title: DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

abstract: Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.


title: Microfluidic devices for bioapplications.

abstract: Harnessing the ability to precisely and reproducibly actuate fluids and manipulate bioparticles such as DNA, cells, and molecules at the microscale, microfluidics is a powerful tool that is currently revolutionizing chemical and biological analysis by replicating laboratory bench-top technology on a miniature chip-scale device, thus allowing assays to be carried out at a fraction of the time and cost while affording portability and field-use capability. Emerging from a decade of research and development in microfluidic technology are a wide range of promising laboratory and consumer biotechnological applications from microscale genetic and proteomic analysis kits, cell culture and manipulation platforms, biosensors, and pathogen detection systems to point-of-care diagnostic devices, high-throughput combinatorial drug screening platforms, schemes for targeted drug delivery and advanced therapeutics, and novel biomaterials synthesis for tissue engineering. The developments associated with these technological advances along with their respective applications to date are reviewed from a broad perspective and possible future directions that could arise from the current state of the art are discussed.


Overview of the Photovoltaic System Topologies


title: Revisiting stepped septum circular polarizer using full-wave simulations

abstract: Septum is an effective polarizer to generate circular polarizations from the linear excitations in a waveguide. In this paper, the functionality of the septum is revisited using full-wave simulations, and a graphical visualization is presented to aid in better understanding the fundamental principals of a stepped septum circular polarizer. Septum responses to even and odd mode excitations are analyzed, and the electric field distributions inside the septum polarizer are presented. The simulated S-parameters and axial ratio validates its effectiveness.


Data Hiding Using Fibonacci EDGE Based Steganography for Cloud Data


title: Physical-Layer Security in the Internet of Things: Sensing and Communication Confidentiality Under Resource Constraints

abstract: The Internet of Things (IoT) will feature pervasive sensing and control capabilities via a massive deployment of machine-type communication (MTC) devices. The limited hardware, low-complexity, and severe energy constraints of MTC devices present unique communication and security challenges. As a result, robust physical-layer security methods that can supplement or even replace lightweight cryptographic protocols are appealing solutions. In this paper, we present an overview of low-complexity physical-layer security schemes that are suitable for the IoT. A local IoT deployment is modeled as a composition of multiple sensor and data subnetworks, with uplink communications from sensors to controllers, and downlink communications from controllers to actuators. The state of the art in physical-layer security for sensor networks is reviewed, followed by an overview of communication network security techniques. We then pinpoint the most energy-efficient and low-complexity security techniques that are best suited for IoT sensing applications. This is followed by a discussion of candidate low-complexity schemes for communication security, such as on–off switching and space-time block codes. The paper concludes by discussing open research issues and avenues for further work, especially the need for a theoretically well-founded and holistic approach for incorporating complexity constraints in physical-layer security designs.


title: Smart Parking System (SPS) Architecture Using Ultrasonic Detector

abstract: With the increase in vehicle production and world population, more and more parking spaces and facilities are required. In this paper a new parking system called Smart Parking System (SPS) is proposed to assist drivers to find vacant spaces in a car park in a shorter time. The new system uses ultrasonic (ultrasound) sensors to detect either car park occupancy or improper parking actions. Different detection technologies are reviewed and compared to determine the best technology for developing SPS. Features of SPS include vacant parking space detection, detection of improper parking, display of available parking spaces, and directional indicators toward vacant parking spaces, payment facilities and different types of parking spaces (vacant, occupied, reserved and handicapped) through the use of specific LEDs. This paper also describes the use of an SPS system from the entrance into a parking lot until the finding of a vacant parking space. The system is designed for a four-level parking lot with 100 parking spaces and five aisles on each floor. The system architecture defines the essential design features such as location of sensors, required number of sensors and LEDs for each level, and indoor and outdoor display boards.


title: Applications of Artificial Neural Networks in Financial Economics: A Survey

abstract: This paper is a survey on the application of artificial neural networks in forecasting financial market prices. The objective of this paper is to appraise the potential of using artificial neural networks to predict the financial system, as it is reflected in many relevant articles. It will provide some guidelines and references for the research and implementation. This paper begins with an introduction to the theory of artificial neural networks. Subsequently it focuses on the forecast of stock prices and option pricing based on a non-linear ANN model. It proceeded with a presentation of the application of ANN in predicting exchange rates. The paper then reviewed the theoretical literature on the prediction of banking and financial crisis based on artificial neural networks. In general artificial neural network is a valuable forecast tool in financial economics due to the learning, generalization and nonlinear behavior properties. Finally it identifies a number of important opportunities for future research on the application of neural networks in financial economics.


title: Visualizing State Spaces with Petri Nets

abstract: Abstract. Many analysis techniques are based on state spaces, e.g., based on some system model or system log, state spaces are generated that can be used to verify behavioral properties (e.g., absence of deadlocks). State spaces can be inspected automatically without any human interpretation. However, for a good understanding of the system’s behavior the analyst needs to inspect and interpret the corresponding state space. Therefore, state space visualization is important. Unfortunately, this aspect has been neglected and today’s analysis tools typically represent states spaces in such a way that they are difficult to interpret and inspection is only possible for toy examples. Attribute-based visualization methods address this issue by enabling users to study state spaces in terms of data they understand: the attributes associated with every state. These techniques can deal with many types of attributes. In this paper we propose an approach based on Petri nets. Using existing synthesis techniques based on regions, we automatically derive a Petri net from a given state space. We consider the places of this Petri net as new derived state attributes. Using these, we combine attribute-based visualizations of the state space with diagrams of the associated Petri net. This approach has been implemented and provides an innovative and versatile way to visualize state spaces.


title: DNA sequence compression - Based on the normalized maximum likelihood model

abstract: Genomic data provide challenging problems that have been studied in a number of fields such as statistics, signal processing, information theory, and computer science. This article shows that the methodologies and tools that have been recently developed in these fields for modeling signals and processes appear to be most promising for genomic research


title: Resilient cities: meaning, models, and metaphor for integrating the ecological, socio-economic, and planning realms

abstract: Urban designers, ecologists, and social scientists have called for closer links among their disciplines. We examine a promising new tool for promoting this linkage—the metaphor of “cities of resilience.” To put this tool to best use, we indicate how metaphor fits with other conceptual tools in science. We then present the two opposing definitions of resilience from ecology, and give reasons why one is more appropriate for linking with design. Additional specific tools and insights that are emerging from, or being increasingly used in, ecology can further support the linkage with urban design. These include recognizing the role of spatial heterogeneity in both ecological and social functioning of urban areas, the integrating power of watersheds, social and ecological patch dynamics of cities, the utility of spatial mosaic models to capture function, the use of an integrated “human ecosystem” modeling framework, and the consequent perspective of metropolitan areas as integrated ecological-social systems. Three additional tools are related to the adaptability of people and human institutions. First is the recognition of a “learning loop” in metropolitan ecosystems in which people respond to and affect ecological change, the use of urban design as experiments whose ecological and social outcomes can be measured, and finally the potency of a dialog between professionals and citizens, communities, and institutions, to support both research and design. The metaphor of resilience, and its technical specifications, draw these diverse strands for linking ecology and planning together. © 2003 Elsevier B.V. All rights reserved.


title: A comparison of the toxicity of mono, bis, tris and tetrakis phosphino silver complexes on SNO esophageal cancer cells

abstract: Several silver(I) triphenylphosphine adducts were prepared as 1:1 to 1:4 ratios of silver nitrate to triphenylphosphine were used to determine their anticancer activity in a malignant SNO esophageal cell line. The silver-phosphine adducts include: [Ag(PPh3)]NO3 (1); [Ag(PPh3)2]NO3 (2); [Ag(PPh3)3]NO3 (3) and [Ag(PPh3)4]NO3 (4). In addition, the activity of 1-4 where compared to previously reported complexes [Ag(Ph2P(CH2)2PPh2)2]NO3 (5) and [Ag(Ph2P(CH2)3PPh2)2]NO3 (6). The cytotoxicity of complexes 1 to 6 was also evaluated in non-malignant human dermal fibroblast cells (HDF-a). The majority of the complexes were found to be highly toxic to the SNO cells and less toxic towards HDF-a cells, as determined by the alamarBlue® assay. Morphological studies and flow cytometry confirmed that the silver(I) complexes induced apoptosis in the malignant cells. These results may have an impact on research related to drug discovery and silver(I) phosphine complexes could be added to the arsenal of anticancer agents. in addition to silver-bis-diphenylphosphinoethane and silver-bis-diphenylphosphinopropane adducts.


title: Multi-Instance Multi-Label Learning with Weak Label ∗

abstract: Multi-Instance Multi-Label learning (MIML) deals with data objects that are represented by a bag of instances and associated with a set of class labels simultaneously. Previous studies typically assume that for every training example, all positive labels are tagged whereas the untagged labels are all negative. In many real applications such as image annotation, however, the learning problem often suffers from weak label; that is, users usually tag only a part of positive labels, and the untagged labels are not necessarily negative. In this paper, we propose the MIMLwel approach which works by assuming that highly relevant labels share some common instances, and the underlying class means of bags for each label are with a large margin. Experiments validate the effectiveness of MIMLwel in handling the weak label problem.


title: A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

abstract: Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.


title: System-Level Design: Orthogonalization of Concerns and Platform-Based Design

abstract: System-level design issues become critical as implementation technology evolves toward increasingly complex integrated circuits and the time-to-market pressure continues relentlessly. To cope with these issues, new methodologies that emphasize re-use at all levels of abstraction are a "must", and this is a major focus of our work in the Gigascale Silicon Research Center. We present some important concepts for system design that are likely to provide at least some of the gains in productivity postulated above. In particular, we focus on a method that separates parts of the design process and makes them nearly independent so that complexity could be mastered. In this domain, architecture-function co-design and communication-based design are introduced and motivated. Platforms are essential elements of this design paradigm. We define system platforms and we argue about their use and relevance. Then we present an application of the design methodology to the design of wireless systems. Finally, we present a new approach to platform-based design called modern embedded systems, compilers, architectures and languages, based on highly concurrent and software programmable architectures and associated design tools.


title: Symbol interdependency in symbolic and embodied cognition

abstract: Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information.


title: Privacy-preserving data integration and sharing

abstract: Integrating data from multiple sources has been a longstanding challenge in the database community. Techniques such as privacy-preserving data mining promises privacy, but assume data has integration has been accomplished. Data integration methods are seriously hampered by inability to share the data to be integrated. This paper lays out a privacy framework for data integration. Challenges for data integration in the context of this framework are discussed, in the context of existing accomplishments in data integration. Many of these challenges are opportunities for the data mining community.


title: Anti-monotonic Overlap-Graph Support Measures

abstract: In graph mining, a frequency measure is anti-monotonic if the frequency of a pattern never exceeds the frequency of a subpattern. The efficiency and correctness of most graph pattern miners relies critically on this property. We study the case where the dataset is a single graph. Vanetik, Gudes and Shimony already gave sufficient and necessary conditions for anti-monotonicity of measures depending only on the edge-overlaps between the instances of the pattern in a labeled graph. We extend these results to homomorphisms, isomorphisms and homeomorphisms on both labeled and unlabeled, directed and undirected graphs, for vertex and edge overlap. We show a set of reductions between the different morphisms that preserve overlap. We also prove that the popular maximum independent set measure assigns the minimal possible meaningful frequency, introduce a new measure based on the minimum clique partition that assigns the maximum possible meaningful frequency and introduce a new measure sandwiched between the former two based on the poly-time computable Lovasz thetas-function.


title: Indoor deployment of low-power wide area networks (LPWAN): A LoRaWAN case study

abstract: The last decade saw the emergence of the Internet of Things (IoT) paradigm, which aims to connect any object to the Internet. In this context, a new type of wireless communication network emerged known as Low-Power Wire-Area Network (LPWAN). By contrast to well-known short range and multi-hop wireless networks, LPWAN networks allow long range communications at a low bit rate. Furthermore, LPWAN networks are considered to be integrated into 5G. Among LPWAN networks, the LoRaWAN technology gains more and more interest from the research and industrial communities. In this article, we have led a thorough experimental performance evaluation of LoRaWAN in an indoor environment. From this study, we quantify the limits of this technology and expose the merits of using LoRaWAN for IoT communications in the context of 5G.


title: Proximity-Coupled Stacked Patch Antenna for Tri-Band GPS Applications

abstract: We introduce a novel proximity-coupled stacked patch antenna to cover all three GPS bands (L1, L2 and L5 bands). The proposed antenna has an aperture size of 1.2" times 1.2" (lambda/8 at the L5 band) and has an RHCP gain greater than 2 dBi over all three GPS bands. Design challenges, corresponding numerical simulations and measurements are presented


title: Understanding latent interactions in online social networks

abstract: Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.


title: Autonomous Tracking of Hexacopter on Moving Mobile Robot Using Gazebo ROS Simulation

abstract: In this paper, we provide simulation of hexacopter UAV and mobile robot UGV collaboration. Cooperative robot simulation is done by using gazebo simulator based on robot operating system (ROS). Mission collaboration provides a tracking object hexacopter to moving mobile robot. Object tracking used a fix bottom camera that covered a view of mobile robot. Tracking performance will used to control the UAV movement. The result of movement is to follow the motion of mobile robot to a desired goal.


title: Joint Word Representation Learning using a Corpus and a Semantic Lexicon

abstract: Methods for learning word representations using large text corpora have received much attention lately due to their impressive performance in numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection. Despite their success, these data-driven word representation learning methods do not consider the rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNet that represent the meanings of words by defining the various relationships that exist among the words in a language. We consider the question, can we improve the word representations learnt using a corpora by integrating the knowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predicts the co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon. We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus. Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into word representations on several benchmark datasets for semantic similarity and word analogy.


title: Grouping Similar Trajectories for Carpooling Purposes

abstract: Vehicle congestion is a serious concern in metropolitan areas. Some policies have been adopted in order to soften the problem: construction of alternative routes, encouragement for the use of bicycles, improvement on public transportation, among others. A practice that might help is carpooling. Carpooling consists in sharing private vehicle space among people with similar trajectories. Although there exist some software initiatives to facilitate the carpooling practice, none of them actually provides some key facilities such as searching for people with similar trajectories. The way in which such a trajectory is represented is also central. In the specific context of carpooling, the use of Points of Interest (POI) as a method for trajectory discretization is rather relevant. In this paper, we consider that and other assumptions to propose an innovative approach to generate trajectory clusters for carpooling purposes, based on Optics algorithm. We also propose a new similarity measure for trajectories. Two experiments have been performed in order to prove the feasibility of the approach. Furthermore, we compare our approach with K-means and Optics. Results have showed that the proposed approach has results similar for Davies-Boulding index (DBI).


title: Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Matrix Games

abstract: This paper aims to investigate the fuzzy constrained matrix game (MG) problems using the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first in attempting to find a solution for fuzzy game problems using RNN models. For this purpose, a fuzzy game problem is reformulated into a weighting problem. Then, the Karush–Kuhn–Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Moreover, the Lyapunov stability and the global convergence of the RNN model are also confirmed. Finally, three illustrative examples are presented to demonstrate the effectiveness of this approach. The obtained results are compared with the results obtained by the previous approaches for solving fuzzy constrained MG.


Object Recognition and Tracking based on Object Feature Extracting


title: Principal component particle swarm optimization (PCPSO)

abstract: Particle swarm optimization (PSO) is based on the notion of particles flying through solution space. Each particle is assumed to have n-dimensions that are mapped to the variables of the function that is being evaluated. The standard PSO algorithm updates a particle by moving towards the particle's past personal best and the best particle that has been found. This paper introduces the principal component particle swarm optimization (PCPSO) procedure. The principal component particle swarm optimization procedure flies the particles in two separates spaces at the same time; the traditional n-dimensional x space and a rotated w-dimensional z space where m /spl les/ n. The Griewank function is used for introducing the PCPSO algorithm and a PCPSO time complexity study.


title: A 0.6 V Low-Power Wide-Range Delay-Locked Loop in 0.18 $\mu$m CMOS

abstract: In this letter, a delay-locked loop (DLL) suitable for low-power and low-voltage operations is presented. To overcome the performance limitations, such as a restricted locking range and elevated output jitters, a novel voltage-controlled delay cell and a phase/frequency detector with a start controller are employed in the proposed DLL. Using a standard 0.18 mum CMOS process, the fabricated circuit exhibits a locking range from 85 to 550 MHz. The measured peak-to-peak and rms jitters at 550 MHz are 25.6 and 3.8 ps, respectively. Operated at a supply voltage of 0.6 V, the power consumption of the DLL circuit varies from 2.4 to 4.2 mW within the entire locking range.


title: Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power)

abstract: We review our work towards achieving competitive performance (classification accuracies) for on-chip machine learning (ML) of large-scale artificial neural networks (ANN) using Non-Volatile Memory (NVM)-based synapses, despite the inherent random and deterministic imperfections of such devices. We then show that such systems could potentially offer faster (up to 25×) and lower-power (from 120–2850×) ML training than GPU-based hardware.


title: Designing empathic computers: the effect of multimodal empathic feedback using animated agent

abstract: Experiencing emotional distress is the number one reason why people who are undergoing behaviour modification (e.g. quitting smoking, dieting) suffer from relapses. Providing emotional support is an effective way to help them overcome the unpleasant effects of negative affect and adhere to their regimen. Building computers with such ability has grabbed the attention of the HCI community in recent years. Early research has shown some promising results when adopting strategies of how we comfort others, but many questions on how to build such systems remain unanswered. This paper presents the results of a 2 (modality: animated vs. no visual) by 3 (intervention: non-empathy vs. empathy vs. empathy and expressivity) between-subjects study that investigates the impact of two important factors and their interaction in the design of such systems: (1) different ways of expressing empathy, and (2) the modality of delivering such content. Findings and implications for the design of empathic computer systems are discussed and directions for future research are suggested.


Towards Constructing Sports News from Live Text Commentary


title: The length of parallel fibers in the cat cerebellar cortex. An experimental light and electron microscopic study

abstract: Experimental light and electron microscopic studies were carried out to determine the length of parallel fibers in the cat cerebellar cortex. Using a fine surgical knife, vermal and hemispheral folia were cut perpendicular to their long axis. The animals were sacrificed 1–10 days after the operation. Sections of the transected folia were then stained with a Fink-Heimer procedure. The resulting degeneration appeared as fine dots that extended lateral to the lesion, as predictable from the course of the parallel fibers. Densitometer readings indicate that the density of degeneration declines gradually lateral to the lesion. The specificity of the silver impregnation was checked by processing silver stained sections for electron microscopy. This confirmed the location of the silver precipitate on degenerating parallel fibers. The pattern of parallel fiber degeneration in the molecular layer has a trapezoidal configuration centered on the lesion. The shorter parallel fibers are located at the base of the molecular layer and extend for 5 mm. The parallel fibers become progressively longer as they approach the pial surface where they attain a maximum length of 7 mm. Our studies suggest that in folia longer than 7 mm parallel fibers are 6 mm long on the average.


title: Entrapment of the sciatic nerve at the linea aspera: A case report and literature review

abstract: BACKGROUND ::: Nontraumatic, non-neoplastic sciatic nerve entrapment at the level of the thigh is extremely rare. In its course, in proximity of the linea aspera, the nerve is exposed to unexpected neuropathic syndromes associated with bone disorders. ::: ::: ::: CASE DESCRIPTION ::: A 67-year-old woman presented with a painful, neuropathic syndrome of the sciatic nerve, not resulting from any trauma and persisting for approximately 2 years. Imaging studies of the thigh showed a delimited zone of hyperostosis in the proximal third of the femoral diaphysis. The symptoms dramatically resolved after the patient underwent neurolysis of the tract of the nerve adjoining to the linea aspera. At the clinical checkup 2 years later, the patient remained free of pain. ::: ::: ::: CONCLUSION ::: The diagnosis of sciatic nerve entrapment at the linea aspera may present considerable difficulties. The clinical history and physical examination sometimes motivate the exploration and neurolysis of the nerve at this site.


title: 72 dB SNR, 240 Hz Frame Rate Readout IC With Differential Continuous-Mode Parallel Architecture for Larger Touch-Screen Panel Applications

abstract: This paper presents a mutual capacitive touch screen panel (TSP) readout IC (ROIC) with a differential continuous-mode parallel operation architecture (DCPA). The proposed architecture achieves a high product of signal-to-noise ratio (SNR) and frame rate, which is a requirement of ROIC for large-sized TSP. DCPA is accomplished by using the proposed differential sensing method with a parallel architecture in a continuous-mode. This architecture is implemented using a continuous-type transmitter for parallel signaling and a differential-architecture receiver. A continuous-type differential charge amplifier removes the common-mode noise component, and reduces the self-noise by the band-pass filtering effect of the continuous-mode charge amplifier. In addition, the differential parallel architecture cancels the timing skew problem caused by the continuous-mode parallel operation and effectively enhances the power spectrum density of the signal. The proposed ROIC was fabricated using a 0.18- $\mu$ m CMOS process and occupied an active area of 1.25 $\text{mm}^{2}$ . The proposed system achieved a 72 dB SNR and 240 Hz frame rate with a 32 channel TX by 10 channel RX mutual capacitive TSP. Moreover, the proposed differential-parallel architecture demonstrated higher immunity to lamp noise and display noise. The proposed system consumed 42.5 mW with a 3.3-V supply.


title: Characterizing 3D Floating Gate NAND Flash: Observations, Analyses, and Implications

abstract: As both NAND flash memory manufacturers and users are turning their attentions from planar architecture towards three-dimensional (3D) architecture, it becomes critical and urgent to understand the characteristics of 3D NAND flash memory. These characteristics, especially those different from planar NAND flash, can significantly affect design choices of flash management techniques. In this article, we present a characterization study on the state-of-the-art 3D floating gate (FG) NAND flash memory through comprehensive experiments on an FPGA-based 3D NAND flash evaluation platform. We make distinct observations on its performance and reliability, such as operation latencies and various error patterns, followed by careful analyses from physical and circuit-level perspectives. Although 3D FG NAND flash provides much higher storage densities than planar NAND flash, it faces new performance challenges of garbage collection overhead and program performance variations and more complicated reliability issues due to, e.g., distinct location dependence and value dependence of errors. We also summarize the differences between 3D FG NAND flash and planar NAND flash and discuss implications on the designs of NAND flash management techniques brought by the architecture innovation. We believe that our work will facilitate developing novel 3D FG NAND flash-oriented designs to achieve better performance and reliability.


title: Multi-modality Sensor Data Classification with Selective Attention

abstract: Multimodel wearable sensor data classificationplays an important role in ubiquitous computingand has a wide range of applications in variousscenarios from healthcare to entertainment. How-ever, most of the existing work in this field em-ploys domain-specific approaches and is thus inef-fective in complex situations where multi-modalitysensor data is collected. Moreover, the wearablesensor data is less informative than the conven-tional data such as texts or images. In this paper,to improve the adaptability of such classificationmethods across different application contexts, weturn this classification task into a game and applya deep reinforcement learning scheme to dynami-cally deal with complex situations. We also intro-duce a selective attention mechanism into the rein-forcement learning scheme to focus on the crucialdimensions of the data. This mechanism helps tocapture extra information from the signal, and canthus significantly improve the discriminative powerof the classifier. We carry out several experimentson three wearable sensor datasets, and demonstratecompetitive performance of the proposed approachcompared to several state-of-the-art baselines.


title: Topic sentiment mixture: modeling facets and opinions in weblogs

abstract: In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments. It could also provide general sentiment models that are applicable to any ad hoc topics. With a specifically designed HMM structure, the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics. Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections. The TSM model is quite general; it can be applied to any text collections with a mixture of topics and sentiments, thus has many potential applications, such as search result summarization, opinion tracking, and user behavior prediction.


title: Cascading effects of attention disengagement and sensory seeking on social symptoms in a community sample of infants at-risk for a future diagnosis of autism spectrum disorder

abstract: Abstract Recent work suggests sensory seeking predicts later social symptomatology through reduced social orienting in infants who are at high-risk for autism spectrum disorder (ASD) based on their status as younger siblings of children diagnosed with ASD . We drew on extant longitudinal data from a community sample of at-risk infants who were identified at 12 months using the First Year Inventory, and followed to 3–5 years. We replicate findings of Damiano et al. (in this issue) that a) high-risk infants who go on to be diagnosed with ASD show heightened sensory seeking in the second year of life relative to those who do not receive a diagnosis, and b) increased sensory seeking indirectly relates to later social symptomatology via reduced social orienting. We extend previous findings to show that sensory seeking has more clinical utility later in the second year of life (20–24 months) than earlier (13–15 months). Further, this study suggests that diminished attention disengagement at 12–15 months may precede and predict increased sensory seeking at 20–24 months. Findings add support for the notion that sensory features produce cascading effects on social development in infants at risk for ASD, and suggest that reduced attention disengagement early in life may set off this cascade.


title: A novel single-phase ac to high voltage dc converter based on Cockcroft-Walton cascade rectifier

abstract: This paper proposes a novel single-phase ac to high voltage dc converter based on Cockcroft-Walton cascade rectifier with only adding one bi-directional switch and one boost inductor. This paper also derives a new method of circuit representation for Cockcroft-Walton cascade rectifier, which simplifies the equivalent circuit and is convenient for simulation. Compared with conventional Cockcroft-Walton rectifier, the proposed converter provides half-wave symmetric and low-distorted line current, adjustable power factor at the ac source, and regulated dc output voltage. Unit power factor is achieved by the average-current method and the dc output voltage is regulated by a PI compensator. Moreover, this paper employs a digital signal processor to implement a digital controller for operating the proposed converter and a prototype with 500w rating is implemented for test. Both simulation and experimental results demonstrate the validity of the proposed converter.


title: High Voltage Tolerant Level Shifters and DCVSL in Standard Low Voltage CMOS Technologies

abstract: In this paper, high voltage (HV) tolerant level-shifters with combinational functionality are proposed based on differential cascode voltage switch logic (DCVSL). These level-shifters are tolerant to supply voltages higher than the process limit for individual CMOS transistors. The proposed HV DCVSL level shifters are particularly useful when it is mandatory to ensure a specific behavior during out of the normal mode periods (power up; power down; reset; etc.). These high voltage tolerant logic circuits were used in the power block of buck converter designed in a standard 3.3 V, 0.13 mum CMOS process, powered by an input voltage range from 2.7 V to 4.2 V.


title: "I just wanted to tell you that loperamide WILL WORK": a web-based study of extra-medical use of loperamide.

abstract: AIMS ::: Many websites provide a means for individuals to share their experiences and knowledge about different drugs. Such User-Generated Content (UGC) can be a rich data source to study emerging drug use practices and trends. This study examined UGC on extra-medical use of loperamide among illicit opioid users. ::: ::: ::: METHODS ::: A website that allows for the free discussion of illicit drugs and is accessible for public viewing was selected for analysis. Web-forum posts were retrieved using web crawlers and retained in a local text database. The database was queried to extract posts with a mention of loperamide and relevant brand/slang terms. Over 1290 posts were identified. A random sample of 258 posts was coded using NVivo to identify intent, dosage, and side-effects of loperamide use. ::: ::: ::: RESULTS ::: There has been an increase in discussions related to loperamide's use by non-medical opioid users, especially in 2010-2011 Loperamide was primarily discussed as a remedy to alleviate a broad range of opioid withdrawal symptoms, and was sometimes referred to as "poor man's" methadone. Typical doses ranged 70-100mg per day, much higher than an indicated daily dose of 16mg. ::: ::: ::: CONCLUSIONS ::: This study suggests that loperamide is being used extra-medically to self-treat opioid withdrawal symptoms. There is a growing demand among people who are opioid dependent for drugs to control withdrawal symptoms, and loperamide appears to fit that role. The study also highlights the potential of the Web as a "leading edge" data source in identifying emerging drug use practices.


title: What color is your Jacobian? Graph coloring for computing derivatives

abstract: Graph coloring has been employed since the 1980s to efficiently compute sparse Jacobian and Hessian matrices using either finite differences or automatic differentiation. Several coloring problems occur in this context, depending on whether the matrix is a Jacobian or a Hessian, and on the specifics of the computational techniques employed. We consider eight variant vertex coloring problems here. This article begins with a gentle introduction to the problem of computing a sparse Jacobian, followed by an overview of the historical development of the research area. Then we present a unifying framework for the graph models of the variant matrix estimation problems. The framework is based upon the viewpoint that a partition of a matrix into structurally orthogonal groups of columns corresponds to distance-2 coloring an appropriate graph representation. The unified framework helps integrate earlier work and leads to fresh insights; enables the design of more efficient algorithms for many problems; leads to new algorithms for others; and eases the task of building graph models for new problems. We report computational results on two of the coloring problems to support our claims. Most of the methods for these problems treat a column or a row of a matrix as an atomic entity, and partition the columns or rows (or both). A brief review of methods that do not fit these criteria is provided. We also discuss results in discrete mathematics and theoretical computer science that intersect with the topics considered here.


title: IF-THEN-ELSE considered harmful

abstract: Although the if-then-else construct for flow of control is simple in some senses, and certainly superior to unrestricted GO TO logic, the indefinite replication of this structure leads to undesireable program constructs. Other, more extensible control structures may be added to programming languages, while regulating the unrestricted use of if-then-else, in such a way as to reduce the complexity of programs.


title: Information technology outsourcing, knowledge transfer, and firm productivity: an empirical analysis

abstract: Firms are increasingly sourcing internal information systems functions from external service providers. However, there is limited empirical evidence of the economic impact of this delivery option and, more specifically, of the productivity gains accruing to firms that have outsourced. Moreover, there is little evidence of the role and contributions of the individual mechanisms by which service providers create value for client firms. We are particularly interested in whether client firms benefit from the accumulated knowledge held by information technology (IT) service firms. In this paper, we examine the impact of IT outsourcing on the productivity of firms that choose this mode of services delivery focusing, on the role of IT-related knowledge. Since firms selfselect into their optimal sourcing mode, we use a variety of econometric techniques including propensity scorebased matching and switching regression to control for potential bias arising from endogenously determined sourcing modes. We demonstrate that IT outsourcing does lead to productivity gains for firms that select this mode of service delivery. Our results also suggest that IT-related knowledge held by IT services vendors enables these productivity gains, the magnitude of which is moderated by a firm's IT intensity. Moreover, the value of outsourcing to a client firm increases with its propensity for outsourcing, which in turn depends on firm-specific attributes including efficiency level, financial leverage, and variability in business conditions. Our analyses also show that firms that outsource have been able to achieve additional productivity gains from contracting out compared with their counterfactuals.


title: Allostatic load biomarkers of chronic stress and impact on health and cognition

abstract: The allostatic load model expands the stress-disease literature by proposing a temporal cascade of multi-systemic physiological dysregulations that contribute to disease trajectories. By incorporating an allostatic load index representing neuroendocrine, immune, metabolic, and cardiovascular system functioning, numerous studies have demonstrated greater prediction of morbidity and mortality over and beyond traditional detection methods employed in biomedical practice. This article reviews theoretical and empirical work using the allostatic load model vis-a-vis the effects of chronic stress on physical and mental health. Specific risk and protective factors associated with increased allostatic load are elucidated and policies for promoting successful aging are proposed.


title: Online comments of multi-category commodities based on emotional tendency analysis

abstract: As the promotion competition of comprehensive e-commerce platforms becomes increasingly keener, this paper aims at finding the differences and key elements of consumer perceptions of different products from the perspective of category subdivision, and exploring the transformation mechanism between text comments and graded comments in order for accurate recommendation of products. First online commodities were generally divided into six categories; and the dictionary-based method was employed to calculate the emotional distribution of each category; then the key factors affecting user experience were identified through word frequency analysis; next, by adjusting emotion intensity and emotion weights, the correlation between text comment and graded comment was studied; finally, the prediction model was built for grade correction. Significant differences exist in the emotional perceptions of consumers whose concerns have similar dimensions but different degrees. Text comments are correlated with graded comments, but deviation between the two occurs with external interference. Adjustment of emotion intensity and emotion weight has an impact on the comprehensive emotion value of products, based on which the recommendation sequencing can be optimized.


title: Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering

abstract: While increasingly complex approaches to question answering (QA) have been proposed, the true gain of these systems, particularly with respect to their expensive training requirements, can be inflated when they are not compared to adequate baselines. Here we propose an unsupervised, simple, and fast alignment and information retrieval baseline that incorporates two novel contributions: a \textit{one-to-many alignment} between query and document terms and \textit{negative alignment} as a proxy for discriminative information. Our approach not only outperforms all conventional baselines as well as many supervised recurrent neural networks, but also approaches the state of the art for supervised systems on three QA datasets. With only three hyperparameters, we achieve 47\% P@1 on an 8th grade Science QA dataset, 32.9\% P@1 on a Yahoo! answers QA dataset and 64\% MAP on WikiQA. We also achieve 26.56\% and 58.36\% on ARC challenge and easy dataset respectively. In addition to including the additional ARC results in this version of the paper, for the ARC easy set only we also experimented with one additional parameter -- number of justifications retrieved.


title: LiBrA-CAN: A Lightweight Broadcast Authentication Protocol for Controller Area Networks

abstract: Security in vehicular networks established itself as a highly active research area in the last few years. However, there are only a few results so far on assuring security for communication buses inside vehicles. Here we advocate the use of a protocol based entirely on simple symmetric primitives that takes advantage of two interesting procedures which we call key splitting and MAC mixing. Rather than achieving authentication independently for each node, we split authentication keys between groups of multiple nodes. This leads to a more efficient progressive authentication that is effective especially in the case when compromised nodes form only a minority and we believe such an assumption to be realistic in automotive networks. To gain more security we also account an interesting construction in which message authentication codes are amalgamated using systems of linear equations. We study several protocol variants which are extremely flexible allowing different trade-offs on bus load, computational cost and security level. Experimental results are presented on state-of-the-art Infineon TriCore controllers which are contrasted with low end controllers with Freescale S12X cores, all these devices are wide spread in the automotive industry. Finally, we discuss a completely backward compatible solution based on CAN+, a recent improvement of CAN.


title: Nutrition-Related Knowledge, Attitudes, and Practices (KAP) among Kindergarten Teachers in Chongqing, China: A Cross-Sectional Survey

abstract: Kindergarten teachers play an important role in providing kindergarten children with education on nutrition. However, few studies have been published on nutrition-related knowledge, attitudes, and practices (KAP) of Chinese kindergarten teachers. This study aimed to assess the nutrition-related knowledge, attitudes, and practices (KAP) of kindergarten teachers in Chongqing, China. Thus, a cross-sectional survey was conducted using a structured KAP model questionnaire administered to 222 kindergarten teachers, who were senior teachers from 80 kindergartens in 19 districts and 20 counties in Chongqing. Multiple regression analysis was used to analyze the influential factors. Among the participants, 54.2% were familiar with simple nutrition-related knowledge; only 9.9% of them were satisfied with their knowledge of childhood nutrition; and 97.7% of them had a positive attitude to learn nutrition-related knowledge. Only 38.7% of the participants had attended pediatric nutrition knowledge courses or training. Multiple regression analysis confirmed significant independent effects on the nutrition knowledge score (p < 0.0001) of respondents on age, type of residence, type of kindergarten, body mass index(BMI), professional training of kindergarten teachers, behavior of having ever participated in childhood nutrition education knowledge courses or training, and behavior of having ever paid attention to children's nutrition knowledge. The model indicated that independent variables explained 45.4% (adjusted R²) of the variance found in the knowledge scores of respondents. While there were low levels of nutrition knowledge and training, it was still encouraging to note that there were positive attitudes towards acquiring nutrition-related knowledge among kindergarten teachers in Chongqing, China. These findings provide some implications that necessary training measures need to be carried out to improve the nutrition-related knowledge level among kindergarten teachers in China.


title: Emergence of structural and dynamical properties of ecological mutualistic networks

abstract: Cooperation among species tends to result in mutualistic networks with a nested structure, which is thought to increase biodiversity and persistence but may be less stable than unstructured networks: here nested networks are shown to result from a mechanism that maximizes species abundances in mutualistic communities, and the abundance of nested species is found to be directly linked to the resilience of the community.


ADAM: Genomics Formats and Processing Patterns for Cloud Scale Computing


title: Implementation and Analysis of AES Encryption on GPU

abstract: GPU is continuing its trend of vastly outperforming CPU while becoming more general purpose. In order to improve the efficiency of AES algorithm, this paper proposed a CUDA implementation of Electronic Codebook (ECB) mode encoding process and Cipher Feedback (CBC) mode decoding process on GPU. In our implementation, the frequently accessed T-boxes were allocated on on-chip shared memory and the granularity that one thread handles a 16 Bytes AES block was adopted. Finally, we achieved the highest performance of around 60 Gbps throughput on NVIDIA Tesla C2050 GPU, which runs up to 50 times faster than a sequential implementation based on Intel Core i7-920 2.66GHz CPU. In addition, we discussed the optimization under some practical application scenarios such as overlapping GPU processing and data transfer.


title: 3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection

abstract: 3D Statistical Shape Models (3D-SSM) are widely used for medical image segmentation. However, during segmentation, they typically perform a very limited unidirectional search for suitable landmark positions in the image, relying on weak learners or use-case specific appearance models that solely take local image information into account. As a consequence, segmentation errors arise, and results in general depend on the accuracy of a previous model initialization. Furthermore, these methods become subject to a tedious and use-case dependent parameter tuning in order to obtain optimized results. To overcome these limitations, we propose an extension of 3D-SSM by landmark-wise random regression forests that perform an enhanced omni-directional search for landmark positions, thereby taking rich non-local image information into account. In addition, we provide a long distance model fitting based on a multi-scale approach, that allows an accurate and reproducible segmentation even from distant image positions, thus enabling an application without model initialization. Finally, translation of the proposed method to different organs is straightforward and requires no adaptation of the training process. In segmentation experiments on 45 clinical CT volumes, the proposed omni-directional search significantly increased accuracy and displayed great precision regardless of model initialization. Furthermore, for liver, spleen and kidney segmentation in a competitive multi-organ labeling challenge on publicly available data, the proposed method achieved similar or better results than the state of the art. Finally, liver segmentation results were obtained that successfully compete with specialized state-of-the-art methods from the well-known liver segmentation challenge SLIVER.


title: Penile size and penile enlargement surgery: a review

abstract: Penile size is a considerable concern for men of all ages. Herein, we review the data on penile size and conditions that will result in penile shortening. Penile augmentation procedures are discussed, including indications, procedures and complications of penile lengthening procedures, penile girth enhancement procedures and penile skin reconstruction.


Productivity and Multi-Screen Computer Displays


Bitsliced implementation of the AES algorithm on GPU Jardim


title: Advanced Butler Matrices With Integrated Bandpass Filter Functions

abstract: A novel class of Butler matrix with inherent bandpass filter (BPF) transfer functions is presented in this paper. The Butler matrix is the fundamental network to split and recombine the signal in multi-port power amplifiers, however, to suppress spurious frequencies generated by the amplifiers or to provide near-band rejection in order not to interfere with other transmission/receiving bands separate filtering is often required. Here, the traditional power division and phase distribution of the Butler matrix are included together with filtering selectivity into one single device based only on coupled resonators. An analytical synthesis procedure of the coupling matrix for ${\hbox{2}}^{k}\times {\hbox{2}}^{k}$ networks is presented here for the first time. The proposed solution has shown significant advantages in terms of size reduction compared to the traditional baseline consisting of a distribution network plus a bank of BPFs. The synthesis and design of a 2 $\times$ 2, 180 $^{\circ}$ hybrid coupler at 10 GHz and a 4 $\times$ 4 Butler matrix with an equal-ripple four-pole Chebyshev bandpass characteristic centred at 12.5 GHz with 500-MHz bandwidth are described, confirming the synthesis technique proposed. Two models of the 4 $\times$ 4, one built with additive manufacturing and the other with milling, are also presented and compared. Experimental measurements are in good agreement with both simulations and theoretical expectations.


title: Machine learning algorithms for event detection

abstract: A common task in many machine learning application domains involves monitoring routinely collected data for ‘interesting’ events. This task is prevalent in surveillance, but also in tasks ranging from the analysis of scientific data to the monitoring of naturally occurring events, and from supervising industrial processes to observing human behavior. We will refer to this monitoring process with the purpose of identifying interesting occurrences, as event detection. We put together this special issue of the Machine Learning journal with the belief that principled machine learning approaches can and will be a differentiator in addressing event detection tasks, and that theoretical and practical advances of machine learning in this area have the potential to impact a wide range of important real-world applications such as security, public health and medicine, biology, environmental sciences, manufacturing, astrophysics, business, and economics. In the recent past, domain experts in these areas have had the laborious job of manually examining the collected data for events of interest. With the emergence of computers, many efforts have been made to replace manual inspection with an automated process. Data, however, have become increasingly complex, and the quantities of collected data have become extremely large in recent years. Multivariate records, images, video footage, audio recordings, spatial and spatio-temporal data, text documents, and even relational data are now routinely collected. We all expect that advances in machine learning would be well-suited for this class of tasks. However, in practice, the peculiarities of the application often grossly violate the


title: DeepRefiner: Multi-layer Android Malware Detection System Applying Deep Neural Networks

abstract: As malicious behaviors vary significantly across mobile malware, it is challenging to detect malware both efficiently and effectively. Also due to the continuous evolution of malicious behaviors, it is difficult to extract features by laborious human feature engineering and keep up with the speed of malware evolution. To solve these challenges, we propose DeepRefiner to identify malware both efficiently and effectively. The novel technique enabling effectiveness is the semantic-based deep learning. We use Long Short Term Memory on the semantic structure of Android bytecode, avoiding missing the details of method-level bytecode semantics. To achieve efficiency, we apply Multilayer Perceptron on the xml files based on the finding that most malware can be efficiently identified using information only from xml files. We evaluate the detection performance of DeepRefiner with 62,915 malicious applications and 47,525 benign applications, showing that DeepRefiner effectively detects malware with an accuracy of 97.74% and a false positive rate of 2.54%. We compare DeepRefiner with a state-of-the-art single classifierbased detection system, StormDroid, and ten widely used signature-based anti-virus scanners. The experimental results show that DeepRefiner significantly outperforms StormDroid and anti-virus scanners. In addition, we evaluate the robustness of DeepRefiner against typical obfuscation techniques and adversarial samples. The experimental results demonstrate that DeepRefiner is robust in detecting obfuscated malicious applications.


title: Could This Be True?: I Think So! Expressed Uncertainty in Online Rumoring

abstract: Rumors are regular features of crisis events due to the extreme uncertainty and lack of information that often characterizes these settings. Despite recent research that explores rumoring during crisis events on social media platforms, limited work has focused explicitly on how individuals and groups express uncertainty. Here we develop and apply a flexible typology for types of expressed uncertainty. By applying our framework across six rumors from two crisis events we demonstrate the role of uncertainty in the collective sensemaking process that occurs during crisis events.


title: Improved Transliteration Mining Using Graph Reinforcement

abstract: Mining of transliterations from comparable or parallel text can enhance natural language processing applications such as machine translation and cross language information retrieval. This paper presents an enhanced transliteration mining technique that uses a generative graph reinforcement model to infer mappings between source and target character sequences. An initial set of mappings are learned through automatic alignment of transliteration pairs at character sequence level. Then, these mappings are modeled using a bipartite graph. A graph reinforcement algorithm is then used to enrich the graph by inferring additional mappings. During graph reinforcement, appropriate link reweighting is used to promote good mappings and to demote bad ones. The enhanced transliteration mining technique is tested in the context of mining transliterations from parallel Wikipedia titles in 4 alphabet-based languages pairs, namely English-Arabic, English-Russian, English-Hindi, and English-Tamil. The improvements in F1-measure over the baseline system were 18.7, 1.0, 4.5, and 32.5 basis points for the four language pairs respectively. The results herein outperform the best reported results in the literature by 2.6, 4.8, 0.8, and 4.1 basis points for the four language pairs respectively.


title: Ranking: Compare, don't score

abstract: The need to rank items based on user input arises in many practical applications such as elections, group-decision making and recommendation systems. The primary challenge in such scenarios is to decide a global ranking based on partial preferences provides by users. The standard approach to address this challenge is to ask users to provide explicit numerical ratings (cardinal information) of a subset of items. The main appeal of such an approach is the ease of aggregation. However, the rating scale as well as the individual ratings are often arbitrary and may not be consistent from one user to another. A more natural alternative to numerical ratings requires users to compare pairs of items (ordinal information). In contrast to cardinal information, such comparisons provide an “absolute” indicator of the user's preference. However, it is often hard to combine or aggregate comparisons to obtain a consistent global ranking. In this work, we provide a tractable framework for utilizing comparison data as well as first-order marginal information for the purpose of ranking. We treat the available information as partial samples from an unknown distribution over permutations. Using the Principle of Maximum Entropy, we devise a concise parameterization of distribution consistent with observations using only O(n2) parameters, where n is the number of items in question. We propose a distributed, iterative algorithm for estimating the parameters of the distribution. We establish the correctness of the algorithm as well as identify the rate of convergence explicitly. Using the learnt distribution, we provide efficient approach to (a) learn the mode of the distribution using ‘maximum weight matching’, (b) identification of top k items, and (c) an aggregate ranking of all n items. Through evaluation of our approach on real-data, we verify effectiveness of our solutions as well as the scalability of the algorithm.


title: IMAGE-BASED AIRBORNE LiDAR POINT CLOUD ENCODING FOR 3D BUILDING MODEL RETRIEVAL

abstract: Abstract. With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.


title: Design & Check Cyclic Redundancy Code using VERILOG HDL

abstract: the CRC or cyclic redundancy check is a widely used technique for error checking in many protocols used in data transmission. The aim of this project is to design the CRC RTL generator or a tool that calculates the CRC equations for the given CRC polynomials and generates the Verilog RTL code .This block deals with the calculation of equations for standard polynomials like CRC-4, CRC-8, CRC-16, CRC-32 and CRC-48, CRC-64 and also user defined proprietary polynomial. To use PERL as the platform it also aims at having a simpler user interface. To generate the RTLs for any data width and for any standard polynomial or user defined polynomial, this design aims to be complete generic. The RTLs generated by this tool are verified by System Verilog constrained random testing to make it more robust and reliable.


title: AccessMiner: using system-centric models for malware protection

abstract: Models based on system calls are a popular and common approach to characterize the run-time behavior of programs. For example, system calls are used by intrusion detection systems to detect software exploits. As another example, policies based on system calls are used to sandbox applications or to enforce access control. Given that malware represents a significant security threat for today's computing infrastructure, it is not surprising that system calls were also proposed to distinguish between benign processes and malicious code. Most proposed malware detectors that use system calls follows program-centric analysis approach. That is, they build models based on specific behaviors of individual applications. Unfortunately, it is not clear how well these models generalize, especially when exposed to a diverse set of previously-unseen, real-world applications that operate on realistic inputs. This is particularly problematic as most previous work has used only a small set of programs to measure their technique's false positive rate. Moreover, these programs were run for a short time, often by the authors themselves. In this paper, we study the diversity of system calls by performing a large-scale collection (compared to previous efforts) of system calls on hosts that run applications for regular users on actual inputs. Our analysis of the data demonstrates that simple malware detectors, such as those based on system call sequences, face significant challenges in such environments. To address the limitations of program-centric approaches, we propose an alternative detection model that characterizes the general interactions between benign programs and the operating system (OS). More precisely, our system-centric approach models the way in which benign programs access OS resources (such as files and registry entries). Our experiments demonstrate that this approach captures well the behavior of benign programs and raises very few (even zero) false positives while being able to detect a significant fraction of today's malware.


Quality of Service ( QoS )-Aware Wireless Sensor Networks ( WSNs ) MAC Protocol for Time-Critical Smart Grid Applications


title: Heartbeat Anomaly Detection using Adversarial Oversampling

abstract: Cardiovascular diseases are one of the most common causes of death in the world. Prevention, knowledge of previous cases in the family, and early detection is the best strategy to reduce this fact. Different machine learning approaches to automatic diagnostic are being proposed to this task. As in most health problems, the imbalance between examples and classes is predominant in this problem and affects the performance of the automated solution. In this paper, we address the classification of heartbeats images in different cardiovascular diseases. We propose a two-dimensional Convolutional Neural Network for classification after using a InfoGAN architecture for generating synthetic images to unbalanced classes. We call this proposal Adversarial Oversampling and compare it with the classical oversampling methods as SMOTE, ADASYN, and Random Oversampling. The results show that the proposed approach improves the classifier performance for the minority classes without harming the performance in the balanced classes.


title: Two-Stream Multi-Task Network for Fashion Recognition

abstract: In this paper, we present a two-stream multi-task network for fashion recognition. This task is challenging as fashion clothing always contain multiple attributes, which need to be predicted simultaneously for real-time industrial systems. To handle these challenges, we formulate fashion recognition into a multi-task learning problem, including landmark detection, category and attribute classifications, and solve it with the proposed deep convolutional neural network. We design two knowledge sharing strategies which enable information transfer between tasks and improve the overall performance. The proposed model achieves state-of-the-art results on large-scale fashion dataset comparing to the existing methods, which demonstrates its great effectiveness and superiority for fashion recognition.


title: Towards the Decentralised Cloud: Survey on Approaches and Challenges for Mobile, Ad hoc, and Edge Computing

abstract: Cloud computing emerged as a centralised paradigm that made “infinite” computing resources available on demand. Nevertheless, the ever-increasing computing capacities present on smart connected things and devices calls for the decentralisation of Cloud computing to avoid unnecessary latencies and fully exploit accessible computing capacities at the edges of the network. Whilst these decentralised Cloud models represent a significant breakthrough from a Cloud perspective, they are rooted in existing research areas such as Mobile Cloud Computing, Mobile Ad hoc Computing, and Edge computing. This article analyses the pre-existing works to determine their role in Decentralised Cloud and future computing development.


title: Computing Linear Discriminants for Idiomatic Sentence Detection

abstract: In this paper, we describe the binary classication of sen- tences into idiomatic and non-idiomatic. Our idiom detection algorithm is based on linear discriminant analysis (LDA). To obtain a discriminant subspace, we train our model on a small number of randomly selected idiomatic and non-idiomatic sentences. We then project both the train- ing and the test data on the chosen subspace and use the three nearest neighbor (3NN) classier to obtain accuracy. The proposed approach is more general than the previous algorithms for idiom detection | neither does it rely on target idiom types, lexicons, or large manually annotated corpora, nor does it limit the search space by a particular linguistic con- struction.


title: Enabling Automated Dynamic Demand Response: From Theory to Practice

abstract: Demand response (DR) is used in smart grids to shape customer load during peak hours. Automated DR offers utilities a fine grained control and a high degree of confidence in the outcome. However the impact on the customer's comfort means this technique is more suited for industrial and commercial settings than for residential homes. In this paper we present a real-life system for achieving automated controlled DR in a heterogeneous environment. The system is integrated with the USC microgrid. Results show that while on a per building per event basis the accuracy of our prediction and customer selection techniques varies, it performs well on average when considering several events and buildings.


title: Combination of microneedling and glycolic acid peels for the treatment of acne scars in dark skin.

abstract: INTRODUCTION ::: Acne scars can cause emotional and psychosocial disturbance to the patient. Various modalities have been used for the treatment of acne scars like punch excision, subcision, peels, microdermabrasion, unfractionated and fractioned lasers. The latest in the treatment armamentarium is microneedling. Acne scars commonly coexist with postinflammatory hyperpigmentation. A combination of microneedling and glycolic acid (GA) peels was found to give excellent results in the treatment of such scars. The aim was to study the efficacy of a combination of microneedling with glycolic peel for the treatment of acne scars in pigmented skin. ::: ::: ::: METHOD ::: Thirty patients in the age group of 20-40 years with atrophic box type or rolling scars with postinflammatory hyperpigmentation were chosen for the study. Two groups were made. The first group comprised of 30 patients in whom only microneedling was performed once in 6 weeks for five sessions. In the second group of 30 patients, a combination of microneedling and 35% GA peels was carried out. Patients from both groups were evaluated on the basis of Echelle d'Evaluation clinique des Cicatrices d'acné classification. ::: ::: ::: RESULTS ::: Based on the objective scoring and its statistical analysis, there was significant improvement in superficial and moderately deep scars (grade 1-3). There was also improvement in skin texture, reduction in postacne pigmentation in the second group. ::: ::: ::: CONCLUSION ::: Microneedling is a simple, inexpensive office procedure with no downtime. It is safe in Indian skin (skin types III-IV). The combined sequential treatment with GA peel caused a significant improvement in the acne scars without increasing morbidity.


title: Automated Music Emotion Recognition: A Systematic Evaluation

abstract: Abstract Automated music emotion recognition (MER) is a challenging task in Music Information Retrieval with wide-ranging applications. Some recent studies pose MER as a continuous regression problem in the Arousal-Valence (AV) plane. These consist of variations on a common architecture having a universal model of emotional response, a common repertoire of low-level audio features, a bag-of-frames approach to audio analysis, and relatively small data sets. These approaches achieve some success at MER and suggest that further improvements are possible with current technology. Our contribution to the state of the art is to examine just how far one can go within this framework, and to investigate what the limitations of this framework are. We present the results of a systematic study conducted in an attempt to maximize the prediction performance of an automated MER system using the architecture described. We begin with a carefully constructed data set, emphasizing quality over quantity. We address affect ind...


title: Vascular Tortuosity May Be Related to Intracranial Artery Atherosclerosis

abstract: BackgroundConventional risk factors cannot explain the individual or ethnic difference in the location of cerebral atherosclerosis. Vascular geometry may play a role on this.AimTo investigate the association between the geometrical properties of the middle cerebral artery and intracranial atherosclerosis.MethodsStroke patients with either symptomatic middle cerebral artery or proximal internal carotid artery diseases were enrolled. The diameter, length, and tortuosity (arc-chord ratio, by percent) of the middle cerebral artery was compared between stroke patients and the age- and gender-matched healthy subjects. The geometrical property was also compared between the symptomatic middle cerebral artery and the normal-looking contralateral middle cerebral artery, and between the contralateral middle cerebral arteries in patients with symptomatic middle cerebral artery and proximal internal carotid artery diseases.ResultsOne hundred seventy-seven patients (101 with middle cerebral artery disease and 76 with p...


title: Iot based smart and adaptive lighting in street lights

abstract: The system is mainly used for smart and weather adaptive lighting in street lights. The project is implemented with smart embedded system that controls the street light based on detection of sunlight. During the night time the street light gets automatically ON and during the day time it gets automatically OFF. The ON/OFF can be accessed anywhere anytime through internet. A camera is placed on top of the street light to track the actions performed on the road where the footages are stored in a server. In addition to this, a panic button is placed on the pole, in-case of any emergency or danger, the person in danger can press this button which raises an alarm at the nearby police station. Whenever the panic button is pressed, the footage at that time recorded by the camera is sent directly to the cloud account. The access of the account is given to the particular police station by which they can view the incident's spot. Each area's street lights are connected to the particular area's police station and each of them has a cloud accessible account. The manual operation using GSM technology is completely eliminated. Thus the system is mainly designed to ensure safety and to prevent energy wastage.


title: Novel rotor design optimization of Synchronous Reluctance Machine for low torque ripple

abstract: Torque ripple can be an important issue in Synchronous Reluctance Machine (SynRM) design, similar to the Induction Machine (IM). A novel, simple and systematic method for ripple reduction in SynRM (that can also be implemented for IPM and PMaSynRM) design is presented in this paper. The decoupling between stator and rotor structure during the torque ripple minimization is another goal. This can be achieved using a general method that minimizes the ripple independent of the stator structure, specially the slots number of the stator, and the number of barriers in the rotor or rotor slots number. Furthermore, it is shown that torque maximization and torque ripple minimization can be achieved independently in the SynRM design using limitedly, FEM simulations.


title: Line-Start PM-Assisted Synchronous Motor Design, Optimization, and Tests

abstract: Line-start synchronous motors (LSSMs) have been recently introduced in the motor market to meet the new efficiency class requirements. They exhibit high efficiency and power factor compared with induction motors (IMs), but the complex design, manufacture, and high cost due to presence of permanent magnets (PM) limit their widespread use. This paper proposes a new approach for the design of LSSMs, in which an important torque contribution is given by the reluctance torque component. Both steady-state and dynamic capabilities are considered. A stochastic optimization is used to determine the parameters that maximize the synchronous torque. The possibility to apply the recent improvements on the synchronous reluctance machine design to LSSMs is discussed. Experimental measurements are carried out on an LSSM prototype achieved from the optimization. The results are compared with the predictions. Two different PM volumes are tested in the same rotor structure, evaluating their impact on motor performance. From the experimental tests, an overview comparison between the IM and LSSM performance is given.


title: A Survey of 5G Network: Architecture and Emerging Technologies

abstract: In the near future, i.e., beyond 4G, some of the prime objectives or demands that need to be addressed are increased capacity, improved data rate, decreased latency, and better quality of service. To meet these demands, drastic improvements need to be made in cellular network architecture. This paper presents the results of a detailed survey on the fifth generation (5G) cellular network architecture and some of the key emerging technologies that are helpful in improving the architecture and meeting the demands of users. In this detailed survey, the prime focus is on the 5G cellular network architecture, massive multiple input multiple output technology, and device-to-device communication (D2D). Along with this, some of the emerging technologies that are addressed in this paper include interference management, spectrum sharing with cognitive radio, ultra-dense networks, multi-radio access technology association, full duplex radios, millimeter wave solutions for 5G cellular networks, and cloud technologies for 5G radio access networks and software defined networks. In this paper, a general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G cellular network architecture. A detailed survey is included regarding current research projects being conducted in different countries by research groups and institutions that are working on 5G technologies.


Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Prediction


title: An Ontology-Based Annotation Framework for Representing the Functionality of Engineering Devices

abstract: This paper proposes a metadata schema relating to functionality based on Semantic Web technology for the management of the information content of engineering design documents. The schema enables us to annotate web-documents with RDF metadata, which represents devices as having specific functions. The metadata provide a clear and operational semantics for the functional terms in documents. The metadata schema is based on our own functional ontologies, which we believe to provide effective guidelines for consistent functional annotation. These ontologies have been developed over many years and deployed successfully in industry. We then demonstrate a document search system using the metadata, which enables us to retrieve web-documents using the types of function and relationships defined in the schema rather than more superficial terms. Such function-oriented management of information is especially useful in the conceptual design phase. It allows one to find previous cases of the same function in earlier designs and to find related patents. We go on to discuss the following two issues on the interoperability of the functional knowledge. The first is automatic transformation of the functional model in our modeling framework into a chart-style form commonly used for the FMEA (Failure Modes and Effects Analysis) activity using a mapping between our ontology and an FMEA ontology. The second is translation of the functional model between our functional vocabulary and other taxonomy of function, that is, Functional Basis proposed by Stone et al. Another mapping between our functional vocabulary and Functional Basis can be done via a richer, generic reference ontology of functions. This mapping would aim at clarifying ontological differences between functional taxonomies and enabling translation between them.


title: Software Design and Architecture The once and future focus of software engineering

abstract: The design of software has been a focus of software engineering research since the field's beginning. This paper explores key aspects of this research focus and shows why design will remain a principal focus. The intrinsic elements of software design, both process and product, are discussed: concept formation, use of experience, and means for representation, reasoning, and directing the design activity. Design is presented as being an activity engaged by a wide range of stakeholders, acting throughout most of a system's lifecycle, making a set of key choices which constitute the application's architecture. Directions for design research are outlined, including: (a) drawing lessons, inspiration, and techniques from design fields outside of computer science, (b) emphasizing the design of application "character" (functionality and style) as well as the application's structure, and (c) expanding the notion of software to encompass the design of additional kinds of intangible complex artifacts.


title: From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering

abstract: We present an improved method for clustering in the presence of very limited supervisory information, given as pairwise instance constraints. By allowing instance-level constraints to have space-level inductive implications, we are able to successfully incorporate constraints for a wide range of data set types. Our method greatly improves on the previously studied constrained k-means algorithm, generally requiring less than half as many constraints to achieve a given accuracy on a range of real-world data, while also being more robust when over-constrained. We additionally discuss an active learning algorithm which increases the value of constraints even further.


title: Neuroanatomical Correlates of Visually Evoked Sexual Arousal in Human Males

abstract: Brain areas activated in human male sexualbehavior have not been characterized precisely. For thefirst time, positron emission tomography (PET) was usedto identify the brain areas activated in healthy males experiencing visually evoked sexualarousal. Eight male subjects underwent six measurementsof regional brain activity following the administrationof [15O]H2O as they viewedthree categories of film clips: sexually explicit clips,emotionally neutral control clips, and humorous controlclips inducing positive but nonsexual emotions.Statistical Parametric Mapping was used to identifybrain regions demonstrating an increased activity associatedwith the sexual response to the visual stimulus.Visually evoked sexual arousal was characterized by athreefold pattern of activation: the bilateralactivation of the inferior temporal cortex, a visualassociation area; the activation of the right insula andright inferior frontal cortex, which are two paralimbicareas relating highly processed sensory information with motivational states; and the activation ofthe left anterior cingulate cortex, another paralimbicarea known to control autonomic and neuroendocrinefunctions. Activation of some of these areas was positively correlated with plasma testosteronelevels. Although this study should be consideredpreliminary, it identified brain regions whoseactivation was correlated with visually evoked sexualarousal in males.


title: ZVS range extension of 10A 15kV SiC MOSFET based 20kW Dual Active Half Bridge (DHB) DC-DC converter

abstract: SiC MOSFET is favorable for high voltage applications due to its fast switching speed, low loss and high voltage rating compared with silicon power devices. This paper presents the switching performance of 10A 15kV SiC MOSFET and analyzes its switching loss when considering the impact of parasitic capacitance. A 20 kHz 20 kW Dual Active Half Bridge (DHB) dc-dc converter based on this new device is designed with full ZVS range. Simulation and experiment results are given to validate the analysis.


title: A Design Approach for Collaboration Processes: A Multimethod Design Science Study in Collaboration Engineering

abstract: Collaboration engineering is an approach for the design and deployment of repeatable collaboration processes that can be executed by practitioners without the support of collaboration professionals such as facilitators. A critical challenge in collaboration engineering concerns how the design activities have to be executed and which design choices have to be made to create a process design. We report on a four-year design science study in which we developed a design approach for collaboration engineering that incorporates existing process design methods, pattern-based design principles, and insights from expert facilitators regarding design challenges and choices. The resulting approach was evaluated and continuously improved in four trials with 37 students. Our findings suggest that this approach is useful to support the design of repeatable collaboration processes. Our study further serves as an example of how a design approach can be developed and improved following a multimethod design science approach.


title: A 1.8-V 1.8 GHz LC VCO for WSN application

abstract: A 1.8 GHz LC VCO in 1.8-V supply is presented. The VCO achieves low power consumption by optimum selection of inductance in the L-C tank. To increase the tuning range, a three-bit switching capacitor array is used for digital switched tuning. Designed in 0.18μm RF CMOS technology, the proposed VCO achieves a phase noise of −126.2dBc/Hz at 1MHz offset and consumes 1.38mA core current at 1.8-V voltage supply.


title: Scalable Distributed Real-Time Clustering for Big Data Streams

abstract: Ever growing volume of data production is the reality we are living in. Social networks, smart cities, telephone networks, the internet are some of the main data producers in the modern world and much of this information is discarded due to the high storage space it would require. Relevant data can be extracted from this massive amount of information and be used to build better cities, offers better services, make predictive analysis, group similar information and many other applications. All of this is possible due to machine learning and data mining where patterns can be found in the ocean of data generated every second. In order to cope with the volume, velocity and variety of data produced a streaming model has been studied were analysis of data has to use low memory and process items only once, therefore only the summary and statistical information is kept reducing enormously the storage requirements. In the highly connected world we live and the advance of grid and cloud computing the missing component to help crunch this large amount of data is the power of distributed computing. Stream Processing Engines (SPE) have revealed to be a more flexible and powerful tool for dealing with BigData, thus a natural evolution from the batch MapReduce tools. The advantage of SPEs is that of processing an unbound, possibly infinite flow of data and returning results in any time. This project merges the concepts of machine learning, streaming model and distributed computing to build a framework for developing, testing and applying algorithms on large volume of data streams. The Scalable Advanced Massive Online Analysis (SAMOA) project is an initiative from the Yahoo! Research labs in Barcelona and abstracts the use of distributed stream processing platforms by using the concept of code once and run everywhere. Our implementation includes a realtime distributed clustering algorithm and a realtime classification algorithm, which can be run on Twitter Storm and Apache S4 platforms.


title: The mole concept using MS Excel

abstract: A program has been developed to assist chemistry students in understanding the mole concept. The mole concept is a fundamental concept in chemistry that students need to understand before they can solve problems such as calculating the number of atoms in an element, mass of a substance or finding the stoichiometric amounts of reactants or products in a given chemical reaction. This program will be able to calculate the number of moles of a substance, the empirical formula of a compound and solve problems related to reaction stoichiometry. In addition, there are short notes, glossary of terms and quiz in the form of true/false and multiple-choice type questions. Another feature of this program is the periodic table of elements where the physical properties of the elements can be displayed. The program is written in Visual Basic language using the Visual Basic editor of the Microsoft Excel software. The data required for developing this program are stored in the Excel worksheets and the user interface is linked to the data using Visual Basic programming. This program receives a positive feedback based on a perception survey on 50 students.


title: Ethical Artificial Intelligence - An Open Question

abstract: Artificial Intelligence (AI) is an effective science which employs strong enough approaches, methods, and techniques to solve unsolvable real world based problems. Because of its unstoppable rise towards the future, there are also some discussions about its ethics and safety. Shaping an AI friendly environment for people and a people friendly environment for AI can be a possible answer for finding a shared context of values for both humans and robots. In this context, objective of this paper is to address the ethical issues of AI and explore the moral dilemmas that arise from ethical algorithms, from pre set or acquired values. In addition, the paper will also focus on the subject of AI safety. As general, the paper will briefly analyze the concerns and potential solutions to solving the ethical issues presented and increase readers awareness on AI safety as another related research interest.


title: Spoken Arabic Dialect Identification Using Phonotactic Modeling

abstract: The Arabic language is a collection of multiple variants, among which Modern Standard Arabic (MSA) has a special status as the formal written standard language of the media, culture and education across the Arab world. The other variants are informal spoken dialects that are the media of communication for daily life. Arabic dialects differ substantially from MSA and each other in terms of phonology, morphology, lexical choice and syntax. In this paper, we describe a system that automatically identifies the Arabic dialect (Gulf, Iraqi, Levantine, Egyptian and MSA) of a speaker given a sample of his/her speech. The phonotactic approach we use proves to be effective in identifying these dialects with considerable overall accuracy --- 81.60% using 30s test utterances.