title: I2t: Image parsing to text description

abstract: In this paper, we present an image parsing to text description (I2T) framework that generates text descriptions of image and video content based on image understanding. The proposed I2T framework follows three steps: 1) input images (or video frames) are decomposed into their constituent visual patterns by an image parsing engine, in a spirit similar to parsing sentences in natural language; 2) the image parsing results are converted into semantic representation in the form of Web ontology language (OWL), which enables seamless integration with general knowledge bases; and 3) a text generation engine converts the results from previous steps into semantically meaningful, human readable, and query-able text reports. The centerpiece of the I2T framework is an and-or graph (AoG) visual knowledge representation, which provides a graphical representation serving as prior knowledge for representing diverse visual patterns and provides top-down hypotheses during the image parsing. The AoG embodies vocabularies of visual elements including primitives, parts, objects, scenes as well as a stochastic image grammar that specifies syntactic relations (i.e., compositional) and semantic relations (e.g., categorical, spatial, temporal, and functional) between these visual elements. Therefore, the AoG is a unified model of both categorical and symbolic representations of visual knowledge. The proposed I2T framework has two objectives. First, we use semiautomatic method to parse images from the Internet in order to build an AoG for visual knowledge representation. Our goal is to make the parsing process more and more automatic using the learned AoG model. Second, we use automatic methods to parse image/video in specific domains and generate text reports that are useful for real-world applications. In the case studies at the end of this paper, we demonstrate two automatic I2T systems: a maritime and urban scene video surveillance system and a real-time automatic driving scene understanding system.


title: Efficiently illuminated broadside-directed 1D and 2D tapered Fabry-Perot leaky-wave antennas

abstract: The study of Fabry-Perot leaky-wave antennas which are properly tapered to optimize their directivity at broadside, is presented in this work. For this purpose, a careful synthesis of the complex aperture illumination (amplitude and phase) induced by the radiating leaky mode is for the first time employed in the synthesis of this type of antennas. The study commences from the one-dimensional (1D) fan-beam Fabry-Perot antennas and extends to two-dimensional (2D) pencil-beam Fabry-Perot antennas. Theoretical and simulated results are reported, illustrating an increase in the antenna directivity at broadside direction in the order of 2dBs for the 1D case, and of 4dBs for the 2D case.


Software to Sketch Interface Designs


title: “Salience network” dysfunction hypothesis in autism spectrum disorders

abstract: Autism spectrum disorder (ASD) is a neurodevelopmental disorder charac- terized by impaired social interaction and communication, as well as repetitive and stereotyped patterns of behavior. Although most patients with ASD show sensory abnormalities such as hyperesthesia and hypoesthesia, its relation to social cognition has not been well studied. Recently, a salience network (SN) dysfunction hypothesis of ASD has been proposed. This neuroscientific hypothesis might explain how a SN integrating external sensory stimuli with internal states mediates interactions between large-scale networks involved in externally and internally oriented cognitive processing. In the brain of patients with ASD, areas of the SN, including the anterior insula, become dysfunctional, which results in difficulty in operating social cognition and self-referential processing. Here we discuss the controversial points and future directions of this hypothesis.


title: Hybrid distribution transformer: Concept development and field demonstration

abstract: Today's distribution system is expected to supply power to loads for which it was not designed. Moreover, high penetration of distributed generation units is redefining the requirements for the design, control and operation of the electric distribution system. A Hybrid Distribution Transformer is a potential cost-effective alternative solution to various distribution grid control devices. The Hybrid Distribution Transformer is realized by augmenting a regular transformer with a fractionally rated power electronic converter, which provides the transformer with additional control capabilities. The Hybrid Distribution Transformer concept can provide dynamic ac voltage regulation, reactive power compensation and, in future designs, form an interface with energy storage devices. Other potential functionalities that can be realized from the Hybrid Distribution Transformer include voltage phase angle control, harmonic compensation and voltage sag compensation. This paper presents the concept of a Hybrid Distribution Transformer and the status of our efforts towards a 500 kVA, 12.47 kV/480 V field demonstrator.


title: Automatic distractor generation for domain specific texts

abstract: This paper presents a system which uses Natural Language Processing techniques to generate multiple-choice questions. The system implements different methods to find distractors semantically similar to the correct answer. For this task, a corpus-based approach is applied to measure similarities. The target language is Basque and the questions are used for learners' assessment in the science domain. In this article we present the results of an evaluation carried out with learners to measure the quality of the automatically generated distractors.


title: Gysel Power Divider With Arbitrary Power Ratios and Filtering Responses Using Coupling Structure

abstract: This paper presents a novel method for designing Gysel power dividers with arbitrary power-division ratios as well as filtering responses. A coupling structure is utilized to replace the quarter-wavelength microstrip line in the power divider. By altering the coupling strength, the power ratio can be arbitrarily controlled with the highest ratio of 10:1. Furthermore, the coupling structures enable the filtering function of the power divider. Two transmission zeros are created near the passband edges, resulting in high-selectivity quasi-elliptic responses. Theoretical analysis is carried out, and closed-form equations are derived based on circuit theory and transmission line theory. For demonstration, three filtering Gysel power dividers are implemented with different power-division ratios (1:1, 3:1, and 10:1). In all of these circuits, power splitting and bandpass filtering functions are observed with good performance. Comparisons of the measured and simulated results are presented to verify the theoretical predications.


title: Viewpoint Invariant 3D Human Pose Estimation with Recurrent Error Feedback

abstract: We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve viewpoint invariance, our deep discriminative model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our approach leverages a convolutional and recurrent network with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner. We evaluate our model on a previously published depth dataset and a newly collected human pose dataset containing 100K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints.


title: An evaluation of white- and gray-box testing tools for C#, C++, Eiffel, and Java

abstract: Automating the process of software testing is a very popular research topic and of real interest to industry. Test automation can take part on different levels, e.g., test execution,testcasegeneration,testdatageneration.Thissur- vey gives an overview of state-of-the art test data generation tools, either academic or commercial. The survey focuses on white- and gray-box techniques. The list of existing tools was filtered with respect to their public availability, their maturity, and activity. The remaining seven tools, i.e., Agi- tarOne, CodePro AnalytiX, AutoTest, C++test, Jtest, RAN- DOOP, and PEX, are briefly introduced and their evaluation results are summarized. For the evaluation we defined 31 benchmark tests, which check the tools capabilities to gener- ate test data that satisfies a given specification: 24 primitive type benchmarks and 7 non-primitive type and more com- plex with respect to the specification benchmarks. Most of the commercial tools implement a test data strategy that uses constant values found in the method under test or values that are slightly modified by means of mathematical operations. This strategy turns out to be very effective. In general, all tools that combine multiple techniques perform very well. For example PEX uses constraint solving techniques, but in cases where the constraint solver reaches its limitations it uses random based techniques to overcome those limita- tions. Especially, the two commercial tools AgitarOne and PEX that combine multiple approaches to test data genera- tionareabletopassall31tests.Thissurveyreflectsthestatus in 2011.


title: Examining CNN Representations with respect to Dataset Bias

abstract: Given a pre-trained CNN without any testing samples, this paper proposes a simple yet effective method to diagnose feature representations of the CNN. We aim to discover representation flaws caused by potential dataset bias. More specifically, when the CNN is trained to estimate image attributes, we mine latent relationships between representations of different attributes inside the CNN. Then, we compare the mined attribute relationships with ground-truth attribute relationships to discover the CNN's blind spots and failure modes due to dataset bias. In fact, representation flaws caused by dataset bias cannot be examined by conventional evaluation strategies based on testing images, because testing images may also have a similar bias. Experiments have demonstrated the effectiveness of our method.


title: Multilayer perceptron for EEG signal classification during listening to emotional music

abstract: In this study an electroencephalography (EEG) signal-based emotion classification algorithm was investigated. Several excerpts of emotional music were used as stimulus for elicitation of emotion-specific EEG signal. Besides, the hemispheric asymmetry alpha power indices of brain activation were extracted as feature vector for training multilayer perceptron classifier (MLP) in order to learn four targeted emotion categories, including joy, angry, sadness, and pleasure. The results demonstrated that the average classification accuracy of MLP could be 69.69% in five subjects for four emotional categories, which is much higher than chance probability of 25%.


title: Automatic Checking of Conformance to Requirement Boilerplates via Text Chunking: An Industrial Case Study

abstract: Context. Boilerplates have long been used in Requirements Engineering (RE) to increase the precision of natural language requirements and to avoid ambiguity problems caused by unrestricted natural language. When boilerplates are used, an important quality assurance task is to verify that the requirements indeed conform to the boilerplates. Objective. If done manually, checking conformance to boilerplates is laborious, presenting a particular challenge when the task has to be repeated multiple times in response to requirements changes. Our objective is to provide automation for checking conformance to boilerplates using a Natural Language Processing (NLP) technique, called Text Chunking, and to empirically validate the effectiveness of the automation. Method. We use an exploratory case study, conducted in an industrial setting, as the basis for our empirical investigation. Results. We present a generalizable and tool-supported approach for boilerplate conformance checking. We report on the application of our approach to the requirements document for a major software component in the satellite domain. We compare alternative text chunking solutions and argue about their effectiveness for boilerplate conformance checking. Conclusion. Our results indicate that: (1) text chunking provides a robust and accurate basis for checking conformance to boilerplates, and (2) the effectiveness of boilerplate conformance checking based on text chunking is not compromised even when the requirements glossary terms are unknown. This makes our work particularly relevant to practice, as many industrial requirements documents have incomplete glossaries.


title: High-gain dual-polarization higher order mode substrate integrated cavity antenna array

abstract: A dual-polarization higher order mode substrate integrated cavity (SIC) antenna array operating in Ka band is proposed. Cross-shaped radiating slots are employed to achieve the dual polarization performance. An 8×8-elements array is designed by using three layers of double-side printed circuit broads (PCBs), which is composed of two layers feeding networks and one layer radiating elements. For better expansibility and higher efficiency, full corporate substrate integrated waveguide (SIW) feeding networks are specially designed and a 2×2-slot antenna subarray excited by higher order mode SIC is presented. Simulated results show that this 8×8 slot array achieves a maximum gain of 24.4 dBi and an impedance matching bandwidth of 34.75–35.75 GHz. High isolation above 50 dB is realized between the two input ports and cross polarization is under 𢀒30 dB. In addition, the aperture efficiency is 65%.


title: CT Saturation Calculations: Are They Applicable in the Modern World?—Part I: The Question

abstract: Previously, ANSI/IEEE relay current transformer (CT) sizing criteria was based on traditional symmetrical calculations usually discussed by technical articles and manufacturers' guidelines. In 1996, IEEE Standard C37.110-7996 introduced (1 + X/R) offset multiplying, current asymmetry and current distortion factors; officially changing the CT sizing guideline. A critical concern is the performance of fast protective schemes (instantaneous or differential elements) during severe saturation of low ratio CTs. Will the instantaneous element operate before the upstream breaker relay trips? Will the differential element mis-operate for out-of-zone faults? The use of electromagnetic and analog relay technology does not assure selectivity. Modern microprocessor relays introduce additional uncertainty into the design/verification process with different sampling techniques and proprietary sensing/recognition/trip algorithms. This paper discusses the application of standard CT accuracy classes with modern ANSI/IEEE CT calculation methodology. This paper is the first of a two-part series; Part II, the Findings provides analytical waveform analysis discussions to illustrate the concepts conveyed in Part I.


title: Applications of Convolutional Neural Networks

abstract: In recent years, deep learning has been used extensively in a wide range of fields. In deep learning, Convolutional Neural Networks are found to give the most accurate results in solving real world problems. In this paper, we give a comprehensive summary of the applications of CNN in computer vision and natural language processing. We delineate how CNN is used in computer vision, mainly in face recognition, scene labelling, image classification, action recognition, human pose estimation and document analysis. Further, we describe how CNN is used in the field of speech recognition and text classification for natural language processing. We compare CNN with other methods to solve the same problem and explain why CNN is better than other methods.


title: Business Models in the Software Industry

abstract: Business models describe the strategic direction of a firm and have significant impact on the success of innovations. While the term business model is broadly used, a comprehensive definition in the context of the software industry has not yet been provided. In this paper, we conceptualize a software industry business model, supporting the description of software firms' business models in a standardized manner. Our model aggregates and unifies findings from a literature review in three disciplines, generic and software industry specific business model concepts as well as economic properties of the software industry. The resulting business model concept comprises 20 elements. A first empirical proof of concept among 10 software business models confirms the applicability of the presented concept. The business models are instantiated based on the proposed framework and the interviewees confirm the applicability, relevance, and importance of the proposed business model.


title: A Wait-List Randomized Controlled Trial of Loving-Kindness Meditation Programme for Self-Criticism

abstract: Self-criticism is a vulnerability risk factor for a number of psychological disorders, and it predicts poor response to psychological and pharmacological treatments. In the current study, we evaluated the efficacy of a loving-kindness meditation (LKM) programme designed to increase self-compassion in a sample of self-critical individuals. Thirty-eight individuals with high scores on the self-critical perfectionism subscale of the Dysfunctional Attitude Scale were randomized to an LKM condition (n = 19) or a wait-list (WL) condition (n = 19). Measures of self-criticism, self-compassion and psychological distress were administered before and immediately following the intervention (LKM or WL). WL participants received the intervention immediately after the waiting period. Both groups were assessed 3 months post-intervention. Intent-to-treat (n = 38) and per-protocol analyses (n = 32) showed significant reductions in self-criticism and depressive symptoms as well as significant increases in self-compassion and positive emotions in the LKM condition compared with the WL condition. A follow-up per-protocol analysis in both groups together (n = 20) showed that these gains were maintained 3 months after the intervention. These preliminary results suggest that LKM may be efficacious in alleviating self-criticism, increasing self-compassion and improving depressive symptoms among self-critical individuals. Copyright © 2014 John Wiley & Sons, Ltd. ::: ::: Key Practitioner Message ::: Self-criticism plays a major role in many psychological disorders and predicts poor response to brief psychological and pharmacological treatments for depression. ::: The current study shows that loving-kindness meditation, designed to foster self-compassion, is efficacious in helping self-critical individuals become less self-critical and more self-compassionate. ::: The study also suggests that practising loving-kindness may reduce depressive symptoms and increase positive emotions.


title: Internet of Things and Edge Cloud Computing Roadmap for Manufacturing

abstract: The manufacturing industry is exploring the use of Internet of Things and cloud computing to enhance the efficiency of manufacturing plant operations, improve product quality, increase manufacturers' ability to respond to changing customer demands, and expand to new markets. Productivity, quality, safety, and the ability to respond quickly to changing conditions are essential to maintaining the industry's competitiveness.


title: Multiuser MIMO-OFDM for Next-Generation Wireless Systems

abstract: This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has nu- merous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station's or radio port's coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment in multiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems.


title: Do stack traces help developers fix bugs?

abstract: A widely shared belief in the software engineering community is that stack traces are much sought after by developers to support them in debugging. But limited empirical evidence is available to confirm the value of stack traces to developers. In this paper, we seek to provide such evidence by conducting an empirical study on the usage of stack traces by developers from the ECLIPSE project. Our results provide strong evidence to this effect and also throws light on some of the patterns in bug fixing using stack traces. We expect the findings of our study to further emphasize the importance of adding stack traces to bug reports and that in the future, software vendors will provide more support in their products to help general users make such information available when filing bug reports.


title: Automatic and scalable fault detection for mobile applications

abstract: This paper describes the design, implementation, and evaluation of VanarSena, an automated fault finder for mobile applications (``apps''). The techniques in VanarSena are driven by a study of 25 million real-world crash reports of Windows Phone apps reported in 2012. Our analysis indicates that a modest number of root causes are responsible for many observed failures, but that they occur in a wide range of places in an app, requiring a wide coverage of possible execution paths. VanarSena adopts a ``greybox'' testing method, instrumenting the app binary to achieve both coverage and speed. VanarSena runs on cloud servers: the developer uploads the app binary; VanarSena then runs several app ``monkeys'' in parallel to emulate user, network, and sensor data behavior, returning a detailed report of crashes and failures. We have tested VanarSena with 3000 apps from the Windows Phone store, finding that 1108 of them had failures; VanarSena uncovered 2969 distinct bugs in existing apps, including 1227 that were not previously reported. Because we anticipate VanarSena being used in regular regression tests, testing speed is important. VanarSena uses two techniques to improve speed. First, it uses a ``hit testing'' method to quickly emulate an app by identifying which user interface controls map to the same execution handlers in the code. Second, it generates a ProcessingCompleted event to accurately determine when to start the next interaction. These features are key benefits of VanarSena's greybox philosophy.


title: Recurrent Neural Network Regularization

abstract: We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation.


title: Large scale ransomware detection by cognitive security

abstract: Attacks of Ransomware [14] are increasing every day. This type of threads explodes the system vulnerabilities, especially those that have a base on Microsoft Windows. From May 2017 millions of computers around the world experienced this virus. For this reason, the necessity of creating different mechanisms which act proactively, it is the way to prevent high levels of its spreading. This proposed research will create a model for ransomware detection and prevention. It will analyze nonstructured data stored in Ecuadorian control and regulatory institution (EcuCERT) logs. Also, it will create a corpus to detect behavior patterns of the main vulnerabilities related to ransomware [26] [27], found in Microsoft Windows Systems [20] and using machine learning techniques. Methods for selecting important variables inside Logs will be used to decide which features best represent the data that makes up the threat. These variables will form the corpus, and they will be the input from automatic learning algorithms. Algorithms will model patterns which will serve to detect early the ransomware before they hijack the data. Finally, the researcher will report the detection of the thread.


title: Collective Information Practice: Exploring Privacy and Security as Social and Cultural Phenomena

abstract: As everyday life is increasingly conducted online, and as the electronic world continues to move out into the physical, the privacy of information and action and the security of information systems are increasingly a focus of concern both for the research community and the public at large. Accordingly, privacy and security are active topics of investigation from a wide range of perspectives-institutional, legislative, technical, interactional, and more. In this article, we wish to contribute toward a broad understanding of privacy and security not simply as technical phenomena but as embedded in social and cultural contexts. Privacy and security are difficult concepts to manage from a technical perspective precisely because they are caught up in larger collective rhetorics and practices of risk, danger, secrecy, trust, morality, identity, and more. Reductive attempts to deal with these issues separately produce incoherent or brittle results. We argue for a move away from narrow views of privacy and security and toward a holistic view of situated and collective information practice.


title: The Nutrition Transition: New Trends in the Global Diet

abstract: Analyses of economic and food availability data for 1962-1994 reveal a major shift in the structure of the global diet marked by an uncoupling of the classic relationship between incomes and fat intakes. Global availability of cheap vegetable oils and fats has resulted in greatly increased fat consumption among low-income nations. Consequently, the nutrition transition now occurs at lower levels of the gross national product than previously, and is accelerated further by high urbanization rates. Data from Asian nations, where diet structure is rapidly changing, suggest that diets higher in fats and sweeteners are also more diverse and more varied. Given that preferences for palatable diets are a universal human trait, fat consumption may be governed not by physiological mechanisms but by the amount of fat available in the food supply. Whereas economic development has led to improved food security and better health, adverse health effects of the nutrition transition include growing rates of childhood obesity. The implications of these trends are explored.


title: Learning Behavior Models for Hybrid Timed Systems

abstract: A tailored model of a system is the prerequisite for various analysis tasks, such as anomaly detection, fault identification, or quality assurance. This paper deals with the algorithmic learning of a system's behavior model given a sample of observations. In particular, we consider real-world production plants where the learned model must capture timing behavior, dependencies between system variables, as well as mode switches--in short: hybrid system's characteristics. Usually, such model formation tasks are solved by human engineers, entailing the well-known bunch of problems including knowledge acquisition, development cost, or lack of experience. ::: ::: Our contributions to the outlined field are as follows. (1) We present a taxonomy of learning problems related to model formation tasks. As a result, an important open learning problem for the domain of production system is identified: The learning of hybrid timed automata. (2) For this class of models, the learning algorithm HyBUTLA is presented. This algorithm is the first of its kind to solve the underlying model formation problem at scalable precision. (3) We present two case studies that illustrate the usability of this approach in realistic settings. (4) We give a proof for the learning and runtime properties of HyBUTLA.


title: $\ell_p$-Box ADMM: A Versatile Framework for Integer Programming

abstract: This paper revisits the integer programming (IP) problem, which plays a fundamental role in many computer vision and machine learning applications. The literature abounds with many seminal works that address this problem, some focusing on continuous approaches (e.g. linear program relaxation) while others on discrete ones (e.g., min-cut). However, a limited number of them are designed to handle the general IP form and even these methods cannot adequately satisfy the simultaneous requirements of accuracy, feasibility, and scalability. To this end, we propose a novel and versatile framework called $\ell_p$-box ADMM, which is based on two parts. (1) The discrete constraint is equivalently replaced by the intersection of a box and a $(n-1)$-dimensional sphere (defined through the $\ell_p$ norm). (2) We infuse this equivalence into the ADMM (Alternating Direction Method of Multipliers) framework to handle these continuous constraints separately and to harness its attractive properties. More importantly, the ADMM update steps can lead to manageable sub-problems in the continuous domain. To demonstrate its efficacy, we consider an instance of the framework, namely $\ell_2$-box ADMM applied to binary quadratic programming (BQP). Here, the ADMM steps are simple, computationally efficient, and theoretically guaranteed to converge to a KKT point. We demonstrate the applicability of $\ell_2$-box ADMM on three important applications: MRF energy minimization, graph matching, and clustering. Results clearly show that it significantly outperforms existing generic IP solvers both in runtime and objective. It also achieves very competitive performance vs. state-of-the-art methods specific to these applications.


title: Password management strategies for online accounts

abstract: Given the widespread use of password authentication in online correspondence, subscription services, and shopping, there is growing concern about identity theft. When people reuse their passwords across multiple accounts, they increase their vulnerability; compromising one password can help an attacker take over several accounts. Our study of 49 undergraduates quantifies how many passwords they had and how often they reused these passwords. The majority of users had three or fewer passwords and passwords were reused twice. Furthermore, over time, password reuse rates increased because people accumulated more accounts but did not create more passwords. Users justified their habits. While they wanted to protect financial data and personal communication, reusing passwords made passwords easier to manage. Users visualized threats from human attackers, particularly viewing those close to them as the most motivated and able attackers; however, participants did not separate the human attackers from their potentially automated tools. They sometimes failed to realize that personalized passwords such as phone numbers can be cracked given a large enough dictionary and enough tries. We discuss how current systems support poor password practices. We also present potential changes in website authentication systems and password managers.


title: Toward Off-Policy Learning Control with Function Approximation

abstract: We present the first temporal-difference learning algorithm for off-policy control with unrestricted linear function approximation whose per-time-step complexity is linear in the number of features. Our algorithm, Greedy-GQ, is an extension of recent work on gradient temporal-difference learning, which has hitherto been restricted to a prediction (policy evaluation) setting, to a control setting in which the target policy is greedy with respect to a linear approximation to the optimal action-value function. A limitation of our control setting is that we require the behavior policy to be stationary. We call this setting latent learning because the optimal policy, though learned, is not manifest in behavior. Popular off-policy algorithms such as Q-learning are known to be unstable in this setting when used with linear function approximation.


title: A unified control scheme for a whole robotic arm-fingers system in grasping and manipulation

abstract: This paper proposes a novel control method for enabling a robotic arm with a pair of two degrees of freedom (DOFs) robotic fingers as an endeffector to execute a variety of superimposed tasks in a coordinated way. Differently from conventional control methods previously proposed for arm-hand systems that adopt a separate design to control each robotic part, such as an arm and a hand, the proposed method designs a unified single control scheme for movements of the whole robotic arm-fingers system. It is assumed that finger-tips are rigid and there are rolling constraints between the finger-tips and a target object, and whole motion of the system is confined to the horizontal plane. The results of numerical computer simulation demonstrate that the proposed method is sufficiently effective in control of such a whole robotic arm-fingers system, and in particular is applicable to superimposed tasks, grasping, regulating, and replacing a target object through composition of the control signal based on the principal of superposition. It is concluded that special attentions in design of control signals should be paid to the wrist that combines the finger parts to the arm


Illumination Invariant Imaging : Applications in Robust Vision-based Localisation , Mapping and Classification for Autonomous Vehicles


title: Rapid development of a Latvian speech-to-text system

abstract: This paper describes the development of a Latvian speech-to-text (STT) system at LIMSI within the Quaero project. One of the aims of the speech processing activities in the Quaero project is to cover all official European languages. However, for some of the languages only very limited, if any, training resources are available via corpora agencies such as LDC and ELRA. The aim of this study was to show the way, taking Latvian as example, an STT system can be rapidly developed without any transcribed training data. Following the scheme proposed in this paper, the Latvian STT system was developed in about a month and obtained a word error rate of 20% on broadcast news and conversation data in the Quaero 2012 evaluation campaign.


The strange geometry of skip-gram with negative sampling


Teacher effectiveness in relation to work satisfaction media utilization and attitude towards the use of information and communication technology among secondary school teachers of Nepal


title: Artificial potential functions for highway driving with collision avoidance

abstract: We present a set of potential function components to assist an automated or semi-automated vehicle in navigating a multi-lane, populated highway. The resulting potential field is constructed as a superposition of disparate functions for lane- keeping, road-staying, speed preference, and vehicle avoidance and passing. The construction of the vehicle avoidance potential is of primary importance, incorporating the structure and protocol of laned highway driving. Particularly, the shape and dimensions of the potential field behind each obstacle vehicle can appropriately encourage control vehicle slowing and/or passing, depending on the cars' velocities and surrounding traffic. Hard barriers on roadway edges and soft boundaries between navigable lanes keep the vehicle on the highway, with a preference to travel in a lane center.


title: Trichostasis spinulosa confirmed by standard skin surface biopsy.

abstract: Trichostasis spinulosa (TS) is a common but rarely diagnosed disease. For diagnosis, it's sufficient to see a bundle of vellus hair located in a keratinous sheath microscopically. In order to obtain these vellus hair settled in comedone-like openings, Standard skin surface biopsy (SSSB), a non-invasive method was chosen. It's aimed to remind the differential diagnosis of TS in treatment-resistant open comedone-like lesions and discuss the SSSB method in diagnosis. A 25-year-old female patient was admitted with a complaint of the black spots located on bilateral cheeks and nose for 12 years. In SSSB, multiple vellus hair bundles in funnel-shaped structures were observed under the microscope, and a diagnosis of 'TS' was made. After six weeks of treatment with tretinoin 0.025% and 4% erythromycin jel topically, the appearance of black macules was significantly reduced. Treatment had to be terminated due to her pregnancy, and the lesions recurred within 1 month. It's believed that TS should be considered in the differential diagnosis of treatment-resistant open comedone-like lesions, and SSSB might be an inexpensive and effective alternative method for the diagnosis of TS.


title: Recognizing Depression from the Microbiota–Gut–Brain Axis

abstract: Major depression is one of the leading causes of disability, morbidity, and mortality worldwide. The brain–gut axis functions are disturbed, revealed by a dysfunction of the brain, immune system, endocrine system, and gut. Traditional depression treatments all target the brain, with different drugs and/or psychotherapy. Unfortunately, most of the patients have never received any treatment. Studies indicate that gut microbiota could be a direct cause for the disorder. Abnormal microbiota and the microbiota–gut–brain dysfunction may cause mental disorders, while correcting these disturbance could alleviate depression. Nowadays, the gut microbiota modulation has become a hot topic in treatment research of mental disorders. Depression is closely related with the health condition of the brain–gut axis, and maintaining/restoring the normal condition of gut microbiota helps in the prevention/therapy of mental disorders.


title: Implementing BLAKE with AVX, AVX2, and XOP ∗

abstract: In 2013 Intel will release the AVX2 instructions, which introduce 256-bit singleinstruction multiple-data (SIMD) integer arithmetic. This will enable desktop and server processors from this vendor to support 4-way SIMD computation of 64-bit add-rotate-xor algorithms, as well as 8-way 32-bit SIMD computations. AVX2 also includes interesting instructions for cryptographic functions, like any-to-any permute and vectorized table-lookup. In this paper, we explore the potential of AVX2 to speed-up the SHA-3 finalist BLAKE, and present the first working assembly implementations of BLAKE-256 and BLAKE-512 with AVX2. We then investigate the potential of the recent AVX and XOP instructions to accelerate BLAKE, and report new speed records on Sandy Bridge and Bulldozer microarchitectures (7.47 and 11.64 cycles per byte for BLAKE-256, 5.71 and 6.95 for BLAKE-512).


title: An Empirical Analysis of the Current State of Phishing Attack and Defence

abstract: Banks and other organisations deal with fraudulent phishing websites by pressing the hosting service providers to remove the sites from the Internet. Until they are removed, the fraudsters will learn the passwords, personal identication numbers (PINs) and other personal details of the users who are fooled into visiting them. We analyse empirical data on actual phishing website removal times and the number of visitors that the websites attract, and conclude that website removal is part of the answer to phishing, but it is not fast enough to completely mitigate the problem. We also identify a subset of phishing websites (operated by the ‘rock-phish’ gang) which through architectural innovations have extended the average lifetime of their phishing websites.


title: Anomaly detection in online social networks

abstract: Anomalies in online social networks can signify irregular, and often illegal behaviour. Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these different types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas of future research.


title: Crowdsourcing for a better world: On the relation between IT affordances and donor motivations in charitable crowdfunding

abstract: Purpose – The purpose of the paper is to better understand the relation between information technology (IT) affordances and donor motivations in charitable crowdfunding. Design/methodology/approach – This paper reports the findings from a comparative case study of two charitable crowdfunding campaigns. Findings – The affordances of crowdfunding platforms support types of donor motivation that are not supported effectively, or at all, in offline charity. Research limitations/implications – For future researchers, the paper provides a theoretical model of the relation between IT affordances and motivations in the context of charitable crowdfunding. Practical/implications – For practitioners in the charity space, the paper suggests why they may wish to consider the use of charitable crowdfunding and how they may go about its implementation. Originality/value – Based on field research at two charitable crowdfunding campaigns, the paper provides a new theoretical model.


title: A Systematic Mapping Study on Legacy System Modernization.

abstract: Legacy system modernization has gained increasing attention from both researchers and practitioners, mainly due to the need of maintaining legacy systems towards business needs and technology advances. In this way, a set of techniques, tools and terms related to software modernization have been proposed— although they have not been consolidated yet. This hinders the characterization of real modernization scenarios according to the existing literature. This paper synthesize the existing contributions related to software modernization by means of a mapping study that characterizes the main results in terms of proposed processes, techniques, and tools. As one of our main findings, we report a lack of empirical studies trying to understand the benefits of using the existing approaches for software mod-


title: Comparison of Cogging Torque Reduction in Permanent Magnet Brushless Machines by Conventional and Herringbone Skewing Techniques

abstract: Cogging torque, as one of the main parasitic demerits of permanent magnet brushless machines, is of particular importance and primary concern during the machine design stage in many high-performance applications. Hence, numerous design techniques have been proposed and employed to effectively alleviate the cogging torque in permanent magnet brushless machines. The effects of rotor step skewing techniques including both conventional and herringbone styles on the cogging torque of permanent magnet brushless machine are comprehensively investigated and compared by synthesized 2-D and 3-D finite-element analysis in this paper. The results have revealed that both the conventional and herringbone rotor step skewing techniques can reduce the cogging torque significantly, but the latter is less effective than the former especially with small skewing step numbers. Moreover, the machine with herringbone rotor step skewing technique has rather peculiar and asymmetric cogging torque profiles, while the machine with conventional rotor step skewing technique exhibits normal and symmetric ones. The validity of the obtained results and findings is underpinned by the experiments on the prototype machine.


title: Glioblastoma multiforme tissue histopathology images based disease stage classification with deep CNN

abstract: Recently, many feature extraction methods for histopathology images have been reported for automatic quantitative analysis. One of the severe brain tumors is the Glioblastoma multiforme (GBM) and histopathology tissue images can provide unique insights into identifying and grading disease stages. However, the number of tissue samples to be examined is enormous, and is a burden to pathologists because of tedious manual evaluation traditionally required for efficient evaluation. In this study, we consider feature extraction and disease stage classification for brain tumor histopathology images using automatic image analysis methods. In particular, we utilized an automatic feature extraction and labeling for histopathology imagery data given by The Cancer Genome Atlas (TCGA) and checked the classification accuracy of disease stages in GBM tissue images using deep Convolutional Neural Network (CNN). Experimental results indicate promise in automatic disease stage classification and high level of accuracy were obtained for tested image data.


title: Potential of flex-PCB winding in coreless tubular permanent magnet synchronous machines

abstract: Many applications are limited by the performance of the motors that power them. In the small power range, slotless BLDC motors are commonly used due to their intrinsic high power density and smooth torque characteristic. However, recent work has shown that the power density of radial flux rotating motors could be significantly increased by replacing their windings, made of copper wire by windings printed on flexible PCB, taking most advantage of the possibilities offered this technology. The aim of this work is to evaluate the performance gain that could be obtained by using this technology in linear tubular motors. To that end, an analytical model is implemented and used for comparing different winding shapes regarding to two criteria, one related to the power density, the other to the force smoothness. A track shape which annihilates the higher harmonics is developed and compared to different 3-segment PCB windings and to a conventional wire winding.


title: Gabor Feature Based Unsupervised Change Detection of Multitemporal SAR Images Based on Two-Level Clustering

abstract: In this letter, we propose a simple yet effective unsupervised change detection approach for multitemporal synthetic aperture radar images from the perspective of clustering. This approach jointly exploits the robust Gabor wavelet representation and the advanced cascade clustering. First, a log-ratio image is generated from the multitemporal images. Then, to integrate contextual information in the feature extraction process, Gabor wavelets are employed to yield the representation of the log-ratio image at multiple scales and orientations, whose maximum magnitude over all orientations in each scale is concatenated to form the Gabor feature vector. Next, a cascade clustering algorithm is designed in this discriminative feature space by successively combining the first-level fuzzy c-means clustering with the second-level nearest neighbor rule. Finally, the two-level combination of the changed and unchanged results generates the final change map. Experimental results are presented to demonstrate the effectiveness of the proposed approach.


title: Application of distributed fault detection in WSN to dangerous chemical products based on Bayesian approach

abstract: Wireless Sensor Networks (WSN) are employed in several applications, including military, medical, environmental and household applications. Among the most important problems in WSN are their vulnerability to faults and high energy consumption. Both of these problems require the implementation of fault distributed detection methods, capable of distinguishing faulty nodes from normal nodes while taking into account energy problems. Several approaches of distributed fault detection are proposed; for example, we include the Bayesian approach, which ensures a study oscillating between Theory of Signal Detection (TSD), fault tolerance and energy optimization by determining optimal parameters of the detection. It is in this context that our paper is inscribed, where we try to apply this approach in the industrial security's field. The Bayesian approach ensures a pre study of WSN's deployment in the dangerous chemical products stores and guarantees a best performing fault detection. The experimental simulations will testify that the proposed algorithm is able to provide a good detection performance while optimizing energy in WSN.


title: Batch and continuous extraction of bromelain enzyme by reversed micelles

abstract: The main aim of this study was to optimize the conditions for bromelain extraction by reversed micelles from pineapple juice (Ananas comosus). The purification was carried out in batch extraction and a micro-column with pulsed caps for continuous extraction. The cationic micellar solution was made of BDBAC as a surfactant, isooctane as a solvent and hexanol as a co-solvent. For the batch process, a purification factor of 3 times at the best values of surfactant agent, co-solvent and salt concentrations, pH of the back and forward extractions were, 100 mM, 10% v/v, 1 M, 3.5 and 8, respectively. For the continuous operation, independent variables optimal point was determined: ratio between light phase flow rate and total flow rate equal to 0.67 and 1 second for the time interval between the pulses. This optimal point led to a productivity of 1.29 mL/min and a purification factor of 4.96.


title: Cross-App Tracking via Nearby Bluetooth Low Energy Devices

abstract: Today an increasing number of consumer devices such as head phones, wearables, light bulbs and even baseball bats, are Bluetooth-enabled thanks to the widespread support of the technology by phone manufacturers and mobile operating system vendors. The ability for any device to seamlessly connect and exchange information with smartphones via Bluetooth Low Energy (BLE) protocol promises unlimited room for innovation. However, it also brings about new privacy challenges. We show that the BLE protocol together with the Bluetooth permission model implemented in the Android and iOS operating systems can be used for cross-app tracking unbeknownst to the individuals. Specifically, through experiments and analyses based on real-world smartphone data we show that by listening to advertising packets broadcasted by nearby BLE-enabled devices and recording information contained in them, app developers can derive fairly unique "fingerprints" for their users, which can be used for cross-app tracking, i.e., linking pseudonymous users of different apps to each other. We demonstrate that privacy protections put in place by the Bluetooth Special Interest Group, Google, and Apple are not sufficient to prevent such fingerprinting or to make cross-app tracking difficult to execute. Our main contribution is to demonstrate the feasibility of cross-app tracking using nearby BLE and raise awareness that changes are needed in order to prevent it from becoming widespread. We also propose mitigation strategies to decrease the feasibility of tracking using nearby BLE devices while preserving the utility of the BLE technology.


title: Efficient magnetic localization and orientation technique for capsule endoscopy

abstract: To build a new wireless robotic capsule endoscope with external guidance for controllable and interactive GI tract examination, a sensing system is needed for tracking 3D location and 2D orientation of the capsule endoscope movement. An appropriate sensing method is to enclose a small permanent magnet in the capsule. The intensities of the magnetic field produced by the magnet in different spatial points can be measured by the magnetic sensors outside the patient's body. With the sensing data of magnetic sensor array, the 3D location and 2D orientation of the capsule can be calculated. Higher calculation accuracy can be obtained if more sensors and optimal algorithm are applied. In this paper, different nonlinear optimization algorithms were evaluated to solve the magnet's 5D parameters, e.g. Powell's, Downhill Simplex, DIRECT, Multilevel Coordinate Search, and Levenberg Marquardt method. We have found that Levenberg-Marquardt method provides satisfactory calculation accuracy and faster speed. Simulations were done for investigating the de-noise ability of this algorithm based on different sensor arrays. Also the real experiment shows that the results are satisfactory with high accuracy (average localization error is 5.6 mm).


title: Shape matching and object recognition using low distortion correspondences

abstract: We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.


title: Exploiting social relations for sentiment analysis in microblogging

abstract: Microblogging, like Twitter and Sina Weibo, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various topics. Existing sentiment analysis approaches often assume that texts are independent and identically distributed (i.i.d.), usually focusing on building a sophisticated feature space to handle noisy and short texts, without taking advantage of the fact that the microblogs are networked data. Inspired by the social sciences findings that sentiment consistency and emotional contagion are observed in social networks, we investigate whether social relations can help sentiment analysis by proposing a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification. In particular, we present a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process; and utilize sparse learning to tackle noisy texts in microblogging. An empirical study of two real-world Twitter datasets shows the superior performance of our framework in handling noisy and short tweets.


title: Arsenic , Tobacco Smoke , and Occupation Associations of Multiple Agents with Lung and Bladder Cancer

abstract: O riginal A rticle Arsenic, Tobacco Smoke, and Occupation Associations of Multiple Agents with Lung and Bladder Cancer Catterina Ferreccio, a Yan Yuan, b Jacqueline Calle, a Hugo Benitez, c Roxana L. Parra, c Johanna Acevedo, a Allan H. Smith, b Jane Liaw, b and Craig Steinmaus b Background: Millions of people worldwide are exposed to arsenic in drinking water, and many are likely coexposed to other agents that could substantially increase their risks of arsenic-related cancer. Methods: We performed a case-control study of multiple chemical exposures in 538 lung and bladder cancer cases and 640 controls in northern Chile, an area with formerly high drinking water arse- nic concentrations. Detailed information was collected on lifetime arsenic exposure, smoking, secondhand smoke, and other known or suspected carcinogens, including asbestos, silica, and wood dust. Results: Very high lung and bladder cancer odds ratios (ORs), and evidence of greater than additive effects, were seen in people exposed to arsenic concentrations >335 µg/L and who were tobacco smokers (OR = 16, 95% confidence interval = 6.5–40 for lung cancer; and OR = 23 [8.2–66] for bladder cancer; Rothman Synergy Indices = 4.0 [1.7–9.4] and 2.0 [0.92–4.5], respectively). Evidence of greater than additive effects were also seen in people coexposed to arsenic and secondhand tobacco smoke and several other known or suspected carcinogens, including asbestos, silica, and wood dust. Conclusions: These findings suggest that people coexposed to arse- nic and other known or suspected carcinogens have very high risks of lung or bladder cancer. (Epidemiology 2013;24: 898–905) A rsenic in drinking water is a well-established cause of lung and bladder cancer, and tens of millions of people world- wide are exposed. 1 Exposure to other agents such as tobacco smoke or occupational carcinogens, like asbestos or silica, could create large subgroups of people in arsenic-exposed areas whose cancer risks are especially high. Numerous pub- lic health agencies have called for research and policies that take into account the cumulative effects of multiple agents. 2 To date, however, relatively few regulations and policies have done this, primarily because of lack of data. We used data from a large case-control study in northern Chile to investi- gate the possible cumulative risks of arsenic, tobacco smoke, and other known or suspected carcinogens. METHODS Study Area The study area consisted of regions I and II in northern Chile, two contiguous regions with a population of 922,579 people. 3 In the late 1950s, river water from the nearby Andes mountains containing high concentrations of natu- rally occurring arsenic was diverted to the largest city in the area (Antofagasta) for drinking, leading to a 13-year period (1958–1970) with an average concentration of 860 µg/L in the city’s water supply. Installation of a treatment plant reduced this to <10 µg/L today. 4 Other cities in the area offer a wide range of exposure (Table 1). The major occupation in this area was mining of copper and other minerals, and this produced a variety of exposures, including silica, dusts, welding fumes, asbestos, and solvents. Participant Selection and Interviews Submitted 19 September 2012; accepted 19 March 2013; posted 12 September 2013. From the a Departamento de Salud Publica, Universidad Catolica de Chile, Santiago, Chile; b Arsenic Research Group, School of Public Health, University of California, Berkeley, CA; and c Hospital de Antofagasta, Antofagasta, Chile. Supported by US National Institute of Environmental Health Sciences grants 5R01ES014032 and P42ES04705. The authors report no conflicts of interest. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole respon- sibility of the author. Correspondence: Craig Steinmaus, University of California, School of Public Health, 50 University Hall, MC7360, Berkeley, CA 94720–7360. E-mail: craigs@berkeley.edu. Copyright © 2013 by Lippincott Williams & Wilkins ISSN: 1044-3983/13/2406-0898 DOI: 10.1097/EDE.0b013e31829e3e03 898 | www.epidem.com Cases included people who 1) had primary lung or blad- der cancer first diagnosed between October 2007 and December 2010; 2) lived in the study area at the time of diagnosis; 3) were over age 25 years when diagnosed; and 4) were able to pro- vide interview data or had a close relative who could. Lung and bladder cancer were selected because lung cancer is the main cause of arsenic-related death, 5 and bladder cancer is associ- ated with higher relative risks than any other arsenic-related cancer. 6 Cases were ascertained from all pathologists, hospitals, and radiologists in the area. Few people leave the study area for medical care because the nearest large medical facilities outside the area are in Santiago, 675 miles away. The large majority of cases were histologically confirmed (98% for bladder can- cer and 72% for lung cancer), with the remaining diagnoses based on a combination of radiologic (computed tomography) Epidemiology • Volume 24, Number 6, November 2013


title: Multiple clone row DRAM: a low latency and area optimized DRAM

abstract: Several previous works have changed DRAM bank structure to reduce memory access latency and have shown performance improvement. However, changes in the area-optimized DRAM bank can incur large area-overhead. To solve this problem, we propose Multiple Clone Row DRAM (MCR-DRAM), which uses existing DRAM bank structure without any modification. Our key idea is Multiple Clone Row (MCR), in which multiple rows are simultaneously turned on or off to consist of a logically single row. MCR provides two advantages which enable our low-latency mechanisms (Early-Access, Early-Precharge and Fast-Refresh). First, MCR increases the speed of the sensing process by increasing the number of sensed-cells. Thus, it enables a READ/WRITE command to an MCR to be issued earlier than possible for a normal row (Early-Access). Second, DRAM cells in an MCR exhibit more frequent refreshes without additional REFRESH commands, thereby reducing the amount of charge leakage during the refresh interval for the identical cell. The reduced amount of charge leakage enables a PRECHARGE command to be served before the activated-cells are fully restored (Early-Precharge) and a REFRESH operation to be completed before the refreshed-cells are fully restored (Fast-Refresh). Even though MCR-DRAM sacrifices memory capacity for low-latency, it can be dynamically reconfigured from low-latency to full-capacity DRAM. MCR-DRAM improves both performance and energy efficiency for both single-core and multi-core systems.


title: A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

abstract: Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.


title: Charateristics of decade-bandwidth, Balanced Antipodal Vivaldi Antenna (BAVA) phased arrays with time-delay beamformer systems

abstract: The first part of this paper presents measured performance of the Balanced Antipodal Vivaldi Antenna (BAVA) with U-shaped metallic channels. This new BAVA array is dual polarized with decade bandwidth and wide scan volume. Its depth is half-wavelength at the highest frequency of operation. It can be constructed as modular individual BAVA elements or as a subarray with integrated transmitter / receiver (T/R) modules. This is a unique feature that reduces manufacturing complexity and the cost of field repair. The second part of this paper overviews performance of the BAVA array scanned at wide scan angles using AFRL's Transformational Element Level Array (TELA) Time-Delay Units between 2 GHz to 8 GHz and using fixed delay lines between 2 GHz to 18 GHz. The authors offer guidelines for minimum aperture size to realize decade bandwidth beam scan performance without beam squinting at the lower end of the frequency band.


title: Predicting solar generation from weather forecasts using machine learning

abstract: A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables. One challenge with integrating renewables into the grid is that their power generation is intermittent and uncontrollable. Thus, predicting future renewable generation is important, since the grid must dispatch generators to satisfy demand as generation varies. While manually developing sophisticated prediction models may be feasible for large-scale solar farms, developing them for distributed generation at millions of homes throughout the grid is a challenging problem. To address the problem, in this paper, we explore automatically creating site-specific prediction models for solar power generation from National Weather Service (NWS) weather forecasts using machine learning techniques. We compare multiple regression techniques for generating prediction models, including linear least squares and support vector machines using multiple kernel functions. We evaluate the accuracy of each model using historical NWS forecasts and solar intensity readings from a weather station deployment for nearly a year. Our results show that SVM-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for our site than existing forecast-based models.


title: Table detection in document images using header and trailer patterns

abstract: This paper presents a new approach to detect tabular structures present in document images and in low resolution video images. The algorithm for table detection is based on identifying the unique table start pattern and table trailer pattern. We have formulated perceptual attributes to characterize the patterns. The performance of our table detection system is tested on a set of document images picked from UW-III (University of Washington) dataset, UNLV dataset, video images of NPTEL videos, and our own dataset. Our approach demonstrates improved detection for different types of table layouts, with or without ruling lines. We have obtained correct table localization on pages with multiple tables aligned side-by-side.


title: Zero-shot Image Tagging by Hierarchical Semantic Embedding

abstract: Given the difficulty of acquiring labeled examples for many fine-grained visual classes, there is an increasing interest in zero-shot image tagging, aiming to tag images with novel labels that have no training examples present. Using a semantic space trained by a neural language model, the current state-of-the-art embeds both images and labels into the space, wherein cross-media similarity is computed. However, for labels of relatively low occurrence, its similarity to images and other labels can be unreliable. This paper proposes Hierarchical Semantic Embedding (HierSE), a simple model that exploits the WordNet hierarchy to improve label embedding and consequently image embedding. Moreover, we identify two good tricks, namely training the neural language model using Flickr tags instead of web documents, and using partial match instead of full match for vectorizing a WordNet node. All this lets us outperform the state-of-the-art. On a test set of over 1,500 visual object classes and 1.3 million images, the proposed model beats the current best results (18.3% versus 9.4% in hit@1).


title: Developing a Telepresence Robot for Interpersonal Communication with the Elderly in a Home Environment

abstract: "Telepresence" is an interesting field that includes virtual reality implementations with human-system interfaces, communication technologies, and robotics. This paper describes the development of a telepresence robot called Telepresence Robot for Interpersonal Communication (TRIC) for the purpose of interpersonal communication with the elderly in a home environment. The main aim behind TRIC's development is to allow elderly populations to remain in their home environments, while loved ones and caregivers are able to maintain a higher level of communication and monitoring than via traditional methods. TRIC aims to be a low-cost, lightweight robot, which can be easily implemented in the home environment. Under this goal, decisions on the design elements included are discussed. In particular, the implementation of key autonomous behaviors in TRIC to increase the user's capability of projection of self and operation of the telepresence robot, in addition to increasing the interactive capability of the participant as a dialogist are emphasized. The technical development and integration of the modules in TRIC, as well as human factors considerations are then described. Preliminary functional tests show that new users were able to effectively navigate TRIC and easily locate visual targets. Finally the future developments of TRIC, especially the possibility of using TRIC for home tele-health monitoring and tele-homecare visits are discussed.


title: Scale Up vs. Scale Out in Cloud Storage and Graph Processing Systems

abstract: Deployers of cloud storage and iterative processing systems typically have to deal with either dollar budget constraints or throughput requirements. This paper examines the question of whether such cloud storage and iterative processing systems are more cost-efficient when scheduled on a COTS (scale out) cluster or a single beefy (scale up) machine. We experimentally evaluate two systems: 1) a distributed key-value store (Cassandra), and 2) a distributed graph processing system (Graph Lab). Our studies reveal scenarios where each option is preferable over the other. We provide recommendations for deployers of such systems to decide between scale up vs. Scale out, as a function of their dollar or throughput constraints. Our results indicate that there is a need or adaptive scheduling in heterogeneous clusters containing scale up and scale out nodes.


title: Impact of stray field on the switching properties of perpendicular MTJ for scaled MRAM

abstract: For the first time, we show the stray field (Hs) from the pinned layer of perpendicular magnetic tunnel junctions (pMTJs) plays multiple important roles: its out-of-plane component degrades pinned layer stability of scaled MTJ, while its in-plane component assists spin-transfer torque switching. Through stray field engineering, one can retain its advantage, and minimized its detrimental effect. We also show that etching process plays a vital role in the stray-field engineering. Our pMTJ exhibits robust pinned layer performance, symmetrical R-H loop and balanced spin-torque switching current. The switching current of our pMTJ is ∼60uA@20us for a 80nm diameter pMTJ.


title: Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX)

abstract: Many places in the world are too rugged or enclosed for vehicles to access. Even today, material transport to such areas is limited to manual labor and beasts of burden. Modern advancements in wearable robotics may make those methods obsolete. Attempts to navigate difficult terrain via purely autonomous robotics have been only moderately successful as highly unstructured environments have proved too unpredictable for pre-programmed robotics with limited sensory inputs. Lower extremity exoskeletons seek to circumvent these challenges by combining the innate intelligence, dexterity and sensory capabilities of a human with the significant strength and endurance of a pair of wearable robotic legs capable of supporting a payload. This dissertation outlines the development of one such system - the Berkeley Lower Extremity Exoskeleton (BLEEX). Previous lower extremity exoskeletons have been limited by difficulties in sensing the human operator and power supply limitations. The BLEEX however utilizes a novel control architecture that estimates the forces exerted on the human by the exoskeleton structure via measurements of only the exoskeleton itself. The BLEEX also utilizes a simplified kinematical architecture with powered joints only in the sagittal plane to minimize power demands. The wearer connects to the BLEEX at a pair of foot bindings and a shoulder harness. Extensive mock-up testing was used to develop the flexible anthropomorphic architecture. The BLEEX wearer can squat, bend, swing from side to side, twist, walk on slopes, and traverse obstacles while carrying significant payloads with ease. Clinical Gait Analysis (CGA) data was used to provide the framework for the design of the hydraulic BLEEX actuation system. Six double-acting hydraulic cylinders actuate the BLEEX ankles, knees and hips in the sagittal plane. Applying CGA motion data to the actuation design yielded hydraulic flow and prime mover requirements. A suitable self-contained hydraulic power supply was designed and built, making the BLEEX one of the first energetically autonomous lower extremity exoskeletons in the world. The BLEEX prototype has been walked, un-tethered on a treadmill at speeds of up to 1.3 m/s. The prototype has been tested in both indoor and outdoor environments and demonstrated short duration (∼30 min) energetic autonomy.


Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment


title: A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries

abstract: We extend latent Dirichlet allocation by introducing a topic model, hierarchically dual latent Dirichlet allocation, the output of which provides a basis for estimating consumers’ content preferenc...


title: A Compact 212.5-Gbit/s Transmitter Optical Sub-Assembly with DMLs and Quad Linear Driver

abstract: We present a compact 212.5-Gbit/s transmitter optical sub-assembly equipped with directly modulated lasers and quad 53.125-Gbit/s linear driver. We demonstrate high-quality and high-output-power operation with low-power consumption (1.01 W), which is desirable for 200GBASE-LR4 application.


Assessing and improving removable partial denture learning outcome by using proficiency tests results.


title: Epidural anesthesia and acutely increased intracranial pressure : Lumbar epidural space hydrodynamics in a porcine model

abstract: Background : The effects of epidural injection on intracranial pressure (ICP), lumbar epidural pressure, cerebral blood flow (CBF), and spinal cord blood flow (SCBF) were studied after acutely increased ICP in swine. Methods : Twenty pigs, anesthetized with isoflurane and mechanically ventilated to maintain normocarbia, had two Tuohy needles placed in the lumbar epidural space. The ICP, lumbar epidural pressure, heart rate, mean arterial pressure, and central venous pressure were monitored. All animals had a Fogarty catheter placed in the parietal epidural space. Six pigs were randomized to a normal ICP group (group N) and eight pigs to an increased ICP group by inflation of the Fogarty catheter balloon (group R). Each pig had 0.33 ml. kg -1 of 2.0% carbonated lidocaine injected over 20 s via an epidural needle placed at L3. The ICP and lumbar epidural pressure were then monitored continuously for 30 min. Pressure-time data were fit to traditional compartmental models. Epidural elastance and resistance were calculated using a derivation of the Windk-essel theory. An additional six pigs had ICP elevated as in group R and CBF and SCBF measured using radioactive microspheres at five time periods : baseline, 0-60 s, 100-160 s, 200-260 s, and at 30 min after epidural injection. Results : The animals did not differ with respect to heart rate, central venous pressure, or mean arterial pressure at baseline. The ICP was 10 ± 2 mmHg in group N, and 24 ± 2 mmHg after balloon inflation in group R. After epidural injection, peak ICP was significantly greater in group R (76 ± 22 vs. 54 ± 17 mmHg) but not different by 30 min (17 ± 5 vs. 11 ± 1 mmHg). Epidural elastance in group N was 8.3 ± 3.1 mmHg. ml -1 and 12.8 ± 3.0 mmHg. ml -1 in group R (P = 0.045). Epidural resistance was 1,330 ± 590 mmHg. s. ml -1 in group N and 2,220 ± 600 mmHg. s. ml -1 in group R (P = 0.038). The CBF and SCBF were less than 10% of baseline during the 0- to 60-s time period after epidural injection. Thereafter, CBF and SCBF did not differ from baseline values. Conclusions : In this porcine model, epidural injection increased ICP. With increased ICP at baseline, more pronounced increases in ICP followed epidural injection. With increased baseline ICP, both epidural elastance and resistance increased compared with controls. The CBF and SCBF were markedly reduced immediately after local anesthetic injection into the epidural space.


title: Inverse Graphics with Probabilistic CAD Models

abstract: Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perception from natural images have focused on low-dimensional latent scenes, due to challenges in both modeling and inference. Accounting for the enormous variability in 3D object shape and 2D appearance via realistic generative models seems intractable, as does inverting even simple versions of the many-to-many computations that link 3D scenes to 2D images. This paper proposes and evaluates an approach that addresses key aspects of both these challenges. We show that it is possible to solve challenging, real-world 3D vision problems by approximate inference in generative models for images based on rendering the outputs of probabilistic CAD (PCAD) programs. Our PCAD object geometry priors generate deformable 3D meshes corresponding to plausible objects and apply affine transformations to place them in a scene. Image likelihoods are based on similarity in a feature space based on standard mid-level image representations from the vision literature. Our inference algorithm integrates single-site and locally blocked Metropolis-Hastings proposals, Hamiltonian Monte Carlo and discriminative data-driven proposals learned from training data generated from our models. We apply this approach to 3D human pose estimation and object shape reconstruction from single images, achieving quantitative and qualitative performance improvements over state-of-the-art baselines.


title: Detecting spam web pages through content analysis

abstract: In this paper, we continue our investigations of "web spam": the injection of artificially-created pages into the web in order to influence the results from search engines, to drive traffic to certain pages for fun or profit. This paper considers some previously-undescribed techniques for automatically detecting spam pages, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms. When combined, our heuristics correctly identify 2,037 (86.2%) of the 2,364 spam pages (13.8%) in our judged collection of 17,168 pages, while misidentifying 526 spam and non-spam pages (3.1%).


title: Argument Schemes for Legal Case-based Reasoning

abstract: In this paper we use the notion of argument schemes to analyse some leading approaches to case-based reasoning in Law. We identify a set of argument schemes that can express the argument provided by such systems and draw attention to some important differences between the various approaches.


title: Understanding Convolutional Neural Networks in Terms of Category-Level Attributes

abstract: It has been recently reported that convolutional neural networks (CNNs) show good performances in many image recognition tasks. They significantly outperform the previous approaches that are not based on neural networks particularly for object category recognition. These performances are arguably owing to their ability of discovering better image features for recognition tasks through learning, resulting in the acquisition of better internal representations of the inputs. However, in spite of the good performances, it remains an open question why CNNs work so well and/or how they can learn such good representations. In this study, we conjecture that the learned representation can be interpreted as category-level attributes that have good properties. We conducted several experiments by using the dataset AwA (Animals with Attributes) and a CNN trained for ILSVRC-2012 in a fully supervised setting to examine this conjecture. We report that there exist units in the CNN that can predict some of the 85 semantic attributes fairly accurately, along with a detailed observation that this is true only for visual attributes and not for non-visual ones. It is more natural to think that the CNN may discover not only semantic attributes but non-semantic ones (or ones that are difficult to represent as a word). To explore this possibility, we perform zero-shot learning by regarding the activation pattern of upper layers as attributes describing the categories. The result shows that it outperforms the state-of-the-art with a significant margin.


title: The existence of refinement mappings

abstract: Abstract Refinement mappings are used to prove that a lower-level specification correctly implements a higher-level one. We consider specifications consisting of a state machine (which may be infinite- state) that specifies safety requirements, and an arbitrary supplementary property that specifies liveness requirements. A refinement mapping from a lower-level specification S 1 to a higher-level one S 2 is a mapping from S 1 's state space to S 2 's state space. It maps steps of S 1 's state machine to steps of S 2 's state machine and maps behaviors allowed by S 1 to behaviors allowed by S 2 . We show that, under reasonable assumptions about the specification, if S 1 implements S 2 , then by adding auxiliary variables to S 1 we can guarantee the existence of a refinement mapping. This provides a completeness result for a practical, hierarchical specification method.


EnsembleNet: Improving Grasp Detection using an Ensemble of Convolutional Neural Networks


title: A Captcha Design Based on Visual Reasoning

abstract: CAPTCHA is a reverse Turing test to distinguish humans from machines. It is widely used in the internet industry for cyber security. A good CAPTCHA is supposed to be easy for humans but difficult for machines. Many existing CAPTCHA implementations leverage the inability of automatic visual recognition, e.g., recognizing the text or other objects in an image. These CAPTCHAs are becoming more and more vulnerable recently, due to the rapid development of visual recognition techniques. This paper presents our study of using visual reasoning in CAPTCHA design. This CAPTCHA asks the users to find specific object(s) in an image according to a given text query. It is generally easy for humans to understand the text query and make sophisticated reasoning about the image, but still remains difficult and computationally expensive for machines. We describe the CAPTCHA design, provide usability analysis and present security experiments. Moreover, we show that the security can be further improved by the use of neural style transfer.


title: Improvements on Hindsight Learning

abstract: Sparse reward problems are one of the biggest challenges in Reinforcement Learning. Goal-directed tasks are one such sparse reward problems where a reward signal is received only when the goal is reached. One promising way to train an agent to perform goal-directed tasks is to use Hindsight Learning approaches. In these approaches, even when an agent fails to reach the desired goal, the agent learns to reach the goal it achieved instead. Doing this over multiple trajectories while generalizing the policy learned from the achieved goals, the agent learns a goal conditioned policy to reach any goal. One such approach is Hindsight Experience replay which uses an off-policy Reinforcement Learning algorithm to learn a goal conditioned policy. In this approach, a replay of the past transitions happens in a uniformly random fashion. Another approach is to use a Hindsight version of the policy gradients to directly learn a policy. In this work, we discuss different ways to replay past transitions to improve learning in hindsight experience replay focusing on prioritized variants in particular. Also, we implement the Hindsight Policy gradient methods to robotic tasks.


title: Document Informed Neural Autoregressive Topic Models with Distributional Prior

abstract: We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g.,"networks"used in the contexts artificial neural networks vs. biological neuron networks. Generative topic models infer topic-word distributions, taking no or only little context into account. Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion. The proposed model is named as iDocNADE. (2) Due to the small number of word occurrences (i.e., lack of context) in short text and data sparsity in a corpus of few documents, the application of topic models is challenging on such texts. Therefore, we propose a simple and efficient way of incorporating external knowledge into neural autoregressive topic models: we use embeddings as a distributional prior. The proposed variants are named as DocNADE2 and iDocNADE2. We present novel neural autoregressive topic model variants that consistently outperform state-of-the-art generative topic models in terms of generalization, interpretability (topic coherence) and applicability (retrieval and classification) over 6 long-text and 8 short-text datasets from diverse domains.


title: Optimization of Resource Provisioning Cost in Cloud Computing

abstract: In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments.


title: Blurred image region detection and classification

abstract: Many digital images contain blurred regions which are caused by motion or defocus. Automatic detection and classification of blurred image regions are very important for different multimedia analyzing tasks. This paper presents a simple and effective automatic image blurred region detection and classification technique. In the proposed technique, blurred image regions are first detected by examining singular value information for each image pixels. The blur types (i.e. motion blur or defocus blur) are then determined based on certain alpha channel constraint that requires neither image deblurring nor blur kernel estimation. Extensive experiments have been conducted over a dataset that consists of 200 blurred image regions and 200 image regions with no blur that are extracted from 100 digital images. Experimental results show that the proposed technique detects and classifies the two types of image blurs accurately. The proposed technique can be used in many different multimedia analysis applications such as image segmentation, depth estimation and information retrieval.


A Longitudinal Study of Topic Classification on Twitter.


title: Unsupervised Techniques for Extracting and Clustering Complex Events in News

abstract: Structured machine-readable representations of news articles can radically change the way we interact with information. One step towards obtaining these representations is event extraction - the identification of event triggers and arguments in text. With previous approaches mainly focusing on classifying events into a small set of predefined types, we analyze unsupervised techniques for complex event extraction. In addition to extracting event mentions in news articles, we aim at obtaining a more general representation by disambiguating to concepts defined in knowledge bases. These concepts are further used as features in a clustering application. Two evaluation settings highlight the advantages and shortcomings of the proposed approach.


title: Bayesian adaptive estimation of the contrast sensitivity function: the quick CSF method.

abstract: The contrast sensitivity function (CSF) predicts functional vision better than acuity, but long testing times prevent its psychophysical assessment in clinical and practical applications. This study presents the quick CSF (qCSF) method, a Bayesian adaptive procedure that applies a strategy developed to estimate multiple parameters of the psychometric function (A. B. Cobo-Lewis, 1996; L. L. Kontsevich & C. W. Tyler, 1999). Before each trial, a one-step-ahead search finds the grating stimulus (defined by frequency and contrast) that maximizes the expected information gain (J. V. Kujala & T. J. Lukka, 2006; L. A. Lesmes et al., 2006), about four CSF parameters. By directly estimating CSF parameters, data collected at one spatial frequency improves sensitivity estimates across all frequencies. A psychophysical study validated that CSFs obtained with 100 qCSF trials (~10 min) exhibited good precision across spatial frequencies (SD < 2–3 dB) and excellent agreement with CSFs obtained independently (mean RMSE = 0.86 dB). To estimate the broad sensitivity metric provided by the area under the log CSF (AULCSF), only 25 trials were needed to achieve a coefficient of variation of 15–20%. The current study demonstrates the method’s value for basic and clinical investigations. Further studies, applying the qCSF to measure wider ranges of normal and abnormal vision, will determine how its efficiency translates to clinical assessment.


title: Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems

abstract: Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha Go Zero [1]) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace [2]. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines?


title: One time password authentication scheme based on elliptic curves for Internet of Things (IoT)

abstract: Establishing end-to-end authentication between devices and applications in Internet of Things (IoT) is a challenging task. Due to heterogeneity in terms of devices, topology, communication and different security protocols used in IoT, existing authentication mechanisms are vulnerable to security threats and can disrupt the progress of IoT in realizing Smart City, Smart Home and Smart Infrastructure, etc. To achieve end-to-end authentication between IoT devices/applications, the existing authentication schemes and security protocols require a two-factor authentication mechanism. Therefore, as part of this paper we review the suitability of an authentication scheme based on One Time Password (OTP) for IoT and proposed a scalable, efficient and robust OTP scheme. Our proposed scheme uses the principles of lightweight Identity Based Elliptic Curve Cryptography scheme and Lamport's OTP algorithm. We evaluate analytically and experimentally the performance of our scheme and observe that our scheme with a smaller key size and lesser infrastructure performs on par with the existing OTP schemes without compromising the security level. Our proposed scheme can be implemented in real-time IoT networks and is the right candidate for two-factor authentication among devices, applications and their communications in IoT.


title: Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer

abstract: Interest in precision diagnostics has been fuelled by the concept that early detection of cancer would benefit patients; that is, if detected early, more tumours should be resectable and treatment more efficacious. Serum contains massive amounts of potentially diagnostic information, and affinity proteomics has risen as an accurate approach to decipher this, to generate actionable information that should result in more precise and evidence-based options to manage cancer. To achieve this, we need to move from single to multiplex biomarkers, a so-called signature, that can provide significantly increased diagnostic accuracy. This Opinion article focuses on the progress being made in identifying protein biomarker signatures of clinical utility, using blood-based proteomics.


title: EC-cache: load-balanced, low-latency cluster caching with online erasure coding

abstract: Data-intensive clusters and object stores are increasingly relying on in-memory object caching to meet the I/O performance demands. These systems routinely face the challenges of popularity skew, background load imbalance, and server failures, which result in severe load imbalance across servers and degraded I/O performance. Selective replication is a commonly used technique to tackle these challenges, where the number of cached replicas of an object is proportional to its popularity. In this paper, we explore an alternative approach using erasure coding. ::: ::: EC-Cache is a load-balanced, low latency cluster cache that uses online erasure coding to overcome the limitations of selective replication. EC-Cache employs erasure coding by: (i) splitting and erasure coding individual objects during writes, and (ii) late binding, wherein obtaining any k out of (k + r) splits of an object are sufficient, during reads. As compared to selective replication, EC-Cache improves load balancing by more than 3× and reduces the median and tail read latencies by more than 2×, while using the same amount of memory. EC-Cache does so using 10% additional bandwidth and a small increase in the amount of stored metadata. The benefits offered by EC-Cache are further amplified in the presence of background network load imbalance and server failures.


title: D3: Abnormal driving behaviors detection and identification using smartphone sensors

abstract: Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarsegrained result, i.e. distinguishing abnormal driving behaviors from normal ones. To improve drivers' awareness of their driving habits so as to prevent potential car accidents, we need to consider a finegrained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e. Weaving, Swerving, Sideslipping, Fast U-turn, Turning with a wide radius and Sudden braking. Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a finegrained abnormal Driving behavior Detection and iDentification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. By extracting unique features from readings of smartphones' accelerometer and orientation sensor, we first identify sixteen representative features to capture the patterns of driving behaviors. Then, a machine learning method, Support Vector Machine (SVM), is employed to train the features and output a classifier model which conducts fine-grained identification. From results of extensive experiments with 20 volunteers driving for another 4 months in real driving environments, we show that D3 achieves an average total accuracy of 95.36%.