title: A PEM-AFROW based algorithm for acoustic feedback control in automotive speech reinforcement systems

abstract: Developing performing speech reinforcement systems to improve the intra-cabin communication quality among car passengers in different row seats, typically degraded by the distance between speakers (for instance in SUV and minivan) and the noise presence within the cockpit, has represented a challenging issue within the related scientific community. One of the main problem to solve in this scenario is the reduction of the electroacoustic coupling between louds and mics in order to avoid the system reaching instability (howling), namely acoustic feedback cancellation (AFC). One of the most performing technique for AFC is the PEM-AFROW approach, recently appeared in the literature. In this work, we propose an innovative feedback suppressor scheme based on the PEM-AFROW concept, which allows to achieve a valuable balance of feedback reduction, maximum stable gain values and overall sound quality. Results obtained by computer simulations in the dual channel communication scenario confirm the effectiveness of the idea.


title: Full-bridge dc-dc converter with planar transformer and center-tap rectifier for fuel cell powered uninterruptible power supply

abstract: This paper shows the design of a galvanically isolated phase-shifted dc-dc converter. The converter, rated at a power of 1kW and an input current of 50 A, is designed to connect a fuel cell to a 48V dc bus of a UPS system. Special attention is given to the design of the planar transformer, which consists of several stacked PCBs. The copper losses, including skin and proximity effects, as well as the core losses are analyzed for the design process of the transformer. A construction method to reach high interleaving of the primary and secondary windings is developed and efficiency measurements of the system are performed and analyzed. A snubber circuit is used to limit the overvoltage on the rectifier. The effects of the snubber circuit on the current waveforms and the switching behavior of the input H bridge are analyzed. Furthermore, a control strategy is shown that reduces the effect of a fluctuating dc-bus voltage on the transferred power.


title: Sociodemographic, clinical, and psychiatric characteristics of transsexuals from Spain.

abstract: The aim of this study was to examine the characteristics of transsexuals from Spain. A total of 252 consecutive applicants for sex reassignment were evaluated using a standardized semistructured clinical interview and the Mini International Neuropsychiatric Interview (Spanish Version 5.0.0) to record demographic, clinical, and psychiatric data. Transsexualism was diagnosed in 230 patients, with a male to female (MF)/female to male (FM) ratio of 2.2:1. Transsexual patients frequently had low employment status, lived with their parents, and mainly had a sexual orientation toward same-sex partners. The most frequent psychiatric diagnoses were adjustment disorder and social phobia in both groups, and alcohol and substance-related disorders in the MF group. MF transsexuals were older than FM transexuals when requesting sex reassignment, but did not differ in age when starting hormonal therapy (often on their own); fewer MFs were in employment requiring high educational qualification, more were non-Spanish natives, and more had previous and current histories of alcohol and substance abuse or dependence. The basic characteristics of transsexuals from Spain were similar to those of other European countries, except for the higher proportion of patients living with their parents and the higher proportion of MFs who reported same-sex sexual orientation compared with previous studies.


title: Behavioral/Systems/Cognitive EEG-Informed fMRI Reveals Spatiotemporal Characteristics of Perceptual Decision Making

abstract: Single-unit and multiunit recordings in primates have already established that decision making involves at least two general stages of neural processing: representation of evidence from early sensory areas and accumulation of evidence to a decision threshold from decision-related regions. However, the relay of information from early sensory to decision areas, such that the accumulation process is instigated, is not well understood. Using a cued paradigm and single-trial analysis of electroencephalography (EEG), we previously reported on temporally specific components related to perceptual decision making. Here, we use information derived from our previous EEG recordings to inform the analysis of fMRI data collected for the same behavioral task to ascertain the cortical origins of each of these EEG components. We demonstrate that a cascade of events associated with perceptual decision making takes place in a highly distributed neural network. Of particular importance is an activation in the lateral occipital complex implicating perceptual persistence as a mechanism by which object decision making in the human brain is instigated.


title: Soft robotics: Technologies and systems pushing the boundaries of robot abilities

abstract: The proliferation of soft robotics research worldwide has brought substantial achievements in terms of principles, models, technologies, techniques, and prototypes of soft robots. Such achievements are reviewed here in terms of the abilities that they provide robots that were not possible before. An analysis of the evolution of this field shows how, after a few pioneering works in the years 2009 to 2012, breakthrough results were obtained by taking seminal technological and scientific challenges related to soft robotics from actuation and sensing to modeling and control. Further progress in soft robotics research has produced achievements that are important in terms of robot abilities—that is, from the viewpoint of what robots can do today thanks to the soft robotics approach. Abilities such as squeezing, stretching, climbing, growing, and morphing would not be possible with an approach based only on rigid links. The challenge ahead for soft robotics is to further develop the abilities for robots to grow, evolve, self-heal, develop, and biodegrade, which are the ways that robots can adapt their morphology to the environment.


title: Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

abstract: Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity. In this paper, we develop a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm. In the potential outcomes framework with unconfoundedness, we show that causal forests are pointwise consistent for the true treatment effect, and have an asymptotically Gaussian and centered sampling distribution. We also discuss a practical method for constructing asymptotic confidence intervals for the true treatment effect that are centered at the causal forest estimates. Our theoretical results rely on a generic Gaussian theory for a large family of random forest algorithms. To our knowledge, this is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference. In experiments, we find causal forests to be substantially more powerful than classical methods based on nearest-neighbor matching, especially in the presence of irrelevant covariates.


title: Creating robust high-throughput traffic sign detectors using centre-surround HOG statistics

abstract: In this paper, we detail a system for creating object detectors which meet the extreme demands of real-world traffic sign detection applications such as GPS map making and real-time in-car traffic sign detection. The resulting detectors are designed to detect and locate multiple traffic sign types in high-definition video (high throughput) from several cameras captured along thousands of kilometers of road with minimal false-positives and detection rates in excess of 99%. This allows for the accurate detection and location of traffic signs in geo-tagged video datasets of entire national road networks in reasonable time using only moderate computing infrastructure. A key to the success of the methods described in this paper is the use of extremely efficient classifier features. In this paper, we identify two obstacles to achieving the desired performance for all target traffic sign types, feature memory bandwidth requirements and feature discriminance. We introduce our use of centre-surround histogram of oriented gradient (HOG) statistics which greatly reduce the per-feature memory bandwidth requirements. Subsequently we extend our use of centre-surround HOG statistics to the color domain, raising the discriminant power of the final classifiers for more challenging sign types.


title: Fast Face-Swap Using Convolutional Neural Networks

abstract: We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression and lighting. To perform this mapping, we use convolutional neural networks trained to capture the appearance of the target identity from an unstructured collection of his/her photographs. This approach is enabled by framing the face swapping problem in terms of style transfer, where the goal is to render an image in the style of another one. Building on recent advances in this area, we devise a new loss function that enables the network to produce highly photorealistic results. By combining neural networks with simple pre- and post-processing steps, we aim at making face swap work in real-time with no input from the user.


title: Congestion control mechanism for Internet-of-Things (IOT) paradigm

abstract: Wireless Sensor Network (WSN) is one of the Internet-of-Things (IoT) paradigms which provide monitoring services for natural disasters such as volcanoes eruption and earthquake which can affect the life of human being. As such, the Quality-of-Service (QoS) of the underlying applications is of important issue in order to ensure its effectiveness and robustness. Besides its undoubtedly services and contributions in monitoring systems, WSN's limited resources can severely degrade the QoS in IoT applications. Congestion in WSN will further reduce the expected QoS of the associated applications. In this case, efficient used of the scarce resources is important to ensure seamless data transmission. The power consumption of a sensor node can be reduced by reducing the rate of packet retransmission which is caused by congestion. In this paper, a selective packet discarding method which is known as Packet Discarding based Node Clustering (PDNC) is introduced. All nodes deployed in the targeted area will be clustered into several groups and a cluster head will be selected at a time. The packet discarding process will then be carried at each node to reduce the number of packets contributing to congestion. Simulation analysis using NS-2 shows that the proposed technique is able to reduce congestion thus improve the overall performance.


title: Design of Low-Voltage Integrated Step-up Oscillators with Microtransformers for Energy Harvesting Applications

abstract: This paper describes the modeling of startup circuits in battery-less micropower energy harvesting systems and investigates the use of bond wire micromagnetics. The analysis focuses on step-up Meissner oscillators based on magnetic core transformers operating with input voltages down to $\approx$ 100 mV, e.g., from thermoelectric generators. As a key point, this paper examines the effect of core losses and leakage inductances on the startup requirements obtained with the classical Barkhausen criterion, and demonstrates the minimum transconductance for oscillations to occur. For validation purposes, a step-up oscillator IC is fabricated in a STMicroelectronics 0.32 $\mu{\rm m}$ technology, and connected to two bond wire microtransformers, respectively, with a 1:38 MnZn ferrite core and with a 1:52 ferromagnetic low-temperature co-fired ceramic (LTCC) core. Coherently with the proposed model, experimental measurements show a minimum startup voltage of 228 mV for the MnZn ferrite core and of 104 mV for the LTCC core.


title: Event-related brain potentials distinguish processing stages involved in face perception and recognition

abstract: Objectives: An event-related brain potential (ERP) study investigated how different processing stages involved in face identification are reflected by ERP modulations, and how stimulus repetitions and attentional set influence such effects. Methods: ERPs were recorded in response to photographs of familiar faces, unfamiliar faces, and houses. In Part I, participants had to detect infrequently presented targets (hands), in Part II, attention was either directed towards or away from the pictorial stimuli. Results: The face-specific N170 component elicited maximally at lateral temporal electrodes was not affected by face familiarity. When compared with unfamiliar faces, familiar faces elicited an enhanced negativity between 300 and 500 ms (‘N400f’) which was followed by an enhanced positivity beyond 500 ms post-stimulus (‘P600f’). In contrast to the ‘classical’ N400, these effects were parietocentrally distributed. They were attenuated, but still reliable, for repeated presentations of familiar faces. When attention was directed to another demanding task, no ‘N400f’ was elicited, but the ‘P600f’ effect remained to be present. Conclusions: While the N170 reflects the pre-categorical structural encoding of faces, the ‘N400f’ and ‘P600f’ are likely to indicate subsequent processes involved in face recognition. Impaired structural encoding can result in the disruption of face identification. This is illustrated by a neuropsychological case study, demonstrating the absence of the N170 and later ERP indicators of face recognition in a prosopagnosic patient. q 2000 Elsevier Science Ireland Ltd. All rights reserved.


title: Light load efficiency enhancement of a LLC resonant converter

abstract: Zero Voltage Switching (ZVS) technique minimizes the turn-on loss of the MOSFETs in LLC resonant converter structure. However, as switching frequency increases, the converter efficiency will suffer due to the increased turn-off loss and gate charge loss, and these effects are especially problematic at light load conditions, where the switching frequency has to be further increased in order to regulate the output. A new hysteretic control scheme is proposed in this paper. By fixing the switching frequency equal to the resonant frequency all the time and skipping pulses proportional to the load condition, the switching losses are reduced and the light load efficiency is improved. The experimental results prove the functionality of the proposed hysteretic control scheme.


title: Community structure in networks: Girvan-Newman algorithm improvement

abstract: Real world networks often have community structure. It is characteristic that the groups of nodes are connected denser within themselves and rarely with each other. The Girvan-Newman method for the detection and analysis of community structure is based on the iterative elimination of edges with the highest number of the shortest paths that go through them. By eliminating edges the network breaks down into smaller networks, i.e. communities. This paper introduces improved Girvan-Newman method where multi-edge removal is allowed, and presents the results of the application of both methods to the existing real social network (Zachary karate club), the computergenerated network and the tumor genes and their mutations network. The improved algorithm in practice reduces the number of operations, but retains the same computational complexity, so it cannot be applied to networks with a very large number of nodes. The most important feature of our improvement is that the result is graph-theoretical invariant, while original algorithm depends on the vertex labeling.


title: Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation

abstract: Recent work in computer vision has yielded impressive results in automatically describing images with natural language. Most of these systems generate captions in a sin- gle language, requiring multiple language-specific models to build a multilingual captioning system. We propose a very simple technique to build a single unified model across languages, using artificial tokens to control the language, making the captioning system more compact. We evaluate our approach on generating English and Japanese captions, and show that a typical neural captioning architecture is capable of learning a single model that can switch between two different languages.


title: Radiating Elements for Shared Aperture Tx/Rx Phased Arrays at K/Ka Band

abstract: A dual-band, Tx/Rx, self-diplexing phased array is presented. The antenna has been designed to cover Tx/Rx satellite communications at K/Ka band with a frequency ratio 1.5:1. To obtain dual-band operations with a single radiating surface, a novel dual-band radiator is adopted and placed in a configuration in which dual-band and single-band elements are interleaved. The proposed configuration reduces the number of radiating elements required by other solutions while avoiding the insurgence of grating lobes. The tightly packed arrangement of the elements poses many integration issues, which are solved with a novel integration technique. The array elements are optimized to scan the beam in excess of 50° in both bands. A subarray with 49 Rx elements and 105 Tx elements was built and measured confirming the results obtained in simulations.


title: Customer Service on Social Media: The Effect of Customer Popularity and Sentiment on Airline Response

abstract: Many companies are now providing customer service through social media, helping and engaging their customers on a real-time basis. To study this increasingly popular practice, we examine how major airlines respond to customer comments on Twitter by exploiting a large data set containing all Twitter exchanges between customers and four major airlines from June 2013 to August 2014. We find that these airlines pay significantly more attention to Twitter users with more followers, suggesting that companies literarily discriminate customers based on their social influence. Moreover, our findings suggest that companies in the digital age are increasingly more sensitive to the need to answer both customer complaints and customer compliments.


How to debrief teamwork interactions: using circular questions to explore and change team interaction patterns


title: Relationship between Community Periodontal Index and Fecal Hemoglobin Concentration, an Indicator for Colorectal Neoplasm

abstract: Periodontal disease and colorectal cancer have inflammatory processes in common. It is therefore worthwhile to investigate whether there is an association between periodontal probing depth and fecal hemoglobin concentration (FHbC), an indicator of colorectal neoplasms, in 40- to 44-year-old Taiwanese. We enrolled a total of 6,214 attendees aged 40 to 44 yr who were participating in a community-based integrated screening program and who received both periodontal and FHbC examinations between 2003 and 2008. A proportional odds logistic regression model was used to estimate the odds ratios of different FHbC levels in treating an increased level of community periodontal index (CPI) measuring periodontal probing depth as ordinary data from 0 to 4. Periodontal probing depth with the order of CPI was in parallel with an increase in the mean values of FHbC: 21.3 ± 156.3, 26.0 ± 167.7, 27.2 ± 151.1, and 39.5 ± 255.7 ng/mL for CPI 0, CPI 1, CPI 2, and CPI 3/4, respectively. The log-FHbC varied across the categories of CPI (p = .0078). After adjusting for age, sex, education level, smoking, alcohol intake, exercise, body mass index, and intake of meat and vegetables, subjects with positive fecal immunochemical test results (FHbC ≥ 100 ng/mL) had a 33% higher risk of deteriorating to severe CPI than did those within the normal range of fecal immunochemical test (FHbC < 100 ng/mL) (adjusted odds ratio = 1.33, 95% confidence interval: 1.03-1.73). A positive association was demonstrated between FHbC and periodontal probing depth assessed by CPI among 6,214 Taiwanese aged 40 to 44 yr who participated in a community-based integrated health screening program. These results could have significant implications for early identification of high-risk individuals, as those with deep periodontal pockets should be advised to undergo screening for colorectal cancer at a younger age than commonly recommended.


title: Large-Scale Identification of Malicious Singleton Files

abstract: We study a dataset of billions of program binary files that appeared on 100 million computers over the course of 12 months, discovering that 94% of these files were present on a single machine. Though malware polymorphism is one cause for the large number of singleton files, additional factors also contribute to polymorphism, given that the ratio of benign to malicious singleton files is 80:1. The huge number of benign singletons makes it challenging to reliably identify the minority of malicious singletons. We present a large-scale study of the properties, characteristics, and distribution of benign and malicious singleton files. We leverage the insights from this study to build a classifier based purely on static features to identify 92% of the remaining malicious singletons at a 1.4% percent false positive rate, despite heavy use of obfuscation and packing techniques by most malicious singleton files that we make no attempt to de-obfuscate. Finally, we demonstrate robustness of our classifier to important classes of automated evasion attacks.


title: Integrating both Visual and Audio Cues for Enhanced Video Caption

abstract: Video caption refers to generating a descriptive sentence for a specific short video clip automatically, which has achieved remarkable success recently. However, most of the existing methods focus more on visual information while ignoring the synchronized audio cues. We propose three multimodal deep fusion strategies to maximize the benefits of visual-audio resonance information. The first one explores the impact on cross-modalities feature fusion from low to high order. The second establishes the visual-audio short-term dependency by sharing weights of corresponding front-end networks. The third extends the temporal dependency to long-term through sharing multimodal memory across visual and audio modalities. Extensive experiments have validated the effectiveness of our three cross-modalities fusion strategies on two benchmark datasets, including Microsoft Research Video to Text (MSRVTT) and Microsoft Video Description (MSVD). It is worth mentioning that sharing weight can coordinate visual-audio feature fusion effectively and achieve the state-of-art performance on both BELU and METEOR metrics. Furthermore, we first propose a dynamic multimodal feature fusion framework to deal with the part modalities missing case. Experimental results demonstrate that even in the audio absence mode, we can still obtain comparable results with the aid of the additional audio modality inference module.


title: Multipath mitigation in Time of Flight 3D sensor based on direct and global seperation technique

abstract: 3D Time-of-Flight sensing technology provides distant measurements from the camera to the scene in the field of view, for complete depth map of a scene. It works by illuminating the scene with a modulated light sources and measuring the phase change between illuminated and reflected light. This is translated to distance, for each pixel simultaneously. The sensor receives the radiance which is combination of light received along multiple paths due to global illumination. This global radiance causes multi-path interference. Separating these components to recover scene depths is challenging for corner shaped and coronel shaped scene as number of multiple path increases. It is observed that for different scenes, global radiance disappears with increase in frequencies beyond some threshold level. This observation is used to develop a novel technique to recover unambiguous depth map of a scene. It requires minimum two frequencies and 3 to 4 measurements which gives minimum computations.


title: Mobile Application Platform Heterogeneity: Android vs Windows Phone vs iOS vs Firefox OS

abstract: Modern smartphones have a rich spectrum of increasingly sophisticated features, opening opportunities for software-led innovation. Of the large number of platforms to develop new software on, in this paper we look closely at three platforms identified as market leaders for the smartphone market by Gartner Group in 2013 and one platform, Firefox OS, representing a new paradigm for operating systems based on web technologies. We compare the platforms in several different categories, such as software architecture, application development, platform capabilities and constraints, and, finally, developer support. Using the implementation of a mobile version of the tic-tac-toe game on all the four platforms, we seek to investigate strengths, weaknesses and challenges of mobile application development on these platforms. Big differences are highlighted when inspecting community environments, hardware abilities and platform maturity. These inevitably impact upon developer choices when deciding on mobile platform development strategies.


title: ELC resonator based narrow band metamaterial absorber

abstract: The aim of this paper is to construct an absorber using metamaterial structure which operates at narrow band. The proposed structure is simulated using Microwave studio by CST. Simulations show that S11 of our first structure is −15.20 dB and the absorbance is 97.74% in the resonant frequency at 5.962 GHz and for second structure absorption occurs at 5.296 GHz with absorbance of 99.859% and S11 of −28.243 dB. The frequency of the proposed structures where absorption occurs is reduced significantly as compared to existing structure, but can be more efficient in absorbance.


title: Wireless sensor networks for healthcare: A survey

abstract: Becoming mature enough to be used for improving the quality of life, wireless sensor network technologies are considered as one of the key research areas in computer science and healthcare application industries. The pervasive healthcare systems provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. This minimizes the need for caregivers and helps the chronically ill and elderly to survive an independent life, besides provides quality care for the babies and little children whose both parents have to work. Although having significant benefits, the area has still major challenges which are investigated in this paper. We provide several state of the art examples together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provide a comprehensive analysis of the benefits and challenges of these systems.


title: The impact of storage on processing: How is information maintained in working memory?

abstract: Working memory is typically defined as a system devoted to the simultaneous maintenance and processing of information. However, the interplay between these 2 functions is still a matter of debate in the literature, with views ranging from complete independence to complete dependence. The time-based resource-sharing model assumes that a central bottleneck constrains the 2 functions to alternate in such a way that maintenance activities postpone concurrent processing, with each additional piece of information to be maintained resulting in an additional postponement. Using different kinds of memoranda, we examined in a series of 7 experiments the effect of increasing memory load on different processing tasks. The results reveal that, insofar as attention is needed for maintenance, processing times linearly increase at a rate of about 50 ms per verbal or visuospatial memory item, suggesting a very fast refresh rate in working memory. Our results also show an asymmetry between verbal and spatial information, in that spatial information can solely rely on attention for its maintenance while verbal information can also rely on a domain-specific maintenance mechanism independent from attention. The implications for the functioning of working memory are discussed, with a specific focus on how information is maintained in working memory.


title: Optimal Demand Response Using Device-Based Reinforcement Learning

abstract: Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated Energy Management System (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMS's rescheduling problem as a reinforcement learning (RL) problem, and argue that this RL problem can be approximately solved by decomposing it over device clusters. Compared with existing formulations, our new formulation (1) does not require explicitly modeling the user's dissatisfaction on job rescheduling, (2) enables the EMS to self-initiate jobs, (3) allows the user to initiate more flexible requests and (4) has a computational complexity linear in the number of devices. We also demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.


title: A novel permanent magnet motor with doubly salient structure

abstract: A new type of doubly salient machine is presented in which the field excitation is provided by a nonrotating permanent magnet (PM). This doubly salient PM (DSPM) motor is shown to be kindred to square waveform PM brushless DC motors. Linear and nonlinear analysis has been performed to investigate the characteristics of this new type of PM motors. A prototype DSPM motor has been designed, and a comparison has been made between this new type of motor and the induction motor. It is shown that, by fully exploiting modern high-energy PM material and the doubly salient structure, the DSPM motor can offer superior performance over existing motors in terms of efficiency, torque density, torque-to-current ratio, and torque-to-inertia ratio, while retaining a simple structure amenable to automatic manufacture. >


title: A retrospective of knowledge graphs

abstract: Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for machines, and even for humans. Knowledge graphs have become prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and finally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner. ::: ::: In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph systems and discuss the future research directions.


title: Resisting relay attacks on vehicular Passive Keyless Entry and start systems

abstract: Passive Keyless Entry and Start (PKES) systems are popularly embed in modern cars, which allow users to open and start their cars while having their car keys' in their pockets. They bring convenience to users but are vulnerable to relay attacks. A relay attack to PKES is a widely known attack against the challenge- response technique used in the passive keyless vehicle system, which allows to open and start the car while the true distance between the key and car remained large. The main countermeasure against relay attacks is the use of distance bounding protocols measuring the round-trip time between the car and the key. However, most schemes tend to a more complex design to decrease adversary's success probability. In this paper, we propose a novel distance bounding protocol to resist relay attacks in PKES systems, using only 2n bits of memory, which, to our best knowledge, is equal to Hancke and Kuhn's protocol and less than any existing protocols. In addition, by using our protocol, the key is able to detect adversary's malicious queries. We also make a comparison with typical previous distance bounding protocols in both memory and mafia fraud success probability.


title: An Extensible Data Model with Security Support for Dataspace Management

abstract: Based on the iDM concept, this paper proposes an extensible data model with an 8-tuple representation, which can not only describe more complex data sources by extending the Group Component, but also contain rule-based security elements, which consist of Access Component and Authorization Component. Data model is detailed and two corresponding examples are analyzed to verify our approach.


title: Blocking Blog Spam with Language Model Disagreement

abstract: We present an approach for detecting link spam common in blog comments by comparing the language models used in the blog post, the comment, and pages linked by the comments. In contrast to other link spam filtering approaches, our method requires no training, no hard-coded rule sets, and no knowledge of complete-web connectivity. Preliminary experiments with identification of typical blog spam show promising results.


title: Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market

abstract: We analyze how network effects affect competition in the nascent cryptocurrency market. We do so by examining early dynamics of exchange rates among different cryptocurrencies. While Bitcoin essentially dominates this market, our data suggest no evidence of a winner-take-all effect early in the market. Indeed, for a relatively long period, a few other cryptocurrencies competing with Bitcoin (the early industry leader) appreciated much more quickly than Bitcoin. The data in this period are consistent with the use of cryptocurrencies as financial assets (popularized by Bitcoin), and not consistent with winner-take-all dynamics. Toward the end of our sample, however, things change dramatically. Bitcoin appreciates against the USD, while other currencies depreciate against the USD. The data in this period are consistent with strong network effects and winner-take-all dynamics. This trend continues at the time of writing.


title: A Semi-supervised Approach to Measuring User Privacy in Online Social Networks

abstract: During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.


title: AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

abstract: Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to capture the semantic subtlety, we propose the concept of aspects with each aspect being a unit representing one underlying semantic facet. Meanwhile, network embedding has emerged as a powerful method for learning network representation, where the learned embedding can be used as features in various downstream applications. Therefore, we are motivated to propose a novel embedding learning framework---AspEm---to preserve the semantic information in HINs based on multiple aspects. Instead of preserving information of the network in one semantic space, AspEm encapsulates information regarding each aspect individually. In order to select aspects for embedding purpose, we further devise a solution for AspEm based on dataset-wide statistics. To corroborate the efficacy of AspEm, we conducted experiments on two real-words datasets with two types of applications---classification and link prediction. Experiment results demonstrate that AspEm can outperform baseline network embedding learning methods by considering multiple aspects, where the aspects can be selected from the given HIN in an unsupervised manner.


title: The task-based approach: some questions and suggestions

abstract: This article first addresses the question of what tasks are. It suggests that rather than accept the common ‘communicative’ definition, we should return to a broader definition and then focus on key dimensions that distinguish (from the learner’s perspective) different types of task, notably degrees of task-involvement and degrees of focus on form or meaning. This approach helps us to conceptualize the complementary roles of form-focused and meaning-focused tasks in our methodology. It also shows the continuity between task-based language teaching and the broader communicative approach within which it is a development. Finally the article asks whether ‘task-based approach’ is really the most appropriate term at all for describing these developments in language pedagogy.


title: Dynamic feedback controller of Euler angles and wind parameters estimation for a quadrotor unmanned aerial vehicle

abstract: A nonlinear dynamic model for a quadrotor unmanned aerial vehicle is presented with a new vision of state parameter control which is based on Euler angles and open loop positions state observer. This method emphasizes on the control of roll, pitch and yaw angle rather than the translational motions of the UAV. For this reason the system has been presented into two cascade partial parts, the first one relates the rotational motion whose the control law is applied in a closed loop form and the other one reflects the translational motion. A dynamic feedback controller is developed to transform the closed loop part of the system into linear, controllable and decoupled subsystem. The wind parameters estimation of the quadrotor is used to avoid more sensors. Hence an estimator of resulting aerodynamic moments via Lyapunov function is developed. Performance and robustness of the proposed controller are tested in simulation.


title: Astrocytes: biology and pathology

abstract: Astrocytes are specialized glial cells that outnumber neurons by over fivefold. They contiguously tile the entire central nervous system (CNS) and exert many essential complex functions in the healthy CNS. Astrocytes respond to all forms of CNS insults through a process referred to as reactive astrogliosis, which has become a pathological hallmark of CNS structural lesions. Substantial progress has been made recently in determining functions and mechanisms of reactive astrogliosis and in identifying roles of astrocytes in CNS disorders and pathologies. A vast molecular arsenal at the disposal of reactive astrocytes is being defined. Transgenic mouse models are dissecting specific aspects of reactive astrocytosis and glial scar formation in vivo. Astrocyte involvement in specific clinicopathological entities is being defined. It is now clear that reactive astrogliosis is not a simple all-or-none phenomenon but is a finely gradated continuum of changes that occur in context-dependent manners regulated by specific signaling events. These changes range from reversible alterations in gene expression and cell hypertrophy with preservation of cellular domains and tissue structure, to long-lasting scar formation with rearrangement of tissue structure. Increasing evidence points towards the potential of reactive astrogliosis to play either primary or contributing roles in CNS disorders via loss of normal astrocyte functions or gain of abnormal effects. This article reviews (1) astrocyte functions in healthy CNS, (2) mechanisms and functions of reactive astrogliosis and glial scar formation, and (3) ways in which reactive astrocytes may cause or contribute to specific CNS disorders and lesions.


title: A Deep Learning-Based Customer Forecasting Tool

abstract: In this study, we developed a deep learning-based customer forecasting tool to improve the management quality of decision making. The proposed approach extracted the relationship between the number of customers and leading factors of huge amount of historical data. The conducted leading factors provide innovative approaches to adjust the manage settings regarding the labor arrangement strategies. In particular, the Point of Sale (POS) data was collected. Deep Learning were utilized to execute significant sales analysis and produce reliable predictions that would become administrative tools, especially with the building of a forecasting model based on customer behavior with weather as the prediction parameter. As a result of the information obtained by this research, recommendations concerning the pharmacy's administration were made, understanding the usefulness of basing the study on the customers. The deep learning-based customer forecasting tool can be used on different industries, which broadens the future work of this research.


title: Arduino based door unlocking system with real time control

abstract: The system proposed is a door unlocking system containing multiple doors any of which can be used to access a certain zone e.g. a laboratory or library. The system is implemented using a central server which contains a central database gathering all the information about the authorized personnel. The hardware components required are RFID reader, passive RFID tags, wireless transmitter & receiver (433 MHz) and an Arduino microcontroller. Software assistance of Arduino IDE and Processing Development Environment (PDE) are required for control. There is also provision for real-time monitoring of users' activities i.e. entry and exit. This is made possible by automatic synchronization of the system with a secured webpage via internet.


title: A direct method for measuring user experience in E-government portals (May 2016)

abstract: The transition from an informative to a service oriented interactive governmental portals has become a necessity due to the time and cost saving benefits for both governments and users. User experience is a key factor in maintaining these benefits. In this study we propose an E-government Portal Assessment Method (EGPAM), which is a direct method for measuring user experience in e-government portals. We present a case study assessing the portal of the Ministry of Public Works (MOW) in Kuwait. Results showed that having a direct measurement to user experience enabled easier identification of the current level of user satisfaction and provided a guidance on ways to improve user experience and addressing identified issues.


title: Exploiting Category-Specific Information for Multi-Document Summarization

abstract: We show that by making use of information common to document sets belonging to a common category, we can improve the quality of automatically extracted content in multi-document summaries. This simple property is widely applicable in multi-document summarization tasks, and can be encapsulated by the concept of category-specific importance (CSI). Our experiments show that CSI is a valuable metric to aid sentence selection in extractive summarization tasks. We operationalize the computation CSI of sentences through the introduction of two new features that can be computed without needing any external knowledge. We also generalize this approach, showing that when manually-curated document-to-category mappings are unavailable, performing automatic categorization of document sets also improves summarization performance. We have incorporated these features into a simple, freely available, open-source extractive summarization system, called SWING. In the recent TAC-2011 guided summarization task, SWING outperformed all other participant summarization systems as measured by automated ROUGE measures.


title: On-line rigid object recognition and pose estimation based on inertial parameters

abstract: This paper proposes an object recognition and gripping pose estimation approach based on on-line estimation of the complete set of inertial parameters, i.e. the mass, the coordinates of the center of mass, and the elements of the inertia matrix, of an object gripped by or attached to a manipulator. A multi-sensor fusion approach combining 6D force/torque, 6D acceleration, 3D angular velocity, and joint angle data to estimate these parameters is presented. In order to facilitate practical implementation, approaches to handling force/torque sensor offsets and to compensating the forces/torques caused by the distal mounting plate of the force/torque sensor and the gripper are incorporated. Regarding the joint angle signals, preprocessing steps to derive the angular velocity, linear acceleration and angular acceleration vector w.r.t. the sensor frame are addressed. The estimation of the complete set of inertial parameters employing the recursive instrumental variables (RIV) method is discussed. The extraction of features that are invariant w.r.t. translation and rotation, i.e. the mass and the principal moments of inertia, as well as a recognition approach based on the Kullback-Leibler divergence are presented. Experimental results show very low errors in the estimates of the inertial parameters, good pose estimation accuracy, and the viability of the recognition approach.


title: Optimized Product Quantization for Approximate Nearest Neighbor Search

abstract: Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search. The essence of product quantization is to decompose the original high-dimensional space into the Cartesian product of a finite number of low-dimensional subspaces that are then quantized separately. Optimal space decomposition is important for the performance of ANN search, but still remains unaddressed. In this paper, we optimize product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks. We present two novel methods for optimization: a non-parametric method that alternatively solves two smaller sub-problems, and a parametric method that is guaranteed to achieve the optimal solution if the input data follows some Gaussian distribution. We show by experiments that our optimized approach substantially improves the accuracy of product quantization for ANN search.


title: TurboGraph: a fast parallel graph engine handling billion-scale graphs in a single PC

abstract: Graphs are used to model many real objects such as social networks and web graphs. Many real applications in various fields require efficient and effective management of large-scale graph structured data. Although distributed graph engines such as GBase and Pregel handle billion-scale graphs, the user needs to be skilled at managing and tuning a distributed system in a cluster, which is a nontrivial job for the ordinary user. Furthermore, these distributed systems need many machines in a cluster in order to provide reasonable performance. In order to address this problem, a disk-based parallel graph engine called Graph-Chi, has been recently proposed. Although Graph-Chi significantly outperforms all representative (disk-based) distributed graph engines, we observe that Graph-Chi still has serious performance problems for many important types of graph queries due to 1) limited parallelism and 2) separate steps for I/O processing and CPU processing. In this paper, we propose a general, disk-based graph engine called TurboGraph to process billion-scale graphs very efficiently by using modern hardware on a single PC. TurboGraph is the first truly parallel graph engine that exploits 1) full parallelism including multi-core parallelism and FlashSSD IO parallelism and 2) full overlap of CPU processing and I/O processing as much as possible. Specifically, we propose a novel parallel execution model, called pin-and-slide. TurboGraph also provides engine-level operators such as BFS which are implemented under the pin-and-slide model. Extensive experimental results with large real datasets show that TurboGraph consistently and significantly outperforms Graph-Chi by up to four orders of magnitude! Our implementation of TurboGraph is available at ``http://wshan.net/turbograph}" as executable files.


title: Literacy acquisition reduces the influence of automatic holistic processing of faces and houses

abstract: Abstract Writing was invented too recently to have influenced the human genome. Consequently, reading acquisition must rely on partial recycling of pre-existing brain systems. Prior fMRI evidence showed that in literates a left-hemispheric visual region increases its activation to written strings relative to illiterates and reduces its response to faces. Increasing literacy also leads to a stronger right-hemispheric lateralization for faces. Here, we evaluated whether this reorganization of the brain's face system has behavioral consequences for the processing of non-linguistic visual stimuli. Three groups of adult illiterates, ex-illiterates and literates were tested with the sequential composite face paradigm that evaluates the automaticity with which faces are processed as wholes. Illiterates were consistently more holistic than participants with reading experience in dealing with faces. A second experiment replicated this effect with both faces and houses. Brain reorganization induced by literacy seems to reduce the influence of automatic holistic processing of faces and houses by enabling the use of a more analytic and flexible processing strategy, at least when holistic processing is detrimental to the task.


title: Click Fraud Detection on the Advertiser Side

abstract: Click fraud--malicious clicks at the expense of pay-per-click advertisers--is posing a serious threat to the Internet economy. Although click fraud has attracted much attention from the security community, as the direct victims of click fraud, advertisers still lack effective defense to detect click fraud independently. In this paper, we propose a novel approach for advertisers to detect click frauds and evaluate the return on investment ROI of their ad campaigns without the helps from ad networks or publishers. Our key idea is to proactively test if visiting clients are full-fledged modern browsers and passively scrutinize user engagement. In particular, we introduce a new functionality test and develop an extensive characterization of user engagement. Our detection can significantly raise the bar for committing click fraud and is transparent to users. Moreover, our approach requires little effort to be deployed at the advertiser side. To validate the effectiveness of our approach, we implement a prototype and deploy it on a large production website; and then we run 10-day ad campaigns for the website on a major ad network. The experimental results show that our proposed defense is effective in identifying both clickbots and human clickers, while incurring negligible overhead at both the server and client sides.


title: Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing

abstract: Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed importance sampling (AIS) is widely used to estimate MRF partition functions, and often yields quite accurate results. However, AIS is prone to overestimate the log-likelihood with little indication that anything is wrong. We present the Reverse AIS Estimator (RAISE), a stochastic lower bound on the log-likelihood of an approximation to the original MRF model. RAISE requires only the same MCMC transition operators as standard AIS. Experimental results indicate that RAISE agrees closely with AIS log-probability estimates for RBMs, DBMs, and DBNs, but typically errs on the side of underestimating, rather than overestimating, the log-likelihood.


Image reconstruction from projections : the fundamentals of computerized tomography


title: Decentralized deadlock-free control for AGV systems

abstract: This paper proposes a decentralized control strategy to assign tasks to Autonomous Guided Vehicles (AGV) and coordinate their paths to avoid deadlock and collisions. We consider a zone-controlled guidepath network where a set of intelligent vehicles (agents) has to autonomously reach a consensus about the distribution of a set of tasks, i.e., a set of zones to be reached. To this aim the agents apply a discrete consensus algorithm in order to locally minimize the global cost for reaching the destination zone. Moreover, we present a decentralized coordination protocol that is based on a zone-controlled approach with the aim of avoiding deadlock and collisions.


title: Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques

abstract: Nowadays, as internet speed has increased and the prices of mobile have decreased very much in past few years. Also the data prices too are very much affordable to most of the people. This has resulted into the digitization of most of the institutes as it is easy and convenient for the people and also for the authority to maintain the records. So, it resulted in most of the banks and other institutes receiving and transferring money through credit cards. But with the hackers and other cyber criminals around there is always chances of the frauds in the transactions. The possibility of the fraud transaction is very less but it is not negligible and even having one fraud transaction is unacceptable because it is crime and we can’t neglect it even if it is very less as it harms both the customer and credibility of the institute. So this paper aims at analyzing various classification techniques using various metrics for judging various classifiers. This model aims at improving fraud detection rather than misclassifying a genuine transaction as fraud.


title: BPSK-based MIMO FMCW Automotive-Radar Concept for 3D Position Measurement

abstract: A novel concept of employing BPSK codes to achieve a simultaneous transmission in a 79 GHz FMCW automotive radar is presented in this paper. With a MIMO topology, simultaneous azimuth and elevation measurements become possible by utilizing the angle-dependent phase difference between the antenna arrays. Hence, this approach can offer a 4D detection of range, azimuth, elevation and Doppler-velocity for each detected target. This concept has been verified through test drives in multiple scenarios and the measurements showed that the elevation accuracy is only limited by calibration.


title: Robust Lane Detection and Tracking for Real-Time Applications

abstract: An effective lane-detection algorithm is a fundamental component of an advanced driver assistant system, as it provides important information that supports driving safety. The challenges faced by the lane detection and tracking algorithm include the lack of clarity of lane markings, poor visibility due to bad weather, illumination and light reflection, shadows, and dense road-based instructions. In this paper, a robust and real-time vision-based lane detection algorithm with an efficient region of interest is proposed to reduce the high noise level and the calculation time. The proposed algorithm also processes a gradient cue and a color cue together and a line clustering with scan-line tests to verify the characteristics of the lane markings. It removes any false lane markings and tracks the real lane markings using the accumulated statistical data. The experiment results show that the proposed algorithm gives accurate results and fulfills the real-time operation requirement on embedded systems with low computing power.


title: A Single Attention-Based Combination of CNN and RNN for Relation Classification

abstract: Relation classification is an essential task in natural language processing (NLP) in order to extract structured data from sentences. In this paper, we propose a novel model Att-ComNN combining convolutional neural network (CNN) and bidirectional recurrent neural network (RNN) for relation classification. By combining RNN and CNN, we obtain more accurate context representations of words, which benefits classifying relations. Besides, with both shortest dependency path (SDP) attention and pooling attention added, this model captures the most informative context representation for better classification without using other handcrafted features. The results of experiments show that our model improves the relation classification performance on the SemEval-2010 Task 8 and outperforms most of previous state-of-the-art methods, including those depending on much richer forms of handcrafted features and prior knowledge.


title: Event-Based Visual Flow

abstract: This paper introduces a new methodology to compute dense visual flow using the precise timings of spikes from an asynchronous event-based retina. Biological retinas, and their artificial counterparts, are totally asynchronous and data-driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework to estimate visual flow from the local properties of events' spatiotemporal space. We will show that precise visual flow orientation and amplitude can be estimated using a local differential approach on the surface defined by coactive events. Experimental results are presented; they show the method adequacy with high data sparseness and temporal resolution of event-based acquisition that allows the computation of motion flow with microsecond accuracy and at very low computational cost.


title: Automatic Web Service Composition Based on Uncertainty Execution Effects

abstract: By arranging multiple existing web services into workflows to create value-added services, automatic web service composition has received much attention in service-oriented computing. A large number of methods have been proposed for it although most of them are merely based on the matching of input-output parameters of services. Besides these parameters, some other elements can affect the execution of services and their composition, such as the preconditions and service execution results. In particular, the execution effects of some services are often uncertain because of the complex and dynamically changing application environments in the real world, and this can cause the emergence of nondeterministic choices in the workflows of composite services. However, the previous methods for automatic service composition mainly rely on sequential structures, which make them difficult to take into account uncertain effects during service composition. In this paper, Graphplan is employed and extended to tackle this problem. In order to model services with uncertain effects, we first extend the original form of Graphplan. Then, we propose a novel approach that can introduce branch structures into composite solutions to cope with such uncertainty in the service composition process. Extensive experiments are performed to evaluate and analyze the proposed methodology.


Book review: Selected Papers on Computer Science by Donald E. Knuth


title: A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image

abstract: Abstract The spatially e nhanced l ocal b inary p attern (LBP) h istogram (eLBPH) methodology has attained an established position in the field of face recognition (FR) and derived many face analysis approaches. Their implementations follow a similar procedure: first divide a full facial image into some regions (subimages) and individually extract LBP histogram for each region, then concatenate all these regional histograms into a single (global) histogram for final recognition. It has been reported that eLBPH is more effective than the naive h olistic LBP h istogram (hLBPH), while the adoption of h olistic LBP i mage (hLBPI) in FR is relatively few. So, this paper aims to systematically empirically address these issues: (1) Why the simple hLBPH is hardly adopted in FR? (2) Why eLBPH is more effective than hLBPH for FR? (3) hLBPI enjoys what kind of properties for FR. Concretely, we (1) compare the hLBPHs for large-variational facial images with those for standard texture images, and suggest that the LBP histogram feature generally needs certain preprocessing or post-processing for good FR performances; (2) illuminate the reason that eLBPH is more effective than hLBPH for FR, i.e., the enhanced histogram tends to be uniform (more stable than the holistic histogram) and relatively preserve spatial relations of faces, and show the sensitivity of eLBPH to the division region parameter; (3) we study the properties of hLBPI for FR, i.e., hLBPI faithfully preserves the both spatial structure and intrinsic appearance details of a facial image, inherits the attractive properties of the LBP operator and does not require the calculation of histogram for FR; (4) comprehensively evaluate and compare hLBPI, hLBPH, eLBPH and some subspace algorithms on the benchmark face datasets ( FERET , Extended YaleB , CMU PIE , AR ); (5) conclude that hLBPI, hLBPH and eLBPH respectively are suitable for face representation under what conditions, and expect providing practitioners with guidance in selecting appropriate approaches for real tasks.


title: Location optimization for data concentrator unit in IEEE 802.15.4 smart grid networks

abstract: In the smart grid system, an advanced meter infrastructure (AMI) is an integral subsystem mainly used to collect monthly consumption and load profile. Hence, a large amount of information will be exchanged within these systems. Data concentrator unit (DCU) is used to collect the information from smart meters before forwarding to meter data management system. In order to meet the AMI's QoS such as throughput and delay, the optimal placement for DCU has to be thoroughly investigated. This paper aims at developing an optimal location algorithm for the DCU placement in a non-beacon-mode IEEE 802.15.4 smart grid network. The optimization algorithm preliminarily computes the DCU position based on a minimum hop count metric. Nevertheless, it is possible that multiple positions achieving the minimum hop count may be found; therefore, the additional performance metric, i.e. the averaged throughput and delay, will be used to select the ultimately optimal location. In this paper, the maximum throughput with the acceptable averaged delay constraint is proposed by considering the behavior of the AMI meters which is almost stationary in the network. From the simulation results, it is obvious that the proposed methodology is significantly effective.


title: LeakMiner: Detect Information Leakage on Android with Static Taint Analysis

abstract: With the growing popularity of Android platform, Android application market becomes a major distribution center where Android users download apps. Unlike most of the PC apps, Android apps manipulates personal information such as contract and SMS messages, and leakage of such information may cause great loss to the Android users. Thus, detecting information leakage on Android is in urgent need. However, till now, there is still no complete vetting process applied to Android markets. State-of-the-art approaches for detecting Android information leakage apply dynamic analysis on user site, thus they introduce large runtime overhead to the Android apps. This paper proposes a new approach called Leak Miner, which detects leakage of sensitive information on Android with static taint analysis. Unlike dynamic approaches, Leak Miner analyzes Android apps on market site. Thus, it does not introduce runtime overhead to normal execution of target apps. Besides, Leak Miner can detect information leakage before apps are distributed to users, so malicious apps can be removed from market before users download them. Our evaluation result shows that Leak Miner can detect 145 true information leakages inside a 1750 app set.


title: Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer

abstract: Convolutional Neural Networks (CNNs) are a popular deep learning architecture widely applied in different domains, in particular in classifying over images, for which the concept of convolution with a filter comes naturally. Unfortunately, the requirement of a distance (or, at least, of a neighbourhood function) in the input feature space has so far prevented its direct use on data types such as omics data. However, a number of omics data are metrizable, i.e., they can be endowed with a metric structure, enabling to adopt a convolutional based deep learning framework, e.g., for prediction. We propose a generalized solution for CNNs on omics data, implemented through a dedicated Keras layer. In particular, for metagenomics data, a metric can be derived from the patristic distance on the phylogenetic tree. For transcriptomics data, we combine Gene Ontology semantic similarity and gene co-expression to define a distance; the function is defined through a multilayer network where 3 layers are defined by the GO mutual semantic similarity while the fourth one by gene co-expression. As a general tool, feature distance on omics data is enabled by OmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learning framework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, for Inflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then a metagenomics collection of gut microbiota of 222 IBD patients.


title: A New Recurrent Neural CRF for Learning Non-linear Edge Features

abstract: Conditional Random Field (CRF) and recurrent neural models have achieved success in structured prediction. More recently, there is a marriage of CRF and recurrent neural models, so that we can gain from both non-linear dense features and globally normalized CRF objective. These recurrent neural CRF models mainly focus on encode node features in CRF undirected graphs. However, edge features prove important to CRF in structured prediction. In this work, we introduce a new recurrent neural CRF model, which learns non-linear edge features, and thus makes non-linear features encoded completely. We compare our model with different neural models in well-known structured prediction tasks. Experiments show that our model outperforms state-of-the-art methods in NP chunking, shallow parsing, Chinese word segmentation and POS tagging.


title: Unilateral Right Parietal Damage Leads to Bilateral Deficit for High-Level Motion

abstract: Summary level motion tasks, namely multiple object tracking and apparent motion. Similarities and differences in the imPatients with right parietal damage demonstrate a vapairments for these two high-level motion tasks should riety of attentional deficits in their left visual field conreveal similarities and differences in the attention mechtralateral to their lesion. We now report that patients anisms they may call on. with right lesions also show a severe loss in the perSelective attention mechanisms were tested with a ception of apparent motion in their “good” right visual task of divided attention in which the subject had to field ipsilateral to their lesion. Three tests of attention report a letter presented among three different letters were conducted, and losses were found only in the for 66 or 300 ms. At the offset of the display, the subject contralesional fields for a selective attention and a was required to identify a letter based on its position in multiple object tracking task. Losses in apparent mo- the letter string. The cued report for brief visual displays tion, however, were bilateral in all cases. The deficit was first developed by Sperling (1960), and we used a in apparent motion in the parietal patients supports modified version of his task. Recent studies have shown previous claims that this relatively effortless percept how patients with parietal lobe lesions can be affected is mediated by attention. However, the bilateral deficit in their ability to perform this type of task (Duncan et suggests that the disruption is due to a bilateral loss in al., 1999). In contrast to the tasks of Duncan et al., we the temporal resolution of attention to transient events asked our subjects to report only one letter among four, that drive the apparent motion percept. and the subject never knew which letter until briefly after the letters were presented. With only four items, normal


title: Numerical analysis of the SF6 dielectric recovery in high voltage circuit-breakers around current zero

abstract: This paper is devoted to the numerical simulation of breakdown process for high voltage circuit-breakers. To optimise the design of switchgears for dielectric phase, it is required to build criteria based on the breakdown electric field strength calculations of hot SF6. This critical electric field is calculated for large temperature and pressure ranges including both electron and ion kinetics. Gas behaviour is calculated thanks to specific simulation tool which takes into account the electric arc, its interaction with material and numerous compressibility effects of the gas . Thus, using different geometrical configurations, it is possible to get an insight into the capabilities of the circuit -breaker to succeed in tests from the dielectric phase point of view. Two studies for both arcing contacts and parallel contacts are presented, compared to test results and commented.


title: Hybrid retinal image registration

abstract: This work studies retinal image registration in the context of the National Institutes of Health (NIH) Early Treatment Diabetic Retinopathy Study (ETDRS) standard. The ETDRS imaging protocol specifies seven fields of each retina and presents three major challenges for the image registration task. First, small overlaps between adjacent fields lead to inadequate landmark points for feature-based methods. Second, the non-uniform contrast/intensity distributions due to imperfect data acquisition will deteriorate the performance of area-based techniques. Third, high-resolution images contain large homogeneous nonvascular/texureless regions that weaken the capabilities of both feature-based and area-based techniques. In this work, we propose a hybrid retinal image registration approach for ETDRS images that effectively combines both area-based and feature-based methods. Four major steps are involved. First, the vascular tree is extracted by using an efficient local entropy-based thresholding technique. Next, zeroth-order translation is estimated by maximizing mutual information based on the binary image pair (area-based). Then image quality assessment regarding the ETDRS field definition is performed based on the translation model. If the image pair is accepted, higher-order transformations will be involved. Specifically, we use two types of features, landmark points and sampling points, for affine/quadratic model estimation. Three empirical conditions are derived experimentally to control the algorithm progress, so that we can achieve the lowest registration error and the highest success rate. Simulation results on 504 pairs of ETDRS images show the effectiveness and robustness of the proposed algorithm


title: The FrameNet Database and Software Tools

abstract: The FrameNet Project is producing a lexicon of English for both human use and NLP applications, based on the principles of Frame Semantics, in which sentences are described on the basis of predicators which evoke semantic frames and other constituents which express the participants (frame elements) in these frames. Our lexicon contains detailed information about the possible syntactic realizations of frame elements, derived from annotated corpus examples. In the process, we have developed a suite of tools for the definition of semantic frames, for annotating sentences, for searching the results, and for creating a variety ofreports. We will discuss the conceptual basis ofour work and demonstrate the tools we work with, the results we produce, and how they may be of use to other NLP projects.


title: MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

abstract: MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. ::: This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines.


title: Implementation of Lean Manufacturing in Romanian Organisations

abstract: The implementation of lean manufacturing is one of the main tools for attaining a higher performance level in the Romanian organisations. The study uses data collected at middle management level from organisations located in Transylvania. The organisations come from different fields of activity and are in different stages of lean implementation. The article makes use of quantitative and qualitative instruments for the evaluation of lean in the above mentioned companies. Characteristics as waste reduction, cost, quality, product design have different approaches in the companies. Their way of approach gives useful information regarding the type of company from the point of view of lean manufacturing implementation. This knowledge provides a useful support at different managerial levels in the process of decision making.


title: Combining static and dynamic analysis for the detection of malicious documents

abstract: The widespread adoption of the PDF format for document exchange has given rise to the use of PDF files as a prime vector for malware propagation. As vulnerabilities in the major PDF viewers keep surfacing, effective detection of malicious PDF documents remains an important issue. In this paper we present MDScan, a standalone malicious document scanner that combines static document analysis and dynamic code execution to detect previously unknown PDF threats. Our evaluation shows that MDScan can detect a broad range of malicious PDF documents, even when they have been extensively obfuscated.


title: Magnetic Two-Way Valves for Paper-Based Capillary-Driven Microfluidic Devices

abstract: This article presents a magnetically actuated two-way, three-position (+, 0, -), paper-based microfluidic valve that includes a neutral position (0)-the first of its kind. The system is highly robust, customizable, and fully automated. The advent of a neutral position and the ability to precisely control switching frequencies establish a new platform for highly controlled fluid flows in paper-based wicking microfluidic devices. The potential utility of these valves is demonstrated in automated, programmed, patterning of dyed liquids in a wicking device akin to a colorimetric assay but with a programmed fluid/reagent delivery. These valves are fabricated using facile methods and thus remain cost-effective for adoption into affordable point-of-care/bioanalytical devices.


title: Practical algorithm for unlimited scale terrain rendering

abstract: The paper presents an algorithm for rendering digital terrain models of unlimited scale and detail. The algorithm is straightforward and simple to implement. It integrates easily with rendering of other geographical data. It has minimal CPU requirements as it harnesses the power of modern graphics processing units. It has implicit triangle budget and provides stable frame rates.


title: Mobile marketing: the role of permission and acceptance

abstract: The escalation and convergence of distributed networks and wireless telecommunications has created a tremendous potential platform for providing business services. In consumer markets, mobile marketing is expected to be a key growth area. The immediacy, interactivity and mobility of wireless devices provide a novel platform for marketing. The personal and ubiquitous nature of devices means that interactivity can be provided anytime and anywhere. However, as experience has shown, it is important to keep the consumer in mind. Mobile marketing permission and acceptance are core issues that marketers have yet to fully explain or resolve. This paper provides direction in this area. After briefly discussing some background on mobile marketing, the paper conceptualises key characteristics for mobile marketing permission and acceptance. The paper concludes with predictions on the future of mobile marketing and some core areas of further research.


title: Learning to mediate perceptual differences in situated human-robot dialogue

abstract: In human-robot dialogue, although a robot and its human partner are co-present in a shared environment, they have significantly mismatched perceptual capabilities (e.g., recognizing objects in the surroundings). When a shared perceptual basis is missing, it becomes difficult for the robot to identify referents in the physical world that are referred to by the human (i.e., a problem of referential grounding). To overcome this problem, we have developed an optimization based approach that allows the robot to detect and adapt to perceptual differences. Through online interaction with the human, the robot can learn a set of weights indicating how reliably/unreliably each dimension (e.g., object type, object color, etc.) of its perception of the environment maps to the human’s linguistic descriptors and thus adjust its word models accordingly. Our empirical evaluation has shown that this weight-learning approach can successfully adjust the weights to reflect the robot’s perceptual limitations. The learned weights, together with updated word models, can lead to a significant improvement for referential grounding in future dialogues.


title: Constraint-based sample propagation for improved state estimation in robotic assembly

abstract: In fast changing assembly scenarios, it is required to adapt the task execution to the current state of the setup without extensive calibration routines. Therefore, it is important to estimate the geometric uncertainties and contact states during the assembly execution. We use a sequential Monte Carlo (SMC) method to track the relative poses between workpieces during a robotic assembly based on joint torque and position measurements only. In contrast to existing approaches, we focus on assembly tasks where the workpiece is not fixed in the workcell, but can, for example, slide on a table surface. We propose a new constraint-based propagation model for the SMC approach: a compensation motion for the samples dependent on the violation of contact constraints is derived. This allows us to track the motion of the workpieces in cases where a common random diffusion model fails. The method is evaluated with experiments using an assembly scenario with two KUKA LBR iiwa robot arms and shows accurate tracking performance.


Community Detection in Signed Networks: the Role of Negative ties in Different Scales


title: Bagging to improve the accuracy of a clustering procedure

abstract: MOTIVATION:The microarray technology is increasingly being applied in biological and medical research to address a wide range of problems such as the classification of tumors. An important statistical question associated with tumor classification is the identification of new tumor classes using gene expression profiles. Essential aspects of this clustering problem include identifying accurate partitions of the tumor samples into clusters and assessing the confidence of cluster assignments for individual samples. RESULTS:Two new resampling methods, inspired from bagging in prediction, are proposed to improve and assess the accuracy of a given clustering procedure. In these ensemble methods, a partitioning clustering procedure is applied to bootstrap learning sets and the resulting multiple partitions are combined by voting or the creation of a new dissimilarity matrix. As in prediction, the motivation behind bagging is to reduce variability in the partitioning results via averaging. The performances of the new and existing methods were compared using simulated data and gene expression data from two recently published cancer microarray studies. The bagged clustering procedures were in general at least as accurate and often substantially more accurate than a single application of the partitioning clustering procedure. A valuable by-product of bagged clustering are the cluster votes which can be used to assess the confidence of cluster assignments for individual observations. SUPPLEMENTARY INFORMATION:For supplementary information on datasets, analyses, and software, consult http://www.stat.berkeley.edu/~sandrine and http://www.bioconductor.org.


title: Using Out-of-Domain Data for Lexical Addressee Detection in Human-Human-Computer Dialog

abstract: Addressee detection (AD) is an important problem for dialog systems in human-humancomputer scenarios (contexts involving multiple people and a system) because systemdirected speech must be distinguished from human-directed speech. Recent work on AD (Shriberg et al., 2012) showed good results using prosodic and lexical features trained on in-domain data. In-domain data, however, is expensive to collect for each new domain. In this study we focus on lexical models and investigate how well out-of-domain data (either outside the domain, or from single-user scenarios) can fill in for matched in-domain data. We find that human-addressed speech can be modeled using out-of-domain conversational speech transcripts, and that human-computer utterances can be modeled using single-user data: the resulting AD system outperforms a system trained only on matched in-domain data. Further gains (up to a 4% reduction in equal error rate) are obtained when in-domain and out-of-domain models are interpolated. Finally, we examine which parts of an utterance are most useful. We find that the first 1.5 seconds of an utterance contain most of the lexical information for AD, and analyze which lexical items convey this. Overall, we conclude that the H-H-C scenario can be approximated by combining data from H-C and H-H scenarios only.


title: Linux kernel hash table behavior: Analysis and improvements

abstract: The Linux kernel stores high-usage data objects such as pages, buffers, and inodes in data structures known as hash tables. In this report we analyze existing static hash tables to study the benefits of dynamically sized hash tables. We find significant performance boosts with careful analysis and tuning of these critical kernel data structures.


title: Supervised domain adaptation for emotion recognition from speech

abstract: One of the main barriers in the deployment of speech emotion recognition systems in real applications is the lack of generalization of the emotion classifiers. The recognition performance achieved in controlled recordings drops when the models are tested with different speakers, channels, environments and domain conditions. This paper explores supervised model adaptation, which can improve the performance of systems evaluated with mismatched training and testing conditions. We address the following key questions in the context of supervised adaptation for speech emotion recognition: (a) how much labeled data is needed for adaptation to achieve good performance? (b) how important is speaker diversity in the labeled set? (c) can spontaneous acted data provide similar performance than naturalistic non-acted recordings? and (d) what is the best approach to adapt the models (domain adaptation versus incremental/online training)? We address these problems by using a multi-corpus framework where the models are trained and tested with different databases. The results indicate that even small portion of data used for adaptation can significantly improve the performance. Increasing the speaker diversity in the labeled data used for adaptation does not provide significant gain in performance. Also, we observe similar performance when the classifiers are trained with naturalistic non-acted data and spontaneous acted data.


title: Comparison of Two Optimal Rotary Transformer Designs for Highly Constrained Applications

abstract: The lack of reliability of gliding contacts in highly constrained environments induces manufacturers to develop contactless transmission power systems such as rotary transformers. The following paper proposes an optimal design methodology for rotary transformers supplied from a low-voltage source at high temperatures. The method is based on an accurate multidisciplinary analysis model divided into magnetic, thermal and electrical parts, optimized thanks to a sequential quadratic programming method. The technique is used to discuss the design particularities of rotary transformers. Two optimally designed structures of rotary transformers : an iron silicon coaxial one and a ferrite pot core one, are compared.


title: Staff Performance Appraisal using Fuzzy Evaluation

abstract: Most organizations use performance appraisal system to evaluate the effectiveness and efficiency of their employees. In evaluating staff performance, it usually involves awarding numerical values or linguistic labels to their performance. These values and labels are used to represent each staff’s achievement by reasoning incorporated in the arithmetical or statistical methods. However, the staff performance appraisal may involve judgments which are based on imprecise data especially when human (the superior) tries to interpret another human (his/her subordinate) performance. Thus, the scores awarded by the appraiser are only approximations. From fuzzy logic perspective, the performance of the appraisee involves the measurement of his/her ability, competence and skills, which are actually fuzzy concepts that can be captured in fuzzy terms. Accordingly, fuzzy approach can be used to handle these imprecision and uncertainty information. Therefore, the performance appraisal system can be examined using Fuzzy Logic Approach and this was carried out in the study. The study utilized hierarchical fuzzy inference approach since performance evaluation comprises of four criteria; namely work achievement, skill knowledge, personal quality, and community services. The output of the study provides the ranking for staff performance. From this study, it is expected that reasoning based on fuzzy models will provide an alternative way in handling various kinds of imprecise data, which often reflected in the way people think and make judgments.


title: Comparing the consistency of expert land cover knowledge

abstract: Data integration can be hindered by differences in data semantics and meaning. The problem is that different data encapsulate different conceptual views of the world. Integration approaches have been developed based on modelling expert opinion of how datasets relate, rather than statistical descriptions of data correspondence. But different experts have different opinions and this is a problem in the interpretation of remotely sensed data as much as in other areas of endeavour. In work reported here, the opinions of three experts were used to examine the semantics of land cover information derived from satellite imagery. We examined the integration of two land cover datasets of the same area at different dates where the land cover mapping classes are very different, and apparently incompatible. The approach adopted involves expert opinion of how the two land cover datasets relate under a scenario of idealised relations. The work reported here compares the performance of three different experts in three different scenarios, and evaluates their performance at identifying areas of land cover change. The results show that overall they identify the same parcels as potential change areas but different experts are more reliable at identifying change in specific landscape types.


title: Using Model-Independent Lower Bounds to Improve Pricing of Asian Style Options in Lévy Markets

abstract: Albrecher et al. (2008) have proposed model-independent lower bounds for arithmetic Asian options. In this paper we provide an alternative and more elementary derivation of their results. We use the bounds as control variates to develop a simple Monte Carlo method for pricing contracts with Asian style features. The conditioning idea that is inherent in our approach also inspires us to propose a new semi-analytic pricing approach. We compare both approaches and conclude that they both have their merits and are useful in practice. In particular, we point out that our newly proposed Monte Carlo method allows to deal with Asian style products that appear in insurance (e.g. unit linked contracts) in a very efficient way, and outperforms other known Monte Carlo methods that are based on control variates.


title: Fast visual road recognition and horizon detection using multiple artificial neural networks

abstract: The development of autonomous vehicles is a highly relevant research topic in mobile robotics. Road recognition using visual information is an important capability for autonomous navigation in urban environments. Over the last three decades, a large number of visual road recognition approaches have been appeared in the literature. This paper proposes a novel visual road detection system based on multiple artificial neural networks that can identify the road based on color and texture. Several features are used as inputs of the artificial neural network such as: average, entropy, energy and variance from different color channels (RGB, HSV, YUV). As a result, our system is able to estimate the classification and the confidence factor of each part of the environment detected by the camera. Experimental tests have been performed in several situations in order to validate the proposed approach.


title: Add English to image Chinese captioning

abstract: Image captioning is a crucial task in the interaction of computer vision and natural language processing. It is an important way that help human understand the world better. There are many studies on image English captioning, but little work on image Chinese captioning because of the lack of the corresponding datasets. This paper focuses on image Chinese captioning by using abundant English datasets for the issue. In this paper, a method of adding English information to image Chinese captioning is proposed. We validate the use of English information with state-of-the art performance on the datasets: Flickr8K-CN.


title: Analysis of a Resonant Transistor DC-DC Converter with Capacitive Output Filter

abstract: Resonant dc-dc converters offer several advantages over the more conventional PWM converters. Some of these advantages include: 1) low switching losses and low transistor stresses; 2) medium speed diodes are sufficient (transistor parasitic, inverse-parallel diodes can be used, even for frequencies in the hundreds of kilohertz); and 3) ability to step the input voltage up or down. This paper presents an analysis of a resonant converter which contains a capacitive-input output filter, rather than the more conventional inductor-input output filter. The switching waveforms are derived and design curves presented along with experimental data. The results are compared to the inductor-input filter case obtained from an earlier paper.


title: S-FTL: An efficient address translation for flash memory by exploiting spatial locality

abstract: The solid-state disk (SSD) is becoming increasingly popular, especially among users whose workloads exhibit substantial random access patterns. As SSD competes with the hard disk, whose per-GB cost keeps dramatically falling, the SSD must retain its performance advantages even with low-cost configurations, such as those with a small built-in DRAM cache for mapping table and using MLC NAND. To this end, we need to make the limited cache space efficiently used to support fast logical-to-physical address translation in the flash translation layer (FTL) with minimal access of flash memory and minimal merge operations. Existing schemes usually require a large number of overhead accesses, either for accessing uncached entries of the mapping table or for the merge operation, and achieve suboptimal performance when the cache space is limited. In this paper we take into account spatial locality exhibited in the workloads to obtain a highly efficient FTL even with a relatively small cache, named as S-FTL. Specifically, we identify three access patterns related to spatial locality, including sequential writes, clustered access, and sparse writes. Accordingly we propose designs to take advantage of these patterns to reduce mapping table size, increase hit ratio for in-cache address translation, and minimize expensive writes to flash memory. We have conducted extensive trace-driven simulations to evaluate S-FTL and compared it with other state-of-the-art FTL schemes. Our experiments show that S-FTL can reduce accesses to the flash for address translation by up to 70% and reduce response time of SSD by up to 25%, compared with the state-of-the-art FTL strategies such as FAST and DFTL.


title: Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation

abstract: The indigenous human microbiota is essential to the health of the host. Although the microbiota can be affected by many features of modern life, we know little about its responses to disturbance, especially repeated disturbances, and how these changes compare with baseline temporal variation. We examined the distal gut microbiota of three individuals over 10 mo that spanned two courses of the antibiotic ciprofloxacin, analyzing more than 1.7 million bacterial 16S rRNA hypervariable region sequences from 52 to 56 samples per subject. Interindividual variation was the major source of variability between samples. Day-to-day temporal variability was evident but constrained around an average community composition that was stable over several months in the absence of deliberate perturbation. The effect of ciprofloxacin on the gut microbiota was profound and rapid, with a loss of diversity and a shift in community composition occurring within 3–4 d of drug initiation. By 1 wk after the end of each course, communities began to return to their initial state, but the return was often incomplete. Although broadly similar, community changes after ciprofloxacin varied among subjects and between the two courses within subjects. In all subjects, the composition of the gut microbiota stabilized by the end of the experiment but was altered from its initial state. As with other ecosystems, the human distal gut microbiome at baseline is a dynamic regimen with a stable average state. Antibiotic perturbation may cause a shift to an alternative stable state, the full consequences of which remain unknown.


title: A hybrid scheme for Automated Essay Grading based on LVQ and NLP techniques

abstract: This paper presents a hybrid approach to an Automated Essay Grading System (AEGS) that provides automated grading and evaluation of student essays. The proposed system has two complementary components: Writing Features Analysis tools, which rely on natural language processing (NLP) techniques and neural network grading engine, which rely on a set of pre-graded essays to judge the student answer and assign a grade. By this way, students essays could be evaluated with a feedback that would improve their writing skills. The proposed system is evaluated using datasets from computer and information sciences college students' essays in Mansoura University. These datasets was written as part of mid-term exams in introduction to information systems course and Systems analysis and design course. The obtained results shows an agreement with teachers' grades in between 70% and nearly 90% with teachers' grades. This indicates that the proposed might be useful as a tool for automatic assessment of students' essays, thus leading to a considerable reduction in essay grading costs.