title: Game AI revisited

abstract: More than a decade after the early research efforts on the use of artificial intelligence (AI) in computer games and the establishment of a new AI domain the term ``game AI'' needs to be redefined. Traditionally, the tasks associated with game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation and pathfinding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial productions has been mainly due to the lack of constructive communication between academia and industry in the early days of academic game AI, and the inability of academic game AI to propose methods that would significantly advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI tradition. A number of those alternative AI uses have already shown a significant potential for the design of better games. This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI field and evidently put the game AI term under a new perspective. These game AI flagship research areas include the computational modeling of player experience, the procedural generation of content, the mining of player data on massive-scale and the alternative AI research foci for enhancing NPC capabilities.


title: Predicting Online Purchase Conversion for Retargeting

abstract: Generally 2% of shoppers make a purchase on the first visit to an online store while the other 98% enjoys only window-shopping. To bring people back to the store and close the deal, "retargeting" has been a vital online advertising strategy that leads to "conversion" of window-shoppers into buyers. As such retargeting is more effective as a focused tool, in this paper, we study the problem of identifying a conversion rate for a given product and its current customers, which is an important analytics metric for retargeting process. Compared to existing approaches using either of customer- or product-level conversion pattern, we propose a joint modeling of both level patterns based on the well-studied buying decision process. To evaluate the effectiveness of our method, we perform extensive experiments on the simulated dataset generated based on a set of real-world web logs. The evaluation results show that conversion predictions by our approach are consistently more accurate and robust than those by existing baselines in dynamic market environment.


title: Generative Visual Manipulation on the Natural Image Manifold

abstract: Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result. Unless the user has considerable artistic skill, it is easy to “fall off” the manifold of natural images while editing. In this paper, we propose to learn the natural image manifold directly from data using a generative adversarial neural network. We then define a class of image editing operations, and constrain their output to lie on that learned manifold at all times. The model automatically adjusts the output keeping all edits as realistic as possible. All our manipulations are expressed in terms of constrained optimization and are applied in near-real time. We evaluate our algorithm on the task of realistic photo manipulation of shape and color. The presented method can further be used for changing one image to look like the other, as well as generating novel imagery from scratch based on user’s scribbles.


title: Attention-Based Super Resolution from Videos

abstract: A video from a moving camera produces different number of observations of different scene areas. We can construct an attention map of the scene by bringing the frames to a common reference and counting the number of frames that observed each scene point. Different representations can be constructed from this. The base of the attention map gives the scene mosaic. Super-resolved images of parts of the scene can be obtained using a subset of observations or video frames. We can combine mosaicing with super-resolution by using all observations, but the magnification factor will vary across the scene based on the attention received. The height of the attention map indicates the amount of super-resolution for that scene point. We modify the traditional super-resolution framework to generate a varying resolution image for panning cameras in this paper. The varying resolution image uses all useful data available in a video. We introduce the concept of attention-based super-resolution and give the modified framework for it. We also show its applicability on a few indoor and outdoor videos.


title: Defending Web Servers with Feints, Distraction and Obfuscation

abstract: In this paper we investigate deceptive defense strategies for web servers. Web servers are widely exploited resources in the modern cyber threat landscape. Often these servers are exposed in the Internet and accessible for a broad range of valid as well as malicious users. Common security strategies like firewalls are not sufficient to protect web servers. Deception based Information Security enables a large set of counter measures to decrease the efficiency of intrusions. In this work we depict several techniques out of the reconnaissance process of an attacker. We match these with deceptive counter measures. All proposed measures are implemented in an experimental web server with deceptive counter measure abilities. We also conducted an experiment with honeytokens and evaluated delay strategies against automated scanner tools.


title: Decentralized Energy Management for Networked Microgrids in Future Distribution Systems

abstract: An increasing amount of distributed energy resources is being integrated into both distribution systems (DSs) and networked microgrids (MGs). The coordination among DSs and MGs becomes essential for DS operators and MG operators considering the high uncertainties of renewable energies and load demands. In this paper, we propose a decentralized energy management framework to coordinate the power exchange between DS and MGs based on the alternating direction method of multipliers algorithm in a fully decentralized fashion. The energy management model in each entity (DSs or MGs) is formulated using two-stage robust optimization to address the uncertainties of renewables and load demands. The problem is treated as a second order cone programming one based on a relaxed distflow model. Moreover, the robust model is solved by column and constraint generation algorithm, where cutting planes are introduced to ensure the exactness of second-order cone relaxation. Numerical results on a modified IEEE 33-bus system with three MGs validate the effectiveness of the proposed method.


title: Collaborative exploratory search for information filtering and large‐scale information triage

abstract: Modern information seekers face dynamic streams of large-scale heterogeneous data that are both intimidating and overwhelming. They need a strategy to filter this barrage of massive data sets, and to find all of the information responding to their information needs, despite the pressures imposed by schedules and budgets. In this applied research, we present an exploratory search strategy that allows professional information seekers to efficiently and effectively triage all of the data. We demonstrate that exploratory search is particularly useful for information filtering and large-scale information triage, regardless of the language of the data, and regardless of the particular industry, whether finance, medical, business, government, information technology, news, or legal. Our strategy reduces a dauntingly large volume of information into a manageable, high-precision data set, suitable for focused reading. This strategy is interdisciplinary, integrating concepts from information filtering, information triage, and exploratory search. Key aspects include advanced search software, interdisciplinary paired search, asynchronous collaborative search, attention to linguistic phenomena, and aggregated search results in the form of a search matrix or search grid. We present the positive results of a task-oriented evaluation in a real-world setting, discuss these results from a qualitative perspective, and share future research areas.


title: Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

abstract: Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes-such as transcriptional profiles-at scale. Here, we develop Perturb-seq, combining single-cell RNA sequencing (RNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbations to perform many such assays in a pool. We demonstrate Perturb-seq by analyzing 200,000 cells in immune cells and cell lines, focusing on transcription factors regulating the response of dendritic cells to lipopolysaccharide (LPS). Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions. We posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation. By decomposing many high content measurements into the effects of perturbations, their interactions, and diverse cell metadata, Perturb-seq dramatically increases the scope of pooled genomic assays.


title: Completely Distributed Power Allocation using Deep Neural Network for Device to Device communication Underlaying LTE

abstract: Device to device (D2D) communication underlaying LTE can be used to distribute traffic loads of eNBs. However, a conventional D2D link is controlled by an eNB, and it still remains burdens to the eNB. We propose a completely distributed power allocation method for D2D communication underlaying LTE using deep learning. In the proposed scheme, a D2D transmitter can decide the transmit power without any help from other nodes, such as an eNB or another D2D device. Also, the power set, which is delivered from each D2D node independently, can optimize the overall cell throughput. We suggest a distirbuted deep learning architecture in which the devices are trained as a group, but operate independently. The deep learning can optimize total cell throughput while keeping constraints such as interference to eNB. The proposed scheme, which is implemented model using Tensorflow, can provide same throughput with the conventional method even it operates completely on distributed manner.


title: Average current-mode control for a boost converter using an 8-bit microcontroller

abstract: This paper presents hybrid average current-mode control (ACMC) for a boost converter using an 8-bit microcontroller. In order to demonstrate the feasibility of a hybrid ACMC DC-DC converter, an ACMC boost converter operating in the continuous conduction mode has been designed using a PIC16C782 microcontroller. System modeling, main design procedures, as well as some hardware and software implementation issues are discussed. Experimental results are presented, and encouragingly demonstrate the performance that a hybrid ACMC DC-DC converter can achieve. A pure analog controller using a UC3K86 analog control chip from Texas Instruments has also been implemented for comparison.


title: On the Effects of Batch and Weight Normalization in Generative Adversarial Networks

abstract: Generative adversarial networks (GANs) are highly effective unsupervised learning frameworks that can generate very sharp data, even for data such as images with complex, highly multimodal distributions. However GANs are known to be very hard to train, suffering from problems such as mode collapse and disturbing visual artifacts. Batch normalization (BN) techniques have been introduced to address the training problem. However, though BN accelerates training in the beginning, our experiments show that the use of BN can be unstable and negatively impact the quality of the trained model. The evaluation of BN and numerous other recent schemes for improving GAN training is hindered by the lack of an effective objective quality measure for GAN models. To address these issues, we first introduce a weight normalization (WN) approach for GAN training that significantly improves the stability, efficiency and the quality of the generated samples. To allow a methodical evaluation, we introduce a new objective measure based on a squared Euclidean reconstruction error metric, to assess training performance in terms of speed, stability, and quality of generated samples. Our experiments indicate that training using WN is generally superior to BN for GANs. We provide statistical evidence for commonly used datasets (CelebA, LSUN, and CIFAR-10), that WN achieves 10% lower mean squared loss for reconstruction and significantly better qualitative results than BN.


Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola


title: Detecting Mastication: A Wearable Approach

abstract: We explore using the Outer Ear Interface (OEI) to recognize eating activities. OEI contains a 3D gyroscope and a set of proximity sensors encapsulated in an off-the-shelf earpiece to monitor jaw movement by measuring ear canal deformation. In a laboratory setting with 20 participants, OEI could distinguish eating from other activities, such as walking, talking, and silently reading, with over 90% accuracy (user independent). In a second study, six subjects wore the system for 6 hours each while performing their normal daily activities. OEI correctly classified five minute segments of time as eating or non-eating with 93% accuracy (user dependent).


title: OPEN ACCESS Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background

abstract: In the present study k-Nearest Neighbor classification method, have been studied for economic forecasting. Due to the effects of companies’ financial distress on stakeholders, financial distress prediction models have been one of the most attractive areas in financial research. In recent years, after the global financial crisis, the number of bankrupt companies has risen. Since companies' financial distress is the first stage of bankruptcy, using financial ratios for predicting financial distress have attracted too much attention of the academics as well as economic and financial institutions. Although in recent years studies on predicting companies’ financial distress in Iran have been increased, most efforts have exploited traditional statistical methods; and just a few studies have used nonparametric methods. Recent studies demonstrate this method is more capable than other methods.


Natural Language Access to Data : It Takes Common Sense !


title: Hybrid Routing Approach for Heterogeneous Wireless Sensor Networks Using Fuzzy Logic Technique

abstract: Wireless Sensor Networks are composed of thousands of sensor nodes, with restricted energy that cooperates to accomplish a sensing task. The homogeneous sensor nodes have same capacity in term of sensing, computation, communication, and power. The different application leads different types of sensor network in real life so the heterogeneous sensors exist. This leads to the researcher to develop the new concept for data acquisition. The Region-based Clustering Approach is used for deployment of different types of nodes in different regions according to their energy level to provide efficient utilization of total coverage area. The proposed Hybrid Routing Protocol is used for some nodes to transmit data directly to base station, while some uses clustering technique to send data to base station. The proposed protocol uses the fuzzy level information for cluster head selection to minimize the selection process time and re-election process of cluster head. This reduces the overall consumption of energy and improves the lifetime of the network.


title: Probabilistic Principal Component Analysis

abstract: Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA.


title: Bursty and hierarchical structure in streams

abstract: A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise—that the appearance of a topic in a document stream is signaled by a “burst of activity,” with certain features rising sharply in frequency as the topic emerges. ::: ::: The goal of the present work is to develop a formal approach for modeling such “bursts,” in such a way that they can be robustly and efficiently identified, and can provide an organizational framework for analyzing the underlying content. The approach is based on modeling the stream using an infinite-state automaton, in which bursts appear naturally as state transitionss it can be viewed as drawing an analogy with models from queueing theory for bursty network traffic. The resulting algorithms are highly efficient, and yield a nested representation of the set of bursts that imposes a hierarchical structure on the overall stream. Experiments with e-mail and research paper archives suggest that the resulting structures have a natural meaning in terms of the content that gave rise to them.


title: A Multi-Stage Strategy to Perspective Rectification for Mobile Phone Camera-Based Document Images

abstract: Document images captured by a mobile phone camera often have perspective distortions. Efficiency and accuracy are two important issues in designing a rectification system for such perspective documents. In this paper, we propose a new perspective rectification system based on vanishing point detection. This system achieves both the desired efficiency and accuracy using a multi-stage strategy: at the first stage, document boundaries and straight lines are used to compute vanishing points; at the second stage, text baselines and block aligns are utilized; and at the last stage, character tilt orientations are voted for the vertical vanishing point. A profit function is introduced to evaluate the reliability of detected vanishing points at each stage. If vanishing points at one stage are reliable, then rectification is ended at that stage. Otherwise, our method continues to seek more reliable vanishing points in the next stage. We have tested this method with more than 400 images including paper documents, signboards and posters. The image acceptance rate is more than 98.5% with an average speed of only about 60 ms.


UNDERSTANDING OF SUPPLY CHAIN : A LITERATURE REVIEW


title: Real-time multiply recursive reflections and refractions using hybrid rendering

abstract: We present a new method for real-time rendering of multiple recursions of reflections and refractions. The method uses the strengths of real-time ray tracing for objects close to the camera, by storing them in a per-frame constructed bounding volume hierarchy (BVH). For objects further from the camera, rasterization is used to create G-buffers which store an image-based representation of the scene outside the near objects. Rays that exit the BVH continue tracing in the G-buffers' perspective space using ray marching, and can even be reflected back into the BVH. Our hybrid renderer is to our knowledge the first method to merge real-time ray tracing techniques with image-based rendering to achieve smooth transitions from accurately ray-traced foreground objects to image-based representations in the background. We are able to achieve more complex reflections and refractions than existing screen space techniques, and offer reflections by off-screen objects. Our results demonstrate that our algorithm is capable of rendering multiple bounce reflections and refractions, for scenes with millions of triangles, at 720p resolution and above 30 FPS.


title: A Focused Dynamic Attention Model for Visual Question Answering

abstract: Visual Question and Answering (VQA) problems are attracting increasing interest from multiple research disciplines. Solving VQA problems requires techniques from both computer vision for understanding the visual contents of a presented image or video, as well as the ones from natural language processing for understanding semantics of the question and generating the answers. Regarding visual content modeling, most of existing VQA methods adopt the strategy of extracting global features from the image or video, which inevitably fails in capturing fine-grained information such as spatial configuration of multiple objects. Extracting features from auto-generated regions -- as some region-based image recognition methods do -- cannot essentially address this problem and may introduce some overwhelming irrelevant features with the question. In this work, we propose a novel Focused Dynamic Attention (FDA) model to provide better aligned image content representation with proposed questions. Being aware of the key words in the question, FDA employs off-the-shelf object detector to identify important regions and fuse the information from the regions and global features via an LSTM unit. Such question-driven representations are then combined with question representation and fed into a reasoning unit for generating the answers. Extensive evaluation on a large-scale benchmark dataset, VQA, clearly demonstrate the superior performance of FDA over well-established baselines.


Perceptual and Conceptual Fluency as Antecedents of the Mere Exposure Effect


title: Management of a giant ovarian cyst by keyless abdominal rope-lifting surgery (KARS)

abstract: Ovarian cysts over 5 and 15 cm in diameter are described as large and giant, respectively3. In addition, women having large cysts without regression in 6-8 weeks time are candidates for surgery. Although data has been published on laparoscopic or laparoscopy assisted management of large and giant cysts, midline laparotomy is still preferred by many surgeons, particularly in cases of giant cysts.


Nexus : A GPU Cluster for Accelerating Neural Networks for Video Analysis


title: Building a Reproducible Machine Learning Pipeline

abstract: Reproducibility of modeling is a problem that exists for any machine learning practitioner, whether in industry or academia. The consequences of an irreproducible model can include significant financial costs, lost time, and even loss of personal reputation (if results prove unable to be replicated). This paper will first discuss the problems we have encountered while building a variety of machine learning models, and subsequently describe the framework we built to tackle the problem of model reproducibility. The framework is comprised of four main components (data, feature, scoring, and evaluation layers), which are themselves comprised of well defined transformations. This enables us to not only exactly replicate a model, but also to reuse the transformations across different models. As a result, the platform has dramatically increased the speed of both offline and online experimentation while also ensuring model reproducibility.


title: Investigating Enactive Learning for Autonomous Intelligent Agents

abstract: The enactive approach to cognition is typically proposed as a viable alternative to traditional cognitive science. Enactive cognition displaces the explanatory focus from the internal representations of the agent to the direct sensorimotor interaction with its environment. In this paper, we investigate enactive learning through means of artificial agent simulations. We compare the performances of the enactive agent to an agent operating on classical reinforcement learning in foraging tasks within maze environments. The characteristics of the agents are analysed in terms of the accessibility of the environmental states, goals, and exploration/exploitation tradeoffs. We confirm that the enactive agent can successfully interact with its environment and learn to avoid unfavourable interactions using intrinsically defined goals. The performance of the enactive agent is shown to be limited by the number of affordable actions.


Track-RNN : Joint Detection and Tracking Using Recurrent Neural Networks


title: Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology

abstract: Mobile payment is receiving growing attention globally, from consumers to merchants, as an alternative to using cash, check, or credit cards. The potential of this technology is enormous. This study aims to identify the main determinants of mobile payment adoption and the intention to recommend this technology. We advance the body of knowledge on this subject by proposing an innovative research model that combines the strengths of two well-known theories; the extended unified theory of acceptance and use of technology (UTAUT2) with the innovation characteristics of the diffusion of innovations (DOI), with perceived security and intention to recommend the technology constructs. The research model was empirically tested using 301 responses from an online survey conducted in a European country, Portugal. Data was analyzed using the structured equation modeling (SEM). We found compatibility, perceived technology security, performance expectations, innovativeness, and social influence to have significant direct and indirect effects over the adoption of mobile payment and the intention to recommend this technology. The relevance of customer's intention to recommend mobile payment technology in social networks and other means of communication was also confirmed, supporting the recommendation to include it in social marketing campaigns and in future technology adoption studies. For researchers this study provides a basis for further refinement of individual models of acceptance. For practitioners, understanding the key constructs is crucial to design, refine, and implement mobile payment services, applications, and products that achieve high consumer acceptance, value, and high rates of positive recommendations in social networks.


title: Congestion Control in Linux TCP

abstract: The TCP protocol is used by the majority of the network applications on the Internet. TCP performance is strongly influenced by its congestion control algorithms that limit the amount of transmitted traffic based on the estimated network capacity and utilization. Because the freely available Linux operating system has gained popularity especially in the network servers, its TCP implementation affects many of the network interactions carried out today. We describe the fundamentals of the Linux TCP design, concentrating on the congestion control algorithms. The Linux TCP implementation supports SACK, TCP timestamps, Explicit Congestion Notification, and techniques to undo congestion window adjustments after incorrect congestion notifications. In addition to features specified by IETF, Linux has implementation details beyond the specifications aimed to further improve its performance. We discuss these, and finally show the performance effects of Quick acknowledgements, Rate-halving, and the algorithms for correcting incorrect congestion window adjustments by comparing the performance of Linux TCP implementing these features to the performance achieved with an implementation that does not use the algorithms in question.


title: An automated recirculation aquaculture system based on fuzzy logic control for aquaculture production of tilapia (Oreochromis niloticus)

abstract: The development of an automated recirculation aquaculture system (ARAS) is presented here. The production system facility was built in a 672-m2 polyethylene greenhouse using four 20-m3 tanks, each of which was stocked with 1,000 tilapia (O. niloticus). The growth of the fish was evaluated from a starting weight of 2 g to a final average weight of 418 g, during a production cycle from July 2008 to January 2009. Our objective was to develop new technological approaches, based on fuzzy logic control, to integrate into novel automated equipment for recirculation aquaculture systems used for fish production under intensive conditions. This technique allows farmers to incorporate their knowledge and experience into the control system. The results demonstrated a substantial water savings of 97.42%. There was a total fish biomass production of 1,655.3 kg and an average feed conversion rate of 1.55. The environmental variables were kept within acceptable ranges for tilapia production. We conclude that it is possible to increase the productivity and profitability of aquaculture systems, by applying new approaches based on modern control techniques, such as fuzzy logic control, that promote resource optimization.


title: Multi-Criteria Decision Making: An Operations Research Approach

abstract: The core of operations research is the development of approaches for optimal decision making. A prominent class of such problems is multi-criteria decision making (MCDM). The typical MCDM problem deals with the evaluation of a set of alternatives in terms of a set of decision criteria. This paper provides a comprehensive survey of some methods for eliciting data for MCDM problems and also for processing such data.


title: Cheese: tracking mouse movement activity on websites, a tool for user modeling

abstract: Conventional web interfaces respond to and consider only mouse clicks when defining a user model. We have extended this and take into account all mouse movements on a page as an additional layer of information for inferring user interest. We have developed a straightforward way to record all mouse movements on a page, and conducted a user study to analyze and investigate mouse behavior trends. We found certain mouse behaviors, common across many users, which are useful for content providers in increasing the effectiveness of their interface design.


title: Automatic Question Generation Using Software Agents for Technical Institutions

abstract: In the attempt of producing quality graduates, a major factor that needs a considerable amount of attention is the institutions evaluation system. Now, in order to produce quality result their examination system must be very effective, questions must be able to assess students in every domain. Preparation of question paper with high standard that really kindles the student’s thinking ability is very challengeable task that need to be performed by the academicians. There arises a need for automatic question generation. For all the existing automatic question generating systems, the problem lies either in eliminating the user’s role or in developing factual questions based on Bloom’s taxonomy. To overcome these issues, in the proposed system, the focus is to take input in form of a text file from user which contains of the text upon which the user desires to fetch questions; the output is produced in form of a text file containing questions based on Bloom’s taxonomy. The entire process is carried out by software agents, which eliminates the major problems of existing systems.


title: A survey of aesthetics-driven image recomposition

abstract: The advancement of digital photography and the popularity of photo sharing social media such as Instagram and Facebook have undoubtedly stimulated growing interest in aesthetics quality improvement. One aspect of photography that contributes to high quality photos is image composition; the spatial arrangement of photo subjects in the image frame. Professional photographers often apply a wealth of photographic composition rules, e.g., rule of thirds, visual balance and simplicity to capture compelling photos. In the recent years, aesthetics-driven recomposition that attempts to computationally modify the composition of an image to mimic a professional photo has started to receive considerable research interest. Researchers have proposed numerous recomposition techniques that utilize a single or a combination of multiple image operators, i.e., cropping, warping and patch rearrangement operators, to modify the composition of an image. In this paper, we present a survey on the state-of-the-arts aesthetic-driven image recomposition. We define the image recomposition problem, outline its objectives, and provide a comprehensive review of the existing image recompositoin techniques, together with a detailed analysis of the effectiveness of each technique in achieving the recomposition objectives. This survey is intended as a good reference for researchers interested in image recomposition.


title: FSNet: An Identity-Aware Generative Model for Image-based Face Swapping

abstract: This paper presents FSNet, a deep generative model for image-based face swapping. Traditionally, face-swapping methods are based on three-dimensional morphable models (3DMMs), and facial textures are replaced between the estimated three-dimensional (3D) geometries in two images of different individuals. However, the estimation of 3D geometries along with different lighting conditions using 3DMMs is still a difficult task. We herein represent the face region with a latent variable that is assigned with the proposed deep neural network (DNN) instead of facial textures. The proposed DNN synthesizes a face-swapped image using the latent variable of the face region and another image of the non-face region. The proposed method is not required to fit to the 3DMM; additionally, it performs face swapping only by feeding two face images to the proposed network. Consequently, our DNN-based face swapping performs better than previous approaches for challenging inputs with different face orientations and lighting conditions. Through several experiments, we demonstrated that the proposed method performs face swapping in a more stable manner than the state-of-the-art method, and that its results are compatible with the method thereof.


title: Localization Based on Social Big Data Analysis in the Vehicular Networks

abstract: Location-based services, especially for vehicular localization, are an indispensable component of most technologies and applications related to the vehicular networks. However, because of the randomness of the vehicle movement and the complexity of a driving environment, attempts to develop an effective localization solution face certain difficulties. In this paper, an overlapping and hierarchical social clustering model (OHSC) is first designed to classify the vehicles into different social clusters by exploring the social relationship between them. By using the results of the OHSC model, we propose a social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks. The experiment results validate the performance of the OHSC model and show that the presented SBL algorithm demonstrates superior localization performance compared with the existing methods.


title: Development of GaN HEMT based High Power High Efficiency Distributed Power Amplifier for Military Applications

abstract: Implementing wide bandgap GaN HEMT device into broadband distributed power amplifier creates a tremendous opportunity for RF designers to develop high power high efficiency very broadband power amplifiers for military applications. Several prototypes of 10-40 W GaN based distributed power amplifiers, including MMIC distributed PA, are currently under the development at Rockwell Collins, Inc. In this paper, we will discuss the results of a 10 W distributed power amplifier with the maximum power output of more than 40 dBm and a power-added efficiency of 30-70% over the bandwidth of 20-2000 MHz.


title: The Influence of Perceived Belonging on Social Network Site Adoption

abstract: Adoption research on Social Network Sites (SNSs) indicates that all three popular Technology Acceptance Model (TAM) constructs, Perceived Ease of Use, Perceived Enjoyment, and Perceived Usefulness, influence the Behavioral Intention to Use SNSs. In contrast, little is known about the specific antecedents of Perceived Enjoyment and Perceived Usefulness in an SNS context. We address this gap by studying whether Perceived Belonging, which we describe as the degree to which a person feels connected to and accepted by other individuals, has an influence on these two constructs. After surveying 415 students and applying a structural equation modeling approach, we confirm that Perceived Belonging positively influences both Perceived Enjoyment and Perceived Usefulness and, hence, also indirectly influences overall SNS adoption behavior.


title: An Optimal Vector-Network-Analyzer Calibration Algorithm

abstract: We present an iterative algorithm for calibrating vector network analyzers based on orthogonal distance regression. The algorithm features a robust, yet efficient, search algorithm, an error analysis that includes both random and systematic errors, a full covariance matrix relating calibration and measurement errors, 95% coverage factors, and an easy-to-use user interface that supports a wide variety of calibration standards. We also discuss evidence that the algorithm outperforms the MultiCal software package in the presence of measurement errors and accurately estimates the uncertainty of its results.


title: A hand gesture interface device

abstract: This paper reports on the development of a hand to machine interface device that provides real-time gesture, position and orientation information. The key element is a glove and the device as a whole incorporates a collection of technologies. Analog flex sensors on the glove measure finger bending. Hand position and orientation are measured either by ultrasonics, providing five degrees of freedom, or magnetic flux sensors, which provide six degrees of freedom. Piezoceramic benders provide the wearer of the glove with tactile feedback. These sensors are mounted on the light-weight glove and connected to the driving hardware via a small cable. Applications of the glove and its component technologies include its use in conjunction with a host computer which drives a real-time 3-dimensional model of the hand allowing the glove wearer to manipulate computer-generated objects as if they were real, interpretation of finger-spelling, evaluation of hand impairment in addition to providing an interface to a visual programming language.


title: Dynamic Brain Sources of Visual Evoked Responses

abstract: It has been long debated whether averaged electrical responses recorded from the scalp result from stimulus-evoked brain events or stimulus-induced changes in ongoing brain dynamics. In a human visual selective attention task, we show that nontarget event-related potentials were mainly generated by partial stimulus-induced phase resetting of multiple electroencephalographic processes. Independent component analysis applied to the single-trial data identified at least eight classes of contributing components, including those producing central and lateral posterior alpha, left and right mu, and frontal midline theta rhythms. Scalp topographies of these components were consistent with their generation in compact cortical domains.


title: Bangla news classification using naive Bayes classifier

abstract: Web is gigantic and being constantly update. Bangla news in web are rapidly grown in the era of information age where each news site has its own different layout and categorization for grouping news. These heterogeneity of layout and categorization can not always satisfy individual user's need. Removing these heterogeneity and classifying the news articles according to user preference is a formidable task. In this paper, we propose an approach that provides a user to find out news articles which are related to a specific classification. We use our own developed web crawler to extract useful text from HTML pages of news article contents to construct a Full-Text-RSS. Each news article contents is tokenized with a modified light-weight Bangla Stemmer. In order to achieve better classification result, we remove the less significant words i.e. stop - word from the document. We apply the naive Bayes classifier for classification of Bangla news article contents based on news code of IPTC. Our experimental result shows the effectiveness of our classification system.


title: Statistical fraud detection: A review

abstract: Fraud is increasing dramatically with the expansion of modem technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are essential if we are to catch fraudsters once fraud prevention has failed. Statistics and machine learning provide effective technologies for fraud detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, telecommunications fraud and computer intrusion, to name but a few. We describe the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used.


Human Motion Reconstruction from Action Video Data Using a 3-Layer-LSTM


title: State-of-the-Art and Open Challenges in RTS Game-AI and Starcraft

abstract: This paper presents a review of artificial intelligence for different approaches used in real-time strategy games. Real-time strategy (RTS) based games are quick combat games in which the objective is to dominate and destroy the opposing enemy such as Rome-total war, Starcraft, the age of empires, and command & conquer, etc. In such games, each player needs to utilize resources efficiently, which includes managing different types of soldiers, units, equipment’s, economic status, positions and the uncertainty during the combat in real time. Now the best human players face difficulty in defeating the best RTS games due to the recent success and advancement of deep mind technologies. In this paper, we explain state-of-the-art and challenges in artificial intelligence (AI) for RTS games and Starcraft, describing problems and issues carried out by RTS based games with some solutions that are addressed to them. Finally, we conclude by emphasizing on game ‘CIG & AIIDE’ competitions along with open research problems and questions in the context of RTS Game-AI, where some of the problems and challenges are mostly considered improved and solved but yet some are open for further research.


title: Visual Analytics for MOOC Data

abstract: With the rise of massive open online courses (MOOCs), tens of millions of learners can now enroll in more than 1,000 courses via MOOC platforms such as Coursera and edX. As a result, a huge amount of data has been collected. Compared with traditional education records, the data from MOOCs has much finer granularity and also contains new pieces of information. It is the first time in history that such comprehensive data related to learning behavior has become available for analysis. What roles can visual analytics play in this MOOC movement? The authors survey the current practice and argue that MOOCs provide an opportunity for visualization researchers and that visual analytics systems for MOOCs can benefit a range of end users such as course instructors, education researchers, students, university administrators, and MOOC providers.


title: Employee Surveys Administered Online Attitudes Toward the Medium, Nonresponse, and Data Representativeness

abstract: This field study of military and civilian workers offers a multimethod approach for studying nonrespondents while investigating (a) how employees feel about taking surveys online, (b) whether dissatisfaction with Web-based survey media discourages response, and (c) the representativeness of attitudinal data produced by workers who opt to complete an online climate survey. Results suggested that employees were not as comfortable with Web-based surveys as suggested in previous research. Moreover, issues pertaining to the online medium discouraged workers from completing the Web-based climate survey. Additional factors driving active and passive nonresponse were also uncovered. Overall, those who did and did not complete the survey held similar views of organizational climate. Results are discussed in terms of the factors driving nonresponse bias.


title: Semantic annotation and reasoning for sensor data

abstract: Developments in (wireless) sensor and actuator networks and the capabilities to manufacture low cost and energy efficient networked embedded devices have lead to considerable interest in adding real world sense to the Internet and the Web. Recent work has raised the idea towards combining the Internet of Things (i.e. real world resources) with semantic Web technologies to design future service and applications for the Web. In this paper we focus on the current developments and discussions on designing Semantic Sensor Web, particularly, we advocate the idea of semantic annotation with the existing authoritative data published on the semantic Web. Through illustrative examples, we demonstrate how rule-based reasoning can be performed over the sensor observation and measurement data and linked data to derive additional or approximate knowledge. Furthermore, we discuss the association between sensor data, the semantic Web, and the social Web which enable construction of context-aware applications and services, and contribute to construction of a networked knowledge framework.


title: A vulnerability in the UMTS and LTE authentication and key agreement protocols

abstract: We report on a deficiency in the specifications of the Authentication and Key Agreement (AKA) protocols of the Universal Mobile Telecommunications System (UMTS) and Long-Term Evolution (LTE) as well as the specification of the GSM Subscriber Identity Authentication protocol, which are all maintained by the 3rd Generation Partnership Program (3GPP), an international consortium of telecommunications standards bodies. The flaw, although found using the computational prover CryptoVerif, is of symbolic nature and could be exploited by both an outside and an inside attacker in order to violate entity authentication properties. An inside attacker may impersonate an honest user during a run of the protocol and apply the session key to use subsequent wireless services on behalf of the honest user.


title: A Performance and Energy Evaluation of OpenCL-accelerated Molecular Docking

abstract: Molecular Docking is a methodology used extensively in modern drug design. It aims to predict the binding position of two molecules by calculating the energy of their possible binding poses. One of the most cited docking tools is AutoDock. At its core, it solves an optimization problem by generating a large solution space of possible poses, and searches among them for the one having the lowest energy. These complex algorithms thus benefit from parallelization based run-time acceleration. This work presents an OpenCL implementation of AutoDock, and a corresponding performance evaluation on two different platforms based on multi-core CPU and GPU accelerators. It shows that OpenCL allows highly efficient docking simulations, achieving speedups of ~4x and ~56x over the original serial AutoDock version, as well as energy efficiency gains of ~2x and ~6x. respectively. To the best of our knowledge, this work is the first one also considering the energy efficiency of molecular docking programs.


Recognition and treatment of ju venile-onset spondyloarthritis


title: Stability of Omega-3 LC-PUFA-rich Photoautotrophic Microalgal Oils Compared to Commercially Available Omega-3 LC-PUFA Oils

abstract: Microalgae are the primary producers of omega-3 LC-PUFA, which are known for their health benefits. Their oil may thus be a potential alternative for fish oil. However, oxidative and hydrolytic stability of omega-3 LC-PUFA oils are important parameters. The purpose of this work was therefore to evaluate these parameters in oils from photoautotrophic microalgae (Isochrysis, Phaeodactylum, Nannochloropsis gaditana, and Nannochloropsis sp.) obtained with hexane/isopropanol (HI) and hexane (H) and compare them with commercial omega-3 LC-PUFA oils. When the results of both the primary and secondary oxidation parameters were put together, it was clear that fish, tuna, and heterotrophic microalgae oil are the least oxidatively stable oils, whereas krill oil and the microalgae oils performed better. The microalgal HI oils were shown to be more oxidatively stable than the microalgal H oils. The hydrolytic stability was shown not to be a problem during the storage of any of the oils.


Universal Semantic Communication


title: Characterization of the compact Hokuyo URG-04LX 2D laser range scanner

abstract: This paper presents a detailed characterization of the Hokuyo URG-04LX 2D laser range finder. While the sensor specifications only provide a rough estimation of the sensor accuracy, the present work analyzes issues such as time drift effects and dependencies on distance, target properties (color, brightness and material) as well as incidence angle. Since the sensor is intended to be used for measurements of a tubelike environment on an inspection robot, the characterization is extended by investigating the influence of the sensor orientation and dependency on lighting conditions. The sensor characteristics are compared to those of the Sick LMS 200 which is commonly used in robotic applications when size and weight are not critical constraints. The results show that the sensor accuracy is strongly depending on the target properties (color, brightness, material) and that it is consequently difficult to establish a calibration model. The paper also identifies cases for which the sensor returns faulty measurements, mainly when the surface has low reflectivity (dark surfaces, foam) or for high incidence angles on shiny surfaces. On the other hand, the repeatability of the sensor seems to be competitive with the LMS 200.


title: Mind the Gap: Learning to Choose Gaps for Question Generation

abstract: Not all learning takes place in an educational setting: more and more self-motivated learners are turning to on-line text to learn about new topics. Our goal is to provide such learners with the well-known benefits of testing by automatically generating quiz questions for online text. Prior work on question generation has focused on the grammaticality of generated questions and generating effective multiple-choice distractors for individual question targets, both key parts of this problem. Our work focuses on the complementary aspect of determining what part of a sentence we should be asking about in the first place; we call this "gap selection." We address this problem by asking human judges about the quality of questions generated from a Wikipedia-based corpus, and then training a model to effectively replicate these judgments. Our data shows that good gaps are of variable length and span all semantic roles, i.e., nouns as well as verbs, and that a majority of good questions do not focus on named entities. Our resulting system can generate fill-in-the-blank (cloze) questions from generic source materials.


title: Power-Aware Optimized RRH to BBU Allocation in C-RAN

abstract: Wireless networks have faced increasing demand to cope with the exponential growth of data. Conventional architectures have hindered the evolution of network scalability. However, the introduction of cloud technology has brought tremendous flexible and scalable on demand resources. Thus, cloud radio access networks (C-RANs) have been introduced as a new trend in wireless technologies. Despite the novel advancements that C-RAN offers, remote radio head (RRH)-to-base band unit (BBU) resource allocation can cause significant downgrade in efficiency, particularly the allocation of computational resources in the BBU pool to densely deployed small cells. This causes an increase in power consumption and wasted resources. Consequently, an efficient resource allocation method is vital for achieving efficient resource consumption. In this paper, the optimal allocation of computational resources between RRHs and BBUs is modeled. This is dependent on having an optimal physical resource allocation for users to determine the required computational resources. For this purpose, an optimization problem that models the assignment of resources at these two levels is formulated. A decomposition model is adopted to solve the problem by formulating two binary integer programming subproblems; one for each level. Furthermore, two low complexity heuristic algorithms are developed to solve each subproblem. Results show that the computational resource requirements and the power consumption of BBUs and the physical machines decrease as the channel quality worsens. Moreover, the developed heuristic solution achieves a close to optimal performance while having a lower complexity. Finally, both models achieve high resource utilization, cementing the efficiency of the proposed solutions.


title: A study of the behavior of several methods for balancing machine learning training data

abstract: There are several aspects that might influence the performance achieved by existing learning systems. It has been reported that one of these aspects is related to class imbalance in which examples in training data belonging to one class heavily outnumber the examples in the other class. In this situation, which is found in real world data describing an infrequent but important event, the learning system may have difficulties to learn the concept related to the minority class. In this work we perform a broad experimental evaluation involving ten methods, three of them proposed by the authors, to deal with the class imbalance problem in thirteen UCI data sets. Our experiments provide evidence that class imbalance does not systematically hinder the performance of learning systems. In fact, the problem seems to be related to learning with too few minority class examples in the presence of other complicating factors, such as class overlapping. Two of our proposed methods deal with these conditions directly, allying a known over-sampling method with data cleaning methods in order to produce better-defined class clusters. Our comparative experiments show that, in general, over-sampling methods provide more accurate results than under-sampling methods considering the area under the ROC curve (AUC). This result seems to contradict results previously published in the literature. Two of our proposed methods, Smote + Tomek and Smote + ENN, presented very good results for data sets with a small number of positive examples. Moreover, Random over-sampling, a very simple over-sampling method, is very competitive to more complex over-sampling methods. Since the over-sampling methods provided very good performance results, we also measured the syntactic complexity of the decision trees induced from over-sampled data. Our results show that these trees are usually more complex then the ones induced from original data. Random over-sampling usually produced the smallest increase in the mean number of induced rules and Smote + ENN the smallest increase in the mean number of conditions per rule, when compared among the investigated over-sampling methods.


title: Progressive skyline computation in database systems

abstract: The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-and-bound skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.


title: Bibliometric impact measures leveraging topic analysis

abstract: Measurements of the impact and history of research literature provide a useful complement to scientific digital library collections. Bibliometric indicators have been extensively studied, mostly in the context of journals. However, journal-based metrics poorly capture topical distinctions in fast-moving fields, and are increasingly problematic with the rise of open-access publishing. Recent developments in latent topic models have produced promising results for automatic sub-field discovery. The fine-grained, faceted topics produced by such models provide a clearer view of the topical divisions of a body of research literature and the interactions between those divisions. We demonstrate the usefulness of topic models in measuring impact by applying a new phrase-based topic discovery model to a collection of 300,000 Computer Science publications, collected by the Rexa automatic citation indexing system.


title: Data center TCP (DCTCP)

abstract: Cloud data centers host diverse applications, mixing workloads that require small predictable latency with others requiring large sustained throughput. In this environment, today's state-of-the-art TCP protocol falls short. We present measurements of a 6000 server production cluster and reveal impairments that lead to high application latencies, rooted in TCP's demands on the limited buffer space available in data center switches. For example, bandwidth hungry "background" flows build up queues at the switches, and thus impact the performance of latency sensitive "foreground" traffic. To address these problems, we propose DCTCP, a TCP-like protocol for data center networks. DCTCP leverages Explicit Congestion Notification (ECN) in the network to provide multi-bit feedback to the end hosts. We evaluate DCTCP at 1 and 10Gbps speeds using commodity, shallow buffered switches. We find DCTCP delivers the same or better throughput than TCP, while using 90% less buffer space. Unlike TCP, DCTCP also provides high burst tolerance and low latency for short flows. In handling workloads derived from operational measurements, we found DCTCP enables the applications to handle 10X the current background traffic, without impacting foreground traffic. Further, a 10X increase in foreground traffic does not cause any timeouts, thus largely eliminating incast problems.


title: All-dielectric subwavelength metasurface focusing lens

abstract: We have proposed, designed, manufactured and tested low loss dielectric micro-lenses for infrared (IR) radiation based on a dielectric metamaterial layer. This metamaterial layer was created by patterning a dielectric surface and etching to sub-micron depths. For a proof-of-concept lens demonstration, we have chosen a fine patterned array of nano-pillars with variable diameters. Gradient index (GRIN) properties were achieved by engineering the nano-pattern characteristics across the lens, so that the effective optical density of the dielectric metamaterial layer peaks around the lens center, and gradually drops at the lens periphery. A set of lens designs with reduced reflection and tailorable phase gradients have been developed and tested, demonstrating focal distances of a few hundred microns, beam area contraction ratio up to three, and insertion losses as low as 11%.


title: A Bayesian Approach to Learning Causal Networks

abstract: Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component independence. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.


title: The skin microbiome

abstract: Nature Reviews Microbiology 9, 244–253 (2011) It has been brought to our attention that in Fig. 1 of the original article the morphology and localization of the Demodex mites were not accurate. We have corrected the figure to show a cartoon that is more representative of the straight body and short limbs of these mites, and of their localization in the hair follicle.


title: A diffusion mechanism for social advertising over microblogs

abstract: Social media have increasingly become popular platforms for information dissemination. Recently, companies have attempted to take advantage of social advertising to deliver their advertisements to appropriate customers. The success of message propagation in social media depends greatly on the content relevance and the closeness of social relationships. In this paper, considering the factors of user preference, network influence, and propagation capability, we propose a diffusion mechanism to deliver advertising information over microblogging media. Our experimental results show that the proposed model could provide advertisers with suitable targets for diffusing advertisements continuously and thus efficiently enhance advertising effectiveness.


title: VLSI Design of an SVM Learning Core on Sequential Minimal Optimization Algorithm

abstract: The sequential minimal optimization (SMO) algorithm has been extensively employed to train the support vector machine (SVM). This work presents an efficient application specific integrated circuit chip design for sequential minimal optimization. This chip is implemented as an intellectual property core, suitable for use in an SVM-based recognition system on a chip. The proposed SMO chip was tested and found to be fully functional, using a prototype system based on the Altera DE2 board with a Cyclone II 2C70 field-programmable gate array.


title: Gender Schema and Social Judgments: A Developmental Study of Children from Hong Kong

abstract: The purpose of the study was to examine inferences and judgments of gender schematic and aschematic children. Kindergarten, third grade, and sixth grade Hong Kong Chinese children (n = 196) were categorized into schematic and aschematic groups on the basis of their reaction times to gender-stereotypic and counterstereotypic items. The children were then asked to make several inferences and judgments about four male and female targets behaving gender-stereotypically or counterstereotypically. Both age and gender schematicity were related to the children's inferences. Older children and gender-schematic children relied more on individuating information and younger children relied more on the gender label. The results are discussed within the framework of gender-schema theories.


Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task


Text Based Image Recognition using Multilayer Perceptron


title: A CMOS High-Power Broadband 260-GHz Radiator Array for Spectroscopy

abstract: A high-power broadband 260-GHz radiation source using 65-nm bulk CMOS technology is reported. The source is an array of eight harmonic oscillators with mutual coupling through four 130-GHz quadrature oscillators. Based on a novel self-feeding structure, the harmonic oscillator simultaneously achieves the optimum conditions for the fundamental oscillation and the 2nd-harmonic generation. The signals at 260 GHz radiate through eight on-chip slot antennas, and are in-phase combined inside a hemispheric silicon lens attached at the backside of the chip. Similar to the laser pulse-driven photoconductive emitter in many THz spectrometers, the radiation of this source can also be modulated by narrow pulses generated on chip, which achieves broad radiation bandwidth. Without modulation, the chip achieves a measured continuous-wave radiated power of 1.1 mW, and an EIRP of 15.7 dBm. Under modulation, the measured bandwidth of the source is 24.7 GHz. This radiator array consumes 0.8-W DC power from a 1.2-V supply.


title: Puberty-related influences on brain development

abstract: Puberty is a time of striking changes in cognition and behavior. To indirectly assess the effects of puberty-related influences on the underlying neuroanatomy of these behavioral changes we will review and synthesize neuroimaging data from typically developing children and adolescents and from those with anomalous hormone or sex chromosome profiles. The trajectories (size by age) of brain morphometry differ between boys and girls, with girls generally reaching peak gray matter thickness 1-2 years earlier than boys. Both boys and girls with congenital adrenal hyperplasia (characterized by high levels of intrauterine testosterone), have smaller amygdala volume but the brain morphometry of girls with CAH did not otherwise significantly differ from controls. Subjects with XXY have gray matter reductions in the insula, temporal gyri, amygdala, hippocampus, and cingulate-areas consistent with the language-based learning difficulties common in this group.


title: Comparison of antenna for DTI rocket telemetry system

abstract: This paper focuses on design, implementation and test of antennas for transmitting telemetry data from a DTI's 122 mm rocket. It is a ballistic rocket which rolls about its longitudinal axis during the flight, making it challenging to design a suitable antenna. Three types of antenna are studied, namely monopole, patch, and wraparound antennas. They are designed, realized, verified, and compared based on their return losses and radiation patterns. An analysis is made on the effect of antenna design and rocket structure towards the radiation pattern. Finally, suitability of each antenna for using on the rocket is discussed.


title: Three-dimensional printing surgical instruments: are we there yet?

abstract: BACKGROUND ::: The applications for rapid prototyping have expanded dramatically over the last 20 y. In recent years, additive manufacturing has been intensely investigated for surgical implants, tissue scaffolds, and organs. There is, however, scant literature to date that has investigated the viability of three-dimensional (3D) printing of surgical instruments. ::: ::: ::: MATERIALS AND METHODS ::: Using a fused deposition modeling printer, an Army/Navy surgical retractor was replicated from polylactic acid (PLA) filament. The retractor was sterilized using standard Food and Drug Administration approved glutaraldehyde protocols, tested for bacteria by polymerase chain reaction, and stressed until fracture to determine if the printed instrument could tolerate force beyond the demands of an operating room (OR). ::: ::: ::: RESULTS ::: Printing required roughly 90 min. The instrument tolerated 13.6 kg of tangential force before failure, both before and after exposure to the sterilant. Freshly extruded PLA from the printer was sterile and produced no polymerase chain reaction product. Each instrument weighed 16 g and required only $0.46 of PLA. ::: ::: ::: CONCLUSIONS ::: Our estimates place the cost per unit of a 3D-printed retractor to be roughly 1/10th the cost of a stainless steel instrument. The PLA Army/Navy retractor is strong enough for the demands of the OR. Freshly extruded PLA in a clean environment, such as an OR, would produce a sterile ready-to-use instrument. Because of the unprecedented accessibility of 3D printing technology world wide and the cost efficiency of these instruments, there are far reaching implications for surgery in some underserved and less developed parts of the world.


title: A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means

abstract: Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.


title: Face Recognition Using Local Binary Patterns Histograms (LBPH) on an FPGA-Based System on Chip (SoC)

abstract: The need for facial recognition systems that are fast and accurate is continuously increasing. In this paper, a face recognition implementation on a System on Chip (SoC), integrated with an FPGA, is presented. This implementation utilizes Local Binary Patterns Histograms to extract features from test face images and Manhattan distance to retrieve the correct match from the system's face database. The SoC utilized is a Zynq-7030. The feature extraction and the distance computations, between the database, are implemented on the FPGA. The ARM processor of the SoC is responsible for receiving the input stream and presenting the output result, using the acquired distances. Real-time face recognition, with an execution time of 8.6 ms and accuracy of 79%, is achieved through this implementation.


title: Shielding-cancellation technique for suppressing common mode EMI in isolated power converters

abstract: In an isolated power converter, the inter-winding capacitances of the power transformer are critical parasitic capacitances for the generating of common mode (CM) noise. To suppress the CM noise in isolated power converters, shielding-cancellation (SC) technique is proposed in this paper. The SC technique is realized by introducing noise cancellation on the basis of shielding, and it is easy to be implemented, suitable for various isolated power converters and independent of the transformer inter-winding capacitance. To verify the effectiveness of the SC technique on suppressing the CM noise, a flyback converter prototype is built, and the CM noise of the converter is measured and compared using three different transformers. It is revealed that the CM noise of the converter, which utilizes the transformer with SC technique, is lower than that using the transformer without shielding or with traditional shielding layers.


title: The application of improved NeuroEvolution of Augmenting Topologies neural network in Marcellus Shale lithofacies prediction

abstract: The organic-rich Marcellus Shale was deposited in a foreland basin during Middle Devonian. In terms of mineral composition and organic matter richness, we define seven mudrock lithofacies: three organic-rich lithofacies and four organic-poor lithofacies. The 3D lithofacies model is very helpful to determine geologic and engineering sweet spots, and consequently useful for designing horizontal well trajectories and stimulation strategies. The NeuroEvolution of Augmenting Topologies (NEAT) is relatively new idea in the design of neural networks, and shed light on classification (i.e., Marcellus Shale lithofacies prediction). We have successfully enhanced the capability and efficiency of NEAT in three aspects. First, we introduced two new attributes of node gene, the node location and recurrent connection (RCC), to increase the calculation efficiency. Second, we evolved the population size from an initial small value to big, instead of using the constant value, which saves time and computer memory, especially for complex learning tasks. Third, in multiclass pattern recognition problems, we combined feature selection of input variables and modular neural network to automatically select input variables and optimize network topology for each binary classifier. These improvements were tested and verified by true if an odd number of its arguments are true and false otherwise (XOR) experiments, and were powerful for classification.


title: PUC5 inverter - a promising topology for single-phase and three-phase applications

abstract: The newly emerged packed U-cell (PUC5) inverter topology is investigated in this paper considering the design criteria, switching technique, voltage balancing of DC capacitor and switches voltages ratings. Afterwards, It is compared with other popular multilevel inverter topologies such as 3-level full-bridge (FB), 5-level cascaded H-bridge (CHB), neutral point clamped (NPC) and T3, in terms of components count, voltage balancing complexity, voltage rating, switching frequency, etc. in continue, the 3-phase configuration of the PUC5 inverter is presented and its proper application would be discussed. Some experimental results are shown to accredit the performance of the 3-phase PUC5 inverter in 3-wire and 4-wire systems to supply 1-phase and 3-phase AC loads.


title: A high performance file system for non-volatile main memory

abstract: Emerging non-volatile main memories (NVMMs) provide data persistence at the main memory level. To avoid the double-copy overheads among the user buffer, the OS page cache, and the storage layer, state-of-the-art NVMM-aware file systems bypass the OS page cache which directly copy data between the user buffer and the NVMM storage. However, one major drawback of existing NVMM technologies is the slow writes. As a result, such direct access for all file operations can lead to suboptimal system performance. In this paper, we propose HiNFS, a high performance file system for non-volatile main memory. Specifically, HiNFS uses an NVMM-aware Write Buffer policy to buffer the lazy-persistent file writes in DRAM and persists them to NVMM lazily to hide the long write latency of NVMM. However, HiNFS performs direct access to NVMM for eager-persistent file writes, and directly reads file data from both DRAM and NVMM as they have similar read performance, in order to eliminate the double-copy overheads from the critical path. To ensure read consistency, HiNFS uses a combination of the DRAM Block Index and Cacheline Bitmap to track the latest data between DRAM and NVMM. Finally, HiNFS employs a Buffer Benefit Model to identify the eager-persistent file writes before issuing the write operations. Using software NVMM emulators, we evaluate HiNFS's performance with various workloads. Comparing with state-of-the-art NVMM-aware file systems - PMFS and EXT4-DAX, surprisingly, our results show that HiNFS improves the system throughput by up to 184% for filebench microbenchmarks and reduces the execution time by up to 64% for data-intensive traces and macro-benchmarks, demonstrating the benefits of hiding the long write latency of NVMM.


title: Benchmarking derivative-free optimization algorithms

abstract: We propose data profiles as a tool for analyzing the performance of derivative-free optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems. Our results provide estimates for the performance difference between these solvers, and show that on these problems, the model-based solver tested performs better than the two direct search solvers tested.


title: A 2.5MHz, 97%-accuracy on-chip current sensor with dynamically-biased shunt feedback for current-mode switching DC-DC converters

abstract: A 2.5 MHz, 97%-accuracy on-chip current sensor for current-mode switching dc-dc converters is presented in this paper. By employing dynamically-biased shunt feedback, the non-dominant pole of the current sensor is pushed to higher frequencies, thereby improving the speed of the current sensor with low quiescent current consumption. Additionally, a feed-forward gain stage in the proposed current sensor increases the dc loop-gain magnitude and thus improves the accuracy of the current sensing. The proposed current sensor for the buck converter is designed using a standard 0.35 mum CMOS process. Results show that the proposed sensor achieves at least 97% sensing accuracy and <30 ns settling time by consuming the quiescent current of 43 muA. The sensor allows the current-mode buck converter to operate at switching frequencies up to 2.5 MHz with a duty cycle down to 0.2.


title: A Unified Framework for Link Recommendation Using Random Walks

abstract: The phenomenal success of social networking sites, such as Facebook, Twitter and LinkedIn, has revolutionized the way people communicate. This paradigm has attracted the attention of researchers that wish to study the corresponding social and technological problems. Link recommendation is a critical task that not only helps increase the linkage inside the network and also improves the user experience. In an effective link recommendation algorithm it is essential to identify the factors that influence link creation. This paper enumerates several of these intuitive criteria and proposes an approach which satisfies these factors. This approach estimates link relevance by using random walk algorithm on an augmented social graph with both attribute and structure information. The global and local influences of the attributes are leveraged in the framework as well. Other than link recommendation, our framework can also rank the attributes in the network. Experiments on DBLP and IMDB data sets demonstrate that our method outperforms state-of-the-art methods for link recommendation.


title: Learning grasping affordances from local visual descriptors

abstract: In this paper we study the learning of affordances through self-experimentation. We study the learning of local visual descriptors that anticipate the success of a given action executed upon an object. Consider, for instance, the case of grasping. Although graspable is a property of the whole object, the grasp action will only succeed if applied in the right part of the object. We propose an algorithm to learn local visual descriptors of good grasping points based on a set of trials performed by the robot. The method estimates the probability of a successful action (grasp) based on simple local features. Experimental results on a humanoid robot illustrate how our method is able to learn descriptors of good grasping points and to generalize to novel objects based on prior experience.


title: Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks

abstract: We introduce a new method for proving explicit upper bounds on the VC dimension of general functional basis networks and prove as an application, for the first time, that the VC dimension of analog neural networks with the sigmoidal activation function?(y)=1/1+e?yis bounded by a quadratic polynomialO((lm)2) in both the numberlof programmable parameters, and the numbermof nodes. The proof method of this paper generalizes to much wider class of Pfaffian activation functions and formulas and gives also for the first time polynomial bounds on their VC dimension. We present also some other applications of our method.


title: Software defined networking for security enhancement in wireless mobile networks

abstract: Abstract In recent years we have seen a fast change in the networking industry: leading by the Software Defined Networking (SDN) paradigm that separates the control plane from the data plane to enable programmability and centralized control of the network infrastructure, the SDN design not only simplifies the network management but also accelerates the innovation speed of deploying advanced network applications. Meanwhile, the landscape of the wireless and mobile industry is changing dramatically as well. Given the advance of wireless technologies such as 4G and WiFi offering a pervasive Internet access, the traffic growth from the smartphone-alike devices has placed an increasing strain on the mobile network infrastructure and infringed the profit. Since the demand is increasing together with the growth of mobile users, the incumbent legacy infrastructure is already calling for an upgrade to overcome its existing limitations in terms of network management and security. In this paper, we advocate that the way forward is to integrate SDN and fully utilize its feature to solve the problem. As the security issue has raise serious concern in the networking community recently, we focus on the security aspect and investigate how to enhance the security with SDN for the wireless mobile networks.


title: CRT calibration techniques for better accuracy including low-luminance colors

abstract: We present a new CRT characterization technique that improves the accuracy of the characterization. This is achieved by optimizing the linear transformation matrix of the two stage model in the uniform CIE/L*a*b* space. We also introduce an approach to improve the characterization performance for low luminance colors. These methods are used to calibrate two CRT monitors and better accuracies are obtained compared to existing methods, especially for low luminance colors. We present a systematic way to adjust the white point of the monitor using hardware settings. This allows us to adjust the ::: monitor white accurately without losing any digital counts which is the case if a software approach is used. We propose a novel search algorithm to achieve very high accuracy calibration for experiments where a limited number of colors has to be displayed. We apply this search algorithm to the case of monochrome image display application and verify the performance our method.


title: Next generation molecular diagnosis of mitochondrial disorders.

abstract: Mitochondrial disorders are by far the most genetically heterogeneous group of diseases, involving two genomes, the 16.6k b mitochondrial genome and ~1500 genes encoded in the nuclear genome. For maternally inherited mitochondrial DNA disorders, a complete molecular diagnosis requires several different methods for the detection and quantification of mtDNA point mutations and large deletions. For mitochondrial disorders caused by autosomal recessive, dominant, and X-linked nuclear genes, the diagnosis has relied on clinical, biochemical, and molecular studies to point to a group of candidate genes followed by stepwise Sanger sequencing of the candidate genes one-by-one. The development of Next Generation Sequencing (NGS) has revolutionized the diagnostic approach. Using massively parallel sequencing (MPS) analysis of the entire mitochondrial genome, mtDNA point mutations and deletions can be detected and quantified in one single step. The NGS approach also allows simultaneous analyses of a group of genes or the whole exome, thus, the mutations in causative gene(s) can be identified in one-step. New approaches make genetic analyses much faster and more efficient. Huge amounts of sequencing data produced by the new technologies brought new challenges to bioinformatics, analytical pipelines, and interpretation of numerous novel variants. This article reviews the clinical utility of next generation sequencing for the molecular diagnoses of complex dual genome mitochondrial disorders.


title: A Wideband Full-Corporate-Feed Waveguide Slot Planar Array

abstract: A wideband full-metal planar antenna array with full-corporate feed-network is proposed. Cavity and slot are used as the basic radiation element and T-shaped walls are employed in the cavity to achieve wide bandwidth. $E$ -plane waveguide feed-network configuration is utilized so as to suppress wave leakage between adjacent layers when the antenna is fabricated by a conventional machining technique. An $8 \times 8$ antenna array prototype with optimized parameters is tested. Experimental results show that the bandwidth of ${\text{VSWR}} is up to 36.9% ( $10.24-14.87\;{\text{GHz}}$ ). More than 60% aperture efficiency and higher than 23.4 dBi gain are achieved over the operational bandwidth.


title: Negative Refractive Index in Left-Handed Materials

abstract: The real part of the refractive index n(omega) of a nearly transparent and passive medium is usually taken to have only positive values. Through an analysis of a current source radiating into a 1D "left-handed" material (LHM)-where the permittivity and permeability are simultaneously less than zero-we determine the analytic structure of n(omega), demonstrating frequency regions where the sign of Re[n(omega)] must, in fact, be negative. The regime of negative index, made relevant by a recent demonstration of an effective LHM, leads to unusual electromagnetic wave propagation and merits further exploration.


title: Bayesian measures of model complexity and fit

abstract: Summary. We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the ‘hat’ matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding pD to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.


title: Deep multimodal semantic embeddings for speech and images

abstract: In this paper, we present a model which takes as input a corpus of images with relevant spoken captions and finds a correspondence between the two modalities. We employ a pair of convolutional neural networks to model visual objects and speech signals at the word level, and tie the networks together with an embedding and alignment model which learns a joint semantic space over both modalities. We evaluate our model using image search and annotation tasks on the Flickr8k dataset, which we augmented by collecting a corpus of 40,000 spoken captions using Amazon Mechanical Turk.


The Quest to Automate Fact-Checking


title: Dynamic task scheduling for linear algebra algorithms on distributed-memory multicore systems

abstract: This paper presents a dynamic task scheduling approach to executing dense linear algebra algorithms on multicore systems (either shared-memory or distributed-memory). We use a task-based library to replace the existing linear algebra subroutines such as PBLAS to transparently provide the same interface and computational function as the ScaLAPACK library. Linear algebra programs are written with the task-based library and executed by a dynamic runtime system. We mainly focus our runtime system design on the metric of performance scalability. We propose a distributed algorithm to solve data dependences without process cooperation. We have implemented the runtime system and applied it to three linear algebra algorithms: Cholesky, LU, and QR factorizations. Our experiments on both shared-memory machines (16, 32 cores) and distributed-memory machines (1024 cores) demonstrate that our runtime system is able to achieve good scalability. Furthermore, we provide analytical analysis to show why the tiled algorithms are scalable and the expected execution time.


Zone Routing Protocol (ZRP) - A Novel Routing Protocol for Vehicular Ad-hoc Networks


title: Efficient purely-dynamic information flow analysis

abstract: We present a novel approach for efficiently tracking information flow in a dynamically-typed language such as JavaScript. Our approach is purely dynamic, and it detects problems with implicit paths via a dynamic check that avoids the need for an approximate static analyses while still guaranteeing non-interference. We incorporate this check into an efficient evaluation strategy based on sparse information labeling that leaves information flow labels implicit whenever possible, and introduces explicit labels only for values that migrate between security domains. We present experimental results showing that, on a range of small benchmark programs, sparse labeling provides a substantial (30%-50%) speed-up over universal labeling.


title: Face detection in color images

abstract: Human face detection is often the first step in applications such as video surveillance, human computer interface, face recognition, and image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful detection over a wide variety of facial variations in color, position, scale, rotation, pose, and expression from several photo collections.


title: Design and Performance Analysis of 2.5 GHz Microstrip Bandpass Filter for LTE

abstract: Microwave filters since a long time have been an integral part of the radar and the telecommunication systems. Designing better and more accurate filters is the major requirement for the various possible communication techniques like the LTE (Long Term Evaluation) system and WiMAX systems. This paper aims to design microstrip bandpass filters for Parallel - Edge Coupled Line Filter and Interdigital Filter configurations for the frequency of 2.5 GHz used for LTE systems. For microstrip implementation, substrate Rogers TMM10 is used for both designs. The filters have been designed on the ADS software. The final conclusions have been drawn by analysing various performance parameters such as return loss, insertion loss, 3-dB bandwidth etc and found very much acceptable for applications.


title: Service quality and ERP implementation: A conceptual and empirical study of semiconductor-related industries in Taiwan

abstract: This paper examines the effectiveness of the implementation of enterprise resource planning (ERP) in improving service quality in the Taiwanese semiconductor industry by assessing the expectations and the perceptions of service quality from the perspectives of both upstream manufacturers and downstream customers. The study first establishes a modified service quality gap model incorporating: (i) the downstream customers' expectations and perceptions, and (ii) the upstream manufacturers' perceptions of the customers' expectations and perceptions. An empirical study by questionnaire survey is then undertaken to investigate the gaps proposed in the research model. The results show that service quality gaps do exist in the Taiwanese semiconductor industry between upstream manufacturers that are implementing ERP and their downstream customers. The study shows that the proposed model provides valuable guidance to manufacturers with respect to the prevention, detection, and elimination of the demonstrated service quality gaps. The model thus helps manufacturers to evaluate the contribution of various ERP modules to improved customer satisfaction with service quality and also provides guidance on improvement strategies to enhance service quality by eliminating quality gaps.


Size versus fairness and strategyproofness in the assignment problem


title: Comparison of price change and health message interventions in promoting healthy food choices.

abstract: This study examines the feasibility and effectiveness of an environmental intervention for improving diet by comparing the impact of health messages, lowered prices, and their combination on the purchase of healthy food items in a restaurant. Price decreases alone, rather than a combination of price decreases and health messages, were associated with a higher level of increased purchases of some healthy food items as compared with control items over a 4-month period. Price decreases may be a more powerful means than health messages of increasing consumption of healthy foods. Health messages may have paradoxical effects if foods labeled as healthy are assumed to taste bad.


title: Non-Autoregressive Neural Machine Translation

abstract: Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower latency during inference. Through knowledge distillation, the use of input token fertilities as a latent variable, and policy gradient fine-tuning, we achieve this at a cost of as little as 2.0 BLEU points relative to the autoregressive Transformer network used as a teacher. We demonstrate substantial cumulative improvements associated with each of the three aspects of our training strategy, and validate our approach on IWSLT 2016 English-German and two WMT language pairs. By sampling fertilities in parallel at inference time, our non-autoregressive model achieves near-state-of-the-art performance of 29.8 BLEU on WMT 2016 English-Romanian.


title: Discriminative Multidimensional Scaling for Low-Resolution Face Recognition

abstract: Face images captured by surveillance videos usually have limited resolution. Due to resolution mismatch, it is hard to match high-resolution (HR) faces with low-resolution (LR) faces directly. Recently, multidimensional scaling (MDS) has been employed to solve the problem. In this letter, we proposed a more discriminative MDS method to learn a mapping matrix, which projects the HR images and LR images to a common subspace. Our method is discriminative since both interclass distances and intraclass distances are taken into consideration. We add an interclass constraint to enlarge the distances of different subjects in the subspace to ensure discriminability. Besides, we consider not only the relationship of HR–LR images, but also the relationship of HR–HR images and LR–LR images in order to preserve local consistency. Experimental results on FERET, Multi-PIE, and SCface databases demonstrate the effectiveness of our proposed approach.


title: Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot

abstract: Finding a fire fast is crucial in firefighting. For risky situations, it would be idealistic to send a firefighting robot that could quickly and efficiently find the fire and suppress it. This paper introduces an algorithm developed for an intelligent firefighting mobile robot to find a fire efficiently by fusing long wave infrared camera, ultraviolet radiation sensor, and LIDAR. For its validation, an experimental test-bed was constructed with a hallway and two rooms, with one of the rooms containing a real size fire created by propane gas. The robot immediately calculates its path towards the fire, moves towards it avoiding obstacles, and ultimately finds the fire. When the fire is out, the robot returns to its original starting place.