title: Context-aware services adaptation for a smart living room

abstract: Pervasive or ubiquitous computing was developed thanks to the technological evolution of embedded systems and computer communication means. Ubiquitous computing has given birth to the concept of smart spaces that facilitate our daily life and increase our comfort where devices provide proactively adpated services. In spite of the significant previous works done in this domain, there still a lot of work and enhancement to do in particular the taking into account of current user's context when providing adaptable services. In this paper we propose an approach for context-aware services adaptation for a smart living room using two machine learning methods.


title: Streaming Big Data Processing in Datacenter Clouds

abstract: Today, we live in a digital universe in which information and technology are not only around us but also play important roles in dictating the quality of our lives. As we delve deeper into this digital universe, we're witnessing explosive growth in the variety, velocity, and volume of data1,2 being transmitted over the Internet. A zetabyte of data passed through the Internet in the past year; IDC predicts that this digital universe will explode to an unimaginable eight Zbytes by 2015. These data are and will be generated mainly from Internet search, social media, mobile devices, the Internet of Things, business transactions, next-generation radio astronomy telescopes, high-energy physics synchrotron, and content distribution. Government and business organizations are now overflowing with data, easily aggregating to terabytes or even petabytes of information.


title: De-anonymizing Social Networks

abstract: Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social-network graphs. To demonstrate its effectiveness on real-world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate.Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy "sybil" nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary's auxiliary information is small.


title: Towards an iso standard for dialogue act annotation

abstract: This paper describes an ISO project which aims at developing a standard for annotating spoken and multimodal dialogue with semantic information concerning the communicative functions of utterances, the kind of semantic content they address, and their relations with what was said and done earlier in the dialogue. The project, ISO 24617-2 "Semantic annotation framework, Part 2: Dialogue acts", is currently at DIS stage. The proposed annotation schema distinguishes 9 orthogonal dimensions, allowing each functional segment in dialogue to have a function in each of these dimensions, thus accounting for the multifunctionality that utterances in dialogue often have. A number of core communicative functions is defined in the form of ISO data categories, available at http://semantic-annotation.uvt.nl/dialogue-acts/iso-datcats.pdf; they are divided into "dimension-specific" functions, which can be used only in a particular dimension, such as Turn Accept in the Turn Management dimension, and "general-purpose" functions, which can be used in any dimension, such as Inform and Request. An XML-based annotation language, "DiAML" is defined, with an abstract syntax, a semantics, and a concrete syntax.


title: A fast algorithm for skew detection of document images using morphology

abstract: Abstract. Any paper document when converted to electronic form through standard digitizing devices, like scanners, is subject to a small tilt or skew. Meanwhile, a de-skewed document allows a more compact representation of its components, particularly text objects, such as words, lines, and paragraphs, where they can be represented by their rectilinear bounding boxes. This simplified representation leads to more efficient, robust, as well as simpler algorithms for document image analysis including optical character recognition (OCR). This paper presents a new method for automatic detection of skew in a document image using mathematical morphology. The proposed algorithm is extremely fast as well as independent of script forms.


title: Filter Importance Sampling

abstract: High quality ray tracing requires pixel sampling and filtering for antialiasing, as well as illumination sampling for complex lighting effects. Both problems are well understood and sophisticated sampling techniques are available for each of them. However, it turns out that weighted pixel sampling spoils the benefits of clever illumination sampling, even with the best filter functions. We propose to circumvent this problem by using pixel sampling and filtering based on importance sampling. Although this technique disallows sharing of samples between neighboring pixels, it results in significant noise reduction and improvement of image quality. The method is easy to implement and well suited for parallel, tile based rendering. I.3.3Computer Graphics Three-dimensional Graphics and RealismRaytracing


title: Accelerated Degradation Tests Applied to Software Aging Experiments

abstract: In the past ten years, the software aging phenomenon has been systematically researched, and recognized by both academic, and industry communities as an important obstacle to achieving dependable software systems. One of its main effects is the depletion of operating system resources, causing system performance degradation or crash/hang failures in running applications. When conducting experimental studies to evaluate the operational reliability of systems suffering from software aging, long periods of runtime are required to observe system failures. Focusing on this problem, we present a systematic approach to accelerate the software aging manifestation to reduce the experimentation time, and to estimate the lifetime distribution of the investigated system. First, we introduce the concept of ?aging factor? that offers a fine control of the aging effects at the experimental level. The aging factors are estimated via sensitivity analyses based on the statistical design of experiments. Aging factors are then used together with the method of accelerated degradation test to estimate the lifetime distribution of the system under test at various stress levels. This approach requires us to estimate a relationship model between stress levels and aging degradation. Such models are called stress-accelerated aging relationships. Finally, the estimated relationship models enable us to estimate the lifetime distribution under use condition. The proposed approach is used in estimating the lifetime distribution of a web server with software aging symptoms. The main result is the reduction of the experimental time by a factor close to 685 in comparison with experiments executed without the use of our technique.


title: Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks

abstract: Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios. As such, most contemporary RL relies on simple heuristics such as epsilon-greedy exploration or adding Gaussian noise to the controls. This paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics. We propose a practical implementation, using variational inference in Bayesian neural networks which efficiently handles continuous state and action spaces. VIME modifies the MDP reward function, and can be applied with several different underlying RL algorithms. We demonstrate that VIME achieves significantly better performance compared to heuristic exploration methods across a variety of continuous control tasks and algorithms, including tasks with very sparse rewards.


title: An extended Playfair Cipher using rotation and random swap patterns

abstract: In this paper, we have proposed an encryption method which is developed by extending Playfair Cipher to make it more secure. Known as Polyalphabetic cipher, Playfair uses a key matrix containing 25 characters for encryption. This extended playfair cipher supports all 26 characters filled in two different key matrices, second matrix having extra checksum characters. As Playfair is susceptible to various attacks, so to avoid that, we included rotation operation on key matrices and swapping of rows and columns of key matrices according to randomly generated swap patterns. Cryptanalysis of new proposed extended cipher is done by performing various attacks and we conclude that this technique is more secure and can be used as a part of advanced encryption algorithms like AES, DES.


title: A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks

abstract: The Internet of things (IoT) is composed of billions of sensing devices that are subject to threats stemming from increasing reliance on communications technologies. A Trust-Based Secure Routing (TBSR) scheme using the traceback approach is proposed to improve the security of data routing and maximize the use of available energy in Energy-Harvesting Wireless Sensor Networks (EHWSNs). The main contributions of a TBSR are (a) the source nodes send data and notification to sinks through disjoint paths, separately; in such a mechanism, the data and notification can be verified independently to ensure their security. (b) Furthermore, the data and notification adopt a dynamic probability of marking and logging approach during the routing. Therefore, when attacked, the network will adopt the traceback approach to locate and clear malicious nodes to ensure security. The probability of marking is determined based on the level of battery remaining; when nodes harvest more energy, the probability of marking is higher, which can improve network security. Because if the probability of marking is higher, the number of marked nodes on the data packet routing path will be more, and the sink will be more likely to trace back the data packet routing path and find malicious nodes according to this notification. When data packets are routed again, they tend to bypass these malicious nodes, which make the success rate of routing higher and lead to improved network security. When the battery level is low, the probability of marking will be decreased, which is able to save energy. For logging, when the battery level is high, the network adopts a larger probability of marking and smaller probability of logging to transmit notification to the sink, which can reserve enough storage space to meet the storage demand for the period of the battery on low level; when the battery level is low, increasing the probability of logging can reduce energy consumption. After the level of battery remaining is high enough, nodes then send the notification which was logged before to the sink. Compared with past solutions, our results indicate that the performance of the TBSR scheme has been improved comprehensively; it can effectively increase the quantity of notification received by the sink by 20%, increase energy efficiency by 11%, reduce the maximum storage capacity needed by nodes by 33.3% and improve the success rate of routing by approximately 16.30%.


title: Robot navigation via spatial and temporal coherent semantic maps

abstract: The ability of mobile robots to sense, interpret and map their environments in human terms is decisive for their applicability to everyday activities hereafter. Bearing this view in mind, we present here, for the first time, an integrated framework that aims: (i) to introduce a semantic mapping method and (ii) to use this semantic map, as a means to provide a hierarchical navigation solution. The semantic map is formed in a bottom-up fashion, along the robot's course, relying on the conceptual space quantization, the time proximity and the spatial coherence integrated into the labeled sparse topological map. A novel time-evolving augmented navigation graph determines the semantic topology of the explored environment and the connectivity among the recognized places expressed by the inter-place transition probability. The robot navigation part is addressed through an interface that facilitates human robot interaction. High level orders are passed to the robots and unfolded recursively, in a top-down fashion, into local navigation data. The performance of the proposed framework was evaluated on long range real world data and exhibited remarkable results. HighlightsLabeled sparse topological map.Augmented navigation graph.Semantic mapping.Robot navigation.


title: Evaluating and comparing MBSE methodologies for practitioners

abstract: The objective of Model-Based System Engineering (MBSE) is to provide the right tools to create and manage all life-cycle information in a pragmatic, concise, consistent and traceable way across the numerous perspectives and architectural levels. Its practical use, however, is currently impeded by a universal lack of experience and integration with development processes. Interested engineers therefore find it difficult to select the methodology best suited for their particular context. This paper proposes a Framework for the Evaluation of MBSE Methodologies for Practitioners (FEMMP) based on a catalogue of standard criteria to assess the practical use of the available methodologies, which are evaluated using a standard case study. The paper illustrates the evaluation process comparing the authors' own methodologies: SYSMOD+ and the MDDM.


A generic approach to bulk loading multidimensional index structures


title: Efficient fuzzy edge detection using successive Otsu's method

abstract: In this paper we propose fuzzy based edge detection using Successive Otsu's method. Edge detection has always been a vital aspect of majority in image processing techniques. In this work we describe the algorithm of Successive Otsu technique and utilize same in grouping image histogram into different partitions. The algorithm proposed here is implemented to provide the threshold parameter to classical Sobel Operator in order to enhance its edge detection capability using the fuzzy logic. The results are compared with Canny Edge Detector and finally concludes that proposed method based Sobel edge detection process behaves much alike to Canny edge detection process.


title: Body, biometrics and identity

abstract: According to a popular aphorism, biometrics are turning the human body into a passport or a password. As usual, aphorisms say more than they intend. Taking the dictum seriously, we would be two: ourself and our body. Who are we, if we are not our body? And what is our body without us? The endless history of identification systems teaches that identification is not a trivial fact but always involves a web of economic interests, political relations, symbolic networks, narratives and meanings. Certainly there are reasons for the ethical and political concerns surrounding biometrics but these reasons are probably quite different from those usually alleged.


title: Text Processing for Text-to-SpeechSystems in Indian Languages

abstract: To build a natural sounding speech synthesis system, it is essential that the text processing component produce an appropriate sequence of phonemic units corresponding to an arbitrary input text. In this paper we discuss our efforts in addressing the issues of Font-to-Akshara mapping, pronunciation rules for Aksharas, text normalization in the context of building text-to-speech systems in Indian languages.


title: Design of a Tubular Permanent Magnet Actuator for Active Lateral Secondary Suspension of a Railway Vehicle

abstract: This paper describes the finite element (FE)-based design of a slotted tubular permanent magnet actuator (TPMA) used in railway vehicle active lateral secondary suspension that improves the actuator’s thrust and lowers its cogging force under thermal and geometric constraints. To consider the electromagnetic and thermal fields and the complex interactions among the design variables, design was carried out in an electromagnet and thermal field environment using accurate and time-effective FE analysis. A six-slot prototype model was fabricated to estimate critical thermal parameters, which are difficult to compute without experiments. Three-dimensional FE analysis using the determined thermal parameters was adopted to calculate the precise thermal distribution of the TPMA and verify the forced air-cooling effect. A prototype TPMA with a quasi-Halbach array of permanent magnets and a moving magnet was manufactured through the FE-based design process; the dynamic, electromagnetic, and thermal characteristics of the prototype TPMA were validated experimentally.


title: An Introduction to Dimensionality Reduction Using Matlab

abstract: Dimensionality reduction is an important task in machine learning, for it facilitates classification, compression, and visualization of high-dimensional data by mitigating undesired properties of high-dimensional spaces. Over the last decade, a large number of new (nonlinear) techniques for dimensionality reduction have been proposed. Most of these techniques are based on the intuition that data lies on or near a complex low-dimensional manifold that is embedded in the high-dimensional space. New techniques for dimensionality reduction aim at identifying and extracting the manifold from the high-dimensional space. A systematic empirical evaluation of a large number of dimensionality reduction techniques has been presented in [86]. This work has led to the development of the Matlab Toolbox for Dimensionality Reduction, which contains implementations of 27 techniques for dimensionality reduction. In addition, the toolbox contains implementation of 6 intrinsic dimensionality estimators and functions for out-of-sample extension, data generation, and data prewhitening. The report describes the techniques that are implemented in the toolbox in detail. Furthermore, it presents a number of examples that illustrate the functionality of the toolbox, and provide insight in the capabilities of state-of-the-art techniques for dimensionality reduction.


Convolutional Neural Network Language Models


COGNITIVE LOAD THEORY , LEARNING DIFFICULTY , AND INSTRUCTIONAL


title: Full Wheatstone Bridge Spin-Valve Based Sensors for IC Currents Monitoring

abstract: Full Wheatstone bridge spin-valve-based electrical current sensors at the IC level are presented. Prototypes with different geometrical parameters have been designed, fabricated and fully characterized. DC characterization has been carried out, for measurement of insertion losses, linearity, voltage offset and sensitivity. Current ranges from 10 muA to 100 mA can be covered with these sensors with excellent linearity and sensitivities above 1 mV/(VmiddotmA) . AC characteristics have also been analyzed and bandwidths exceeding 100 kHz are demonstrated. Moreover, the temperature coefficients have been extracted in the range of -20degC to +60degC. In order to highlight the design properties, dependence of the sensor's performance with external magnetic perturbations and self-heating have also been measured and quantified. The associated errors are in the range of 1%-2% of the full scale.


title: Effect of Dual-tasking on Visual and Auditory Simple Reaction Times.

abstract: Visual and auditory simple reaction times were measured in Sri Lankan young adults to see the effect of dual-tasking on reaction time. Reaction times were measured using a reaction time program in a computer. Subjects responded to stimuli by pressing the spacebar with their dominant index finger. Visual and auditory simple reaction times (ms) in males (mean ± SD) were 293.5 ± 42.4, 302.2 ± 41.9 respectively. In females respective values were 315.1 ± 55.5, 313.1 ± 45. Males had statistically significant faster reaction times (p 0.05) within the same sex. Dual-tasking significantly increases (p < 0.05) both reaction times in both sexes. In females, dual-tasking affects simple auditory reaction time significantly more than simple visual reaction time (p < 0.05).


Information Retrieval Applications in Software Maintenance and Evolution


title: Spatially Binned ROC: A Comprehensive Saliency Metric

abstract: A recent trend in saliency algorithm development is large-scale benchmarking and algorithm ranking with ground truth provided by datasets of human fixations. In order to accommodate the strong bias humans have toward central fixations, it is common to replace traditional ROC metrics with a shuffled ROC metric which uses randomly sampled fixations from other images in the database as the negative set. However, the shuffled ROC introduces a number of problematic elements, including a fundamental assumption that it is possible to separate visual salience and image spatial arrangement. We argue that it is more informative to directly measure the effect of spatial bias on algorithm performance rather than try to correct for it. To capture and quantify these known sources of bias, we propose a novel metric for measuring saliency algorithm performance: the spatially binned ROC (spROC). This metric provides direct in-sight into the spatial biases of a saliency algorithm without sacrificing the intuitive raw performance evaluation of traditional ROC measurements. By quantitatively measuring the bias in saliency algorithms, researchers will be better equipped to select and optimize the most appropriate algorithm for a given task. We use a baseline measure of inherent algorithm bias to show that Adaptive Whitening Saliency (AWS) [14], Attention by Information Maximization (AIM) [8], and Dynamic Visual Attention (DVA) [20] provide the least spatially biased results, suiting them for tasks in which there is no information about the underlying spatial bias of the stimuli, whereas algorithms such as Graph Based Visual Saliency (GBVS) [18] and Context-Aware Saliency (CAS) [15] have a significant inherent central bias.


title: D3S: Debugging Deployed Distributed Systems

abstract: Testing large-scale distributed systems is a challenge, because some errors manifest themselves only after a distributed sequence of events that involves machine and network failures. D3S is a checker that allows developers to specify predicates on distributed properties of a deployed system, and that checks these predicates while the system is running. When D3S finds a problem it produces the sequence of state changes that led to the problem, allowing developers to quickly find the root cause. ::: ::: Developers write predicates in a simple and sequential programming style, while D3S checks these predicates in a distributed and parallel manner to allow checking to be scalable to large systems and fault tolerant. By using binary instrumentation, D3S works transparently with legacy systems and can change predicates to be checked at runtime. An evaluation with 5 deployed systems shows that D3S can detect non-trivial correctness and performance bugs at runtime and with low performance overhead (less than 8%).


title: A Measurement Based Rogue AP Detection Scheme

abstract: This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing based technique that allows the user to avoid connecting to rogue APs. Our method employs the round trip time between the user and the DNS server to independently determine whether an AP is legitimate or not without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance.


title: Challenges and Innovations in Building a Product Knowledge Graph

abstract: Knowledge graphs have been used to support a wide range of applications and enhance search results for multiple major search engines, such as Google and Bing. At Amazon we are building a Product Graph, an authoritative knowledge graph for all products in the world. The thousands of product verticals we need to model, the vast number of data sources we need to extract knowledge from, the huge volume of new products we need to handle every day, and the various applications in Search, Discovery, Personalization, Voice, that we wish to support, all present big challenges in constructing such a graph. In this talk we describe four scientific directions we are investigating in building and using such a knowledge graph. First, we have been developing advanced extraction technologies to harvest product knowledge from semi-structured sources on the web and from text product profiles. Our annotation-based extraction tool selects a few webpages (typically below 10 pages) from a website for annotations, and can derive XPaths to extract from the whole website with average precision and recall of 97% [1]. Our distantly supervised extraction tool, CERES, uses an existing knowledge graph to automatically generate (noisy) training labels, and can obtain a precision over 90% when extracting from long-tail websites in various languages [1]. Our OpenTag technique extends state-of-the-art techniques such as Recursive Neural Network (RNN) and Conditional Random Field with attention and active learning, to achieve over 90% precision and recall in extracting attribute values (including values unseen in training data) from product titles, descriptions, and bullets [3].


title: SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints

abstract: This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present SoPhie; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends a social attention mechanism with physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path. Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors. SoPhie also takes advantage of GAN to generates more realistic samples and to capture the uncertain nature of the future paths by modeling its distribution. All these mechanisms enable our approach to predict socially and physically plausible paths for the agents and to achieve state-of-the-art performance on several different trajectory forecasting benchmarks.


title: FoldMecha: Design for Linkage-Based Paper Toys

abstract: We present FoldMecha, a computational tool to help non-experts design and build paper mechanical toys. By customizing templates a user can experiment with basic mechanisms, design their own model, print and cut out a folding net to construct the toy. We used the tool to build two kinds of paper automata models: walkers and flowers.


Analysis of systems to process massive data stream


6 Temporal Ontology and Temporal Reference


title: Robot carers, ethics, and older people

abstract: This paper offers an ethical framework for the development of robots as home companions that are intended to address the isolation and reduced physical functioning of frail older people with capacity, especially those living alone in a noninstitutional setting. Our ethical framework gives autonomy priority in a list of purposes served by assistive technology in general, and carebots in particular. It first introduces the notion of "presence" and draws a distinction between humanoid multi-function robots and non-humanoid robots to suggest that the former provide a more sophisticated presence than the latter. It then looks at the difference between lower-tech assistive technological support for older people and its benefits, and contrasts these with what robots can offer. This provides some context for the ethical assessment of robotic assistive technology. We then consider what might need to be added to presence to produce care from a companion robot that deals with older people's reduced functioning and isolation. Finally, we outline and explain our ethical framework. We discuss how it combines sometimes conflicting values that the design of a carebot might incorporate, if informed by an analysis of the different roles that can be served by a companion robot.


title: Using a user-level memory thread for correlation prefetching

abstract: This paper introduces the idea of using a User-Level Memory Thread (ULMT) for correlation prefetching. In this approach, a user thread runs on a general-purpose processor in main memory, either in the memory controller chip or in a DRAM chip. The thread performs correlation prefetching in software, sending the prefetched data into the L2 cache of the main processor. This approach requires minimal hardware beyond the memory processor: the correlation table is a software data structure that resides in main memory, while the main processor only needs a few modifications to its L2 cache so that it can accept incoming prefetches. In addition, the approach has wide usability, as it can effectively prefetch even for irregular applications. Finally, it is very flexible, as the prefetching algorithm can be customized by the user on an application basis. Our simulation results show that, through a new design of the correlation table and prefetching algorithm, our scheme delivers good results. Specifically, nine mostly-irregular applications show an average speedup of 1.32. Furthermore, our scheme works well in combination with a conventional processor-side sequential prefetcher, in which case the average speedup increases to 1.46. Finally, by exploiting the customization of the prefetching algorithm, we increase the average speedup to 1.53.


title: Multi-class object tracking algorithm that handles fragmentation and grouping

abstract: We propose a framework for detecting and tracking multiple interacting objects, while explicitly handling the dual problems of fragmentation (an object may be broken into several blobs) and grouping (multiple objects may appear as a single blob). We use foreground blobs obtained by background subtraction from a stationary camera as measurements. The main challenge is to associate blob measurements with objects, given the fragment-object-group ambiguity when the number of objects is variable and unknown, and object-class-specific models are not available. We first track foreground blobs till they merge or split. We then build an inference graph representing merge-split relations between the tracked blobs. Using this graph and a generic object model based on spatial connectedness and coherent motion, we label the tracked blobs as whole objects, fragments of objects or groups of interacting objects. The outputs of our algorithm are entire tracks of objects, which may include corresponding tracks from groups during interactions. Experimental results on multiple video sequences are shown.


title: 600V-class Super Junction MOSFET with High Aspect Ratio P/N Columns Structure

abstract: A Super Junction (SJ) MOSFET with high aspect ratio p/n columns structure has been proposed to improve the trade-off relationship between breakdown voltage and specific on-resistance (Ron)- We have proposed a new trench filling epitaxial growth technique to fabricate this structure. In this work, we tried to apply this method to 600 V-class SJ-MOSFETs. We succeeded in fabricating p/n columns structure of which the aspect ratio is 25. It shows that its Ron is lower than that of IGBTs, and is the lowest in the reported 600 V-class Si devices.


title: Modification of UCT with patterns in Monte-Carlo go

abstract: Algorithm UCB1 for multi-armed bandit problem has already been extended to Algorithm UCT (Upper bound Confidence for Tree) which works for minimax tree search. We have developed a Monte-Carlo Go program, MoGo, which is the first computer Go program using UCT. We explain our modification of UCT for Go application and also the intelligent random simulation with patterns which has improved significantly the performance of MoGo. UCT combined with pruning techniques for large Go board is discussed, as well as parallelization of UCT. MoGo is now a top level Go program on $9\times9$ and $13\times13$ Go boards.


title: SwingPong: analysis and suggestion based on motion data from mobile sensors for table tennis strokes using decision tree

abstract: Table tennis is a sport that everyone can play, regardless of age or gender. It becomes a popular sport since it does not require much space and its equipment is affordable. For beginners, they may not have a chance to get some helpful advice to learn how to control a table tennis ball. Thus, we decided to analyze user's motion data and build a model to develop an application called SwingPong detecting basic table-tennis strokes in order to provide helpful suggestion to players. SwingPong is an android application on a smartphone analyzing user's forehand and backhand strokes and providing suggestion back to the user in order to improve his or her swing. There are two main modules in the application: Module #1: storing angular acceleration and angular velocity of strokes. When a table tennis ball is hit on the racket, the angular acceleration and angular velocity of user's swing are collected using accelerometer and gyroscope sensors in smartphones. We collected the training data set from table tennis national players, Thai youth national team table tennis athletes and coaches. J48 Decision Tree was used in order to build a model of hitting classification. Module #2: providing suggestions. We applied the model from Module#1 to evaluate user's basic strokes and provide alert sound when a ball is hit. After a user hits the ball in a given time, the application analyzes the user's strokes and shows suggestions. The experimental results show that SwingPong is able to recognize strokes whether the ball is good (or in), out, or hits the net. The accuracy of classification of forehand strokes is 77.21% and the accuracy of classification of backhand strokes is 69.63%. In addition, SwingPong can provide helpful suggestions so that users can use the feedbacks to improve their swings.


title: Wholebody Teleoperation for Humanoid Robot by Marionette System

abstract: Marionette system provides an intuitive teleoperation system for the difficulty controlling whole-body motion of a multi-joint robot and the complicated observation of its condition. This system employs a small robot which has similar form to a control target as an operating/displaying device, so that the device provides an operational feeling like manipulating a doll, so it is named Marionette device. Since the characteristics of the Marionette device and the target robot are synchronized bilaterally in the system, the operation of the Marionette device is reflected intuitively in the target, and the robot motion is also displayed simultaneously by the Marionette device. In this paper, we develop a humanoid type device as a Marionette device in order to operate a humanoid robot HRP-2 and implement a wholebody teleoperation method. The remote walking and manipulation experiments in an unknown environment are introduced. In this experiment, the operator should guess the remote environment using head camera view and control legs and arms by the Marionette device. We propose a new operation method for controlling the foot position and leg joints with the Marionette device. It makes possible to walk by pointing a foot stamp step by step in a stable place


title: TwitterStand: news in tweets

abstract: Twitter is an electronic medium that allows a large user populace to communicate with each other simultaneously. Inherent to Twitter is an asymmetrical relationship between friends and followers that provides an interesting social network like structure among the users of Twitter. Twitter messages, called tweets, are restricted to 140 characters and thus are usually very focused. We investigate the use of Twitter to build a news processing system, called TwitterStand, from Twitter tweets. The idea is to capture tweets that correspond to late breaking news. The result is analogous to a distributed news wire service. The difference is that the identities of the contributors/reporters are not known in advance and there may be many of them. Furthermore, tweets are not sent according to a schedule: they occur as news is happening, and tend to be noisy while usually arriving at a high throughput rate. Some of the issues addressed include removing the noise, determining tweet clusters of interest bearing in mind that the methods must be online, and determining the relevant locations associated with the tweets.


title: Extending the Chin and Lee's End User Computing Satisfaction Model with the Task-Technology Fit Model

abstract: The measurement of satisfaction has had a long history within the IS discipline. In general, most early studies focused only on the characteristics of the system, without trying to understand the process of the formation of satisfaction. This research focuses on combining the two theories of user satisfaction from the world of information technology. The first theory is Chin and Lee’s end user computing satisfaction model that explains the formation process of user satisfaction from expectation and desire. The second theory is Task-Technology Fit model explains the fit between user’s task with the support of information technology. The theoretical combination between the two theories was validated using datasets from field tests.


title: Keloid scarring: new treatments ahead.

abstract: Keloid disease presents a healthcare challenge: patients suffer from pruritus, pain, inflammation, and cosmetic disfigurement. There is no single effective therapeutic regimen for keloids. Numerous treatment options have been described including occlusive dressings, compression therapy, intralesional steroid injections, laser and radiation therapy, cryosurgery, 5-fluorouracil, interferon, and imiquimod cream, but managing keloid disease still is a considerable problem for clinicians. Better understanding of the molecular mechanisms behind keloid disease led to the development of new promising therapies like the application of recombinant TGF-b3, interleukin 10, and imatinib mesylate. This review provides an overview of the existing therapeutic options for keloid disease and summarizes upcoming future therapies with a special focus on blocking the transforming growth factor-beta pathway.


Mashtree: a rapid comparison of whole genome sequence files


title: MPPT control method of PV system based on active disturbance rejection control

abstract: This paper presents a maximum power point tracking method based on the active disturbance rejection controller. The tracking-differentiator is used to obtain the differential signal of current and voltage, and the system is controlled by the active disturbance rejection controller to ensure the system works at the maximum power point. The proposed strategy is validated in Matlab/Simulink, and the feasibility and superiority of the algorithm are verified by comparing with the traditional incremental conductance method based on PI controller.


Concept Learning from Natural Language Interactions


title: A Robust Approach for Multivariate Time Series Forecasting

abstract: Time series forecasting is often confronted with multivariate data, but few model is available in this situation. Besides, data distortion aggravates the difficulty to predict multivariate time series. To tackle such problems, we propose an approach based on convolutional neural network with a feature extraction layer added before convolution layer to extract multivariate features and handle multivariate time series data, as well as decreases the effect of distortion by transforming the sample into a denser representation with both its information and the information of its temporal neighbors. A full connection layer then fuses these extracted features and gets the final result. Given that events in the world are always related, using both the target time series and other related time series to forecast the future changes of the target dimension would achieve a better prediction. The proposed approach can process multivariate time series data and is robust to the number of samples, numeric ranges of data etc. Extensive experiments validate the effectiveness of the approach in accomplishing multivariate time series forecasting.


title: Profile and effects of consumer involvement in fresh meat.

abstract: This study investigates the profile and effects of consumer involvement in fresh meat as a product category based on cross-sectional data collected in Belgium. Analyses confirm that involvement in meat is a multidimensional construct including four facets: pleasure value, symbolic value, risk importance and risk probability. Four involvement-based meat consumer segments are identified: straightforward, cautious, indifferent, and concerned. Socio-demographic differences between the segments relate to gender, age and presence of children. The segments differ in terms of extensiveness of the decision-making process, impact and trust in information sources, levels of concern, price consciousness, claimed meat consumption, consumption intention, and preferred place of purchase. The two segments with a strong perception of meat risks constitute two-thirds of the market. They can be typified as cautious meat lovers versus concerned meat consumers. Efforts aiming at consumer reassurance through quality improvement, traceability, labelling or communication may gain effectiveness when targeted specifically to these two segments. Whereas straightforward meat lovers focus mainly on taste as the decisive criterion, indifferent consumers are strongly price oriented.


title: Shape-Driven Gabor Jets for Face Description and Authentication

abstract: This paper proposes, through the combination of concepts and tools from different fields within the computer vision community, an alternative path to the selection of key points in face images. The classical way of attempting to solve the face recognition problem using algorithms which encode local information is to localize a predefined set of points in the image, extract features from the regions surrounding those locations, and choose a measure of similarity (or distance) between correspondent features. Our approach, namely shape-driven Gabor jets, aims at selecting an own set of points and features for a given client. After applying a ridges and valleys detector to a face image, characteristic lines are extracted and a set of points is automatically sampled from these lines where Gabor features (jets) are calculated. So each face is depicted by R2 points and their respective jets. Once two sets of points from face images have been extracted, a shape-matching algorithm is used to solve the correspondence problem (i.e., map each point from the first image to a point within the second image) so that the system is able to compare shape-matched jets. As a byproduct of the matching process, geometrical measures are computed and compiled into the final dissimilarity function. Experiments on the AR face database confirm good performance of the method against expression and, mainly, lighting changes. Moreover, results on the XM2VTS and BANCA databases show that our algorithm achieves better performance than implementations of the elastic bunch graph matching approach and other related techniques.


title: Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States

abstract: Establishing up-to-date large scale building maps is essential to understand the urban dynamics, such as estimating population, urban planning, and many other applications. Although many computer vision tasks have been successfully carried out with deep convolutional neural networks, there is a growing need to understand their large scale impact on building mapping with remote sensing imagery. Taking advantage of the scalability of convolutional neural networks (CNNs) and using only few areas with the abundance of building footprints, for the first time we conduct a comparative analysis of four state-of-the-art CNNs for extracting building footprints across the entire continental United States. The four CNN architectures namely: Branch-out CNN, fully convolutional network (FCN), conditional random field as recurrent neural network (CRFasRNN), and SegNet, support semantic pixelwise labeling and focus on capturing textural information at multiscale. We use 1-meter resolution aerial images from National Agriculture Imagery Program as the test-bed, and compare the extraction results across the four methods. In addition, we propose to combine signed-distance labels with SegNet, the preferred CNN architecture identified by our extensive evaluations, to advance building extraction results to instance level. We further demonstrate the usefulness of fusing additional near IR information into the building extraction framework. Large scale experimental evaluations are conducted and reported using metrics that include: Precision, recall rate, intersection over union, and the number of buildings extracted. With the improved CNN model and no requirement of further postprocessing, we have generated building maps for the United States with an average processing time less than one minute for an area of size $\sim {\text{56}}$ $\text{km}^2$ . The quality of extracted buildings and processing time demonstrated that the proposed CNN based framework fits the need of building extraction at scale.


Controls for Assistive Robots


title: CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy

abstract: Marginal tables are the workhorse of capturing the correlations among a set of attributes. We consider the problem of constructing marginal tables given a set of user's multi-dimensional data while satisfying Local Differential Privacy (LDP), a privacy notion that protects individual user's privacy without relying on a trusted third party. Existing works on this problem perform poorly in the high-dimensional setting; even worse, some incur very expensive computational overhead. In this paper, we propose CALM, Consistent Adaptive Local Marginal, that takes advantage of the careful challenge analysis and performs consistently better than existing methods. More importantly, CALM can scale well with large data dimensions and marginal sizes. We conduct extensive experiments on several real world datasets. Experimental results demonstrate the effectiveness and efficiency of CALM over existing methods.


title: Application-managed flash

abstract: In flash storage, an FTL is a complex piece of code that resides completely inside the storage device and is provided by the manufacturer. Its principal virtue is providing interoperability with conventional HDDs. However, this virtue is also its biggest impediment in reaching the full performance of the underlying flash storage. We propose to refactor the flash storage architecture so that it relies on a new block I/O interface which does not permit overwriting of data without intervening erasures. We demonstrate how high-level applications, in particular file systems, can deal with this restriction efficiently by employing append-only segments. This refactoring dramatically reduces flash management overhead and improves performance of applications, such as file systems and databases, by permitting them to directly manage flash storage. Our experiments on a machine with the new block I/O interface show that DRAM in the flash controller is reduced by 128X and the performance of the file system improves by 80% over conventional SSDs.


title: An Intelligent V2I-Based Traffic Management System

abstract: Vehicles equipped with intelligent systems designed to prevent accidents, such as collision warning systems (CWSs) or lane-keeping assistance (LKA), are now on the market. The next step in reducing road accidents is to coordinate such vehicles in advance not only to avoid collisions but to improve traffic flow as well. To this end, vehicle-to-infrastructure (V2I) communications are essential to properly manage traffic situations. This paper describes the AUTOPIA approach toward an intelligent traffic management system based on V2I communications. A fuzzy-based control algorithm that takes into account each vehicle's safe and comfortable distance and speed adjustment for collision avoidance and better traffic flow has been developed. The proposed solution was validated by an IEEE-802.11p-based communications study. The entire system showed good performance in testing in real-world scenarios, first by computer simulation and then with real vehicles.


title: Anaplastology in times of facial transplantation: Still a reasonable treatment option?

abstract: Optimum functional and aesthetic facial reconstruction is still a challenge in patients who suffer from inborn or acquired facial deformity. It is known that functional and aesthetic impairment can result in significant psychosocial strain, leading to the social isolation of patients who are affected by major facial deformities. Microvascular techniques and increasing experience in facial transplantation certainly contribute to better restorative outcomes. However, these technologies also have some drawbacks, limitations and unsolved problems. Extensive facial defects which include several aesthetic units and dentition can be restored by combining dental prostheses and anaplastology, thus providing an adequate functional and aesthetic outcome in selected patients without the drawbacks of major surgical procedures. Referring to some representative patient cases, it is shown how extreme facial disfigurement after oncological surgery can be palliated by combining intraoral dentures with extraoral facial prostheses using individualized treatment and without the need for major reconstructive surgery.


title: Five-factor model of personality and job satisfaction: A meta-analysis.

abstract: This study reports results of a meta-analysis linking traits from the 5-factor model of personality to overall job satisfaction. Using the model as an organizing framework, 334 correlations from 163 independent samples were classified according to the model. The estimated true score correlations with job satisfaction were .29 for Neuroticism, .25 for Extraversion, .02 for Openness to Experience, .17 for Agreeableness, and .26 for Conscientiousness. Results further indicated that only the relations of Neuroticism and Extraversion with job satisfaction generalized across studies. As a set, the Big Five traits had a multiple correlation of .41 with job satisfaction, indicating support for the validity of the dispositional source of job satisfaction when traits are organized according to the 5-factor model. Research on the dispositional source of job satisfaction has had a spotty history in job satisfaction research. The personological basis of job satisfaction was considered in the earliest treatments of job satisfaction. Hoppock (1935), for example, noted a strong correlation between workers’ emotional adjustment and their levels of job satisfaction. Similarly, Fisher and Hanna (1931) concluded that a large part of dissatisfaction resulted from emotional maladjustment. With some noteworthy exceptions (P. C. Smith, 1955; Weitz, 1952), these early considerations of the dispositional source of job satisfaction lay dormant until the 1980s, when a series of provocative studies (Arvey, Bouchard, Segal, & Abra


title: An Implementation of Scoped Memory for Real-Time Java

abstract: This paper presents our experience implementing the memory management extensions in the Real-Time Specification for Java. These extensions are designed to given real-time programmers the control they need to obtain predictable memory system behavior while preserving Java's safe memory model.We describe our implementation of certain dynamic checks required by the Real-Time Java extensions. In particular, we describe how to perform these checks in a way that avoids harmful interactions between the garbage collector and the memory management system. We also found that extensive debugging support was necessary during the development of Real-Time Java programs. We therefore used a static analysis and a dynamic debugging package during the development of our benchmark applications.


title: Information Characteristics and the Structure of Landscapes

abstract: Various techniques for statistical analysis of the structure of fitness landscapes have been proposed. An important feature of these techniques is that they study the ruggedness of landscapes by measuring their correlation characteristics. This paper proposes a new information analysis of fitness landscapes. The underlying idea is to consider a fitness landscape as an ensemble of objects that are related to the fitness of neighboring points. Three information characteristics of the ensemble are defined and studied. They are termed: information content, partial information content, and information stability. The information characteristics of a range of landscapes with known correlation features are analyzed in an attempt to reveal the advantages of the information analysis. We show that the proposed analysis is an appropriate tool for investigating the structure of fitness landscapes.


A Constructive Memory System for Situated Design Agents


title: Detection of road surface damage using mobile robot equipped with 2D laser scanner

abstract: This paper introduces a road surface damage detection using mobile robot. Our research is aimed autonomous sidewalk investigation with mobile robot, for reduce the burden of human workers engaged in road maintenance. A mobile robot moves along the route for investigation and obtain shape information of road surface using 2D laser scanner. From this road surface information, road damage section will be automatically detected. By showing the detection result instead of site investigation by human workers, it expects to reduce the burden of human workers. Road surface have gradual curves and some road damage is small and less than 2 cm. Hence, our method uses random sampling to detect irregularity as road damage. This paper explains the measurement of road surface using mobile robot equipped with 2D laser scanner and the road damage detection method. In this paper, some experimental results also is shown.


title: Compulsivity in obsessive–compulsive disorder and addictions

abstract: Compulsive behaviors are driven by repetitive urges and typically involve the experience of limited voluntary control over these urges, a diminished ability to delay or inhibit these behaviors, and a tendency to perform repetitive acts in a habitual or stereotyped manner. Compulsivity is not only a central characteristic of obsessive-compulsive disorder (OCD) but is also crucial to addiction. Based on this analogy, OCD has been proposed to be part of the concept of behavioral addiction along with other non-drug-related disorders that share compulsivity, such as pathological gambling, skin-picking, trichotillomania and compulsive eating. In this review, we investigate the neurobiological overlap between compulsivity in substance-use disorders, OCD and behavioral addictions as a validation for the construct of compulsivity that could be adopted in the Research Domain Criteria (RDoC). The reviewed data suggest that compulsivity in OCD and addictions is related to impaired reward and punishment processing with attenuated dopamine release in the ventral striatum, negative reinforcement in limbic systems, cognitive and behavioral inflexibility with diminished serotonergic prefrontal control, and habitual responding with imbalances between ventral and dorsal frontostriatal recruitment. Frontostriatal abnormalities of compulsivity are promising targets for neuromodulation and other interventions for OCD and addictions. We conclude that compulsivity encompasses many of the RDoC constructs in a trans-diagnostic fashion with a common brain circuit dysfunction that can help identifying appropriate prevention and treatment targets.


title: Direct torque control of induction machines using space vector modulation

abstract: A direct induction machine torque control method based on predictive, deadbeat control of the torque and flux is presented. By estimating the synchronous speed and the voltage behind the transient reactance, the change in torque and flux over the switching period is calculated. The stator voltage required to cause the torque and flux to be equal to their respective reference values is calculated. Space vector PWM is used to define the inverter switching state. An alternative approach to deadbeat control for use in the transient or pulse-dropping mode is also presented. An alternative modulation scheme is presented in which transient performance is improved by specifying the inverter switching states and then calculating the required switched instants to maintain deadbeat control of the flux while reducing the torque error during the entire switching interval. A similar approach is used for a transient in the flux. The implementation of the control scheme using DSP-based hardware is described, with complete experimental results given. >


title: Linguistic Traces of a Scientific Fraud: The Case of Diederik Stapel

abstract: When scientists report false data, does their writing style reflect their deception? In this study, we investigated the linguistic patterns of fraudulent (N = 24; 170,008 words) and genuine publications (N = 25; 189,705 words) first-authored by social psychologist Diederik Stapel. The analysis revealed that Stapel's fraudulent papers contained linguistic changes in science-related discourse dimensions, including more terms pertaining to methods, investigation, and certainty than his genuine papers. His writing style also matched patterns in other deceptive language, including fewer adjectives in fraudulent publications relative to genuine publications. Using differences in language dimensions we were able to classify Stapel's publications with above chance accuracy. Beyond these discourse dimensions, Stapel included fewer co-authors when reporting fake data than genuine data, although other evidentiary claims (e.g., number of references and experiments) did not differ across the two article types. This research supports recent findings that language cues vary systematically with deception, and that deception can be revealed in fraudulent scientific discourse.


title: Efficacy and Safety of Penile Girth Enhancement by Autologous Fat Injection for Patients with Thin Penises

abstract: Background ::: This study aimed to investigate the efficacy and safety of autologous fat injection (AFI) for penile girth enhancement (PGE) in patients with thin penises.


title: M5AIE a method for body part detection and tracking using RGB-D images

abstract: The automatic detection and tracking of human body parts in color images is highly sensitive to appearance features such as illumination, skin color and clothes. As a result, the use of depth images has been shown to be an attractive alternative over color images due to its invariance to lighting conditions. However, body part detection and tracking is still a challenging problem, mainly because the shape and depth of the imaged body can change depending on the perspective. We present a hybrid approach, called M5AIE, that uses both color and depth information to perform body part detection, tracking and pose classification. We have developed a modified Accumulative Geodesic Extrema (AGEX) approach for detecting body part candidates. We also have used the Affine-SIFT (ASIFT) algorithm for feature extraction, and we have adapted the conventional matching method to perform tracking and labeling of body parts in a sequence of images that has color and depth information. The results produced by our tracking system were used with the C4.5 Gain Ratio Decision Tree, the Naive Bayes and the KNN classification algorithms for the identification of the users pose.


title: Application of a second-order stochastic optimization algorithm for fitting stochastic epidemiological models

abstract: Epidemiological models have tremendous potential to forecast disease burden and quantify the impact of interventions. Detailed models are increasingly popular, however these models tend to be stochastic and very costly to evaluate. Fortunately, readily available high-performance cloud computing now means that these models can be evaluated many times in parallel. Here, we briefly describe PSPO, an extension to Spall's second-order stochastic optimization algorithm, Simultaneous Perturbation Stochastic Approximation (SPSA), that takes full advantage of parallel computing environments. The main focus of this work is on the use of PSPO to maximize the pseudo-likelihood of a stochastic epidemiological model to data from a 1861 measles outbreak in Hagelloch, Germany. Results indicate that PSPO far outperforms gradient ascent and SPSA on this challenging likelihood maximization problem.


title: AutoComment: Mining question and answer sites for automatic comment generation

abstract: Code comments improve software maintainability. To address the comment scarcity issue, we propose a new automatic comment generation approach, which mines comments from a large programming Question and Answer (Q&A) site. Q&A sites allow programmers to post questions and receive solutions, which contain code segments together with their descriptions, referred to as code-description mappings. We develop AutoComment to extract such mappings, and leverage them to generate description comments automatically for similar code segments matched in open-source projects. We apply AutoComment to analyze Java and Android tagged Q&A posts to extract 132,767 code-description mappings, which help AutoComment to generate 102 comments automatically for 23 Java and Android projects. The user study results show that the majority of the participants consider the generated comments accurate, adequate, concise, and useful in helping them understand the code.


Operationalization of the Capability Approach, from Theory to Practice: A Review of Techniques and Empirical Applications


title: A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

abstract: Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloudcomputing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradationwithin an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework forsolving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner. Experiment results using actual Google cluster traces showthat our proposed hierarchical framework significantly savesthe power consumption and energy usage than the baselinewhile achieving no severe latency degradation. Meanwhile, the proposed framework can achieve the best trade-off between latency and power/energy consumption in a server cluster.


title: Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics

abstract: A combined approach of deductive and inductive reasoning can leverage the clear separation between the evolving (streaming) and static parts of online knowledge at conceptual and technological levels. What are the hottest topics discussed on Twitter? Which topics have my close friends discussed in the last hour? Which movie is my friend most likely to watch next? Which Tuscan red wine should I recommend? With many popular social networks publishing microblogs and feeds, the information required to answer these queries is becoming available on the Web.


title: Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

abstract: Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.


Context Representation for Named Entity Linking


title: A Novel Method of Combined Feature Extraction for Recognition

abstract: Multimodal recognition is an emerging technique to overcome the non-robustness of the unimodal recognition in real applications. Canonical correlation analysis (CCA) has been employed as a powerful tool for feature fusion in the realization of such multimodal system. However, CCA is the unsupervised feature extraction and it does not utilize the class information of the samples, resulting in the constraint of the recognition performance. In this paper, the class information is incorporated into the framework of CCA for combined feature extraction, and a novel method of combined feature extraction for multimodal recognition, called discriminative canonical correlation analysis (DCCA), is proposed. The experiments show that DCCA outperforms some related methods of both unimodal recognition and multimodal recognition.


title: The Gamification of Learning and Instruction: Game-Based Methods and Strategies for Training and Education

abstract: Learning professionals are finding success applying game-based sensibilities to the development of instruction. This is the first book to show how to design online instruction that leverages the best elements of online games to increase learning, retention, and application. It explains how to match different game strategies to types of learning content for the right learning outcome and discusses how gamification techniques can be used in a variety of settings to improve learning, retention and application of knowledge. Supported by peer-reviewed studies and examples from corporations who have adopted game-based learning successfully, the book illustrates how combining instructional design thinking with game concepts can create engaged and interactive learning experiences across a variety of media, from online to face-to-face.


title: Authentic leadership and eudaemonic well-being: Understanding leader-follower outcomes

abstract: We sought to examine the concept of authentic leadership and discuss the influences of authenticity and authentic leadership on leader and follower eudaemonic well-being, as well as examine the processes through which these influences are realized. This was accomplished in four ways. First, we provide an ontological definition of authentic leadership, rooted in two distinct yet related philosophical approaches to human well-being: hedonism and eudaemonia. Second, we develop a multi-component model of authentic leadership based on recent theoretical developments in the area of authenticity. The resulting model consists of self-awareness, unbiased processing, authentic behavior/acting and authentic relational orientation. Third, we discuss the personal antecedents (leader characteristics) of authentic leadership as well as the outcomes of authentic leadership for both leaders and followers and examine the processes linking authentic leadership to its antecedents and outcomes. Fourth, we discuss the implications of this work for authentic leadership theory and then provide some practical


title: Robust Control for Robotic Manipulators Base on Adaptive Neural Network

abstract: As robotic manipulators are increasingly applied in industrial production, higher precision control methods are being studied by researchers. But robotic manipulators are a coupled system with a lot of the uncertainties; higher precision is difficult to obtain by traditional control methods. A novel adaptive robust control method based on neural network is proposed by the paper. Neural network controller is designed for adaptive learning and compensate for the unknown system and approach errors as disturbance are eliminated by robust controller. The weight adaptive laws on-line based on Lyapunov theory are designed. Robust controller is proposed based on H ∞ theory. These can assure the stability of the whole system, and L 2 gain also is less than the index value. Simulation studies show that the proposed control strategy is able to achieve higher control precision and has important engineering applications value.


title: Chicken Farm Monitoring System

abstract: In this paper, the chicken farm monitoring system is proposed and developed based on wireless communication unit to transfer data by using the wireless module combined with the sensors that enable to detect temperature, humidity, light and water level values. This system is focused on the collecting, storing, and controlling the information of the chicken farm so that the high quality and quantity of the meal production can be produced. This system is developed to solve several problems in the chicken farm which are many human workers is needed to control the farm, high cost in maintenance, and inaccurate data collected at one point. The proposed methodology really helps in finishing this project within the period given. Based on the research that has been carried out, the system that can monitor and control environment condition (temperature, humidity, and light) has been developed by using the Arduino microcontroller. This system also is able to collect data and operate autonomously.


title: Classify, predict, detect, anticipate and synthesize: Hierarchical recurrent latent variable models for human activity modeling.

abstract: Human activity modeling operates on two levels: high-level action modeling, such as classification, prediction, detection and anticipation, and low-level motion trajectory prediction and synthesis. In this work, we propose a semi-supervised generative latent variable model that addresses both of these levels by modeling continuous observations as well as semantic labels. We extend the model to capture the dependencies between different entities, such as a human and objects, and to represent hierarchical label structure, such as high-level actions and sub-activities. In the experiments we investigate our model's capability to classify, predict, detect and anticipate semantic action and affordance labels and to predict and generate human motion. We train our models on data extracted from depth image streams from the Cornell Activity 120, the UTKinect-Action3D and the Stony Brook University Kinect Interaction Dataset. We observe that our model performs well in all of the tasks and often outperforms task-specific models.


Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection.


title: A new dual-polarized broadband horn antenna

abstract: This letter describes a new design of dual-polarized broadband horn antennas. The bandwidth of the designed horn antenna is from 2 to 26.5 GHz. A novel coaxial line to quadruple-ridged waveguide transition is introduced to improve the return loss performance of the horn antenna. Measured results for VSWR, isolation, gain, and radiation pattern of the designed horn antenna are presented.


title: Social ramifications of autonomous urban land vehicles

abstract: Autonomous vehicle technology may arrive much sooner than most people expect and it has profound implications for transportation. The technology facilitates a rail-less personal rapid transit (PRT) system using both public and private vehicles. Road traffic fatalities and injuries may decline by one to two orders of magnitude. A PRT system can provide mobility to the blind, elders suffering from dementia, children and the intoxicated. The system can make use of existing infrastructure, reduce urban sprawl and ease congestion. Autonomous vehicle based systems can improve fuel efficiency. The technology presents a window of opportunity for a new mode of transportation that obtains efficiencies of up to 0.25 l/100 km (1000 mpg equivalent), reducing U.S. petroleum consumption by up to 16%. The U.S. carbon savings could reach the equivalent of 12 trains of 100 coal cars daily.


title: Money walks: a human-centric study on the economics of personal mobile data

abstract: In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made/received, applications, e.g. time spent on different apps, location and media, e.g. photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data.


title: Self-compassion , Interpersonal Conflict Resolutions , and Well-being

abstract: We examined the role of self-compassion in freshmen students’ goal pursuit and well-being across the first year of university. Multilevel analyses of 1 week of daily diary assessment revealed that individuals high in self-compassion appeared to be less vulnerable to the affective consequences of thwarted goal progress. We also found that trait self-compassion moderated the relation of autonomous goal motivation to negative affect, such that autonomous motivation was especially related to low negative affect for students high in self-compassion. Longitudinally, we found that self-compassion was associated with positive changes in life satisfaction, identity development, and decreases in negative affectivity over the academic year. In summary, we suggest that selfcompassion is an adaptive trait for new college students.


title: Novel Unobtrusive Approach for Sleep Monitoring Using Fiber Optics in an Ambient Assisted Living Platform

abstract: Sleep plays a vital role in a person’s health and well-being. Unfortunately, most people suffering from sleep disorders remain without diagnosis and treatment since the current sleep assessment systems are cumbersome and expensive. As a result, there is an increasing demand for cheaper and more affordable sleep monitoring systems in real-life environments. In this paper, we propose a novel non-intrusive system for sleep quality monitoring using a microbend fiber optic mat placed under the bed mattress. The sleep quality is assessed based on different parameters. Moreover, the sensor has been integrated into an existing Ambient Assisted Living framework to be validated in real scenarios. Three senior female residents participated in our study and the sleep data was collected over a one-month period in a home-living situation. The proposed system shows accurate and consistent results with a survey collected from each participant showing their sleep patterns and other in-home activities.


Modeling the Dynamics of Driver ’ s Dilemma Zone Perception Using Machine Learning Methods for Safer Intersection Control


title: Android Malware Detection Based on Factorization Machine

abstract: With the increasing popularity of Android smart phones in recent years, the number of Android malware is growing rapidly. Due to its great threat and damage to mobile phone users, Android malware detection has become increasingly important in cyber security. Traditional methods like signature-based ones cannot protect users from the ever increasing sophistication and rapid behavior changes of new types of Android malware. Therefore, lots of recent efforts have been made to use machine learning to characterize and discover the malicious behavior patterns of mobile apps for malware detection. In this paper, we propose a novel and highly reliable machine learning algorithm for Android Malware detection based on the use of Factorization Machine and the extensive study of Android app features. We first extract 77 types of features that are highly relevant to malware detection from the manifest file and source code of each mobile app, including Application Programming Interface (API) calls, permissions, etc. We have observed that the numerical feature representation of an app usually forms a long and highly sparse vector, and that the interactions among different features are critical to revealing some malicious behavior patterns. Based on these observations, we propose to use factorization machine, which fit the problem the best, as a supervised classifier for malware detection. According to extensive performance evaluation, our proposed method achieved a test result of 99.01% detection rate with 0.09% false positive rate on the DREBIN dataset and 99.2% detection rate with only 0.93% false positive rate on the AMD dataset, significantly outperformed a number of state-of-the-art machine-learning-based Android malware detection methods as well as commercial antivirus engines.


title: Evaluating search features of Google Knowledge Graph and Bing Satori

abstract: Purpose – The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage of entity types, the extent of their support for list search services and the capabilities of their natural language query interfaces. Design/methodology/approach – The authors manually submitted selected queries to these two semantic web search engines and evaluated the returned results. To test the coverage of entity types, the authors selected the entity types from Freebase database. To test the capabilities of natural language query interfaces, the authors used a manually developed query data set about US geography. Findings – The results indicate that both semantic search engines cover only the very common entity types. In addition, the list search service is provided for a small percentage of entity types. Moreover, both search engines support queries with very limited complexity and with limited set of recognised ...


title: Electronic Commerce Benefits and Adoption Barriers in Small and Medium Enterprises in Gweru, Zimbabwe

abstract: This paper reports on the findings of an exploratory survey research whose main purpose was to explore electronic commerce benefits and adoption barriers in small and medium enterprises (SMEs) in Gweru, Zimbabwe. Using an exploratory research design, a structured survey instrument was developed and administered to thirty SMEs conveniently selected from a database of SMEs in Gweru. The study found that though SMEs in Gweru appear to have adopted electronic commerce related technologies, the usage level of these technologies is still in its infancy, while the predominant use of electronic commerce applications was for communication/informative purposes. Perceptions and attitudes of SMEs towards electronic commerce and its relayed technologies were strongly favourable. The most significant benefits derived from electronic commerce adoption and usage were improved quality of information and communicating with suppliers and customers. Regarding barriers, the technology was deemed too expensive and incompatible with the way SMEs/customers do business. The study concludes that in order to reap significant benefits from electronic commerce, survey SMEs need to adopt sophisticated e-commerce technologies.


title: Software Implementation of Cyclic Abelian Elliptic Curve using Matlab.

abstract: of products and standards that use public-key cryptography for encryption and digital signature use RSA. The key length for secure RSA has increased over recent years ,and this has put heavier processing load on applications using RSA. Recently, a competing system has begun to challenge RSA: Elliptic curve cryptography (ECC).The principle attraction of ECC, compared to RSA, is that it appears to offer equal security for a far smaller key size, thereby reducing processor overhead. Cryptographers are interested only in elliptic curve that belongs to cyclic abelian group. This paper implements cyclic abelian elliptic curve in MATLAB. The properties of abelian group is proved over the coordinates satisfying the curve. Base points of elliptic curve are generated to prove that the elliptic curve belongs to cyclic abelian group.


title: Position assignment on an enterprise level using combinatorial optimization

abstract: We developed a tool to solve a problem of position assignment within the IT Ford College Graduate program. This position assignment tool was first developed in 2012 and has been used successfully since then. The tool has since evolved for use with several other position assignment and related tasks with other similar programs in Ford Motor Company. This paper will describe the creation of this tool and how we have applied it, focusing on the need for developing such a tool, and how the continued development of this tool will benefit its users and the company.


title: Oracle MCG: A first peek into COCO Detection Challenges

abstract: The recently presented COCO detection challenge will most probably be the reference benchmark in object detection in the next years. COCO is two orders of magnitude larger than Pascal and has four times the number of categories; so in all likelihood researchers will be faced with a number of new challenges. At this point, without any finished round of the competition, it is difficult for researchers to put their techniques in context, or in other words, to know how good their results are. In order to give a little context, this note evaluates a hypothetical object detector consisting in an oracle picking the best object proposal from a state-of-the-art technique. This oracle achieves a AP=0.292 in segmented objects and AP=0.317 in bounding boxes, showing that indeed the database is challenging, given that this value is the best one can expect if working on object proposals without refinement.


title: Deep neural networks for syllable based acoustic modeling in Chinese speech recognition

abstract: Recently, the deep neural networks (DNNs) based acoustic modeling methods have been successfully applied to many speech recognition tasks. This paper reports the work about applying DNNs for syllable based acoustic modeling in Chinese automatic speech recognition (ASR). Compared with initial/finals (IFs), syllable can implicitly model the intra-syllable variations in better accuracy. However, the context dependent syllable based modeling set holds too many units, bringing about heavy problems on modeling and decoding implementation. In this paper, a WFST decoding framework is applied. Moreover, the decision tree based state tying and DNNs based models are discussed for the acoustic model training. The experimental results show that compared with the traditional IFs based modeling method, the proposed syllable modeling method using DNNs is more robust for data sparsity problem, which indicates that it has the potential to obtain better performance for Chinese ASR.


title: Travel Q: Questifying Micro Activities Using Travel Photos to Enhance Travel Experience

abstract: Travel is a series of micro activities. With the proliferation of digital cameras and smartphones, it is now common for travelers to record their micro activities in fine detail by taking photos without ever missing a moment. However, how to enhance the travel experience by utilizing such recorded micro activities has not been determined. Thus, we aim to 1) investigate how travelers record micro activities in photos through a user study and 2) design a system to enhance travel experiences utilizing travel photos. Based on the user study, we devise a "questification" strategy that motivates travelers to change their photos into quests. We implement a research prototype, Travel Q, a community platform that allows users to share their micro activities through photos. The results of a field study with the prototype suggest that questification is an applicable strategy for enhancing travel experiences.


Assessing the Business Value of Knowledge Retention Projects: Results of


title: GRAPH/Z: A Key-Value Store Based Scalable Graph Processing System

abstract: The emerging applications in big data and social networks issue rapidly increasing demands on graph processing. Graph query operations that involve a large number of vertices and edges can be tremendously slow on traditional databases. The state-of-the-art graph processing systems and databases usually adopt master/slave architecture that potentially impairs their The contributions of this paper are as follows: scalability. This work describes the design and implementation of a new graph processing system based on Bulk Synchronous Parallel model. Our system is built on top of ZHT, a scalable distributed key-value store, which benefits the graph processing in terms of scalability, performance and persistency. The experiment results imply excellent scalability.


title: Sinkhorn Solves Sudoku

abstract: The Sudoku puzzle is a discrete constraint satisfaction problem, as is the error correction decoding problem. We propose here an algorithm for solution to the Sinkhorn puzzle based on Sinkhorn balancing. Sinkhorn balancing is an algorithm for projecting a matrix onto the space of doubly stochastic matrices. The Sinkhorn balancing solver is capable of solving all but the most difficult puzzles. A proof of convergence is presented, with some information theoretic connections. A random generalization of the Sudoku puzzle is presented, for which the Sinkhorn-based solver is also very effective.


title: Oculomotor responses during partial and total sleep deprivation.

abstract: INTRODUCTION: Oculomotor responses related to the pupil light reflex (PLR) and saccadic velocity may be sensitive to the effects of sleepiness and therefore could be used to evaluate an individual's fitness for duty. METHODS: There were 12 normal subjects who completed an 8-d study. They were allowed 8 h in bed on the first three nights, 4 h in bed on the fourth night, and then were sleep deprived for the following 64 h. Approximately every 3 h, subjects performed a battery of tests which included a 45-s automated oculomotor test and a 40-min PC-based driving simulator task. Sleepiness was evaluated with a self-assessment instrument. Subjects were allowed 10 h of recovery sleep following sleep deprivation. RESULTS: Oculomotor results for nine subjects showed a significant increase in latency to pupil constriction and a significant decrease in saccadic velocity with total, but not partial, sleep deprivation. The most robust changes during sleep deprivation occurred for saccadic velocity. A night of recovery sleep reversed the effects of total sleep deprivation on latency to pupil constriction and saccadic velocity. Subjective sleepiness and off-road accidents were found to significantly increase over the sleep deprivation period. A significant positive correlation between increasing latency to pupil constriction and increasing sleepiness and driving accidents, and a significant negative correlation between decreasing saccadic velocity and increasing sleepiness and driving accidents during sleep deprivation were found. CONCLUSION: These findings suggest that oculomotor functions, particularly saccadic velocity, are feasible for assessing neurophysiological changes associated with and predictive of sleep deprivation-induced operational performance degradation. Language: en


title: A high-power, low-loss W-band SPDT switch using SiGe PIN diodes

abstract: This paper presents a W-band SPDT switch implemented using PIN diodes in a new 90 nm SiGe BiCMOS technology. The SPDT switch achieves a minimum insertion loss of 1.4 dB and an isolation of 22 dB at 95 GHz, with less than 2 dB insertion loss from 77-134 GHz, and greater than 20 dB isolation from 79-129 GHz. The input and output return losses are greater than 10 dB from 73-133 GHz. By reverse biasing the off-state PIN diodes, the P1dB is larger than +24 dBm. To the authors' best knowledge, these results demonstrate the lowest loss and highest power handling capability achieved by a W-band SPDT switch in any silicon-based technology reported to date.


title: People detection in top-view fisheye imaging

abstract: We address the problem of people detection in top-view fisheye imaging. Even within the same top-view fisheye frame, upright people appear slanted in various directions and are distorted in different ways. Due to this variability, standard people detectors are not directly applicable to top-view fisheye frames, and dedicated people detectors for the top-view fisheye domain are hard to design. We extract features in the fisheye frame, unwrap them to a perspectivelike feature map, and forward them to a people detector designed and trained for common perspective images. Extracting the features before unwrapping prevents harmful smoothing of the gradient information. To facilitate feature unwrapping, we employ dense feature extraction and compute the unwrapping Jacobian. Distortion-free unwrapping is known to be impossible. We optimize the unwrapping method for the explicit goal of people detection performance. Applying a tunable fisheye camera model to project the fisheye image plane onto a unit half sphere, followed by the stereographic map projection, we obtain people detection performance similar to the standard perspective case. We complete the solution by introducing a convenient purposive fisheye-camera calibration process, optimized for subsequent people detection performance.


title: Continuous mobile authentication using touchscreen gestures

abstract: Securing the sensitive data stored and accessed from mobile devices makes user authentication a problem of paramount importance. The tension between security and usability renders however the task of user authentication on mobile devices a challenging task. This paper introduces FAST (Fingergestures Authentication System using Touchscreen), a novel touchscreen based authentication approach on mobile devices. Besides extracting touch data from touchscreen equipped smartphones, FAST complements and validates this data using a digital sensor glove that we have built using off-the-shelf components. FAST leverages state-of-the-art classification algorithms to provide transparent and continuous mobile system protection. A notable feature is FAST 's continuous, user transparent post-login authentication. We use touch data collected from 40 users to show that FAST achieves a False Accept Rate (FAR) of 4.66% and False Reject Rate of 0.13% for the continuous post-login user authentication. The low FAR and FRR values indicate that FAST provides excellent post-login access security, without disturbing the honest mobile users.


Adapting word2vec to Named Entity Recognition


title: Context Mover's Distance&Barycenters: Optimal Transport of Contexts for Building Representations

abstract: We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space. This enables us to consider representation learning from the perspective of Optimal Transport and take advantage of its tools such as Wasserstein distance and barycenters. We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance (quantitatively as well as qualitatively) on tasks such as measuring sentence similarity, word entailment and similarity, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec, GenSen). The key benefits of the proposed approach include: (a) capturing uncertainty and polysemy via modeling the entities as distributions, (b) utilizing the underlying geometry of the particular task (with the ground cost), (c) simultaneously providing interpretability with the notion of optimal transport between contexts and (d) easy applicability on top of existing point embedding methods. The code, as well as prebuilt histograms, are available under https://github.com/context-mover/.


title: Recent developments and recommendations for improving harmonic radar tracking systems

abstract: The paper presents some recent developments in harmonic radar tracking systems. These are widely used for monitoring and tracking of low flying insects like honey bees, butterflies, snail and carabid beetles, and come under the category of individual marking techniques and use harmonic range detection or range finding for tracking insects tagged with harmonic transponders. In most cases the transponder is, however, a vertical rod or trailing wire both of which hinder insect movement. This paper presents recommendations for improving the harmonic radar, detectable range and minimizing the weight and size of transponder. It presents externally biased microstrip antenna based prototype harmonic transponder.


title: LC-MS Analysis, 15-Lipoxygenase Inhibition, Cytotoxicity, and Genotoxicity of Dissotis multiflora (Sm) Triana (Melastomataceae) and Paullinia pinnata Linn (Sapindaceae)

abstract: This study aims to evaluate the anti-inflammatory, cytotoxicity, and genotoxicity activities of Dissotis multiflora (Sm) Triana and Paullinia pinnata Linn used traditionally in Cameroon to treat infectious diseases. Phytochemical screening was carried out using the LC-MS procedure. The ferrous oxidation-xylenol orange (FOX) assay was used to determine the 15-lipoxygenase (15-LOX) inhibitory activity of the plant samples. The tetrazolium-based colorimetric (MTT) assay was performed using Vero cells. The Ames test was carried out using Salmonella typhimurium TA98 and TA100 tester strains. LC-MS chromatogram of D. multiflora led to the identification of four known compounds, namely, 5-(3,5-dinitrophenyl)-2H-tetrazol (2), 2,2'-{[2-(6-amino-9H-purine-9-yl)ethyl]imino}diethanol (14), 1,2,5-oxadiazolo [3,4-b]pyrazine, 5,6-di (3,5-dimethyl-1-piperidyl) (19), and nimbolinin D (20) while four compounds were also identified in P. pinnata known as 2-hydroxycarbamoyl-4-methyl-pentanoic acid (2), pheophorbide A (16), 1-[4-({2-[(1-methyl-1H-indol-5-yl)amino]-4-pyrimidinyl}oxy)-1-naphthyl]-3-[1-(4 methylphenyl)-3-(2-methyl-2-propanyl)-1H-pyrazol-5-yl]urea (17), and nimbolinin D (18). D. multiflora and P. pinnata inhibited 15-LOX activity in concentration-dependent manner. The LC50 (concentration that kills 50% of cells) values of the extracts ranged from 0.13 ± 00 to 1 ± 00 mg/mL for P. pinnata and D. multiflora, respectively. P. pinnata was cytotoxic at concentrations tested while D. multiflora was not. The selectivity index (SI) values ranged from 0.16 to 10.30 on Vero cell lines. No genotoxic effect was observed against both strains tested. These extracts are sources of compounds which can be used to control infectious diseases and associated inflammation. However, caution should be taken while using P. pinnata for medicinal purposes.