title: Effective Multi-Modal Retrieval based on Stacked Auto-Encoders

abstract: Multi-modal retrieval is emerging as a new search paradigm that enables seamless information retrieval from various types of media. For example, users can simply snap a movie poster to search relevant reviews and trailers. To solve the problem, a set of mapping functions are learned to project high-dimensional features extracted from data of different media types into a common low-dimensional space so that metric distance measures can be applied. In this paper, we propose an effective mapping mechanism based on deep learning (i.e., stacked auto-encoders) for multi-modal retrieval. Mapping functions are learned by optimizing a new objective function, which captures both intra-modal and inter-modal semantic relationships of data from heterogeneous sources effectively. Compared with previous works which require a substantial amount of prior knowledge such as similarity matrices of intra-modal data and ranking examples, our method requires little prior knowledge. Given a large training dataset, we split it into mini-batches and continually adjust the mapping functions for each batch of input. Hence, our method is memory efficient with respect to the data volume. Experiments on three real datasets illustrate that our proposed method achieves significant improvement in search accuracy over the state-of-the-art methods.


Consciousness, emotion, and imagination: A brain-inspired architecture for cognitive robotics


title: Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages

abstract: In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding. We present an end-to-end graph-based neural network dependency parser that can be trained to reproduce matrices of edge scores, which can be directly projected across word alignments. We show that our approach to cross-lingual dependency parsing is not only simpler, but also achieves an absolute improvement of 2.25% averaged across 10 languages compared to the previous state of the art.


title: Making workplaces safer: The influence of organisational climate and individual differences on safety behaviour

abstract: Abstract Current work health and safety practices focus predominately on fostering a safety climate to promote safety behaviours and reduce workplace accidents. Despite the importance of safety climates in accident prevention, recent research has demonstrated that individual factors can also predict work safety behaviour. This study considered the importance of organisational climate together with individual characteristics including differences in personality, impulsiveness, and perceptions of safety within the workplace on safety behaviour. 203 participants consisting of 67 males and 136 females aged 18 to 71 years, completed an online questionnaire. Results revealed that safety behaviour was directly related to safety climate, and conscientiousness. In contrast, neuroticism, and impulsiveness were not significantly related to safety behaviour. The present study findings support previous findings in the literature regarding the importance of safety climate as well as the personality trait of conscientiousness in applying safety behaviours. However, the present study findings did not support previous research in relation to the personality trait of high neuroticism resulting in decreased safety behaviour, nor did not confirm an inverse relationship between high impulsivity and low safety behaviour as theoretical models would suggest. This new finding may warrant further research into the precursors for safety behaviour.


arulesViz: Interactive Visualization of Association Rules with R


title: Towards a smart city based on cloud of things

abstract: Smart City represents one of the most promising and prominent Internet of Things (IoT) applications. In the last few years, smart city concept has played an important role in academic and industry fields, with the development and deployment of various middleware platforms. However, this expansion has followed distinct approaches creating a fragmented scenario, in which different IoT ecosystems are not able to communicate between them. To fill this gap, there is a need to revisit the smart city IoT semantic and offer a global common approach. To this purpose, this paper browses the semantic annotation of the sensors in the cloud, and innovative services can be implemented and considered by bridging Clouds and IoT. Things-like semantic will be considered to perform the aggregation of heterogeneous resources by defining the Clouds of Things (CoT) paradigm. We survey the smart city vision, providing information on the main requirements and highlighting the benefits of integrating different IoT ecosystems within the cloud under this new CoT vision and discuss relevant challenges in this research area.


title: 3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation

abstract: Advanced deep learning methods have been developed to conduct prostate MR volume segmentation in either a 2D or 3D fully convolutional manner. However, 2D methods tend to have limited segmentation performance, since large amounts of spatial information of prostate volumes are discarded during the slice-by-slice segmentation process; and 3D methods also have room for improvement, since they use isotropic kernels to perform 3D convolutions whereas most prostate MR volumes have anisotropic spatial resolution. Besides, the fully convolutional structural methods achieve good performance for localization issues but neglect the per-voxel classification for segmentation tasks. In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation. We first design a 3D ResNet encoder to extract 3D features from prostate scans, and then develop the decoder, which is composed of a multi-scale 3D global convolutional block and a 3D boundary refinement block, to address the classification and localization issues simultaneously for volumetric segmentation. Additionally, we combine the encoder-decoder segmentation network with an adversarial network in the training phrase to enforce the contiguity of long-range spatial predictions. Throughout the proposed model, we use anisotropic convolutional processing for better feature learning on prostate MR scans. We evaluated our 3D GCA-Net model on two public prostate MR datasets and achieved state-of-the-art performances.


title: Smart and Energy Efficient LED Street Light Control System Using ZigBee Network

abstract: The world is converging towards wireless as a communication channel and at the same time facing energy and environmental problems. The solution is by mingling the information technology (IT) and power. Streetlights are among a city's most important and expensive assets usually costs one third of the electricity bill. The proposed smart light control system outclasses the previous systems because it integrates the low power communication protocol Zig Bee with the LED lights. The system reduces the power of the LED lights according to outside conditions of light intensity as it has the ability to dim the lights. The initial experimental results show that it saves remarkable power as compared to conventional systems. This efficiency increases even two fold by considering the advantage of remotely monitoring and controlling the lights through the centralized point. Thus the proposed system is the cost effective and efficient system satisfying the needs of the modern users.


title: DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension

abstract: We present DREAM, the first dialogue-based multiple-choice reading comprehension dataset. Collected from English-as-a-foreign-language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our dataset contains 10,197 multiple-choice questions for 6,444 dialogues. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge. We apply several popular neural reading comprehension models that primarily exploit surface information within the text and find them to, at best, just barely outperform a rule-based approach. We next investigate the effects of incorporating dialogue structure and different kinds of general world knowledge into both rule-based and (neural and non-neural) machine learning-based reading comprehension models. Experimental results on the DREAM dataset show the effectiveness of dialogue structure and general world knowledge. DREAM will be available at https://dataset.org/dream/.


title: Use of Word and Character N-Grams for Low-Resourced Local Languages

abstract: Language identification is a text classification task for identifying the language of a given text. Several works use this as a preprocessing technique prior to sentiment analysis, mood analysis, and named entity recognition among others. Thus, building an accurate language identification engine is important given that the Philippines is home to more than 170 languages, and is scarce of language documents and resources. We compare machine learning algorithms such as Naive Bayes, Linear Support Vector Machines (SVM), and Random Forest for classification of Philippine languages. Results show that the Linear SVM model had the best performance with 0.97 Fl-score.


title: A research agenda for distributed software development

abstract: In recent years, a number of business reasons have caused software development to become increasingly distributed. Remote development of software offers several advantages, but it is also fraught with challenges. In this paper, we report on our study of distributed software development that helped shape a research agenda for this field. Our study has identified four areas where important research questions need to be addressed to make distributed development more effective. These areas are: collaborative software tools, knowledge acquisition and management, testing in a distributed set-up and process and metrics issues. We present a brief summary of related research in each of these areas, and also outline open research issues.


Realtime learning and detection of 3D texture-less objects: A scalable approach


title: Lyra: An Interactive Visualization Design Environment

abstract: We present Lyra, an interactive environment for designing customized visualizations without writing code. Using drag-and-drop interactions, designers can bind data to the properties of graphical marks to author expressive visualization designs. Marks can be moved, rotated and resized using handles; relatively positioned using connectors; and parameterized by data fields using property drop zones. Lyra also provides a data pipeline interface for iterative, visual specification of data transformations and layout algorithms. Visualizations created with Lyra are represented as specifications in Vega, a declarative visualization grammar that enables sharing and reuse. We evaluate Lyra's expressivity and accessibility through diverse examples and studies with journalists and visualization designers. We find that Lyra enables users to rapidly develop customized visualizations, covering a design space comparable to existing programming-based tools.


title: Anomalies with variable partition paging algorithms

abstract: Five types of anomalous behaviour which may occur in paged virtual memory operating systems are defined. One type of anomaly, for example, concerns the fact that, with certain reference strings and paging algorithms, an increase in mean memory allocation may result in an increase in fault rate. Two paging algorithms, the page fault frequency and working set algorithms, are examined in terms of their anomaly potential, and reference string examples of various anomalies are presented. Two paging algorithm properties, the inclusion property and the generalized inclusion property, are discussed and the anomaly implications of these properties presented.


title: Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

abstract: The proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diffusion is known as early rumor detection, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. Thus, identifying trending rumors demands an efficient yet flexible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. However, it is a challenging task to apply conventional classification algorithms to rumor detection in earliness since they rely on hand-crafted features which require intensive manual efforts in the case of large amount of posts. This paper presents a deep attention model based on recurrent neural networks (RNNs) to selectively learn temporal representations of sequential posts for rumor identification. The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time. Extensive experiments on real datasets collected from social media websites demonstrate that the deep attention based RNN model outperforms state-of-the-art baselines by detecting rumors more quickly and accurately than competitors.


title: ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

abstract: Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new tool for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at this https URL


title: Strategies for mHealth Research: Lessons from 3 Mobile Intervention Studies

abstract: The capacity of Mobile Health (mHealth) technologies to propel healthcare forward is directly linked to the quality of mobile interventions developed through careful mHealth research. mHealth research entails several unique characteristics, including collaboration with technologists at all phases of a project, reliance on regional telecommunication infrastructure and commercial mobile service providers, and deployment and evaluation of interventions “in the wild”, with participants using mobile tools in uncontrolled environments. In the current paper, we summarize the lessons our multi-institutional/multi-disciplinary team has learned conducting a range of mHealth projects using mobile phones with diverse clinical populations. First, we describe three ongoing projects that we draw from to illustrate throughout the paper. We then provide an example for multidisciplinary teamwork and conceptual mHealth intervention development that we found to be particularly useful. Finally, we discuss mHealth research challenges (i.e. evolving technology, mobile phone selection, user characteristics, the deployment environment, and mHealth system “bugs and glitches”), and provide recommendations for identifying and resolving barriers, or preventing their occurrence altogether.


title: Compressed sensing for Synthetic Aperture Radar imaging

abstract: In this paper, we introduce a new Synthetic Aperture Radar (SAR) imaging modality that provides a high resolution map of the spatial distribution of targets and terrain based on a significant reduction in the number of transmitted and/or received electromagnetic waveforms. This new imaging scheme, which requires no new hardware components, allows the aperture to be compressed and presents many important applications and advantages among which include resolving ambiguities, strong resistance to countermesasures and interception, and reduced on-board storage constraints.


title: Design and analysis of a 6-DOF force/torque sensor for human gait analysis

abstract: This paper describes the development of a compact and robust 6-DOF force/torque sensor for human gait analysis. The forces and torques exerted on human foot are important information for the gait analysis. Multi-axis force/torque sensor can be installed under human foot the sense the forces and torques. The sensor should have large force range without reducing the accuracy to support the human weight and small size not to disturb the motion of the subject. In this paper, we present a 6-DOF force/torque sensor which has good accuracy, large measuring range, and new compact structure. And it is much cheaper than a commercial one. The proposed design is simulated by FEM software (ANSYS), and it shows good results being applied large forces and moments. This new sensor can be used to sense the center of pressure (CoP) and zero moment point (ZMP) of the human foot for gait analysis.


title: A reliable triangular mesh intersection algorithm and its application in geological modelling

abstract: We introduce a reliable intersection algorithm for manifold surface meshes. The proposed algorithm builds conforming surface meshes from a set of intersecting triangulated surfaces. This algorithm effectively handles all degenerate triangle---triangle intersection cases. The key idea of the algorithm is based on an extensive set of triangle---edge intersection cases, combined with an intersection curve tracking method. The intersection operations do not rely on global spatial search operations and no remeshing steps are needed. The intersection curves are introduced into each surface mesh using a unique curve imprinting algorithm. The imprinting algorithm naturally handles degenerate intersection cases of many surfaces at an edge or at a point. The algorithm produces a consistent mesh data structure for subsequent mesh optimization operations. The mesh intersection algorithm is used within a general framework for modelling and meshing of geological formations, which are essential for reliable mathematical modelling of oil reservoirs.


title: A Convolutional Learning System for Object Classification in 3-D Lidar Data

abstract: In this brief, a convolutional learning system for classification of segmented objects represented in 3-D as point clouds of laser reflections is proposed. Several novelties are discussed: (1) extension of the existing convolutional neural network (CNN) framework to direct processing of 3-D data in a multiview setting which may be helpful for rotation-invariant consideration, (2) improvement of CNN training effectiveness by employing a stochastic meta-descent (SMD) method, and (3) combination of unsupervised and supervised training for enhanced performance of CNN. CNN performance is illustrated on a two-class data set of objects in a segmented outdoor environment.


title: Vehicle anti-theft tracking system based on Internet of things

abstract: As the amount of urban vehicle grows rapidly, vehicle theft has become a shared concern for all citizens. However, present anti-theft systems lack the tracking and monitoring function. The vehicle anti-theft tracking system based on Internet of things is designed in this article, which can provide all-round active service for the owners. This system is controlled by an RFID module to switch on and off. When the car is stolen, the vibration sensors and pyroelectric infrared sensors mounted inside the vehicle are triggered, and GSM module will send the location information obtained by GPS module to the owner's mobile phone, thus owners can check the position of the vehicle with an android software developed by our team. This system uses android mobile phones as mobile terminal, which is more convenient and flexible than other kinds of like products since the owner can check and track the position of the car immediately with android mobile phone application once the car is stolen.


title: Dialectical behavior therapy for depressed older adults: a randomized pilot study

abstract: OBJECTIVE Although there is evidence for the efficacy of antidepressants and for some individual and group psychotherapy interventions for depressed older adults, a significant number of these do not respond to treatment. Authors assessed the benefits of augmenting medication with group psychotherapy. METHODS They randomly assigned 34 (largely chronically) depressed individuals age 60 and older to receive 28 weeks of antidepressant medication plus clinical management, either alone (MED) or with the addition of dialectical behavior therapy skills-training and scheduled telephone coaching sessions (MED+DBT). RESULTS Only MED+DBT showed significant decreases on mean self-rated depression scores, and both treatment groups demonstrated significant and roughly equivalent decreases on interviewer-rated depression scores. However, on interviewer-rated depression, 71% of MED+DBT patients were in remission at post-treatment, in contrast to 47% of MED patients. At a 6-month follow-up, 75% of MED+DBT patients were in remission, compared with only 31% of MED patients, a significant difference. Only patients receiving MED+DBT showed significant improvements from pre- to post-treatment on dependency and adaptive coping that are proposed to create vulnerability to depression. CONCLUSION Results from this pilot study suggest that DBT skills training and telephone coaching may offer promise to effectively augment the effects of antidepressant medication in depressed older adults.


title: An Effective Model Between Mobile Phone Usage and P2P Default Behavior

abstract: P2P online lending platforms have become increasingly developed. However, these platforms may suffer a serious loss caused by default behaviors of borrowers. In this paper, we present an effective default behavior prediction model to reduce default risk in P2P lending. The proposed model uses mobile phone usage data, which are generated from widely used mobile phones. We extract features from five aspects, including consumption, social network, mobility, socioeconomic, and individual attribute. Based on these features, we propose a joint decision model, which makes a default risk judgment through combining Random Forests with Light Gradient Boosting Machine. Validated by a real-world dataset collected by a mobile carrier and a P2P lending company in China, the proposed model not only demonstrates satisfactory performance on the evaluation metrics but also outperforms the existing methods in this area. Based on these results, the proposed model implies the high feasibility and potential to be adopted in real-world P2P online lending platforms.


title: Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data

abstract: With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form. In this paper, we develop a deep learning-based method, called Drive2Vec, for embedding such sensor data in a low-dimensional yet actionable form. Our method is based on stacked gated recurrent units (GRUs). It accepts a short interval of automobile sensor data as input and computes a low-dimensional representation of that data, which can then be used to accurately solve a range of tasks. With this representation, we (1) predict the exact values of the sensors in the short term (up to three seconds in the future), (2) forecast the long-term average values of these same sensors, (3) infer additional contextual information that is not encoded in the data, including the identity of the driver behind the wheel, and (4) build a knowledge base that can be used to auto-label data and identify risky states. We evaluate our approach on a dataset collected by Audi, which equipped a fleet of test vehicles with data loggers to store all sensor readings on 2,098 hours of driving on real roads. We show in several experiments that our method outperforms other baselines by up to 90%, and we further demonstrate how these embeddings of sensor data can be used to solve a variety of real-world automotive applications.


title: Ku-Band Low-Profile Rx-only and Tx-Rx antennas for Mobile Satellite Communications

abstract: This paper presents two different Ku-Band Low-Profile antenna concepts for Mobile Satellite Communications. The antennas are based on low-cost hybrid mechanical-electronic steerable solutions but, while the first one allows a broadband reception of a satellite signal (Receive-only antenna concept), the second one provides transmit and receive functions for a bi-directional communication link between the satellite and the mobile user terminal (Transmit-Receive antenna). Both examples are suitable for integration in land vehicles and aircrafts.


title: A Survey of Digital Television Broadcast Transmission Techniques

abstract: This paper is a survey of the transmission techniques used in digital television (TV) standards worldwide. With the increase in the demand for High-Definition (HD) TV, video-on-demand and mobile TV services, there was a real need for more bandwidth-efficient, flawless and crisp video quality, which motivated the migration from analogue to digital broadcasting. In this paper we present a brief history of the development of TV and then we survey the transmission technology used in different digital terrestrial, satellite, cable and mobile TV standards in different parts of the world. First, we present the Digital Video Broadcasting standards developed in Europe for terrestrial (DVB-T/T2), for satellite (DVB-S/S2), for cable (DVB-C) and for hand-held transmission (DVB-H). We then describe the Advanced Television System Committee standards developed in the USA both for terrestrial (ATSC) and for hand-held transmission (ATSC-M/H). We continue by describing the Integrated Services Digital Broadcasting standards developed in Japan for Terrestrial (ISDB-T) and Satellite (ISDB-S) transmission and then present the International System for Digital Television (ISDTV), which was developed in Brazil by adopteding the ISDB-T physical layer architecture. Following the ISDTV, we describe the Digital Terrestrial television Multimedia Broadcast (DTMB) standard developed in China. Finally, as a design example, we highlight the physical layer implementation of the DVB-T2 standard.


title: Architecture design with STT-RAM: Opportunities and challenges

abstract: The emerging spin-transfer torque magnetic random-access memory (STT-RAM) has attracted a lot of interest from both academia and industry in recent years. It has been considered as a promising replacement of SRAM and DRAM in the cache and memory system design thanks to many advantages, including non-volatility, low leakage power, SRAM comparable read performance and read energy consumption, higher density than SRAM, better scalability than conventional CMOS technologies, and good CMOS compatibility. However, the disadvantages of STT-RAM, such as higher write energy and longer write latency than SRAM, also bring design challenges. This paper introduces state-of-the-art architectural approaches to adopt STT-RAM in the cache and memory system design by taking advantage of the opportunities brought by STT-RAM as well as overcoming the challenges.


THE DESIGN OF A SENSOR NETWORK BASED ON IOT TECHNOLOGY FOR LANDSLIDE HAZARD ASSESSMENT


title: COMPARISON OF DATA ENCRYPTION ALGORITHMS

abstract: This paper tries to present a fair comparison between the most common and used algorithms in the data encryption field. The two main characteristics that identify and differentiate one encryption algorithm from another are its ability to secure the protected data against attacks and its speed and efficiency in doing so. This paper provides a performance comparison between four of the most common encryption algorithms: DES, 3DES, Blowfish and AES. The comparison has been conducted by running several encryption settings to process different sizes of data blocks


title: Distributed deployment of asynchronous guards in art galleries

abstract: This paper presents deployment algorithms for multiple mobile robots with line-of-sight sensing and communication capabilities in a simple nonconvex polygonal environment. The objective of the proposed algorithms is to achieve full visibility of the environment. We solve the problem by constructing a novel data structure called the vertex-induced tree and designing schemes to deploy over the nodes of this tree by means of distributed algorithms. The agents are assumed to have access to a local memory and their operation is partially asynchronous.


title: Analysis on data collection using mobile robot in wireless sensor networks

abstract: Recently, owing to the improvement of mass construction, the charge of sensor nodes has reduced considerably, and resource-rich sensor nodes can be prepared with high-technology procedures, for instance GPS. Nevertheless, the capability of the battery for all sensor nodes is measured inadequate in terms of energy. When energy diminution of sensor node fallout in a dead mode that is not capable to correspond with other nodes, this generates a bottleneck in a WSN. The data collection algorithm efficiently retrieved the sensed data from the dead node. Collecting sensed data using this approach will consume more time. Various techniques to approach the process of collecting sensed data using mobile robot in wireless sensor networks. In this paper, observed the latest sensed data collection methods and supply a summary in the applying automatic mobile robot with wireless sensor networks and also associated investigation work with that place. In addition side by side evaluations usually are done between various techniques to explain the advantages along with limitations. In this paper, experimental analysis has been conducted along with various factors to demonstrate the development as well as deployment of the sensed information collecting procedure inside wireless sensor networks.


title: A high-efficiency, dual-mode, dynamic, buck-boost power supply IC for portable applications

abstract: Integrated power supplies are critical building blocks in state-of-the-art portable applications, where they efficiently and accurately transform a battery supply into various regulated voltages, as required by their loads. This paper presents a novel low voltage, dual-mode, buck-boost converter IC targeted for dynamic supplies of linear RF power amplifiers (PAs) in wireless handsets. Maintaining high efficiency of the converter over a wide loading range is critical for improving battery life in such systems. The use of a novel, supply-voltage adaptive, on time control for pulse-frequency-modulation (PFM) mode achieves an accurate output ripple voltage, not to mention higher efficiency under light loads. In the high-power mode, the converter is operated in a modified pulse-width-modulation (PWM) control, where its operation is changed adaptively between buck, buck-boost, and boost regions, thereby saving unnecessary switching losses. Appropriate circuit topologies are developed and designed to overcome the challenges of a low supply voltage environment requiring a wide dynamic range converter. The converter is designed and simulated using a 0.5-/spl mu/m n-well CMOS process for a supply voltage range of 1.4-4.2 V, which is compatible with state-of-the-art Li-ion batteries (2.7-4.2 V), and alternate power sources, e.g. NiCd and NiMH batteries. Simulation results show that the converter generates an output voltage of 0.5-5 V while delivering up to 0.5 A of load current with a maximum ripple of 40 mV. The converter exhibits efficiency of 60-93% in PWM mode and 80% in PFM mode. A 0.9-1 V transient control-step response in PWM mode, which refers to a change in output voltage of 4.5-5 V, from an input supply of 1.4 V, is less than 200 /spl mu/sec.


title: Research of phase-shift series-load resonant power supply for DBD-ozone generator

abstract: In this paper, the power supply of a full bridge series-load resonant DBD type ozone generator is analyzed in detail under phase-shift control. The working modes of the circuit in two main states is obtained by combining the on-off state of power switches and the equivalent circuit of DBD type ozone generator. The experimental results indicate that: the series-load resonant power supply of ozone generator not only can realize the soft switching, but also can adjust the discharge power of ozone generator by changing the phase-shift angle under phase-shift control strategy. The series-load resonant power supply is therefore very suitable for ozone generator.


title: Underground cable fault detection and identification via fourier analysis

abstract: In this paper, the analysis of underground power cables is performed using Fourier analysis with the objective of detecting fault and average life of the cable. Three types of cables are used in this experiment; a normal cable, a shorted cable, and a cable with holes. The impedance in each case is computed and Fourier transformation is applied so that the resulting impedance magnitude and impedance phase can be examined in the frequency domain. Various windowing techniques are applied to the experimental data to eliminate any interference. Fourier analysis is then applied to the impedance data calculated from both sending end voltage and differential voltage. This analysis reveals differences in the frequency response of the three different types of a cable and eventually can be used as a measure for fault detection. Preliminary results reveal the differences in the frequency response. Accordingly, Fourier type methods can be effectively used as low cost and viable solutions to identify and detect faults in underground cables.


title: Microcharacterization of polycrystalline semiconductor thin films for photovoltaic applications

abstract: We have investigated the properties of individual grains in polycrystalline thin films of CuInSe2 and CdTe by transmission electron microscopy (TEM), transmission electron diffraction (TED), scanning electron microscopy (SEM), and energy dispersive x‐ray spectroscopy (EDS) in a Scanning Transmission Microscope (STEM). TED experiments showed that most grains possess the chalcopyrite structure, which is expected for CuInSe2. In some cases, however, a complex arrangement of different phases was found within a single grain, allowing a glimpse at the kinetics of the grain boundaries, EDS spectra were recorded in a SEM. The spectra show unambiguously the presence of grains with disparate composition. To assess the compositional changes within single grains, EDS spectra were taken at various locations on the grains in a STEM. Using the total photon count as a measure of the local grain thickness and the ratio of SeKα/SeLα intensities to correct for absorption losses, we can analyze the relative concentrations of...


title: Personalized pricing recommender system: multi-stage epsilon-greedy approach

abstract: Many e-commerce sites use recommender systems, which suggest items that customers prefer. Though recommender systems have achieved great success, their potential is not yet fulfilled. One weakness of current systems is that the actions of the system toward customers are restricted to simply showing items. We propose a system that relaxes this restriction to offer price discounting as well as recommendations. The system can determine whether or not to offer price discounting for individual customers, and such a pricing scheme is called price personalization. We discuss how the introduction of price personalization improves the commercial viability of managing a recommender system, and thereby improving the customers' sense of the system's reliability. We then propose a method for adding price personalization to standard recommendation algorithms which utilize two types of customer data: preferential data and purchasing history. Based on the analysis of the experimental results, we reveal further issues in designing a personalized pricing recommender system.


title: An Oblivious General-Purpose SQL Database for the Cloud

abstract: We present ObliDB, a secure SQL database for the public cloud that supports both transactional and analytics workloads and protects against access pattern leakage. With databases being a critical component in many applications, there is significant interest in outsourcing them securely. Hardware enclaves offer a strong practical foundation towards this goal by providing encryption and secure execution, but they still suffer from access pattern leaks that can reveal a great deal of information. The naive way to address this issue--using generic Oblivious RAM (ORAM) primitives beneath a database--adds prohibitive overhead. Instead, ObliDB co-designs both its data structures (e.g., oblivious B+ trees) and query operators to accelerate SQL processing, giving up to 329x speedup over naive ORAM. On analytics workloads, ObliDB ranges from competitive to 19x faster than systems designed only for analytics, such as Opaque, and comes within 2.6x of Spark SQL. Moreover, ObliDB also supports point queries, insertions, and deletions with latencies of 1-10ms, making it usable for transactional workloads too. To our knowledge, ObliDB is the first oblivious database that supports both transactional and analytic workloads.


title: Lack of effect of long-term supplementation with beta carotene on the incidence of malignant neoplasms and cardiovascular disease.

abstract: BACKGROUND ::: Observational studies suggest that people who consume more fruits and vegetables containing beta carotene have somewhat lower risks of cancer and cardiovascular disease, and earlier basic research suggested plausible mechanisms. Because large randomized trials of long duration were necessary to test this hypothesis directly, we conducted a trial of beta carotene supplementation. ::: ::: ::: METHODS ::: In a randomized, double-blind, placebo-controlled trial of beta carotene (50 mg on alternate days), we enrolled 22,071 male physicians, 40 to 84 years of age, in the United States; 11 percent were current smokers and 39 percent were former smokers at the beginning of the study in 1982. By December 31, 1995, the scheduled end of the study, fewer than 1 percent had been lost to follow-up, and compliance was 78 percent in the group that received beta carotene. ::: ::: ::: RESULTS ::: Among 11,036 physicians randomly assigned to receive beta carotene and 11,035 assigned to receive placebo, there were virtually no early or late differences in the overall incidence of malignant neoplasms or cardiovascular disease, or in overall mortality. In the beta carotene group, 1273 men had any malignant neoplasm (except nonmelanoma skin cancer), as compared with 1293 in the placebo group (relative risk, 0.98; 95 percent confidence interval, 0.91 to 1.06). There were also no significant differences in the number of cases of lung cancer (82 in the beta carotene group vs. 88 in the placebo group); the number of deaths from cancer (386 vs. 380), deaths from any cause (979 vs. 968), or deaths from cardiovascular disease (338 vs. 313); the number of men with myocardial infarction (468 vs. 489); the number with stroke (367 vs. 382); or the number with any one of the previous three end points (967 vs. 972). Among current and former smokers, there were also no significant early or late differences in any of these end points. ::: ::: ::: CONCLUSIONS ::: In this trial among healthy men, 12 years of supplementation with beta carotene produced neither benefit nor harm in terms of the incidence of malignant neoplasms, cardiovascular disease, or death from all causes.


title: Optimizing an Approximation of ROUGE - a Problem-Reduction Approach to Extractive Multi-Document Summarization

abstract: This paper presents a problem-reduction ::: approach to extractive multi-document ::: summarization: we propose a reduction ::: to the problem of scoring individual sentences ::: with their ROUGE scores based on ::: supervised learning. For the summarization, ::: we solve an optimization problem ::: where the ROUGE score of the selected ::: summary sentences is maximized. To this ::: end, we derive an approximation of the ::: ROUGE-N score of a set of sentences, and ::: define a principled discrete optimization ::: problem for sentence selection. Mathematical ::: and empirical evidence suggests ::: that the sentence selection step is solved ::: almost exactly, thus reducing the problem ::: to the sentence scoring task. We perform ::: a detailed experimental evaluation on two ::: DUC datasets to demonstrate the validity ::: of our approach.


Advancing the “E” in K-12 STEM Education


title: Detouring: Translating software to circumvent hard faults in simple cores

abstract: CMOS technology trends are leading to an increasing incidence of hard (permanent) faults in processors. These faults may be introduced at fabrication or occur in the field. Whereas high-performance processor cores have enough redundancy to tolerate many of these faults, the simple, low-power cores that are attractive for multicore chips do not. We propose Detouring, a software-based scheme for tolerating hard faults in simple cores. The key idea is to automatically modify software such that its functionality is unchanged but it does not use any of the faulty hardware. Our initial implementation of Detouring tolerates hard faults in several hardware components, including the instruction cache, registers, functional units, and the operand bypass network. Detouring has no hardware cost and no performance overhead for fault-free cores.


title: Predict anchor links across social networks via an embedding approach

abstract: Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent we can address the anchor link prediction problem, if only structural information of networks is available. Most existing methods, unsupervised or supervised, directly work on networks themselves rather than on their intrinsic structural regularities, and thus their effectiveness is sensitive to the high dimension and sparsity of networks. To offer a robust method, we propose a novel supervised model, called PALE, which employs network embedding with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor links. Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods.


title: PCS/W-CDMA dual-band MMIC power amplifier with a newly proposed linearizing bias circuit

abstract: A personal communications service/wide-band code division multiple access (PCS/W-CDMA) dual-band monolithic microwave integrated circuit (MMIC) power amplifier with a single-chip MMIC and a single-path output matching network is demonstrated by adopting a newly proposed on-chip linearizer. The linearizer is composed of the base-emitter diode of an active bias transistor and a capacitor to provide an RF short at the base node of the active bias transistor. The linearizer enhances the linearity of the power amplifier effectively for both PCS and W-CDMA bands with no additional DC power consumption, and has negligible insertion power loss with almost no increase in die area. It improves the input 1-dB gain compression point by 18.5 (20) dB and phase distortion by 6.1/spl deg/ (12.42/spl deg/) at an output power of 28 (28) dBm for the PCS (W-CDMA) band while keeping the base bias voltage of the power amplifier as designed. A PCS and W-CDMA dual-band InGaP heterojunction bipolar transistor MMIC power amplifier with single input and output and no switch for band selection is embodied by implementing the linearizer and by designing the amplifier to have broad-band characteristics. The dual-band power amplifier exhibits an output power of 30 (28.5) dBm, power-added efficiency of 39.5 % (36 %), and adjacent channel power ratio of -46 (-50) dBc at the output power of 28 (28) dBm under 3.4-V operation voltage for PCS (W-CDMA) applications.


A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA


title: Exploiting Objective Annotations for Measuring Translation Post-editing Effort

abstract: With the noticeable improvement in the overall quality of Machine Translation (MT) systems in recent years, post-editing of MT output is starting to become a common practice among human translators. However, it is well known that the quality of a given MT system can vary significantly across translation segments and that post-editing bad quality translations is a tedious task that may require more effort than translating texts from scratch. Previous research dedicated to learning quality estimation models to flag such segments has shown that models based on human annotation achieve more promising results. However, it is not yet clear what is the most appropriate form of human annotation for building such models. We experiment with models based on three annotation types (post-editing time, post-editing distance and post-editing effort scores) and show that estimations resulting from using post-editing time, a simple and objective annotation, can reliably indicate translation post-editing effort in a practical, taskbased scenario. We also discuss some perspectives on the effectiveness, reliability and cost of each type of annotation.


title: Debunking Myths about Gender and Mathematics Performance

abstract: G ender differences in mathematics participation rate, mean and high-end performance, and variance in distribution of performance have been reported on numerous occasions. The reasons for these findings have been the subject of much debate. For example, the greater male variability hypothesis, originally proposed by Ellis in 1894 [42] and reiterated in 2005 by Lawrence Summers when he was president of Harvard University [48], states that variability in intellectual abilities is intrinsically greater among males. If true, it could account for the fact that all Fields medalists have been male. If gender differences in means and variances are primarily a consequence of innate, biologically determined differences between the sexes, one would expect these differences to be similar among countries regardless of their culture and to remain fairly constant across time. Such a finding would suggest that little can be done to diminish these differences. In support of this hypothesis, Machin and Pekkarinen [26] claimed that greater male variance in mathematics performance was a “robust phenomenon”, that is, observed among fifteen-year-olds in thirty-five out of the forty countries that participated in the 2003 Programme for International Student Assessment (PISA). In addition, women’s nature might include a tendency to prefer the more nurturing fields, such as nursing and teaching young children, to the more quantitative ones, such as mathematics, physics, and engineering. If so, it might not make sense to encourage and direct any but the unusual female toward studying and seeking employment in these latter fields. This viewpoint has led some folks to propose that it may be a waste of time and money to expend resources directed toward trying to increase participation of women in these mathematics-intensive fields (e.g., [5], [6], [46], [49], [50]). Alternatively, boys and girls may be born similar in their innate intellectual potential but end up displaying differences due to a variety of sociocultural factors present in their environment, for example, gender-stratification ([2]). If true, one might see differences among countries and changes over time in mathematics variances and mean performances. This gender-stratified hypothesis is consistent with several recent findings. For example, Hyde and collaborators ([20], [25]) reported that girls have now reached parity with boys in mean mathematics performance in the United States, even in high school, where a significant gap in mean performance existed in the 1970s. Likewise, both Brody and Mills ([3]) and Wai et al. ([51]) noted a drop in nonrandom samples of students under thirteen years of age, from 13:1 in the 1970s down to approximately 3:1 by the 1990s in the ratio of U.S. boys to girls scoring above 700 on the quantitative section of the college-entrance SAT examination. The percentage of Ph.D.’s in the mathematical sciences awarded to U.S. citizens who are women has increased from 6 percent in the 1960s to 30 percent in the past decade ([4], [9]). Sociocultural, legal, and educational changes that took place during this time span may account for these dramatic improvements in mathematics performance and participation by U.S. females. Gender differences in opportunities and outcomes within countries have been quantified by a variety of measures. The Gender Gap Index (GGI) is a composite, weighted measure of the gap Jonathan M. Kane is professor of mathematics and computer science at the University of Wisconsin-Whitewater. His email address is kanej@uww.edu.


title: Securing the cloud with reconfigurable computing: An FPGA accelerator for homomorphic encryption

abstract: A hot topic in current cloud security research, homomorphic encryption is a recently introduced technique allowing computation to take place on encrypted data. This work presents the architecture and implementation of a dedicated FPGA-based accelerator addressing the prohibitive computing demand of homomorphic encryption. In particular, the accelerator targets the most time consuming operation used by the encryption primitive, large integer multiplication. Based on an Altera's Stratix V FPGA platform, the prototype implementation achieves significant improvements in terms of execution time - under a comparable hardware cost- against alternative solutions previously presented in the technical literature.


title: Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization

abstract: Can one parallelize complex exploration-exploitation tradeoffs? As an example, consider the problem of optimal high-throughput experimental design, where we wish to sequentially design batches of experiments in order to simultaneously learn a surrogate function mapping stimulus to response and identify the maximum of the function. We formalize the task as a multiarmed bandit problem, where the unknown payoff function is sampled from a Gaussian process (GP), and instead of a single arm, in each round we pull a batch of several arms in parallel. We develop GP-BUCB, a principled algorithm for choosing batches, based on the GP-UCB algorithm for sequential GP optimization. We prove a surprising result; as compared to the sequential approach, the cumulative regret of the parallel algorithm only increases by a constant factor independent of the batch size B. Our results provide rigorous theoretical support for exploiting parallelism in Bayesian global optimization. We demonstrate the effectiveness of our approach on two real-world applications.


title: Modeling, simulation and analisis of noninverting buck-boost converter using PSIM

abstract: This paper presents circuit modeling, design, simulation and analysis of noninverting buck boost converter. Closed loop simulation is carried out using PI and digital gates in voltage mode control technique using PSIM 8.0.7. The system can operate in buck, buck-boost or boost mode according to the condition of the supply voltage. Load and line regulations analysis is done and it is found to be 0.0125 % and 2.676 % respectively with tight regulations. efficiency is found to be 91.33%.under performance analysis it is found that ripple content in the output is reduced and enhanced the efficiency. non-inverting converter is employed to serve the need of the battery-powered applications. A voltage control pwm technique is adapted in the controller to enhance the efficiency and voltage regulation of the converter by reducing the transients caused during the transition from the buck mode to the boost mode.


title: Three Approaches from Europe: Waldorf, Montessori, and Reggio Emilia.

abstract: Waldorf, Montessori, and Reggio Emilia are three progressive approaches to early childhood education that appear to be growing in influence in North America and to have many points in common. This article provides a brief comparative introduction and highlights several key areas of similarity and contrast. All three approaches represent an explicit idealism and turn away from war and violence toward peace and reconstruction. They are built on coherent visions of how to improve human society by helping children realize their full potential as intelligent, creative, whole persons. In each approach, children are viewed as active authors of their own development, strongly influenced by natural, dynamic, selfrighting forces within themselves, opening the way toward growth and learning. Teachers depend for their work with children on carefully prepared, aesthetically pleasing environments that serve as a pedagogical tool and provide strong messages about the curriculum and about respect for children. Partnering with parents is highly valued in all three approaches, and children are evaluated by means other than traditional tests and grades. However, there are also many areas of difference, some at the level of principle and others at the level of strategy. Underlying the three approaches are variant views of the nature of young children's needs, interests, and modes of learning that lead to contrasts in the ways that teachers interact with children in the classroom, frame and structure learning experiences for children, and follow the children through observation/documentation. The article ends with discussion of the methods that researchers apply to analyze the strengths and weaknesses of each approach.


title: SkePU: a multi-backend skeleton programming library for multi-GPU systems

abstract: We present SkePU, a C++ template library which provides a simple and unified interface for specifying data-parallel computations with the help of skeletons on GPUs using CUDA and OpenCL. The interface is also general enough to support other architectures, and SkePU implements both a sequential CPU and a parallel OpenMP backend. It also supports multi-GPU systems. Copying data between the host and the GPU device memory can be a performance bottleneck. A key technique in SkePU is the implementation of lazy memory copying in the container type used to represent skeleton operands, which allows to avoid unnecessary memory transfers. We evaluate SkePU with small benchmarks and a larger application, a Runge-Kutta ODE solver. The results show that a skeleton approach to GPU programming is viable, especially when the computation burden is large compared to memory I/O (the lazy memory copying can help to achieve this). It also shows that utilizing several GPUs have a potential for performance gains. We see that SkePU offers good performance with a more complex and realistic task such as ODE solving, with up to 10 times faster run times when using SkePU with a GPU backend compared to a sequential solver running on a fast CPU.


title: Dense reconstruction on-the-fly

abstract: We present a novel system that is capable of generating live dense volumetric reconstructions based on input from a micro aerial vehicle. The distributed reconstruction pipeline is based on state-of-the-art approaches to visual SLAM and variational depth map fusion, and is designed to exploit the individual capabilities of the system components. Results are visualized in real-time on a tablet interface, which gives the user the opportunity to interact. We demonstrate the performance of our approach by capturing several indoor and outdoor scenes on-the-fly and by evaluating our results with respect to a ground-truth model.


title: A frequent pattern mining algorithm for feature extraction of customer reviews

abstract: Online shoppers often have different idea about the same product. They look for the product features that are consistent with their goal. Sometimes a feature might be interesting for one, while it does not make that impression for someone else. Unfortunately, identifying the target product with particular features is a tough task which is not achievable with existing functionality provided by common websites. In this paper, we present a frequent pattern mining algorithm to mine a bunch of reviews and extract product features. Our experimental results indicate that the algorithm outperforms the old pattern mining techniques used by previous researchers.


title: Readout interface for capacitive MEMS microphone in CMOS technology

abstract: This paper presents the frontend part of the readout interface for a capacitive MEMS microphone designed and fabricated in 0.35 μm CMOS technology. The developed readout interface will be used in a noise dosimeter applicable in very noisy and harsh environment. The prototype chips were measured and the main parameters evaluated. The achieved results demonstrate low harmonic distortion, low noise and high dynamic range of the developed readout interface.


title: Folliculitis decalvans: the use of dermatoscopy as an auxiliary tool in clinical diagnosis*

abstract: Folliculitis decalvans is an inflammatory presentation of cicatrizing alopecia characterized by inflammatory perifollicular papules and pustules. It generally occurs in adult males, predominantly involving the vertex and occipital areas of the scalp. The use of dermatoscopy in hair and scalp diseases improves diagnostic accuracy. Some trichoscopic findings, such as follicular tufts, perifollicular erythema, crusts and pustules, can be observed in folliculitis decalvans. More research on the pathogenesis and treatment options of this disfiguring disease is required for improving patient management.


title: Emerging Adulthood: What Is It, and What Is It Good For?

abstract: ABSTRACT—This article asserts that the theory of emerging adulthood is a useful way of conceptualizing the lives of people from their late teens to their mid- to late 20s in industrialized societies. The place of emerging adulthood within the adult life course is discussed. The weaknesses of previous terms for this age period are examined, and emerging adulthood is argued to be preferable as a new term for a new phenomenon. With respect to the question of whether emerging adulthood is experienced positively or negatively by most people, it is argued that it is positive for most people but entails developmental challenges that may be difficult and there is great heterogeneity, with some emerging adults experiencing serious problems. With respect to the question of whether or not emerging adulthood is good for society, it is argued that claims of the dangers of emerging adulthood are overblown, but emerging adulthood is probably a mixed blessing for society.


Image Caption Validation


title: Recurrent Memory Array Structures

abstract: The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalization capabilities. It considers both deterministic and stochastic variants of memory operation. It is shown that the nondeterministic Array-LSTM approach improves state-of-the-art performance on character level text prediction achieving 1.402 BPC on enwik8 dataset. Furthermore, this report estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9 and enwik10 datasets respectively.


title: Lignocellulosic residues: biodegradation and bioconversion by fungi.

abstract: The ability of fungi to degrade lignocellulosic materials is due to their highly efficient enzymatic system. Fungi have two types of extracellular enzymatic systems; the hydrolytic system, which produces hydrolases that are responsible for polysaccharide degradation and a unique oxidative and extracellular ligninolytic system, which degrades lignin and opens phenyl rings. Lignocellulosic residues from wood, grass, agricultural, forestry wastes and municipal solid wastes are particularly abundant in nature and have a potential for bioconversion. Accumulation of lignocellulosic materials in large quantities in places where agricultural residues present a disposal problem results not only in deterioration of the environment but also in loss of potentially valuable material that can be used in paper manufacture, biomass fuel production, composting, human and animal feed among others. Several novel markets for lignocellulosic residues have been identified recently. The use of fungi in low cost bioremediation projects might be attractive given their lignocellulose hydrolysis enzyme machinery.


title: Are ResNets Provably Better than Linear Predictors?

abstract: A residual network (or ResNet) is a standard deep neural net architecture, with state-of-the-art performance across numerous applications. The main premise of ResNets is that they allow the training of each layer to focus on fitting just the residual of the previous layer's output and the target output. Thus, we should expect that the trained network is no worse than what we can obtain if we remove the residual layers and train a shallower network instead. However, due to the non-convexity of the optimization problem, it is not at all clear that ResNets indeed achieve this behavior, rather than getting stuck at some arbitrarily poor local minimum. In this paper, we rigorously prove that arbitrarily deep, nonlinear residual units indeed exhibit this behavior, in the sense that the optimization landscape contains no local minima with value above what can be obtained with a linear predictor (namely a 1-layer network). Notably, we show this under minimal or no assumptions on the precise network architecture, data distribution, or loss function used. We also provide a quantitative analysis of approximate stationary points for this problem. Finally, we show that with a certain tweak to the architecture, training the network with standard stochastic gradient descent achieves an objective value close or better than any linear predictor.


title: CSGNet: Neural Shape Parser for Constructive Solid Geometry

abstract: We present a neural architecture that takes as input a 2D or 3D shape and induces a program to generate it. The in- structions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this task that rely on primitive detection are inherently slow since the search space over possible primitive combi- nations is large. In contrast, our model uses a recurrent neural network conditioned on the input shape to produce a sequence of instructions in a top-down manner and is sig- nificantly faster. It is also more effective as a shape detec- tor than existing state-of-the-art detection techniques. We also demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.


title: Reducing MOSFET 1/f noise and power consumption by switched biasing

abstract: Switched biasing is proposed as a technique for reducing the 1/f noise in MOSFET's. Conventional techniques, such as chopping or correlated double sampling, reduce the effect of 1/f noise in electronic circuits, whereas the switched biasing technique reduces the 1/f noise itself. Whereas noise reduction techniques generally lead to more power consumption, switched biasing can reduce the power consumption. It exploits an intriguing physical effect: cycling a MOS transistor from strong inversion to accumulation reduces its intrinsic 1/f noise. As the 1/f noise is reduced at its physical roots, high frequency circuits, in which 1/f noise is being upconverted, can also benefit. This is demonstrated by applying switched biasing in a 0.8 /spl mu/m CMOS sawtooth oscillator. By periodically switching off the bias currents, during time intervals that they are not contributing to the circuit operation, a reduction of the 1/f noise induced phase noise by more than 8 dB is achieved, while the power consumption is also reduced by 30%.


title: The Adoption of Electronic Banking in Tunisia : An Exploratory Study

abstract: This paper aims to check if the current and prompt technological revolution altering the whole world has crucial impacts on the Tunisian banking sector. Particularly, this study seeks some clues on which we can rely in order to understand the customers’ behavior regarding the adoption of electronic banking. To achieve this purpose, an empirical research is carried out in Tunisia and it reveals that a panoply of factors is affecting the customers-attitude toward e-banking. For instance; age, gender and educational qualifications seem to be important and they split up the group into electronic banking adopters and traditional banking defenders and so, they have significant influence on the customers’ adoption of e-banking. Furthermore, this study shows that despite the presidential incentives and in spite of being fully aware of the e-banking’s benefits, numerous respondents are still using the conventional banking. It is worthy to mention that the fear of loss because of transactions errors or hackers plays a significant role in alienating Tunisian customers from online banking. Finally, a number of this study’s limitations are highlighted and some research perspectives are suggested.


title: Population genetic structure and ecological niche modelling of the leafhopper Hishimonus phycitis

abstract: Witches’ broom disease of lime, caused by ‘Candidatus Phytoplasma aurantifolia’, is responsible for major losses of Mexican lime trees in Southern Iran, Oman and the United Arab Emirates. The causative phytoplasma is transmitted by the leafhopper, Hishimonus phycitis. We combined ecological niche modelling with environmental and genetic data for six populations of H. phycitis from Iran and one from Oman. The mitochondrial cytochrome c oxidase I (COI) gene and nine microsatellite DNA markers were used for the genetic analyses. Although the Oman population had specific haplotypes, the COI sequences were highly conserved among all populations studied. In contrast, the microsatellite data divided the populations from Iran and Oman into two separate clades. An analysis of molecular variance indicated a high level of variation within populations. The Mantel test showed no correlation between genetic and geographical distances. Gene flow values were small between the populations from Iran and north of Oman but significantly higher among the Iranian populations supporting the differentiation between Iran and Oman. In addition, we found that patterns of genetic divergence within Iranian populations were associated strongly with divergence in terms of their ecological niches. Data on six climatic variables, including elevation, were used to create ecological niche models. Our results suggest that the genetic differentiation of H. phycitis may be attributable to climatic conditions and/or geographical barriers.


title: Motivational Beliefs, Values, and Goals

abstract: ▪ Abstract This chapter reviews the recent research on motivation, beliefs, values, and goals, focusing on developmental and educational psychology. The authors divide the chapter into four major sections: theories focused on expectancies for success (self-efficacy theory and control theory), theories focused on task value (theories focused on intrinsic motivation, self-determination, flow, interest, and goals), theories that integrate expectancies and values (attribution theory, the expectancy-value models of Eccles et al., Feather, and Heckhausen, and self-worth theory), and theories integrating motivation and cognition (social cognitive theories of self-regulation and motivation, the work by Winne & Marx, Borkowski et al., Pintrich et al., and theories of motivation and volition). The authors end the chapter with a discussion of how to integrate theories of self-regulation and expectancy-value models of motivation and suggest new directions for future research.


title: Regularity Preserved Superpixels and Supervoxels

abstract: Most existing superpixel algorithms ignore the spatial structure and regularity properties, which result in undesirable sizes and location relationships for the subsequent processing. In this paper, we introduce a new method to generate the regularity preserved superpixels. Starting from the lattice seeds, our method relocates them to the pixel with locally maximal edge magnitudes and treats them as the superpixel junctions. Then, the shortest path algorithm is employed to find the local optimal boundary connecting each adjacent junction pair. Thanks to the local constraints, our method obtains homogeneous superpixels with adjacency in lowly textured and uniform regions and simultaneously preserves the boundary adherence in the high contrast contents. Our method preserves the regularity property without significantly sacrificing the segmentation accuracy. Moreover, we extend this regular constraint for generating the supervoxels. Our method obtains the regular supervoxels, which preserves the structural relation on both spatial and temporal spaces of the video. Quantitative and qualitative experimental results on benchmark datasets demonstrate that our simple but effective method outperforms the existing regular superpixel methods.


title: Covert Timing Channels Exploiting Cache Coherence Hardware: Characterization and Defense

abstract: Information leakage of sensitive data has become one of the fast growing concerns among computer users. With adversaries turning to hardware for exploits, caches are frequently a target for timing channels since they present different timing profiles for cache miss and hit latencies. Such timing channels operate by having an adversary covertly communicate secrets to a spy simply through modulating resource timing without leaving any physical evidence. In this article, we demonstrate a new vulnerability exposed by cache coherence protocols where adversaries could manipulate the coherence states on certain cache blocks to alter cache access timing and communicate secrets illegitimately. Our threat model assumes the trojan and spy can either exploit explicitly shared read-only physical pages (e.g., shared library code), or use memory deduplication feature to implicitly force create shared physical pages. We demonstrate a template that adversaries may use to construct covert timing channels through manipulating combinations of coherence states and data placement in different caches. We investigate several classes of cache coherence protocols, and observe that both directory-based and snoopy protocols can be subject to covert timing channel attacks. We identify that the root cause of the vulnerability to be the existence of access latency difference for cache lines in read-only cache coherence states: Exlusive and Shared. For defense, we propose a slightly modified cache coherence scheme that will enable the last level cache to directly respond to read data requests in these read-only coherence states, and avoid any latency difference that could enable timing channels.


title: Fast Exact Euclidean Distance (FEED): A New Class of Adaptable Distance Transforms

abstract: A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast Exact Euclidean Distance (FEED) transforms. FEED class algorithms calculate the DT starting directly from the definition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their efficient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity O(N), even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand. The source code of all algorithms included as well as the data sets used for both benchmarks are provided as supplementary material to this article.


title: Syntactically Informed Text Compression with Recurrent Neural Networks

abstract: We present a self-contained system for constructing natural language models for use in text compression. Our system improves upon previous neural network based models by utilizing recent advances in syntactic parsing -- Google's SyntaxNet -- to augment character-level recurrent neural networks. RNNs have proven exceptional in modeling sequence data such as text, as their architecture allows for modeling of long-term contextual information.


Seq2Tree: A Tree-Structured Extension of LSTM Network


title: WDAqua-core0: A Question Answering Component for the Research Community

abstract: We describe and present a new Question Answering (QA) component that can be easily used by the QA research community.


title: A Sizing Methodology of the Synchronous Reluctance Motor for Traction Applications

abstract: Synchronous reluctance machines (SynRM) have been used widely in industries for instance, in ABB's new VSD product package based on SynRM technology. It is due to their unique merits such as high efficiency, fast dynamic response, and low cost. However, considering the major requirements for traction applications such as high torque and power density, low torque ripple, wide speed range, proper size, and capability of meeting a specific torque envelope, this machine is still under investigation to be developed for traction applications. Since the choice of motor for traction is generally determined by manufacturers with respect to three dominant factors: cost, weight, and size, the SynRM can be considered a strong alternative due to its high efficiency and lower cost. Hence, the machine's proper size estimation is a major step of the design process before attempting the rotor geometry design. This is crucial in passenger vehicles in which compactness is a requirement and the size and weight are indeed the design limitations. This paper presents a methodology for sizing a SynRM. The electric and magnetic parameters of the proposed machine in conjunction with the core dimensions are calculated. Then, the proposed method's validity and evaluation are done using FE analysis.


title: Catalytic Conversion of Carbohydrates to Initial Platform Chemicals: Chemistry and Sustainability.

abstract: The replacement of fossil resources that currently provide more than 90% of our energy needs and feedstocks of the chemical industry in combination with reduced emission of carbon dioxide is one of the most pressing challenges of mankind. Biomass as a globally available resource has been proposed as an alternative feedstock for production of basic building blocks, which could partially or even fully replace the currently utilized fossil-based ones in well-established chemical processes. The destruction of lignocellulosic feed followed by oxygen removal from its cellulose and hemicellulose content by catalytic processes results in the formation of initial platform chemicals (IPCs). However, their sustainable production strongly depends on the availability of resources, their efficient or even industrially viable conversion processes, and replenishment time of feedstocks. Herein, we overview recent advances and developments in catalytic transformations of the carbohydrate content of lignocellulosic biomass to IPCs (i.e., ethanol, 3-hydroxypropionic acid, isoprene, succinic and levulinic acids, furfural, and 5-hydroxymethylfurfural). The mechanistic aspects, development of new catalysts, different efficiency indicators (yield and selectivity), and conversion conditions of their production are presented and compared. The potential biochemical production routes utilizing recently engineered microorganisms are reviewed, as well. The sustainability metrics that could be applied to the chemical industry (individual set of sustainability indicators, composite indices methods, material and energy flow analysis-based metrics, and ethanol equivalents) are also overviewed as well as an outlook is provided to highlight challenges and opportunities associated with this huge research area.


title: Sparse polynomial multiplication and division in Maple 14

abstract: We demonstrate new routines for sparse multivariate polynomial multiplication and division over the integers that we have integrated into Maple 14 through the expand and divide commands. These routines are currently the fastest available, and the multiplication routine is parallelized with superlinear speedup. The performance of Maple is significantly improved. We describe our polynomial data structure and compare it with Maple's. Then we present benchmarks comparing Maple 14 with Maple 13, Magma, Mathematica, Singular, Pari, and Trip.


title: Groupwise Maximin Fair Allocation of Indivisible Goods

abstract: We study the problem of allocating indivisible goods among n agents in a fair manner. For this problem, maximin share (MMS) is a well-studied solution concept which provides a fairness threshold. Specifically, maximin share is defined as the minimum utility that an agent can guarantee for herself when asked to partition the set of goods into n bundles such that the remaining (n-1) agents pick their bundles adversarially. An allocation is deemed to be fair if every agent gets a bundle whose valuation is at least her maximin share. ::: Even though maximin shares provide a natural benchmark for fairness, it has its own drawbacks and, in particular, it is not sufficient to rule out unsatisfactory allocations. Motivated by these considerations, in this work we define a stronger notion of fairness, called groupwise maximin share guarantee (GMMS). In GMMS, we require that the maximin share guarantee is achieved not just with respect to the grand bundle, but also among all the subgroups of agents. Hence, this solution concept strengthens MMS and provides an ex-post fairness guarantee. We show that in specific settings, GMMS allocations always exist. We also establish the existence of approximate GMMS allocations under additive valuations, and develop a polynomial-time algorithm to find such allocations. Moreover, we establish a scale of fairness wherein we show that GMMS implies approximate envy freeness. ::: Finally, we empirically demonstrate the existence of GMMS allocations in a large set of randomly generated instances. For the same set of instances, we additionally show that our algorithm achieves an approximation factor better than the established, worst-case bound.


title: Procedural City Layout Generation Based on Urban Land Use Models

abstract: Training and simulation applications in virtual worlds require significant amounts of urban environments. Procedural generation is an efficient way to create such models. Existing approaches for procedural modelling of cities aim at facilitating the work of urban planners and artists, but either require expert knowledge or external input data to generate results that resemble real-life cities, or they have long computation times, and thus are unsuitable for non-experts such as training instructors. We propose a method that procedurally creates layouts of structurally plausible cities from high-level, intuitive user input such as city size, location and historic background. The resulting layouts consist of different kinds of city districts which are arranged using constraints derived from established models of urban land use. Our approach avoids the need for external expert engagement in the creation process, and allows for the generation of large city layouts in seconds, making it significantly faster than comparable agent-based software and thus supporting the needs of non-expert creators of virtual cities for many applications.


title: Learning ELM-Tree from big data based on uncertainty reduction

abstract: A challenge in big data classification is the design of highly parallelized learning algorithms. One solution to this problem is applying parallel computation to different components of a learning model. In this paper, we first propose an extreme learning machine tree (ELM-Tree) model based on the heuristics of uncertainty reduction. In the ELM-Tree model, information entropy and ambiguity are used as the uncertainty measures for splitting decision tree (DT) nodes. Besides, in order to resolve the over-partitioning problem in the DT induction, ELMs are embedded as the leaf nodes when the gain ratios of all the available splits are smaller than a given threshold. Then, we apply parallel computation to five components of the ELM-Tree model, which effectively reduces the computational time for big data classification. Experimental studies demonstrate the effectiveness of the proposed method.


title: Entity Recommendations in Web Search

abstract: While some web search users know exactly what they are looking for, others are willing to explore topics related to an initial interest. Often, the user's initial interest can be uniquely linked to an entity in a knowledge base. In this case, it is natural to recommend the explicitly linked entities for further exploration. In real world knowledge bases, however, the number of linked entities may be very large and not all related entities may be equally relevant. Thus, there is a need for ranking related entities. In this paper, we describe Spark, a recommendation engine that links a user's initial query to an entity within a knowledge base and provides a ranking of the related entities. Spark extracts several signals from a variety of data sources, including Yahoo! Web Search, Twitter, and Flickr, using a large cluster of computers running Hadoop. These signals are combined with a machine learned ranking model in order to produce a final recommendation of entities to user queries. This system is currently powering Yahoo! Web Search result pages.


title: A Q-band CMOS LNA with noise cancellation

abstract: In this paper, a Q-band CMOS low noise amplifier (LNA) exploiting transformer positive-negative feedback and noise cancelling is presented. The proposed low noise amplifier consists of a positive transformer feedback to achieve noise-reduction and a negative transformer feedback to obtain good reverse isolation and stability. The noise cancellation technique is applied in this LNA to achieve a low noise figure. This LNA has been fabricated by standard commercial 90 nm technology. Based on measurements, this proposed LNA achieve a voltage gain of 11.5 dB, a noise figure of 6.5 dB and an input P1dB of -18 dBm. It consumes 11.5 mA from a 1.2 V supply and the total area of this design is 0.6*0.7 mm 2 .


title: Use Fewer Instances of the Letter “i”: Toward Writing Style Anonymization

abstract: This paper presents Anonymouth, a novel framework for anonymizing writing style. Without accounting for style, anonymous authors risk identification. This framework is necessary to provide a tool for testing the consistency of anonymized writing style and a mechanism for adaptive attacks against stylometry techniques. Our framework defines the steps necessary to anonymize documents and implements them. A key contribution of this work is this framework, including novel methods for identifying which features of documents need to change and how they must be changed to accomplish document anonymization. In our experiment, 80% of the user study participants were able to anonymize their documents in terms of a fixed corpus and limited feature set used. However, modifying pre-written documents were found to be difficult and the anonymization did not hold up to more extensive feature sets. It is important to note that Anonymouth is only the first step toward a tool to acheive stylometric anonymity with respect to state-of-the-art authorship attribution techniques. The topic needs further exploration in order to accomplish significant anonymity.


title: Predicting semantic annotations on the real-time web

abstract: The explosion of the real-time web has spurred a growing need for new methods to organize, monitor, and distill relevant information from these large-scale social streams. One especially encouraging development is the self-curation of the real-time web via user-driven linking, in which users annotate their own status updates with lightweight semantic annotations -- or hashtags. Unfortunately, there is evidence that hashtag growth is not keeping pace with the growth of the overall real-time web. In a random sample of 3 million tweets, we find that only 10.2% contain at least one hashtag. Hence, in this paper we explore the possibility of predicting hashtags for un-annotated status updates. Toward this end, we propose and evaluate a graph-based prediction framework. Three of the unique features of the approach are: (i) a path aggregation technique for scoring the closeness of terms and hashtags in the graph; (ii) pivot term selection, for identifying high value terms in status updates; and (iii) a dynamic sliding window for recommending hashtags reflecting the current status of the real-time web. Experimentally we find encouraging results in comparison with Bayesian and data mining-based approaches.


title: Towards versatile legged robots through active impedance control

abstract: Robots with legs and arms have the potential to support humans in dangerous, dull or dirty tasks. A major motivation behind research on such robots is their potential versatility. However, these robots come at a high price in mechanical and control complexity. Hence, until they can demonstrate a clear advantage over their simpler counterparts, robots with arms and legs will not fulfill their true potential. In this paper, we discuss the opportunities for versatile robots that arise by actively controlling the mechanical impedance of joints and particularly legs. In contrast to passive elements such as springs, active impedance is achieved by torque-controlled joints allowing real-time adjustment of stiffness and damping. Adjustable stiffness and damping in real-time is a fundamental building block towards versatility. Experiments with our 80 kg hydraulic quadruped robot HyQ demonstrate that active impedance alone i.e. no springs in the structure can successfully emulate passively compliant elements during highly dynamic locomotion tasks running, jumping and hopping; and that no springs are needed to protect the actuation system. Here we present results of a flying trot, also referred to as a running trot. To the best of the authors' knowledge this is the first time a flying trot has been successfully implemented on a robot without passive elements such as springs. A critical discussion on the pros and cons of active impedance concludes the paper. This article is an extension of our previous work presented at the International Symposium on Robotics Research ISRR 2013.


title: 5 – Distributed Information Retrieval

abstract: A multi-database model of distributed information retrieval is presented, in which people are assumed to have access to many searchable text databases. In such an environment, full-text information retrieval consists of discovering database contents, ranking databases by their expected ability to satisfy the query, searching a small number of databases, and merging results returned by different databases. This paper presents algorithms for each task. It also discusses how to reorganize conventional test collections into multi-database testbeds, and evaluation methodologies for multi-database experiments. A broad and diverse group of experimental results is presented to demonstrate that the algorithms are effective, efficient, robust, and scalable.


title: The state-of-the-art in personalized recommender systems for social networking

abstract: With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users' personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.


title: Analysis and design of a fully integrated SoC for UHF RFID reader in CMOS technology

abstract: This paper presents a fully integrated single-chip UHF radio frequency identification (RFID) reader SoC for short distance handheld applications. The SoC integrates all building blocks—including an RF transceiver, a PLL frequency synthesizer, a digital baseband and a MCU—in a 0.18um CMOS process. A high-linearity RX front-end and a low-phase-noise sythesizer are designed to handle the large self-interferer. A class-E power amplifier with high power efficiency is also integrated to fulfill the function of a RFID reader. The chip has a die area of 5.1mm*3.8mm including pads1.


title: Regularizing graph centrality computations

abstract: Centrality metrics such as betweenness and closeness have been used to identify important nodes in a network. However, it takes days to months on a high-end workstation to compute the centrality of today's networks. The main reasons are the size and the irregular structure of these networks. While today's computing units excel at processing dense and regular data, their performance is questionable when the data is sparse. In this work, we show how centrality computations can be regularized to reach higher performance. For betweenness centrality, we deviate from the traditional fine-grain approach by allowing a GPU to execute multiple BFSs at the same time. Furthermore, we exploit hardware and software vectorization to compute closeness centrality values on CPUs, GPUs and Intel Xeon Phi. Experiments show that only by reengineering the algorithms and without using additional hardware, the proposed techniques can speed up the centrality computations significantly: an improvement of a factor 5.9 on CPU architectures, 70.4 on GPU architectures and 21.0 on Intel Xeon Phi. We propose parallel algorithms to compute centrality on accelerators.We apply multiple breadth-first search operations simultaneously.Vectorization is applied to make the closeness computation faster.All the algorithms and techniques are experimentally validated.We get better performance than the best existing centrality computation solutions.


title: Learning Less is More - 6D Camera Localization via 3D Surface Regression

abstract: Popular research areas like autonomous driving and augmented reality have renewed the interest in image-based camera localization. In this work, we address the task of predicting the 6D camera pose from a single RGB image in a given 3D environment. With the advent of neural networks, previous works have either learned the entire camera localization process, or multiple components of a camera localization pipeline. Our key contribution is to demonstrate and explain that learning a single component of this pipeline is sufficient. This component is a fully convolutional neural network for densely regressing so-called scene coordinates, defining the correspondence between the input image and the 3D scene space. The neural network is prepended to a new end-to-end trainable pipeline. Our system is efficient, highly accurate, robust in training, and exhibits outstanding generalization capabilities. It exceeds state-of-the-art consistently on indoor and outdoor datasets. Interestingly, our approach surpasses existing techniques even without utilizing a 3D model of the scene during training, since the network is able to discover 3D scene geometry automatically, solely from single-view constraints.


title: A novel JXTA-based architecture for implementing heterogenous Networks of Things

abstract: Abstract This paper presents EmbJXTAChord, a novel peer-to-peer (P2P) architecture that integrates the good features of different sources, such as JXTA, EXI, CoAP, combining and augmenting them to provide a framework that is specifically devised for developing IoT applications over heterogeneous networks. EmbJXTAChord provides for several interesting properties, such as, distributed and fault-tolerant resource discovery, transparent routing over subnetworks, application protocol independence from the transport protocol in narrowband Wireless Sensor Networks, thus eliminating the need for using dedicated software or configuring custom gateways to achieve these functionalities. Moreover, EmbJXTAChord offers native support not only for TCP/HTTP, but also for Bluetooth RFCOMM and 6LoWPAN, thus opening to a broad range of IoT devices in supernetworks composed of networks using different interconnection technologies, not necessarily IP-based. In addition, EmbJXTAChord offers security over heterogeneous networks providing support for secure peergroups (even nested) and for group encryption, thus allowing for unicast and multicast communication between groups of objects sharing the same resources. The users of the proposed architecture will benefit from an integrated solution and the applications developed on the proposed framework will be able to reconfigure themselves, adapting automatically to the network topology of the execution environment. Finally, EmbJXTAChord provides jxCOAP-E, a new CoAP implementation that leverages on the transport mechanisms for heterogeneous networks offered by EmbJXTAChord. jxCOAP-E enables to realize a RESTful service architecture for peer-to-peer narrowband or broadband networks composed of devices connected via Ethernet, Wi-Fi, Bluetooth, BLE or IEEE 802.15.4. Differently from CoAP, jxCOAP-E provides a distributed and fault-tolerant service discovery mechanism and support for secure multicast communications. The paper presents EmbJXTAChord, discusses all the relevant design challenges and presents a comparative experimental performance assessment with state-of-the-art solutions on commercial-off-the-shelf devices.


title: Optimal test sequence generation using firefly algorithm

abstract: Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.


title: Elderly victim: an unusual autoerotic fatality involving an 87-year-old male

abstract: The average age of autoerotic victims is in the early thirties. Elderly victims of more than 65 years of age are uncommon, consisting of less than 1% of cases. The following case represents the oldest victim ever reported: a 87-year-old man found hanging by a belt in his living room. The body of the deceased was completely naked, with a complex bondage system of ropes. In the victim’s bedroom closet, a suitcase contained similar ropes and belts suggesting prior autoerotic activity. Semen was also found at the scene.


title: A single-chip highly linear 2.4GHz 30dBm power amplifier in 90nm CMOS

abstract: In recent years, there has been tremendous interest in trying to implement power amplifiers (PAs) in CMOS, due to cost and integration benefits. However, the low supply voltage, conductive substrate, and high loss of on-chip passives make monolithic, linear, high-power PA design challenging in CMOS. Most of the high-power CMOS PAs reported to date are switching-type [1,2], and have not exhibited sufficient linearity required for modern wireless standards. In this paper, a single-chip linear CMOS PA with sufficient power and linearity for emerging OFDM-based applications is reported. This 90nm fully-integrated PA adopts a differential topology and operates at 3.3V supply.