title: Static Path Planning for Mobile Beacons to Localize Sensor Networks

abstract: In this paper, we study the static path planning problem with wireless sensor network localization as the primary objective. We consider a model in which sensors are assumed to be uniformly deployed to a predefined deployment area. We then deploy a robot to serve as a mobile beacon to enable the localization of the sensor nodes. The robot follows a pre-determined static path while periodically broadcasting its current location coordinates to the nearby sensors. The static path planning problem looks for good paths that result in better localization accuracy and coverage of the sensor network while keeping the path length bounded. We propose two new path types, CIRCLES and S-CURVES, that are specifically designed to reduce the collinearity during localization. We compare our solution with existing ones using the Cramer Rao bound (CRB) as the evaluation tool, which gives an unbiased evaluation regardless of localization algorithm used. The evaluation shows that our solutions cope with collinearity in a more effective manner than previous solutions. Our solutions provide significantly better localization accuracy and coverage in the cases where collinearity is the greatest problem


title: Joint Head Pose Estimation and Face Alignment Framework Using Global and Local CNN Features

abstract: In this paper, we explore global and local features obtained from Convolutional Neural Networks (CNN) for learning to estimate head pose and localize landmarks jointly. Because there is a high correlation between head pose and landmark locations, the head pose distributions from a reference database and learned local deep patch features are used to reduce the error in the head pose estimation and face alignment tasks. First, we train GNet on the detected face region to obtain a rough estimate of the pose and to localize the seven primary landmarks. The most similar shape is selected for initialization from a reference shape pool constructed from the training samples according to the estimated head pose. Starting from the initial pose and shape, LNet is used to learn local CNN features and predict the shape and pose residuals. We demonstrate that our algorithm, named JFA, improves both the head pose estimation and face alignment. To the best of our knowledge, this is the first system that explores the use of the global and local CNN features to solve head pose estimation and landmark detection tasks jointly.


title: Astrophysical image denoising using bivariate isotropic Cauchy distributions in the undecimated wavelet domain

abstract: Within the framework of wavelet analysis, we describe a novel technique for removing noise from astrophysical images. We design a Bayesian estimator, which relies on a particular member of the family of isotropic /spl alpha/-stable distributions, namely the bivariate Cauchy density. Using the bivariate Cauchy model we develop a noise-removal processor that takes into account the interscale dependencies of wavelet coefficients. We show through simulations that our proposed technique outperforms existing methods both visually and in terms of root mean squared error.


Stability of Immunoglobulin G During Storage of Human Serum


title: Classification of electrocorticography based motor imagery movements using continuous wavelet transform

abstract: Recently electrocorticography (ECoG) has emerged as a potential tool for Brain Computer Interfacing applications. In this paper, a continuous wavelet transform (CWT) based method is proposed for classifying ECoG motor imagery signals corresponding to left pinky and tongue movement. The total experiment is carried out with the publicly available benchmark BCI competition III, data set I. The L 2 norms of the CWT coefficients obtained from ECoG signals are shown to be separable for the two classes of motor imagery signals. Then the L 2 norm based features are subjected to principal component analysis, yielding a feature set with lower dimension. Among various types of classifiers used, support vector machine based classifiers have been shown to provide a good accuracy of 92% which is shown to be better than several existing techniques. In addition, unlike most of the existing methods, our proposed method involves no pre-processing and thus can have better potential for practical implementation while requiring much lower computational time in extracting the features.


title: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

abstract: Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.


title: Deep Discriminative Representation Learning with Attention Map for Scene Classification

abstract: Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were based on handcrafted features. However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is only to use original RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show class activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using original RGB patches and attention map based class information provide complementary information to the standard RGB deep models. To the best of our knowledge, we are the first to investigate attention information encoded CNNs. Additionally, to enhance the discriminability, we further employ a recently developed object function called "center loss," which has proved to be very useful in face recognition. Finally, our framework provides attention guidance to the model in an end-to-end fashion. Extensive experiments on two benchmark datasets show that our approach matches or exceeds the performance of other methods.


Application of parametric linear programming in Coca-cola Company using a developed algorithm


Exploring the musical taste of expert listeners: musicology students reveal tendency toward omnivorous taste


Water Quality Measurement and Control from Remote Station for Pisiculture Using NI myRIO Beena


title: A Unified Virtual Power Decoupling Method for Droop-Controlled Parallel Inverters in Microgrids

abstract: The coupling between the active and the reactive power of the droop-controlled inverter is critical in the low-voltage microgrid. The conventional virtual power-based control strategy can decouple the active and the reactive power of the inverter, whereas the active power of the load is probably not equally shared among the parallel inverters in the microgrid. To tackle this issue, this paper proposes an improved virtual power-based control method for the droop-controlled parallel inverters with a unified rotation angle in the power transformation. Meanwhile, the optimal value of the unified rotation angle is rigorously derived through investigating the relative stability of the system by innovatively adopting Routh Stability Criterion. Moreover, the proposed method is enhanced by introducing low-pass filters in the coupling path for further mitigating the coupling between the active and the reactive power of the inverter. Additionally, the stability analysis and parameter design for the proposed method are comprehensively presented. Finally, the effectiveness of the proposed method is validated by the simulation and experimental results.


title: Constructing a Desiring User: Discourse, Rurality, and Design in Location-Based Social Networks

abstract: A growing body of literature addresses the use of Grindr and SCRUFF, location-based networking applications for gay, bisexual, and queer men. This study builds on that work, asking whose sexuality is produced in the design and use of these applications. Drawing from ethnographic research and discourse analysis, we build on analytical frames from science and technology studies, feminist HCI, and sexuality studies, proposing what we call the desiring user: a user whose desires and sexuality are mediated through technological devices in particular ways. In doing so, we demonstrate how the discursive constructions of the user put forth by the creators of Grindr and SCRUFF clash with the lived reality of our rural interlocutors. We address emerging themes in CSCW and HCI related to the construction of sexual subjectivities and social computing in rural settings.


title: The Resonating Star Gyroscope: A Novel Multiple-Shell Silicon Gyroscope With Sub-5 deg/hr Allan Deviation Bias Instability

abstract: We report on the design, fabrication and characterization of a novel multiple-shell silicon vibratory microgyroscope. The resonating star gyroscope (RSG) is formed as a merged superposition of two square shells, yielding in-plane flexural modes that are utilized to sense rotation along the normal axis. The first prototypes of the single-shell RSG were implemented with 65 mum thick trench-refilled polysilicon structural material using the HARPSS process. These devices exhibited open-loop rate sensitivity of approximately 800 muV/deg/s. Despite high-aspect ratio sensing gaps, the device yielded poor sensitivity caused by low resonant-mode quality factors. To alleviate the Q TED losses caused by the inevitable formation of voids in trench-refilled structural material, the RSG was implemented in (111) single crystalline silicon. A 2.5-mm multiple-shell RSG was fabricated in 40 mum-thick SOI device layer using a simple two-mask process. Multiple-shells enable a higher operating frequency and larger resonant mass, essential components for reducing the mechanical noise floor of the sensor. Experimental data of a high-Q (111) multiple-shell prototype indicates sub-5 deg/hr Brownian noise floor, with a measured Allan deviation bias drift of 3.5 deg/hr. The gyroscope exhibits an open-loop rate sensitivity of approximately 16.7 mV/deg/s in vacuum.


title: DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa({\lambda}) Reinforcement Learners

abstract: This paper describes an architecture for controlling non-player characters (NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004. Specifically, the DRE-Bot architecture is made up of three reinforcement learners, Danger, Replenish and Explore, which use the tabular Sarsa({\lambda}) algorithm. This algorithm enables the NPC to learn through trial and error building up experience over time in an approach inspired by human learning. Experimentation is carried to measure the performance of DRE-Bot when competing against fixed strategy bots that ship with the game. The discount parameter, {\gamma}, and the trace parameter, {\lambda}, are also varied to see if their values have an effect on the performance.


title: LCANet: End-to-End Lipreading with Cascaded Attention-CTC

abstract: Machine lipreading is a special type of automatic speech recognition (ASR) which transcribes human speech by visually interpreting the movement of related face regions including lips, face, and tongue. Recently, deep neural network based lipreading methods show great potential and have exceeded the accuracy of experienced human lipreaders in some benchmark datasets. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data. In this paper, we propose LCANet, an end-to-end deep neural network based lipreading system. LCANet encodes input video frames using a stacked 3D convolutional neural network (CNN), highway network and bidirectional GRU network. The encoder effectively captures both short-term and long-term spatio-temporal information. More importantly, LCANet incorporates a cascaded attention-CTC decoder to generate output texts. By cascading CTC with attention, it partially eliminates the defect of the conditional independence assumption of CTC within the hidden neural layers, and this yields notably performance improvement as well as faster convergence. The experimental results show the proposed system achieves a 1.3% CER and 3.0% WER on the GRID corpus database, leading to a 12.3% improvement compared to the state-of-the-art methods.


title: Development and validation of blue ray, an optical modem for the MEDUSA class AUVs

abstract: Wireless optical communications are emerging as a viable solution for high-speed data transmission over short ranges in the ocean, complementing mainstream acoustic communication systems that operate over much longer ranges, but at lower data rates. The current drive to develop cooperative autonomous vehicular systems to carry out surveying and other complex missions in the ocean critically depends on the existence of such communication links to share sensory and coordination information. This paper presents a compact, low power consumption, cost-efficient, and lightweight optical modem designed for fast data transmission between MEDUSA underwater vehicles at ranges on the order of 10 m. The transmitter uses LEDs for ON-OFF keying, while the receiver front-end adopts a transimpedance architecture with a photodiode detector. This simplistic transmission and reception technique reduces the overall design and hardware complexities compared to, e.g., laser diodes and photomultiplier tubes. The LEDs are arranged in a circular array, with a photodiode at the centre to enable half-duplex operation. These modems are designed to achieve data rates of 20–200 kb/s over short-range, line-of-sight, configurations. A transparent casing is customised to fit the Medusa vehicles, with proper alignment for inter-vehicle communication when they move in formation.


title: Economic valuation of a toll road concession with traffic guarantees and the abandonment option

abstract: Governments worldwide have been encouraging private participation in transportation infrastructure. To increase the feasibility of a project, public-private partnership (PPP) may include guarantees or other support to reduce the risks for private investors. It is necessary to value these opportunities under a real options framework and thereby analyze the project's economic feasibility and risk allocation. However, within this structure, sponsors have an implicit option to abandon the project that should be simultaneously valued. Thus, this article proposes a hypothetical toll road concession in Brazil with a minimum traffic guarantee, a maximum traffic ceiling, and an implicit abandonment option. Different combinations of the minimum and maximum levels are presented, resulting in very high or even negative value added to the net present value (NPV). The abandonment option impacts the level of guarantee to be given. Governments should calibrate an optimal level of guarantees to avoid unnecessarily high costs, protect the returns of the sponsor, and lower the probability of abandonment.


title: Sentiment Analysis on Twitter Data using KNN and SVM

abstract: Millions of users share opinions on various topics using micro-blogging every day. Twitter is a very popular micro-blogging site where users are allowed a limit of 140 characters; this kind of restriction makes the users be concise as well as expressive at the same time. For that reason, it becomes a rich source for sentiment analysis and belief mining. The aim of this paper is to develop such a functional classifier which can correctly and automatically classify the sentiment of an unknown tweet. In our work, we propose techniques to classify the sentiment label accurately. We introduce two methods: one of the methods is known as sentiment classification algorithm (SCA) based on k-nearest neighbor (KNN) and the other one is based on support vector machine (SVM). We also evaluate their performance based on real tweets.


title: Dyslexia: the Role of Vision and Visual Attention

abstract: Dyslexia is more than just difficulty with translating letters into sounds. Many dyslexics have problems with clearly seeing letters and their order. These difficulties may be caused by abnormal development of their visual “magnocellular” (M) nerve cells; these mediate the ability to rapidly identify letters and their order because they control visual guidance of attention and of eye fixations. Evidence for M cell impairment has been demonstrated at all levels of the visual system: in the retina, in the lateral geniculate nucleus, in the primary visual cortex and throughout the dorsal visuomotor “where” pathway forward from the visual cortex to the posterior parietal and prefrontal cortices. This abnormality destabilises visual perception; hence, its severity in individuals correlates with their reading deficit. Treatments that facilitate M function, such as viewing text through yellow or blue filters, can greatly increase reading progress in children with visual reading problems. M weakness may be caused by genetic vulnerability, which can disturb orderly migration of cortical neurones during development or possibly reduce uptake of omega-3 fatty acids, which are usually obtained from fish oils in the diet. For example, M cell membranes require replenishment of the omega-3 docosahexaenoic acid to maintain their rapid responses. Hence, supplementing some dyslexics’ diets with DHA can greatly improve their M function and their reading.


Mr. Risk! Please Trust Me: Trust Antecedents that Increase Online Consumer Purchase Intention


title: Relating Schizotypy and Personality to the Phenomenology of Creativity

abstract: Introduction: Although a considerable amount of research has addressed psychopathological and personality correlates of creativity, the relationship between these characteristics and the phenomenology of creativity has been neglected. Relating these characteristics to the phenomenology of creativity may assist in clarifying the precise nature of the relationship between psychopathology and creativity. The current article reports on an empirical study of the relationship between the phenomenology of the creative process and psychopathological and personality characteristics in a sample of artists. Method: A total of 100 artists (43 males, 57 females, mean age = 34.69 years) from a range of disciplines completed the Experience of Creativity Questionnaire and measures of “positive” schizotypy, affective disturbance, mental boundaries, and normal personality. Results: The sample of artists was found to be elevated on “positive” schizotypy, unipolar affective disturbance, thin boundaries, and the personality dimensions of Openness to Experience and Neuroticism, compared with norm data. Schizotypy was found to be the strongest predictor of a range of creative experience scales (Distinct Experience, Anxiety, Absorption, Power/Pleasure), suggesting a strong overlap of schizotypal and creative experience. Discussion: These findings indicate that “positive” schizotypy is associated with central features of “flow”-type experience, including distinct shift in phenomenological experience, deep absorption, focus on present experience, and sense of pleasure. The neurologically based construct of latent inhibition may be a mechanism that facilitates entry into flow-type states for schizotypal individuals. This may occur by reduced latent inhibition providing a “fresh” awareness and therefore a greater absorption in present experience, thus leading to flow-type states.


title: SIGDROP: Signature-based ROP Detection using Hardware Performance Counters

abstract: Return-Oriented Programming (ROP) is a software exploit for system compromise. By chaining short instruction sequences from existing code pieces, ROP can bypass static code-integrity checking approaches and non-executable page protections. Existing defenses either require access to source code or binary, a customized compiler or hardware modifications, or suffer from high performance and storage overhead. In this work, we propose SIGDROP, a low-cost approach for ROP detection which uses low-level properties inherent to ROP attacks. Specifically, we observe special patterns of certain hardware events when a ROP attack occurs during program execution. Such hardware event-based patterns form signatures to flag ROP attacks at runtime. SIGDROP leverages Hardware Performance Counters, which are already present in commodity processors, to efficiently capture and extract the signatures. Our evaluation demonstrates that SIGDROP can effectively detect ROP attacks with acceptable performance overhead and negligible storage overhead.


title: Multi-ontology sense making: a new simplicity in decision making

abstract: Imagine organising a birthday party for a group of young children. Would you agree a set of learning objectives with their parents in advance of the party? Would those objectives be aligned with the mission statement for education in the society to which you belong? Would you create a project plan for the party with clear milestones associated with empirical measures of achievement? Would you start the party with a motivational video so that the children did not waste time in play not aligned with the learning objectives? Would you use PowerPoint to demonstrate to the children that their pocket money is linked to achievement of the empirical measures at each milestone? Would you conduct an after-action review at the end of the party, update your best practice database and revise standard operating procedures for party management? ::: No! Instead, like most parents, you would create barriers to prevent certain types of behaviour, you would use attractors (party games, a football, a videotape) to encourage the formation of beneficial largely self-organising identities; you would disrupt negative patterns early, to prevent the party becoming chaotic, or necessitating the draconian imposition of authority. At the end of the party you would know whether it had been a success, but you could not have defined (in other than the most general terms) what that success would look like in advance.


title: Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications

abstract: Neurofeedback is a kind of biofeedback, which teaches self-control of brain functions to subjects by measuring brain waves and providing a feedback signal. Neurofeedback usually provides the audio and or video feedback. Positive or negative feedback is produced for desirable or undesirable brain activities, respectively. In this review, we provided clinical and technical information about the following issues: (1) Various neurofeedback treatment protocols i.e. alpha, beta, alpha/theta, delta, gamma, and theta; (2) Different EEG electrode placements i.e. standard recording channels in the frontal, temporal, central, and occipital lobes; (3) Electrode montages (unipolar, bipolar); (4) Types of neurofeedback i.e. frequency, power, slow cortical potential, functional magnetic resonance imaging, and so on; (5) Clinical applications of neurofeedback i.e. treatment of attention deficit hyperactivity disorder, anxiety, depression, epilepsy, insomnia, drug addiction, schizophrenia, learning disabilities, dyslexia and dyscalculia, autistic spectrum disorders and so on as well as other applications such as pain management, and the improvement of musical and athletic performance; and (6) Neurofeedback softwares. To date, many studies have been conducted on the neurofeedback therapy and its effectiveness on the treatment of many diseases. Neurofeedback, like other treatments, has its own pros and cons. Although it is a non-invasive procedure, its validity has been questioned in terms of conclusive scientific evidence. For example, it is expensive, time-consuming and its benefits are not long-lasting. Also, it might take months to show the desired improvements. Nevertheless, neurofeedback is known as a complementary and alternative treatment of many brain dysfunctions. However, current research does not support conclusive results about its efficacy.


title: Resource Management in Clouds: Survey and Research Challenges

abstract: Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .


title: Marrow cell infusion attenuates vascular remodeling in a murine model of monocrotaline-induced pulmonary hypertension.

abstract: There have been reports of marrow cells converting into pulmonary epithelial cells after marrow transplantation in irradiated mice. We evaluated the impact of whole bone marrow (WBM) infusion in mice, with or without total body irradiation (TBI), treated with saline or monocrotaline (MCT), which induces pulmonary hypertension (PH). C57BL/6 mice were injected with MCT or saline weekly for 4 weeks. Cohorts were then infused with saline vehicle (vehicle) or WBM from C57BL/-Tg(UBC-GFP)30Scha/J mice, with or without previous TBI (WBM or WBM/TBI). Four weeks later, right ventricular peak pressures (RVPP), right ventricular free wall-to-body weight ratios (RV/BW), and pulmonary vessel wall thickness-to-blood vessel diameter ratios (PVWT/D) were determined. WBM infusion and WBM following TBI induced increases in RVPP and RV/BW in saline-treated mice, while only TBI-exposed mice showed additional increases in PVWT/D. MCT increased RVPP, RV/BW, and PVWT/D in mice given vehicle or WBM alone, but not in mice given WBM/TBI. RVPP and RV/BW were not significantly lower in MCT mice given WBM/TBI than in MCT mice treated with vehicle, but MCT-treated mice given WBM or TBI/WBM had significantly lower PVWT/D compared to MCT-treated mice given saline vehicle. No donor WBM-derived pulmonary vascular cells were detected, suggesting that the observed effects of WBM infusion may be due to paracrine effects separate from cell conversions. The observation of PH after marrow infusion suggests an additional mechanism for lung toxicity seen in marrow transplantation. In conclusion, WBM alone appears to increase RVPP and RV/BW in normal mice but the combination of WBM and TBI attenuates MCT-induced PH.


title: Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!

abstract: The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices.


title: Cantilever Beam Equation for Almost Arbitrary Deflections: Derivation and Worked Examples

abstract: We derived a non-linear 4th-order ordinary differential equation the solutions of which lead to the exact shapes of the cantilever beam. The result of the equation in a non-dimensional form was found to depend on two parameters only: the angle of the beam at the fixed end, and the parameter encompassing the material characteristics and geometry of the beam. The parameter space was explored in detail and the results were used to suggest the areas in which they could be applied.


title: Financing low income housing in Nigeria

abstract: Purpose – The purpose of this paper is to identify and analyse the methods of housing finance adopted by the low income and informal groups in Nigeria.Design/methodology/approach – A survey of 300 households in selected areas (low‐income/informal) of Jos Metropolis, Nigeria, was carried out, concerning the methods of housing finance used for building and home improvement.Findings – The survey showed that 75 per cent of the households utilized traditional methods of financing and 25 per cent using modern methods.Research limitations/implications – Based on data collected from the survey, the research serves as a basis for further research into traditional methods of housing finance in developing countries.Practical implications – The analysis of traditional financing methods highlights the range and structure of the traditional methods of financing in operation in informal and low income areas of Jos Metropolis, Nigeria. For example, informal and customary/traditional methods (Esusu/Asusu, Age grade associ...


title: DIGS: A Framework for Discovering Goals for Security Requirements Engineering

abstract: Context: The security goals of a software system provide a foundation for security requirements engineering. Identifying security goals is a process of iteration and refinement, leveraging the knowledge and expertise of the analyst to secure not only the core functionality but the security mechanisms as well. Moreover, a comprehensive security plan should include goals for not only preventing a breach, but also for detecting and appropriately responding in case a breach does occur. Goal: The objective of this research is to support analysts in security requirements engineering by providing a framework that supports a systematic and comprehensive discovery of security goals for a software system. Method: We develop a framework, Discovering Goals for Security (DIGS), that models the key entities in information security, including assets and security goals. We systematically develop a set of security goal patterns that capture multiple dimensions of security for assets. DIGS explicitly captures the relations and assumptions that underlie security goals to elicit implied goals. We map the goal patterns to NIST controls to help in operationalizing the goals. We evaluate DIGS via a controlled experiment where 28 participants analyzed systems from mobile banking and human resource management domains. Results: Participants considered security goals commensurate to the knowledge available to them. Although the overall recall was low given the empirical constraints, participants using DIGS identified more implied goals and felt more confident in completing the task. Conclusion: Explicitly providing the additional knowledge for the identification of implied security goals significantly increased the chances of discovering such goals, thereby improving coverage of stakeholder security requirements, even if they are unstated.


title: Sentiment analysis of Twitter data for predicting stock market movements

abstract: Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from Twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets.


Two-factor authentication: too little, too late


title: Initialization Strategies of Spatio-Temporal Convolutional Neural Networks

abstract: We propose a new way of incorporating temporal information present in videos into Spatial Convolutional Neural Networks (ConvNets) trained on images, that avoids training Spatio-Temporal ConvNets from scratch. We describe several initializations of weights in 3D Convolutional Layers of Spatio-Temporal ConvNet using 2D Convolutional Weights learned from ImageNet. We show that it is important to initialize 3D Convolutional Weights judiciously in order to learn temporal representations of videos. We evaluate our methods on the UCF-101 dataset and demonstrate improvement over Spatial ConvNets.


title: Super-resolution of compressed videos using convolutional neural networks

abstract: Convolutional neural networks (CNN) have been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this paper, we consider the problem of compressed video super-resolution. Traditional SR algorithms for compressed videos rely on information from the encoder such as frame type or quantizer step, whereas our algorithm only requires the compressed low resolution frames to reconstruct the high resolution video. We propose a CNN that is trained on both the spatial and the temporal dimensions of compressed videos to enhance their spatial resolution. Consecutive frames are motion compensated and used as input to a CNN that provides super-resolved video frames as output. Our network is pretrained with images, which significantly improves the performance over random initialization. In extensive experimental evaluations, we trained the state-of-the-art image and video superresolution algorithms on compressed videos and compared their performance to our proposed method.


title: Forwarding metamorphosis: fast programmable match-action processing in hardware for SDN

abstract: In Software Defined Networking (SDN) the control plane is physically separate from the forwarding plane. Control software programs the forwarding plane (e.g., switches and routers) using an open interface, such as OpenFlow. This paper aims to overcomes two limitations in current switching chips and the OpenFlow protocol: i) current hardware switches are quite rigid, allowing ``Match-Action'' processing on only a fixed set of fields, and ii) the OpenFlow specification only defines a limited repertoire of packet processing actions. We propose the RMT (reconfigurable match tables) model, a new RISC-inspired pipelined architecture for switching chips, and we identify the essential minimal set of action primitives to specify how headers are processed in hardware. RMT allows the forwarding plane to be changed in the field without modifying hardware. As in OpenFlow, the programmer can specify multiple match tables of arbitrary width and depth, subject only to an overall resource limit, with each table configurable for matching on arbitrary fields. However, RMT allows the programmer to modify all header fields much more comprehensively than in OpenFlow. Our paper describes the design of a 64 port by 10 Gb/s switch chip implementing the RMT model. Our concrete design demonstrates, contrary to concerns within the community, that flexible OpenFlow hardware switch implementations are feasible at almost no additional cost or power.


title: Pubertal hormones organize the adolescent brain and behavior

abstract: Maturation of the reproductive system during puberty results in elevated levels of gonadal steroid hormones. These hormones sculpt neural circuits during adolescence, a time of dramatic rewiring of the nervous system. Here, we review the evidence that steroid-dependent organization of the adolescent brain programs a variety of adult behaviors in animals and humans. Converging lines of evidence indicate that adolescence may be a sensitive period for steroid-dependent brain organization and that variation in the timing of interactions between the hormones of puberty and the adolescent brain leads to individual differences in adult behavior and risk of sex-biased psychopathologies.


title: Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning

abstract: Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as"the curse of dimensionality". This paper presents a deep learning-based approach that can handle general high-dimensional parabolic PDEs. To this end, the PDEs are reformulated as a control theory problem and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function. Numerical results on examples including the nonlinear Black-Scholes equation, the Hamilton-Jacobi-Bellman equation, and the Allen-Cahn equation suggest that the proposed algorithm is quite effective in high dimensions, in terms of both accuracy and speed. This opens up new possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their inter-relationships.


Bayesian Nonparametric Learning: Expressive Priors for Intelligent Systems


title: BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification

abstract: In this paper, we propose a simple, efficient, yet robust multiresolution approach to texture classification-binary rotation invariant and noise tolerant (BRINT). The proposed approach is very fast to build, very compact while remaining robust to illumination variations, rotation changes, and noise. We develop a novel and simple strategy to compute a local binary descriptor based on the conventional local binary pattern (LBP) approach, preserving the advantageous characteristics of uniform LBP. Points are sampled in a circular neighborhood, but keeping the number of bins in a single-scale LBP histogram constant and small, such that arbitrarily large circular neighborhoods can be sampled and compactly encoded over a number of scales. There is no necessity to learn a texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different data sets. Extensive experimental results on representative texture databases show that the proposed BRINT not only demonstrates superior performance to a number of recent state-of-the-art LBP variants under normal conditions, but also performs significantly and consistently better in presence of noise due to its high distinctiveness and robustness. This noise robustness characteristic of the proposed BRINT is evaluated quantitatively with different artificially generated types and levels of noise (including Gaussian, salt and pepper, and speckle noise) in natural texture images.


Real-time Servo Control using EtherCAT Master on Real-time Embedded Linux Extensions


title: Cardinal rules: visual orientation perception reflects knowledge of environmental statistics

abstract: Orientation judgments are more accurate at the horizontal and vertical orientations, possibly reflecting a statistical inference. Here the authors provide evidence for this idea, finding that observers' internal models for orientation match the local orientation distribution measured in photographs, and suggest how such information could be encoded in a neural population.


title: Discriminant analysis in correlation similarity measure space

abstract: Correlation is one of the most widely used similarity measures in machine learning like Euclidean and Mahalanobis distances. However, compared with proposed numerous discriminant learning algorithms in distance metric space, only a very little work has been conducted on this topic using correlation similarity measure. In this paper, we propose a novel discriminant learning algorithm in correlation measure space, Correlation Discriminant Analysis (CDA). In this framework, based on the definitions of within-class correlation and between-class correlation, the optimum transformation can be sought for to maximize the difference between them, which is in accordance with good classification performance empirically. Under different cases of the transformation, different implementations of the algorithm are given. Extensive empirical evaluations of CDA demonstrate its advantage over alternative methods.


FROM ROAD EXTRACTION TO MAN-MADE OBJECT DISCRIMINATION IN HIGH RESOLUTION SAR DATA


title: Regulation of proline biosynthesis, degradation, uptake and transport in higher plants : Its implications in plant growth and abiotic stress tolerance

abstract: Dramatic accumulation of proline due to increased synthesis and decreased degradation under a variety of stress conditions such as salt, drought and metal has been documented in many plants. Similarly, a decrease in the level of accumulated proline in the rehydrated plants is due to both down regulation of proline biosynthetic pathway enzymes and upregulation of proline degrading enzymes. But, the role of proline during plant development and the molecular basis of the effect of proline accumulation during stress and upon relief of stress are still largely obscure. Here, we summarize the genes governing the proline biosynthetic pathway, its degradation and regulation. Sequentially, we provide an account on transgenics raised so far to engineer the overproduction of osmolyte proline. Also, the identification of specific cellular pathways involved in proline biosynthesis and metabolic changes occurring in transgenic plants developed for proline enhancements are discussed. Further, emphasis is also made on an untouched area of signal transduction of proline biosynthetic pathway.


title: Fast concurrent queues for x86 processors

abstract: Conventional wisdom in designing concurrent data structures is to use the most powerful synchronization primitive, namely compare-and-swap (CAS), and to avoid contended hot spots. In building concurrent FIFO queues, this reasoning has led researchers to propose combining-based concurrent queues. This paper takes a different approach, showing how to rely on fetch-and-add (F&A), a less powerful primitive that is available on x86 processors, to construct a nonblocking (lock-free) linearizable concurrent FIFO queue which, despite the F&A being a contended hot spot, outperforms combining-based implementations by 1.5x to 2.5x in all concurrency levels on an x86 server with four multicore processors, in both single-processor and multi-processor executions.


title: Wafer-Scale Millimeter-Wave Phased-Array RFICs

abstract: This invited talk will present a summary of the millimeter-wave wafer-scale phased array work at UCSD. This concept can drastically reduce the cost of millimeter- wave phased arrays by combining the RFIC blocks, antennas, power distribution and summing, digital control and up and down converters all on the same wafer (or large piece of silicon), and eliminates all RF transitions in and out of the chip, therefore resulting in more efficient systems and lower cost systems. Examples at 90-100 GHz, 108-114 GHz and 400 GHz will be presented in this paper, together with their measured antenna patterns.


title: Towards Scalable Publish/Subscribe Systems

abstract: Despite suffering from inefficiency and flexibility limitations, the filter-based routing (FBR) algorithm is widely used in content-based publish/subscribe (pub/sub) systems. To address its limitations, we propose a dynamic destination-based routing algorithm called D-DBR, which decomposes pub/sub into two independent parts: Content-based matching and destination based multicasting. D-DBR exhibits low event matching cost and high efficiency, flexibility, and robustness for event routing in small-scale overlays. To improve its scalability to large-scale overlays, we further extend D-DBR to a new routing algorithm called MERC. MERC divides the overlay into interconnected clusters and applies content-based and destination-based mechanisms to route events inter- and intra-cluster, respectively. We implemented all algorithms in the PADRES pub/sub system. Experimental results show that our algorithms outperform the FBR algorithm.


title: Song recommendation with non-negative matrix factorization and graph total variation

abstract: This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using a rich combination of audio, meta-data and social features. As we demonstrate, our hybrid recommendation system is very versatile and incorporates several well-known methods while outperforming them. Particularly, we show on real-world data that our model overcomes w.r.t. two evaluation metrics the recommendation of models solely based on low-rank information, graph-based information or a combination of both.


title: Detection and velocity estimation of moving vehicles in high-resolution spaceborne synthetic aperture radar data

abstract: Automatic estimation of traffic parameters has evolved to an important topic of research. Current and upcoming SAR satellite missions offer new possibilities for traffic monitoring and control from space as an alternative to conventional traffic data acquisition. In this paper a detection approach is presented which evaluates simultaneously the effects moving objects suffer from in the SAR focusing process. Information about the measured signal and the expected signal are utilized in the detection framework. Analyses of the proposed technique are done with real spaceborne SAR data.


title: Clinical and dermoscopic features of atypical Spitz tumors: A multicenter, retrospective, case-control study.

abstract: BACKGROUND ::: Few studies have described the clinical and dermoscopic features of atypical Spitz tumors. ::: ::: ::: OBJECTIVE ::: We sought to describe the clinical and dermoscopic features of a series of atypical Spitz tumors as compared with those of conventional Spitz nevi. ::: ::: ::: METHODS ::: This was a multicenter, retrospective, case-control study, analyzing the clinical and dermoscopic characteristics of 55 atypical Spitz tumors and 110 Spitz nevi that were excised and diagnosed histopathologically. ::: ::: ::: RESULTS ::: The majority of atypical Spitz tumors presented clinically as a plaque or nodule, dermoscopically typified by a multicomponent or nonspecific pattern. A proportion of lesions (16.4%) exhibited the typical nonpigmented Spitzoid pattern of dotted vessels and white lines under dermoscopy. Nodularity, ulceration, linear vessels, polymorphic vessels, white lines, and blue-white veil were associated with atypical Spitz tumors by univariate analysis, but only nodularity and white lines remained significant after multivariate analysis. In contrast, a pigmented typical Spitzoid pattern was a potent predictor of Spitz nevi, associated with 6.5-fold increased probability. ::: ::: ::: LIMITATIONS ::: Differentiation from Spitzoid melanoma and other nonmelanocytic lesions was not investigated. ::: ::: ::: CONCLUSION ::: Atypical Spitz tumors are polymorphic melanocytic proliferations with a nodular clinical appearance. Dermoscopically they demonstrate a multicomponent and nonspecific pattern. A typical nonpigmented Spitzoid pattern on dermoscopy (with dotted vessels and white lines) does not exclude atypical Spitz tumors.


title: k-Shape: Efficient and Accurate Clustering of Time Series

abstract: The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data mining methods, not only due to its exploratory power, but also as a preprocessing step or subroutine for other techniques. In this paper, we describe k-Shape, a novel algorithm for time-series clustering. k-Shape relies on a scalable iterative refinement procedure, which creates homogeneous and well-separated clusters. As its distance measure, k-Shape uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them. Based on the properties of that distance measure, we develop a method to compute cluster centroids, which are used in every iteration to update the assignment of time series to clusters. An extensive experimental evaluation against partitional, hierarchical, and spectral clustering methods, with the most competitive distance measures, showed the robustness of k-Shape. Overall, k-Shape emerges as a domain-independent, highly accurate, and efficient clustering approach for time series with broad applications.


title: A Review of Recent Developments in Electrical Machine Design Optimization Methods With a Permanent-Magnet Synchronous Motor Benchmark Study

abstract: This paper systematically covers the significant developments of the last decade, including surrogate modeling of electrical machines and direct and stochastic search algorithms for both single- and multi-objective design optimization problems. The specific challenges and the dedicated algorithms for electric machine design are discussed, followed by benchmark studies comparing response surface (RS) and differential evolution (DE) algorithms on a permanent-magnet-synchronous-motor design with five independent variables and a strong nonlinear multiobjective Pareto front and on a function with eleven independent variables. The results show that RS and DE are comparable when the optimization employs only a small number of candidate designs and DE performs better when more candidates are considered.


title: Service Blueprinting: A Practical Technique for Service Innovation:

abstract: With the global focus on service-led growth has come increased need for practical techniques for service innovation. Services are fluid, dynamic, experiential, and frequently -produced in real time by customers, employees, and technology, often with few static physical properties. However, most product innovation approaches focus on the design of relatively static products with physical properties. Thus, many of the invention and prototype design techniques used for physical goods and technologies do not work well for human and interactive services. This article describes one technique—service blueprinting—that has proven useful for service innovation. Service blueprinting is securely grounded in the customer's experience and it allows the clear visualization of dynamic service processes. The technique is described in detail including real case examples that illustrate the value and breadth of its applications.


Subgroup Discovery – Advanced Review


title: A system for converting print into braille

abstract: Describes a method for converting text into braille, in the form in which it is stored as in a computer. The system has been designed to be configurable for a wide range of languages and character sets, and uses a predominantly table driven method to achieve this. The algorithm is explained in the context of the conversion of text into Standard English Braille (British), and the tables for this transformation are given. Particular importance has been attached to enabling braille specialists, who are not experts in computer algorithms, to be able to modify the system for either slight modifications to an existing braille code translator, or for producing a braille code translator for a new language.


title: Stack-propagation: Improved Representation Learning for Syntax

abstract: Traditional syntax models typically leverage part-of-speech (POS) information by constructing features from hand-tuned templates. We demonstrate that a better approach is to utilize POS tags as a regularizer of learned representations. We propose a simple method for learning a stacked pipeline of models which we call "stack-propagation". We apply this to dependency parsing and tagging, where we use the hidden layer of the tagger network as a representation of the input tokens for the parser. At test time, our parser does not require predicted POS tags. On 19 languages from the Universal Dependencies, our method is 1.3% (absolute) more accurate than a state-of-the-art graph-based approach and 2.7% more accurate than the most comparable greedy model.


title: On Compiling CNF into Decision-DNNF

abstract: Decision-DNNF is a strict subset of decomposable negation normal form (DNNF) that plays a key role in analyzing the complexity of model counters (the searches performed by these counters have their traces in Decision-DNNF). This paper presents a number of results on Decision-DNNF. First, we introduce a new notion of CNF width and provide an algorithm that compiles CNFs into Decision-DNNFs in time and space that are exponential only in this width. The new width strictly dominates the treewidth of the CNF primal graph: it is no greater and can be bounded when the treewidth of the primal graph is unbounded. This new result leads to a tighter bound on the complexity of model counting. Second, we show that the output of the algorithm can be converted in linear time to a sentential decision diagram (SDD), which leads to a tighter bound on the complexity of compiling CNFs into SDDs.


title: Mixed Membership Markov Models for Unsupervised Conversation Modeling

abstract: Recent work has explored the use of hidden Markov models for unsupervised discourse and conversation modeling, where each segment or block of text such as a message in a conversation is associated with a hidden state in a sequence. We extend this approach to allow each block of text to be a mixture of multiple classes. Under our model, the probability of a class in a text block is a log-linear function of the classes in the previous block. We show that this model performs well at predictive tasks on two conversation data sets, improving thread reconstruction accuracy by up to 15 percentage points over a standard HMM. Additionally, we show quantitatively that the induced word clusters correspond to speech acts more closely than baseline models.


title: An Efficient Algorithm of Frequent Itemsets Mining Based on MapReduce

abstract: Mainstream parallel algorithms for mining frequent itemsets (patterns) were designed by implementing FP-Growth or Apriori algorithms on MapReduce (MR) framework. Existing MR FP-Growth algorithms can not distribute data equally among nodes, and MR Apriori algorithms utilize multiple map/reduce procedures and generate too many key-value pairs with value of 1; these disadvantages hinder their performance. This paper proposes an algorithm FIMMR: it firstly mines local frequent itemsets for each data chunk as candidates, applies prune strategies to the candidates, and then identifies global frequent itemsets from candidates. Experimental results show that the time efficiency of FIMMR outperforms PFP and SPC significantly; and under small minimum support threshold, FIMMR can achieve one order of magnitude improvement than the other two algorithms; meanwhile, the speedup of FIMMR is also satisfactory.


title: The Ontop Framework for Ontology Based Data Access

abstract: Ontology Based Data Access (OBDA) [4] is a paradigm of accessing data trough a conceptual layer. Usually, the conceptual layer is expressed in the form of an RDF(S) [10] or OWL [15] ontology, and the data is stored in relational databases.


title: Improving text classification with word embedding

abstract: One challenge in text classification is that it is difficult to make feature reductions based on the definition of the features. An ineffective feature reduction may even worsen the classification accuracy. Word2Vec, a word embedding method, has recently been gaining popularity due to its high precision rate of analyzing the semantic similarity between words at relatively low computational cost. However, there is limited research about feature reduction using Word2Vec. In this project, we developed a method using Word2Vec to reduce the feature size while increasing the classification accuracy. We achieved feature reduction by loosely clustering similar features using graph search techniques. Similarity thresholds above 0.5 were used in our method to pair and cluster the features. Finally, we utilized Multinomial Naive Bayes classifier, Support Vector Machine, K Nearest Neighbor and Random Forest classifier to evaluate the effect of our method. Four datasets with dimensions up to 100,000 feature size and 400,000 document size were used to evaluate the result of our method. The result showed that around 4–10% feature reduction was achieved with up to 1–4% improvement of classification accuracy in terms of different datasets and classifiers. Meanwhile, we also succeeded in improving feature reduction and classification accuracy by combining our method with other classic feature reduction techniques such as chi-square and mutual information.


title: Typographic style is more than cosmetic

abstract: There is disagreement about the role and importance of typographicstyle (source code formatting and commenting) in program comprehension.Results from experiments and opinions in programming style books aremixed. This article presents principles of typographic style consistentand compatible with the results of program comprehension studies. Fourexperiments demonstrate that the typographic style principles embodiedin the book format significantly aid program comprehension and reducemaintenance effort. —Authors' Abstract


title: Robust high-dimensional precision matrix estimation

abstract: The dependency structure of multivariate data can be analyzed using the covariance matrix $\Sigma$. In many fields the precision matrix $\Sigma^{-1}$ is even more informative. As the sample covariance estimator is singular in high-dimensions, it cannot be used to obtain a precision matrix estimator. A popular high-dimensional estimator is the graphical lasso, but it lacks robustness. We consider the high-dimensional independent contamination model. Here, even a small percentage of contaminated cells in the data matrix may lead to a high percentage of contaminated rows. Downweighting entire observations, which is done by traditional robust procedures, would then results in a loss of information. In this paper, we formally prove that replacing the sample covariance matrix in the graphical lasso with an elementwise robust covariance matrix leads to an elementwise robust, sparse precision matrix estimator computable in high-dimensions. Examples of such elementwise robust covariance estimators are given. The final precision matrix estimator is positive definite, has a high breakdown point under elementwise contamination and can be computed fast.


title: Evaluation of biomechanical properties of human skin.

abstract: Measurement of the physical properties of skin may seem "esoteric" and of little relevance to clinical science; however, the noninvasive nature of available techniques provides unique opportunities for monitoring the effects of disease, drugs, or cosmetics over time on exactly the same area of skin. In vitro testing gives repeatable standardized methods that can supply basic elastic and viscoelastic moduli for skin, which for low strain are comparable to results obtained from in vivo tests. Interpretation of in vivo tests can be difficult, as no analytical model has been developed that can relate measurements from these tests directly to basic skin properties. Each method and each implementation of that method has subtle differences from every other method so that results between studies are difficult to compare; however, the alternative to the bioengineering tests is the hand and eye, which provide subjective (and often biased), nonlinear, and notoriously variable data between individuals. Judging the severity of involvement, the changes due to treatment or progress of disease, and the efficacy of competing treatments is very difficult, even for an experienced clinician. Objective, numerical information on the effects of different active compounds and their formulations is essential if new preparations are to be optimized. Measurement of mechanical properties is one aspect of this important field of endeavor.


title: 90° Hybrid coupler design technique for wideband and multimode mm-wave operations featuring lateral ground planes virtual expansion in 28nm FD-SOI CMOS technology

abstract: This paper focuses on 90° hybrid coupler design enhancement for wideband and multimode operations to fit the needs of balanced circuits for future wireless network systems. Lateral ground planes placement is explored to take advantage of induced effects, generally avoided by designers, enabling frequency shift and wideband operations compared to a classical standalone 40GHz 90° hybrid coupler. 90° hybrid coupler structure featuring virtual ground plane expansion for multimode operations simulated in 28nm FD-SOI CMOS technology is then proposed. This structure exhibits three modes. A first mode with 42GHz operations similar to standalone coupler, a second mode featuring two operation frequencies of 46.4GHz and 86.1GHz and a wideband mode exhibiting 37.5GHz 1dB ΔAmplitude bandwidth centred at 60.5GHz with ΔPhase=93°±1.7°.


title: SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks

abstract: Inference for state-of-the-art deep neural networks is computationally expensive, making them difficult to deploy on constrained hardware environments. An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributions with a limited entry codebook. For very low-precisions, such as binary or ternary networks with 1-8-bit activations, the information loss from quantization leads to significant accuracy degradation due to large gradient mismatches between the forward and backward functions. In this paper, we introduce a quantization method to reduce this loss by learning a symmetric codebook for particular weight subgroups. These subgroups are determined based on their locality in the weight matrix, such that the hardware simplicity of the low-precision representations is preserved. Empirically, we show that symmetric quantization can substantially improve accuracy for networks with extremely low-precision weights and activations. We also demonstrate that this representation imposes minimal or no hardware implications to more coarse-grained approaches. Source code is available at https://www.github.com/julianfaraone/SYQ.


title: Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning

abstract: The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread. However, the current justification is intuitive rather than analytical. This work proposes one minor modification to the normal ensembling methodology, which we prove allows the ensemble to perform Bayesian inference, hence converging to the corresponding Gaussian Process as both the total number of NNs, and the size of each, tend to infinity. This working paper provides early-stage results in a reinforcement learning setting, analysing the practicality of the technique for an ensemble of small, finite number. Using the uncertainty estimates produced by anchored ensembles to govern the exploration-exploitation process results in steadier, more stable learning.


title: Reducing DRAM footprint with NVM in facebook

abstract: Popular SSD-based key-value stores consume a large amount of DRAM in order to provide high-performance database operations. However, DRAM can be expensive for data center providers, especially given recent global supply shortages that have resulted in increasing DRAM costs. In this work, we design a key-value store, MyNVM, which leverages an NVM block device to reduce DRAM usage, and to reduce the total cost of ownership, while providing comparable latency and queries-per-second (QPS) as MyRocks on a server with a much larger amount of DRAM. Replacing DRAM with NVM introduces several challenges. In particular, NVM has limited read bandwidth, and it wears out quickly under a high write bandwidth. We design novel solutions to these challenges, including using small block sizes with a partitioned index, aligning blocks post-compression to reduce read bandwidth, utilizing dictionary compression, implementing an admission control policy for which objects get cached in NVM to control its durability, as well as replacing interrupts with a hybrid polling mechanism. We implemented MyNVM and measured its performance in Facebook's production environment. Our implementation reduces the size of the DRAM cache from 96 GB to 16 GB, and incurs a negligible impact on latency and queries-per-second compared to MyRocks. Finally, to the best of our knowledge, this is the first study on the usage of NVM devices in a commercial data center environment.


title: Call graph construction for Java libraries

abstract: Today, every application uses software libraries. Yet, while a lot of research exists w.r.t. analyzing applications, research that targets the analysis of libraries independent of any application is scarce. This is unfortunate, because, for developers of libraries, such as the Java Development Kit (JDK), it is crucial to ensure that the library behaves as intended regardless of how it is used. To fill this gap, we discuss the construction of call graphs for libraries that abstract over all potential library usages. Call graphs are particularly relevant as they are a precursor of many advanced analyses, such as inter-procedural data-flow analyses. We show that the current practice of using call graph algorithms designed for applications to analyze libraries leads to call graphs that, at the same time, lack relevant call edges and contain unnecessary edges. This motivates the need for call graph construction algorithms dedicated to libraries. Unlike algorithms for applications, call graph construction algorithms for libraries must take into consideration the goals of subsequent analyses. Specifically, we show that it is essential to distinguish between the scenario of an analysis for potential exploitable vulnerabilities from the scenario of an analysis for general software quality attributes, e.g., dead methods or unused fields. This distinction affects the decision about what constitutes the library-private implementation, which therefore, needs special treatment. Thus, building one call graph that satisfies all needs is not sensical. Overall, we observed that the proposed call graph algorithms reduce the number of call edges up to 30% when compared to existing approaches.


title: Software project management: the manager's view

abstract: As businesses become more dependent on information technology for their operations, IS managers are under increasing pressure to deliver quality applications software on time and within budget. Thus, in addition to their technical skills, they must master the necessary management skills to lead and control software development projects. The purpose of this tutorial is to present the fundamental concepts of modern project management and show how these concepts can be applied to software development projects. The tutorial presents a broad overview of current software project management practices that evolved over the years from a variety of complex projects. The subject is presented from the manager's rather than from the developer's perspective. The focus is on large and complex projects because these projects are the most challenging and in need of an effective project management discipline.


title: Monte-Carlo tree search and rapid action value estimation in computer Go

abstract: A new paradigm for search, based on Monte-Carlo simulation, has revolutionised the performance of computer Go programs. In this article we describe two extensions to the Monte-Carlo tree search algorithm, which significantly improve the effectiveness of the basic algorithm. When we applied these two extensions to the Go program MoGo, it became the first program to achieve dan (master) level in 9x9 Go. In this article we survey the Monte-Carlo revolution in computer Go, outline the key ideas that led to the success of MoGo and subsequent Go programs, and provide for the first time a comprehensive description, in theory and in practice, of this extended framework for Monte-Carlo tree search.


title: Canada (2012)" A shared “passengers & goods ” city logistics system

abstract: Many strategic planning models have been developed to help decision making in city logistics. Such models do not take into account, or very few, the flow of passengers because the considered unit does not have the same nature (a person is active and a good is passive). However, it seems fundamental to gather the goods and the passengers in one model when their respective transports interact with each other. In this context, we suggest assessing a shared passengers & goods city logistics system where the spare capacity of public transport is used to distribute goods toward the city core. We model the problem as a vehicle routing problem with transfers and give a mathematical formulation. Then we propose an Adaptive Large Neighborhood Search (ALNS) to solve it. This approach is evaluated on data sets generated following a field study in the city of La Rochelle in France.


title: Railway fail-safe signalization and interlocking design based on automation Petri Net

abstract: Safe transportation on the railways can be achieved by the use of a reliable interlocking and signalization systems in order to provide safety on the railways so as to avoid fatal accidents. Presently, the relay based interlocking systems are being replaced by the programmable interlocking systems. It has both hardware and software aspects. Looking at the designing aspect a formal method can be used to fulfill the need of an efficient interlocking system so as to avoid the accidents caused due to manual operations. In this paper Automation Petri-Nets (APNs) which is an extension of Petri-Nets are being used to model a railway track interlocking and signalization operation systems. The obtained model can be converted into PLC ladder logic program easily so as to verify the accuracy for the chosen railway yard.


Story Cloze Task: UW NLP System


title: Detecting Abnormal Events in Video Using Narrowed Normality Clusters

abstract: We formulate the abnormal event detection problem as an outlier detection task and we propose a two-stage algorithm based on k-means clustering and one-class Support Vector Machines (SVM) to eliminate outliers. In the feature extraction stage, we propose to augment spatio-temporal cubes with deep appearance features extracted from the last convolutional layer of a pre-trained neural network. After extracting motion and appearance features from the training video containing only normal events, we apply k-means clustering to find clusters representing different types of normal motion and appearance features. In the first stage, we consider that clusters with fewer samples (with respect to a given threshold) contain mostly outliers, and we eliminate these clusters altogether. In the second stage, we shrink the borders of the remaining clusters by training a one-class SVM model on each cluster. To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality. If the test sample does not fit well in any narrowed normality cluster, then it is labeled as abnormal. We compare our method with several state-of-the-art methods on three benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve better results in most cases, while processing the test video in real-time at 24 frames per second on a single CPU.


Playing Tetris with Deep Reinforcement Learning


title: Circular polarization bandwidth for a bowtie turnstile over a ground plane

abstract: This report estimates the circular polarization bandwidth for a bowtie turnstile over a finite ground plane.


title: AESTHETICS OF STREETSCAPES: INFLUENCE OF FUNDAMENTAL PROPERTIES ON AESTHETIC JUDGMENTS OF URBAN SPACE ','

abstract: This experiment was designed to investigate the relationship between the fundamental visual, spatial properties of existing complex streetscapes and aesthetic judgments. Participants performed aesthetic judgments over 35 photographs of typical spatial situations taken along the Meisner Strase in Radebeul, Germany. In a modified Q-Sort procedure, the participants assigned the pictures to five categories, including 1 (beautiful) and 5 (not beautiful). Vegetation, Stylistic Uniformity, Homogeneity of Scale, and Symmetry were identified as primary components of aesthetic judgment by using principal component analysis. Stimuli, photographically edited according to these factors, were then tested using the same Q-Sort procedure, which confirmed these determinants. These results are intended to help the development of the theoretical understanding of the link between the influence of selected spatial properties and the aesthetic judgment of the visual quality of urban spaces.


title: Improving Air-to-Air Combat Behavior through Transparent Machine Learning

abstract: Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events’ in a scenario (e.g. a new opponent tactic). Machine learning techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic scripting technique to the generation of CGF behavior for training simulations. Dynamic scripting is a machine learning technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic scripting generates behavior in a transparent manner. Also, dynamic scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination in multi-agent systems remains a non-trivial problem. We enabled explicit team coordination through communication between team members. This coordination method was tested in an air combat simulation experiment, and compared against a baseline that consisted of a similar dynamic scripting setup, without explicit coordination. In terms of combat performance, the team using the explicit team coordination was 20% more effective than the baseline. Finally, the paper will discuss the application of dynamic scripting in a practical setting.


title: Attentive Neural Processes

abstract: Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled.


title: Multi-Authority Attribute Based Encryption

abstract: In an identity based encryption scheme, each user is identified by a unique identity string. An attribute based encryption scheme (ABE), in contrast, is a scheme in which each user is identified by a set of attributes, and some function of those attributes is used to determine decryption ability for each ciphertext. Sahai and Waters introduced a single authority attribute encryption scheme and left open the question of whether a scheme could be constructed in which multiple authorities were allowed to distribute attributes [SW05]. We answer this question in the affirmative. ::: ::: Our scheme allows any polynomial number of independent authorities to monitor attributes and distribute secret keys. An encryptor can choose, for each authority, a number dk and a set of attributes; he can then encrypt a message such that a user can only decrypt if he has at least dk of the given attributes from each authority k. Our scheme can tolerate an arbitrary number of corrupt authoritites. ::: ::: We also show how to apply our techniques to achieve a multiauthority version of the large universe fine grained access control ABE presented by Gopal et al. [GPSW06].


title: PATHOLOGY OF NEURO-GLIAL α-SYNUCLEINOPATHY (LEWY BODY DISEASE AND MULTIPLE SYSTEM ATROPHY)

abstract: α‑Synucleinopathies comprise a group of neurodegenerative disorders that share α‑synuclein (αS) accumulation in selected vulnerable neurons and glia i.e. Parkinsons disease (PD) dementia with Lewy bodies and multiple system atrophy (MSA) . The histological hallmark of PD is neuronal αS aggregates called Lewy bodies (LBs) . LB formation has been considered to be a marker for neuronal degeneration because neuronal loss is found in the predilection sites for LBs. However recent studies have suggested that oligomers and protoﬁ brils of αS are cytotoxic and that LBs may represent a cytoprotective mechanism in PD. The histological hallmark of MSA is αS aggregates in the oligodendrocytes referred to as glial cytoplasmic inclusions (GCIs) . αS inclusions are also found in the neuronal somata axons and nucleus. At present two degenerative processes have been considered in this disease; one is due to the widespread occurrence of GCIs associated with oligodendroglia‑myelin degeneration and the other is due to the aggregation of αS in neurons in several brain regions. These two processes might synergistically cause neuronal depletion in MSA. Hirosaki Med.J. 61 Supplement:S80―S88,2010


title: EXTENSION OF THE LUGRE DYNAMIC TIRE FRICTION MODEL TO 2D MOTION

abstract: An extension of the LuGre dynamic friction model from longitudinal motion to longitudinal/lateral motion is developed. Applying this model to the motion of a tire we derive a model for tire-road contact forces and moments. A comparison of the steadystate behaviour of the dynamic model with existing static tire friction models is also presented. This comparison allows one to determine the values of the parameters for the new model. Introducing a set of mean states we reduce the order of the system and derive a model in lumped form which is useful for control purposes.


title: What’s in a picture? Evidence of discrimination from Prosper.com

abstract: We find evidence of significant racial disparities in a new type of credit market known as peer-to-peer lending. Loan listings with blacks in the attached picture are 25 to 35 percent less likely to receive funding than those of whites with similar credit profiles. Despite the higher average interest rates charged to blacks, lenders making such loans earn a lower net return compared to loans made to whites with similar credit profiles because blacks have higher relative default rates. These results provide insight into whether the discrimination we find is taste-based or statistical.


title: STREAM: the stanford stream data manager (demonstration description)

abstract: A cam cleat includes a pair of spring loaded cams or pawls rotatably mounted on a base. The cams have specially formed teeth and open and close in opposite rotary directions to releasably grip a line therebetween. The cams or pawls are rotatably mounted on a shaft and the case by a plurality of rotatable bearings to substantially reduce the effort required in opening and closing the cams and to reduce the spring force maintained on the cams, resulting in substantial advantages in performance. An alternate single cam version is also disclosed.


title: Computer vision based feature extraction of leaves for identification of medicinal values of plants

abstract: Plants are considered as one of the greatest assets in the field of Indian Science of Medicine called Ayurveda. Some plants have its medicinal values apart from serving as the source of food. The innovation in the allopathic medicines has degraded the significance of these therapeutic plants. People failed to have their medications at their door step instead went behind the fastest cure unaware of its side effects. One among the reasons is the lack of knowledge about identifying medicinal plants among the normal ones. So, a Vision based approach is being employed to create an automated system which identifies the plants and provides its medicinal values thus helping even a common man to be aware of the medicinal plants around them. This paper discusses about the formation of the feature set which is the important step in recognizing any plant species.


title: Knowledge building using visualizations

abstract: In this paper, we describe our efforts in knowledge building by creating visualizations. Our efforts include problem-based learning. We have identified a problem that the students have in learning OOP. To solve that problem we engage our students in the classroom using existing visualizations created by students of the earlier class, then we have the currents students improve the existing visualizations, or create new visualizations for future use in the same class. We describe the process of building knowledge, problem based learning, the details of the visualizations, our observations and the merits of this approach.


Effect of combining organic and inorganic phosphorus sources on maize grain yield in a humic-nitisol in Western Kenya


title: Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding

abstract: Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the role of feature selection, training data selection, and probabilistic threshold optimization in improving different multi-label classification approaches. We conduct experiments based on two different datasets: a recent gold standard dataset used for this task and a second larger and more complex EMR dataset we curated from the University of Kentucky Medical Center. While conventional approaches achieve results comparable to the state-of-the-art on the gold standard dataset, on our complex in-house dataset, we show that feature selection, training data selection, and probabilistic thresholding provide significant gains in performance.


title: Monkey Says, Monkey Does: Security and Privacy on Voice Assistants

abstract: The introduction of smart mobile devices has radically redesigned user interaction, as these devices are equipped with numerous sensors, making applications context-aware. To further improve user experience, most mobile operating systems and service providers are gradually shipping smart devices with voice controlled intelligent personal assistants, reaching a new level of human and technology convergence. While these systems facilitate user interaction, it has been recently shown that there is a potential risk regarding devices, which have such functionality. Our independent research indicates that this threat is not merely potential, but very real and more dangerous than initially perceived, as it is augmented by the inherent mechanisms of the underlying operating systems, the increasing capabilities of these assistants, and the proximity with other devices in the Internet of Things (IoT) era. In this paper, we discuss and demonstrate how these attacks can be launched, analysing their impact in real world scenarios.


Deep Context Model for Grammatical Error Correction


title: Cyber-attack detection and accommodation algorithm for energy delivery systems

abstract: Cyber-attack accommodation in a cyber-physical system is to ensure system operation, integrity and availability while maintaining a reasonable operational performance under attack. In this paper, we present a novel cyber-attack accommodation algorithm by estimating the true operational states of the system with new boundary & performance constrained resilient estimators while the system is continuously operating and is under attack. Our approach is based on combining data driven machine learning and physics based domain knowledge with traditional resilient estimation. The results were evaluated using a high fidelity model-based simulation environment.


title: Clustering Techniques: A Brief Survey of Different Clustering Algorithms

abstract: Partitioning a set of objects into homogeneous clusters is a fundamental operation in data mining. The operation is needed in a number of data mining tasks. Clustering or data grouping is the key technique of the data mining. It is an unsupervised learning task where one seeks to identify a finite set of categories termed clusters to describe the data . The grouping of data into clusters is based on the principle of maximizing the intra class similarity and minimizing the inter class similarity. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good clustering? This paper deal with the study of various clustering algorithms of data mining and it focus on the clustering basics, requirement, classification, problem and application area of the clustering algorithms.


title: Visual Place Recognition with Repetitive Structures

abstract: Repeated structures such as building facades, fences or road markings often represent a significant challenge for place recognition. Repeated structures are notoriously hard for establishing correspondences using multi-view geometry. They violate the feature independence assumed in the bag-of-visual-words representation which often leads to over-counting evidence and significant degradation of retrieval performance. In this work we show that repeated structures are not a nuisance but, when appropriately represented, they form an important distinguishing feature for many places. We describe a representation of repeated structures suitable for scalable retrieval and geometric verification. The retrieval is based on robust detection of repeated image structures and a suitable modification of weights in the bag-of-visual-word model. We also demonstrate that the explicit detection of repeated patterns is beneficial for robust visual word matching for geometric verification. Place recognition results are shown on datasets of street-level imagery from Pittsburgh and San Francisco demonstrating significant gains in recognition performance compared to the standard bag-of-visual-words baseline as well as the more recently proposed burstiness weighting and Fisher vector encoding.


title: Online self-calibration for robotic systems

abstract: We present a generic algorithm for self-calibration of robotic systems that utilizes two key innovations. First, it uses an information-theoretic measure to automatically identify and store novel measurement sequences. This keeps the computation tractable by discarding redundant information and allows the system to build a sparse but complete calibration dataset from data collected at different times. Second, as the full observability of the calibration parameters may not be guaranteed for an arbitrary measurement sequence, the algorithm detects and locks unobservable directions in parameter space using a combination of rank-revealing QR and singular value decompositions of the Fisher information matrix. The result is an algorithm that listens to an incoming sensor stream, builds a minimal set of data for estimating the calibration parameters, and updates parameters as they become observable, leaving the others locked at their initial guess. We validate our approach through an extensive set of simulated and real-world experiments.


title: Real-time tracking of non-rigid objects using mean shift

abstract: A new method for real time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and finds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) and the target candidates is expressed by a metric derived from the Bhattacharyya coefficient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution. The capability of the tracker to handle in real time partial occlusions, significant clutter, and target scale variations, is demonstrated for several image sequences.


title: Cross-domain sentiment classification with word embeddings and canonical correlation analysis

abstract: A common approach for automatic sentiment classification is using classifiers trained on labeled text data (reviews, blog posts etc.) to predict the sentiment polarity of new data. Because people express sentiment differently in different domains, this approach requires annotated corpora for each domain. However, annotating data for every domain of interest is laborious and impractical. In this paper, we address the domain adaptation problem for sentiment classification. We explore the effect of generic methods for feature learning and feature subspace mapping, namely word embeddings and canonical correlation analysis (CCA), on cross-domain sentiment classifiers. We show that by using only such rather generic methods, it is possible to get results very competitive with those of sophisticated methods specially developed for the considered problem. An advantage of using word embeddings and CCA is their availability out-of-the-box, which is important for the applicability of the proposed method. Experiments on a widely used benchmark dataset shows that both word embeddings and CCA contribute to accuracy improvement and their combination provides the best results.


title: Reducing false positives of network anomaly detection by local adaptive multivariate smoothing

abstract: Abstract Network intrusion detection systems based on the anomaly detection paradigm have high false alarm rate making them difficult to use. To address this weakness, we propose to smooth the outputs of anomaly detectors by online Local Adaptive Multivariate Smoothing (LAMS). LAMS can reduce a large portion of false positives introduced by the anomaly detection by replacing the anomaly detector's output on a network event with an aggregate of its output on all similar network events observed previously. The arguments are supported by extensive experimental evaluation involving several anomaly detectors in two domains: NetFlow and proxy logs. Finally, we show how the proposed solution can be efficiently implemented to process large streams of non-stationary data.


title: Gaze gestures and haptic feedback in mobile devices

abstract: Anticipating the emergence of gaze tracking capable mobile devices, we are investigating the use of gaze as an input modality in handheld mobile devices. We conducted a study of combining gaze gestures with vibrotactile feedback. Gaze gestures were used as an input method in a mobile device and vibrotactile feedback as a new alternative way to give confirmation of interaction events. Our results show that vibrotactile feedback significantly improved the use of gaze gestures. The tasks were completed faster and rated easier and more comfortable when vibrotactile feedback was provided.


Profitability of Momentum Strategies: An Evaluation of Alternative Explanations


title: Comparative validation of the D. melanogaster modENCODE transcriptome annotation

abstract: Accurate gene model annotation of reference genomes is critical for making them useful. The modENCODE project has improved the D. melanogaster genome annotation by using deep and diverse high-throughput data. Since transcriptional activity that has been evolutionarily conserved is likely to have an advantageous function, we have performed large-scale interspecific comparisons to increase confidence in predicted annotations. To support comparative genomics, we filled in divergence gaps in the Drosophila phylogeny by generating draft genomes for eight new species. For comparative transcriptome analysis, we generated mRNA expression profiles on 81 samples from multiple tissues and developmental stages of 15 Drosophila species, and we performed cap analysis of gene expression in D. melanogaster and D. pseudoobscura. We also describe conservation of four distinct core promoter structures composed of combinations of elements at three positions. Overall, each type of genomic feature shows a characteristic divergence rate relative to neutral models, highlighting the value of multispecies alignment in annotating a target genome that should prove useful in the annotation of other high priority genomes, especially human and other mammalian genomes that are rich in noncoding sequences. We report that the vast majority of elements in the annotation are evolutionarily conserved, indicating that the annotation will be an important springboard for functional genetic testing by the Drosophila community.


title: Digitise or Perish: Unlocking cultural memory online

abstract: Who we are is determined by what we remember; after all, history is nothing but a narrative of cobbled-together facts. Through the internet and various digitisation technologies, we've seen an unprecedented change in how and what we can archive - and therefore our ability to access history. Mel Campbell sheds light on these informational catacombs.