title: Prediction of time series by statistical learning: general losses and fast rates

abstract: Abstract We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates for the classical penalized ERM procedure. We apply this method for quantile forecasting of the French GDP. Under additional conditions on the loss functions (satisfied by the quadratic loss function) and for uniformly mixing processes, we prove that the Gibbs estimator actually achieves fast rates of convergence d/n. We discuss the optimality of these different rates pointing out references to lower bounds when they are available. In particular, these results bring a generalization the results of [29] on sparse regression estimation to some autoregression.


title: Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets

abstract: This article describes a strategy based on a naive-bayes classifier for detecting the polarity of English tweets. The experiments have shown that the best performance is achieved by using a binary classifier between just two sharp polarity categories: positive and negative. In addition, in order to detect tweets with and without polarity, the system makes use of a very basic rule that searchs for polarity words within the analysed tweets/texts. When the classifier is provided with a polarity lexicon and multiwords it achieves 63% F-score.


title: PALM: Parallel Architecture-Friendly Latch-Free Modifications to B+ Trees on Many-Core Processors

abstract: Concurrency control on B + trees is primarily achieved with latches, but serialization and contention can hinder scalability. As core counts on current processors increase, it is imperative to develop scalable latch-free techniques for concurrency control. We present PALM, a novel technique for performing multiple concurrent queries on in-memory B + trees. PALM is based on the Bulk Synchronous Parallel model, which guarantees freedom from deadlocks and race conditions. Input queries are grouped and processed in atomic batches, and work proceeds in stages that preclude contention. Transitions between stages are accomplished with scalable point-to-point communication. PALM exploits dataand thread-level parallelism on modern many-core architectures, and performs 40M 1 updates/second on trees with 128M keys, and 128M updates/second on trees with 512K keys on the latest CPU architectures. Our throughput is 2.3X‐19X that of state-of-theart concurrent update algorithms on in-memory B + trees. PALM obtains close to peak throughput at very low response times of less than 350 s, even for large trees. We also evaluate PALM on the Intel


title: Approximate FPGA-based LSTMs under Computation Time Constraints

abstract: Recurrent Neural Networks, with the prominence of Long Short-Term Memory (LSTM) networks, have demonstrated state-of-the-art accuracy in several emerging Artificial Intelligence tasks. Nevertheless, the highest performing LSTM models are becoming increasingly demanding in terms of computational and memory load. At the same time, emerging latency-sensitive applications including mobile robots and autonomous vehicles often operate under stringent computation time constraints. In this paper, we address the challenge of deploying computationally demanding LSTMs at a constrained time budget by introducing an approximate computing scheme that combines iterative low-rank compression and pruning, along with a novel FPGA-based LSTM architecture. Combined in an end-to-end framework, the approximation method parameters are optimised and the architecture is configured to address the problem of high-performance LSTM execution in time-constrained applications. Quantitative evaluation on a real-life image captioning application indicates that the proposed system required up to 6.5\(\times \) less time to achieve the same application-level accuracy compared to a baseline method, while achieving an average of 25\(\times \) higher accuracy under the same computation time constraints.


title: Change-Point Detection with Feature Selection in High-Dimensional Time-Series Data

abstract: Change-point detection is the problem of finding abrupt changes in time-series, and it is attracting a lot of attention in the artificial intelligence and data mining communities. In this paper, we present a supervised learning based change-point detection approach in which we use the separability of past and future data at time t (they are labeled as +1 and -1) as plausibility of change-points. Based on this framework, we propose a detection measure called the additive Hilbert-Schmidt Independence Criterion (aHSIC), which is defined as the weighted sum of the HSIC scores between features and its corresponding binary labels. Here, the HSIC is a kernel-based independence measure. A novel aspect of the aHSIC score is that it can incorporate feature selection during its detection measure estimation. More specifically, we first select features that are responsible for an abrupt change by using a supervised approach, and then compute the aHSIC score by employing the selected features. Thus, compared with traditional detection measures, our approach tends to be robust as regards noise features, and so the aHSIC is suitable for a use with high-dimensional time-series change-point detection problems. We demonstrate that the proposed change-point detection method is promising through extensive experiments on synthetic data sets and a real-world human activity data set.


title: QRS Detection Algorithm for Telehealth Electrocardiogram Recordings

abstract: Objective: QRS detection algorithms are needed to analyze electrocardiogram (ECG) recordings generated in telehealth environments. However, the numerous published QRS detectors focus on clean clinical data. Here, a “UNSW” QRS detection algorithm is described that is suitable for clinical ECG and also poorer quality telehealth ECG. Methods: The UNSW algorithm generates a feature signal containing information about ECG amplitude and derivative, which is filtered according to its frequency content and an adaptive threshold is applied. The algorithm was tested on clinical and telehealth ECG and the QRS detection performance is compared to the Pan–Tompkins (PT) and Gutierrez–Rivas (GR) algorithm. Results: For the MIT-BIH Arrhythmia database (virtually artifact free, clinical ECG), the overall sensitivity ( Se ) and positive predictivity (+ P ) of the UNSW algorithm was >99%, which was comparable to PT and GR. When applied to the MIT-BIH noise stress test database (clinical ECG with added calibrated noise) after artifact masking, all three algorithms had overall Se >99%, and the UNSW algorithm had higher + P (98%, p Se and 95% + P which was superior to PT (+ P : p Se and + P : p Conclusion: This is the first study to describe a QRS detection algorithm for telehealth data and evaluate it on clinical and telehealth ECG with superior results to published algorithms. Significance: The UNSW algorithm could be used to manage increasing telehealth ECG analysis workloads.


title: Understanding Online Consumer Stickiness in E-commerce Environment: A Relationship Formation Model

abstract: Consumers with online shopping experience often stick to some special websites. Does it mean that long-term relationships between consumers and the websites form? To solve the problem, this paper analyzed the belief and attitude factors influencing online consumer stickiness intention. Based on the relationships between belief-attitude-intention implied in TPB (Theory of Planned Behavior), according to Expectation-Confirmation theory and Commitment-trust theory, we developed a concept model on online consumer stickiness. Six research hypotheses derived from this model were empirically validated using a survey of online shoppers. SEM (Structure Equation Modeling) was as a data analysis method. The results suggest that online consumer stickiness intention is influenced by consumers’ commitment to the website (e-vendors) and their overall satisfaction to the transaction process, and commitment’s effect is stronger; in turn, both consumers’ commitment and overall satisfaction are influenced by consumers’ ongoing trust significantly; confirmation has a significant effect on overall satisfaction but has not the same effect on commitment. The findings show that the long-term relationships between consumers and special websites exactly exist during online repeat consumption.


title: Reformulating and optimizing the Mumford–Shah functional on a graph—A faster, lower energy solution

abstract: Active contour formulations predominate current minimization of the Mumford-Shah functional (MSF) for image segmentation and filtering. Unfortunately, these formulations necessitate optimization of the contour by evolving via gradient descent, which is known for its sensitivity to initialization and the tendency to produce undesirable local minima. In order to reduce these problems, we reformulate the corresponding MSF on an arbitrary graph and apply combinatorial optimization to produce a fast, low-energy solution. The solution provided by this graph formulation is compared with the solution computed via traditional narrow-band level set methods. This comparison demonstrates that our graph formulation and optimization produces lower energy solutions than gradient descent based contour evolution methods in significantly less time. Finally, by avoiding evolution of the contour via gradient descent, we demonstrate that our optimization of the MSF is capable of evolving the contour with non-local movement.


title: An Investigation into Android In-App Ad Practice: Implications for App Developers

abstract: In-app advertising has served as the major revenue source for millions of app developers in the mobile Internet ecosystem. Ad networks play an important role in app monetization by providing third-party libraries for developers to choose and embed into their apps. However, developers lack guidelines on how to choose from hundreds of ad networks and various ad features to maximize their revues without hurting the user experience of their apps. Our work aims to uncover the best practice and provide app developers guidelines on ad network selection and ad placement. To this end, we investigate 697 unique APIs from 164 ad networks which are extracted from 277,616 Android apps, develop a methodology of ad type classification based on UI interaction and behavior, and perform a large scale measurement study of in-app ads with static analysis techniques at the API granularity. We found that developers have more choices about ad networks than several years before. Most developers are conservative about ad placement and about 71% apps contain at most one ad library. In addition, the likeliness of an app containing ads depends on the app category to which it belongs. The app categories featuring young audience usually contain the most ad libraries maybe because of the ad-tolerance characteristic of young people. Furthermore, we propose a terminology and classify mobile ads into five ad types: Embedded, Popup, Notification, Offerwall, and Floating. We found that embedded and popup ad types are popular with apps in nearly all categories. Our results also suggest that developers should embed at most 6 ad libraries into an app, which otherwise would anger the app users. Also, a developer should use at most one ad network when her app is still at the initial stage and could start using more (2 or 3) ad networks when the app becomes popular. Our research is the first to reveal the preference of both developers and users for ad networks and ad types.


title: A Survey of Data Prefetching Techniques

abstract: The expanding gap between microprocessor and DRAM performance has necessitated the use of increasingly aggressive techniques designed to reduce or hide the latency of main memory accesses. Although large cache hierarchies have proven to be effective in reducing this latency for the most frequently used data, it is still not uncommon for scientific programs to spend more than half their run times stalled on memory requests. Data prefetching has been proposed as a technique for hiding the access latency of data referencing patterns that defeat caching strategies. Rather than waiting for a cache miss to perform a memory fetch, data prefetching anticipates such misses and issues a fetch to the memory system in advance of the actual memory reference. To be effective, prefetching must be implemented in such a way that prefetches are timely, useful, and introduce little overhead. Secondary effects in the memory system must also be taken into consideration. Despite these obstacles, prefetching has the potential to significantly improve overall program execution time by overlapping computation with memory accesses. Prefetching strategies are diverse and no single strategy has yet been proposed which provides optimal performance. The following survey examines several alternative approaches and discusses the design tradeoffs involved when implementing a data prefetch strategy. Note to the Reviewer : The survey style of this paper has necessarily led to a larger number of references than is normally allowed in IEEE Computer articles. The authors ask your assistance in identifying any references which may be excluded, either because they seem unnecessary or because the referenced topic adds little to the paper. Steven VanderWiel David J. Lilja svw@ee.umn.edu lilja@ee.umn.edu


title: Cyclostationary Feature Detection in Cognitive Radio using Different Modulation Schemes

abstract: various spectrum sensing schemes involved in cognitive radio have always been researched and discussed. An ideal detection scheme should be fast, accurate and efficient. Cyclostationary feature detection is a detection scheme that satisfies all these criteria. The method also possesses the ability to distinguish between noise and the primary user signal. One major advantage of cyclostationary feature detection method is that in addition to identifying the primary user signal, it also identifies the modulation scheme used by the primary user. This paper investigates the cyclostationary feature detection method under different modulation schemes. In this paper, cooperative spectrum sensing is performed which involves the cooperation among various cognitive relay nodes. Cooperative spectrum sensing is thus found to be an effective technique to improve the detection performance by exploring the spatial diversity of various relay nodes.


title: Design and Implementation of DSSS-CDMA Transmitter and Receiver for Reconfigurable Links Using FPGA

abstract: Direct sequence spread Spectrum (DSSS), is also called as direct sequence code division multiplexing (DS-CDMA). In direct sequence spread spectrum, the stream of information to be transmitted is divided into small pieces, each of which is allocated across to a frequency channel across the spectrum. A data signal at the point of transmission is combined with a higher data-rate bit sequence (also called chipping code) that divides the data according to a spreading ratio. The redundant chipping code helps the signal resist interference and also enables the original data to be recovered if data bits are damaged during the transmission. Direct sequence contrasts with the other spread spectrum process, known as frequency hopping spread spectrum. Frequency hopping code division multiple access (FH-CDMA), in which a broad slice of the bandwidth spectrum is divided into many possible broadcast frequencies. In general, frequency-hopping devices use, less power and are cheaper, but the performance of DS-CDMA systems is usually better and more reliable. In this project direct sequence spread spectrum principle based code division multiple access (CDMA) transmitter and receiver is implemented in VHDL for FPGA. The transmitter module mainly consists of data generator, programmable chip sequence generator (PN sequence generator), direct digital frequency synthesizer (DDFS), BPSK modulator blocks. The receiver modular mainly consists of BPSK demodulator, programmable chip sequence generator (PN sequence generator), matched filters, threshold detector blocks. Modelsim 6.2(MXE) tool will be used for functional and logic verification at each block. The Xilinx synthesis technology (XST) of Xilinx ISE 9.2i tool will be used for synthesis of transmitter and receiver on FPGA Spartan 3E.


1H,13C and15N NMR and GIAO CPHF calculations on two quinoacridinium salts


Stacked Similarity-Aware Autoencoders.


title: Past and Future Steps for Adaptive Storage Data Systems: From Shallow to Deep Adaptivity

abstract: Data systems with adaptive storage can autonomously change their behavior by altering how data is stored and accessed. Such systems have been studied primarily for the case of adaptive indexing to automatically create the right indexes at the right granularity. More recently work on adaptive loading and adaptive data layouts brought even more flexibility. We survey this work and describe the need for even deeper adaptivity that goes beyond adjusting knobs in a single architecture; instead it can adapt the fundamental architecture of a data system to drastically alter its behavior.


title: Macro Benthic Assemblage and Temporal Interactions at Palk Strait, Southeast Coast of India

abstract: The present study was made to map out the seasonal variation (from March 2007 to Feb 2008) of macro faunal diversity at different depths viz. 0mt, 5m, 10m and 20m in Arkattuthurai region of Palk Strait, southeast coast of India. A total of 66 species of macro fauna were recorded. Among them, polychaetes were found to be the dominant group (38 species), followed by bivalves (15 species) and gastropods (13 species). The density of organisms varied from 62 during post monsoon at 10m depth to 126 during summer at 20m depth. Similarly species diversity varied from 4.075 at 10m in the sample collected during pre monsoon to 4.935 at 20m depth during summer. In a nutshell, the distribution of macro fauna was found to increase with increasing depth and the probable reasons for the distribution pattern of organisms is discussed in this paper.


title: Levofloxacin implants with predefined microstructure fabricated by three-dimensional printing technique.

abstract: A novel three-dimensional (3D) printing technique was utilized in the preparation of drug implants that can be designed to have complex drug release profiles. The method we describe is based on a lactic acid polymer matrix with a predefined microstructure that is amenable to rapid prototyping and fabrication. We describe how the process parameters, especially selection of the binder, were optimized. Implants containing levofloxacin (LVFX) with predefined microstructures using an optimized binder solution of ethanol and acetone (20:80, v/v) were prepared by a 3D printing process that achieved a bi-modal profile displaying both pulsatile and steady state LVFX release from a single implant. The pulse release appeared from day 5 to 25, followed by a steady state phase of 25 days. The next pulse release phase then began at the 50th day and ended at the 80th day. To evaluate the drug implants structurally and analytically, the microscopic morphologies and the in vitro release profiles of the implants fabricated by both the 3D printing technique and the conventional lost mold technique were assessed using environmental scanning electron microscopy (ESEM) and UV absorbance spectrophotometry. The results demonstrate that the 3D printing technology can be used to fabricate drug implants with sophisticated micro- and macro-architecture in a single device that may be rapidly prototyped and fabricated. We conclude that drug implants with predefined microstructure fabricated by 3D printing techniques can have clear advantages compared to implants fabricated by conventional compressing methods.


A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations


title: Sideslip angle, lateral tire force and road friction estimation in simulations and experiments

abstract: Transversal tire forces and sideslip angle are essential data for improving vehicle safety, handling, steerability, comfort and performance. This paper proposes and compares four observers designed to calculate vehicle sideslip angle and lateral tire forces. The different observers are derived from the Extended Kalman filter (EKF), the single-track model, and use different on tire-force models. The first three tire-force models are the linear model, the Burckhardt model and the Pacejka model. The remaining tire-force model, the “linear adaptive” model, is proposed in this paper in order to construct an adaptive observer. The “linear adaptive” model is based on the linear model, but with an additional variable used to correct wheel cornering stiffness errors. The paper describes models and observers, along with a road-friction identification method. Observers are first compared in a simulation context using a professional vehicle simulator. Subsequently, observers are applied to real experimental data acquired on the Heudiasyc Laboratory car.


title: Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios

abstract: In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments, we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning. On the one side, the generation of a high rate of structured data on the Web led to the creation and publication of the so-called knowledge graphs. On the other side, deep learning emerged as one of the most promising approaches in the generation and training of models that can be applied to a wide variety of application fields. More recently, autoencoders have proven their strength in various scenarios, playing a fundamental role in unsupervised learning. In this paper, we instigate how to exploit the semantic information encoded in a knowledge graph to build connections between units in a Neural Network, thus leading to a new method, SEM-AUTO, to extract and weight semantic features that can eventually be used to build a recommender system. As adding content-based side information may mitigate the cold user problems, we tested how our approach behaves in the presence of a few ratings from a user on the Movielens 1M dataset and compare results with BPRSLIM.


title: The merits of sharing a ride

abstract: The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.


Why Neural Translations are the Right Length


title: A Psychophysiological Investigation of the Continuous Flow Model of Human Information Processing

abstract: Twelve subjects responded to target letters "H" or "S" by squeezing dynamometers with the left or right hand. Targets could be surrounded by compatible (e.g., HHHHH) or incompatible noise (SSHSS) letters. Measures of the P300 component of the event-related brain potential and of correct and incorrect electromyographic and squeeze activity were used to study stimulus evaluation and response-related processes. When incorrect squeeze activity was present, execution of the correct response was prolonged, indicating a process of response competition. This process occurred more often under incompatible noise conditions, which were also associated with a delayed P300. Thus, the noise/compatibility manipulation influenced both stimulus evaluation and response competition processes. In contrast, a warning tone that preceded array presentation on half the trials, increased response speed without influencing evaluation time. The data suggest that the latency and accuracy of overt behavioral responses are a function of (a) a response activation process controlled by an evaluation process that accumulates evidence gradually, (b) a response priming process that is independent of stimulus evaluation, and (c) a response competition process. Language: en


title: Pairwise meta-rules for better meta-learning-based algorithm ranking

abstract: In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset.


title: Hardware Design of Smart Home Energy Management System With Dynamic Price Response

abstract: The smart grid initiative and electricity market operation drive the development known as demand-side management or controllable load. Home energy management has received increasing interest due to the significant amount of loads in the residential sector. This paper presents a hardware design of smart home energy management system (SHEMS) with the applications of communication, sensing technology, and machine learning algorithm. With the proposed design, consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer. Also, consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. Further, SHEMS is designed with sensors to detect human activities and then a machine learning algorithm is applied to intelligently help consumers reduce total payment on electricity without or with little consumer involvement. Finally, simulation and experiment results are presented based on an actual SHEMS prototype to verify the hardware system.


title: On the relationship between REA and SAP

abstract: Resources–Events–Agents (REA) is the best known theoretical accounting enterprise database model, while SAP is the dominant enterprise resource-planning system. The purpose of this paper is to investigate some of the relationships between the underlying data models in REA and SAP. The criteria of Dunn and McCarthy [J. Inf. Syst. (1997) 31] for differentiating accounting systems (database orientation, semantic orientation, and structuring orientation) are used to structure a summary of those relationships. Although it is found that there are substantial similarities, SAP does have some implementation compromises that generally keep it from being fully REA. These compromises are based on the use of accounting artifacts and other, often, implementation-specific compromises. In addition, there are emerging differences between the two. A comparison between REA and SAP is important for a number of reasons. First, the tie between SAP and REA provides SAP with a theoretic basis. Second, the existence of a relationship between REA and SAP provides an important basis for the coverage of REA in the curriculum. Third, the opening of a dialogue between SAP and REA could spur further development of both.


title: A 1.3GHz fifth generation SPARC64 microprocessor

abstract: A 5th generation SPARC64 processor is fabricated in 130nm SOI CMOS process with 8 layers of Cu metallization. It runs at 1.3GHz with 34.7W power dissipation in the laboratory. The chip contains over 190M transistors with 19M in logic circuits. The chip size is 18.14mm x 15.99mm. The error detection and recovery mechanism is implemented for execution units and data path logic circuits in addition to on-chip arrays to detect and recover from data logic error. This processor is developed by using mostly in-house CAD tools.


title: Multi-Vehicle Cooperative Control Using Mixed Integer Linear Programming

abstract: We present methods to synthesize cooperative strategies for multi-vehicle control problems using mixed integer linear programming. Complex multi-vehicle control problems are expressed as mixed logical dynamical systems. Optimal strategies for these systems are then solved for using mixed integer linear programming. We motivate the methods on problems derived from an adversarial game between two teams of robots called RoboFlag. We assume the strategy for one team is fixed and governed by state machines. The strategy for the other team is generated using our methods. Finally, we perform an average case computational complexity study on our approach.


title: Modeling and characterization of GPS spoofing

abstract: The Global Positioning System (GPS) grows into a ubiquitous utility that provides positioning, navigation, and timing (PNT) services. As an essential element of the global information infrastructure, cyber security of GPS faces serious challenges. Some mission-critical systems even rely on GPS as a security measure. However, civilian GPS itself has no protection against malicious acts such as spoofing. GPS spoofing breaches authentication by forging satellite signals to mislead users with wrong location/timing data that threatens homeland security. In order to make civilian GPS secure and resilient for diverse applications, we must understand the nature of attacks. This paper proposes a novel attack modeling of GPS spoofing with event-driven simulation package. Simulation supplements usual experiments to limit incidental harms and to comprehend a surreptitious scenario. We also provide taxonomy of GPS spoofing through characterization. The work accelerates the development of defense technology against GPS-based attacks.


title: Drone & me: an exploration into natural human-drone interaction

abstract: Personal drones are becoming popular. It is challenging to design how to interact with these flying robots. We present a Wizard-of-Oz (WoZ) elicitation study that informs how to naturally interact with drones. Results show strong agreement between participants for many interaction techniques, as when gesturing for the drone to stop. We discovered that people interact with drones as with a person or a pet, using interpersonal gestures, such as beckoning the drone closer. We detail the interaction metaphors observed and offer design insights for human-drone interactions.


title: A Naive Bayesian Multi-label Classication Algorithm With Application to Visualize Text Search Results

abstract: Search results visualization has emerged as an important research topic due to its ap- plication on search engine amelioration. From the perspective of machine learning, the text search results visualization task fits to the multi-label learning framework that a document is usually related to multiple category labels. In this paper, a Na¨ ive Bayesian (NB) multi-label classification algorithm is proposed by incorporating a two-step feature selection strategy which aims to satisfy the assumption of conditional independency in NB classification theory. The experiments over public data set demonstrate that the proposed method has highly competitive performance with several well-established multi-label clas- sification algorithms. We implement a prototype system named TJ-MLWC based on the proposed algorithm, which acts as an intermediate layer between users and a commercial Internet Search Engine, allowing the search results of a query displaying by one or multi- ple categories. Testing results indicate that our prototype improves search experience by adding the function of browsing search results by category.


title: Building a Large-scale Multimodal Knowledge Base for Visual Question Answering

abstract: The complexity of the visual world creates significant challenges for comprehensive visual understanding. In spite of recent successes in visual recognition, a lack of common sense knowledge and the insufficiencies of joint reasoning still leave a large gap between computer vision models and their human counterparts for higher-level tasks such as question answering (QA). In this work, we build a multimodal knowledge base (KB) incorporating visual, textual and structured data, as well as their diverse relations for visual QA. Such a general-purpose approach presents major challenges in terms of scalability and quality. Our approach is to cast large-scale MRFs into a KB representation, and introduce a scalable knowledge base construction system by leveraging database techniques. We unify several visual QA tasks in a principled query language. Our KB achieves competitive results compared to purpose-built models, while exhibiting greater flexibility in visual QA.


title: AUTONOMOUS INTELLIGENT FLIGHT CONTROL OF FIXED-WING UAV BASED ON ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

abstract: This paper develops an autonomous flight controller for fixed-wing UAV based on ANFIS. Three ANFIS modules are designed for controlling the altitude, the heading angle, and the speed of the UAV. In this way, the UAV position is controlled in threedimensional space: altitude, longitude, and latitude position. The simulation results show the capability of the designed approach and its very satisfied performance with good stability and robustness against UAV parametric uncertainties and external wind disturbance.


title: Alkaloids: an overview of their antibacterial, antibiotic-enhancing and antivirulence activities.

abstract: With reports of pandrug-resistant bacteria causing untreatable infections, the need for new antibacterial therapies is more pressing than ever. Alkaloids are a large and structurally diverse group of compounds that have served as scaffolds for important antibacterial drugs such as metronidazole and the quinolones. In this review, we highlight other alkaloids with development potential. Natural, semisynthetic and synthetic alkaloids of all classes are considered, looking first at those with direct antibacterial activity and those with antibiotic-enhancing activity. Potent examples include CJ-13,136, a novel actinomycete-derived quinolone alkaloid with a minimum inhibitory concentration of 0.1 ng/mL against Helicobacter pylori, and squalamine, a polyamine alkaloid from the dogfish shark that renders Gram-negative pathogens 16- to >32-fold more susceptible to ciprofloxacin. Where available, information on toxicity, structure-activity relationships, mechanisms of action and in vivo activity is presented. The effects of alkaloids on virulence gene regulatory systems such as quorum sensing and virulence factors such as sortases, adhesins and secretion systems are also described. The synthetic isoquinoline alkaloid virstatin, for example, inhibits the transcriptional regulator ToxT in Vibrio cholerae, preventing expression of cholera toxin and fimbriae and conferring in vivo protection against intestinal colonisation. The review concludes with implications and limitations of the described research and directions for future research.


title: Muscle tissue adaptations to hypoxia.

abstract: This review reports on the effects of hypoxia on human skeletal muscle tissue. It was hypothesized in early reports that chronic hypoxia, as the main physiological stress during exposure to altitude, per se might positively affect muscle oxidative capacity and capillarity. However, it is now established that sustained exposure to severe hypoxia has detrimental effects on muscle structure. Short-term effects on skeletal muscle structure can readily be observed after 2 months of acute exposure of lowlanders to severe hypoxia, e.g. during typical mountaineering expeditions to the Himalayas. The full range of phenotypic malleability of muscle tissue is demonstrated in people living permanently at high altitude (e.g. at La Paz, 3600-4000 m). In addition, there is some evidence for genetic adaptations to hypoxia in high-altitude populations such as Tibetans and Quechuas, who have been exposed to altitudes in excess of 3500 m for thousands of generations. The hallmark of muscle adaptation to hypoxia in all these cases is a decrease in muscle oxidative capacity concomitant with a decrease in aerobic work capacity. It is thought that local tissue hypoxia is an important adaptive stress for muscle tissue in exercise training, so these results seem contra-intuitive. Studies have therefore been conducted in which subjects were exposed to hypoxia only during exercise sessions. In this situation, the potentially negative effects of permanent hypoxic exposure and other confounding variables related to exposure to high altitude could be avoided. Training in hypoxia results, at the molecular level, in an upregulation of the regulatory subunit of hypoxia-inducible factor-1 (HIF-1). Possibly as a consequence of this upregulation of HIF-1, the levels mRNAs for myoglobin, for vascular endothelial growth factor and for glycolytic enzymes, such as phosphofructokinase, together with mitochondrial and capillary densities, increased in a hypoxia-dependent manner. Functional analyses revealed positive effects on V(O(2)max) (when measured at altitude) on maximal power output and on lean body mass. In addition to the positive effects of hypoxia training on athletic performance, there is some recent indication that hypoxia training has a positive effect on the risk factors for cardiovascular disease.


title: Virtual training of fire wardens through immersive 3D environments

abstract: This paper presents an interactive application addressed to training experts on occupational hazard prevention and more precisely in fire safety in buildings. The platform allows the simulation of fires throughout a building. The fire warden trainee has to deal with that fire by a) finding the safest evacuation route or b) putting the fire out. The simulated fire will react to the environment, propagating in different ways depending on several factors; air currents in the building, material of the floor and walls, furniture, etc. The heat release of different materials has to be studied for this purpose. The fire will also react in real time to the response of the trainee depending on the method used to put it out. BIM standards will be applied to the logistic of the simulation. Thanks to this work methodology the application is provided with precise information regarding the building. The application is developed using a multiplatform game engine, so it can be used either as a web player serious game or in immersive environments such as the Oculus Rift HMD. In this publication we discuss the work methodology planed to fulfil the aforementioned issues.


title: On the Systematic Analysis of Natural Language Requirements with CIRCE

abstract: This paper presents Circe, an environment for the analysis of natural language requirements. Circe is first presented in terms of its architecture, based on a transformational paradigm. Details are then given for the various transformation steps, including (i) a novel technique for parsing natural language requirements, and (ii) an expert system based on modular agents, embodying intensional knowledge about software systems in general. The result of all the transformations is a set of models for the requirements document, for the system described by the requirements, and for the requirements writing process. These models can be inspected, measured, and validated against a given set of criteria. ::: ::: Some of the features of the environment are shown by means of an example. Various stages of requirements analysis are covered, from initial sketches to pseudo-code and UML models.


title: Perseverance of social theories: The role of explanation in the persistence of discredited information

abstract: The perseverance of social theories was examined in two experiments within a debriefing paradigm. Subjects were initially given two case studies suggestive of either a positive or a negative relationship between risk taking and success as a firefighter. Some subjects were asked to provide a written explanation of the relationship; others were not. In addition, experimental subjects were thoroughly debriefed concerning the fictitious nature of the initial case studies. Subsequent assessments of subjects' personal beliefs about the relationship indicated that even when initially based on weak data, social theories can survive the total discrediting of that initial evidential base. Both correlational and experimental results suggested that such unwarranted theory perseverance may be mediated, in part, by the cognitive process of formulating causal scenarios or explanations. Normative issues and the cognitive processes underlying perseverance were examined in detail, and possible techniques for overcoming unwarranted theory perseverance were discussed. All of us have tried to change a friend's view about some social, political or scientific issue-from the efficacy of capital punishment as a deterrent to crime to the validity of the IQ test-only to experience frustrating failure. We offer seemingly compelling evidence or thoroughly rebut opposing arguments but produce little if any change in our friend's beliefs. Indeed, we suspect that we ourselves frequently may be guilty of similar intransigence when our views come under attack. From such everyday observations, two obvious questions arise that form the focus of the present article: Are we, in fact, prone to persist in our beliefs and theories about the world to a degree that is normatively indefensible, and if so, why?


title: Comparing Click Logs and Editorial Labels for Training Query Rewriting

abstract: Clicks on web advertisements in response to web search queries is a major source of revenue for search companies. Query rewrites can signican tly increase the coverage of web advertisements. In previous work we focused on optimizing the relevance between the query issued by the web searcher, and rewritten queries used to place advertisements. In this preliminary study, we examine some features of query rewrites which are predictive of click-throughs on sponsored search listings retrieved for those rewrites, by mining web search-click logs. We also compare the features which are predictive of relevance (judged by human editors) and the clicks in user query logs during query rewriting. Our preliminary results suggest that similar features are predictive, and so we may be able to train our models on click log data in place of human relevance judgments.


title: Time is of the essence: improving recency ranking using Twitter data

abstract: Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.


title: Reference Architecture for Holonic Manufacturing Systems : PROSA

abstract: Abstract Future manufacturing systems need to cope with frequent changes and disturbances. As such, their control requires constant adaptation and high flexibility. Holonic manufacturing is a highly distributed control paradigm that promises to handle these problems successfully. It is based on the concept of autonomous co-operating agents, called `holons'. This paper gives an overview of the holonic reference architecture for manufacturing systems as developed at PMA-KULeuven. This architecture, called PROSA, consists of three types of basic holons: order holons, product holons, and resource holons. They are structured using the object-oriented concepts of aggregation and specialisation. Staff holons can be added to assist the basic holons with expert knowledge. The resulting architecture has a high degree of self-similarity, which reduces the complexity to integrate new components and enables easy reconfiguration of the system. PROSA shows to cover aspects of both hierarchical as well as heterarchical control approaches. As such, it can be regarded as a generalisation of the two former approaches. More importantly, PROSA introduces significant innovations: the system structure is decoupled from the control algorithm, logistical aspects can be decoupled from technical ones, and PROSA opens opportunities to achieve more advanced hybrid control algorithms.


title: The Framework of Home Remote Automation System Based on Smartphone

abstract: The purpose of this paper is creating a mobile app on a Smartphone device so that the user can control electronic devices; see the amount of flow that has been used in the amount of dollars, so the problem is the difficulty in saving electricity can be resolved. Development and design was done by collecting data using questionnaires to the respondents. Design method using observations, distributing questionnaires and to study literature, and then after that do the design in hardware (microcontroller) made United Modeling Language (UML), database design, code implementation and creation of user interfaces on IOS and Android. The result of this research is the implementation of a remote home automation application in mobile which can help users in order to control the home and determine the cost of electricity that has been used in every electronic device so that the optimization can be achieved.


title: Roadmap to adopting OPC UA

abstract: This paper highlights a roadmap, consisting of three alternative paths, to adopting OPC UA, a new generation OPC specification. The OPC UA specification aims to improve and extend the scope of OPC in system integration. It introduces SOA paradigm and platform independence into OPC world, enabling a wide variety of new applications. It also provides integration from an embedded device up into the ERP level. This paper details ways to adapt existing systems to utilize OPC UA in order to timely take advantage of the new possibilities. Novel key features and application possibilities offered by OPC UA are also discussed. At last a small sample OPC UA application is presented as a proof of concept.


title: Masquerade Attack Detection Using a Search-Behavior Modeling Approach

abstract: Masquerade attacks are unfortunately a familiar security problem that is a consequence of identity theft. Detecting masqueraders is very hard. Prior work has focused on user command modeling to identify abnormal behavior indicative of impersonation. This paper extends prior work by presenting one-class Hellinger distance-based and one-class SVM modeling techniques that use a set of novel features to reveal user intent. The specific objective is to model user search profiles and detect deviations indicating a masquerade attack. We hypothesize that each individual user knows their own file system well enough to search in a limited, targeted and unique fashion in order to find information germane to their current task. Masqueraders, on the other hand, will likely not know the file system and layout of another user’s desktop, and would likely search more extensively and broadly in a manner that is different than the victim user being impersonated. We extend prior research that uses UNIX command sequences issued by users as the audit source by relying upon an abstraction of commands. We devise taxonomies of UNIX commands and Windows applications that are used to abstract sequences of user commands and actions. We also gathered our own normal and masquerader data sets captured in a Windows environment for evaluation. The datasets are publicly available for other researchers who wish to study masquerade attack rather than author identification as in much of the prior reported work. The experimental results show that modeling search behavior reliably detects all masqueraders with a very low false positive rate of 0.1%, far better than prior published results. The limited set of features used for search behavior modeling also results in huge performance gains over the same modeling techniques that use larger sets of features.


title: Classification of hardwood species using ANN classifier

abstract: In this paper, an approach for the classification of different hardwood species of open access database, using texture feature extraction and supervised machine learning technique has been implemented. Edges of complex cellular structure of microscopic images of hardwood are enhanced with the application of Gabor filter, and Gray Level Co-occurrence Matrix (GLCM) as an effective texture feature extraction technique is being revalidated. About, 44 features have been extracted from GLCM; these features have been further normalized in the range [0.1, 1]. Multilayer Perceptron Backpropagation Artificial Neural Network have been used for classification. Experiments conducted on 25 wood species have resulted in recognition accuracy of about 88.60% and 92.60% using Levenberg-Marquardt backpropagation training function with two different datasets for training, validation and testing ratio (70%, 15%, 15% and 80%, 10%, 10%) respectively. Proposed methodology can be extended with optimized machine learning techniques for online identification of wood.


title: Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion

abstract: Deep Neural Networks (DNNs) provide state-of-the-art solutions in several difficult machine perceptual tasks. However, their performance relies on the availability of a large set of labeled training data, which limits the breadth of their applicability. Hence, there is a need for new {\em semi-supervised learning} methods for DNNs that can leverage both (a small amount of) labeled and unlabeled training data. In this paper, we develop a general loss function enabling DNNs of any topology to be trained in a semi-supervised manner without extra hyper-parameters. As opposed to current semi-supervised techniques based on topology-specific or unstable approaches, ours is both robust and general. We demonstrate that our approach reaches state-of-the-art performance on the SVHN ($9.82\%$ test error, with $500$ labels and wide Resnet) and CIFAR10 (16.38% test error, with 8000 labels and sigmoid convolutional neural network) data sets.


title: Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.

abstract: It is well known that when data are nonnormally distributed, a test of the significance of Pearson’s r may inflate Type I error rates and reduce power. Statistics textbooks and the simulation literature provide several alternatives to Pearson’s correlation. However, the relative performance of these alternatives has been unclear. Two simulation studies were conducted to compare 12 methods, including Pearson, Spearman’s rank-order, transformation, and resampling approaches. With most sample sizes (n 20), Type I and Type II error rates were minimized by transforming the data to a normal shape prior to assessing the Pearson correlation. Among transformation approaches, a general purpose rank-based inverse normal transformation (i.e., transformation to rankit scores) was most beneficial. However, when samples were both small (n 10) and extremely nonnormal, the permutation test often outperformed other alternatives, including various bootstrap tests.


title: An implementation of GEM: supporting a semantic data model on a relational back-end.

abstract: This paper presents a simple approach for extending the relational system INGRES into one supporting a semantic data model it describe a DBMS consisting of (i) a user-friendly front-end, supporting the GEM semantic data model and query language under the UNIX time-sharing system, and (ii) a dedicated back-end processor providing efficient support for database transactions, concurrency control and recovery GEM extends the relational model to support the notions of entities with surrogates, the relationships of aggregation and generalization, null values and set-valued attributes, and provides simple extensions of QUEL to handle these new constructs in this proposed implementation of GEM, the relational database processor IDM 500 by Britton-Lee is used as the back-end machine


title: Model Extraction for ARINC 653 based Avionics Software ⋆

abstract: One of the most exciting and promising approaches to ensure the correctness of critical systems is software model checking, which considers real code, written with standard programming languages like C. One general technique to implement this approach is producing a reduced model of the software in order to employ existing and efficient tools, like spin. This paper presents the application of the technique to avionics software constructed on top of an application interface (API) compliant with the ARINC 653 specification (APEX), which is widely employed by the manufacturers in the avionics industry. The paper uses techniques to automatically extract promela models from C source code. These techniques were previously developed by the authors. However, they are now extended to deal with new problems, like real-time aspects and APEX scheduling. In order to close the extracted model during the verification, we built a reusable APEX-specific environment. This APEX environment models the execution engine (i.e. an APEX compliant RTOS) that implements APEX services. Finally, this paper also contains a novel testing method to ensure the correctness of this APEX environment. This testing method uses SPIN to execute official ARINC 653 test cases.


title: Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

abstract: Online multi-object tracking with a single moving camera is a challenging problem as the assumptions of 2D conventional motion models (e.g., first or second order models) in the image coordinate no longer hold because of global camera motion. In this paper, we consider motion context from multiple objects which describes the relative movement between objects and construct a Relative Motion Network (RMN) to factor out the effects of unexpected camera motion for robust tracking. The RMN consists of multiple relative motion models that describe spatial relations between objects, thereby facilitating robust prediction and data association for accurate tracking under arbitrary camera movements. The RMN can be incorporated into various multi-object tracking frameworks and we demonstrate its effectiveness with one tracking framework based on a Bayesian filter. Experiments on benchmark datasets show that online multi-object tracking performance can be better achieved by the proposed method.


PPDDL1.0: An Extension to PDDL for Expressing Planning Domains with Probabilistic Effects


Joint Speaker and Lexical Modeling for Short-Term Characterization of Speaker


title: Effect of Protein Ingestion on Energy Expenditure and Substrate Utilization after Exercise in Middle-Aged Women

abstract: Research suggests that ingesting protein after resistance exercise (RE) increases muscle protein synthesis and results in greater muscle gains. The effect on energy expenditure and substrate utilization, however, is unclear. This study evaluated the effect of RE and postexercise protein on recovery energy expenditure and substrate utilization in 17 women (age 46.5 ± 1.2 y). A whey-protein supplement (120 kcal, 30 g protein) was ingested immediately after 1 bout of RE (PRO) and a noncaloric placebo after another (PLA). VO 2 and respiratory-exchange ratio (RER) were measured before and for 120 min after each exercise session. RE resulted in a significant increase in VO 2 that persisted through 90 min of recovery (P < 0.01) and was not affected by protein supplementation. RE significantly lowered RER, resulting in an increase in fat oxidation for both PLA and PRO (P < 0.01). For PRO, however, RER returned to baseline values earlier than for PLA, resulting in a reduced fat-oxidation response (P = 0.02) and earlier return to preexercise baseline values than for PLA. Substrate utilization was significantly different between conditions (P = 0.02), with fat contributing 77.76% ± 2.19% for PLA and 72.12% ± 2.17% for PRO, while protein oxidation increased from 17.18% ± 1.33% for PLA to 20.82% ± 1.47% for PRO. Postexercise protein did not affect energy expenditure, but when protein was available as an alternate fuel fat oxidation was diminished. Based on these findings it might be beneficial for middle-aged women to delay protein intake after RE to maximize fat utilization.


title: 5.2-GHz RF Power Harvester in 0.18- $\mu$m CMOS for Implantable Intraocular Pressure Monitoring

abstract: A first fully integrated 5.2-GHz CMOS-based RF power harvester with an on-chip antenna is presented in this paper. The design is optimized for sensors implanted inside the eye to wirelessly monitor the intraocular pressure of glaucoma patients. It includes a five-stage RF rectifier with an on-chip antenna, a dc voltage limiter, two voltage sensors, a low dropout voltage regulator, and MOSCAP based on-chip storage. The chip has been designed and fabricated in a standard 0.18- $\mu$ m CMOS technology. To emulate the eye environment in measurements, a custom test setup is developed that comprises Plexiglass cavities filled with saline solution. Measurements in this setup show that the proposed chip can be charged to 1 V wirelessly from a 5-W transmitter 3 cm away from the harvester chip. The energy that is stored on the 5-nF on-chip MOSCAP when charged to 1 V is 2.5 nJ, which is sufficient to drive an arbitrary 100- $\mu$ W load for 9 $\ \mu$ s at regulated 0.8 V. Simulated efficiency of the rectifier is 42% at ${-}$ 7 dBm of input power.


Meta-analysis Associations between vitamin D receptor gene polymorphisms and osteoarthritis : an updated meta-analysis


title: Predictability of energy use in homes

abstract: Predictability of home energy usage forms the basis of many home energy management and demand-response systems. While existing studies focus on designing more accurate prediction algorithms, a comprehensive energy management solution requires a broad understanding of prediction accuracy at different granularities, for example appliance and home, as well as different time horizons, for example an hour, day, or week into the future. In this paper, we undertake an analysis of predictability of power draw of appliances and whole-home energy consumption at four different time horizons: an hour, a quarter-day, a day, and a week in the future. Our analysis presents two research contributions. Our first contribution is a diverse dataset, GreenHomes, that includes appliance power draw and whole-home energy consumption data from seven homes across three states in the United States over a two-year period. Our second and primary contribution is a set of insights into the predictability of home energy usage. We show that simple statistic-based algorithms perform as well as sophisticated machine learning algorithms and time-series based predictors. These simple algorithms can considerably reduce the computational need for large-scale predictive analysis of home energy data. We also show that appliance-level power draw is more predictable than whole-home energy consumption at shorter time horizons while home-level energy consumption is more predictable at longer time horizons. Finally, we show that there is large variation in predictability across homes. This variation may be attributed to home type and points to the need for personalized energy management systems.


title: Vehicle Localization along a Previously Driven Route Using Image Database

abstract: In most autonomous driving applications, such as parking and commuting, a vehicle follows a previously taken route, or almost the same route. In this paper, we propose a method to localize a vehicle along a previously driven route using images. The proposed method consists of two stages: offline creation of a database, and online localization. In the offline stage, a database is created from images that are captured when the vehicle drives a route for the first time. The database consists of images, 3D positions of feature points estimated by structure-from-motion, and a topological graph. In the online stage, the method first identifies the database image that is most similar to the current image by topometric localization, which considers topological information on a metric scale. The vehicle poses are then estimated from the 3D-2D correspondences of matching feature points between the current image and the identified database image. In an experiment, we estimated vehicle poses using images captured in an indoor parking lot.


Content Based Image Retrieval Systems using SIFT: A Survey


Predicting Rare Events Using Specialized Sampling Techniques in SAS


title: Autonomous Driving in the Real World: Experiences with Tesla Autopilot and Summon

abstract: As autonomous driving emerges, it is important to understand drivers' experiences with autonomous cars. We report the results of an online survey with Tesla owners using two autonomous driving features, Autopilot and Summon. We found that current users of these features have significant driving experience, high self-rated computer expertise and care about how automation works. Surprisingly, although automation failures are extremely common they were not perceived as risky. The most commonly occurring failures included the failure to detect lanes and uncomfortable speed changes of the vehicle. Additionally, a majority of the drivers emphasized the importance of being alert while driving with autonomous features and aware of the limitations of the current technology. Our main contribution is to provide a picture of attitudes and experiences towards semi-autonomous driving, revealing that some drivers adopting these features may not perceive autonomous driving as risky, even in an environment with regular automation failures.


title: BusyBody: creating and fielding personalized models of the cost of interruption

abstract: Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user's current interruptability from computer activity and relevant contextual information. We describe a system that intermittently asks users to assess their perceived interruptability during a training phase and that builds decision-theoretic models with the ability to predict the cost of interrupting the user. The models are used at run-time to compute the expected cost of interruptions, providing a mediator for incoming notifications, based on a consideration of a user's current and recent history of computer activity, meeting status, location, time of day, and whether a conversation is detected.


title: Anomaly detection on ATMs via time series motif discovery

abstract: Cash machines or automated teller machines (ATMs) are one of the typical ways to get cash around the world. Such machines are under a variety of criminal attacks. Most of the manipulations are performed through skimming. In 2014, such attacks led to a damage of approx. 280 million Euro within the EU. In this paper, we propose an approach to detect anomalies and attacks on ATMs via motif discovery. Motifs are frequently unknown occurring sequences or events in a time series signal. State of the ATM is captured by innovative piezoelectric sensor networks to analyse the occurring vibrations. The captured signals are inspected by the Complex Quad-Tree Wavelet Packet transform which provides broad frequency analysis of a signal in various scales. Next, features are extracted from the selected scale based on the information content, to detect motifs. Detected motifs provide the prototype patterns for anomaly detection or classification tasks.


title: Deep Portrait Image Completion and Extrapolation

abstract: General image completion and extrapolation methods often fail on portrait images where parts of the human body need to be recovered - a task that requires accurate human body structure and appearance synthesis. We present a two-stage deep learning framework for tackling this problem. In the first stage, given a portrait image with an incomplete human body, we extract a complete, coherent human body structure through a human parsing network, which focuses on structure recovery inside the unknown region with the help of full-body pose estimation. In the second stage, we use an image completion network to fill the unknown region, guided by the structure map recovered in the first stage. For realistic synthesis the completion network is trained with both perceptual loss and conditional adversarial loss. We further propose a face refinement network to improve the fidelity of the synthesized face region. We evaluate our method on publicly-available portrait image datasets, and show that it outperforms other state-of-the-art general image completion methods. Our method enables new portrait image editing applications such as occlusion removal and portrait extrapolation. We further show that the proposed general learning framework can be applied to other types of images, e.g. animal images.


title: Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

abstract: The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that an edge-based deployment can also be used as an enabler of advanced Machine Learning (ML) applications in cellular networks, thanks to the balance it strikes between a completely distributed and a centralized approach. First, we will present an edge-controller-based architecture for cellular networks. Second, by using real data from hundreds of base stations of a major U.S. national operator, we will provide insights on how to dynamically cluster the base stations under the domain of each controller. Third, we will describe how these controllers can be used to run ML algorithms to predict the number of users, and a use case in which these predictions are used by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that prediction accuracy improves when based on machine learning algorithms that exploit the controllers' view with respect to when it is based only on the local data of each single base station.


Effects of Algorithmic Decision-Making and Interpretability on Human Behavior: Experiments using Crowdsourcing


title: Predicting Pinterest: Automating a Distributed Human Computation

abstract: Everyday, millions of users save content items for future use on sites like Pinterest, by ''pinning'' them onto carefully categorised personal pinboards, thereby creating personal taxonomies of the Web. This paper seeks to understand Pinterest as a distributed human computation that categorises images from around the Web. We show that despite being categorised onto personal pinboards by individual actions, there is a generally a global agreement in implicitly assigning images into a coarse-grained global taxonomy of 32 categories, and furthermore, users tend to specialise in a handful of categories. By exploiting these characteristics, and augmenting with image-related features drawn from a state-of-the-art deep convolutional neural network, we develop a cascade of predictors that together automate a large fraction of Pinterest actions. Our end-to-end model is able to both predict whether a user will repin an image onto her own pinboard, and also which pinboard she might choose, with an accuracy of 0.69 (Accuracy@5 of 0.75).


title: Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations

abstract: An increasing number of interactive visualization tools stress the integration with computational software like MATLAB and R to access a variety of proven algorithms. In many cases, however, the algorithms are used as black boxes that run to completion in isolation which contradicts the needs of interactive data exploration. This paper structures, formalizes, and discusses possibilities to enable user involvement in ongoing computations. Based on a structured characterization of needs regarding intermediate feedback and control, the main contribution is a formalization and comparison of strategies for achieving user involvement for algorithms with different characteristics. In the context of integration, we describe considerations for implementing these strategies either as part of the visualization tool or as part of the algorithm, and we identify requirements and guidelines for the design of algorithmic APIs. To assess the practical applicability, we provide a survey of frequently used algorithm implementations within R regarding the fulfillment of these guidelines. While echoing previous calls for analysis modules which support data exploration more directly, we conclude that a range of pragmatic options for enabling user involvement in ongoing computations exists on both the visualization and algorithm side and should be used.


title: Direct Measurement of Small Antenna Radiation Efficiency by Calorimetric Method

abstract: A novel method for radiation efficiency measurement of small-size radiators such as mobile phones and other handheld wireless devices is presented. The method is based on the accurate determination of the power which is dissipated in the antenna under test using a calorimetric approach. The principle of the method, some of its variants, and the experimental setup are described. Application of the method is demonstrated for several test objects with different efficiencies. Accuracy is validated by comparison of calorimetric radiation efficiency against numerical estimates and independent measurements using the reflection efficiency method, a generalized variant of the Wheeler cap method, whose basic limitations are briefly discussed. Due to the calorimetric principle employed in the proposed new method, it qualifies equally well as a highly accurate reference method for radiation efficiency measurements and as a low-cost alternative to three-dimensional pattern integration and Wheeler cap type efficiency estimates


title: Effective deep learning-based multi-modal retrieval

abstract: Multi-modal retrieval is emerging as a new search paradigm that enables seamless information retrieval from various types of media. For example, users can simply snap a movie poster to search for relevant reviews and trailers. The mainstream solution to the problem is to learn a set of mapping functions that project data from different modalities into a common metric space in which conventional indexing schemes for high-dimensional space can be applied. Since the effectiveness of the mapping functions plays an essential role in improving search quality, in this paper, we exploit deep learning techniques to learn effective mapping functions. In particular, we first propose a general learning objective that effectively captures both intramodal and intermodal semantic relationships of data from heterogeneous sources. Given the general objective, we propose two learning algorithms to realize it: (1) an unsupervised approach that uses stacked auto-encoders and requires minimum prior knowledge on the training data and (2) a supervised approach using deep convolutional neural network and neural language model. Our training algorithms are memory efficient with respect to the data volume. Given a large training dataset, we split it into mini-batches and adjust the mapping functions continuously for each batch. Experimental results on three real datasets demonstrate that our proposed methods achieve significant improvement in search accuracy over the state-of-the-art solutions.


title: Exploring Legal Patent Citations for Patent Valuation

abstract: Effective patent valuation is important for patent holders. Forward patent citations, widely used in assessing patent value, have been considered as reflecting knowledge flows, just like paper citations. However, patent citations also carry legal implication, which is important for patent valuation. We argue that patent citations can either be technological citations that indicate knowledge transfer or be legal citations that delimit the legal scope of citing patents. In this paper, we first develop citation-network based methods to infer patent quality measures at either the legal or technological dimension. Then we propose a probabilistic mixture approach to incorporate both the legal and technological dimensions in patent citations, and an iterative learning process that integrates a temporal decay function on legal citations, a probabilistic citation network based algorithm and a prediction model for patent valuation. We learn all the parameters together and use them for patent valuation. We demonstrate the effectiveness of our approach by using patent maintenance status as an indicator of patent value and discuss the insights we learned from this study.


title: Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

abstract: The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports), it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others.


title: Health Beliefs of Muslim Women and Implications for Health Care Providers: Exploratory Study on the Health Beliefs of Muslim Women

abstract: Purpose: The purpose of the study is to determine specific health beliefs important to Muslim women as they relate to participation in medical, psychological or social evaluation and treatment and to determine whether any of their beliefs, attitudes, or perceptions have an effect on the female Muslim patient’s decision to access and follow through with medical evaluation and treatment. Background: Muslim women have special beliefs, attitudes, and perceptions that may directly impact healthcare received within a westernized health care system that may not share the unique sensitivities of the Islamic culture (Bennoune, 2007). Health care providers are able to better advocate for their patients when they have an awareness of the unique cultural beliefs and background of their patients and how to provide a safe and comfortable place for patients to openly participate in health care decision making. It is important to explore the beliefs of practicing Muslim women in order to fully meet the health care needs of this community (Belut and Ebaugh, 2013).Methods: This was a prospective, cross-sectional qualitative survey design of (n=14/20) Muslim women living in three large cluster groups across the USA. IRB approval was granted prior to recruitment of subjects and data collection.Results: This study suggests that Muslim women (1) prefer to make autonomous health care decisions without the assistance of a male family member, (2) prefer to have a female health care provider, (3) are willing to access medical and rehabilitation services if provided by a female, but not when provided by a male health care provider, (4) believe in the use of prayer, recitation of Quran, fasting, charity to be beneficial to their physical health, and (5) are comfortable with the use of physical touch in medicine and rehabilitation evaluation and treatment, if the provider is female. Conclusion: Suggestions for future research should address specific health care provider outcomes as they pertain to optimal clinical decision making for the Muslim female patient.


title: Multiresolution volume visualization with a texture-based octree

abstract: Although 3D texture-based volume rendering guarantees image quality almost interactively, it is difficult to maintain an interactive rate when the technique has to be exploited on large datasets. In this paper, we propose a new texture memory representation and a management policy that substitute the classical one-texel per voxel approach for a hierarchical approach. The hierarchical approach benefits nearly homogeneous regions and regions of lower interest. The proposed algorithm is based on a simple traversal of the octree representation of the volume data. Driven by a user-defined image quality, defined as a combination of data homogeneity and importance, a set of octree nodes (the cut) is selected to be rendered. The degree of accuracy applied for the representation of each one of the nodes of the cut in the texture memory is set independently according to the user-defined parameters. The variable resolution texture model obtained reduces the texture memory size and thus texture swapping, improving rendering speed.


title: A Review of Commonly Used DC Arc Models

abstract: The dc arc hazard is a great concern to industry. Quantitative arc hazard assessments are performed on dc systems to determine a nearby worker's potential incident energy exposure during an arcing event. Four viable dc assessment methods are reviewed in this paper. The most widely used model for predicting dc incident energy is based on Lee's theoretical arc model; the electrical arc power is determined from the maximum power transfer theorem, and the arc is depicted as a spherical radiant source with uniform heat transmission in all directions. Like Lee's model, Ammerman's model assumes complete conversion of electrical arc energy into thermal energy, but arc power is determined from an iterative technique constrained by arc power and circuit characteristics. Ammerman incorporates multiplying factors which account for the higher incident energies associated with arcing in enclosures. Based on dc arc testing, the applicability of an existing software package has been extended to dc systems through multiplying factors, and equations for dc rail and transit systems have also been developed. Model derivation is examined in this paper for suitability to arcing in general and dc specifically. Model performance is assessed using the available limited data (ac or dc). Example calculations are provided.


title: Material Classification Using Raw Time-of-Flight Measurements

abstract: We propose a material classification method using raw time-of-flight (ToF) measurements. ToF cameras capture the correlation between a reference signal and the temporal response of material to incident illumination. Such measurements encode unique signatures of the material, i.e. the degree of subsurface scattering inside a volume. Subsequently, it offers an orthogonal domain of feature representation compared to conventional spatial and angular reflectance-based approaches. We demonstrate the effectiveness, robustness, and efficiency of our method through experiments and comparisons of real-world materials.


title: De novo identification of replication-timing domains in the human genome by deep learning

abstract: Motivation: The de novo identification of the initiation and termination zones—regions that replicate earlier or later than their upstream and downstream neighbours, respectively—remains a key challenge in DNA replication. Results: Building on advances in deep learning, we developed a novel hybrid architecture combining a pre-trained, deep neural network and a hidden Markov model (DNN-HMM) for the de novo identification of replication domains using replication timing profiles. Our results demonstrate that DNNHMM can significantly outperform strong, discriminatively trained Gaussian mixture model–hidden Markov model (GMM-HMM) systems and other six reported methods that can be applied to this challenge. We applied our trained DNN-HMM to identify distinct replication domain types, namely the early replication domain (ERD), the down transition zone (DTZ), the late replication domain (LRD), and the up transition zone (UTZ), using newly replicated DNA sequencing (Repli-Seq) data across 15 human cells. A subsequent integrative analysis revealed that these replication domains harbour unique genomic and epigenetic patterns, transcriptional activity, and higher-order chromosomal structure. Our findings support the ‘replication-domain’ model, which states (1) that ERDs and LRDs, connected by UTZs and DTZs, are spatially compartmentalized structural and functional units of higherorder chromosomal structure, (2) that the adjacent DTZ-UTZ pairs form chromatin loops, and (3) that intra-interactions within ERDs and LRDs tend to be short-range and long-range, respectively. Our model reveals an important chromatin organizational principle of the human genome and represents a critical step toward understanding the mechanisms regulating replication timing. Availability: Our DNN-HMM method and three additional algorithms can be freely accessed at https://github.com/requiem116/DNN-HMM. The replication domain regions identified in this study are available in GEO under the accession ID GSE53984. Contact: shuwj@bmi.ac.cn and boxc@bmi.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.


title: Radar-based extended object tracking under clutter using generalized probabilistic data association

abstract: An important foundation for various vehicular applications is a reliable environment recognition. In this context, the simultaneous estimation of the state and the existence of an unknown number of objects under difficult detection conditions is a particular challenge. In this paper, we propose an algorithm for tracking extended objects under clutter. We propose an extended measurement model which enables the estimation of the object width using a standard Kalman filter implementation without the need for clustering the data. As this implies multiple observations generated by one object and additional clutter observations, the generalized probabilistic data association with a state-depended cardinality model is utilized. The proposed algorithm is evaluated with simulated data of a radar-based vehicle tracking system.


title: Comparative Analysis of Relational and Non-relational Databases in the Context of Performance in Web Applications

abstract: This paper presents comparative analysis of relational and non-relational databases. For the purposes of this paper simple social-media web application was created. The application supports three types of databases: SQL (it was tested with PostgreSQL), MongoDB and Apache Cassandra. For each database the applied data model was described. The aim of the analysis was to compare the performance of these selected databases in the context of data reading and writing. Performance tests showed that MongoDB is the fastest when reading data and PostgreSQL is the fastest for writing. The test application is fully functional, however implementation occurred to be more challenging for Cassandra.


title: Gesture recognition using Kinect in a virtual classroom environment

abstract: In an E-learning classroom, students and professors are at different geographic locations. In the present E-learning system, the professor and the student interact with each other through simple internet applications. The disadvantage in this type of classroom set-up is that students in a remote location will not receive the same attention as those ones in a real classroom. Students in a remote location will only be seen on a small screen and the clarity of the video might be poor. Furthermore, the professor in a real classroom might focus only on students who are interacting face-to-face with him. The professor may not know the details of the remote students and how well they understand his class lecture. To solve this problem, this paper describes a system, based on KinectTM, to make the E-learning class lecture more interactive for the remote students. For example, when a student in a remote location, raises his/her hand to talk to a professor, the Kinect camera detects the student, identifies the gesture of the remote student, zooms onto the remote student, and displays details such as his/her name and background of study. Thus the professor can pay equal attention to both the remote and local students.


title: BarrierPoint: Sampled simulation of multi-threaded applications

abstract: Sampling is a well-known technique to speed up architectural simulation of long-running workloads while maintaining accurate performance predictions. A number of sampling techniques have recently been developed that extend well-known single-threaded techniques to allow sampled simulation of multi-threaded applications. Unfortunately, prior work is limited to non-synchronizing applications (e.g., server throughput workloads); requires the functional simulation of the entire application using a detailed cache hierarchy which limits the overall simulation speedup potential; leads to different units of work across different processor architectures which complicates performance analysis; or, requires massive machine resources to achieve reasonable simulation speedups. In this work, we propose BarrierPoint, a sampling methodology to accelerate simulation by leveraging globally synchronizing barriers in multi-threaded applications. BarrierPoint collects microarchitecture-independent code and data signatures to determine the most representative inter-barrier regions, called barrierpoints. BarrierPoint estimates total application execution time (and other performance metrics of interest) through detailed simulation of these barrierpoints only, leading to substantial simulation speedups. Barrierpoints can be simulated in parallel, use fewer simulation resources, and define fixed units of work to be used in performance comparisons across processor architectures. Our evaluation of BarrierPoint using NPB and Parsec benchmarks reports average simulation speedups of 24.7× (and up to 866.6×) with an average simulation error of 0.9% and 2.9% at most. On average, BarrierPoint reduces the number of simulation machine resources needed by 78×.


title: Game design as marketing: How game mechanics create demand for virtual goods

abstract: Selling virtual goods for real money is an increasingly popular revenue model for massivelymultiplayer online games (MMOs), social networking sites (SNSs) and other online hangouts. In this paper, we argue that the marketing of virtual goods currently falls short of what it could be. Game developers have long created compelling game designs, but having to market virtual goods to players is a relatively new situation to them. Professional marketers, on the other hand, tend to overlook the internal design of games and hangouts and focus on marketing the services as a whole. To begin bridging the gap, we propose that the design patterns and game mechanics commonly used in games and online hangouts should be viewed as a set of marketing techniques designed to sell virtual goods. Based on a review of a number of MMOs, we describe some of the most common patterns and game mechanics and show how their effects can be explained in terms of analogous techniques from marketing science. The results provide a new perspective to game design with interesting implications to developers. Moreover, they also suggest a radically new perspective to marketers of ordinary goods and services: viewing marketing as a form of game design.


title: Novel stability improvement method for V/F controlled induction motor drive systems

abstract: To improve the stability of V/F controlled induction motor drive system,which is always experienced in light load state,a stabilizing control method is presented.Based on the model of induction motor,a deeply analysis revealed the relationship between the system oscillations and the fluctuation of reactive current.And the strategy was implemented by adjusting the output voltage according to the fluctuation of reactive current which was decoupled through stator voltage oriented synchronous rotation frame transformation.The proposed method was parameter indenpent and robust.Experiments show that the system has a very stable operation over the whole speed range from 0 to 50 Hz with the proposed method and the oscillations have be suppressed effectively.


title: Star Coordinates: A Multi-dimensional Visualization Technique with Uniform Treatment of Dimensions

abstract: Visualizing multi-dimensional data has tremendous effects on science, engineering, and business decisionmaking. A new visualization technique called Star Coordinates is presented to support users in early stages of their visual thinking activities. Star Coordinates arranges coordinates on a circle sharing the same origin at the center. It uses simply points to represent data, treating each dimension uniformly at the cost of coarse representation. Current implementation of Star Coordinates provided valuable insight on several real data sets for cluster discovery and multifactor analysis tasks. The work on Star Coordinates will continue on developing advanced transformations that will improve data understanding in multi-dimensions.


title: UUV teams, control from a biological perspective

abstract: Remote Operated Vehicles (ROVs) are used extensively for salvage operations, ocean floor surveying and numerous inspection activities that support a wide range of underwater commercial activities. In deep water (greater than 1000 ft) an ROV is the platform of choice because of the depth and endurance limitations for human divers. The key disadvantage to an ROV is the requirement for the long tether. The tether greatly inhibits the speed of the ROV, requires a ship with deck gear capable of handling this cable, and significantly restricts ship movement while deployed. Un-tethered Unmanned Underwater Vehicles (UUVs) have entered the commercial market and have demonstrated the ability to perform deep-water surveys faster and cheaper than towed vessels. With further technological advances, UUVs have the potential for supplementing and even replacing ROVs for many deep-water operations because of the cost and problems associated with the tether. One promising scenario for the near future is to use an ROV or surface ship to control multiple UUVs in a local work area. Typically in this scenario the UUVs are used to extend the sensor footprint of the ROV or surface ship. Another area of interest is the UUV team concept. A stereotypical UUV team would be a heterogeneous mix of several low-cost specific purpose vehicles, guided and supported by one or two higher cost control vessels. Because of the severe restrictions that the sub-sea environment places on communication and positioning, precision underwater navigation is difficult. Currently most precision underwater navigation relies on some sort of infrastructure such as surface ships or underwater beacons placed in known positions. Using these assets as reference-points sub-sea navigation is carried out. Some situations require that the environmental and/or commercial attributes of an area be assessed before an infrastructure exists. In order to do this the UUV team must be able to navigate to an area, carry out its task and return without any pre-existing infrastructure or step by step guidance. Given basic assumptions about the type and frequency of sensor input we present a biologically inspired, decentralized methodology for safely and efficiently moving a loose formation of UUV's to and from the task area with the goal of minimizing outside guidance.


title: License Plate Localization and Character Segmentation with feedback self-learning and hybrid-binarization techniques

abstract: License plate localization (LPL) and character segmentation (CS) play key roles in the license plate (LP) recognition system. In this paper, we dedicate ourselves to these two issues. In LPL, histogram equalization is employed to solve the low-contrast and dynamic-range problems; the texture properties, e.g., aspect ratio, and color similarity are used to locate the LP; and the Hough transform is adopted to correct the rotation problem. In CS, the hybrid binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms.


title: A hybrid approach to discover semantic hierarchical sections in scholarly documents

abstract: Scholarly documents are usually composed of sections, each of which serves a different purpose by conveying specific context. The ability to automatically identify sections would allow us to understand the semantics of what is different in different sections of documents, such as what was in the introduction, methodologies used, experimental types, trends, etc. We propose a set of hybrid algorithms to 1) automatically identify section boundaries, 2) recognize standard sections, and 3) build a hierarchy of sections. Our algorithms achieve an F-measure of 92.38% in section boundary detection, 96% accuracy (average) on standard section recognition, and 95.51% in accuracy in the section positioning task.


title: A Global Perspective of Smart Cities: A Survey

abstract: The technological revolution that has taken place in recent decades, driven by advances and developments in Information and Communication Technologies (ICT) has revolutionized the way people communicate, work, travel, live, etc. Cities need to evolve towards intelligent dynamic infrastructures that serve citizens fulfilling the criteria of energy efficiency and sustainability. This article provides an overview of the main smart city applications, and their implementation status in major cities around the world. We also present a study of patents on basic smart city technologies in order to show which countries and companies are making greater efforts to register the intellectual property. The relation between patented technologies and current ongoing smart city applications is also investigated.


title: Bilevel Feature Extraction-Based Text Mining for Fault Diagnosis of Railway Systems

abstract: A vast amount of text data is recorded in the forms of repair verbatim in railway maintenance sectors. Efficient text mining of such maintenance data plays an important role in detecting anomalies and improving fault diagnosis efficiency. However, unstructured verbatim, high-dimensional data, and imbalanced fault class distribution pose challenges for feature selections and fault diagnosis. We propose a bilevel feature extraction-based text mining that integrates features extracted at both syntax and semantic levels with the aim to improve the fault classification performance. We first perform an improved $\chi^{2}$ statistics-based feature selection at the syntax level to overcome the learning difficulty caused by an imbalanced data set. Then, we perform a prior latent Dirichlet allocation-based feature selection at the semantic level to reduce the data set into a low-dimensional topic space. Finally, we fuse fault features derived from both syntax and semantic levels via serial fusion. The proposed method uses fault features at different levels and enhances the precision of fault diagnosis for all fault classes, particularly minority ones. Its performance has been validated by using a railway maintenance data set collected from 2008 to 2014 by a railway corporation. It outperforms traditional approaches.


title: Vision based rail track and switch recognition for self-localization of trains in a rail network

abstract: A collision avoidance system for railroad vehicles needs to determine their location in the railroad network precisely and reliably. For a vehicle-based system, that is independent from the infrastructure, it is vital to determine the direction a railroad vehicle turns at switches. In this paper a vision based approach is presented that allows to achieve this reliably, even under difficult conditions. In the images of a camera that observes the area in front of a railroad vehicle the rail tracks are detected in real-time. From the perspective of the moving railroad vehicle rail tracks branch and join from/to the currently travelled rail track. By tracking these rail tracks in the images, switches are detected as they are passed. It is shown that the followed track can be determined at branching switches. The approach is tested with real data from test rides in different locations and under a variety of weather conditions and environments. It proved to be very robust and of high practical use for track-selective self-localization of railroad vehicles, mandatory for collision avoidance.