title: Eyes-Free Barcode Localization and Decoding for Visually Impaired Mobile Phone Users

abstract: An eyes-free barcode localization and decoding method is presented that enables visually impaired (VI) mobile phone users to decode MSI (Modified Plessy) barcodes on shelves and UPC barcodes on individual boxes, cans, and bottles. Simple and efficient barcode localization and decoding techniques are augmented with an interactive haptic feedback loop that allows the VI user to align the phone’s camera with a fixed surface in the pitch and yaw planes. The method is implemented on a Google Nexus One smart phone running Android 2.1. A laboratory study is presented in which the method was evaluated by one VI and four blindfolded sighted participants.


title: Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications

abstract: The web-based e-learning system (WELS) has emerged as a new means of skill training and knowledge acquisition, encouraging both academia and industry to invest resources in the adoption of this system. Traditionally, most pre- and post-adoption tasks related to evaluation are carried out from the viewpoints of technology. Since users have been widely recognized as being a key group of stakeholders in influencing the adoption of information systems, their attitudes toward this system are pivotal. Therefore, based on the theory of multi-criteria decision making and the research products of user satisfaction from the fields of human-computer interaction and information systems, this study proposed a multi-criteria methodology from the perspective of learner satisfaction to support those evaluation-based activities taking place at the pre- and post-adoption phases of the WELS life cycle. In addition, by following this methodology, this study empirically investigated learners' perceptions of the relative importance of decision criteria. This investigation carried out a survey of college students, and the data thus obtained was then analyzed by analytic hierarchy process in order to derive an integrated preference structure of learners as a ground for evaluation. We found that learners regarded the learner interface as being the most important dimension of decision criteria. Future applications of these results are recommended and the implications are discussed.


title: Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development

abstract: This case study examines 18 elementary school teachers' perceptions of the barriers to technology integration (access, vision, professional development, time, and beliefs) and instructional practices with technology after two years of situated professional development. Months after transitioning from mentoring to teacher-led communities of practice, teachers continued to report positive perceptions of several barriers and were observed engaging in desirable instructional practices. Interviews suggest that the situated professional development activities helped create an environment that supported teachers' decisions to integrate technology. Implications for teacher professional development and technology integration are discussed in conjunction with the strengths and limitations of the study.


title: Slide-Down Prevention for Wheeled Mobile Robots on Slopes

abstract: Wheeled mobile robots on inclined terrain can slide down due to loss of traction and gravity. This type of instability, which is different from tip-over, can provoke uncontrolled motion or get the vehicle stuck. This paper proposes slide-down prevention by real-time computation of a straightforward stability margin for a given ground-wheel friction coefficient. This margin is applied to the case study of Lazaro, a hybrid skid-steer mobile robot with caster-leg mechanism that allows tests with four or five wheel contact points. Experimental results for both ADAMS simulations and the actual vehicle demonstrate the effectiveness of the proposed approach.


title: A general edit distance between RNA structures

abstract: Arc-annotated sequences are useful in representing the structural information of RNA sequences. In general, RNA secondary and tertiary structures can be represented as a set of nested arcs and a set of crossing arcs, respectively. Since RNA functions are largely determined by molecular confirmation and therefore secondary and tertiary structures, the comparison between RNA secondary and tertiary structures has received much attention recently. In this paper, we propose the notion of edit distance to measure the similarity between two RNA secondary and tertiary structures, by incorporating various edit operations performed on both bases and arcs (i.e., base-pairs). Several algorithms are presented to compute the edit distance between two RNA sequences with various arc structures and under various score schemes, either exactly or approximately, with provably good performance. Preliminary experimental tests confirm that our definition of edit distance and the computation model are among the most reasonable ones ever studied in the literature.


title: An FPGA-based 77 GHZ MEMS radar signal processing system for automotive collision avoidance

abstract: An FPGA implemented signal processing algorithm to determine the range and velocity of targets using a MEMS based FMCW 77 GHz long range automotive radar has been presented. The MEMS radar uses two MEMS SP3T RF switches, two microfabricated Rotman lenses and two microstrip antenna arrays in addition to other microelectronic components to realize a directional beam that can scan the target area with a 6.8 ms cycle time. By sequencing the FMCW signal through the 3 beamport of the Rotman lens, the beam can be steered by ±4 degrees. The developed signal processing and control algorithm has been implanted in a Xilinx Virtex 5 FPGA. A worst case range accuracy of ±0.25m and velocity accuracy of ±0.83 m/s has been achieved which is better than the state-of-the art Bosch LRR3 radar sensor.


title: Two problems with variational expectation maximisation for time-series

abstract: Variational methods are a key component of the approximate inference and learning toolbox. These methods fill an important middle ground, retaining distributional information about uncertainty in latent variables, unlike maximum a posteriori methods (MAP), and yet generally requiring less computational time than Monte Carlo Markov Chain methods. In particular the variational Expectation Maximisation (vEM) and variational Bayes algorithms, both involving variational optimisation of a free-energy, are widely used in time-series modelling. Here, we investigate the success of vEM in simple probabilistic time-series models. First we consider the inference step of vEM, and show that a consequence of the well-known compactness property of variational inference is a failure to propagate uncertainty in time, thus limiting the usefulness of the retained distributional information. In particular, the uncertainty may appear to be smallest precisely when the approximation is poorest. Second, we consider parameter learning and analytically reveal systematic biases in the parameters found by vEM. Surprisingly, simpler variational approximations (such a mean-field) can lead to less bias than more complicated structured approximations.


title: Anomaly detection of Logo images in the mobile phone using convolutional autoencoder

abstract: There are Logo images printed in the mobile phone. Anomaly detection of Logo image is an important quality control task in the intelligent manufacture. In real applications, there are not enough negative samples in the production line for us to study their difference from normal samples. In this paper, we propose an unsupervised learning method based on convolutional autoencoder (CAE) to generate the template of sample and detect the abnormal information through comparing test images with the adaptive template. Firstly, several methods of data augmentation are introduced to expand the scale of positive samples, aiming to improve the performance of CAE. Secondly, the topology of proposed CAE model is introduced. Thirdly, we introduce the image processing methods to detect and locate the abnormal information in the Logo image. A series of experiments on three group of different Logo image have shown that the method we proposed can effectively detect most of the anomalies in the image and achieve the average accuracy of 98.9%.


title: Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks

abstract: Forum threads are lengthy and rich in content. Concise thread summaries will benefit both newcomers seeking information and those who participate in the discussion. Few studies, however, have examined the task of forum thread summarization. In this work we make the first attempt to adapt the hierarchical attention networks for thread summarization. The model draws on the recent development of neural attention mechanisms to build sentence and thread representations and use them for summarization. Our results indicate that the proposed approach can outperform a range of competitive baselines. Further, a redundancy removal step is crucial for achieving outstanding results.


title: Reviewer Integration and Performance Measurement for Malware Detection

abstract: We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system's ability to keep pace with evolving threats. We conduct our evaluation on a sample of VirusTotal submissions spanning 2.5i?źyears and containing 1.1 million binaries with 778i?źGB of raw feature data. Without reviewer assistance, we achieve 72i?ź% detection at a 0.5i?ź% false positive rate, performing comparable to the best vendors on VirusTotal. Given a budget of 80 accurate reviews daily, we improve detection to 89i?ź% and are able to detect 42i?ź% of malicious binaries undetected upon initial submission to VirusTotal. Additionally, we identify a previously unnoticed temporal inconsistency in the labeling of training datasets. We compare the impact of training labels obtained at the same time training data is first seen with training labels obtained months later. We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20i?ź% points. We release our cluster-based implementation, as well as a list of all hashes in our evaluation and 3i?ź% of our entire dataset.


title: Instantaneous kinematics and design of a novel redundant parallel manipulator

abstract: In this paper we study the velocity and acceleration relations of a novel redundant parallel manipulator. Through this study, we derive the Jacobian matrices of the inverse and direct velocity problems. Moreover, we introduce two practical designs that are kinematically equivalent to the ideal manipulator. The proposed manipulator has potential to be used in a wide range of applications from space robotics to highly dexterous flight simulators. >


title: Organizing for Innovation in the Digitized World

abstract: Our era is one of increasingly pervasive digital technologies, which penetrate deeply into the very core of the products, services, and operations of many organizations and radically change the nature of product and service innovations. The fundamental properties of digital technology are reprogrammability and data homogenization. Together, they provide an environment of open and flexible affordances that are used in creating innovations characterized by convergence and generativity. An analysis of convergence and generativity observed in innovations with pervasive digital technologies reveals three traits: (1) the importance of digital technology platforms, (2) the emergence of distributed innovations, and (3) the prevalence of combinatorial innovation. Each of the six articles in this special issue relates to one or more of these three traits. In this essay, we explore the organizational research implications of these three digital innovation traits and identify research opportunities for organization science scholars. Examples from the articles in this special issue on organizing for innovation in the digitized world are used to demonstrate the kind of organizational scholarship that can faithfully reflect and inform innovation in a world of pervasive digital technologies.


title: Corpus-based Learning of Analogies and Semantic Relations

abstract: We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct; the average college-bound senior high school student answers about 57% correctly). We motivate this research by applying it to a difficult problem in natural language processing, determining semantic relations in noun-modifier pairs. The problem is to classify a noun-modifier pair, such as "laser printer", according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5% (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2% (random: 20%). The performance is state-of-the-art for both verbal analogies and noun-modifier relations.


title: New types of deep neural network learning for speech recognition and related applications: an overview

abstract: In this paper, we provide an overview of the invited and contributed papers presented at the special session at ICASSP-2013, entitled “New Types of Deep Neural Network Learning for Speech Recognition and Related Applications,” as organized by the authors. We also describe the historical context in which acoustic models based on deep neural networks have been developed. The technical overview of the papers presented in our special session is organized into five ways of improving deep learning methods: (1) better optimization; (2) better types of neural activation function and better network architectures; (3) better ways to determine the myriad hyper-parameters of deep neural networks; (4) more appropriate ways to preprocess speech for deep neural networks; and (5) ways of leveraging multiple languages or dialects that are more easily achieved with deep neural networks than with Gaussian mixture models.


title: Building Classifiers to Predict the Start of Glucose-Lowering Pharmacotherapy Using Belgian Health Expenditure Data

abstract: Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related to drug purchases and medical provisions to construct models that predict whether a patient will start glucose-lowering pharmacotherapy in the coming years, based on that patient's recent medical expenditure history. The design and implementation of the modeling strategy are discussed in detail and several learning methods are benchmarked for our application. Our best performing model obtains between 74.9% and 76.8% area under the ROC curve, which is comparable to state-of-the-art risk prediction approaches for T2D based on questionnaires. In contrast to other methods, our approach can be implemented on a population-wide scale at virtually no extra operational cost. Possibly, our approach can be further improved by additional information about some risk factors of T2D that is unavailable in health expenditure data.


title: Dietary Factors in the Etiology of Parkinson's Disease

abstract: Parkinson's disease (PD) is the second most common neurodegenerative disorder. The majority of cases do not arise from purely genetic factors, implicating an important role of environmental factors in disease pathogenesis. Well-established environmental toxins important in PD include pesticides, herbicides, and heavy metals. However, many toxicants linked to PD and used in animal models are rarely encountered. In this context, other factors such as dietary components may represent daily exposures and have gained attention as disease modifiers. Several in vitro, in vivo, and human epidemiological studies have found a variety of dietary factors that modify PD risk. Here, we critically review findings on association between dietary factors, including vitamins, flavonoids, calorie intake, caffeine, alcohol, and metals consumed via food and fatty acids and PD. We have also discussed key data on heterocyclic amines that are produced in high-temperature cooked meat, which is a new emerging field in the assessment of dietary factors in neurological diseases. While more research is clearly needed, significant evidence exists that specific dietary factors can modify PD risk.


title: New Robot for Power Line Inspection

abstract: Power line inspection is of the utmost importance for the reliability and stability of electric power distribution. However, manual inspection is a hazardous, slow, expensive and unreliable task. Therefore, new highly specialized robots are required to improve the overall quality and safety of the power line inspection. The research conducted so far has been mainly focused on the development of climbing and flying robots. This paper first addresses the main achievements in the field of robotic power line inspection. The proposed solutions are critically assessed and the associated problems are outlined. Based on these findings, a new concept for robot-assisted power line inspection, combining both climbing and flying principles, is proposed in the second part of the paper. The proposed concept is critically assessed and related to the other established concepts so as to demonstrate its advantages and feasibility for a routine power line inspection.


title: Mechanical property characterization of LPCVD silicon nitride thin films at cryogenic temperatures

abstract: T-shape, LPCVD silicon nitride cantilevers are fabricated to determine Young's modulus and fracture strength of silicon nitride thin films at room and cryogenic temperatures. A helium-cooled measurement setup is developed and installed inside a focused-ion-beam (FIB) system. A lead-zirconate-titanate (PZT) translator powered by a function generator and a dc voltage is utilized as an actuator, and a silicon diode is used as a temperature sensor in this setup. Resonant frequencies of identical cantilevers with different "milling masses" are measured to obtain thickness and Young's modulus of the silicon nitride thin films, while a bending test is performed to obtain fracture strength. From the experiment, the average Young's modulus of low-pressure chemical-vapor deposition (LPCVD) silicon nitride thin films varies from 260.5 GPa at room temperature (298 K) to 266.6 GPa at 30 K, and the average fracture strength ranges from 6.9 GPa at room temperature to 7.9 GPa at 30 K. The measurement setup and technique presented here can be used to characterize the mechanical properties of different MEMS materials at cryogenic temperatures.


title: Low-inertia serial manipulator with counterbalance mechanism

abstract: This paper proposes a new wire-based counterbalance mechanism for robot manipulator. Industrial robots require high torque due to the large weight. For this reason, a robot should be operated by a high torque motor. The use of a high torque motor increases the producing and operating costs of the robot. In order to resolve the problem, a novel mechanism for gravity compensation is proposed using a spring and a wire. To solve the problem of spring and wire during stretched, the multiple winding mechanism is used. The static analysis is performed to verify the compensation performance. Experiments are going to be performed after assembling the designed prototype soon.


title: Health behavior models in the age of mobile interventions: are our theories up to the task?

abstract: Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.


title: Towards An Information Security Maturity Model for Secure e-Government Services: A Stakeholders View

abstract: The paper proposes a comprehensive information security maturity model (ISMM) that addresses both technical and socio/non-technical security aspects. The model is intended for securing e-government ...


title: Bankshot: caching slow storage in fast non-volatile memory

abstract: Emerging non-volatile storage (e.g., Phase Change Memory, STT-RAM) allow access to persistent data at latencies an order of magnitude lower than SSDs. The density and price gap between NVMs and denser storage make NVM economically most suitable as a cache for larger, more conventional storage (i.e., NAND flash-based SSDs and disks). Existing storage caching architectures (even those that use fast flash-based SSDs) introduce significant software overhead that can obscure the performance benefits of faster memories. We propose Bankshot, a caching architecture that allows cache hits to bypass the OS (and the associated software overheads) entirely, while relying on the OS for heavy-weight operations like servicing misses and performing write backs. We evaluate several design decisions in Bankshot including different cache management policies and different levels of hardware, software support for tracking dirty data and maintaining meta-data. We find that with hardware support Bankshot can offer upto 5× speedup over conventional caching systems.


title: Explicit search result diversification through sub-queries

abstract: Queries submitted to a retrieval system are often ambiguous. In such a situation, a sensible strategy is to diversify the ranking of results to be retrieved, in the hope that users will find at least one of these results to be relevant to their information need. In this paper, we introduce xQuAD, a novel framework for search result diversification that builds such a diversified ranking by explicitly accounting for the relationship between documents retrieved for the original query and the possible aspects underlying this query, in the form of sub-queries. We evaluate the effectiveness of xQuAD using a standard TREC collection. The results show that our framework markedly outperforms state-of-the-art diversification approaches under a simulated best-case scenario. Moreover, we show that its effectiveness can be further improved by estimating the relative importance of each identified sub-query. Finally, we show that our framework can still outperform the simulated best-case scenario of the state-of-the-art diversification approaches using sub-queries automatically derived from the baseline document ranking itself.


title: Individual differences in judging deception: Accuracy and bias.

abstract: The authors report a meta-analysis of individual differences in detecting deception, confining attention to occasions when people judge strangers’ veracity in real-time with no special aids. The authors have developed a statistical technique to correct nominal individual differences for differences introduced by random measurement error. Although researchers have suggested that people differ in the ability to detect lies, psychometric analyses of 247 samples reveal that these ability differences are minute. In terms of the percentage of lies detected, measurement-corrected standard deviations in judge ability are less than 1%. In accuracy, judges range no more widely than would be expected by chance, and the best judges are no more accurate than a stochastic mechanism would produce. When judging deception, people differ less in ability than in the inclination to regard others’ statements as truthful. People also differ from one another as lie- and truth-tellers. They vary in the detectability of their lies. Moreover, some people are more credible than others whether lying or truth-telling. Results reveal that the outcome of a deception judgment depends more on the liar’s credibility than any other individual difference.


title: A nonconvex optimization framework for low rank matrix estimation

abstract: We study the estimation of low rank matrices via nonconvex optimization. Compared with convex relaxation, nonconvex optimization exhibits superior empirical performance for large scale instances of low rank matrix estimation. However, the understanding of its theoretical guarantees are limited. In this paper, we define the notion of projected oracle divergence based on which we establish sufficient conditions for the success of nonconvex optimization. We illustrate the consequences of this general framework for matrix sensing. In particular, we prove that a broad class of nonconvex optimization algorithms, including alternating minimization and gradient-type methods, geometrically converge to the global optimum and exactly recover the true low rank matrices under standard conditions.


title: Reversible Alopecia with Localized Scalp Necrosis After Accidental Embolization of the Parietal Artery with Hyaluronic Acid

abstract: Hyaluronic acid (HA) filler injection is widely used for soft-tissue augmentation. Complications associated with HA filling are not uncommon; however, HA-induced alopecia is a rarely reported complication that could result in severe secondary psychological trauma. The etiology, clinical traits, treatment strategies, outcomes, and possible reversibility of HA-induced alopecia have not been characterized. Here, we report a case in which bilateral temple injections of 6.5 mL of HA led to persistent pain over the left scalp for several days. Although the pain was relieved at day 9 after 600 U of hyaluronidase were injected in the left temple, the patient developed localized alopecia at the left temporoparietal region with central skin necrosis at day 15. After topical applications of recombinant bovine basic fibroblast growth factor gel and 2% minoxidil spay, the necrotic skin wound was healed at day 42. Hair regrowth and normal hair density were restored at day 74. Analyses of Doppler ultrasound examinations and histopathology of the skin biopsy suggested that mild ischemia of the left temporoparietal region led to reversible alopecia, while the permanent hair loss in the left parietal area was associated with severe skin ischemia. Therefore, the key to treatment would be to focus on the effective correction of severe ischemia-induced skin necrosis to prevent permanent hair loss.


Fast and Controllable Simulation of the Shattering of Brittle Objects


title: RLgraph: Flexible Computation Graphs for Deep Reinforcement Learning

abstract: Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing high performance RL computation graphs in both static graph and define-by-run paradigms. The resulting implementations yield high performance across different deep learning frameworks and distributed backends.


title: A Novel Grid Synchronization PLL Method Based on Adaptive Low-Pass Notch Filter for Grid-Connected PCS

abstract: The amount of distributed energy resources (DERs) has increased constantly worldwide. The power ratings of DERs have become considerably high, as required by the new grid code requirement. To follow the grid code and optimize the function of grid-connected inverters based on DERs, a phase-locked loop (PLL) is essential for detecting the grid phase angle accurately when the grid voltage is polluted by harmonics and imbalance. This paper proposes a novel low-pass notch filter PLL (LPN-PLL) control strategy to synchronize with the true phase angle of the grid instead of using a conventional synchronous reference frame PLL (SRF-PLL), which requires a d-q-axis transformation of three-phase voltage and a proportional-integral controller. The proposed LPN-PLL is an upgraded version of the PLL method using the fast Fourier transform concept (FFT-PLL) which is robust to the harmonics and imbalance of the grid voltage. The proposed PLL algorithm was compared with conventional SRF-PLL and FFT-PLL and was implemented digitally using a digital signal processor TMS320F28335. A 10-kW three-phase grid-connected inverter was set, and a verification experiment was performed, showing the high performance and robustness of the proposal under low-voltage ride-through operation.


title: Impedance Control: An Approach to Manipulation

abstract: Manipulation fundamentally requires a manipulator to be mechanically coupled to the object being manipulated. A consideration of the physical constraints imposed by dynamic interaction shows that control of a vector quantity such as position or force is inadequate and that control of the manipulator impedance is also necessary. Techniques for control of manipulator behaviour are presented which result in a unified approach to kinematically constrained motion, dynamic interaction, target acquisition and obstacle avoidance.


title: High-frequency induction heating for small-foreign-metal particle detection using 400 kHz SiC-MOSFETs inverter

abstract: This paper presents a detection method of small-foreign-metal particles using a 400 kHz silicon-carbide (SiC)-metal-oxide-semiconductor field-effect-transistors (MOSFETs) high-frequency inverter. A 400 kHz SiC-MOSFETs high-frequency inverter is developed and applied to small-foreign-metal particles detection on high-performance chemical films (HPCFs). A new placement of induction heating (IH) coils, which is staggered-placement IH coils, is proposed. It is shown that small-foreign-metal particles consisting of 0.7 mm diameter stainless steel balls (SUS304) on an A4-paper-size film can be detected with the proposed staggered-placement IH coils using JSOL JMAG software, which is a 3D full-wave electromagnetic field simulation software. A prototype experimental model with the proposed staggered-placement IH coils is constructed and tested. Experimental results demonstrate that the IH coils with the constructed 400 kHz SiC-MOSFETs can heat small-foreign-metal particles, which can then be detected by a thermographic camera. Experimental results with the proposed staggered-placement of IH coils also demonstrate that the proposed detection method of small-foreign-metal particles using a 400 kHz SiC-MOSFETs high-frequency inverter is applicable to actual HPCF production lines.


A Case of Subungual Melanoma In Situ in an 18-Year-Old Girl Presented with Total Melanonychia


title: An independent shopping experience for wheelchair users through augmented reality and RFID

abstract: ABSTRACTPeople with physical and mobility impairments continue to struggle to attain independence in the performance of routine activities and tasks. For example, browsing in a store and interacting with products located beyond an arm’s length may be impossible without the enabling intervention of a human assistant. This research article describes a study undertaken to design, develop, and evaluate potential interaction methods for motor-impaired individuals, specifically those who use wheelchairs. Our study includes a user-centered approach, and a categorization of wheelchair users based upon the severity of their disability and their individual needs. We designed and developed access solutions that utilize radio frequency identification (RFID), augmented reality (AR), and touchscreen technologies in order to help people who use wheelchairs to carry out certain tasks autonomously. In this way, they have been empowered to go shopping independently, free from reliance upon the assistance of others. A total...


title: Toward an AI Physicist for Unsupervised Learning

abstract: We investigate opportunities and challenges for improving unsupervised machine learning using four common strategies with a long history in physics: divide-and-conquer, Occam's Razor, unification, and lifelong learning. Instead of using one model to learn everything, we propose a novel paradigm centered around the learning and manipulation of *theories*, which parsimoniously predict both aspects of the future (from past observations) and the domain in which these predictions are accurate. Specifically, we propose a novel generalized-mean-loss to encourage each theory to specialize in its comparatively advantageous domain, and a differentiable description length objective to downweight bad data and"snap"learned theories into simple symbolic formulas. Theories are stored in a"theory hub", which continuously unifies learned theories and can propose theories when encountering new environments. We test our implementation, the"AI Physicist"learning agent, on a suite of increasingly complex physics environments. From unsupervised observation of trajectories through worlds involving random combinations of gravity, electromagnetism, harmonic motion and elastic bounces, our agent typically learns faster and produces mean-squared prediction errors about a billion times smaller than a standard feedforward neural net of comparable complexity, typically recovering integer and rational theory parameters exactly. Our agent successfully identifies domains with different laws of motion also for a nonlinear chaotic double pendulum in a piecewise constant force field.


title: Vital, Sophia, and Co.—The Quest for the Legal Personhood of Robots

abstract: The paper examines today’s debate on the legal status of AI robots, and how often scholars and policy makers confuse the legal agenthood of these artificial agents with the status of legal personhood. By taking into account current trends in the field, the paper suggests a twofold stance. First, policy makers shall seriously mull over the possibility of establishing novel forms of accountability and liability for the activities of AI robots in contracts and business law, e.g., new forms of legal agenthood in cases of complex distributed responsibility. Second, any hypothesis of granting AI robots full legal personhood has to be discarded in the foreseeable future. However, how should we deal with Sophia, which became the first AI application to receive citizenship of any country, namely, Saudi Arabia, in October 2017? Admittedly, granting someone, or something, legal personhood is—as always has been—a highly sensitive political issue that does not simply hinge on rational choices and empirical evidence. Discretion, arbitrariness, and even bizarre decisions play a role in this context. However, the normative reasons why legal systems grant human and artificial entities, such as corporations, their status, help us taking sides in today’s quest for the legal personhood of AI robots. Is citizen Sophia really conscious, or capable of suffering the slings and arrows of outrageous scholars?


title: Optimal coordination of directional overcurrent relay coordination

abstract: Directional overcurrent relays are used to protect interconnected power systems and looped distribution systems. Several techniques and formulations have been proposed to solve the directional overcurrent relay problem. In this paper, the proposed formulations for relay coordination problem are discussed. The protective relay coordination problem is reformulated to take into account the discrete pickup current values. The directional overcurrent relay coordination problem is formulated as a mixed integer-programming problem. The problem is solved using the OSL solver of the general algebraic modeling system (GAMS).


title: Mapping the margins: intersectionality, identity politics, and violence against women of color

abstract: Over the last two decades, women have organized against the almost routine violence that shapes their lives. Drawing from the strength of shared experience, women have recognized that the political demands of millions speak more powerfully than the pleas of a few isolated voices. This politicization in turn has transformed the way we understand violence against women. For example, battering and rape, once seen as private (family matters) and aberrational (errant sexual aggression), are now largely recognized as part of a broad-scale system of domination that affects women as a class. This process of recognizing as social and systemic what was formerly perceived as isolated and individual has also characterized the identity politics of people of color and gays and lesbians, among others. For all these groups, identity-based politics has been a source of strength, community, and intellectual development. The embrace of identity politics, however, has been in tension with dominant conceptions of social justice. Race, gender, and other identity categories are most often treated in mainstream liberal discourse as vestiges of bias or domination-that is, as intrinsically negative frameworks in which social power works to exclude or marginalize those who are different. According to this understanding, our liberatory objective should be to empty such categories of any social significance. Yet implicit in certain strands of feminist and racial liberation movements, for example, is the view that the social power in delineating difference need not be the power of domination; it can instead be the source of political empowerment and social reconstruction. The problem with identity politics is not that it fails to transcend difference, as some critics charge, but rather the opposite- that it frequently conflates or ignores intra group differences. In the context of violence against women, this elision of difference is problematic, fundamentally because the violence that many women experience is often shaped by other dimensions of their identities, such as race and class. Moreover, ignoring differences within groups frequently contributes to tension among groups, another problem of identity politics that frustrates efforts to politicize violence against women. Feminist efforts to politicize experiences of women and antiracist efforts to politicize experiences of people of color' have frequently proceeded as though the issues and experiences they each detail occur on mutually exclusive terrains. Al-though racism and sexism readily intersect in the lives of real people, they seldom do in feminist and antiracist practices. And so, when the practices expound identity as "woman" or "person of color" as an either/or proposition, they relegate the identity of women of color to a location that resists telling. My objective here is to advance the telling of that location by exploring the race and gender dimensions of violence against women of color. Contemporary feminist and antiracist discourses have failed to consider the intersections of racism and patriarchy. Focusing on two dimensions of male violence against women-battering and rape-I consider how the experiences of women of color are frequently the product of intersecting patterns of racism and sexism, and how these experiences tend not to be represented within the discourse of either feminism or antiracism... Language: en


title: QR Images: Optimized Image Embedding in QR Codes

abstract: This paper introduces the concept of QR images, an automatic method to embed QR codes into color images with bounded probability of detection error. These embeddings are compatible with standard decoding applications and can be applied to any color image with full area coverage. The QR information bits are encoded into the luminance values of the image, taking advantage of the immunity of QR readers against local luminance disturbances. To mitigate the visual distortion of the QR image, the algorithm utilizes halftoning masks for the selection of modified pixels and nonlinear programming techniques to locally optimize luminance levels. A tractable model for the probability of error is developed and models of the human visual system are considered in the quality metric used to optimize the luminance levels of the QR image. To minimize the processing time, the optimization techniques proposed to consider the mechanics of a common binarization method and are designed to be amenable for parallel implementations. Experimental results show the graceful degradation of the decoding rate and the perceptual quality as a function the embedding parameters. A visual comparison between the proposed and existing methods is presented.


A System for Detection of Distributed Denial of Service ( DDoS ) Attacks using KDD Cup Data Set


title: Hierarchical Bayesian Models for Applications in Information retrieval

abstract: SUMMARY We present a simple hierarchical Bayesian approach to the modeling collections of texts and other large-scale data collections. For text collections, we posit that a document is generated by choosing a random set of multinomial probabilities for a set of possible “topics,” and then repeatedly generating words by sampling from the topic mixture. This model is intractable for exact probabilistic inference, but approximate posterior probabilities and marginal likelihoods can be obtained via fast variational methods. We also present extensions to coupled models for joint text/image data and multiresolution models for topic hierarchies.


title: Convolutional Radio Modulation Recognition Networks

abstract: We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.


title: VR Collide! Comparing Collision-Avoidance Methods Between Co-located Virtual Reality Users

abstract: We present a pilot study comparing visual feedback mechanisms for preventing physical collisions between co-located VR users. These include Avatar (a 3D avatar in co-located with the other user), BoundingBox (similar to HTC's "chaperone"), and CameraOverlay (live video feed overlaid on the virtual environment). Using a simulated second user, we found that CameraOverlay and Avatar had the fastest travel time around an obstacle, but BoundingBox had the fewest collisions at 0.07 collision/trial versus 0.2 collisions/trial for Avatar and 0.4 collisions/trial for CameraOverlay. However, subjective participant impressions strongly favoured Avatar and CameraOverlay over BoundingBox. Based on these results, we propose future studies on hybrid methods combining the best aspects of Avatar (speed, user preference) and BoundingBox (safety).


title: Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate.

abstract: Wine grapes present a unique biogeography model, wherein microbial biodiversity patterns across viticultural zones not only answer questions of dispersal and community maintenance, they are also an inherent component of the quality, consumer acceptance, and economic appreciation of a culturally important food product. On their journey from the vineyard to the wine bottle, grapes are transformed to wine through microbial activity, with indisputable consequences for wine quality parameters. Wine grapes harbor a wide range of microbes originating from the surrounding environment, many of which are recognized for their role in grapevine health and wine quality. However, determinants of regional wine characteristics have not been identified, but are frequently assumed to stem from viticultural or geological factors alone. This study used a high-throughput, short-amplicon sequencing approach to demonstrate that regional, site-specific, and grape-variety factors shape the fungal and bacterial consortia inhabiting wine-grape surfaces. Furthermore, these microbial assemblages are correlated to specific climatic features, suggesting a link between vineyard environmental conditions and microbial inhabitation patterns. Taken together, these factors shape the unique microbial inputs to regional wine fermentations, posing the existence of nonrandom "microbial terroir" as a determining factor in regional variation among wine grapes.


title: Coseismic Deformation from the 1999 Mw 7.1 Hector Mine, California, Earthquake as Inferred from InSAR and GPS Observations

abstract: We use interferometric synthetic aperture radar (InSAR) and Global Positioning System (GPS) observations to investigate static deformation due to the 1999 M_w 7.1 Hector Mine earthquake, that occurred in the eastern California shear zone. Interferometric decorrelation, phase, and azimuth offset measurements indicate regions of surface and near-surface slip, which we use to constrain the geometry of surface rupture. The inferred geometry is spatially complex, with multiple strands. The southern third of the rupture zone consists of three subparallel segments extending about 20 km in length in a N45°W direction. The central segment is the simplest, with a single strand crossing the Bullion Mountains and a strike of N10°W. The northern third of the rupture zone is characterized by multiple splays, with directions subparallel to strikes in the southern and central. The average strike for the entire rupture is about N30°W. The interferograms indicate significant along-strike variations in strain which are consistent with variations in the ground-based slip measurements. Using a variable resolution data sampling routine to reduce the computational burden, we invert the InSAR and GPS data for the fault geometry and distribution of slip. We compare results from assuming an elastic half-space and a layered elastic space. Results from these two elastic models are similar, although the layered-space model predicts more slip at depth than does the half-space model. The layered model predicts a maximum coseismic slip of more than 5 m at a depth of 3 to 6 km. Contrary to preliminary reports, the northern part of the Hector Mine rupture accommodates the maximum slip. Our model predictions for the surface fault offset and total seismic moment agree with both field mapping results and recent seismic models. The inferred shallow slip deficit is enigmatic and may suggest that distributed inelastic yielding occurred in the uppermost few kilometers of the crust during or soon after the earthquake.


title: Recursive co-kriging model for Design of Computer experiments with multiple levels of fidelity with an application to hydrodynamic

abstract: We consider in this paper the problem of building a fast-running approximation—also called surrogate model—of a complex computer code. The co-kriging based surrogate model is a promising tool to build such an approximation when the complex computer code can be run at different levels of accuracy. We present here an original approach to perform a multi-fidelity co-kriging model which is based on a recursive formulation. We prove that the predictive mean and the variance of the presented approach are identical to the ones of the original co-kriging model. However, our new approach allows to obtain original results. First, closed-form formulas for the universal co-kriging predictive mean and variance are given. Second, a fast cross-validation procedure for the multi-fidelity co-kriging model is introduced. Finally, the proposed approach has a reduced computational complexity compared to the previous one. The multi-fidelity model is successfully applied to emulate a hydrodynamic simulator.


title: DCI Closed: A Fast and Memory Efficient Algorithm to Mine Frequent Closed Itemsets

abstract: One of the main problems raising up in the frequent closed itemsetsmining problem is the duplicate detection. In this paper we propose a general technique for promptly detecting and discarding duplicate closed itemsets, without the need of keeping in the main memory the whole set of closed patterns. Our approach can be exploited with substantial performance benefits by any algorithm that adopts a vertical representation of the dataset. We implemented our technique within a new depth-first closed itemsets mining algorithm. The experimental evaluation demonstrates that our algorithm outperforms other state of the art algorithms like CLOSET+ and FPCLOSE.


title: A sweat-gland tumor metastasizing to the axilla: Report of a case

abstract: Sclerosing sweat-duct carcinoma is a rare malignancy of the skin. We herein present the case of a 68-year-old Japanese man who developed sclerosing sweat-duct carcinoma 2 years after undergoing local excision of a primary tumor in his right arm. Firm nodules appeared in the right axilla, which were revealed by surgical resection to be metastases. This rare tumor grows slowly but usually metastasizes to the regional lymph nodes, whereupon they are often associated with a poor prognosis. As the effectiveness of chemotherapy and radiotherapy against this neoplasm remains unclear, we recommend that the disease should initially be aggressively treated by surgery, which would also be indicated for any metastatic lesions.


title: Budgeted optimization with constrained experiments

abstract: Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(ċ) given a budget by requesting a sequence of samples from the function. In our setting, however, evaluating the function at precisely specified points is not practically possible due to prohibitive costs. Instead, we can only request constrained experiments. A constrained experiment, denoted by Q, specifies a subset of the input space for the experimenter to sample the function from. The outcome of Q includes a sampled experiment x, and its function output f(x). Importantly, as the constraints of Q become looser, the cost of fulfilling the request decreases, but the uncertainty about the location x increases. Our goal is to manage this trade-off by selecting a set of constrained experiments that best optimize f(ċ) within the budget. We study this problem in two different settings, the non-sequential (or batch) setting where a set of constrained experiments is selected at once, and the sequential setting where experiments are selected one at a time. We evaluate our proposed methods for both settings using synthetic and real functions. The experimental results demonstrate the efficacy of the proposed methods.


title: An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning

abstract: Although empirical and neural studies show that serotonin (5HT) plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework. The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG). In this model, the DA signal is represented by the temporal difference error (δ), while the 5HT signal is represented by a parameter (α) that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: (1) Risk-sensitive decision making, where 5HT controls risk assessment, (2) Temporal reward prediction, where 5HT controls time-scale of reward prediction, and (3) Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG.


title: Laserlight visual cueing device for freezing of gait in Parkinson's disease: a case study of the biomechanics involved.

abstract: BACKGROUND ::: Freezing of gait (FOG) is a serious gait disorder affecting up to two-thirds of people with Parkinson's disease (PD). Cueing has been explored as a method of generating motor execution using visual transverse lines on the floor. However, the impact of a laser light visual cue remains unclear. ::: ::: ::: OBJECTIVE ::: To determine the biomechanical effect of a laser cane on FOG in a participant with PD compared to a healthy age- and gender-matched control. ::: ::: ::: METHODS ::: The participant with PD and healthy control were given a task of initiating gait from standing. Electromyography (EMG) data were collected from the tibialis anterior (TA) and the medial gastrocnemius (GS) muscles using an 8-channel system. A 10-camera system (Qualisys) recorded movement in 6 degrees of freedom and a calibrated anatomical system technique was used to construct a full body model. Center of mass (COM) and center of pressure (COP) were the main outcome measures. ::: ::: ::: RESULTS ::: The uncued condition showed that separation of COM and COP took longer and was of smaller magnitude than the cued condition. EMG activity revealed prolonged activation of GS, with little to no TA activity. The cued condition showed earlier COM and COP separation. There was reduced fluctuation in GS, with abnormal, early bursts of TA activity. Step length improved in the cued condition compared to the uncued condition. ::: ::: ::: CONCLUSION ::: Laserlight visual cueing improved step length beyond a non-cued condition for this patient indicating improved posture and muscle control.


title: BestConfig: tapping the performance potential of systems via automatic configuration tuning

abstract: An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present Best Config, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment.


title: Attitudes and Dating Aggression: A Cognitive Dissonance Approach

abstract: This study examined the association between attitudes about dating aggression and select dating aggressive behaviors (verbal aggression and jealous behavior) in high school students. Our hypothesis, derived from cognitive dissonance theory, was that discrepancies between self-reported attitudes and aggressive behavior at Time 1 (i.e., putative cognitive dissonance) would predict decreases in aggression between Time 1 and Time 2 beyond what would be predicted by change in attitudes over the same period. Results indicated that cognitive dissonance (as indexed by the discrepancy between attitudes and behavior) was generally a significant predictor of behavior change, providing significant improvement in prediction of behavior over attitude change alone. We discuss the implications of these findings for prevention efforts and directions for future research in this area.


title: MODELING BY MULTI-AGENT SYSTEMS : A SWARM INTELLIGENCE APPROACH

abstract: There are a number of emergent traffic and transportation phenomena that cannot be analyzed successfully and explained using analytical models. The only way to analyze such phenomena is through the development of models that can simulate behavior of every agent. Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual ‘agents’ and that each agent interacts with other agents according to localized knowledge. The agent-based approach is a ‘bottom-up’ approach to modeling where special kinds of artificial agents are created by analogy with social insects. Social insects (including bees, wasps, ants and termites) have lived on Earth for millions of years. Their behavior in nature is primarily characterized by autonomy, distributed functioning and self-organizing capacities. Social insect colonies teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. On the other hand, a large number of traditional engineering models and algorithms are based on control and centralization. In this article, we try to obtain the answer to the following question: Can we use some principles of natural swarm intelligence in the development of artificial systems aimed at solving complex problems in traffic and transportation?


title: 3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation

abstract: We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. 3D-consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from UK Biobank. It is then tested on 756 different cases from UK Biobank and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with 30 cases, RVSC with 16 cases). Results comparable or even better than the state-of-the-art in terms of distance measures are achieved. They also emphasize the assets of our method, namely enhanced spatial consistency (currently neither considered nor achieved by the state-of-the-art), and the generalization ability to unseen cases even from other databases.


title: Personal projects, happiness, and meaning: On doing well and being yourself.

abstract: Personal Projects Analysis (B. R. Little, 1983) was adapted to examine relations between participants' appraisals of their goal characteristics and orthogonal happiness and meaning factors that emerged from factor analyses of diverse well-being measures. In two studies with 146 and 179 university students, goal efficacy was associated with happiness and goal integrity was associated with meaning. A new technique for classifying participants according to emergent identity themes is introduced. In both studies, identity-compensatory predictors of happiness were apparent. Agentic participants were happiest if their goals were supported by others, communal participants were happiest if their goals were fun, and hedonistic participants were happiest if their goals were being accomplished. The distinction between happiness and meaning is emphasized, and the tension between efficacy and integrity is discussed. Developmental implications are discussed with reference to results from archival data from a sample of senior managers. Wisdom literature has long promoted being true to oneself as a desirable alternative to preoccupation with success. Warnings against blind achievement are present in two of the earliest known written records, from about 3,700 years ago. In the Atrahasis epic, the gods punish "noisy" ambition with a terrible flood, and in the Gilgamesh epic, personal accomplishments lose their meaning for the protagonist in light of his friend's death (Fisher, 1970; Guirand, 1977, pp. 49-72). Similarly, in the Genesis Tower of Babel story, ambition is punished by confusion, and in Ecclesiastes, achievements are dismissed as vanity and folly. The corollary to these recommendations is represented by injunctions from Greek philosophy that "the unexamined life is not worth living" and that one should "know thyself." The examples given above converge on a theme so commonplace that it regularly appears in Hollywood films (e.g., "Regarding Henry,'' ' 'The Doctor,'' and ' 'The Fisher King''). Each of these films features a highly successful character absorbed in his accomplishments until some crisis makes his life feel meaningless.


title: Heavy Metal (Hg, Cr, Cd, and Pb) Contamination in Urban Areas and Wildlife Reserves: Honeybees as Bioindicators

abstract: The degree of heavy metal (Hg, Cr, Cd, and Pb) pollution in honeybees (Apis mellifera) was investigated in several sampling sites around central Italy including both polluted and wildlife areas. The honeybee readily inhabits all environmental compartments, such as soil, vegetation, air, and water, and actively forages the area around the hive. Therefore, if it functions in a polluted environment, plant products used by bees may also be contaminated, and as a result, also a part of these pollutants will accumulate in the organism. The bees, foragers in particular, are good biological indicators that quickly detect the chemical impairment of the environment by the high mortality and the presence of pollutants in their body or in beehive products. The experiment was carried out using 24 colonies of honeybees bred in hives dislocated whether within urban areas or in wide countryside areas. Metals were analyzed on the foragers during all spring and summer seasons, when the bees were active. Results showed no presence of mercury in all samples analyzed, but honeybees accumulated several amounts of lead, chromium, and cadmium. Pb reported a statistically significant difference among the stations located in urban areas and those in the natural reserves, showing the highest values in honeybees collected from hives located in Ciampino area (Rome), next to the airport. The mean value for this sampling station was 0.52 mg kg−1, and July and September were characterized by the highest concentrations of Pb. Cd also showed statistically significant differences among areas, while for Cr no statistically significant differences were found.


title: OpenNetMon: Network monitoring in OpenFlow Software-Defined Networks

abstract: We present OpenNetMon, an approach and open-source software implementation to monitor per-flow metrics, especially throughput, delay and packet loss, in OpenFlow networks. Currently, ISPs over-provision capacity in order to meet QoS demands from customers. Software-Defined Networking and OpenFlow allow for better network control and flexibility in the pursuit of operating networks as efficiently as possible. Where OpenFlow provides interfaces to implement fine-grained Traffic Engineering (TE), OpenNetMon provides the monitoring necessary to determine whether end-to-end QoS parameters are actually met and delivers the input for TE approaches to compute appropriate paths. OpenNetMon polls edge switches, i.e. switches with flow end-points attached, at an adaptive rate that increases when flow rates differ between samples and decreases when flows stabilize to minimize the number of queries. The adaptive rate reduces network and switch CPU overhead while optimizing measurement accuracy. We show that not only local links serving variable bit-rate video streams, but also aggregated WAN links benefit from an adaptive polling rate to obtain accurate measurements. Furthermore, we verify throughput, delay and packet loss measurements for bursty scenarios in our experiment testbed.


title: DroidChain: A novel malware detection method for Android based on behavior chain

abstract: Android malware threats have recently become a real concern. The growing amount and diversity of these applications render conventional defenses largely ineffective. To fight against malware variants and zero-day malware, this paper proposes DroidChain, a malware detection method based on behavior chain model, which is composed of typical behavior processes of Android apps. Using the method, we summarize four kinds of malware models, including privacy leakage, SMS financial charge, malware installation and privilege escalation. The detection of 1260 Android applications shows that the accuracy of this method reaches 81.8%.


title: An efficient omnidirectional vision system for soccer robots: From calibration to object detection

abstract: Robotic soccer is nowadays a popular research domain in the area of multi-robot systems. In the context of RoboCup, the Middle Size League is one of the most challenging. This paper presents an efficient omnidirectional vision system for real-time object detection, developed for the robotic soccer team of the University of Aveiro, CAMBADA. The vision system is used to find the ball and white lines, which are used for self-localization, as well as to find the presence of obstacles. Algorithms for detecting these objects and also for calibrating most of the parameters of the vision system are presented in this paper. We also propose an efficient approach for detecting arbitrary FIFA balls, which is an important topic of research in the Middle Size League. The experimental results that we present show the effectiveness of our algorithms, both in terms of accuracy and processing time, as well as the results that the team has been achieving: 1st place in RoboCup 2008, 3rd place in 2009 and 1st place in the mandatory technical challenge in RoboCup 2009, where the robots have to play with an arbitrary standard FIFA ball.


title: Cytoscape App Store

abstract: Summary: Cytoscape is an open source software tool for biological network visualization and analysis, which can be extended with independently developed apps. We launched the Cytoscape App Store to highlight the important features that apps add to Cytoscape, enable researchers to find and install apps they need and help developers promote their apps. ::: ::: Availability: The App Store is available at http://apps.cytoscape.org. ::: ::: Contact: apico@gladstone.ucsf.edu


title: An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method

abstract: In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain–computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 ± 9.6 bit min−1. The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.


title: The Limits of Utilitarianism

abstract: The Limits of Utilitarianism was first published in 1982. Minnesota Archive Editions uses digital technology to make long-unavailable books once again accessible, and are published unaltered from the original University of Minnesota Press editions.Many philosophers have argued that utilitarianism is an unacceptable moral theory and that promoting the general welfare is at best only one of the legitimate goals of public policy. Utilitarian principles seem to place no limits on the extent to which society may legitimately interfere with a person's liberties - provided that such actions can be shown to promote the long-term welfare of its members. These issues have played a central role in discussions of utilitarianism since the time of Bentham and Mill. Despite criticisms, utilitarianism remains the most influential and widely accepted moral theory of recent times.In this volume contemporary philosophers address four aspects of utilitarianism: the principle of utility; utilitarianism vis-a-vis contractarianism; welfare; and voluntary cooperation and helping others. The editors provide an introduction and a comprehensive bibliography that covers all books and articles published in utilitarianism since 1930.


title: Strontium and barium ions and guinea-pig ureter.

abstract: Abstract 1. 1. In guinea-pig ureteral smooth muscle, Sr 2+ substitution for Ca 2+ increased the duration and the number of spikes of an action potential and the duration of phasic contraction. 2. 2. The magnitudep of tension development was decreased in phasic contraction as well as in potassium-induced tonic contraction. Sr 2+ activity could be inhibited by Mn 2+ . 3. 3. Ba 2+ substitution was incapable of maintaining ureteral excitability, although contractile activity was preserved.


title: Data Mining: Web Data Mining Techniques, Tools and Algorithms: An Overview

abstract: Web data mining became an easy and important platform for retrieval of useful information. Users prefer World Wide Web more to upload and download data. As increasing growth of data over the internet, it is getting difficult and time consuming for discovering informative knowledge and patterns. Digging knowledgeable and user queried information from unstructured and inconsistent data over the web is not an easy task to perform. Different mining techniques are used to fetch relevant information from web (hyperlinks, contents, web usage logs). Web data mining is a sub discipline of data mining which mainly deals with web. Web data mining is divided into three different types: web structure, web content and web usage mining. All these types use different techniques, tools, approaches, algorithms for discover information from huge bulks of data over the web.


title: RT-Xen: towards real-time hypervisor scheduling in xen

abstract: As system integration becomes an increasingly important challenge for complex real-time systems, there has been a significant demand for supporting real-time systems in virtualized environments. This paper presents RT-Xen, the first real-time hypervisor scheduling framework for Xen, the most widely used open-source virtual machine monitor (VMM). RT-Xen bridges the gap between real-time scheduling theory and Xen, whose wide-spread adoption makes it an attractive platform for integrating a broad range of real-time and embedded systems. Moreover, RT-Xen provides an open-source platform for researchers and integrators to develop and evaluate real-time scheduling techniques, which to date have been studied predominantly via analysis and simulations. Extensive experimental results demonstrate the feasibility, efficiency, and efficacy of fixed-priority hierarchical real-time scheduling in RT-Xen. RT-Xen instantiates a suite of fixed-priority servers (Deferrable Server, Periodic Server, Polling Server, and Sporadic Server). While the server algorithms are not new, this empirical study represents the first comprehensive experimental comparison of these algorithms within the same virtualization platform. Our empirical evaluation shows that RT-Xen can provide effective real-time scheduling to guest Linux operating systems at a 1ms quantum, while incurring only moderate overhead for all the fixed-priority server algorithms. While more complex algorithms such as Sporadic Server do incur higher overhead, none of the overhead differences among different server algorithms are significant. Deferrable Server generally delivers better soft real-time performance than the other server algorithms, while Periodic Server incurs high deadline miss ratios in overloaded situations.


title: It's Different: Insights into home energy consumption in India

abstract: Residential buildings contribute significantly to the overall energy usage across the world. Real deployments, and collected data thereof, play a critical role in providing insights into home energy consumption and occupant behavior. Existing datasets from real residential deployments are all from the developed countries. Developing countries, such as India, present unique opportunities to evaluate the scalability of existing research in diverse settings. Building upon more than a year of experience in sensor network deployments, we undertake an extensive deployment in a three storey home in Delhi, spanning 73 days from May-August 2013, measuring electrical, water and ambient parameters. We used 33 sensors across the home, measuring these parameters, collecting a total of approx. 400 MB of data daily. We discuss the architectural implications on the deployment systems that can be used for monitoring and control in the context of developing countries. Addressing the unreliability of electrical grid and internet in such settings, we present Sense Local-store Upload architecture for robust data collection. While providing several unique aspects, our deployment further validates the common considerations from similar residential deployments, discussed previously in the literature. We also release our collected data- Indian data for Ambient Water and Electricity Sensing (iAWE), for public use.


title: Fostering Social Cognition through an Imitation- and Synchronization-Based Dance/Movement Intervention in Adults with Autism Spectrum Disorder: A Controlled Proof-of-Concept Study

abstract: Background: Since social cognition is impaired in individuals with autism spectrum disorder (ASD), this study aimed at establishing the efficacy of a newly developed imitation- and synchronization-based dance/movement intervention (SI-DMI) in fostering emotion inference and empathic feelings (emotional reaction to feelings of others) in adults with high-functioning ASD. Methods: Fifty-five adults with ASD (IQ ≥85) who were blinded to the aim of the study were assigned to receive either 10 weeks of a dance/movement intervention focusing on interpersonal movement imitation and synchronization (SI-DMI, n = 27) or a control movement intervention (CMI, n = 24) focusing on individual motor coordination (2 participants from each group declined before baseline testing). The primary outcome measure was the objective Multifaceted Empathy Test targeting emotion inference and empathic feelings. Secondary outcomes were scores on the self-rated Interpersonal Reactivity Index. The well-established automatic imitation task and synchronization finger-tapping task were used to quantify effects on imitation and synchronization functions, complemented by the more naturalistic Assessment of Spontaneous Interaction in Movement. Results: Intention-to-treat analyses revealed that from baseline to 3 months, patients treated with SI-DMI showed a significantly larger improvement in emotion inference (d = 0.58), but not empathic feelings, than those treated with CMI (d = -0.04). On the close generalization level, SI-DMI increased synchronization skills and imitation tendencies, as well as whole-body imitation/synchronization and movement reciprocity/dialogue, compared to CMI. Conclusions: SI-DMI can be successful in promoting emotion inference in adults with ASD and warrants further investigation.


title: A modified microstrip line for excitation of wide-band dielectric resonator antennas

abstract: A novel approach for effectively feeding a wide-band dielectric resonator antenna (DRA) of very low-permittivity is presented in this paper. It is shown that a tapered microstrip line with optimized length and width can provide excellent impedance matching. The impedance bandwidth of the antenna (S 11 < −10 dB) associated with single resonance mode is over 35%. A minimum reflection coefficient of −60 dB is observed in the simulations for a dielectric resonator with dielectric constant of e r = 5. The broadside radiation pattern of the antenna is stable over the impedance bandwidth. Low cross polarization levels (< −20 dB) across the frequency range 21–30 GHz are noticeable.


title: Secure Data Storage in Mobile Cloud Computing

abstract: In cloud computing highly scalable computing resources are supplied as an outer service through internet on pay-as-usability basis. Portio research [1] estimates that mobile subscribers will reach 6.5 billion by the end of 2012, 6.9 billion by the end of 2013. Due to increasing use of mobile devices the requirement of cloud computing in mobile devices arise, which gave birth to Mobile Cloud Computing (MCC). Mobile Cloud Computing refers to an infrastructure where data processing and storage can happen away from mobile device. Mobile devices do not need to have large storage capacity and powerful CPU speed. Due to storing data on cloud there is an issue of data security. Because of the risk associated with data storage many IT professionals are not showing their interest towards Mobile Cloud Computing. This paper explores: (i) The concept of Mobile Cloud Computing and issues associated in it (ii) Security of data stored in cloud with various mechanisms (iii) Proposed a possible solution to provide confidentiality, access control as well as integrity of data.


title: Evaluating the impact of Undetected Disk Errors in RAID systems

abstract: Despite the reliability of modern disks, recent studies have made it clear that a new class of faults, UndetectedDisk Errors (UDEs) also known as silent data corruption events, become a real challenge as storage capacity scales. While RAID systems have proven effective in protecting data from traditional disk failures, silent data corruption events remain a significant problem unaddressed by RAID. We present a fault model for UDEs, and a hybrid framework for simulating UDEs in large-scale systems. The framework combines a multi-resolution discrete event simulator with numerical solvers. Our implementation enables us to model arbitrary storage systems and workloads and estimate the rate of undetected data corruptions. We present results for several systems and workloads, from gigascale to petascale. These results indicate that corruption from UDEs is a significant problem in the absence of protection schemes and that such schemes dramatically decrease the rate of undetected data corruption.


title: Lethal head entrapment--a problem characteristic of early childhood.

abstract: Accidental deaths in infancy and early childhood often result from young childrens' lack of understanding of the dangers of certain situations and their physical inability to extricate themselves from potentially lethal circumstances. Two cases are reported to demonstrate an age-related susceptibility in the young to lethal head entrapment. Case 1: a 5-month-old girl smothered when she slipped down in her stroller, trapping her head beneath the frame and forcing her face into the soft material of the base. Case 2: a 14-month-old boy was hanged while exploring a filing cabinet when his head became caught between two lower drawers. Additional mental and physical characteristics that predispose young children and infants to lethal head entrapment include an inability to effectively problem solve once confronted with a hazardous situation, and relatively large heads and weak neck musculature. Because of these features lethal head entrapment represents a particular circumstance that may predispose to accidental asphyxial deaths in the very young. A combination of careful death scene and autopsy evaluations will be required to confirm the alleged circumstances of death in these cases, including mortuary re-enactments and assessment of the deceased infant's level of physical maturity and mobility.


Measuring ERP Success : Integrated Model of User Satisfaction and Technology Acceptance ; An Empirical Study in Japan


title: Multi-factor Authentication as a Service

abstract: An architecture for providing multi-factor authentication as a service is proposed, resting on the principle of a loose coupling and separation of duties between network entities and end user devices. The multi-factor authentication architecture leverages Identity Federation and Single-Sign-On technologies, such as the OpenID framework, in order to provide for a modular integration of various factors of authentication. The architecture is robust and scalable enabling service providers to define risk-based authentication policies by way of assurance level requirements, which map to concrete authentication factor capabilities on user devices.


title: LQR control for stabilizing triple link inverted pendulum system

abstract: This paper presents the model of car triple inverted pendulum system using Lagrange equation in detail. This model is then linearized to design an LQR controller to maintain the triple inverted pendulum on a cart around its unstable equilibrium position using single control input. The controllability of the triple inverted pendulum system is investigated and the choice of weights in LQR is also discussed. The system is simulated in MATLAB environment and the simulation results establish the satisfactory performance of the LQR controller in stabilizing the system.


title: Performance evaluation of correlative interferometry for angle of arrival estimation

abstract: The paper proposes the implementation of correlative interferometry for angle of arrival (AOA) estimation. The correlative interferometry processing involves the comparison of the measured phase differences between the antenna elements of the direction finder (DF) antenna system with those obtained for the same antenna system at all possible directions of incidence. The comparison is made by calculating the correlation of the two data sets obtained by multiplying the coordinates element by element and summing the result. Using different comparison data sets for different wave directions, the bearing is estimated from the data set for which the correlation is at a maximum. Four and eight uniform linear array (ULA) configuration of the algorithm is investigated so that that the amount of processing and memory was optimized with accuracy in the measurement of the direction. From the optimum structure, Monte Carlo simulation is carried out to evaluate the mean and variance of multiple AOA estimates for various signal-to-noise ratios (SNR). Results obtained shows that eight element ULA performs better than the four element ULA due to its higher resolution, with earlier having an average AOA estimate of 10dB and the latter of 12dB. This paper caters for the high processing burden associated with direction finding, provides a significant relationship between the number of ULA elements, resolution and effect of noise in AOA estimation for direction finding. This paper presents a critical evaluation on the superiority of correlative interferometry for AOA estimation.


title: Is Your Profile Picture Worth 1000 Words? Photo Characteristics Associated with Personality Impression Agreement

abstract: Social-Networking Websites (SNWs) are rapidly becoming a central media for social exchange. A basic question is how well are people able to get to know each other through these websites? In this study, we explore characteristics of the profile photographs and their association with impression agreement. Using a specially designed social networking website, we examined 1,316 first-impressions of profile owners who had posted photographs as part of a complete profile. The results suggest that photographs in which the profile owners were smiling, outdoors, and shown with others were associated with higher impression agreement. Several gender interactions suggested that other aspects of the photographs, including head covering and apparent weight, also affected impression agreement depending on the gender of the profile owner and visitors. These results were interpreted in light of the literature on interpersonal perception.


title: Applications of psychological science for actionable analytics

abstract: According to psychological scientists, humans understand models that most match their own internal models, which they characterize as lists of "heuristic"s (i.e. lists of very succinct rules). One such heuristic rule generator is the Fast-and-Frugal Trees (FFT) preferred by psychological scientists. Despite their successful use in many applied domains, FFTs have not been applied in software analytics. Accordingly, this paper assesses FFTs for software analytics. We find that FFTs are remarkably effective in that their models are very succinct (5 lines or less describing a binary decision tree) while also outperforming result from very recent, top-level, conference papers. Also, when we restrict training data to operational attributes (i.e., those attributes that are frequently changed by developers), the performance of FFTs are not effected (while the performance of other learners can vary wildly). Our conclusions are two-fold. Firstly, there is much that software analytics community could learn from psychological science. Secondly, proponents of complex methods should always baseline those methods against simpler alternatives. For example, FFTs could be used as a standard baseline learner against which other software analytics tools are compared.


title: Policy Search in Continuous Action Domains: an Overview

abstract: Abstract Continuous action policy search is currently the focus of intensive research, driven both by the recent success of deep reinforcement learning algorithms and the emergence of competitors based on evolutionary algorithms. In this paper, we present a broad survey of policy search methods, providing a unified perspective on very different approaches, including also Bayesian Optimization and directed exploration methods. The main message of this overview is in the relationship between the families of methods, but we also outline some factors underlying sample efficiency properties of the various approaches.


title: Identifying bug signatures using discriminative graph mining

abstract: Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single statement or function call which is very likely to contain bugs. Although such methods could be very accurate, it is usually very hard for developers to understand the context of the bug, given each bug location in isolation. In this study, we propose to model software executions with graphs at two levels of granularity: methods and basic blocks. An individual node represents a method or basic block and an edge represents a method call, method return or transition (at the method or basic block granularity). Given a set of graphs of correct and faulty executions, we propose to extract the most discriminative subgraphs which contrast the program flow of correct and faulty executions. The extracted subgraphs not only pinpoint the bug, but also provide an informative context for understanding and fixing the bug. Different from traditional graph mining which mines a very large set of frequent subgraphs, we formulate subgraph mining as an optimization problem and directly generate the most discriminative subgraph with a recently proposed graph mining algorithm LEAP. We further extend it to generate a ranked list of top-k discriminative subgraphs representing distinct locations which may contain bugs. Experimental results and case studies show that our proposed method is both effective and efficient to mine discriminative subgraphs for bug localization and context identification.


title: Zeta converter for power factor correction and voltage regulation

abstract: This paper proposes a power factor correction using an intermediate zeta converter. Average current mode control technique is used in the closed loop, which will force the input source current to be almost in phase with the supply voltage. Further, this control loop also ensures that tight regulation of load voltage against line and load disturbances. Input current is almost smooth and exhibits low ripple current thus results in lower EMI. To verify the proposed control principle a zeta converter is designed to handle a power 250 W and the total system is simulated in PSIM environment. The power factor obtained through simulation is in compliance with the standards.


title: Hamming Distance Metric Learning

abstract: Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity. Binary codes are well suited to large-scale applications as they are storage efficient and permit exact sub-linear kNN search. The framework is applicable to broad families of mappings, and uses a flexible form of triplet ranking loss. We overcome discontinuous optimization of the discrete mappings by minimizing a piecewise-smooth upper bound on empirical loss, inspired by latent structural SVMs. We develop a new loss-augmented inference algorithm that is quadratic in the code length. We show strong retrieval performance on CIFAR-10 and MNIST, with promising classification results using no more than kNN on the binary codes.


title: Sentiment Analysis in Social Media Texts

abstract: This paper presents a method for sentiment analysis specifically designed to work with Twitter data (tweets), taking into account their structure, length and specific language. The approach employed makes it easily extendible to other languages and makes it able to process tweets in near real time. The main contributions of this work are: a) the pre-processing of tweets to normalize the language and generalize the vocabulary employed to express sentiment; b) the use minimal linguistic processing, which makes the approach easily portable to other languages; c) the inclusion of higher order n-grams to spot modifications in the polarity of the sentiment expressed; d) the use of simple heuristics to select features to be employed; e) the application of supervised learning using a simple Support Vector Machines linear classifier on a set of realistic data. We show that using the training models generated with the method described we can improve the sentiment classification performance, irrespective of the domain and distribution of the test sets.


title: Multi-sensor fusion method using kalman filter to improve localization accuracy based on android smart phone

abstract: Android smart phone can be used in ITS (Intelligent Transportation Systems) to obtain people and vehicle’s location since it is integrated with GPS, direction sensor and acceleration sensor. Because the GPS built in smart phone always has an error of dozens of meters, improving the positioning accuracy is necessary before introducing it into ITS. This paper proposed an approach to improve the accuracy to the street level and get a smooth trajectory without jump points. It is convenient for everyone to use it because almost everyone has a smart phone. Firstly, road-matching algorithm is used to improve the localization accuracy to street level; secondly, speed and direction information are introduced to better reflect the real trajectory; thirdly, Kalman filter is used to eliminate the jump points and make the trajectory smooth; finally, the optimal result obtained from the process of Kalman filter is interpolated to reflect more details. The experiment result shows that the approach is effective.


title: Transient Coupling Characteristics between Core and Outer Semiconducting Layer of XLPE Cable Windings

abstract: In this paper,the transient voltage distributions of core and outer semiconduting layer are measured,a circuit model of a circle of cable is described.The computed result perfectly agrees with measured one,which indicates that the capacity coupling exists between core and outer semiconducting layer under impulse voltage.A theoretical basis is provided for the design, protection and experimentation for the new type of electric equipment.


title: A Network Coding and DES Based Dynamic Encryption Scheme for Moving Target Defense

abstract: Unlike prior efforts in cybersecurity research, a dynamic defense theory, called moving target defense, increases the complexity and costs for attacks by effectively restricting the vulnerability exposure and the attack opportunities through various continually-changing evaluation, development mechanisms and strategy. Data encryption standard (DES) was the classical scheme of the traditional symmetric-key encryption schemes. Now it has been gradually replaced by the triple DES or advanced encryption standard (AES) so that the encoder has a larger key space. However, both the triple DES and AES cannot meet the dynamic security requirements of dynamic defense due to their static extension to the key space. In this paper, we propose a dynamic three-layer encryption scheme based on DES and network coding, with a low-complexity partial key update mechanism. Based on the theoretical analysis, the new scheme is shown to have the benefit to achieve a dynamic transition between efficiency and security, which increases its adaptability to various cyber conditions. The simulation results also show that the running ratio of the new scheme is relatively lower than or comparable to the triple DES.


title: Agricultural sustainability and intensive production practices

abstract: A doubling in global food demand projected for the next 50 years poses huge challenges for the sustainability both of food production and of terrestrial and aquatic ecosystems and the services they provide to society. Agriculturalists are the principal managers of global usable lands and will shape, perhaps irreversibly, the surface of the Earth in the coming decades. New incentives and policies for ensuring the sustainability of agriculture and ecosystem services will be crucial if we are to meet the demands of improving yields without compromising environmental integrity or public health.


title: Explaining and Harnessing Adversarial Examples

abstract: Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.


title: Development of the organizational agility maturity model

abstract: The importance of organizational agility in a competitive environment is nowadays widely recognized and accepted. However, despite this awareness, the availability of tools and methods that support an organization in assessing and improving their organizational agility is scarce. Therefore, this study introduces the Organizational Agility Maturity Model in order to provide an easy-to-use yet powerful assessment tool for organizations in the software and IT service industry. Based on a design science research approach with a comprehensive literature review and an empirical investigation utilizing factor analysis, both scientific rigor as well as practical relevance is ensured. The applicability is further demonstrated by a cluster analysis identifying patterns of organizational agility that fit to the maturity model. The Organizational Agility Maturity Model further contributes to the field by providing a theoretically and empirically grounded structure of organizational agility supporting the efforts of developing a common understanding of the concept.


Variational Bayesian Inference for Big Data Marketing Models 1