD) and reading disorders (RD). To improve working memory and other executive function difficulties, and as an alternative to medication treatments for attention and executive function disorders, the Motor Cognition(2)® (MC(2)®)program was developed. Preliminary research on this program indicates statistically significant improvements in working memory, mathematics, and nonsense word decoding for reading. Further research on the MC(2)® program and its impact on working memory, as well as other areas of executive functioning, is warranted.


title: Electroadhesive robots—wall climbing robots enabled by a novel, robust, and electrically controllable adhesion technology

abstract: This paper describes a novel clamping technology called compliant electroadhesion, as well as the first application of this technology to wall climbing robots. As the name implies, electroadhesion is an electrically controllable adhesion technology. It involves inducing electrostatic charges on a wall substrate using a power supply connected to compliant pads situated on the moving robot. High clamping forces (0.2-1.4 Newton supported by 1 square centimeter of clamp area, depending on substrate) have been demonstrated on a wide variety of common building substrates, both rough and smooth as well as both electrically conductive and insulating. Unlike conventional adhesives or dry adhesives, the electroadhesion can be modulated or turned off for mobility or cleaning. The technology uses a very small amount of power (on the order of 20 microwatts/Newton weight held) and shows the ability to repeatably clamp to wall substrates that are heavily covered in dust or other debris. Using this technology, SRI International has demonstrated a variety of wall climbing robots including tracked and legged robots.


title: The structure of the information visualization design space

abstract: Research on information visualization has reached the point where a number of successful point designs have been proposed and a variety of techniques have been discovered. It is now appropriate to describe and analyze portions of the design space so as to understand the differences among designs and to suggest new possibilities. This paper proposes an organization of the information visualization literature and illustrates it with a series of examples. The result is a framework for designing new visualizations and augmenting existing designs.


title: A Grating-Coupler-Enabled CMOS Photonics Platform

abstract: We have developed a silicon photonics platform that allows monolithic integration with electronic circuits in a CMOS-compatible process. In this platform, vertical couplers are found to be a superior solution compared to traditional edge-coupling techniques. Grating couplers are an essential element in developing the optical wafer-scale test infrastructure, which in turn, enables the development of the photonic device library. The photonic devices were assembled into a 4 × 10 Gb/s transceiver die that also contains modulator drive, control, and receive electronics.


title: Evaluating a Representational State Transfer (REST) Architecture: What is the Impact of REST in My Architecture?

abstract: The use of Representational State Transfer (REST) as an architectural style for integrating services and applications brings several benefits, but also poses new challenges and risks. Particularly important among those risks are failures to effectively address quality attribute requirements such as security, reliability, and performance. An architecture evaluation early in the software life cycle can identify and help mitigate those risks. In this paper we present guidelines to assist architecture evaluation activities in REST-based systems. These guidelines can be systematically used in conjunction with scenario-based evaluation methods to reason about design considerations and trade-offs. This paper also presents a proof of concept to describe how to use the guidelines in the context of an Architecture Trade-off Analysis Method (ATAM) evaluation.


HMM-Based Word Alignment in Statistical Translation


The Healthy Child


title: Delving Deeper into MOOC Student Dropout Prediction

abstract: In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice. Yet most prior research on MOOC dropout prediction has measured test accuracy on the same course used for training the classifier, which can lead to overly optimistic accuracy estimates. In order to understand better how accuracy is affected by the training+testing regime, we compared the accuracy of a standard dropout prediction architecture (clickstream features + logistic regression) across 4 different training paradigms. Results suggest that (1) training and testing on the same course ("post-hoc") can overestimate accuracy by several percentage points; (2) dropout classifiers trained on proxy labels based on students' persistence are surprisingly competitive with post-hoc training (87.33% versus 90.20% AUC averaged over 8 weeks of 40 HarvardX MOOCs); and (3) classifier performance does not vary significantly with the academic discipline. Finally, we also research new dropout prediction architectures based on deep, fully-connected, feed-forward neural networks and find that (4) networks with as many as 5 hidden layers can statistically significantly increase test accuracy over that of logistic regression.


title: Artificial moral agents are infeasible with foreseeable technologies

abstract: For an artificial agent to be morally praiseworthy, its rules for behaviour and the mechanisms for supplying those rules must not be supplied entirely by external humans. Such systems are a substantial departure from current technologies and theory, and are a low prospect. With foreseeable technologies, an artificial agent will carry zero responsibility for its behavior and humans will retain full responsibility.


title: Cloudified NGN infrastructure based on SDN and NFV

abstract: Network Function Virtualization (NFV) and Software Defined network (SDN) intend to deliver Next Generation Networks (NGN) related services over an intelligent, cost effective and cloudified infrastructure. This paper discusses a new NGN design based on cloud services such as NFV and SDN in order to improve network scalability, elasticity, usage efficiency and resiliency while decreasing cost. In this paper, we discuss the detailed architectural design along with the functionality of each element of the design. The new design will be investigated against some of the NGN related challenges to prove its efficiency to deliver services. Analysis in terms of performance, flexibility and Deployment Complexity will be performed to explore possible enhancements to the proposed design.


title: Design and characterization of a powered elbow prosthesis

abstract: This paper describes the design of a powered elbow prosthesis, which incorporates a belt and cable drive transmission with a brushless DC motor to achieve an output torque of approximately 18.4 Nm, a backdrive torque of 1.5 Nm, and a speed of up to 360 deg/s while remaining within the anthropomorphic envelope with regard to mass and size. The measured torque and speed of the prosthesis is commensurate with nominal capability of the natural limb (for purposes of performing activities of daily living).


Dynamic Ant Colony System with Three Level Update Feature Selection for Intrusion Detection


title: Shared common ground influences information density in microblog texts

abstract: If speakers use language rationally, they should structure their messages to achieve approximately uniform information density (UID), in order to optimize transmission via a noisy channel. Previous work identified a consistent increase in linguistic information across sentences in text as a signature of the UID hypothesis. This increase was derived from a predicted increase in context, but the context itself was not quantified. We use microblog texts from Twitter, tied to a single shared event (the baseball World Series), to quantify both linguistic and non-linguistic context. By tracking changes in contextual information, we predict and identify gradual and rapid changes in information content in response to in-game events. These findings lend further support to the UID hypothesis and highlights the importance of nonlinguistic common ground for language production and processing.


title: Measuring media-based social interactions provided by smartphone applications in social networks

abstract: Increasing varieties of applications for smartphones allow users to post (upload) multimedia content and interact in social networks. Usually modeled as a graph, social networks are achieved for applying data mining techniques in the analysis of social relations among users. However, these analyses do not make explicit which actions are performed, which types of medias and users' applications for mobile phone are used to provide social interactions. We have been investigating a human-readable technique for representing social interactions as behavioral contingencies -- if-then rules which describe what people do -- and measuring them using data mining procedures. In this paper we apply our technique for analyzing social interactions in group of Facebook users. We present results that enable us to identify the most symmetric social interaction and the smartphone users' application which provides the type of media used in the symmetric social interaction.


title: Plagiarism detection of flowchart images in the texts

abstract: Today, much more than in the past are discussed of plagiarism in the research. Conditions of the Web and Possibility of complex and smart searches in a short time, is rated to this, and as a result has arrived significant damages to the research. Tools designed to deal with plagiarism act on the text and ignore images. On the other, an inseparable part of information transfer are images that transfer the large volume of information in an article or scientific research. Because of the images include a very wide range and especially found large amounts of flowchart images in the computer's texts, and as respects, flowcharts are carrying a lot of information, could be one of the options of plagiarism. The purpose of this paper is examine the plagiarism rate of a paper in terms of flowchart images plagiarism using artificial neural network. The average of flowchart images recognition accuracy in terms of structure, nodes and edges in the proposed method with 81.91 percent, indicating the success of this method.


title: Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm

abstract: The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the Singular Value Decomposition. However, in the presence of missing data and outliers this method is not applicable, and unfortunately, this is often the case in practice. In this paper we present a method for calculating the low-rank factorization of a matrix which minimizes the L 1 norm in the presence of missing data. Our approach represents a generalization the Wiberg algorithm of one of the more convincing methods for factorization under the L 2 norm. By utilizing the differentiability of linear programs, we can extend the underlying ideas behind this approach to include this class of L 1 problems as well. We show that the proposed algorithm can be efficiently implemented using existing optimization software. We also provide preliminary experiments on synthetic as well as real world data with very convincing results.


title: Spam detection framework for Android Twitter application using Naïve Bayes and K-Nearest Neighbor classifiers

abstract: Twitter currently is one of the leading social networks worldwide based on the amount of monthly active users after Facebook and Instagram. People uses Twitter mostly to find out more information about breaking news or keeping up with news in general by following trending topics. As Twitter become a source of news breaks contents in form of comments and replies to share the newest ideas. Therefore, several mobile applications that utilize Twitter API has been developed to provide a convenient way in providing trending topics to their user. Twitter trending topics offers an effective opportunity in marketing point of view for online marketers to promote their marketing contents. Spam contents in Twitter were found to be distracting and annoying for certain users, thus mobile application to deliver spam-free Twitter trending topics contents is needed. This research designs an Android application framework that allow developers to build their own implementation of spam detection classifier for Twitter contents as application library. This research implements two classification methods, i.e. Naive Bayes and K-Nearest Neighbor, to identify spam in Twitter trending topics. The Naive Bayes and K-Nearest Neighbor classification methods are able to detect spam and ham contents with 82% and 71% accuracy respectively.


title: Applied the Technology Acceptance Model in Designing a Questionnaire for Mobile Reminder System

abstract: Usability of software applications is a key to success of any system and the technology is best care because it is not susceptible to forget or to damage. The main aim of the proposed system application is to help two important categories of the society they are elderly and Alzheimer’s patients, these two categories shared in one recipe which is a forgetful. Proposed application gives elderly and Alzheimer’s patients an opportunity to have small memory which can help them to remember tasks, events, medications and people. It is a great opportunity to learn what makes a proposed application good. Which may contribute to the prevention of progression of the disease rapidly. In this study Technology Acceptance Model has been designed to investigate the end-user acceptance of designing Proposed Mobile Reminder System. The purpose of this study is to design a quantitative approach based on the technology acceptance model questionnaire as its primary research methodology. It utilized a quantitative approach based a Technology Acceptance Model (TAM) to evaluate the designing Proposed Mobile Reminder System. The related constructs for evaluation are: Perceived of Usefulness, Perceived Ease of Use, User Satisfaction and Attribute of Usability. All these constructs are modified to suit the context of the study. Moreover, this study outlines the details of each construct and its relevance toward the research issue. The outcome of the study represents series of approaches that will apply for checking the advantages of proposed system by helping the target groups to remember what they may forget, and helping care providers to save time and effort.


title: A High Energy Efficient Reconfigurable Hybrid Neural Network Processor for Deep Learning Applications

abstract: Hybrid neural networks (hybrid-NNs) have been widely used and brought new challenges to NN processors. Thinker is an energy efficient reconfigurable hybrid-NN processor fabricated in 65-nm technology. To achieve high energy efficiency, three optimization techniques are proposed. First, each processing element (PE) supports bit-width adaptive computing to meet various bit-widths of neural layers, which raises computing throughput by 91% and improves energy efficiency by $1.93 \times $ on average. Second, PE array supports on-demand array partitioning and reconfiguration for processing different NNs in parallel, which results in 13.7% improvement of PE utilization and improves energy efficiency by $1.11 \times $ . Third, a fused data pattern-based multi-bank memory system is designed to exploit data reuse and guarantee parallel data access, which improves computing throughput and energy efficiency by $1.11 \times $ and $1.17 \times $ , respectively. Measurement results show that this processor achieves 5.09-TOPS/W energy efficiency at most.


A simple RNN-plus-highway network for statistical parametric speech synthesis


Separate Loss for Basic and Compound Facial Expression Recognition in the Wild


title: Trends of power semiconductor wafer level packaging

abstract: A review of recent advances in power wafer level electronic packaging is presented based on the development of power device integration. The paper covers in more detail how advances in both semiconductor content and power advanced wafer level package design and materials have co-enabled significant advances in power device capability during recent years. Extrapolating the same trends in representative areas for the remainder of the decade serves to highlight where further improvement in materials and techniques can drive continued enhancements in usability, efficiency, reliability and overall cost of power semiconductor solutions. Along with next generation wafer level power packaging development, the role of modeling is a key to assure successful package design. An overview of the power package modeling is presented. Challenges of wafer level power semiconductor packaging and modeling in both next generation design and assembly processes are presented and discussed.


title: An Army of Me: Sockpuppets in Online Discussion Communities

abstract: In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as ``I'', and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.


Ontology-driven Information Extraction


title: PROBLEM SOLVING STRATEGY AND ORGANISATION

abstract: Viewing new product development as a problem-solving activity, the focus in the article is on abduction as a problem-solving strategy. We identify the advantages of problem-solving by way of abduction, as compared to induction and deduction, present a rationale of how to decide between the different problem-solving strategies, and finally, draw on a case study of a main European car manufacturer to analyze how problem-solving by abduction is implemented in practice.


title: Actualization of safe and stable stair climbing and three-dimensional locomotion for wheelchair

abstract: Leg-wheel hybrid mobile vehicle has been regarded as the best combination and many of them have been presented recently because of the high terrain adaptability of legged walking robots on rough ground and the high mobility of wheeled mobile vehicles on flat terrain. We discuss the detailed actualization of safe and stable stair climbing and three-dimensional locomotion for wheelchair - with mainly indoor or ordered terrain environments - in mechanism sensor system and control method based on the developed leg-wheel hybrid mobile vehicle Zero Carrier I in this paper.


Dynamic Time Warping Algorithm Review


title: Seven V's of Big Data understanding Big Data to extract value

abstract: Big Data has shown lot of potential in real world industry and research community. We support the power and potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 7 V's. 7th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 7 Vs of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.


title: Multiuser Resource Allocation Optimization Using Bandwidth-Power Product in Cognitive Radio Networks

abstract: In this paper, the problem of resource allocation optimization is studied for a single-cell multiuser cognitive radio network in the presence of primary user networks. The spectral access of the cognitive radio network is based on Orthogonal Frequency Division Multiple Access (OFDMA). A joint bandwidth and power allocation is performed so that users' rate requirements are satisfied, and the integrity of primary user communication is preserved. In this work, two unique challenges are addressed. The first is the incorporation of primary user activity in the design of resource allocation technique, and the second is the limited hardware capabilities of cognitive terminals compared to those available at the cognitive base station. To address these problems, a novel resource allocation framework is proposed based on the bandwidth-power product minimization, which is an effective metric in evaluating the spectral resource consumption in a cognitive radio environment. The framework takes into consideration the challenges aforementioned. The results show significant enhancement in spectral efficiency by using our framework compared to classical power adaptive optimization using iterative waterfilling scheme.


title: A new fuzzy dempster MCDM method and its application in supplier selection

abstract: Supplier selection is a multi-criterion decision making problem under uncertain environments. Hence, it is reasonable to hand the problem in fuzzy sets theory (FST) and Dempster Shafer theory of evidence (DST). In this paper, a new MCDM methodology, using FST and DST, based on the main idea of the technique for order preference by similarity to an ideal solution (TOPSIS), is developed to deal with supplier selection problem. The basic probability assignments (BPA) can be determined by the distance to the ideal solution and the distance to the negative ideal solution. Dempster combination rule is used to combine all the criterion data to get the final scores of the alternatives in the systems. The final decision results can be drawn through the pignistic probability transformation. In traditional fuzzy TOPSIS method, the quantitative performance of criterion, such as crisp numbers, should be transformed into fuzzy numbers. The proposed method is more flexible due to the reason that the BPA can be determined without the transformation step in traditional fuzzy TOPSIS method. The performance of criterion can be represented as crisp number or fuzzy number according to the real situation in our proposed method. The numerical example about supplier selection is used to illustrate the efficiency of the proposed method.


title: Packet classification on multiple fields

abstract: Routers classify packets to determine which flow they belong to, and to decide what service they should receive. Classification may, in general, be based on an arbitrary number of fields in the packet header. Performing classification quickly on an arbitrary number of fields is known to be difficult, and has poor worst-case performance. In this paper, we consider a number of classifiers taken from real networks. We find that the classifiers contain considerable structure and redundancy that can be exploited by the classification algorithm. In particular, we find that a simple multi-stage classification algorithm, called RFC (recursive flow classification), can classify 30 million packets per second in pipelined hardware, or one million packets per second in software.


title: Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series

abstract: This article proposes a Bayesian nonparametric method for forecasting, imputation, and clustering in sparsely observed, multivariate time series data. The method is appropriate for jointly modeling hundreds of time series with widely varying, non-stationary dynamics. Given a collection of $N$ time series, the Bayesian model first partitions them into independent clusters using a Chinese restaurant process prior. Within a cluster, all time series are modeled jointly using a novel "temporally-reweighted" extension of the Chinese restaurant process mixture. Markov chain Monte Carlo techniques are used to obtain samples from the posterior distribution, which are then used to form predictive inferences. We apply the technique to challenging forecasting and imputation tasks using seasonal flu data from the US Center for Disease Control and Prevention, demonstrating superior forecasting accuracy and competitive imputation accuracy as compared to multiple widely used baselines. We further show that the model discovers interpretable clusters in datasets with hundreds of time series, using macroeconomic data from the Gapminder Foundation.


title: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review.

abstract: Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.


title: Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation

abstract: In this paper, we propose a fine-grained image categorization system with easy deployment. We do not use any object/part annotation (weakly supervised) in the training or in the testing stage, but only class labels for training images. Fine-grained image categorization aims to classify objects with only subtle distinctions (e.g., two breeds of dogs that look alike). Most existing works heavily rely on object/part detectors to build the correspondence between object parts, which require accurate object or object part annotations at least for training images. The need for expensive object annotations prevents the wide usage of these methods. Instead, we propose to generate multi-scale part proposals from object proposals, select useful part proposals, and use them to compute a global image representation for categorization. This is specially designed for the weakly supervised fine-grained categorization task, because useful parts have been shown to play a critical role in existing annotation-dependent works, but accurate part detectors are hard to acquire. With the proposed image representation, we can further detect and visualize the key (most discriminative) parts in objects of different classes. In the experiments, the proposed weakly supervised method achieves comparable or better accuracy than the state-of-the-art weakly supervised methods and most existing annotation-dependent methods on three challenging datasets. Its success suggests that it is not always necessary to learn expensive object/part detectors in fine-grained image categorization.


title: Quadruped walking robots at Tokyo Institute of Technology

abstract: In this article, the design principle of the leg driving mechanism to minimize energy loss and maximize output power is discussed. We will also introduce the gait control methods implemented in our previous quadruped walking robots. Finally, we will survey most of the prototype models of our quadruped walking robots.


title: Student attendance system in classroom using face recognition technique

abstract: Authentication is one of the significant issues in the era of information system. Among other things, human face recognition (HFR) is one of known techniques which can be used for user authentication. As an important branch of biometric verification, HFR has been widely used in many applications, such as video monitoring/surveillance system, human-computer interaction, door access control system and network security. This paper proposes a method for student attendance system in classroom using face recognition technique by combining Discrete Wavelet Transforms (DWT) and Discrete Cosine Transform (DCT) to extract the features of student's face which is followed by applying Radial Basis Function (RBF) for classifying the facial objects. From the experiments which is conducted by involving 16 students situated in classroom setting, it results in 121 out of 148 successful faces recognition.


title: A Review of Depth and Normal Fusion Algorithms

abstract: Geometric surface information such as depth maps and surface normals can be acquired by various methods such as stereo light fields, shape from shading and photometric stereo techniques. We compare several algorithms which deal with the combination of depth with surface normal information in order to reconstruct a refined depth map. The reasons for performance differences are examined from the perspective of alternative formulations of surface normals for depth reconstruction. We review and analyze methods in a systematic way. Based on our findings, we introduce a new generalized fusion method, which is formulated as a least squares problem and outperforms previous methods in the depth error domain by introducing a novel normal weighting that performs closer to the geodesic distance measure. Furthermore, a novel method is introduced based on Total Generalized Variation (TGV) which further outperforms previous approaches in terms of the geodesic normal distance error and maintains comparable quality in the depth error domain.


title: The Unintended Consequences of Overfitting: Training Data Inference Attacks

abstract: Machine learning algorithms that are applied to sensitive data pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker, either through their structure or their observable behavior. However, the underlying cause of this privacy risk is not well understood beyond a handful of anecdotal accounts that suggest overfitting and influence might play a role. ::: This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about training data from machine learning models, either through training set membership inference or model inversion attacks. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We find that overfitting is sufficient to allow an attacker to perform membership inference, and when certain conditions on the influence of certain features are present, model inversion attacks. Interestingly, our formal analysis also shows that overfitting is not necessary for these attacks, and begins to shed light on what other factors may be in play. Finally, we explore the connection between two types of attack, membership inference and model inversion, and show that there are deep connections between the two that lead to effective new attacks.


title: Dynamic source routing in ad hoc wireless networks

abstract: An ad hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, it may be necessary for one mobile host to enlist the aid of other hosts in forwarding a packet to its destination, due to the limited range of each mobile host’s wireless transmissions. This paper presents a protocol for routing in ad hoc networks that uses dynamic source routing. The protocol adapts quickly to routing changes when host movement is frequent, yet requires little or no overhead during periods in which hosts move less frequently. Based on results from a packet-level simulation of mobile hosts operating in an ad hoc network, the protocol performs well over a variety of environmental conditions such as host density and movement rates. For all but the highest rates of host movement simulated, the overhead of the protocol is quite low, falling to just 1% of total data packets transmitted for moderate movement rates in a network of 24 mobile hosts. In all cases, the difference in length between the routes used and the optimal route lengths is negligible, and in most cases, route lengths are on average within a factor of 1.01 of optimal.


title: Automated Content Analysis of Online Political Communication

abstract: Content analysis has a long tradition in the social sciences: it is central to the study of policy preferences, propaganda and mass media, and the framing of social movements. New computational tools and the increasing availability of digitized documents promise to push forward this line of inquiry by reducing the costs of manual annotation and enabling the analysis of large-scale corpora. In particular, the automated analysis of online political communication may yield insights into political sentiment which offline analysis instruments (such as opinion polls) fail to capture; for instance, we are now in a better position to analyze the temporal dimension of opinion formation because of higher resolution data. Several linguistic peculiarities, however, distinguish online political communication from traditional political texts; for starters, it is less formal and structured. Automated content analysis techniques are also not always as reliable or as valid as manual annotation, which makes measurements potentially noisy or misleading. We provide an overview of techniques suited to two common content analysis tasks: classifying documents into specified categories, and discovering unknown categories from documents. This second task is more exploratory in nature: it helps identify topic domains when there are no clear preconceptions of the topics that are discussed in a certain communication environment; the first task, on the other hand, can help label a large volume of text in a more efficient manner than manual annotation. This chapter focuses on the application of these automated techniques to online political communication, and suggests directions for future research in this domain.


title: Distributed, object-based programming systems

abstract: The development of distributed operating systems and object-based programming languages makes possible an environment in which programs consisting of a set of interacting modules, or objects, may execute concurrently on a collection of loosely coupled processors. An object-based programming language encourages a methodology for designing and creating a program as a set of autonomous components, whereas a distributed operating system permits a collection of workstations or personal computers to be treated as a single entity. The amalgamation of these two concepts has resulted in systems that shall be referred to as distributed , object-based programming systems . This paper discusses issues in the design and implementation of such systems. Following the presentation of fundamental concepts and various object models, issues in object management, object interaction management, and physical resource management are discussed. Extensive examples are drawn from existing systems.


title: A switch-beam circular array antenna using pattern reconfigurable Yagi-Uda antenna for space communications

abstract: This paper presents a switch-beam circular array antenna using pattern reconfigurable Yagi-Uda antenna. A two-beam switching Yagi-Uda antenna is applied as an array element. The 3-element circular array is introduced to be able to switch the main beam in azimuth direction by switching the beam and shifting the excitation phase of array elements. The array antenna is designed at 2.4 GHz for drone or UAV radio controlling. The two-beam elements and 90°-shifted phase of excitation are applied to realize the proposed switched-beam array antenna. The simulated results show that the array antenna can provide 7 main beam directions covering a half azimuth plane (0° ≤ φ ≤ 180°) and gain of the array antenna is higher than 7 dBi.


title: Breakthroughs in the treatment and prevention of Clostridium difficile infection

abstract: This Review summarizes the latest advances in the treatment and prevention of Clostridium difficile infection (CDI), which is now the most common health-care-associated infection in the USA. As traditional, standard CDI antibiotic therapies (metronidazole and vancomycin) are limited by their broad spectrum and further perturbation of the intestinal microbiota, which result in unacceptably high recurrence rates, novel therapeutic strategies for CDI are needed. Emerging CDI therapies are focused on limiting further perturbation of the intestinal microbiota and/or restoring the microbiota to its pre-morbid state, reducing colonization of the intestinal tract by toxigenic strains of C. difficile and bolstering the host immune response against C. difficile toxins. Fidaxomicin is associated with reduced CDI recurrences, and other emerging narrow-spectrum CDI antibiotic therapies might eventually demonstrate a similar benefit. Prevention of intestinal colonization of toxigenic strains of C. difficile can be achieved through restoration of the intestinal microbiota with faecal microbiota transplantation, as well as by colonizing the gut with nontoxigenic C. difficile strains. Finally, emerging immunological therapies, including monoclonal antibodies and vaccines against C. difficile toxins, might protect against CDI and subsequent CDI recurrences. The available clinical data for these emerging therapies, and their relative advantages and disadvantages, are described.


title: Flower pollination algorithm: a comprehensive review

abstract: Flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its metaphor from flowers proliferation role in plants. This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications. Besides, it makes a comparison between FPA and six different metaheuristics such as genetic algorithm, cuckoo search, grasshopper optimization algorithm, and others on solving a constrained engineering optimization problem ::: . The experimental results are statistically analyzed with non-parametric Friedman test which indicates that FPA is superior more than other competitors in solving the given problem.


Word Embeddings for Information Extraction from Tweets


ElizabethShafer Automated Redaction of Private and Personal Data in Collections Toward Responsible Stewardship of Digital Heritage


title: Deeply Vulnerable -- a study of the robustness of face recognition to presentation attacks

abstract: The vulnerability of deep-learning-based face-recognition (FR) methods, to presentation attacks (PA), is studied in this study. Recently, proposed FR methods based on deep neural networks (DNN) have been shown to outperform most other methods by a significant margin. In a trustworthy face-verification system, however, maximising recognition-performance alone is not sufficient – the system should also be capable of resisting various kinds of attacks, including PA. Previous experience has shown that the PA vulnerability of FR systems tends to increase with face-verification accuracy. Using several publicly available PA datasets, the authors show that DNN-based FR systems compensate for variability between bona fide and PA samples, and tend to score them similarly, which makes such FR systems extremely vulnerable to PAs. Experiments show the vulnerability of the studied DNN-based FR systems to be consistently higher than 90%, and often higher than 98%.


title: Dual-Band Coaxial Cavity Bandpass Filter With Helical Feeding Structure and Mixed Coupling

abstract: In this letter, stepped-impedance resonators are used in coaxial cavity filter to generate dual-band response. Two key merits of the proposed filter are the helical feeding structure and the introduction of mixed coupling for coupling control and generation of transmission zero. The helical feeding structure makes it possible to conveniently control the external quality factors of two bands. To illustrate the concept, a third-order dual-band coaxial cavity filter is designed, fabricated and measured. The measured results are in good agreement with the full-wave simulation results.


title: The beauty of simplicity

abstract: In this paper we show how discussion about design quality or even usability is often discussion about aesthetics. First, we introduce some definitions of beauty in aesthetics. Then, we introduce how the beauty of the Web design affects the feeling of online trust, and bring forth observations about this design quality as an aesthetic notion. As a result, we present how simplicity and beauty affect the user's experience and interpretation of the design, and contemplate on how this perception may vary according to cultural background, age, and the amount of user experience. We will also envision some future trends for the aesthetics of user interfaces.


Deep Text Style Transfer


Mechanics and Acoustics of Violin Bowing


title: Model-Based Reinforcement Learning for Atari

abstract: Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with fewer interactions than model-free methods. We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games in low data regime of 100k interactions between the agent and the environment, which corresponds to two hours of real-time play. In most games SimPLe outperforms state-of-the-art model-free algorithms, in some games by over an order of magnitude.


Understanding Blockchain Consensus Models


title: Multi-scale characterizations of colon polyps via computed tomographic colonography

abstract: Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.


title: Deep Frank-Wolfe For Neural Network Optimization

abstract: Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms. The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive variants. However, SGD requires a hand-designed schedule for the learning rate. In addition, its adaptive variants tend to produce solutions that generalize less well on unseen data than SGD with a hand-designed schedule. We present an optimization method that offers empirically the best of both worlds: our algorithm yields good generalization performance while requiring only one hyper-parameter. Our approach is based on a composite proximal framework, which exploits the compositional nature of deep neural networks and can leverage powerful convex optimization algorithms by design. Specifically, we employ the Frank-Wolfe (FW) algorithm for SVM, which computes an optimal step-size in closed-form at each time-step. We further show that the descent direction is given by a simple backward pass in the network, yielding the same computational cost per iteration as SGD. We present experiments on the CIFAR and SNLI data sets, where we demonstrate the significant superiority of our method over Adam, Adagrad, as well as the recently proposed BPGrad and AMSGrad. Furthermore, we compare our algorithm to SGD with a hand-designed learning rate schedule, and show that it provides similar generalization while converging faster. The code is publicly available at https://github.com/oval-group/dfw.


title: Novel millimeter-wave flexible antenna for RF energy harvesting

abstract: In this paper, a novel MMW flexible antenna has been proposed for energy harvesting applications. Antenna consists of a triangular patch printed on a flexible PET substrate. Defected ground structures (DGS) have been introduced to generate multiple resonances in order to improve bandwidth. It has been observed that the bandwidth and radiation characteristics are considerably improved by two square split-ring slots incorporated as DGS. Antenna is fabricated by inkjet printing. Measurements show that the dual band operation has been achieved with bandwidth of 15.9–19.7 GHz and 22.5–34 GHz for Band-I and Band-II respectively. Distinctive features of omnidirectional radiation pattern, significant gain profile combined with the flexibility of integration at non-uniform surfaces present this versatile antenna as a valuable contribution in emerging energy harvesting for future wireless networks at MMW spectrum.


title: Distributed Denial of Service: Taxonomies of Attacks, Tools, and Countermeasures

abstract: Distributed Denial of Service (DDoS) attacks have become a large problem for users of computer systems connected to the Internet. DDoS attackers hijack secondary victim systems using them to wage a coordinated large-scale attack against primary victim systems. As new countermeasures are developed to prevent or mitigate DDoS attacks, attackers are constantly developing new methods to circumvent these new countermeasures. In this paper, we describe DDoS attack models and propose taxonomies to characterize the scope of DDoS attacks, the characteristics of the software attack tools used, and the countermeasures available. These taxonomies illustrate similarities and patterns in different DDoS attacks and tools, to assist in the development of more generalized solutions to countering DDoS attacks, including new derivative attacks.


title: Reliability of Ferroelectric Random Access memory embedded within 130nm CMOS

abstract: We present results of a comprehensive reliability evaluation of a 2T–2C, 4Mb, Ferroelectric Random Access Memory embedded within a standard 130nm, 5LM Cu CMOS platform. Wear-out free endurance to 5.4×1013 cycles and data retention equivalent of 10 years at 85°C is demonstrated. The results show that the technology can be used in a wide range of applications including embedded processing.


title: Inhibition of GSK-3 Ameliorates Aβ Pathology in an Adult-Onset Drosophila Model of Alzheimer's Disease

abstract: Abeta peptide accumulation is thought to be the primary event in the pathogenesis of Alzheimer's disease (AD), with downstream neurotoxic effects including the hyperphosphorylation of tau protein. Glycogen synthase kinase-3 (GSK-3) is increasingly implicated as playing a pivotal role in this amyloid cascade. We have developed an adult-onset Drosophila model of AD, using an inducible gene expression system to express Arctic mutant Abeta42 specifically in adult neurons, to avoid developmental effects. Abeta42 accumulated with age in these flies and they displayed increased mortality together with progressive neuronal dysfunction, but in the apparent absence of neuronal loss. This fly model can thus be used to examine the role of events during adulthood and early AD aetiology. Expression of Abeta42 in adult neurons increased GSK-3 activity, and inhibition of GSK-3 (either genetically or pharmacologically by lithium treatment) rescued Abeta42 toxicity. Abeta42 pathogenesis was also reduced by removal of endogenous fly tau; but, within the limits of detection of available methods, tau phosphorylation did not appear to be altered in flies expressing Abeta42. The GSK-3-mediated effects on Abeta42 toxicity appear to be at least in part mediated by tau-independent mechanisms, because the protective effect of lithium alone was greater than that of the removal of tau alone. Finally, Abeta42 levels were reduced upon GSK-3 inhibition, pointing to a direct role of GSK-3 in the regulation of Abeta42 peptide level, in the absence of APP processing. Our study points to the need both to identify the mechanisms by which GSK-3 modulates Abeta42 levels in the fly and to determine if similar mechanisms are present in mammals, and it supports the potential therapeutic use of GSK-3 inhibitors in AD.


title: A Wideband Reconfigurable Transmitarray Element

abstract: Transmitarrays have been shown to be viable architectures for achieving high-directivity reconfigurable apertures. Two reconfigurable transmitarray elements, using single and stacked patches, based on proximity-coupled feeding and two balanced reconfigurable bridged-T phase shifters are presented. Both designs achieve over 400 of phase range and achieve average insertion losses of 3.6 dB. Compared to existing proposed transmitarray elements, the stacked-patch element has a much smaller physical profile, with a lens thickness of only 0.17 λ in free-space, while achieving a large bandwidth of 500 MHz at 5 GHz (10%). Other considerations such as the mutual coupling and beam squint of an array using the stacked-patch element are also discussed.


title: An Optimal Auction Mechanism for Mobile Edge Caching

abstract: With the explosive growth of wireless data, mobile edge caching has emerged as a promising paradigm to support mobile traffic recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality. Finally, extensive simulations are implemented to evaluate the performance of the proposed optimal auction mechanism, and the impact of various model parameters is highlighted to obtain engineering insights into the content caching problem.


title: Sentiment analysis algorithms and applications: A survey

abstract: Abstract Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.


title: An Integrated View of Immunometabolism

abstract: The worldwide obesity epidemic has emerged as a major cause of insulin resistance and Type 2 diabetes. Chronic tissue inflammation is a well-recognized feature of obesity, and the field of immunometabolism has witnessed many advances in recent years. Here, we review the major features of our current understanding with respect to chronic obesity-related inflammation in metabolic tissues and focus on how these inflammatory changes affect insulin sensitivity, insulin secretion, food intake, and glucose homeostasis. There is a growing appreciation of the varied and sometimes integrated crosstalk between cells within a tissue (intraorgan) and tissues within an organism (interorgan) that supports inflammation in the context of metabolic dysregulation. Understanding these pathways and modes of communication has implications for translational studies. We also briefly summarize the state of this field with respect to potential current and developing therapeutics.


title: Characterization and Testing of a Tool for Photovoltaic Panel Modeling

abstract: This paper presents the evaluation of the performance, in terms of uncertainty, of a tool designed to estimate the main parameters of a model of a photovoltaic panel (PVP) under real and/or simulated working conditions. The presented tool permits the characterization of the panel, and it is useful to predict its behavior in whatever working condition; in this way, it is possible to compare the actual and expected performance to prevent any decrease in the output power, so permitting the replacement of the monitored module before it goes out of order or its efficiency falls under a given threshold. The well-known two-diode model is used to estimate the parameters of the electrical equivalent circuit of the PVP and to simulate the I -V and P-V characteristic curves in any given environmental condition of irradiance and/or temperature. The model and the estimation algorithm are implemented with MATLAB functions, whereas data acquisition and result presentation are managed by a LabVIEW graphics user interface. The presented tool has been validated against an experimentally characterized PVP. The environmental parameters of the model such as irradiance and temperature have been set (with their respective uncertainties) during simulations or directly measured during the outdoor tests, whereas the others parameters have been evaluated using a best-fit algorithm on the measured data. The estimation is based on the minimization of a new objective function and on a modified expression of the model resistances, which differ from those mentioned in the available literature. After a review of the state of the art, this paper provides the description of the estimation technique and its validation by means of simulations and experiments. Some results are also provided to illustrate the performance of the proposed test method.


title: Determination of Intensity-Based Stochastic Models for Terrestrial Laser Scanners Utilising 3D-Point Clouds

abstract: Recent advances in stochastic modelling of reflectorless rangefinders revealed an inherent relationship among raw intensity values and the corresponding precision of observed distances. In order to derive the stochastic properties of a terrestrial laser scanner’s (TLS) rangefinder, distances have to be observed repeatedly. For this, the TLS of interest has to be operated in the so-called 1D-mode—a functionality which is offered only by a few manufacturers due to laser safety regulations. The article at hand proposes two methodologies to compute intensity-based stochastic models based on capturing geometric primitives in form of planar shapes utilising 3D-point clouds. At first the procedures are applied to a phase-based Zoller + Fröhlich IMAGER 5006h. The generated results are then evaluated by comparing the outcome to the parameters of a stochastic model which has been derived by means of measurements captured in 1D-mode. Another open research question is if intensity-based stochastic models are applicable for other rangefinder types. Therefore, one of the suggested procedures is applied to a Riegl VZ-400i impulse scanner, as well as a Leica ScanStation P40 TLS that deploys a hybrid rangefinder technology. The generated results successfully demonstrate alternative methods for the computation of intensity-based stochastic models as well as their transferability to other rangefinder technologies.


title: Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

abstract: Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. We developed a method to automatically detect mitotic figures in breast cancer tissue sections based on convolutional neural networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by: (1) noisy and expensive reference standards established by pathologists, (2) lack of generalization due to staining variation across laboratories, and (3) high computational requirements needed to process gigapixel whole-slide images (WSIs). In this paper, we present a method to train and evaluate CNNs to specifically solve these issues in the context of mitosis detection in breast cancer WSIs. First, by combining image analysis of mitotic activity in phosphohistone-H3 (PHH3) restained slides and registration, we built a reference standard for mitosis detection in entire H&E WSIs requiring minimal manual annotation effort. Second, we designed a data augmentation strategy that creates diverse and realistic H&E stain variations by modifying the hematoxylin and eosin color channels directly. Using it during training combined with network ensembling resulted in a stain invariant mitosis detector. Third, we applied knowledge distillation to reduce the computational requirements of the mitosis detection ensemble with a negligible loss of performance. The system was trained in a single-center cohort and evaluated in an independent multicenter cohort from The Cancer Genome Atlas on the three tasks of the Tumor Proliferation Assessment Challenge (TUPAC). We obtained a performance within the top-3 best methods for most of the tasks of the challenge.


title: Sound event detection in real life recordings using coupled matrix factorization of spectral representations and class activity annotations

abstract: Methods for detection of overlapping sound events in audio involve matrix factorization approaches, often assigning separated components to event classes. We present a method that bypasses the supervised construction of class models. The method learns the components as a non-negative dictionary in a coupled matrix factorization problem, where the spectral representation and the class activity annotation of the audio signal share the activation matrix. In testing, the dictionaries are used to estimate directly the class activations. For dealing with large amount of training data, two methods are proposed for reducing the size of the dictionary. The methods were tested on a database of real life recordings, and outperformed previous approaches by over 10%.


title: Using Topic Models in Content-Based News Recommender Systems

abstract: We study content-based recommendation of Finnish news in a system with a very small group of users. We compare three standard methods, Naive Bayes (NB), K-Nearest Neighbor (kNN) Regression and Regulairized Linear Regression in a novel online simulation setting and in a coldstart simulation. We also apply Latent Dirichlet Allocation (LDA) on the large corpus of news and compare the learned features to those found by Singular Value Decomposition (SVD). Our results indicate that Naive Bayes is the worst of the three models. K-Nearest Neighbor performs consistently well across input features. Regularized Linear Regression performs generally worse than kNN, but reaches similar performance as kNN with some features. Regularized Linear Regression gains statistically significant improvements over the word-features with LDA both on the full data set and in the cold-start simulation. In the cold-start simulation we find that LDA gives statistically significant improvements for all the methods.


title: Adaptive Routing Protocol with Energy Efficiency and Event Clustering for Wireless Sensor Networks

abstract: Wireless sensor network (WSN) is a promising approach for a variety of applications. Routing protocol for WSNs is very challenging because it should be simple, scalable, energy-efficient, and robust to deal with a very large number of nodes, and also self-configurable to node failures and changes of the network topology dynamically. Recently, many researchers have focused on developing hierarchical protocols for WSNs. However, most protocols in the literatures cannot scale well to large sensor networks and difficult to apply in the real applications. In this paper, we propose a novel adaptive routing protocol for WSNs called ARPEES. The main design features of the proposed method are: energy efficiency, dynamic event clustering, and multi-hop relay considering the trade-off relationship between the residual energy available of relay nodes and distance from the relay node to the base station. With a distributed and light overhead traffic approach, we spread energy consumption required for aggregating data and relaying them to different sensor nodes to prolong the lifetime of the whole network. In this method, we consider energy and distance as the parameters in the proposed function to select relay nodes and finally select the optimal path among cluster heads, relay nodes and the base station. The simulation results show that our routing protocol achieves better performance than other previous routing protocols.


title: 5G roadmap: 10 key enabling technologies

abstract: The fifth generation (5G) mobile communication networks will require a major paradigm shift to satisfy the increasing demand for higher data rates, lower network latencies, better energy efficiency, and reliable ubiquitous connectivity. With prediction of the advent of 5G systems in the near future, many efforts and revolutionary ideas have been proposed and explored around the world. The major technological breakthroughs that will bring renaissance to wireless communication networks include (1) a wireless software-defined network, (2) network function virtualization, (3) millimeter wave spectrum, (4) massive MIMO, (5) network ultra-densification, (6) big data and mobile cloud computing, (7) scalable Internet of Things, (8) device-to-device connectivity with high mobility, (9) green communications, and (10) new radio access techniques. In this paper, the state-of-the-art and the potentials of these ten enabling technologies are extensively surveyed. Furthermore, the challenges and limitations for each technology are treated in depth, while the possible solutions are highlighted.


New geodesic distance transforms for grayscale images


title: Evidence for Infants' Internal Working Models of Attachment

abstract: Nearly half a century ago, psychiatrist John Bowlby proposed that the instinctual behavioral system that underpins an infant’s attachment to his or her mother is accompanied by ‘‘internal working models’’ of the social world—models based on the infant’s own experience with his or her caregiver (Bowlby, 1958, 1969/1982). These mental models were thought to mediate, in part, the ability of an infant to use the caregiver as a buffer against the stresses of life, as well as the later development of important self-regulatory and social skills. Hundreds of studies now testify to the impact of caregivers’ behavior on infants’ behavior and development: Infants who most easily seek and accept support from their parents are considered secure in their attachments and are more likely to have received sensitive and responsive caregiving than insecure infants; over time, they display a variety of socioemotional advantages over insecure infants (Cassidy & Shaver, 1999). Research has also shown that, at least in older children and adults, individual differences in the security of attachment are indeed related to the individual’s representations of social relations (Bretherton & Munholland, 1999). Yet no study has ever directly assessed internal working models of attachment in infancy. In the present study, we sought to do so. METHOD


title: Control system with sinusoidal PWM three-phase inverter with a frequency scalar control of induction motor

abstract: The principles of implementation of the control system with sinusoidal PWM inverter voltage frequency scalar and vector control induction motor are reviewed. Comparisons of simple control system with sinusoidal PWM control system and sinusoidal PWM control with an additional third-harmonic signal and gain modulated control signal are carried out. There are shown the maximum amplitude and actual values phase and line inverter output voltage at the maximum amplitude of the control signals. Recommendations on the choice of supply voltage induction motor electric drive with frequency scalar control are presented.


title: Applications of soft union sets to hemirings via SU-h-ideals

abstract: The aim of this article is to lay a foundation for providing a soft algebraic tool in considering many problems that contain uncertainties. In order to provide these soft algebraic structures, we introduce the concepts of soft union hemirings soft union h-ideals of hemirings by soft intersection-union product and obtain some related results. Finally, we investigate some characterizations of h-hemiregular hemirings by soft union h-ideals.


title: Active learning strategies for rating elicitation in collaborative filtering: A system-wide perspective

abstract: The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor-quality data during training, that is, garbage in, garbage out. Active learning aims to remedy this problem by focusing on obtaining better-quality data that more aptly reflects a user's preferences. However, traditional evaluation of active learning strategies has two major flaws, which have significant negative ramifications on accurately evaluating the system's performance (prediction error, precision, and quantity of elicited ratings). (1) Performance has been evaluated for each user independently (ignoring system-wide improvements). (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). In this article we show that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system centric). We propose a new evaluation methodology and use it to evaluate some novel and state-of-the-art rating elicitation strategies. We found that the system-wide effectiveness of a rating elicitation strategy depends on the stage of the rating elicitation process, and on the evaluation measures (MAE, NDCG, and Precision). In particular, we show that using some common user-centric strategies may actually degrade the overall performance of a system. Finally, we show that the performance of many common active learning strategies changes significantly when evaluated concurrently with the natural acquisition of ratings in recommender systems.


title: Handlers in action

abstract: Plotkin and Pretnar's handlers for algebraic effects occupy a sweet spot in the design space of abstractions for effectful computation. By separating effect signatures from their implementation, algebraic effects provide a high degree of modularity, allowing programmers to express effectful programs independently of the concrete interpretation of their effects. A handler is an interpretation of the effects of an algebraic computation. The handler abstraction adapts well to multiple settings: pure or impure, strict or lazy, static types or dynamic types. This is a position paper whose main aim is to popularise the handler abstraction. We give a gentle introduction to its use, a collection of illustrative examples, and a straightforward operational semantics. We describe our Haskell implementation of handlers in detail, outline the ideas behind our OCaml, SML, and Racket implementations, and present experimental results comparing handlers with existing code.


title: Fast kernel entropy estimation and optimization

abstract: Differential entropy is a quantity used in many signal processing problems. Often we need to calculate not only the entropy itself, but also its gradient with respect to various variables, for efficient optimization, sensitivity analysis, etc. Entropy estimation can be based on an estimate of the probability density function, which is computationally costly if done naively. Some prior algorithms use computationally efficient non-parametric entropy estimators. However, differentiation of the previously proposed estimators is difficult and may even be undefined. To counter these obstacles, we consider non-parametric kernel entropy estimation that is differentiable. We present two different accelerated kernel algorithms. The first accelerates the entropy gradient calculation based on a back propagation principle. It allows calculating the differential entropy gradient in the same complexity as that of calculating the entropy itself. The second algorithm accelerates the estimation of both entropy and its gradient by using fast convolution over a uniform grid. As an example, we apply both algorithms to blind source separation.


title: Increasing the Action Gap: New Operators for Reinforcement Learning

abstract: This paper introduces new optimality-preserving operators on Q-functions. We first describe an operator for tabular representations, the consistent Bellman operator, which incorporates a notion of local policy consistency. We show that this local consistency leads to an increase in the action gap at each state; increasing this gap, we argue, mitigates the undesirable effects of approximation and estimation errors on the induced greedy policies. This operator can also be applied to discretized continuous space and time problems, and we provide empirical results evidencing superior performance in this context. Extending the idea of a locally consistent operator, we then derive sufficient conditions for an operator to preserve optimality, leading to a family of operators which includes our consistent Bellman operator. As corollaries we provide a proof of optimality for Baird's advantage learning algorithm and derive other gap-increasing operators with interesting properties. We conclude with an empirical study on 60 Atari 2600 games illustrating the strong potential of these new operators.


title: Sugar, Stress, and the Supplemental Nutrition Assistance Program: Early Childhood Obesity Risks Among a Clinic-Based Sample of Low-Income Hispanics

abstract: The nationwide epidemic of pediatric obesity is more prevalent among Hispanic children than white children. Recent literature suggests that obesity has early origins, leading scholars to call for interventions in pregnancy and infancy. However, there is little theoretical or empirical research to guide the development of early prevention programs for Hispanics. The present study seeks to identify risk factors for early childhood obesity among a low-income, predominately Hispanic sample. Data were gathered to inform the design of a primary care childhood obesity prevention program targeting pregnancy through age 12 months. Baseline data were gathered on 153 women attending the clinic for prenatal care or for their child's 2, 6 or 12 month well-check. All women completed surveys on diet, exercise, social support, food security, stress, infant feeding practices, health, and demographics. For women with children (n = 66), survey data were matched with medical records data on infant weight. Results reveal that 55 % of women in the sample had an infant profiling in the 85th percentile or higher, confirming the need for an early childhood obesity intervention. While mothers exhibited several potential risk factors for childhood obesity (e.g. fast food consumption), only maternal consumption of sweets and sugar-sweetened beverages, stress, and SNAP (food stamp receipt) were associated with infant overweight. Findings further reveal that stress and SNAP relate to child overweight, in part, through mothers' sugar-sweetened beverage consumption. Results suggest that obesity prevention efforts must address specific individual choices as well as the external environment that shapes these consumption patterns.


title: AHEad: Privacy-preserving online behavioural advertising using homomorphic encryption

abstract: Online advertising is a rapidly growing industry, forming the primary source of income for many publishers that offer free web content. The practice of serving advertisements based on individuals' interests greatly improves the expected effectiveness of advertisements, and is believed to be beneficial to publishers and users alike. However, the widespread data collection required for such behavioural advertising sparks concerns over user privacy. In this paper, we present AHEad, a privacy-preserving protocol for Online Behavioural Advertising that ensures user privacy by processing data in encrypted form. AHEad combines homomorphic encryption with a machine learning method commonly encountered in existing advertising systems. Advertisements are served based on detailed user profiles, while achieving performance linear in the size of user profiles. To the best of our knowledge, AHEad is the first protocol that preserves user privacy in behavioural advertising while allowing the use of detailed user profiles and machine learning methods.


title: Risk and maintenance factors for eating pathology: A meta-analytic review.

abstract: This meta-analytic review of prospective and experimental studies reveals that several accepted risk factors for eating pathology have not received empirical support (e.g., sexual abuse) or have received contradictory support (e.g., dieting). There was consistent support for less-accepted risk factors(e.g., thin-ideal internalization) as well as emerging evidence for variables that potentiate and mitigate the effects of risk factors(e.g., social support) and factors that predict eating pathology maintenance(e.g., negative affect). In addition, certain multivariate etiologic and maintenance models received preliminary support. However, the predictive power of individual risk and maintenance factors was limited, suggesting it will be important to search for additional risk and maintenance factors, develop more comprehensive multivariate models, and address methodological limitations that attenuate effects. Eating disorders are one of the most common psychiatric problems faced by women and girls and are characterized by chronicity and relapse (Fairburn, Cooper, Doll, Norman, & O’Connor, 2000; Lewinsohn, Streigel-Moore, & Seeley, 2000). Anorexia nervosa involves emaciation, fear of becoming fat, disturbed perception of body shape, undue influence of shape on self-evaluation, denial of the seriousness of low body weight, and amenorrhea. Bulimia nervosa is marked by uncontrollable binge eating, compensatory behavior to prevent weight gain (e.g., vomiting), and undue influence of shape on self-evaluation. Binge eating disorder entails uncontrollable binge eating in the absence of compensatory behaviors. Eating disorders are marked by psychosocial impairment and comorbid psychopathology and have the highest levels of treatment seeking, inpatient hospitalization, suicide attempts, and mortality of the most common psychiatric syndromes (Newman et al., 1996; Wilson, Heffernan, & Black, 1996). Furthermore, eating pathology increases the risk for onset of obesity, depression, and substance abuse (e.g., Stice, Cameron, Killen, Hayward, & Taylor, 1999; Stice, Hayward, Cameron, Killen, & Taylor, 2000). Although there has been a burgeoning of longitudinal and experimental studies on the risk and maintenance factors for eating pathology, this literature has not been critically reviewed or synthesized. A review of this nature is needed because many of the risk factors that are widely accepted by researchers and clinicians have not been empirically supported. Moreover, there are several less-accepted risk factors that have received empirical support. Thus, the first aimof this article is to provide such a review in the hope that it will advance knowledge of the etiologic and maintenance processes for this serious psychiatric problem. I used a meta-analytic approach to document the magnitude of effects and provide an objective summary of the literature. The second aim is to review multivariate etiologic and maintenance models that have been tested prospectively, which is crucial because little attention has been paid to how risk factors work together. The third aimis to discuss the methodological, statistical, and theoretical limitations of this literature in an effort to promote more rigorous research. The fourth aimis to delineate the preventive and treatment implications of this literature. I conclude the article with a discussion of directions for future research.


title: Alternating proximal gradient method for sparse nonnegative Tucker decomposition

abstract: Multi-way data arises in many applications such as electroencephalography classification, face recognition, text mining and hyperspectral data analysis. Tensor decomposition has been commonly used to find the hidden factors and elicit the intrinsic structures of the multi-way data. This paper considers sparse nonnegative Tucker decomposition (NTD), which is to decompose a given tensor into the product of a core tensor and several factor matrices with sparsity and nonnegativity constraints. An alternating proximal gradient method is applied to solve the problem. The algorithm is then modified to sparse NTD with missing values. Per-iteration cost of the algorithm is estimated scalable about the data size, and global convergence is established under fairly loose conditions. Numerical experiments on both synthetic and real world data demonstrate its superiority over a few state-of-the-art methods for (sparse) NTD from partial and/or full observations. The MATLAB code along with demos are accessible from the author’s homepage.


Automatic Ontology Generation : State of the Art


title: Finding planes in LiDAR point clouds for real-time registration

abstract: We present a robust plane finding algorithm that when combined with plane-based frame-to-frame registration gives accurate real-time pose estimation. Our plane extraction is capable of handling large and sparse datasets such as those generated from spinning multi-laser sensors such as the Velodyne HDL-32E LiDAR. We test our algorithm on frame-to-frame registration in a closed-loop indoor path comprising 827 successive 3D laser scans (over 57 million points), using no additional information (e.g., odometry, IMU). Our algorithm outperforms, in both accuracy and time, three state-of-the-art methods, based on iterative closest point (ICP), plane-based randomized Hough transform, and planar region growing.


title: Software selection: a case study of the application of the analytical hierarchical process to the selection of a multimedia authoring system

abstract: This paper discusses the multimedia processing environment, the applicability of analytic hierarchy process (AHP) in problem solving, and how AHP can be applied to the selection of multimedia authorizing systems (MAS) in a group decision environment. A MAS selection model is proposed to facilitate the group's decision making in the selection of MAS. Six software engineers, who are technically competent and experienced, participated in our study. They were trained to use AHP and then applied this technique to evaluate three MAS products for adoption decision. The results indicated that AHP offers chances for every participant to fully understand, discuss, and objectively evaluate all MAS products before identifying and selecting the most efficient MAS.


title: Nested LSTMs

abstract: We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory. Nested LSTMs add depth to LSTMs via nesting as opposed to stacking. The value of a memory cell in an NLSTM is computed by an LSTM cell, which has its own inner memory cell. Specifically, instead of computing the value of the (outer) memory cell as $c^{outer}_t = f_t \odot c_{t-1} + i_t \odot g_t$, NLSTM memory cells use the concatenation $(f_t \odot c_{t-1}, i_t \odot g_t)$ as input to an inner LSTM (or NLSTM) memory cell, and set $c^{outer}_t$ = $h^{inner}_t$. Nested LSTMs outperform both stacked and single-layer LSTMs with similar numbers of parameters in our experiments on various character-level language modeling tasks, and the inner memories of an LSTM learn longer term dependencies compared with the higher-level units of a stacked LSTM.


title: Future Internet of Things: open issues and challenges

abstract: Internet of Things (IoT) and its relevant technologies have been attracting the attention of researchers from academia, industry, and government in recent years. However, since the requirements of the IoT are quite different from what the Internet today can offer, several innovative techniques have been gradually developed and incorporated into IoT, which is referred to as the Future Internet of Things (FIoT). Among them, how to extract "data" and transfer them into "knowledge" from sensing layer to application layer has become a vital issue. This paper begins with an overview of IoT and FIoT, followed by discussions on how to apply data mining and computational intelligence to FIoT. An intelligent data management framework inspired by swarm optimization will then given. Finally, open issues and future trends of this field will be addressed.


title: on Obesity and Heart Disease From the Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism

abstract: Obesity is becoming a global epidemic in both children and adults. It is associated with numerous comorbidities such as cardiovascular diseases (CVD), type 2 diabetes, hypertension, certain cancers, and sleep apnea/sleep-disordered breathing. In fact, obesity is an independent risk factor for CVD, and CVD risks have also been documented in obese children. Obesity is associated with an increased risk of morbidity and mortality as well as reduced life expectancy. Health service use and medical costs associated with obesity and related diseases have risen dramatically and are expected to continue to rise. Besides an altered metabolic profile, a variety of adaptations/alterations in cardiac structure and function occur in the individual as adipose tissue accumulates in excess amounts, even in the absence of comorbidities. Hence, obesity may affect the heart through its influence on known risk factors such as dyslipidemia, hypertension, glucose intolerance, inflammatory markers, obstructive sleep apnea/hypoventilation, and the prothrombotic state, in addition to as-yet-unrecognized mechanisms. On the whole, overweight and obesity predispose to or are associated with numerous cardiac complications such as coronary heart disease, heart failure, and sudden death because of their impact on the cardiovascular system. The pathophysiology of these entities that are linked to obesity will be discussed. However, the cardiovascular clinical evaluation of obese patients may be limited because of the morphology of the individual. In this statement, we review the available evidence of the impact of obesity on CVD with emphasis on the evaluation of cardiac structure and function in obese patients and the effect of weight loss on the cardiovascular system.


title: CBC: An associative classifier with a small number of rules

abstract: Associative classifiers have been proposed to achieve an accurate model with each individual rule being interpretable. However, existing associative classifiers often consist of a large number of rules and, thus, can be difficult to interpret. We show that associative classifiers consisting of an ordered rule set can be represented as a tree model. From this view, it is clear that these classifiers are restricted in that at least one child node of a non-leaf node is never split. We propose a new tree model, i.e., condition-based tree (CBT), to relax the restriction. Furthermore, we also propose an algorithm to transform a CBT to an ordered rule set with concise rule conditions. This ordered rule set is referred to as a condition-based classifier (CBC). Thus, the interpretability of an associative classifier is maintained, but more expressive models are possible. The rule transformation algorithm can be also applied to regular binary decision trees to extract an ordered set of rules with simple rule conditions. Feature selection is applied to a binary representation of conditions to simplify/improve the models further. Experimental studies show that CBC has competitive accuracy performance, and has a significantly smaller number of rules (median of 10 rules per data set) than well-known associative classifiers such as CBA (median of 47) and GARC (median of 21). CBC with feature selection has even a smaller number of rules.


title: Query-driven Active Surveying for Collective Classification

abstract: In network classification problems such as those found in intelligence gathering, public health, and viral marketing, one is often only interested in inferring the labels of a subset of the nodes. We refer to this subset as the query set, and define the problem as query-driven collective classification. We study this problem in a practical active learning framework, in which the learning algorithm can survey non-query nodes to obtain their labels and network structure. We derive a surveying strategy aimed toward optimal inference on the query set. Considering both feature and structural smoothness, concepts that we formally define, we develop an algorithm which adaptively selects survey nodes by estimating which form of smoothness is most appropriate. We evaluate our algorithm on several network datasets and demonstrate its improvements over standard active learning methods.


title: Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

abstract: Despite the steady progress in video analysis led by the adoption of convolutional neural networks (CNNs), the relative improvement has been less drastic as that in 2D static image classification. Three main challenges exist including spatial (image) feature representation, temporal information representation, and model/computation complexity. It was recently shown by Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained on ImageNet, could be a promising way for spatial and temporal representation learning. However, as for model/computation complexity, 3D CNNs are much more expensive than 2D CNNs and prone to overfit. We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices. In particular, we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions. Rather surprisingly, best result (in both speed and accuracy) is achieved when replacing the 3D convolutions at the bottom of the network, suggesting that temporal representation learning on high-level “semantic” features is more useful. Our conclusion generalizes to datasets with very different properties. When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating, our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks (Kinetics, Something-something, UCF101 and HMDB), as well as two action detection (localization) benchmarks (JHMDB and UCF101-24).


title: Video and audio processing in paediatrics: a review.

abstract: OBJECTIVE ::: Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU). ::: ::: ::: APPROACH ::: For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts-full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization. ::: ::: ::: MAIN RESULTS ::: The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed. ::: ::: ::: SIGNIFICANCE ::: Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.


title: Intestinal microbiota of salmonids and its changes upon introduction of soy proteins to fish feed

abstract: World tendency in partial substitution of fishmeal-based diet in feeding fish reared in aquaculture systems for plant proteins is caused by exhaustion of fishery resources. The use of products of fat-free soy processing in feeds has provoked troubles in salmonid health, in particular the onset of inflammation in the intestine. Numerous investigations devoted to the analysis of reasons for such negative consequences have attracted attention to the intestinal microbiota of fish. In this review, we analyze the studies on the effect of soy proteins in feed on the intestinal microbiota of salmonids.


Self-Supervised Feature Learning for Semantic Segmentation of Overhead Imagery