title: A Survey on Topology issues in Wireless Sensor Network

abstract: Topology issues have received more and more attentions in Wireless Sensor Networks (WSN). While WSN applications are normally optimized by the given underlying network topology, another trend is to optimize WSN by means of topology control. A number of approaches have been invested in this area, such as topology directed routing, cooperating schemes, sensor coverage based topology control and network connectivity based topology control. Most of the schemes have proven to be able to provide a better network monitoring and communication performance with prolonged system lifetime. In this survey paper, we provide a full view of the studies in this area. By summarizing previous achievements and analyzing existed problems, we also point out possible research directions for future work .


title: Prior Exposure Increases Perceived Accuracy of Fake News

abstract: The 2016 U.S. presidential election brought considerable attention to the phenomenon of "fake news": entirely fabricated and often partisan content that is presented as factual. Here we demonstrate one mechanism that contributes to the believability of fake news: fluency via prior exposure. Using actual fake-news headlines presented as they were seen on Facebook, we show that even a single exposure increases subsequent perceptions of accuracy, both within the same session and after a week. Moreover, this "illusory truth effect" for fake-news headlines occurs despite a low level of overall believability and even when the stories are labeled as contested by fact checkers or are inconsistent with the reader's political ideology. These results suggest that social media platforms help to incubate belief in blatantly false news stories and that tagging such stories as disputed is not an effective solution to this problem. It is interesting, however, that we also found that prior exposure does not impact entirely implausible statements (e.g., "The earth is a perfect square"). These observations indicate that although extreme implausibility is a boundary condition of the illusory truth effect, only a small degree of potential plausibility is sufficient for repetition to increase perceived accuracy. As a consequence, the scope and impact of repetition on beliefs is greater than has been previously assumed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).


title: Exponential Antipodal Vivaldi Antenna With Exponential Dielectric Lens

abstract: The expansion of wireless technologies poses various requirements such as stable radiation characteristics in a wide frequency range. This letter presents an exponential antipodal Vivaldi antenna with a dielectric lens replacing part of the substrate in antenna aperture, with dimensions of 76 × 130 mm2. The shape of the lens also follows exponential structure in its inner (where lens meets the antenna substrate) and outer edges. Using a dielectric with higher constant in the proposed antenna provides 1 dB improvement in antenna gain and 24% of 3-dB beamwidth reduction.


title: Speech emotion recognition using hidden Markov models

abstract: Abstract In emotion classification of speech signals, the popular features employed are statistics of fundamental frequency, energy contour, duration of silence and voice quality. However, the performance of systems employing these features degrades substantially when more than two categories of emotion are to be classified. In this paper, a text independent method of emotion classification of speech is proposed. The proposed method makes use of short time log frequency power coefficients (LFPC) to represent the speech signals and a discrete hidden Markov model (HMM) as the classifier. The emotions are classified into six categories. The category labels used are, the archetypal emotions of Anger, Disgust, Fear, Joy, Sadness and Surprise. A database consisting of 60 emotional utterances, each from twelve speakers is constructed and used to train and test the proposed system. Performance of the LFPC feature parameters is compared with that of the linear prediction Cepstral coefficients (LPCC) and mel-frequency Cepstral coefficients (MFCC) feature parameters commonly used in speech recognition systems. Results show that the proposed system yields an average accuracy of 78% and the best accuracy of 96% in the classification of six emotions. This is beyond the 17% chances by a random hit for a sample set of 6 categories. Results also reveal that LFPC is a better choice as feature parameters for emotion classification than the traditional feature parameters.


title: Design and implementation of a robot for maze-solving with turning indicators using PID controller

abstract: Autonomous navigation is an important feature that allows a mobile robot to independently move from a point to another without an intervention from a human operator. Autonomous navigation within an unknown area requires the robot to explore, localize and map its surroundings. By solving a maze, the pertaining algorithms and behaviour of the robot can be studied and improved upon. This paper describes an implementation of a maze-solving robot designed to solve a maze with turning indicators. The black turning indicators tell the robot which way to turn at the intersections to reach at the centre of the maze. Detection of intersection line and turning indicators in the maze was done by using LDR sensors. Algorithm for straight-line correction was based on PI(D) controller.


title: Query Classification Using Convolutional Neural Networks

abstract: Nowadays, search engines become more and more important for people, it can help us to obtain the useful information. Capturing the query intent from use's query can improve the search efficiency and provide them a better user experiment. Query classification task can help us identify query intent. In this paper, we utilize convolutional neural networks for query classification, it is a supervise learning method, in the training process, we input the query and corresponding category to train this model, and in test process this model can predict query's category. In our experiments, we use two different convolutional neural networks to learn the query representation automatically based on the semantically similarity. Experimental results show that our model is competitive and can better predict query's category, the precision of one model improve 3% compared to logistic regression.


title: Group multi-criteria supplier selection using an extended VIKOR method with interval 2-tuple linguistic information

abstract: Propose an extended VIKOR method for group multi-criteria supplier selection.Diversity and uncertainty of decision makers' assessments are modeled using interval 2-tuples.Multiple and conflicting criteria are considered and evaluated by extended VIKOR method.The new method is more suitable to handle the supplier selection problem under uncertain environment. How to select the suitable suppliers in the supply chain is critical for an organization's success and has attracted much attention of both researchers and practitioners. Supplier selection can be regarded as a complex group multiple criteria decision making problem requiring consideration of a number of alternative suppliers and quantitative and qualitative criteria. Additionally, decision makers cannot easily express their judgments on the alternatives with exact numerical values in many practical situations, and there usually exists uncertain and incomplete assessments. In response, this paper proposes an extended VIKOR method for group multi-criteria supplier selection with interval 2-tuple linguistic information. The feasibility and practicability of the proposed interval 2-tuple linguistic VIKOR (ITL-VIKOR) method are demonstrated through three realistic supplier selection examples and comparisons with the existing approaches. Results show that the ITL-VIKOR method being proposed is more suitable and effective to handle the supplier selection problem under vague, uncertain and incomplete information environment.


Programming parallel algorithms


title: A Digital PWM Current Controller for Switched Reluctance Motor Drives

abstract: In this paper, a PWM current controller for the switched reluctance motor drives is proposed and digitally implemented. Parameter adaption is employed to guarantee both fast dynamics and robustness of the proposed current controller. The relationship between the proposed controller and the conventional controllers including PI and dead-beat controller is also presented. An improved sampling method is designed to avoid PWM delay in the control loop. Simulation and experimental results show that the proposed controller keeps similar dynamic response and accuracy as hysteresis controller under various testing conditions. However, compared with the hysteresis controller, the proposed current controller needs much lower sampling rate and has a constant switching frequency.


title: Vehicle Door Latch with Tracking and Alert System using Global Positioning System Technology and IoT Based Hardware Control for Visibility and Security of Assets

abstract: Distinguishing and classifying different types of malware is important to better understanding how they can infect computers and devices, the threat level they pose and how to protect against them. In this paper, a system for classifying malware programs is presented. The paper describes the architecture of the system and assesses its performance on a publicly available database (provided by Microsoft for the Microsoft Malware Classification Challenge BIG2015) to serve as a benchmark for future research efforts. First, the malicious programs are preprocessed such that they are visualized as gray scale images. We then make use of an architecture comprised of multiple layers (multiple levels of encoding) to carry out the classification process of those images/programs. We compare the performance of this approach against traditional machine learning and pattern recognition algorithms. Our experimental results show that the deep learning architecture yields a boost in performance over those conventional/standard algorithms. A hold-out validation analysis using the superior architecture shows an accuracy in the order of 99.15%.


title: Automatically Inferring Malware Signatures for Anti-Virus Assisted Attacks

abstract: Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats. Anti-virus signatures are a simple and fast detection mechanism that can complement more sophisticated analysis strategies. However, if signatures are not designed with care, they can turn from a defensive mechanism into an instrument of attack. In this paper, we present a novel method for automatically deriving signatures from anti-virus software and discuss how the extracted signatures can be used to attack sensible data with the aid of the virus scanner itself. To this end, we study the practicability of our approach using four commercial products and exemplary demonstrate anti-virus assisted attacks in three different scenarios.


title: Assumption-free anomaly detection in time series

abstract: Recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time. However, because of the sheer volume of data most of it will never be inspected by an algorithm, much less a human being. One way to mitigate this problem is to perform some type of anomaly (novelty /interestingness/surprisingness) detection and flag unusual patterns for further inspection by humans or more CPU intensive algorithms. Most current solutions are “custom made” for particular domains, such as ECG monitoring, valve pressure monitoring, etc. This customization requires extensive effort by domain expert. Furthermore, hand-crafted systems tend to be very brittle to concept drift. In this demonstration, we will show an online anomaly detection system that does not need to be customized for individual domains, yet performs with exceptionally high precision/recall. The system is based on the recently introduced idea of time series bitmaps. To demonstrate the universality of our system, we will allow testing on independently annotated datasets from domains as diverse as ECGs, Space Shuttle telemetry monitoring, video surveillance, and respiratory data. In addition, we invite attendees to test our system with any dataset available on the web.


title: Wireless Fingerprint Attendance System using ZigBee Technology

abstract: In this paper provides the design method of wireless fingerprint attendance system based on ZigBee technology. The system includes terminal fingerprint acquisition module and attendance management module through computer. It can realize automatically such functions as information acquisition of fingerprint, processing, wireless transmission, fingerprint matching, and attendance management. Considering the fact and topology of ZigBee network, a fingerprint acquisition module and a wireless alarm module were designed by using the fingerprint sensor and module respectively. The whole system was implemented wireless alarm through messages and internet in the GSM web. In order to achieve the simple and high real-time system, it realized low-cost and high-performance wireless fingerprint attendance function, which provided a new wireless fingerprint attendance system for enterprises and institutions.


title: Wirtinger Flow Method With Optimal Stepsize for Phase Retrieval

abstract: The recently reported Wirtinger flow (WF) algorithm has been demonstrated as a promising method for solving the problem of phase retrieval by applying a gradient descent scheme. An empirical choice of stepsize is suggested in practice. However, this heuristic stepsize selection rule is not optimal. In order to accelerate the convergence rate, we propose an improved WF with optimal stepsize. It is revealed that this optimal stepsize is the solution of a univariate cubic equation with real-valued coefficients. Finding its roots is computationally simple because a closed-form expression exists. Furthermore, compared with obtaining the coefficients of the cubic equation, calculating the gradient is still the leading cost. Therefore, the proposed approach has the same dominant cost as WF in each iteration. Simulation results are provided to validate its efficiency compared to the existing technique.


title: Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks

abstract: In this letter, we consider cooperative spectrum sensing based on energy detection in cognitive radio networks. Soft combination of the observed energies from different cognitive radio users is investigated. Based on the Neyman-Pearson criterion, we obtain an optimal soft combination scheme that maximizes the detection probability for a given false alarm probability. Encouraged by the performance gain of soft combination, we further propose a new softened hard combination scheme with two-bit overhead for each user and achieve a good tradeoff between detection performance and complexity.


Benchmarks for Temporal Logic Requirements for Automotive Systems


title: Fast and Effective Feature-Preserving Mesh Denoising

abstract: We present a simple and fast mesh denoising method, which can remove noise effectively while preserving mesh features such as sharp edges and corners. The method consists of two stages. First, noisy face normals are filtered iteratively by weighted averaging of neighboring face normals. Second, vertex positions are iteratively updated to agree with the denoised face normals. The weight function used during normal filtering is much simpler than that used in previous similar approaches, being simply a trimmed quadratic. This makes the algorithm both fast and simple to implement. Vertex position updating is based on the integration of surface normals using a least-squares error criterion. Like previous algorithms, we solve the least-squares problem by gradient descent; whereas previous methods needed user input to determine the iteration step size, we determine it automatically. In addition, we prove the convergence of the vertex position updating approach. Analysis and experiments show the advantages of our proposed method over various earlier surface denoising methods.


title: CiteSeerx: A Scholarly Big Dataset

abstract: The CiteSeer x digital library stores and indexes research articles in Computer Science and related fields. Although its main purpose is to make it easier for researchers to search for scientific information, CiteSeer x has been proven as a powerful resource in many data mining, machine learning and information retrieval applications that use rich metadata, e.g., titles, abstracts, authors, venues, references lists, etc. The metadata extraction in CiteSeer x is done using automated techniques. Although fairly accurate, these techniques still result in noisy metadata. Since the performance of models trained on these data highly depends on the quality of the data, we propose an approach to CiteSeer x metadata cleaning that incorporates information from an external data source. The result is a subset of CiteSeer x , which is substantially cleaner than the entire set. Our goal is to make the new dataset available to the research community to facilitate future work in Information Retrieval.


title: Evaluating a tool for improving accessibility to charts and graphs

abstract: We discuss factors in the design and evaluation of natural language-driven assistive technologies that generate descriptions of, and allow interaction with, graphical representations of numerical data. In particular, we provide data in favor of 1) screen-reading technologies as a usable, useful, and cost-effective means of interacting with graphs. The data also show that by carrying out evaluation of Assistive Technologies on populations other than the target communities, certain subtleties of navigation and interaction may be lost or distorted.


title: Convolutional Attention-based Seq2Seq Neural Network for End-to-End ASR

abstract: This thesis introduces the sequence to sequence model with Luong's attention mechanism for end-to-end ASR. It also describes various neural network algorithms including Batch normalization, Dropout and Residual network which constitute the convolutional attention-based seq2seq neural network. Finally the proposed model proved its effectiveness for speech recognition achieving 15.8% phoneme error rate on TIMIT dataset.


title: Janus: Automatic Ontology Builder from XSD Files

abstract: The construction of a reference ontology for a large domain still remains an hard human task. The process is sometimes assisted by software tools that facilitate the information extraction from a textual corpus. Despite of the great use of XML Schema files on the internet and especially in the B2B domain, tools that offer a complete semantic analysis of XML schemas are really rare. In this paper we introduce Janus, a tool for automatically building a reference knowledge base starting from XML Schema files. Janus also provides different useful views to simplify B2B application integration.


title: Coexistence challenges for heterogeneous cognitive wireless networks in TV white spaces

abstract: In order to improve utilization of TV spectrum, regulatory bodies around the world have been developing rules to allow operation by unlicensed users in these bands provided that interference to incumbent broadcasters is avoided. Thus, new services may opportunistically use temporarily unoccupied TV channels, known as television white space. This has motivated several standardization efforts such as IEEE 802.22, 802.11af, 802.19 TG1, and ECMA 392 to further cognitive networking. Specifically, multiple collocated secondary networks are expected to use TVWS, each with distinct requirements (bandwidth, transmission power, different system architectures, and device types) that must all comply with regulatory requirements to protect incumbents. Heterogeneous coexistence in the TVWS is thus expected to be an important research challenge. This article introduces the current regulatory scenario, emerging standards for cognitive wireless networks targeting the TVWS, and discusses possible coexistence scenarios and associated challenges. Furthermore, the article casts an eye on future considerations for these upcoming standards in support of spectrum sharing opportunities as a function of network architecture evolution.


title: Radio propagation characteristics for line-of-sight microcellular and personal communications

abstract: To acquire a knowledge of radio propagation characteristics in the microcellular environments for personal communications services (PCS), a comprehensive measurement program was conducted by Telesis Technologies Laboratory (TTL) in the San Francisco Bay area using three base station antenna heights of 3.2 m, 8.7 m, and 13.4 m and two frequencies at 900 MHz and 1900 MHz. Five test settings were chosen in urban, suburban, and rural areas in order to study propagation in a variety of environments. This paper reports the LOS measurements in different environments, all of which show variations of signal strength with distance that have distinct near and far regions separated by a break point. It was also found that the location of the break point for different frequencies and antenna heights can be calculated based on first Fresnel zone clearance. The regression analysis reveals a slope that is less than two before the break point, while it is greater than two after the break point. This break distance can be used to define the size of microcell and to design for fast hand-off. Beyond the first Fresnel zone break distance the base station antenna height gain was observed to approximately follow the square power law of antenna height. >


title: A compact single layer monopulse microstrip antenna array

abstract: A compact single layer monopulse microstrip patch antenna array for the application of monopulse radar has been designed, manufactured and tested. The array, the monopulse comparator and the feed network are placed on the same layer, resulting in a very compact structure. The limitation of this structure is discussed. The effects of spurious radiation and blockage caused by the comparator on the sidelobe level are estimated. A space mapping (SM) technique is applied to design microstrip subarray. The bandwidth (VSWR<2) of the antenna is 5.6%, the operating frequency is between 13.85-15.1 GHz and the designed central frequency is 14.25 GHz. Our measurements show that, both for the E plane and for the H plane, the sidelobe levels of the sum pattern are less than -17 dB, and the null depths of the difference pattern are less than -30 dB. The maximum gain at the operating frequencies is 24.5 dBi.


title: A Hybrid AMOLED Driver IC for Real-Time TFT Nonuniformity Compensation

abstract: An active matrix organic light emitting diode (AMOLED) display driver IC, enabling real-time thin-film transistor (TFT) nonuniformity compensation, is presented with a hybrid driving method to satisfy fast driving speed, high TFT current accuracy, and a high aperture ratio. The proposed hybrid column-driver IC drives a mobile UHD $\left({{3840} \times {2160}} \right)$ AMOLED panel, with one horizontal time of ${7}.{7}\,{\upmu \text{s}}$ at a scan frequency of 60 Hz, simultaneously senses the TFT current for back-end TFT variation compensation. Due to external compensation, a simple 3T1C pixel circuit is employed in each pixel. Accurate current sensing and high panel noise immunity is guaranteed by a proposed current-sensing circuit. By reusing the hybrid column-driver circuitries, the driver embodies an 8 bit current-mode ADC to measure OLED $V$ – $I$ transfer characteristic for OLED luminance-degradation compensation. Measurement results show that the hybrid driving method reduces the maximum current error between two emulated TFTs with a 60 mV threshold voltage difference under 1 gray-level error of 0.94 gray level (37 nA) in 8 bit gray scales from 12.82 gray level (501 nA). The circuit-reused current-mode ADC achieves 0.56 LSB DNL and 0.75 LSB INL.


title: Preparation of isotopically labelled benzophenone-containing lipid analogues

abstract: Methods have been developed for isotopic labelling of the benzophenone-containing cholesterol analogue 1 and the fatty acid analogue 2 by use of deuteriated sodium borohydride derivatives. These procedures avoid the commercial catalytic tritiation which had previously been used to obtain 3H-1 and 3H-2. Copyright © 2005 John Wiley & Sons, Ltd.


title: Direct semi-dense SLAM for rolling shutter cameras

abstract: In this paper, we present a monocular Direct and Semi-dense SLAM (Simultaneous Localization And Mapping) system for rolling shutter cameras. In a rolling shutter camera, the pose is different for each row of each image, and this yields poor pose estimates and poor structure estimates when using a state-of-the-art semi-dense direct method designed for global shutter cameras. To address this issue in tracking, we model the smooth and continuous camera trajectory using a B-spline curve of degree k??1 for poses in the Lie algebra, se(3).We solve for the camera poses at each row-time by a direct optimisation of photometric error as a function of the control points of the spline. Likewise for mapping, we develop generalised epipolar geometry for the rolling shutter case and solve for point depths using photometric error. Although each of these issues has been previously tackled, to the best of our knowledge ours is the first full solution to monocular, direct (feature-less) SLAM. We benchmark our method for pose accuracy and map accuracy against the state-of-the-art semi-dense SLAM system, LSD-SLAM, demonstrating the improved efficacy of our approach when using rolling shutter cameras via synthetic sequences with known ground-truth and real sequences.


title: Innovative Cost Effective Approach for Cell Phone Based Remote Controlled Embedded System for Irrigation

abstract: This work describes the development of innovative low cost cell phone based remote control application for induction motor-pump based irrigation in agriculture. Rural areas in many states of India are plagued by frequent power cuts and abnormal voltage conditions. The developed system ensures that water is distributed to field whenever normal conditions exist based on task specified. A novel concept of miscall for specified duration has been used to reduce the operational cost of the system and for the convenience of farmers facing difficulty in typing messages. Information is exchanged in form of miscalls / message between the system and the user cell phones. The system is based on AVR ATMega32 micro-controller and includes protection against single phasing, over-current, dry running and other desirable features. DS1307 and DS18S20 are used for time and temperature measurement respectively. It is expected that system will relieve hardships of farmers relating water distribution to a great extent.


title: FAM-FACE-SG: a score for risk stratification of frequent hospital admitters

abstract: Background ::: An accurate risk stratification tool is critical in identifying patients who are at high risk of frequent hospital readmissions. While 30-day hospital readmissions have been widely studied, there is increasing interest in identifying potential high-cost users or frequent hospital admitters. In this study, we aimed to derive and validate a risk stratification tool to predict frequent hospital admitters.


title: Simulation of Image Enhancement Techniques Using Matlab

abstract: This paper emphasis is on the importance of simulation techniques in the field of digital image processing. Simulations are carried out for implementation of various basic image enhancement techniques on MATLAB in spatial domain as well as the frequency domain. Through this simulation, it is concluded that the frequency domain analysis is easier to implement as compared to spatial domain and also it is concluded that one can understand the complex procedures of image processing provided he/she is able to analyze the results and compare it with the theoretical results mentioned in various books


title: vRAM: Faster Verifiable RAM with Program-Independent Preprocessing

abstract: We study the problem of verifiable computation (VC) for RAM programs, where a computationally weak verifier outsources the execution of a program to a powerful (but untrusted) prover. Existing efficient implementations of VC protocols require an expensive preprocessing phase that binds the parties to a single circuit. (While there are schemes that avoid preprocessing entirely, their performance remains significantly worse than constructions with preprocessing.) Thus, a prover and verifier are forced to choose between two approaches: (1) Allow verification of arbitrary RAM programs, at the expense of efficiency, by preprocessing a universal circuit which can handle all possible instructions during each CPU cycle; or (2) Sacrifice expressiveness by preprocessing an efficient circuit which is tailored to the verification of a single specific RAM program. We present vRAM, a VC system for RAM programs that avoids both the above drawbacks by having a preprocessing phase that is entirely circuit-independent (other than an upper bound on the circuit size). During the proving phase, once the program to be verified and its inputs are chosen, the circuit-independence of our construction allows the parties to use a smaller circuit tailored to verifying the specific program on the chosen inputs, i.e., without needing to encode all possible instructions in each cycle. Moreover, our construction is the first with asymptotically optimal prover overhead; i.e., the work of the prover is a constant multiplicative factor of the time to execute the program. Our experimental evaluation demonstrates that vRAM reduces the prover's memory consumption by 55-110x and its running time by 9-30x compared to existing schemes with universal preprocessing. This allows us to scale to RAM computations with more than 2 million CPU cycles, a 65x improvement compared to the state of the art. Finally, vRAM has performance comparable to (and sometimes better than) the best existing scheme with program-specific preprocessing despite the fact that the latter can deploy program-specific optimizations (and has to pay a separate preprocessing cost for every new program).


title: Advances in Pre-Training Distributed Word Representations

abstract: Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl. In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together. The main result of our work is the new set of publicly available pre-trained models that outperform the current state of the art by a large margin on a number of tasks.


title: A unified energy-based framework for unsupervised learning

abstract: We introduce a view of unsupervised learning that integrates probabilistic and nonprobabilistic methods for clustering, dimensionality reduction, and feature extraction in a unified framework. In this framework, an energy function associates low energies to input points that are similar to training samples, and high energies to unobserved points. Learning consists in minimizing the energies of training samples while ensuring that the energies of unobserved ones are higher. Some traditional methods construct the architecture so that only a small number of points can have low energy, while other methods explicitly “pull up” on the energies of unobserved points. In probabilistic methods the energy of unobserved points is pulled by minimizing the log partition function, an expensive, and sometimes intractable process. We explore different and more efficient methods using an energy-based approach. In particular, we show that a simple solution is to restrict the amount of information contained in codes that represent the data. We demonstrate such a method by training it on natural image patches and by applying to image denoising.


title: Greater Trochanteric Pain Syndrome: A Review of Anatomy, Diagnosis and Treatment

abstract: Greater trochanteric pain syndrome (GTPS) is a term used to describe chronic pain overlying the lateral aspect of the hip. This regional pain syndrome, once described as trochanteric bursitis, often mimics pain generated from other sources, including, but not limited to myofascial pain, degenerative joint disease, and spinal pathology. The incidence of greater trochanteric pain is reported to be approximately 1.8 patients per 1000 per year with the prevalence being higher in women, and patients with coexisting low back pain, osteoarthritis, iliotibial band tenderness, and obesity. Symptoms of GTPS consist of persistent pain in the lateral hip radiating along the lateral aspect of the thigh to the knee and occasionally below the knee and/or buttock. Physical examination reveals point tenderness in the posterolateral area of the greater trochanter. Most cases of GTPS are self-limited with conservative measures, such as physical therapy, weight loss, nonsteroidal antiinflammatory drugs and behavior modification, providing resolution of symptoms. Other treatment modalities include bursa or lateral hip injections performed with corticosteroid and local anesthetic. More invasive surgical interventions have anecdotally been reported to provide pain relief when conservative treatment modalities fail.


title: Multi-Task Learning for Mental Health using Social Media Text

abstract: We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework. By modeling multiple conditions, the system learns to make predictions about suicide risk and mental health at a low false positive rate. Conditions are modeled as tasks in a multi-task learning (MTL) framework, with gender prediction as an additional auxiliary task. We demonstrate the effectiveness of multi-task learning by comparison to a well-tuned single-task baseline with the same number of parameters. Our best MTL model predicts potential suicide attempt, as well as the presence of atypical mental health, with AUC > 0.8. We also find additional large improvements using multi-task learning on mental health tasks with limited training data.


title: A 19W high-efficiency wide-band CMOS-GaN class-E Chireix RF outphasing power amplifier

abstract: A class-E Chireix outphasing power amplifier is presented that enables high efficiency across a wide power back-off range and RF bandwidth. In this design the Chireix compensation elements and class-E loading conditions are provided by an asymmetric coupled-line power combiner. The class-E operated GaN HEMT switches are driven by high-speed, high-voltage CMOS drivers, implemented in a standard 65nm process technology. The proposed concept demonstrates 51.6% system average power efficiency and 65.1% average drain efficiency for a 7.5dB PAR WCDMA signal at 1.95GHz, while meeting the ACLR specifications. Moreover, the PA demonstrated more than 60% drain efficiency across a 6dB power back-off range and up to 19W peak power between 1800–2050MHz.


title: Unsupervised Feature Selection Using Feature Similarity

abstract: In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure.


title: A Systematic Review of Software Development Cost Estimation Studies

abstract: This paper aims to provide a basis for the improvement of software-estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A Web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) increase the breadth of the search for relevant studies, 2) search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) conduct more studies on estimation methods commonly used by the software industry, and 4) increase the awareness of how properties of the data sets impact the results when evaluating estimation methods


title: A 28-GHz Harmonic-Tuned Power Amplifier in 130-nm SiGe BiCMOS

abstract: The design methodology and measurement results of a millimeter-wave harmonic-tuned power amplifier (PA) are presented. The PA uses optimum fundamental and second-harmonic terminations to achieve high peak power-added efficiency (PAE). We present a parasitic-aware design technique for the output network realized as a bandpass filter cascaded with or surrounded by a low-pass matching network. This technique demonstrates a method of manipulating the second-harmonic phase of a Chebyshev bandpass filter, while maintaining a suitable impedance match at the fundamental. The technique is applied to a 28-GHz PA in SiGe BiCMOS, which achieves 15.3-dB gain, 18.6-dBm saturated output power, 15.5-dBm output 1-dB compression point, and 35.3% peak PAE. When backed off 6- from 1-dB compression, the PA achieves 11.5% PAE with a third-order intermodulation product of −33.7 dBc.


title: A Geometric Framework for Rectangular Shape Detection

abstract: Rectangular shape detection has a wide range of applications, such as license plate detection, vehicle detection, and building detection. In this paper, we propose a geometric framework for rectangular shape detection based on the channel-scale space of RGB images. The framework consists of algorithms developed to address three issues of a candidate shape (i.e., a connected component of edge points), including: 1) outliers; 2) open shape; and 3) fragmentation. Furthermore, we propose an interestness measure for rectangular shapes by integrating imbalanced points (one type of interest points). Our experimental study shows the promise of the proposed framework.


title: A chat room assignment for teaching network security

abstract: This paper describes a chat room application suitable for teaching basic network programming and security protocols. A client/server design illustrates the structure of current scalable network services while a multicast version demonstrates the need for efficient simultaneous distribution of network content to multiple receivers (e.g., as required by video broadcasts). The system also includes implementations of two security protocols, one similar to Kerberos and another based on public key encryption.


title: Layout guidelines for web text and a web service to improve accessibility for dyslexics

abstract: In this paper, we offer set of guidelines and a web service that presents Web texts in a more more accessible way to people with dyslexia. The layout guidelines for developing this service are based on a user study with a group of twenty two dyslexic users. The data collected from our study combines qualitative data from interviews and questionnaires and quantitative data from tests carried out using eye tracking. We analyze and compare both kinds of data and present a set of layout guidelines for making the text Web more readable for dyslexic users. To the best of our knowledge, our methodology for defining dyslexic-friendly guidelines and our web service are novel.


title: The BROAD study: A randomised controlled trial using a whole food plant-based diet in the community for obesity, ischaemic heart disease or diabetes

abstract: BACKGROUND/OBJECTIVE ::: There is little randomised evidence using a whole food plant-based (WFPB) diet as intervention for elevated body mass index (BMI) or dyslipidaemia. We investigated the effectiveness of a community-based dietary programme. Primary end points: BMI and cholesterol at 6 months (subsequently extended). ::: ::: ::: SUBJECTS ::: Ages 35-70, from one general practice in Gisborne, New Zealand. Diagnosed with obesity or overweight and at least one of type 2 diabetes, ischaemic heart disease, hypertension or hypercholesterolaemia. Of 65 subjects randomised (control n=32, intervention n=33), 49 (75.4%) completed the study to 6 months. Twenty-three (70%) intervention participants were followed up at 12 months. ::: ::: ::: METHODS ::: All participants received normal care. Intervention participants attended facilitated meetings twice-weekly for 12 weeks, and followed a non-energy-restricted WFPB diet with vitamin B12 supplementation. ::: ::: ::: RESULTS ::: At 6 months, mean BMI reduction was greater with the WFPB diet compared with normal care (4.4 vs 0.4, difference: 3.9 kg m-2 (95% confidence interval (CI)±1), P<0.0001). Mean cholesterol reduction was greater with the WFPB diet, but the difference was not significant compared with normal care (0.71 vs 0.26, difference: 0.45 mmol l-1 (95% CI±0.54), P=0.1), unless dropouts were excluded (difference: 0.56 mmol l-1 (95% CI±0.54), P=0.05). Twelve-month mean reductions for the WFPB diet group were 4.2 (±0.8) kg m-2 BMI points and 0.55 (±0.54, P=0.05) mmol l-1 total cholesterol. No serious harms were reported. ::: ::: ::: CONCLUSIONS ::: This programme led to significant improvements in BMI, cholesterol and other risk factors. To the best of our knowledge, this research has achieved greater weight loss at 6 and 12 months than any other trial that does not limit energy intake or mandate regular exercise.


title: Radio access for ultra-reliable and low-latency 5G communications

abstract: Fifth generation wireless networks are currently being developed to handle a wide range of new use cases. One important emerging area is ultra-reliable communication with guaranteed low latencies well beyond what current wireless technologies can provide. In this paper, we explore the viability of using wireless communication for low-latency, high-reliability communication in an example scenario of factory automation, and outline important design choices for such a system. We show that it is possible to achieve very low error rates and latencies over a radio channel, also when considering fast fading signal and interference, channel estimation errors, and antenna correlation. The most important tool to ensure high reliability is diversity, and low latency is achieved by using short transmission intervals without retransmissions, which, however, introduces a natural restriction on coverage area.


title: Solving a fuzzy set-covering problem

abstract: This work proposed a set-covering model using the concept of fuzzy set theory todefine ''fuzzy covers.'' The proposed fuzzy set-covering model can be reduced to a nonlinear integer programming problem which is easily solvable with modern software. This model is a nature extension of the classical set-covering model, and is able to handle uncertainty.


title: A smart home application to eldercare: current status and lessons learned.

abstract: To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes.


title: Reinforcement Learning in Neural Networks: A Survey

abstract: In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applications. Although many surveys investigated general RL, no survey is specifically dedicated to the combination of artificial neural networks and RL. This paper therefore describes the state of the art of NNRL algorithms, with a focus on robotics applications. In this paper, a comprehensive survey is started with a discussion on the concepts of RL. Then, a review of several different NNRL algorithms is presented. Afterwards, the performances of different NNRL algorithms are evaluated and compared in learning prediction and learning control tasks from an empirical aspect and the paper concludes with a discussion on open issues.


A Comprehensive Survey on : Quantum Cryptography


title: Adaptive integrated CMOS circulator

abstract: An adaptive circulator fabricated on a 130 nm CMOS is presented. Circulator has two adaptive blocks for gain and phase mismatch correction and leakage cancelation. The impedance matching circuit corrects mismatches for antenna, divider, and LNTA. The cancelation block cancels the Tx leakage. Measured isolation between transmitter and receiver for single tone at 2.4 GHz is 90 dB, and for a 40 MHz wide-band signal is 50dB. The circulator Rx gain is 10 dB, with NF = 4.7 dB and 5 dB insertion loss.


title: An Optimal Divide-Conquer Algorithm for 2D Skyline Queries

abstract: Skyline query processing is fundamental to many applications including multi-criteria decision making. In this paper, we will present an optimal algorithm for computing skyline in the two dimensional space. The algorithm has the progressive nature and adopts the divide-conquer paradigm. It can be shown that our algorithm achieves the minimum I/O costs, and is more efficient and scalable than the existing techniques. The experiment results demonstrated that our algorithm greatly improves the performance of the existing techniques.


title: Lightfield Recovery from Its Focal Stack

abstract: The Focal Stack Transform integrates a 4D lightfield over a set of appropriately chosen 2D planes. The result of such integration is an image focused on a determined depth in 3D space. The set of such images is the Focal Stack of the lightfield. This paper studies the existence of an inverse for this transform. Such inverse could be used to obtain a 4D lightfield from a set of images focused on several depths of the scene. In this paper, we show that this inversion cannot be obtained for a general lightfield and introduce a subset of lightfields where this inversion can be computed exactly. We examine the numerical properties of such inversion process for general lightfields and examine several regularization approaches to stabilize the transform. Experimental results are provided for focal stacks obtained from several plenoptic cameras. From a practical point of view, results show how this inversion procedure can be used to recover, compress, and denoise the original 4D lightfield.


title: White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

abstract: The accurate assessment of White matter hyperintensities (WMH) burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data. The manual delineation of WMHs is tedious, costly and time consuming. This is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense. Several automated methods aiming to tackle the challenges of WMH segmentation have been proposed, however cannot differentiate between WMH and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. As far as we know, this is the first time such differentiation task has explicitly been proposed. The proposed fully convolutional CNN architecture, is comprised of an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, were found to be in line with the associations found with the expert-annotated volumes.


title: What levels of moral reasoning and values explain adherence to information security rules? An empirical study

abstract: It is widely agreed that employee non-adherence to information security policies poses a major problem for organizations. Previous research has pointed to the potential of theories of moral reasoning to better understand this problem. However, we find no empirical studies that examine the influence of moral reasoning on compliance with information security policies. We address this research gap by proposing a theoretical model that explains non-compliance in terms of moral reasoning and values. The model integrates two well-known psychological theories: the Theory of Cognitive Moral Development by Kohlberg and the Theory of Motivational Types of Values by Schwartz. Our empirical findings largely support the proposed model and suggest implications for practice and research on how to improve information security policy compliance.


title: Brain-Computer Interfaces, Virtual Reality, and Videogames

abstract: Major challenges must be tackled for brain-computer interfaces to mature into an established communications medium for VR applications, which will range from basic neuroscience studies to developing optimal peripherals and mental gamepads and more efficient brain-signal processing techniques.


title: Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition

abstract: Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages. In order to efficiently address the problem, this work first decomposes the aging process into multiple short-term stages. Then, a novel generative probabilistic model, named Temporal Non-Volume Preserving (TNVP) transformation, is presented to model the facial aging process at each stage. Unlike Generative Adversarial Networks (GANs), which requires an empirical balance threshold, and Restricted Boltzmann Machines (RBM), an intractable model, our proposed TNVP approach guarantees a tractable density function, exact inference and evaluation for embedding the feature transformations between faces in consecutive stages. Our model shows its advantages not only in capturing the non-linear age related variance in each stage but also producing a smooth synthesis in age progression across faces. Our approach can model any face in the wild provided with only four basic landmark points. Moreover, the structure can be transformed into a deep convolutional network while keeping the advantages of probabilistic models with tractable log-likelihood density estimation. Our method is evaluated in both terms of synthesizing age-progressed faces and cross-age face verification and consistently shows the state-of-the-art results in various face aging databases, i.e. FG-NET, MORPH, AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). A large-scale face verification on Megaface challenge 1 is also performed to further show the advantages of our proposed approach.


On a test of whether one of two random variables is stochasitcally larger than the other


title: Using the Field Effect in Silicon to Study Charge Processes in a Nematic Cell

abstract: ABSTRACT We propose a simple method for the qualitative study of charge processes in a liquid crystal layer at the silicon surface. The method is based on the field effect that has been induced by charges that are localized near the silicon surface. The method is sensitive to the sign of accumulating charge and does allow monitoring a change of the surface charge.


title: A personal news agent that talks, learns and explains

abstract: Most work on intelligent information agents has thus far focused on systems that are accessible through the World Wide Web. As demanding schedules prohibit people from continuous access to their computers, there is a clear demand for information systems that do not require workstation access or graphical user interfaces. We present a personal news agent that is designed to become part of an intelligent, IP-enabled radio, which uses synthesized speech to read news stories to a user. Based on voice feedback from the user, the system automatically adapts to the user’s preferences and interests. In addition to time-coded feedback, we explore two components of the system that facilitate the automated induction of accurate interest profiles. First, we motivate the use of a multistrategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the use of "concept feedback", a novel form of user feedback that is based on our agent's capability to construct explanations for the reasons that have led to a specific classification. Users can then critique these explanations which, from a machine learning perspective, allows for more direct changes to an induced concept than through the inclusion of additional training examples. We evaluate the proposed algorithms on user data collected with a prototype of our system, and assess the performance contributions of the system's individual components.


title: Hybrid NOrec: a case study in the effectiveness of best effort hardware transactional memory

abstract: Transactional memory (TM) is a promising synchronization mechanism for the next generation of multicore processors. Best-effort Hardware Transactional Memory (HTM) designs, such as Sun's prototype Rock processor and AMD's proposed Advanced Synchronization Facility (ASF), can efficiently execute many transactions, but abort in some cases due to various limitations. Hybrid TM systems can use a compatible software TM (STM) in such cases. We introduce a family of hybrid TMs built using the recent NOrec STM algorithm that, unlike existing hybrid approaches, provide both low overhead on hardware transactions and concurrent execution of hardware and software transactions. We evaluate implementations for Rock and ASF, exploring how the differing HTM designs affect optimization choices. Our investigation yields valuable input for designers of future best-effort HTMs.


title: BiRank: Towards Ranking on Bipartite Graphs

abstract: The bipartite graph is a ubiquitous data structure that can model the relationship between two entity types: for instance, users and items, queries and webpages. In this paper, we study the problem of ranking vertices of a bipartite graph, based on the graph's link structure as well as prior information about vertices (which we term a query vector). We present a new solution, BiRank, which iteratively assigns scores to vertices and finally converges to a unique stationary ranking. In contrast to the traditional random walk-based methods, BiRank iterates towards optimizing a regularization function, which smooths the graph under the guidance of the query vector. Importantly, we establish how BiRank relates to the Bayesian methodology, enabling the future extension in a probabilistic way. To show the rationale and extendability of the ranking methodology, we further extend it to rank for the more generic n-partite graphs. BiRank's generic modeling of both the graph structure and vertex features enables it to model various ranking hypotheses flexibly. To illustrate its functionality, we apply the BiRank and TriRank (ranking for tripartite graphs) algorithms to two real-world applications: a general ranking scenario that predicts the future popularity of items, and a personalized ranking scenario that recommends items of interest to users. Extensive experiments on both synthetic and real-world datasets demonstrate BiRank's soundness (fast convergence), efficiency (linear in the number of graph edges) and effectiveness (achieving state-of-the-art in the two real-world tasks).


title: Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks

abstract: Influence Maximization (IM), that seeks a small set of key users who spread the influence widely into the network, is a core problem in multiple domains. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. Despite the huge amount of effort, IM in billion-scale networks such as Facebook, Twitter, and World Wide Web has not been satisfactorily solved. Even the state-of-the-art methods such as TIM+ and IMM may take days on those networks. In this paper, we propose SSA and D-SSA, two novel sampling frameworks for IM-based viral marketing problems. SSA and D-SSA are up to 1200 times faster than the SIGMOD'15 best method, IMM, while providing the same (1-1/e-e) approximation guarantee. Underlying our frameworks is an innovative Stop-and-Stare strategy in which they stop at exponential check points to verify (stare) if there is adequate statistical evidence on the solution quality. Theoretically, we prove that SSA and D-SSA are the first approximation algorithms that use (asymptotically) minimum numbers of samples, meeting strict theoretical thresholds characterized for IM. The absolute superiority of SSA and D-SSA are confirmed through extensive experiments on real network data for IM and another topic-aware viral marketing problem, named TVM.


title: Understanding the coverage and scalability of place-centric crowdsensing

abstract: Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.


title: Interacting with sexist men triggers social identity threat among female engineers

abstract: Social identity threat is the notion that one of a person’s many social identities may be at risk of being devalued in a particular context (C. M. Steele, S. J. Spencer, & J. Aronson, 2002). The authors suggest that in domains in which women are already negatively stereotyped, interacting with a sexist man can trigger social identity threat, undermining women’s performance. In Study 1, male engineering students who scored highly on a subtle measure of sexism behaved in a dominant and sexually interested way toward an ostensible female classmate. In Studies 2 and 3, female engineering students who interacted with such sexist men, or with confederates trained to behave in the same way, performed worse on an engineering test than did women who interacted with nonsexist men. Study 4 replicated this finding and showed that women’s underperformance did not extend to an English test, an area in which women are not negatively stereotyped. Study 5 showed that interacting with sexist men leads women to suppress concerns about gender stereotypes, an established mechanism of stereotype threat. Discussion addresses implications for social identity threat and for women’s performance in school and at work.


title: Teacher Attitudes Toward Dyslexia: Effects on Teacher Expectations and the Academic Achievement of Students With Dyslexia:

abstract: The present study examined teacher attitudes toward dyslexia and the effects of these attitudes on teacher expectations and the academic achievement of students with dyslexia compared to students without learning disabilities. The attitudes of 30 regular education teachers toward dyslexia were determined using both an implicit measure and an explicit, self-report measure. Achievement scores for 307 students were also obtained. Implicit teacher attitudes toward dyslexia related to teacher ratings of student achievement on a writing task and also to student achievement on standardized tests of spelling but not math for those students with dyslexia. Self-reported attitudes of the teachers toward dyslexia did not relate to any of the outcome measures. Neither the implicit nor the explicit measures of teacher attitudes related to teacher expectations. The results show implicit attitude measures to be a more valuable predictor of the achievement of students with dyslexia than explicit, self-report attitude meas...


title: Comparative study on classifying human activities with miniature inertial and magnetic sensors

abstract: This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.


title: Social media, traditional media, and music sales

abstract: Motivated by the growing importance of social media, this paper examines the relationship between new media, old media, and sales in the context of the music industry. In particular, we study the interplay between blog buzz, radio play, and music sales at both the album and song levels of analysis. We employ the panel vector autoregression (PVAR) methodology, an extension of vector autoregression to panel data. We find that radio play is consistently and positively related to future sales at both the song and album levels. Blog buzz, however, is not related to album sales and negatively related to song sales, suggesting that sales displacement due to free online sampling dominates any positive word-of-mouth effects of song buzz on sales. Further, the negative relationship between song buzz and sales is stronger for niche music relative to mainstream music, and for less popular songs within albums. We discuss the implications of these results for both research and practice regarding the role of new media in the music industry.


title: An Overview of Sustainable Green 5G Networks

abstract: The stringent requirements of a 1,000 times increase in data traffic and one millisecond round trip latency have made limiting the potentially tremendous ensuing energy consumption one of the most challenging problems for the design of the upcoming fifth-generation (5G) networks. To enable sustainable 5G networks, new technologies have been proposed to improve the system energy efficiency and alternative energy sources are introduced to reduce our dependence on traditional fossil fuels. In particular, various 5G techniques target the reduction of the energy consumption without sacrificing the quality-of-service. Meanwhile, energy harvesting technologies, which enable communication transceivers to harvest energy from various renewable resources and ambient radio frequency signals for communi- cation, have drawn significant interest from both academia and industry. In this article, we provide an overview of the latest research on both green 5G techniques and energy harvesting for communication. In addition, some technical challenges and potential research topics for realizing sustainable green 5G networks are also identified.


title: Cost-Efficient SHA Hardware Accelerators

abstract: This paper presents a new set of techniques for hardware implementations of secure hash algorithm (SHA) hash functions. These techniques consist mostly in operation rescheduling and hardware reutilization, therefore, significantly decreasing the critical path and required area. Throughputs from 1.3 Gbit/s to 1.8 Gbit/s were obtained for the SHA implementations on a Xilinx VIRTEX II Pro. Compared to commercial cores and previously published research, these figures correspond to an improvement in throughput/slice in the range of 29% to 59% for SHA-1 and 54% to 100% for SHA-2. Experimental results on hybrid hardware/software implementations of the SHA cores, have shown speedups up to 150 times for the proposed cores, compared to pure software implementations.


title: LCMKL: latent-community and multi-kernel learning based image annotation

abstract: Automatic image annotation is an important function for online photo sharing service. The concurrence of labels is pretty common in multi-label annotation. In this paper, we propose a novel approach called latent-community and multi-kernel learning (LCMKL). The established graph of labels is regarded as a semantic network. Community detection method is introduced that treats the label set as communities. Multi-kernel learning SVM is adopted for specifying communities and settling difficulty of extracting semantically meaningful entities with some simple features. Experiments on NUS-WIDE database demonstrate that LCMKL outperforms other state-of-the-art approaches.


title: 60-GHz electrically reconfigurable reflectarray using p-i-n diode

abstract: A 60-GHz band electrically reconfigurable reflectarray has been investigated. Each reflecting element is printed on a dielectric substrate and has a rectangular microstrip patch and a stub loaded with a single p-i-n diode. The reflection phase is changed by 180° in accordance with the on/off state of the p-i-n diode; namely, the stub works as a single-bit phase shifter. A prototype consisting of 40 × 40 elements spaced at 0.7λ was fabricated, and the reflection coefficients of the elements and the radiation patterns while the beam steering was performed were measured. Good agreement was observed between the results of the simulations and experiments.


title: Glioblastoma survival in the United States before and during the temozolomide era

abstract: The standard-of-care treatment for newly diagnosed glioblastoma changed in 2005, when radiation therapy plus temozolomide chemotherapy replaced radiation therapy alone. It is not yet clear how this change in treatment has influenced patient survival in routine clinical practice, or if a survival benefit extends to patients older than those enrolled in the trial. Data from the Surveillance, Epidemiology, and End Results (SEER) Program was analyzed to compare survival of adult glioblastoma patients diagnosed from 2000–2003 to patients diagnosed from 2005–2008, in order to evaluate pre-temozolomide and post-temozolomide periods. The Kaplan–Meier method and Cox proportional hazards models were used. 6,673 patients with glioblastoma diagnosed from 2000–2003 and 7,259 patients diagnosed from 2005–2008 were identified. Median survival times of all patients diagnosed in the 2000–2003 and 2005–2008 periods were 8.1 and 9.7 months, respectively. Amongst patients treated with surgery and a radiation-containing regimen, median survival was 12.0 months in 2000–2003 and 14.2 months in 2005–2008. In the temozolomide era, median survival times ranged from a high of 31.9 months in patients age 20–29 to a low of 5.6 months in patients age 80 and older. The survival of patients with newly diagnosed glioblastoma improved from 2000–2003 to 2005–2008, likely due to temozolomide use. However, median survival time after glioblastoma diagnosis in the SEER population remains well under one year, largely driven by poor prognosis in elderly patients.


A survey on solid texture synthesis


title: Overlapping coding regions and trancriptional units of two essential chromosomal genes (CCT8, TRP1) in the fungal pathogen Candida albicans

abstract: Sequencing of the 3'-untranslated region of the CCT8 gene of the fungal pathogen Candida albicans revealed that the CCT8 coding region overlaps 13 bp with the coding region of the convergently orientated TRP1 gene. The same overlap was found in three strains with different genetic backgrounds. 3'-RACE was used to determine that the CCT8 and TRP1 transcripts extended significantly into the coding region of the adjacent gene, which also contained sequences encoding the poly(A) addition site. A strain retaining one wild-type CCT8/TRP1 locus on one chromosome and a deletion on the other homologous chromosome contained both CCT8 and TRP1 transcripts; this result indicates that both transcripts are synthesized from the same gene locus. The CCT8/TRP1 gene pair of C . albicans constitutes an extreme natural case of transcriptional overlap in a eukaryote. The results confirm that convergent overlapping transcription units are compatible with expression of the overlapping genes.


title: Software Process Improvement as organizational change: A metaphorical analysis of the literature

abstract: Software Process Improvement (SPI) typically involves rather complex organizational changes. Acknowledging that managers can approach these changes in quite different ways, this paper addresses the following question: what perspectives do the research literature offer on SPI as organizational change and how is this knowledge presented and published? To answer this question, we analyzed SPI research publications with a main emphasis on organizational change using Gareth Morgan's organizational metaphors (1996) as analytical lenses. In addition, we characterized each article along the following dimensions: knowledge orientation (normative versus descriptive), theoretical emphasis (high versus low), main audience (practitioner versus academic), geographical origin (Scandinavia, the Americas, Europe, or the Asia-Pacific), and publication level (high versus low ranked journal). The review demonstrates that the literature as a whole is firmly grounded in both theory and practice, it appropriately targets both practitioner and academic audiences, and Scandinavian and American researchers are the main contributors. However, the distribution of articles across Morgan's metaphors is uneven and reveals knowledge gaps that present new avenues for research. The current literature offers important insights into organizational change in SPI when viewed through machine, organism, flux and transformation, and brain metaphors. Practitioners may use these articles to guide SPI initiatives. In contrast, the impact of culture, dominance, psychic prison, and politics in SPI has only received scant attention. We argue that these perspectives could offer important additional insights into the challenges involved in managing SPI. Researchers are therefore advised to engage in new SPI research based on one or more of these perspectives. Overall, the paper offers research directions and management lessons, and it provides a roadmap to help identify insights and specific articles related to SPI as organizational change.


title: Interpreting Finite Automata for Sequential Data

abstract: Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specifying objectives or desired attributes. In this paper, we identify the key properties used to interpret automata and propose a modification of a state-merging approach to learn variants of finite state automata. We apply the approach to problems beyond typical grammar inference tasks. Additionally, we cover several use-cases for prediction, classification, and clustering on sequential data in both supervised and unsupervised scenarios to show how the identified key properties are applicable in a wide range of contexts.


title: Priority capabilities in LTE supporting national security and emergency preparedness next generation network priority services

abstract: Various priority services exist today in the public networks to support key personnel in their critical communications during a National Security and Emergency Preparedness (NS/EP) condition. These priority services will evolve as the public networks evolve to Next Generation Network (NGN) Internet Protocol (IP)-based infrastructures, including the 4G wireless network called Long Term Evolution (LTE). In addition to evolving the legacy priority Voice services to Voice over IP (VoIP), it is expected that NS/EP NGN Priority Services (NGN-PS) will include priority data and priority video services. NS/EP NGN-PS, and associated network capabilities, have been specified by the Office of Emergency Communications (OEC) and are based on industry Standards. During an NS/EP condition, the public networks can get overloaded due to heavy user traffic and possible failures. NS/EP NGN-PS provides the mechanisms in the access and core networks to facilitate the needed priority communications. In particular, the wireless access networks such as LTE may become severely overloaded in an area, affecting call admissions and Quality of Service (QoS). This paper describes the NS/EP priority mechanisms in LTE that address and mitigate the various potential LTE congestion points, to then facilitate the high probability of call and data admission and completion. These NS/EP priority mechanisms provide capabilities to access, invoke and maintain NS/EP NGN-PS, i.e., NS/EP voice, data and video priority services, for end-to-end user-to-user communications. These mechanisms will result in priority during (a) user attachment, (b) node signaling and processing, (c) device paging, (d) handover, (e) media bearer establishment, (f) radio resource admission, (g) packet transport, and (h) overload controls.


title: The Relationship Between Self-Esteem and Sexual Self-Concept in People With Physical-Motor Disabilities

abstract: Background: Self-esteem is the value that the individuals give themselves, and sexual self-concept is also a part of individuality or sexualself. Impairment or disability exists not only in the physical body of disabled people but also in their attitudes. Negative attitudes affect the mental health of disabled people, causing them to have lower self-esteem. Objectives: This study aimed to examine the relationship between self-esteem and sexual self-concept in people with physical-motor disabilities. Patients and Methods: This cross-sectional study was conducted on 200 random samples with physical-motor disabilities covered by Isfahan Welfare Organization in 2013. Data collection instruments were the Persian Eysenck self-esteem questionnaire, and five domains (sexual anxiety, sexual self-efficacy, sexual self-esteem, sexual fear and sexual depression) of the Persian multidimensional sexual selfconcept questionnaire. Because of incomplete filling of the questionnaires, the data of 183 people were analyzed by the SPSS 16.0 software. Data were analyzed using the t-test, Man-Whitney and Kruskal-Wallis tests and Spearman correlation coefficient. Results: The mean age was 36.88 ± 8.94 years for women and 37.80 ± 10.13 for men. The mean scores of self-esteem among women and men were 15.80 ± 3.08 and 16.2 ± 2.90, respectively and there was no statistically significance difference. Comparison of the mean scores of sexual anxiety, sexual self-efficacy, sexual self-esteem, sexual fear and sexual depression among men and women showed that women scored higher than men in all domains. This difference was statistically significant in other domains except the sexual self-esteem (14.92 ± 3.61 vs. 13.56 ± 4.52) (P < 0.05). The Kruskal-Wallis test showed that except for sexual anxiety and sexual self-esteem, there was a statistical difference between other domains of people’s sexual self-concept and degree of disability (P < 0.05). Moreover, Spearman coefficient showed that there was only a correlation between men’s sexual anxiety, sexual self-esteem and sexual self-efficacy with their self-esteem. This correlation was positive in sexual anxiety and negative in two other domains. Conclusions: Lack of difference in self-esteem of disabled people in different degrees of disability and in both men and women suggests that disabled people should not be presumed to have low self-esteem, and their different aspects of life should be attended to, just like others. Furthermore, studies should be designed and implemented based on psychological, social and environmental factors that can help disabled people to promote their positive sexual self-concept through marriage, and reduce their negative self-concept.


title: Am I normal? A systematic review and construction of nomograms for flaccid and erect penis length and circumference in up to 15 521 men

abstract: OBJECTIVE ::: To systematically review and create nomograms of flaccid and erect penile size measurements. ::: ::: ::: METHODS ::: Study key eligibility criteria: measurement of penis size by a health professional using a standard procedure; a minimum of 50 participants per sample. ::: ::: ::: EXCLUSION CRITERIA ::: samples with a congenital or acquired penile abnormality, previous surgery, complaint of small penis size or erectile dysfunction. Synthesis methods: calculation of a weighted mean and pooled standard deviation (SD) and simulation of 20,000 observations from the normal distribution to generate nomograms of penis size. ::: ::: ::: RESULTS ::: Nomograms for flaccid pendulous [n = 10,704, mean (SD) 9.16 (1.57) cm] and stretched length [n = 14,160, mean (SD) 13.24 (1.89) cm], erect length [n = 692, mean (SD) 13.12 (1.66) cm], flaccid circumference [n = 9407, mean (SD) 9.31 (0.90) cm], and erect circumference [n = 381, mean (SD) 11.66 (1.10) cm] were constructed. Consistent and strongest significant correlation was between flaccid stretched or erect length and height, which ranged from r = 0.2 to 0.6. ::: ::: ::: LIMITATIONS ::: relatively few erect measurements were conducted in a clinical setting and the greatest variability between studies was seen with flaccid stretched length. ::: ::: ::: CONCLUSIONS ::: Penis size nomograms may be useful in clinical and therapeutic settings to counsel men and for academic research.


title: Prolonged survival of Campylobacter species in bovine manure compost.

abstract: The persistence of naturally occurring campylobacteria in aerobic compost constructed of manure from beef cattle that were administered chlortetracycline and sulfamethazine (AS700) or from cattle not administered antibiotics (control) was examined. Although there were no differences in population sizes of heterotrophic bacteria, the temperature of AS700 compost was more variable and did not become as high as that of control compost. There were significant differences in water content, total carbon (C), total nitrogen (N), and electrical conductivity but not in the C/N ratio or pH between the two compost treatments. Campylobacteria were readily isolated from pen manure, for up to day 15 from control compost, and throughout the active phase of AS700 compost. Campylobacter DNA (including Campylobacter coli, Campylobacter fetus, Campylobacter hyointestinalis, and Campylobacter jejuni) was detected over the ca. 10-month composting period, and no reductions in quantities of C. jejuni DNA were observed over the duration of the active phase. The utilization of centrifugation in combination with ethidium monoazide (EMA) significantly reduced (>90%) the amplification of C. jejuni DNA that did not originate from cells with intact cell membranes. No differences were observed in the frequency of Campylobacter DNA detection between EMA- and non-EMA-treated samples, suggesting that Campylobacter DNA amplified from compost was extracted from cells with intact cell membranes (i.e., from viable cells). The findings of this study indicate that campylobacteria excreted in cattle feces persist for long periods in compost and call into question the common belief that these bacteria do not persist in manure.


title: Consumer Brand Engagement in Social Media: Conceptualization, Scale Development and Validation

abstract: In the last three decades, an influential research stream has emerged which highlights the dynamics of focal consumer/brand relationships. Specifically, more recently the ‘consumer brand engagement’ (CBE) concept has been postulated to more comprehensively reflect the nature of consumers' particular interactive brand relationships, relative to traditional concepts, including ‘involvement.’ However, despite the growing scholarly interest regarding the undertaking of marketing research addressing ‘engagement,’ studies have been predominantly exploratory in nature, thus generating a lack of empirical research in this area to date. By developing and validating a CBE scale in specific social media settings, we address this identified literature gap. Specifically, we conceptualize CBE as a consumer's positively valenced brand-related cognitive, emotional and behavioral activity during or related to focal consumer/brand interactions. We derive three CBE dimensions, including cognitive processing, affection, and activation. Within three different social media contexts, we employ exploratory and confirmatory factor analyses to develop a reliable, 10-item CBE scale, which we proceed to validate within a nomological net of conceptual relationships and a rival model. The findings suggest that while consumer brand ‘involvement’ acts as a CBE antecedent, consumer ‘self-brand connection’ and ‘brand usage intent’ represent key CBE consequences, thus providing a platform for further research in this emerging area. We conclude with an overview of key managerial and scholarly implications arising from this research.


title: Big data analytics on Apache Spark

abstract: Apache Spark has emerged as the de facto framework for big data analytics with its advanced in-memory programming model and upper-level libraries for scalable machine learning, graph analysis, streaming and structured data processing. It is a general-purpose cluster computing framework with language-integrated APIs in Scala, Java, Python and R. As a rapidly evolving open source project, with an increasing number of contributors from both academia and industry, it is difficult for researchers to comprehend the full body of development and research behind Apache Spark, especially those who are beginners in this area. In this paper, we present a technical review on big data analytics using Apache Spark. This review focuses on the key components, abstractions and features of Apache Spark. More specifically, it shows what Apache Spark has for designing and implementing big data algorithms and pipelines for machine learning, graph analysis and stream processing. In addition, we highlight some research and development directions on Apache Spark for big data analytics.


title: GeaBase: A High-Performance Distributed Graph Database for Industry-Scale Applications

abstract: Graph analytics have been gaining tractions rapidly in the past few years. It has a wide array of application areas in the industry, ranging from e-commerce, social network and recommendation systems to fraud detection and virtually any problem that requires insights into data connections, not just data itself. In this paper, we present GeaBase, a new distributed graph database that provides the capability to store and analyze graph-structured data in real-time at massive scale. We describe the details of the system and the implementation, including a novel update architecture, called Update Center (UC), and a new language that is suitable for both graph traversal and analytics. We also compare the performance of GeaBase to a widely used open-source graph database Titan. Experiments show that GeaBase is up to 182x faster than Titan in our testing scenarios. We also achieves 22x higher throughput on social network workloads in the comparison.


title: A Power-Efficient Accelerator Based on FPGAs for LSTM Network

abstract: Today, artificial neural networks (ANNs) are widely used in a variety of applications, including speech recognition, face detection, disease diagnosis, etc. And as the emerging field of ANNs, Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) which contains complex computational logic. To achieve high accuracy, researchers always build large-scale LSTM networks which are time-consuming and power-consuming. In this paper, we present a hardware accelerator for the LSTM neural network layer based on FPGA Zedboard and use pipeline methods to parallelize the forward computing process. We also implement a sparse LSTM hidden layer, which consumes fewer storage resources than the dense network. Our accelerator is power-efficient and has a higher speed than ARM Cortex-A9 processor.


title: The analyzing of a cascaded multilevel class-D power amplifier with the carrier-based phase-shifted modulation

abstract: This paper presents a switch-mode power amplifier based on a cascaded multicell multilevel circuit topology. Amplifiers based on the single-phase inverter topology or H-bridge is most easily accomplished by the direct series, or parallel, connection of multiple H-bridge power-stages. Operating these with the phase-shifted PWM control gives higher output power and produces an improved output voltage spectrum and significantly lowers output current-ripple. Two carrier-based PWM schemes, phase-shifted and level-shifted modulations, are analyzed and their performance is compared. A multilevel class-D amplifier's mathematic model is analyzed. The results of measurements taken from a laboratory setup are presented which prove the proposed amplifier improves the dynamic system behavior and have wide bandwidth.


title: Understanding and Supporting Fathers and Fatherhood on Social Media Sites

abstract: Fathers are taking on more childcare and household responsibilities than they used to and many non-profit and government organizations have pushed for changes in policies to support fathers. Despite this effort, little research has explored how fathers go online related to their roles as fathers. Drawing on an interview study with 37 fathers, we find that they use social media to document and archive fatherhood, learn how to be a father, and access social support. They also go online to support diverse family needs, such as single fathers' use of Reddit instead of Facebook, fathers raised by single mothers' search for role models online, and stay-at-home fathers' use of father blogs. However, fathers are constrained by privacy concerns and perceptions of judgment relating to sharing content online about their children. Drawing on theories of fatherhood, we present theoretical and design ideas for designing online spaces to better support fathers and fatherhood. We conclude with a call for a research agenda to support fathers online.


title: Constants across cultures in the face and emotion.

abstract: This study addresses the question of whether any facial expressions of emotion are universal. Recent studies showing that members of literate cultures associated the same emotion concepts with the same facial behaviors could not demonstrate that at least some facial expressions of emotion are universal; the cultures compared had all been exposed to some of the same mass media presentations of facial expression, and these may have taught the people in each culture to recognize the unique facial expressions of other cultures. To show that members of a preliterate culture who had minimal exposure to literate cultures would associate the same emotion concepts with the same facial behaviors as do members of Western and Eastern literate cultures, data were gathered in New Guinea by telling subjects a story, showing them a set of three faces, and asking them to select the face which showed the emotion appropriate to the story. The results provide evidence in support of the hypothesis that the association between particular facial muscular patterns and discrete emotions is universal.


title: Combining content and link for classification using matrix factorization

abstract: The world wide web contains rich textual contents that areinterconnected via complex hyperlinks. This huge database violates the assumption held by most of conventional statistical methods that each web page is considered as an independent and identical sample. It is thus difficult to apply traditional mining or learning methods for solving web mining problems, e.g., web page classification, by exploiting both the content and the link structure. The research in this direction has recently received considerable attention but are still in an early stage. Though a few methods exploit both the link structure or the content information, some of them combine the only authority information with the content information, and the others first decompose the link structure into hub and authority features, then apply them as additional document features. Being practically attractive for its great simplicity, this paper aims to design an algorithm that exploits both the content and linkage information, by carrying out a joint factorization on both the linkage adjacency matrix and the document-term matrix, and derives a new representation for web pages in a low-dimensional factor space, without explicitly separating them as content, hub or authority factors. Further analysis can be performed based on the compact representation of web pages. In the experiments, the proposed method is compared with state-of-the-art methods and demonstrates an excellent accuracy in hypertext classification on the WebKB and Cora benchmarks.


title: On Sampling Focal Length Values to Solve the Absolute Pose Problem

abstract: Estimating the absolute pose of a camera relative to a 3D representation of a scene is a fundamental step in many geometric Computer Vision applications. When the camera is calibrated, the pose can be computed very efficiently. If the calibration is unknown, the problem becomes much harder, resulting in slower solvers or solvers requiring more samples and thus significantly longer run-times for RANSAC. In this paper, we challenge the notion that using minimal solvers is always optimal and propose to compute the pose for a camera with unknown focal length by randomly sampling a focal length value and using an efficient pose solver for the now calibrated camera. Our main contribution is a novel sampling scheme that enables us to guide the sampling process towards promising focal length values and avoids considering all possible values once a good pose is found. The resulting RANSAC variant is significantly faster than current state-of-the-art pose solvers, especially for low inlier ratios, while achieving a similar or better pose accuracy.


title: Multi-city sustainable regional urban growth simulation—MSRUGS: a case study along the mid-section of Silk Road of China

abstract: Urban growth along the middle section of the ancient silk-road of China (so called West Yellow River Corridor—He-Xi Corridor) has taken a unique path deviating from what is commonly seen in the coastal China. Urban growth here has been driven by historical heritage, transportation connection between East and West China, and mineral exploitation. However, it has been constrained by water shortage and harsh natural environment because this region is located in arid and semi-arid climate zones. This paper attempts to construct a multi-city agent-based model to explore possible trajectories of regional urban growth along the entire He-Xi Corridor under a severe environment risk, over urban growth under an extreme threat of water shortage. In contrast with current ABM approaches, our model will simulate urban growth in a large administrative region consisting of a system of cities. It simultaneously considers the spatial variations of these cities in terms of population size, development history, water resource endowment and sustainable development potential. It also explores potential impacts of exogenous inter-city interactions on future urban growth on the basis of urban gravity model. The algorithmic foundations of three types of agents, developers, conservationists and regional-planners, are discussed. Simulations with regard to three different development scenarios are presented and analyzed.