title: Huematic—an automated scorer for the Farnsworth-Munsell 100 hue test

abstract: A cheap, portable automated scorer for the Farnsworth-Munsell (FM) 100 hue test has been developed. It consists of a light pen, a series of omni-directional bar-codes attached to the reverse of the FM 100 hue caps and a small micro-computer. The print-out includes patient details, a linear histogram of the partial errors by cap position, indication of the peak error positions for congenital colour vision deficiencies and appropriate statistical analysis of the total error score. All the results are available within four minutes of completing the testing procedure.


title: Enabling Fine-Grained Edge Offloading for IoT

abstract: In this paper we make the case for IoT edge offloading, which strives to exploit the resources on edge computing devices by offloading fine-grained computation tasks from the cloud closer to the users and data generators (i.e., IoT devices). The key motive is to enhance performance, security and privacy for IoT services. Our proposal bridges the gap between cloud computing and IoT by applying a divide and conquer approach over the multi-level (cloud, edge and IoT) information pipeline. To validate the design of IoT edge offloading, we developed a unikernel-based prototype and evaluated the system under various hardware and network conditions. Our experimentation has shown promising results and revealed the limitation of existing IoT hardware and virtualization platforms, shedding light on future research of edge computing and IoT.


title: Tensor Spectral Clustering for Partitioning Higher-order Network Structures

abstract: Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.


title: Test effort estimation in regression testing

abstract: Software test effort estimation has always been a challenge for the software practitioners, because it consumes approximately half of the overall development costs of any software project. In order to provide effective software maintenance it is necessary to carry out the regression testing of the software. Hence, this research work aims to propose a measure for the estimation of the software test effort in regression testing. Since, the effort required developing or test software shall depend on various major contributing factors like, therefore, the proposed measure first estimates the change type of any software, make test cases for any software, then calculate execution complexity of any software and tester rank. In general, the regression testing takes more time and cost to perform it. Therefore, the effort estimation in regression testing is utmost required in order to compute man-hour for any software. In order to analyze the validity of the proposed test effort estimation measure, the measure is compared for various ranges of problem from small, mid and large size program to real life software projects. The result obtained shows that, the proposed test measure is a comprehensive one and compares well with other prevalent measures proposed in the past.


title: The Depth and Breadth of Google Scholar: An Empirical Study

abstract: The introduction of Google Scholar in November 2004 was accompanied by fanfare, skepticism, and numerous questions about the scope and coverage of this database. Nearly one year after its inception, many of these questions remain unanswered. This study compares the contents of 47 different databases with that of Google Scholar. Included in this investigation are tests for Google Scholar publication date and publication language bias, as well as a study of upload frequency. Tests show Google Scholar's current strengths to be coverage of science and medical databases, open access databases, and single publisher databases. Current weaknesses include lack of coverage of social science and humanities databases and an English language bias.


title: (Psychological) Distance Makes the Heart Grow Fonder Effects of Psychological Distance and Relative Intelligence on Men’s Attraction to Women

abstract: Interpersonal attraction may be shaped by (a) one’s psychological distance from a target (the subjective experience that a target is close to or far from the self) and (b) the perceived standing of a target on a trait relative to the self (as better or worse than the self). We propose that when evaluating a psychologically distant target, individuals may rely on abstract schemas (e.g., the desirability of a partner’s traits) and prefer targets who possess more (vs. less) desirable qualities than themselves. However, when evaluating psychologically near targets, concrete contextual details of the environment (e.g., how a target’s behavior affects self-evaluations in the moment) may determine individuals’ attraction toward targets. Six studies revealed that when evaluating psychologically distant targets, men showed greater attraction toward women who displayed more (vs. less) intelligence than themselves. In contrast, when targets were psychologically near, men showed less attraction toward women who outsm...


title: REVIEW ON OPTIMIZATION TECHNIQUES USED FOR IMAGE COMPRESSION

abstract: Image compression is most essential requirement for efficient utilization of storage space and transmission bandwidth. Image compression technique involves reducing the size of the image without degrading the quality of the image. Currently many image compression algorithms are used to deal with increasing amount of data involved but still finding the alternative solution is the area of research. This paper reviews some of the Meta heuristic optimization algorithms used for image compression. These algorithms are based on swarm intelligence. Swarm intelligence is a relatively new area that deals with the study of behavior among many entities or objects interacting within the natural or artificial systems. In past few years Swarm Intelligence based algorithms have been applied to a wide variety of problems in combinatorial and continuous optimization, telecommunications, swarm robotics, networking, image processing etc. This paper provides an insight of many optimization techniques used for image compression like Ant Colony Optimization (ACO) algorithm , Harmony Search Algorithm (HSA) and Artificial Bee Colony algorithm, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Ant Colony Optimization algorithm is inspired by the behavior among real ant’s while searching for the food source. Harmony Search Algorithm is inspired by the harmony improvisation process followed while playing music. Particle swarm optimization is an optimization technique inspired by social behavior of bird flocking or fish schooling. Artificial Bee Colony algorithm is motivated by the behavior exhibited by honey bees while searching for the food source. Genetic Algorithm is based on processes observed in the natural evolution.


title: Compositional GAN: Learning Image-Conditional Binary Composition

abstract: Generative Adversarial Networks (GANs) can produce images of surprising complexity and realism, but are generally modeled to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem. In this work, we propose to model object composition in a GAN framework as a self-consistent composition-decomposition network. Our model is conditioned on the object images from their marginal distributions to generate a realistic image from their joint distribution by explicitly learning the possible interactions. We evaluate our model through qualitative experiments and user evaluations in both the scenarios when either paired or unpaired examples for the individual object images and the joint scenes are given during training. Our results reveal that the learned model captures potential interactions between the two object domains given as input to output new instances of composed scene at test time in a reasonable fashion.


title: A novel micro-vibration sensor for activity recognition: Potential and limitations

abstract: This paper researches the potential of a novel ball switch as a wearable vibration sensor for activity recognition. The ball switch is available as a commercial, off-the-shelf sensor and is unique among such sensors due to its miniaturized design and the low mass of the ball. We present a detailed analysis of the physical properties of the sensor as well as a recommendation for circuit design, sampling method and a feature generation algorithm for activity recognition. The analysis reveals that it is sensitive to vibrations between 1.5 kHz and 8 kHz, where the acceleration sensor is responsive below 1.6 kHz. Furthermore, the ball switch is substantially cheaper (3x), smaller (2x) and uses less power (50x) than an accelerometer based system, but delivers less information. We also present the results of a case study in activity recognition done in parallel with an acceleration sensor using 5 subjects and 8 different activities. It shows that the ball switch can increase recognition rates when added to an accelerometer-based system, demonstrating that it can sample activity-pertinent information which an accelerometer can not. We conclude that this ball switch can be used to recognize high-frequency activity components and effectively improve recognition rates while representing a very low cost sensor in terms of price, device size and power consumption.


title: Clustering financial time series: an application to mutual funds style analysis

abstract: Abstract Classification can be useful in giving a synthetic and informative description of contexts characterized by high degrees of complexity. Different approaches could be adopted to tackle the classification problem: statistical tools may contribute to increase the degree of confidence in the classification scheme. A classification algorithm for mutual funds style analysis is proposed, which combines different statistical techniques and exploits information readily available at low cost. Objective, representative, consistent and empirically testable classification schemes are strongly sought for in this field in order to give reliable information to investors and fund managers who are interested in evaluating and comparing different financial products. Institutional classification schemes, when available, do not always provide consistent and representative peer groups of funds. A “return-based” classification scheme is proposed, which aims at identifying mutual funds’ styles by analysing time series of past returns. The proposed classification procedure consists of three basic steps: (a) a dimensionality reduction step based on principal component analysis, (b) a clustering step that exploits a robust evolutionary clustering methodology, and (c) a style identification step via a constrained regression model first proposed by William Sharpe. The algorithm is tested on a sample of Italian mutual funds and achieves satisfactory results with respect to (i) the agreement with the existing institutional classification and (ii) the explanatory power of out of sample variability in the cross-section of returns.


title: An analysis matrix for the assessment of smart city technologies: main results of its application

abstract: The paper presents the main results of a previously developed methodology to better evaluate new technologies in Smart Cities, using a tool to evaluate different systems and technologies regarding their usefulness, considering each application and how technologies can impact the physical space and natural environment. Technologies have also been evaluated according to how they are used by citizens, who must be the main concern of all urban development. Through a survey conducted among the Smart City Spanish network (RECI) we found that the ICT’s that change our cities everyday must be reviewed, developing an innovative methodology in order to find an analysis matrix to assess and score all the technologies that affect a Smart City strategy. The paper provides the results of this methodology regarding the three main aspects to be considered in urban developments: mobility, energy efficiency, and quality of life after obtaining the final score for every analyzed technology. This methodology fulfills an identified need to study how new technologies could affect urban scenarios before being applied, developing an analysis system to be used by urban planners and policy-makers to decide how best to use them, and this paper tries to show, in a simple way, how they can appreciate the variances between different solutions.


title: Fifty Years of Vehicle Routing

abstract: The Vehicle Routing Problem (VRP) was introduced 50 years ago by Dantzig and Ramser under the title “The Truck Dispatching Problem.” The study of the VRP has given rise to major developments in the fields of exact algorithms and heuristics. In particular, highly sophisticated exact mathematical programming decomposition algorithms and powerful metaheuristics for the VRP have been put forward in recent years. The purpose of this article is to provide a brief account of this development.


title: A Measurement Study of Hate Speech in Social Media

abstract: Social media platforms provide an inexpensive communication medium that allows anyone to quickly reach millions of users. Consequently, in these platforms anyone can publish content and anyone interested in the content can obtain it, representing a transformative revolution in our society. However, this same potential of social media systems brings together an important challenge---these systems provide space for discourses that are harmful to certain groups of people. This challenge manifests itself with a number of variations, including bullying, offensive content, and hate speech. Specifically, authorities of many countries today are rapidly recognizing hate speech as a serious problem, specially because it is hard to create barriers on the Internet to prevent the dissemination of hate across countries or minorities. In this paper, we provide the first of a kind systematic large scale measurement and analysis study of hate speech in online social media. We aim to understand the abundance of hate speech in online social media, the most common hate expressions, the effect of anonymity on hate speech and the most hated groups across regions. In order to achieve our objectives, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both of these systems. Our results identify hate speech forms and unveil a set of important patterns, providing not only a broader understanding of online hate speech, but also offering directions for detection and prevention approaches.


Illness beliefs and the sociocultural context of diabetes self-management in British South Asians: a mixed methods study


title: Concealed weapon detection from images using SIFT and SURF

abstract: Nowadays the global attacks of terrorism continues to escalate, so it is exigent to find the different techniques to ensure the safety of individuals at the public places like airports, railways and sensitive buildings etc. This paper presents proposed method for feature extraction from images to detect weapons especially different shapes of guns. Most effective algorithms SIFT and SURF used for feature extraction applied on database of images of luggage. Both algorithms give results that are invariant to scale, rotation, occlusion, change in illumination and noise. In the proposed method, input image is divided into parts in order to detect multiple guns of same or different type. Experimentally proposed method shows 90% efficiency and SURF is faster than SIFT. Algorithms are tested on the database of images containing weapons.


title: Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

abstract: Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection.


title: Integrated Localization and Recognition for Inshore Ships in Large Scene Remote Sensing Images

abstract: Automatic inshore ship recognition, which includes target localization and type recognition, is an important and challenging task. However, existing ship recognition methods mainly focus on the classification of ship samples or clips. These methods rely deeply on the detection algorithm to complete localization and recognition in large scene images. In this letter, we present an integrated framework to automatically locate and recognize inshore ships in large scene satellite images. Different from traditional object recognition methods using two steps of detection–classification, the proposed framework could locate inshore ships and identify types without the detection step. Considering ship size is a useful feature, a novel multimodel method is proposed to utilize this feature. And an Euclidean-distance-based fusion strategy is used to combine candidates given by models. This fusion strategy could effectively separate side-by-side ships. To handle large scene images efficiently, scale-invariant feature transform registration is also integrated into the framework to utilize geographic information. All of these make the framework an end-to-end fashion which could automatically recognize inshore ships in large scene satellite images. Experiments on Quickbird images show that this framework could achieve the actual applied requirements.


title: Effects and mechanisms of working memory training: a review

abstract: Can cognitive abilities such as reasoning be improved through working memory training? This question is still highly controversial, with prior studies providing contradictory findings. The lack of theory-driven, systematic approaches and (occasionally serious) methodological shortcomings complicates this debate even more. This review suggests two general mechanisms mediating transfer effects that are (or are not) observed after working memory training: enhanced working memory capacity, enabling people to hold more items in working memory than before training, or enhanced efficiency using the working memory capacity available (e.g., using chunking strategies to remember more items correctly). We then highlight multiple factors that could influence these mechanisms of transfer and thus the success of training interventions. These factors include (1) the nature of the training regime (i.e., intensity, duration, and adaptivity of the training tasks) and, with it, the magnitude of improvements during training, and (2) individual differences in age, cognitive abilities, biological factors, and motivational and personality factors. Finally, we summarize the findings revealed by existing training studies for each of these factors, and thereby present a roadmap for accumulating further empirical evidence regarding the efficacy of working memory training in a systematic way.


title: Consumer perceptual mapping towards e-banking channel: (A study of bank BRI customer in Indonesia)

abstract: Implementation of e-banking services system provides various advantages for the company are cost and time efficiencies, and be able to create differentiation and able to target market segments with low cost. To determine the underlying motives customers to select and prefer one channel and the other required a systematic exploration of the customer perception. This study aims to understand the customers' perception of various e-banking channels and how their motives in choosing the channel usage. By understanding customer perceptions about various banking channels, it is expected to be more helpful in assisting banks in Indonesia to introduce e-banking. A convinience sampling technique used to select 234 customers who surveyed in this study. Findings suggest that Automatic Teller Machine was percieved to be low cost, low complexity and most usefulness. EFT is also almost similar to the ATM, get the low cost and low complexity but low on security. Perception on Internet banking was secure and usefulness and high on privacy. Meanwhile perception on SMS banking was easy to access and also high on privacy. And phone banking was perceived to be the most expensive and inaccurate. Future studies are expected in addition to using other multivariate techniques would also be able to add other attributes are more influential.


title: Sancus: low-cost trustworthy extensible networked devices with a zero-software trusted computing base

abstract: In this paper we propose Sancus, a security architecture for networked embedded devices. Sancus supports extensibility in the form of remote (even third-party) software installation on devices while maintaining strong security guarantees. More specifically, Sancus can remotely attest to a software provider that a specific software module is running uncompromised, and can authenticate messages from software modules to software providers. Software modules can securely maintain local state, and can securely interact with other software modules that they choose to trust. The most distinguishing feature of Sancus is that it achieves these security guarantees without trusting any infrastructural software on the device. The Trusted Computing Base (TCB) on the device is only the hardware. Moreover, the hardware cost of Sancus is low. ::: ::: We describe the design of Sancus, and develop and evaluate a prototype FPGA implementation of a Sancus-enabled device. The prototype extends an MSP430 processor with hardware support for the memory access control and cryptographic functionality required to run Sancus. We also develop a C compiler that targets our device and that can compile standard C modules to Sancus protected software modules.


title: Arctangent Demodulation With DC Offset Compensation in Quadrature Doppler Radar Receiver Systems

abstract: Direct-conversion microwave Doppler radar can be used to detect cardiopulmonary activity at a distance. One challenge for such detection in single channel receivers is demodulation sensitivity to target position, which can be overcome by using a quadrature receiver. This paper presents a mathematical analysis and experimental results demonstrating the effectiveness of arctangent demodulation in quadrature receivers. A particular challenge in this technique is the presence of dc offset resulting from receiver imperfections and clutter reflections, in addition to dc information related to target position and associated phase. These dc components can be large compared to the ac motion-related signal, and thus, cannot simply be included in digitization without adversely affecting resolution. Presented here is a method for calibrating the dc offset while preserving the dc information and capturing the motion-related signal with maximum resolution. Experimental results demonstrate that arctangent demodulation with dc offset compensation results in a significant improvement in heart rate measurement accuracy over quadrature channel selection, with a standard deviation of less than 1 beat/min


Consumer evaluations of brand extensions : an integration of previous research


title: Exploiting Entity Linking in Queries for Entity Retrieval

abstract: The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the task of entity linking in queries. In this paper we make a first attempt at bringing together these two, i.e., leveraging entity annotations of queries in the entity retrieval model. We introduce a new probabilistic component and show how it can be applied on top of any term-based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Using a standard entity retrieval test collection, we show that our extension brings consistent improvements over all baseline methods, including the current state-of-the-art. We further show that our extension is robust against parameter settings.


title: Three-axis NC milling simulation based on adaptive triangular mesh

abstract: NC milling simulation has become an important step in computer aided manufacturing (CAM). To achieve real-time simulation, the total number of polygons has to be reduced, which results in poor image quality. This paper presents an adaptive triangular mesh algorithm to reduce the number of polygons while image quality remains high. Binary tree is used to represent the milling surface, and the optimization of the mesh is performed dynamically in the process of simulation. In this algorithm, the resolution of triangles is automatically updated according to local surface flatness, thus greatly reducing the number of triangles at planar regions. By doing this, real-time and high quality of visual presentation is insured and the translation, rotation and zooming operations are still applicable. When machining precision is evaluated, or overcut, undercut and interference are inspected, full resolution model stored in memory is automatically loaded to ensure the accuracy and correctness of these inspections. Finally, an example is presented to illustrate the validity of proposed algorithm.


title: Problem Based Learning: An instructional model and its constructivist framework

abstract: It is said that there’s nothing so practical as good theory. It may also be said that there’s nothing so theoretically interesting as good practice 1 . This is particularly true of efforts to relate constructivism as a theory of learning to the practice of instruction. Our goal in this paper is to provide a clear link between the theoretical principles of constructivism, the practice of instructional design, and the practice of teaching. We will begin with a basic characterization of constructivism identifying what we believe to be the central principles in learning and understanding. We will then identify and elaborate on eight instructional principles for the design of a constructivist learning environment. Finally, we will examine what we consider to be one of the best exemplars of a constructivist learning environment -- Problem Based Learning as described by Barrows (1985, 1986, 1992).


FROM THE EDITORS GRAND CHALLENGES AND INDUCTIVE METHODS: RIGOR WITHOUT RIGOR MORTIS


title: Nickel Cobalt Sulfide core/shell structure on 3D Graphene for supercapacitor application

abstract: Three-dimensional (3D) core/shell structure of nickel cobalt sulfide is nano-engineered by using series of hydrothermal steps on a CVD grown graphene for supercapacitor application. This core/shell is composited of NiCo2S4 nanotube (NCS) as core and CoxNi(3−x)S2 (CNS) nanosheets as a shell. The as-synthesized composite exhibits excellent electrochemical properties by using the advantage of NCS nanontube core as superhighway for electron and ion transport, and CNS nanosheets shell as high active area pseudocapacitive material. The 3D graphene layer serves as excellent surface area to support 3D NCS/CNS; moreover, it provides excellent electrical conductivity between nickel foam current collector and the 3D NCS/NCS composite. Using these hybrid advantages the as-synthesized graphene/NCS/CNS composite electrode exhibits high areal capacitance of 15.6 F/cm2 at current density of 10 mA/cm2; excellent cycling stability of 93% after 5000 of cycles and excellent rate capability of 74.36% as current increase from 10 to 100 mA/cm2. Moreover, a prototype of asymmetric device fabricated using graphene/NCS/CNS as positive electrode and RGO as negative electrode exhibits high energy density of 23.9 Wh/kg and power density of 2460.6 W/kg at high operating current of 100 mA. Such high performance electrode material may get great application in future energy storage device.


title: Depth really Matters: Improving Visual Salient Region Detection with Depth.

abstract: Depth information has been shown to affect identification of visually salient regions in images. In this paper, we investigate the role of depth in saliency detection in the presence of (i) competing saliencies due to appearance, (ii) depth-induced blur and (iii) centre-bias. Having established through experiments that depth continues to be a significant contributor to saliency in the presence of these cues, we propose a 3D-saliency formulation that takes into account structural features of objects in an indoor setting to identify regions at salient depth levels. Computed 3D-saliency is used in conjunction with 2D-saliency models through non-linear regression using SVM to improve saliency maps. Experiments on benchmark datasets containing depth information show that the proposed fusion of 3D-saliency with 2D-saliency models results in an average improvement in ROC scores of about 9% over state-of-the-art 2D saliency models. The main contributions of this paper are: (i) The development of a 3D-saliency model that integrates depth and geometric features of object surfaces in indoor scenes. (ii) Fusion of appearance (RGB) saliency with depth saliency through non-linear regression using SVM. (iii) Experiments to support the hypothesis that depth improves saliency detection in the presence of blur and centre-bias. The effectiveness of the 3D-saliency model and its fusion with RGB-saliency is illustrated through experiments on two benchmark datasets that contain depth information. Current stateof-the-art saliency detection algorithms perform poorly on these datasets that depict indoor scenes due to the presence of competing saliencies in the form of color contrast. For example in Fig. 1, saliency maps of [1] is shown for different scenes, along with its human eye fixations and our proposed saliency map after fusion. It is seen from the first scene of Fig. 1, that illumination plays spoiler role in RGB-saliency map. In second scene of Fig. 1, the RGB-saliency is focused on the cap though multiple salient objects are present in the scene. Last scene at the bottom of Fig. 1, shows the limitation of the RGB-saliency when the object is similar in appearance with the background. Effect of depth on Saliency: In [4], it is shown that depth is an important cue for saliency. In this paper we go further and verify if the depth alone influences the saliency. Different scenes were captured for experimentation using Kinect sensor. Observations resulted out of these experiments are (i) Humans fixate on the objects at closer depth, in the presence of visually competing salient objects in the background, (ii) Early attention happens on the objects at closer depth, (iii) Effective fixations are high at the low contrast foreground compared to the high contrast objects in the background which are blurred, (iv) Low contrast object placed at the center of the field of view, gets more attention compared to other locations. As a result of all these observations, we develop a 3D-saliency that captures the depth information of the regions in the scene. 3D-Saliency: We adapt the region based contrast method from Cheng et al. [1] in computing contrast strengths for the segmented 3D surfaces or regions. Each segmented region is assigned a contrast score using surface normals as the feature. Structure of the surface can be described based on the distribution of normals in the region. We compute a histogram of angular distances formed by every pair of normals in the region. Every region Rk is associated with a histogram Hk. Contrast score Ck of a region Rk is computed as the sum of the dot products of its histogram with histograms of other regions in the scene. Since the depth of the region is influencing the visual attention, the contrast score is scaled by a value Zk, which is the depth of the region Rk from the sensor. In order to define the saliency, sizes of the regions i.e. the number of the points in the region, have to be considered. We find the ratio of the region dimension to the half of the scene dimension. Considering nk as the number of 3D points in the region Rk, the constrast score becomes Figure 1: Four different scenes and their saliency maps; For each scene from top left (i) Original Image, (ii) RGB-Saliency map using RC [1], (iii) Human fixations from eye-tracker and (iv) Fused RGBD-saliency map


title: Generative Adversarial Nets

abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.


title: Grid Assembly-Free 60-GHz Antenna Module Embedded in FR-4 Transceiver Carrier Board

abstract: A novel antenna module solution which dramatically alleviates cost and fabrication related limitations of traditional 60-GHz antenna modules is demonstrated. The antenna topology is wholly devised using industry standard FR-4 PCB and is integrated into the 60-GHz transceiver carrier board. As a result, the proposed approach effectively bypasses the need for any complicated grid array assemblies for the first time to the author's best knowledge. The antenna element is first designed and confirmed to exhibit more than 9-GHz bandwidth and 71% average radiation efficiency from 57–66 GHz. The design is further expanded into an 8-element phased array module and integrated with a 60-GHz RFIC transceiver and digital interface. The overall footprint of the antenna module within the carrier board is 20 $\,\times\,$ 20 $\,\times\,$ 0.838 mm $^{3}$ . System-level evaluation confirms the 8-element phased array module to be highly sufficient in scan range, side lobe levels and radiation efficiency for 60-GHz wireless systems, particularly in the field of consumer electronics.


title: Recent advances in design, synthesis and biological activity of aminoalkylsulfonates and sulfonamidopeptides.

abstract: The replacement of peptide bond is an important segment in the synthesis of peptidomimetics, because this modification may result in the preparation of biologically active analogues with improved properties, especially regarding bioavailability and metabolical stability. The introduction of sulfonamide group increases polarity of a molecule and the hydrogen-bond donor properties as a sulfonamide N-H is more acidic (pKa=11-12) than carboxamide. Furthermore, due to geometry of sulfur atom the sulfonamido bond shows structural similarity to the tetrahedral transition state present as an intermediate in the enzymatic hydrolysis of an amide bond thus making these compounds candidates in the development of new drugs. Recent advances in the synthesis of building blocks for sulfonamidopeptides, such as alpha or beta- substituted aminoalkylsulfonates and efficient methods for the formation of sulfonamide bond have enabled the preparation of large number of oligomers with potential applications on various fields. These methods have been applied for the synthesis of oligopeptidosulfonamides, catalysts, receptor molecules and enzyme inhibitors. This article deals with physicochemical properties of sulfonamides, synthesis of aminoalkylsulfonates and sulfonamidopeptides, and the biological activity of these compounds.


SIRT 6 protects against pathological damage caused by diet-induced obesity


title: On-line outlier detection and data cleaning

abstract: Outliers are observations that do not follow the statistical distribution of the bulk of the data, and consequently may lead to erroneous results with respect to statistical analysis. Many conventional outlier detection tools are based on the assumption that the data is identically and independently distributed. In this paper, an outlier-resistant data filter-cleaner is proposed. The proposed data filter-cleaner includes an on-li ne outlier-resistant estimate of the process model and combines it with a modified Kalman filter to detect and “clean” outliers. The advantage over existing methods is that the proposed method has the following features: (a) a priori knowledge of the process model is not required; (b) it is applicable to autocorrelated data; (c) it can be implemented on-line; and (d) it tries to only clean (i.e., detects and replaces) outliers and preserves all other information in the data. © 2004 Elsevier Ltd. All rights reserved.


title: The Diagnosis, Staging, and Treatment Options for Mycosis Fungoides

abstract: Background: Cutaneous T-cell lymphoma (CTCL) represents a spectrum of diseases composed of malignant T lymphocytes. The most common type is mycosis fungoides (MF). An accurate diagnosis of early MF may be difficult because of the varied clinical and histologic expressions of the disease. Methods: The authors review the epidemiology, possible risk factors, clinical manifestations, diagnostic techniques, staging, prognosis, and treatment options for MF. Results: The varied and often nonspecific clinical and histologic presentations of MF may delay diagnosis and staging, thus necessitating further studies such as immunophenotyping and T-cell receptor gene rearrangement analysis. Conclusions: A multidisciplinary approach to the diagnosis, staging, and treatment of MF assists in optimizing outcomes from management of patients with this disease. A multidisciplinary approach in diagnosing, staging, and treating mycosis fungoides improves the outlook of patients with this disease.


title: Additively Manufactured Nanotechnology and Origami-Enabled Flexible Microwave Electronics

abstract: Inkjet printing on flexible paper and additive manufacturing technologies (AMT) are introduced for the sus- tainable ultra-low-cost fabrication of flexible radio frequency (RF)/microwave electronics and sensors. This paper covers ex- amples of state-of-the-art integrated wireless sensor modules on paper or flexible polymers and shows numerous inkjet- printed passives, sensors, origami, and microfluidics topolo- gies. It also demonstrates additively manufactured antennas that could potentially set the foundation for the truly conver- gent wireless sensor ad-hoc networks of the future with en- hanced cognitive intelligence and ''zero-power'' operability through ambient energy harvesting and wireless power transfer. The paper also discusses the major challenges for the realization of inkjet-printed/3-D printed high-complexity flexible modules as well as future directions in the area of environmentally-friendly ''Green'') RF electronics and ''Smart- House'' conformal sensors.


title: Power quality improvement techniques in AC-DC Cuk Converter

abstract: The Improved Power Quality AC-DC Converters (IPQCs) provides enhanced power quality in terms of improved power factor and reduced Total Harmonic Distortion (THD) at the utility interface. This paper addresses comparison of power factor correction techniques for high frequency isolation based single phase Buck-Boost AC-DC Cuk Converter, which consists of only one switch resulting in reduced THD and improved power factor. With an awareness of modern design trends towards improved performance, the proposed converter is designed for low power rating and low output voltage, typically 20 W with 13.5 V. To validate the design of the converter at the primary stage, simulation is performed at lower as well as at higher loads.


title: Joint covariate selection and joint subspace selection for multiple classification problems

abstract: We address the problem of recovering a common set of covariates that are relevant simultaneously to several classification problems. By penalizing the sum of ? 2 norms of the blocks of coefficients associated with each covariate across different classification problems, similar sparsity patterns in all models are encouraged. To take computational advantage of the sparsity of solutions at high regularization levels, we propose a blockwise path-following scheme that approximately traces the regularization path. As the regularization coefficient decreases, the algorithm maintains and updates concurrently a growing set of covariates that are simultaneously active for all problems. We also show how to use random projections to extend this approach to the problem of joint subspace selection, where multiple predictors are found in a common low-dimensional subspace. We present theoretical results showing that this random projection approach converges to the solution yielded by trace-norm regularization. Finally, we present a variety of experimental results exploring joint covariate selection and joint subspace selection, comparing the path-following approach to competing algorithms in terms of prediction accuracy and running time.


title: Modeling the Stuxnet attack with BDMP: Towards more formal risk assessments

abstract: Attack modeling has recently been adopted by security analysts as a useful tool in risk assessment of cyber-physical systems. We propose in this paper to model the Stuxnet attack with BDMP (Boolean logic Driven Markov Processes) formalism and to show the advantages of such modeling. After a description of the architecture targeted by Stuxnet, we explain the steps of the attack and model them formally with a BDMP. Based on estimated values of the success probabilities and rates of the elementary attack steps, we give a quantification of the main possible sequences leading to the physical destruction of the targeted industrial facility. This example completes a series of papers on BDMP applied to security by modeling a real case study. It highlights the advantages of BDMP compared to attack trees often used in security assessment.


title: Radiofrequency ablation of liver tumors: influence of technique and tumor size.

abstract: BACKGROUND ::: Radiofrequency thermal ablation (RFA) of liver tumors is done by both radiologists and surgeons by using various techniques for a variety of indications. This report describes our initial experience with RFA in 45 patients with hepatic malignancies. ::: ::: ::: METHODS ::: Patients with primary or secondary hepatic malignancies who were not candidates for resection underwent ultrasound-guided RFA under general anesthesia. End points were recurrence within or adjacent to the ablation zone or new hepatic or extrahepatic lesions. Product limit survival estimates for both ablation site recurrence-free survival and disease-free survival were calculated and compared for tumor size (less than 4 cm or 4 cm or greater), operative approach (percutaneous, laparoscopy, or open), and tumor type (hepatocellular cancer, colorectal cancer, or other metastatic disease). ::: ::: ::: RESULTS ::: Patients with hepatocellular cancer (n = 11) and with secondary hepatic malignancies (n = 34) had 84 lesions ablated with a median follow-up of 12 months. Largest ablated tumor size of 4 cm or greater (P <.001) and the percutaneous approach (P <.02) were associated with worse ablation site recurrence-free survival but not overall disease-free survival (P =.06). The 15 patients with colorectal cancer had worse disease-free survival compared with other tumor types (P <.01). ::: ::: ::: CONCLUSIONS ::: RFA of hepatic malignancies can be done by using a percutaneous, laparoscopic, or open approach. Local control appears superior for tumors less than 4 cm and when an open surgical approach is used. The difficulty in achieving prolonged disease-free survival, especially in colorectal cancer, underscores the need to investigate multimodality approaches that include local ablative techniques. Future RFA studies should consider tumor size, operative technique, and tumor type in trial design.


title: CORRELATION BASED FEATURE SELECTION (CFS) TECHNIQUE TO PREDICT STUDENT PERFROMANCE

abstract: Education data mining is an emerging stream which helps in mining academic data for solving various types of problems. One of the problems is the selection of a proper academic track. The admission of a student in engineering college depends on many factors. In this paper we have tried to implement a classification technique to assist students in predicting their success in admission in an engineering stream.We have analyzed the data set containing information about student’s academic as well as sociodemographic variables, with attributes such as family pressure, interest, gender, XII marks and CET rank in entrance examinations and historical data of previous batch of students. Feature selection is a process for removing irrelevant and redundant features which will help improve the predictive accuracy of classifiers. In this paper first we have used feature selection attribute algorithms Chi-square.InfoGain, and GainRatio to predict the relevant features. Then we have applied fast correlation base filter on given features. Later classification is done using NBTree, MultilayerPerceptron, NaiveBayes and Instance based –K- nearest neighbor. Results showed reduction in computational cost and time and increase in predictive accuracy for the student model


Effect of preparations based on algae extract on the formation of selected biochemical parameters of blood and immune response of laying hens


title: A snapshot of image pre-processing for convolutional neural networks:case study of MNIST

abstract: In the last five years, deep learning methods and particularly Convolutional Neural Networks (CNNs) have exhibited excellent accuracies in many pattern classification problems. Most of the state-of-the-art models apply data-augmentation techniques at the training stage. This paper provides a brief tutorial on data preprocessing and shows its benefits by using the competitive MNIST handwritten digits classification problem. We show and analyze the impact of different preprocessing techniques on the performance of three CNNs, LeNet, Network3 and DropConnect, together with their ensembles. The analyzed transformations are, centering, elastic deformation, translation, rotation and different combinations of them. Our analysis demonstrates that data-preprocessing techniques, such as the combination of elastic deformation and rotation, together with ensembles have a high potential to further improve the state-of-the-art accuracy in MNIST classification.


title: Ray: A Distributed Framework for Emerging AI Applications

abstract: The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray--a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.


title: A step toward computer-assisted mammography using evolutionary programming and neural networks.

abstract: Artificial intelligence techniques can be used to provide a second opinion in medical settings. This may improve the sensitivity and specificity of diagnoses, as well as the cost effectiveness of the physician's effort. In the current study, evolutionary programming is used to train artificial neural networks to detect breast cancer using radiographic features and patient age. Results from 112 suspicious breast masses (63 malignant, 49 benign, biopsy proven) indicate that a significant probability of detecting malignancies can be achieved using simple neural architectures at the risk of a small percentage of false positives.


title: Parallel training of deep stacking networks

abstract: The Deep Stacking Network (DSN) is a special type of deep architecture developed to enable and benefit from parallel learning of its model parameters on large CPU clusters. As a prospective key component of future speech recognizers, the architectural design of the DSN and its parallel training endow the DSN with scalability over a vast amount of training data. In this paper, we present our first parallel implementation of the DSN training algorithm. Particularly, we show the tradeoff between the time/memory saving via training parallelism and the associated cost arising from inter-CPU communication. Further, in phone classification experiments, we demonstrate a significantly lowered error rate using parallel full-batch training distributed over a CPU cluster, compared with sequential minibatch training implemented in a single CPU machine under otherwise identical experimental conditions and as exploited prior to the work reported in this paper.


title: Which Colors Best Catch Your Eyes: a Subjective Study of Color Saliency

abstract: To determine Regions of Interest (ROI) in a scene, percep-tual saliency of regions has to be measured. When scenes are viewed with the same context and motivation, these ROIs are often highly correlated among different people. As a result, it is possible to develop a computational model of visual attention that can analyze a scene and accurately esti-mate the location of viewers ROIs. Color saliency is inves-tigated in this paper. In particular, a subjective experiment has been carried out to estimate which hues attract more human attention. The performance of the visual attention model including color saliency are assessed in the context of a segmentation evaluation application.


title: Task Scheduling Using PSO Algorithm in Cloud Computing Environments

abstract: The Cloud computing has become the fast spread in the field of computing, research and industry in the last few years. As part of the service offered, there are new possibilities to build applications and provide various services to the end user by virtualization through the internet. Task scheduling is the most significant matter in the cloud computing because the user has to pay for resource using on the basis of time, which acts to distribute the load evenly among the system resources by maximizing utilization and reducing task execution Time. Many heuristic algorithms have been existed to resolve the task scheduling problem such as a Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Cuckoo search (CS) algorithms, etc. In this paper, a Dynamic Adaptive Particle Swarm Optimization algorithm (DAPSO) has been implemented to enhance the performance of the basic PSO algorithm to optimize the task runtime by minimizing the makespan of a particular task set, and in the same time, maximizing resource utilization. Also, .a task scheduling algorithm has been proposed to schedule the independent task over the Cloud Computing. The proposed algorithm is considered an amalgamation of the Dynamic PSO (DAPSO) algorithm and the Cuckoo search (CS) algorithm; called MDAPSO. According to the experimental results, it is found that MDAPSO and DAPSO algorithms outperform the original PSO algorithm. Also, a comparative study has been done to evaluate the performance of the proposed MDAPSO with respect to the original PSO.


title: SCALING OF EARTHQUAKE ACCELEROGRAMS FOR NON-LINEAR DYNAMIC ANALYSES TO MATCH THE EARTHQUAKE DESIGN SPECTRA 1

abstract: A complete seismic design of structures requires linear and nonlinear time-history analyses especially for special type buildings. Seismic design codes generally define ground shaking in the form of a response spectrum of acceleration and allow using response spectrum compatible time history records in linear and nonlinear time history analyses. These records can be obtained from natural earthquake records, or can be generated synthetically and artificially. Although using real earthquake records has many advantages, there may exist lack of strong motion earthquake records to satisfy seismological and geological conditions and requirements defined in seismic codes. Artificial accelerograms whose response spectra closely compatible to design response spectra can be generated in either time or frequency domain. Matching techniques are based on scaling of the selected time history in time domain; filtering actual motion in frequency domain by its spectral ratio with the design target spectrum; or elementary wavelets are added or subtracted from the real time history to match a target design spectrum. In this study, the spectrum matching procedures for real accelerograms are summarized and applied to selected real acceleration records to match the proposed Type 1 elastic design spectrum given in the Eurocode 8 for specified seismic region and soil type. Artificial accelerograms, which are compatible with the selected design spectrum, are generated according to specified scenario earthquake. The linear and nonlinear response of single degree of freedom system subjected to the modified and artificially generated time histories acceleration records are compared and the advantages and disadvantages of each one are discussed.


title: Evoked-potential correlates of stimulus uncertainty.

abstract: The average evoked-potential waveforms to sound and light stimuli recorded from scalp in awake human subjects show differences as a function of the subject's degree of uncertainty with respect to the sensory modality of the stimulus to be presented. Differences are also found in the evoked potential as a function of whether or not the sensorymodality of the stimulus was anticipated correctly. The major waveform alteration is in the amplitude of a positive-going component which reaches peak amplitude at about 300 milliseconds.


title: Anomalous Event Detection Using a Semi-Two Dimensional Hidden Markov Model

abstract: The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.


title: Lecture Video Indexing and Analysis Using Video OCR Technology

abstract: The text displayed in a lecture video is closely related to the lecture content. Therefore, it provides a valuable source for indexing and retrieving lecture video contents. Textual content can be detected, extracted and analyzed automatically by video OCR (Optical Character Recognition) techniques. In this paper, we present an approach for automated lecture video indexing based on video OCR technology: Firstly, we developed a novel video segmenter for an automated slide video structure analysis. Having adopted a localization and verification scheme, we perform text detection secondly. We employ SWT (stroke width transform) not only to remove false alarms from the text detection, but also to analyze the slide structure further. To recognize texts, a multi-hypotheses framework is adopted, that consists of multiple text segments, OCR, spell checking and result merging processes. Finally, we implemented a novel algorithm for slide structure analysis and extraction by using the geometrical information of detected text lines. The accuracy of the proposed approach is proven by evaluation.


title: Gaits and gait transitions for legged robots

abstract: This paper introduces the concept of gait transitions, acyclic feedforward motion patterns that allow a robot to switch from one gait to another. Legged robots often utilize collections of gait patterns to locomote over a variety of surfaces. Each feedforward gait is generally tuned for a specific surface and set of operating conditions. To enable locomotion across a changing surface, a robot must be able to stably change between gaits while continuing to locomote. By understanding the fundamentals of gaits, we present methods to correctly transition between differing gaits. On two separate robotic platforms, we show how the application of gait transitions enhances each robot's behavioral suite. Using the RHex robotic hexapod, gait transitions are used to smoothly switch from a tripod walking gait to a metachronal wave gait used to climb stairs. We also introduce the RiSE platform, a hexapod robot capable of vertical climbing, and discuss how gait transitions play an important role in achieving vertical mobility


title: Data Security and Privacy Protection Issues in Cloud Computing

abstract: It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud.


title: Gaussian Processes for Timeseries Modelling.

abstract: In this paper we offer a gentle introduction to Gaussian processes for timeseries data analysis. The conceptual framework of Bayesian modelling for timeseries data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes. We discuss how domain knowledge influences design of the Gaussian process models and provide case examples to highlight the approaches.


title: Study on Logistics Center Site Selection of Jilin Province

abstract: Based on the introduction of the concepts of logistics and logistics center, this paper analyzed the status and problems of Jilin Province’s logistics center, and proposed the steps, principles and factors for site selection of logistics center. Then by listing and comparing nine kinds of logistics center site selection method, including Analytic Hierarchy Process, Cluster Algorithm, Genetic Algorithm, Weight Grade Method, P-Median Method, System Simulation Method, Fuzzy quality function method, Dijkstra Method, we chose the best site selection method, namely the center of gravity method. Finally, this paper found out the exact logistics center location of Jilin Province with the center of gravity method.


title: Power Tensions in Health Information System Integration in Developing Countries: The Need for Distributed Control

abstract: As part of health sector reform, most developing countries are in the process of restructuring the Health Information Systems (HIS) by way of standardizing and integrating various vertical reporting systems. Nevertheless, the pressure resulting from the vertical systems supported by donors renders the integration goal challenging and unachievable. While many studies have argued for the heterogeneity of interests and multiple data needs of the donors as the major cause of the problem, this paper argue for more detailed and critical analysis of the problem. To contribute to this, the paper emphasize the need to understand the main actors involved in the process in terms of the resources and rules available to them; and their implication in the HIS integration venture. By drawing on an empirical case study and Structuration Theory concepts, the paper identified the dialectic power relations between the actors resulting from the asymmetric ownership and control of resources and rules. To alleviate the power tensions, the paper proposed i) the need to build shared meaning of the integration process through communication approaches ii) and the need to distribute the control of the integrated HIS using modularization approach facilitating ‘the tapping on’ of the resources available to each group of actors.


title: The role of orienting in vibrissal touch sensing

abstract: Rodents, such as rats and mice, are strongly tactile animals who explore the environment with their long mobile facial whiskers, or macrovibrissae, and orient to explore objects further with their shorter, more densely-packed, microvibrissae. Although whisker motion (whisking) has been extensively studied, less is know about how rodents orient their vibrissal system to investigate unexpected stimuli. We describe two studies that address this question. In the first we seek to characterise how adult rats orient towards unexpected macrovibrissal contacts with objects and examine the microvibrissal exploration behaviour following such contacts. We show that rats orient to the nearest macrovibrissal contact on an unexpected object, progressively homing in on the nearest contact point on the object in each subsequent whisk. Following contact, rats “dab” against the object with their microvibrissae at an average rate of approximately 8hz, which suggests synchronization of microvibrissal dabbing with macrovibrissal motion, and an amplitude of 5mm. In study two we examine the role of orienting to tactile contacts in developing rat pups for maintaining aggregations (huddles). We show that young pups are able to orient to contacts with nearby conspecifics before their eyes open implying an important role for the macrovibrissae, which are present from birth, in maintaining contact with conspecifics. Overall, these data suggest that orienting to tactile cues, detected by the vibrissal system, plays a crucial role throughout the life of a rat.


title: Is Working Memory Training Effective? A Meta-Analytic Review

abstract: It has been suggested that working memory training programs are effective both as treatments for attention-deficit/hyperactivity disorder (ADHD) and other cognitive disorders in children and as a tool to improve cognitive ability and scholastic attainment in typically developing children and adults. However, effects across studies appear to be variable, and a systematic meta-analytic review was undertaken. To be included in the review, studies had to be randomized controlled trials or quasi-experiments without randomization, have a treatment, and have either a treated group or an untreated control group. Twenty-three studies with 30 group comparisons met the criteria for inclusion. The studies included involved clinical samples and samples of typically developing children and adults. Meta-analyses indicated that the programs produced reliable short-term improvements in working memory skills. For verbal working memory, these near-transfer effects were not sustained at follow-up, whereas for visuospatial working memory, limited evidence suggested that such effects might be maintained. More importantly, there was no convincing evidence of the generalization of working memory training to other skills (nonverbal and verbal ability, inhibitory processes in attention, word decoding, and arithmetic). The authors conclude that memory training programs appear to produce short-term, specific training effects that do not generalize. Possible limitations of the review (including age differences in the samples and the variety of different clinical conditions included) are noted. However, current findings cast doubt on both the clinical relevance of working memory training programs and their utility as methods of enhancing cognitive functioning in typically developing children and healthy adults.


title: An effective diffusion barrier metallization process on copper

abstract: Abstract Copper substrates have been widely used to mount semiconductor device chips due to their high electrical and thermal conductivity. To achieve high heat conduction, solders are utilized to bond the chip on the substrate. Soft and ductile solders such as indium and lead–tin alloys are often used to help release the stress developed in a bonded structure through plastic deformation and strain. In the solder joint, a major concern is the formation of intermetallics caused by the diffusion of copper atoms from the substrate to the joint. The growth of intermetallics makes the solder joint less ductile and less capable of releasing stresses. Prevention of copper diffusion is needed in applications where the solder joint must remain soft and ductile. On the other hand, in applications where creep movement cannot be tolerated, hard solders such as AuSn eutectic alloy should be considered. In this paper, we report a barrier metallization technique on copper substrates to block copper atoms from diffusing into the solder. Indium was selected as the solder medium in this study because of its increasing applications in many photonic devices such as laser diodes and photonic switches. Other solders such as lead–tin alloys can be also used with the metallization process. The metallization consists of Ni buffer layer and Cr blocking layer. The solder joint is fabricated by the fluxless bonding technology. After accelerated aging test at 130°C for 100 h, no sign of copper diffusion through the metallization is detected using energy dispersive X-ray (EDX). If 90°C is the maximum temperature of the solder joint during device operation, the corresponding time for no copper diffusion is 107 500 h, or 12.3 years. This result shows that the metallization process developed in this research is, indeed, very effective.


title: Adomian decomposition method for solving initial value problems in vibration models

abstract: A number of engineering problems have second-order ordinary differential equations as their mathematical models. In practice, we may have a large scale problem with a large number of degrees of freedom, which must be solved accurately. Therefore, treating the mathematical model governing the problems correctly is required in order to get an accurate solution. In this work, we use Adomian decomposition method to solve vibration models in the forms of initial value problems of second-order ordinary differential equations. However, for problems involving an external source, the Adomian decomposition method may not lead to an accurate solution if the external source is not correctly treated. In this paper, we propose a strategy to treat the external source when we implement the Adomian decomposition method to solve initial value problems of second-order ordinary differential equations. Computational results show that our strategy is indeed effective. We obtain accurate solutions to the considered problems. Note that exact solutions are often not available, so they need to be approximated using some methods, such as the Adomian decomposition method.


title: New tapered slot vivaldi antenna for UWB applications

abstract: A Simulation of a small sized antipodal Vivaldi antenna for ultra-wideband (UWB) applications is presented in this paper. By using commercial electromagnetic simulation software CST Microwave, some parameters like return loss (S11), Voltage Standing Wave Ratio (VSWR), radiation pattern has been performed to test the validity of simulation and verify eligibility of the antenna for UWB systems. The antenna design with dimensions of 58×60 mm achieves satisfactory impedance matching and radiation across the frequency band from 2.14 to 11.33 GHz with more than 136% fractional bandwidth.


title: Effect of different docid orderings on dynamic pruning retrieval strategies

abstract: Document-at-a-time (DAAT) dynamic pruning strategies for information retrieval systems such as MaxScore and Wand can increase querying efficiency without decreasing effectiveness. Both work on posting lists sorted by ascending document identifier (docid). The order in which docids are assigned -- and hence the order of postings in the posting lists -- is known to have a noticeable impact on posting list compression. However, the resulting impact on dynamic pruning strategies is not well understood. In this poster, we examine the impact on the efficiency of these strategies across different docid orderings, by experimenting using the TREC ClueWeb09 corpus. We find that while the number of postings scored by dynamic pruning strategies do not markedly vary for different docid orderings, the ordering still has a marked impact on mean query response time. Moreover, when docids are assigned by lexicographical URL ordering, the benefit to response time for is more pronounced for Wand than for MaxScore.


title: A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems

abstract: Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators. In this paper, we propose a neural APE system that encodes the source (src) and machine translated (mt) sentences with two separate encoders, but leverages a shared attention mechanism to better understand how the two inputs contribute to the generation of the post-edited (pe) sentences. Our empirical observations have showed that when the mt is incorrect, the attention shifts weight toward tokens in the src sentence to properly edit the incorrect translation. The model has been trained and evaluated on the official data from the WMT16 and WMT17 APE IT domain English-German shared tasks. Additionally, we have used the extra 500K artificial data provided by the shared task. Our system has been able to reproduce the accuracies of systems trained with the same data, while at the same time providing better interpretability.


title: Accurate Uncertainties for Deep Learning Using Calibrated Regression

abstract: Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use of approximate inference, Bayesian uncertainty estimates are often inaccurate -- for example, a 90% credible interval may not contain the true outcome 90% of the time. Here, we propose a simple procedure for calibrating any regression algorithm; when applied to Bayesian and probabilistic models, it is guaranteed to produce calibrated uncertainty estimates given enough data. Our procedure is inspired by Platt scaling and extends previous work on classification. We evaluate this approach on Bayesian linear regression, feedforward, and recurrent neural networks, and find that it consistently outputs well-calibrated credible intervals while improving performance on time series forecasting and model-based reinforcement learning tasks.


Fast Bayesian hyperparameter optimization on large datasets


title: Walk-Steered Convolution for Graph Classification

abstract: Graph classification is a fundamental but challenging issue for numerous real-world applications. Despite recent great progress in image/video classification, convolutional neural networks (CNNs) cannot yet cater to graphs well because of graphical non-Euclidean topology. In this work, we propose a walk-steered convolutional (WSC) network to assemble the essential success of standard convolutional neural networks as well as the powerful representation ability of random walk. Instead of deterministic neighbor searching used in previous graphical CNNs, we construct multi-scale walk fields (a.k.a. local receptive fields) with random walk paths to depict subgraph structures and advocate graph scalability. To express the internal variations of a walk field, Gaussian mixture models are introduced to encode principal components of walk paths therein. As an analogy to a standard convolution kernel on image, Gaussian models implicitly coordinate those unordered vertices/nodes and edges in a local receptive field after projecting to the gradient space of Gaussian parameters. We further stack graph coarsening upon Gaussian encoding by using dynamic clustering, such that high-level semantics of graph can be well learned like the conventional pooling on image. The experimental results on several public datasets demonstrate the superiority of our proposed WSC method over many state-of-the-arts for graph classification.


title: Documenting the Research Through Design Process

abstract: Documentation of Research through Design (RtD) processes is essential for capturing and translating design knowledge into broader academic knowledge. Moreover, it can support both design and research objectives internally in RtD projects, as well as external concerns such as project management, knowledge dissemination, and education. In spite of this, RtD documentation has received little sustained attention in prior work. Building on what prior work there is as well as an in-depth analysis of two markedly different cases, we present a framework for planning and evaluating RtD documentation. The framework addresses three core concerns: the medium of documentation, the performativity of documentation, and providing equal support for both research and design.


title: Design and optimization of ultra wide band monopole antenna with DGS for microwave imaging

abstract: A compact, low profile UWB monopole antenna is presented for Ultra wideband applications. A Monopole reference antenna design on the FR4 substrate of size 28.3 × 24 mm2 without defected ground structure (DGS) and results are analyzed in terms of impedance bandwidth, mutual coupling, radiation patterns and antenna gain with the help of CST Microwave studio and then proposed UWB monopole antenna is also design on the same substrate with DGS. All the results are also simulated and analyzed for the same. A comparative study carried out between these two designs and analyzed that proposed UWB monopole antenna enhanced impedance bandwidth from 3.7 GHz–14.9 GHz(without DGS) to 3.4 GHz–20 GHz (with DGS) and group delay is also satisfactory, which makes the proposed antenna is a good candidate for portable UWB applications.


title: An Efficient Frequency and Phase Estimation Algorithm With CRB Performance for FMCW Radar Applications

abstract: In this paper, a chirp z-transform (CZT)-based algorithm for frequency-modulated continuous wave (FMCW) radar applications is presented. The proposed algorithm is optimized for real-time implementation in field-programmable gate arrays. To achieve a very high accuracy, the FMCW radar uses an additional phase evaluation. Therefore, a phase calculation based on the CZT algorithm is derived and compared with a correlation based algorithm. For a better classification of the algorithm, the respective Cramer-Rao bounds are calculated. The performance of the algorithm is shown by the evaluation of different radar measurements with a K-band radar. In the measurements, an accuracy of 5 μm with a mean standard deviation of 774 nm is achieved, which nearly matches the theoretically predicted mean standard deviation of 160 nm.


Graph Cut Algorithms in Vision , Graphics and Machine Learning An Integrative Paper


title: Visual Entailment: A Novel Task for Fine-Grained Image Understanding

abstract: Existing visual reasoning datasets such as Visual Question Answering (VQA), often suffer from biases conditioned on the question, image or answer distributions. The recently proposed CLEVR dataset addresses these limitations and requires fine-grained reasoning but the dataset is synthetic and consists of similar objects and sentence structures across the dataset. ::: In this paper, we introduce a new inference task, Visual Entailment (VE) - consisting of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text. To realize this task, we build a dataset SNLI-VE based on the Stanford Natural Language Inference corpus and Flickr30k dataset. We evaluate various existing VQA baselines and build a model called Explainable Visual Entailment (EVE) system to address the VE task. EVE achieves up to 71% accuracy and outperforms several other state-of-the-art VQA based models. Finally, we demonstrate the explainability of EVE through cross-modal attention visualizations. The SNLI-VE dataset is publicly available at this https URL necla-ml/SNLI-VE.


title: Broadband Circularly Polarized Microstrip Antenna Array Using Sequentially Rotated Technique

abstract: In this letter, a broadband circularly polarized microstrip antenna array with a separate feed network layer is presented. A Wilkinson power divider that provides high isolation between elements and subarrays is employed in constructing the non coplanar corporate feed network of the array. The corporate-fed 4×4 antenna array composed of sequentially rotated 2×2 subar rays is designed and fabricated. The measurement results indicate that the proposed array exhibits a 1-dB axial-ratio (AR) bandwidth of 47.8% in which the return loss is below -10 dB.


Factors Predisposing to Perinatal Death Related to Uterine Rupture During Attempted Vaginal Birth After Caesarean Section: Retrospective Cohort Study


title: The Effect of Enteric-Coated, Delayed-Release Peppermint Oil on Irritable Bowel Syndrome

abstract: Herbal remedies, particularly peppermint, have been reported to be helpful in controlling symptoms of irritable bowel syndrome (IBS). We conducted a randomized double-blind placebo-controlled study on 90 outpatients with IBS. Subjects took one capsule of enteric-coated, delayed-release peppermint oil (Colpermin) or placebo three times daily for 8 weeks. We visited patients after the first, fourth, and eighth weeks and evaluated their symptoms and quality of life. The number of subjects free from abdominal pain or discomfort changed from 0 at week 0 to 14 at week 8 in the Colpermin group and from 0 to 6 in controls (P < 0.001). The severity of abdominal pain was also reduced significantly in the Colpermin group as compared to controls. Furthermore, Colpermin significantly improved the quality of life. There was no significant adverse reaction. Colpermin is effective and safe as a therapeutic agent in patients with IBS suffering from abdominal pain or discomfort.


title: The Vulnerable Brain: Biological Factors in the Diagnosis and Treatment of Depression

abstract: Psychiatrists can lay no claim to a rich tradition in classification. They have long looked down on classification as a futile activity and a dangerous one to boot, one suggesting that a “labeling” of patients disregards that which is “essential” for them. This view is, of course, untenable. A scientific study should be preceded by a classification of the phenomena on which it focuses. Classification is the foundation of diagnosis and, without adequate diagnosis, treatment as well as research inevitably gives botched results.


Review on Feature Extraction methods of Image based Sign Language Recognition system


title: Influence of Income Level and Seasons on Quantity and Composition of Municipal Solid Waste: A Case Study of the Capital City of Pakistan

abstract: The current study aims to analyze and compare the quantity and composition of municipal solid waste (MSW) at three socio-economic levels of population during all four seasons of the year (spring, summer, monsoon and winter). In this study, 2164.75 kg of MSW was evaluated, from 1260 samples collected from 45 households. The average waste generation was estimated to be 0.6 kg per capita per day. Waste generation rate for high, middle and low income groups was 0.890, 0.612 and 0.346 kg per capita per day, respectively. Nevertheless, season specific analysis indicated waste generation rates of 0.78, 0.58, 0.48 and 0.75 kg per capita per day in spring, summer, monsoon and winter, respectively. A two way ANOVA statistical analysis further illustrated a significant effect (p = 0.00) of economic level and seasons on the amount and composition of waste generated by the community. Moreover, the collected waste was segregated into 42 categories, where the highest was the organic fraction (57%), then diapers (12%), followed by plastic (8%), cardboard (3%) and paper (2%). The amounts of textile, diapers and plastics were highest in the lowest income group, while tetra packs, metal, paper and yard waste were maximum in the high income group. It is concluded that the high income group generated the highest amount of waste and waste generation rate is higher in the seasons of spring and winter compared to the other two seasons.


title: Differential Dynamic Logic for Hybrid Systems

abstract: Hybrid systems are models for complex physical systems and are defined as dynamical systems with interacting discrete transitions and continuous evolutions along differential equations. With the goal of developing a theoretical and practical foundation for deductive verification of hybrid systems, we introduce a dynamic logic for hybrid programs, which is a program notation for hybrid systems. As a verification technique that is suitable for automation, we introduce a free variable proof calculus with a novel combination of real-valued free variables and Skolemisation for lifting quantifier elimination for real arithmetic to dynamic logic. The calculus is compositional, i.e., it reduces properties of hybrid programs to properties of their parts. Our main result proves that this calculus axiomatises the transition behaviour of hybrid systems completely relative to differential equations. In a case study with cooperating traffic agents of the European Train Control System, we further show that our calculus is well-suited for verifying realistic hybrid systems with parametric system dynamics.


title: Causal Transfer Learning

abstract: An important goal in both transfer learning and causal inference is to make accurate predictions when the distribution of the test set and the training set(s) differ. Such a distribution shift may happen as a result of an external intervention on the data generating process, causing certain aspects of the distribution to change, and others to remain invariant. We consider a class of causal transfer learning problems, where multiple training sets are given that correspond to different external interventions, and the task is to predict the distribution of a target variable given measurements of other variables for a new (yet unseen) intervention on the system. We propose a method for solving these problems that exploits causal reasoning but does neither rely on prior knowledge of the causal graph, nor on the the type of interventions and their targets. We evaluate the method on simulated and real world data and find that it outperforms a standard prediction method that ignores the distribution shift.


title: An Energy-Efficient Hardware Implementation of HOG-Based Object Detection at 1080HD 60 fps with Multi-Scale Support

abstract: A real-time and energy-efficient multi-scale object detector hardware implementation is presented in this paper. Detection is done using Histogram of Oriented Gradients (HOG) features and Support Vector Machine (SVM) classification. Multi-scale detection is essential for robust and practical applications to detect objects of different sizes. Parallel detectors with balanced workload are used to increase the throughput, enabling voltage scaling and energy consumption reduction. Image pre-processing is also introduced to further reduce power and area costs of the image scales generation. This design can operate on high definition 1080HD video at 60 fps in real-time with a clock rate of 270 MHz, and consumes 45.3 mW (0.36 nJ/pixel) based on post-layout simulations. The ASIC has an area of 490 kgates and 0.538 Mbit on-chip memory in a 45 nm SOI CMOS process.


Transparent and flexible fingerprint sensor array with multiplexed detection of tactile pressure and skin temperature


title: Cancer despite immunosurveillance: immunoselection and immunosubversion

abstract: Numerous innate and adaptive immune effector cells and molecules participate in the recognition and destruction of cancer cells, a process that is known as cancer immunosurveillance. But cancer cells avoid such immunosurveillance through the outgrowth of poorly immunogenic tumour-cell variants (immunoselection) and through subversion of the immune system (immunosubversion). At the early stages of carcinogenesis, cell-intrinsic barriers to tumour development seem to be associated with stimulation of an active antitumour immune response, whereas overt tumour development seems to correlate with changes in the immunogenic properties of tumour cells. The permanent success of treatments for cancer might depend on using immunogenic chemotherapy to re-establish antitumour immune responses.


title: CDTA: A Comprehensive Solution for Counterfeit Detection, Traceability, and Authentication in the IoT Supply Chain

abstract: The Internet of Things (IoT) is transforming the way we live and work by increasing the connectedness of people and things on a scale that was once unimaginable. However, the vulnerabilities in the IoT supply chain have raised serious concerns about the security and trustworthiness of IoT devices and components within them. Testing for device provenance, detection of counterfeit integrated circuits (ICs) and systems, and traceability of IoT devices are challenging issues to address. In this article, we develop a novel radio-frequency identification (RFID)-based system suitable for counterfeit detection, traceability, and authentication in the IoT supply chain called CDTA. CDTA is composed of different types of on-chip sensors and in-system structures that collect necessary information to detect multiple counterfeit IC types (recycled, cloned, etc.), track and trace IoT devices, and verify the overall system authenticity. Central to CDTA is an RFID tag employed as storage and a channel to read the information from different types of chips on the printed circuit board (PCB) in both power-on and power-off scenarios. CDTA sensor data can also be sent to the remote server for authentication via an encrypted Ethernet channel when the IoT device is deployed in the field. A novel board ID generator is implemented by combining outputs of physical unclonable functions (PUFs) embedded in the RFID tag and different chips on the PCB. A light-weight RFID protocol is proposed to enable mutual authentication between RFID readers and tags. We also implement a secure interchip communication on the PCB. Simulations and experimental results using Spartan 3E FPGAs demonstrate the effectiveness of this system. The efficiency of the radio-frequency (RF) communication has also been verified via a PCB prototype with a printed slot antenna.


title: An Unsupervised Segmentation Method for Retinal Vessel Using Combined Filters

abstract: Image segmentation of retinal blood vessels is an important procedure for the prediction and diagnosis of cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels appearance. This work develops an unsupervised segmentation procedure for the segmentation of retinal vessels images using a combined matched filter, Frangi filter and Gabor Wavelet Filter. After the vessel enhancement, two segmentation methods are tested. The first method uses an approach based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The results are compared to other state-of-the-art methods described in the literature.


title: Development of Electronic Money and Its Impact on the Central Bank Role and Monetary Policy

abstract: Introduction The development of information technology and the emerging of a number of new innovations are taking place in the area of retail payments known as electronic money (e- money).This development influencing the banking industry due to the increased use of pre-paid card, e-purse, and e-wires of money orders, e-banking, e-loans. These innovations have the potential to challenge the predominant role of cash for making small-value payments and could make retail transactions easier and cheaper for consumers and merchants. However, they also raise a number of policy issues for central banks because of the possible implications for central bank and monetary policy and because of central banks' general interest in payment systems. This paper provides a definition of electronic money and a description of its key features. It discusses the factors influencing the development of e-money products. Finally, it reviews the policy issues raised by e-money developments, as seen from a central bank perspective, and discusses the possible policy responses and try to answer the following question: would E-money development affect central bank role and monetary policy? Problem of the Study "The term [electronic] money refers to various proposed electronic payment mechanisms designed for use by consumers to make retail payments. Digital money products have the potential to replace central bank currency" (Berentsen, 1997). This quote indicates that the advent of electronic money will have an impact on the banking system and monetary policy. While this topic is controversial it seems obvious that some changes will result and that there is no prefect answer to predict this new instruments affect on monetary aggregates and the role of central banks. Its growth will be based on many things: future technology, increased security, regulation, and ease of conversion. It can impact such variables as monetary supply, exchange rates, the money multiplier, velocity of money and seignorage. Increased reliance on electronic money as a substitute for currency will directly affect the central bank and its control over monetary aggregates and policies. Purpose of the Study This paper will focus on the development of electronic money and its impact on the central bank role and monetary policy. Here the focus is primarily on the forces sustaining the development of e-money and on the central bank ability to conduct monetary policy in the presence of e-money. The purpose of the paper aims at discussing the following issues: 1- Does electronic money considered as a form of inside money similar to private notes issued by free banks? 2- If this is the case, does this situation challenge the control of the central bank over the supply of money? In other words will the monetary policy still be efficient? Methodology of the Study This paper depends on analytical method at determining the impact of the development of electronic money in the different areas related directly or indirectly to the emerging of this new innovation at the last decade. In this paper we incentive our effort at analyzing the previous literature review related to this subject to make better understanding to all aspects of this subject depending on a judgment approach which represent our persuasive in this field to determine the most influenced areas to obtain the best conclusions and give the appropriate recommendations to convene our results. Definition of Electronic Money Electronic money is the money balance recorded electronically on a "stored-value" card (Ely, 1996). These cards, "smart cards," have a microprocessor embedded which can be loaded with a monetary value. Another form of electronic money is network money, "software that allows the transfer of value on computer networks, particularly the internet. Like a travelers check, a digital money balance is a floating claim on a private bank or other financial institution that is not linked to any particular account" (Berentsen, 1997). …


title: Security Failures in EMV Smart Card Payment Systems

abstract: New credit cards containing Europay, MasterCard and Visa (EMV) chips for enhanced security used in-store purchases rather than online purchases have been adopted considerably. EMV supposedly protects the payment cards in such a way that the computer chip in a card referred to as chip-and-pin cards generate a unique one time code each time the card is used. The one time code is designed such that if it is copied or stolen from the merchant system or from the system terminal cannot be used to create a counterfeit copy of that card or counterfeit chip of the transaction. However, in spite of this design, EMV technology is not entirely foolproof from failure. In this paper we discuss the issues, failures and fraudulent cases associated with EMV Chip-And-Card technology.


title: Column-Oriented Storage Techniques for MapReduce

abstract: Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from over three decades of research on parallel DBMSs. However, translating these techniques to a Map-Reduce implementation such as Hadoop presents unique challenges that can lead to new design choices. This paper describes how column-oriented storage techniques can be incorporated in Hadoop in a way that preserves its popular programming APIs. ::: ::: We show that simply using binary storage formats in Hadoop can provide a 3x performance boost over the naive use of text files. We then introduce a column-oriented storage format that is compatible with the replication and scheduling constraints of Hadoop and show that it can speed up MapReduce jobs on real workloads by an order of magnitude. We also show that dealing with complex column types such as arrays, maps, and nested records, which are common in MapReduce jobs, can incur significant CPU overhead. Finally, we introduce a novel skip list column format and lazy record construction strategy that avoids deserializing unwanted records to provide an additional 1.5x performance boost. Experiments on a real intranet crawl are used to show that our column-oriented storage techniques can improve the performance of the map phase in Hadoop by as much as two orders of magnitude.