title: Developing Smart Farm and Traceability System for Agricultural Products using IoT Technology

abstract: In the past, most farmers have taken care of their products by relying on basic observation and general knowledge. The quality of agricultural products depends on the farmer’s skill and experience. Therefore, transferring knowledge of planting techniques from one generation to another is not an easy task. In recent years, the smart farm concept has been introduced. However, most current smart farming focuses on monitoring, without utilising the observed data in other ways. Therefore, the aim of this research is to propose a traceability system, summarising and presenting observed data from the smart farm. The Internet of Things (IoT) has been introduced in this research, using several sensors to detect the environmental data in the smart farm. The entire data from the results was computed and presented using a traceability system. Customers are provided with more information support, especially concerning the quality of the planting process, before buying an agricultural product by scanning a quick response (QR) code through a mobile application. This gives the customer greater confidence in the product.


title: Stem Cells: A Revolution in Therapeutics—Recent Advances in Stem Cell Biology and Their Therapeutic Applications in Regenerative Medicine and Cancer Therapies

abstract: Basic and clinical research accomplished during the last few years on embryonic, fetal, amniotic, umbilical cord blood, and adult stem cells has constituted a revolution in regenerative medicine and cancer therapies by providing the possibility of generating multiple therapeutically useful cell types. These new cells could be used for treating numerous genetic and degenerative disorders. Among them, age-related functional defects, hematopoietic and immune system disorders, heart failures, chronic liver injuries, diabetes, Parkinson’s and Alzheimer’s diseases, arthritis, and muscular, skin, lung, eye, and digestive disorders as well as aggressive and recurrent cancers could be successfully treated by stem cell-based therapies. This review focuses on the recent advancements in adult stem cell biology in normal and pathological conditions. We describe how these results have improved our understanding on critical and unique functions of these rare sub-populations of multipotent and undifferentiated cells with an unlimited self-renewal capacity and high plasticity. Finally, we discuss some major advances to translate the experimental models on ex vivo and in vivo expanded and/or differentiated stem cells into clinical applications for the development of novel cellular therapies aimed at repairing genetically altered or damaged tissues/organs in humans. A particular emphasis is made on the therapeutic potential of different tissue-resident adult stem cell types and their in vivo modulation for treating and curing specific pathological disorders.


title: Photoshopping the selfie: Self photo editing and photo investment are associated with body dissatisfaction in adolescent girls.

abstract: Objective ::: Social media engagement by adolescent girls is high. Despite its appeal, there are potential negative consequences for body dissatisfaction and disordered eating from social media use. This study aimed to examine, in a cross-sectional design, the relationship between social media use in general, and social media activities related to taking “selfies” and sharing specifically, with overvaluation of shape and weight, body dissatisfaction, and dietary restraint. ::: ::: Method ::: Participants were 101 grade seven girls (Mage = 13.1, SD = 0.3), who completed self-report questionnaires of social media use and body-related and eating concerns measures. ::: ::: Results ::: Results showed that girls who regularly shared self-images on social media, relative to those who did not, reported significantly higher overvaluation of shape and weight, body dissatisfaction, dietary restraint, and internalization of the thin ideal. In addition, among girls who shared photos of themselves on social media, higher engagement in manipulation of and investment in these photos, but not higher media exposure, were associated with greater body-related and eating concerns, including after accounting for media use and internalization of the thin ideal. ::: ::: Discussion ::: Although cross-sectional, these findings suggest the importance of social media activities for body-related and eating concerns as well as potential avenues for targeted social-media-based intervention. © 2015 Wiley Periodicals, Inc. (Int J Eat Disord 2015; 48:1132–1140).


title: A Linked Data platform for mining software repositories

abstract: The mining of software repositories involves the extraction of both basic and value-added information from existing software repositories. The repositories will be mined to extract facts by different stakeholders (e.g. researchers, managers) and for various purposes. To avoid unnecessary pre-processing and analysis steps, sharing and integration of both basic and value-added facts are needed. In this research, we introduce SeCold, an open and collaborative platform for sharing software datasets. SeCold provides the first online software ecosystem Linked Data platform that supports data extraction and on-the-fly inter-dataset integration from major version control, issue tracking, and quality evaluation systems. In its first release, the dataset contains about two billion facts, such as source code statements, software licenses, and code clones from 18 000 software projects. In its second release the SeCold project will contain additional facts mined from issue trackers and versioning systems. Our approach is based on the same fundamental principle as Wikipedia: researchers and tool developers share analysis results obtained from their tools by publishing them as part of the SeCold portal and therefore make them an integrated part of the global knowledge domain. The SeCold project is an official member of the Linked Data dataset cloud and is currently the eighth largest online dataset available on the Web.


title: Connecting Agriculture to the Internet of Things through Sensor Networks

abstract: The Internet of Things (IOT), the idea of getting real-world objects connected with each other, will change the ways we organize, obtain and consume information radically. Through sensor networks, agriculture can be connected to the IOT, which allows us to create connections among agronomists, farmers and crops regardless of their geographical differences. With the help of the connections, the agronomists will have better understanding of crop growth models and farming practices will be improved as well. This paper reports on the design of the sensor network when connecting agriculture to the IOT. Reliability, management, interoperability, low cost and commercialization are considered in the design. Finally, we share our experiences in both development and deployment.


title: Vibrating insoles and balance control in elderly people

abstract: Somatosensory function declines with age, and such changes have been associated with diminished motor performance. Input noise can enhance sensory and motor function. We asked young and elderly participants to stand quietly on vibrating gel-based insoles, and calculated sway parameters and random-walk variables. In our 27 participants, application of noise resulted in a reduction in seven of eight sway parameters in young participants and all of the sway variables in elderly participants. Elderly participants showed greater improvement than young people in two variables, mediolateral range (p=0.008), and critical mean square displacement (p=0.012). Noise-based devices, such as randomly vibrating insoles, could ameliorate age-related impairments in balance control.


title: Looking Away and Catching Up: Dealing with Brief Attentional Disconnection in Synchronous Groupware

abstract: Awareness information in shared-workspace groupware is often ephemeral, and when users are interrupted or look away from the system, they can miss important information about others' activities. These attentional disconnections are brief (generally less than ten seconds), are frequent in many work environments, and can occur for many reasons. In this paper, we provide visualization techniques that can help groupware users recover from these short absences. First, we provide a simple attention monitor that informs groupware systems whether a user is looking at the screen and whether the workspace is visible. Second, we provide a framework of catch-up techniques for visualizing missed activity -- including highlighting, motion traces, and replay. We implemented these techniques in three applications that cover different communication methods and interaction speeds, and explored the use of the effects in three studies. Participants clearly saw the value of catch-up techniques, and although different techniques were preferred in different situations, nearly all users strongly preferred the catch-up techniques. Our work shows that helping users deal with periods of attentional disconnection can substantially improve the usability of real-time groupware systems.


title: Validity and reliability of the Turkish Version of the Smartphone Addiction Scale in a younger population

abstract: ABSTRACTObjective: Smartphones have many features such as communication, internet, photography, multimedia and navigation, and are currently one of the most popular technological devices. Usage of smartphones has increased rapidly and this rapid increase has brought about addiction and problematic usage. To our knowledge, there is no scale, which can be used to assess addiction to smartphones in the Turkish population. The aim of this study was to adapt Turkish terminology and to assess the reliability and validity of the Turkish version of the Smartphone Addiction Scale.Methods: The sample was composed of 301 students studying at the Faculty of Medicine, Suleyman Demirel University, who used smartphones. In the study, in addition to the Smartphone Addiction Scale, an Information Form, the Internet Addiction Scale and the Problem Mobile Phone Use Scale were used as tools for collecting data. The scales were given to all attendees in mixed order and the Information Form was always given at the first stage....


title: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells

abstract: Motivation: Assessment of protein subcellular location is crucial to proteomics efforts since localization information provides a context for a protein’s sequence, structure, and function. The work described below is the first to address the subcellular localization of proteins in a quantitative, comprehensive manner. Results: Images for ten different subcellular patterns (including all major organelles) were collected using fluorescence microscopy. The patterns were described using a variety of numeric features, including Zernike moments, Haralick texture features, and a set of new features developed specifically for this purpose. To test the usefulness of these features, they were used to train a neural network classifier. The classifier was able to correctly recognize an average of 83% of previously unseen cells showing one of the ten patterns. The same classifier was then used to recognize previously unseen sets of homogeneously prepared cells with 98% accuracy. Availability: Algorithms were implemented using the commercial products Matlab, S-Plus, and SAS, as well as some functions written in C. The scripts and source code generated for this work are available at http://murphylab.


title: Real time video stabilization for handheld devices

abstract: The paper proposes a method and robust algorithm for 2D video stabilization designed for portable devices in real-time. The BSC (Boundary Signal Computation) chip of TI (Texas Instruments) is essentially used (or emulated herein) for searching of correlations between the 1D integral projections, horizontal and vertical ones, by a SAD (Sums of Absolute Differences) approach. The proposed method is based on an accurate vector model allowing interpretations of increasing complexity for the transformations among frames. Experiments, conducted on testing video clips, are very promising for the future R&D of the method.


title: 2.45GHz wide input range CMOS rectifier for RF energy harvesting

abstract: This paper presents a CMOS rectifier operating at 2.45 GHz for RF energy harvesting. The diode forward voltage drop, forward peak current and reverse leakage current issues have been addressed by utilizing a novel DC-boosted biasing technique. The proposed rectifier is implemented in 65 nm 6M/1P standard CMOS technology. In simulation, the proposed rectifier achieved peak efficiency of 59.6% with input power of −12 dBm at 2.45 GHz and 29 kΩ output load. PCE greater than 20% can be achieved over an extended input power range of 19 dB from −18 dBm to 1 dBm. The proposed rectifier has sensitivity of −17 dBm for 1 V output voltage.


title: Caffeine intake is related to successful weight loss maintenance

abstract: The effect of caffeine intake on weight loss maintenance has not been examined in humans. We compared the daily consumption of coffee and caffeinated beverages between 494 weight loss maintainers and 2129 individuals from the general population controlling for sociodemographic variables, body mass index and physical activity level. Weight loss maintainers reported to consume significantly more cups of coffee and caffeinated beverages compared with the participants in the general population sample. Thus, consumption of caffeinated beverages might support weight loss maintenance. Further studies should investigate possible mechanisms.


title: A 0.9–1.5 GHz Microstrip Antenna With Full Polarization Diversity and Frequency Agility

abstract: A microstrip antenna with frequency agility and polarization diversity is presented. Commercially available packaged RF microelectrical-mechanical (MEMS) single-pole double-throw (SPDT) devices are used with a novel feed network to provide four states of polarization control; linear-vertical, linear-horizontal, left-hand circular and right-handed circular. Also, hyper-abrupt silicon junction tuning diodes are used to tune the antenna center frequency from 0.9-1.5 GHz. The microstrip antenna is 1 in x 1 in, and is fabricated on a 4 in x 4 in commercial-grade dielectric laminate. To the authors' knowledge, this is the first demonstration of an antenna element with four polarization states across a tunable bandwidth of 1.4:1.


title: Improvement of power quality for PMSG wind turbine systems

abstract: This paper proposes an LVRT (low-voltage ride-through) of multi-pole permanent-magnet synchronous generator (PMSG) for wind turbine systems under the grid fault condition and varying wind speed using the energy storage system (ESS) and braking chopper (BC). The proposed topology can reduce the ESS power rating and improve the output performance. A detailed design procedure and control strategy for the ESS and BC are presented. The PSIM simulation results prove that the proposed system can give the ride-through capability and power smoothening for the PMSG wind turbine system. The validity of the proposed system is also verified by experiment results.


title: Generating Stopword List for Sanskrit Language

abstract: In the era of information burst, optimization of processes for Information Retrieval, Text Summarization, Text and Data Analytic systems becomes utmost important. Therefore in order to achieve accuracy, redundant words with low or no semantic meaning must be filtered out. Such words are known as Stopwords. Stopwords list has been developed for languages like English, Chinese, Arabic, Hindi, etc but standard stopword list is still missing for Sanskrit language. Identifying stop words manually from Sanskrit text is a herculean task hence this paper reflects an automated stop word generator algorithm based on frequency of word and its implementation to ease the task. To fine-tune the generated list still manual intervention by language expert is required thus following a hybrid approach. The paper presents the first of its kind, a list of seventy-five generic stopwords of Sanskrit language extracted from a data amounting to nearly seventy-six thousand words.


title: A Digital PFC Controller without Input Voltage Sensing

abstract: This paper introduces a novel digital PFC (DPFC) control approach that requires no input voltage sensing or current loop compensation, yet results in low-harmonic operation over a universal input voltage range and loads ranging from high-power operation in continuous conduction mode down to near-zero load. The controller is based on low-resolution DPWM and A/D converters, requires no microcontroller or DSP programming, and is well suited for a simple, low-cost integrated-circuit realization, or as an HDL core suitable for integration with other power control functions. Experimental verification results are shown for a 500 W boost PFC rectifier.


title: A grid-connected photovoltaic system: Mathematical modeling using MATLAB/Simulink

abstract: Renewable Energy Sources, especially solar energy, are important in mitigating environmental problems. Following, a step-by-step modeling of a photovoltaic (PV) system that can be connected to the grid through converters is achieved. The proposed mathematical model is implemented in MATLAB/ Simulink. A maximum power point tracking (MPPT) algorithm finds the maximum power for the operation of the PV system during variations of solar irradiance and ambient temperature. The proposed model is ready to be used for the implementation of energy storage that can provide power and voltage smoothing, peak shaving and standby generation in case of a grid fault.


title: Temporal dithering of illumination for fast active vision

abstract: Active vision techniques use programmable light sources, such as projectors, whose intensities can be controlled over space and time. We present a broad framework for fast active vision using Digital Light Processing (DLP) projectors. The digital micromirror array (DMD) in a DLP projector is capable of switching mirrors "on" and "off" at high speeds (106/s). An off-the-shelf DLP projector, however, effectively operates at much lower rates (30-60Hz) by emitting smaller intensities that are integrated over time by a sensor (eye or camera) to produce the desired brightness value. Our key idea is to exploit this "temporal dithering" of illumination, as observed by a high-speed camera. The dithering encodes each brightness value uniquely and may be used in conjunction with virtually any active vision technique. We apply our approach to five well-known problems: (a) structured light-based range finding, (b) photometric stereo, (c) illumination de-multiplexing, (d) high frequency preserving motion-blur and (e) separation of direct and global scene components, achieving significant speedups in performance. In all our methods, the projector receives a single image as input whereas the camera acquires a sequence of frames.


title: DOI: 10.1080/15298860390129863 Self-Compassion: An Alternative Conceptualization of a HealthyAttitudeToward Oneself

abstract: This article defines and examines the construct of self-compassion. Self-compassion entails three main components: (a) self-kindness—being kind and understanding toward oneself in instances of pain or failure rather than being harshly self-critical, (b) common humanity—perceiving one's experiences as part of the larger human experience rather than seeing them as separating and isolating, and (c) mindfulness—holding painful thoughts and feelings in balanced awareness rather than over-identifying with them. Self-compassion is an emotionally positive self-attitude that should protect against the negative consequences of self-judgment, isolation, and rumination (such as depression). Because of its non-evaluative and interconnected nature, it should also counter the tendencies towards narcissism, self-centeredness, and downward social comparison that have been associated with attempts to maintain self-esteem. The relation of self-compassion to other psychological constructs is examined, its links to psychologi...


title: DiracNets: Training Very Deep Neural Networks Without Skip-Connections

abstract: Deep neural networks with skip-connections, such as ResNet, show excellent performance in various image classification benchmarks. It is though observed that the initial motivation behind them - training deeper networks - does not actually hold true, and the benefits come from increased capacity, rather than from depth. Motivated by this, and inspired from ResNet, we propose a simple Dirac weight parameterization, which allows us to train very deep plain networks without explicit skip-connections, and achieve nearly the same performance. This parameterization has a minor computational cost at training time and no cost at all at inference, as both Dirac parameterization and batch normalization can be folded into convolutional filters, so that network becomes a simple chain of convolution-ReLU pairs. We are able to match ResNet-1001 accuracy on CIFAR-10 with 28-layer wider plain DiracNet, and closely match ResNets on ImageNet. Our parameterization also mostly eliminates the need of careful initialization in residual and non-residual networks. The code and models for our experiments are available at this https URL


Developing a parallel benchmarking suite for Java Grande applications


title: Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle

abstract: In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Trees. Additionally, we incorporate dynamic reconfigurability into the framework by integrating the motion planners with the control kernel of the UAV in a novel manner with little modification to the original algorithms. The framework has been verified through simulation and in actual flight. Empirical results show that these techniques used with such a framework offer a surprisingly efficient method for dynamically reconfiguring a motion plan based on unforeseen contingencies which may arise during the execution of a plan. The framework is generic and can be used for additional platforms.


title: Internet of Things (IoT)-based Learning Framework to Facilitate STEM Undergraduate Education

abstract: Internet of Things (IoT) is rapidly emerging as the next generation of communication infrastructure, where myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. The rapid growth of IoT applications has increased the demand for experienced professionals in the area. Since few, if any, dedicated IoT courses are currently offered, most Science, Technology, Engineering, and Mathematics (STEM) students will have limited or no exposure to IoT development until after graduation, and inadequately prepared in IoT development to enter into the workforce. Therefore, we have implemented an IoT-based learning framework to facilitate STEM undergraduate education. The design challenges of the novel learning framework are discussed in the paper. Subsequently, we established effective learning approaches to address the challenges. Specifically, we have introduced a lab development kit composed of Raspberry™ Pi/Arduino™ boards and sets of sensors with Zigbee™ supporting to provide wireless communication.


title: Object Detection in Video with Spatiotemporal Sampling Networks

abstract: We propose a Spatiotemporal Sampling Network (STSN) that uses deformable convolutions across time for object detection in videos. Our STSN performs object detection in a video frame by learning to spatially sample features from the adjacent frames. This naturally renders the approach robust to occlusion or motion blur in individual frames. Our framework does not require additional supervision, as it optimizes sampling locations directly with respect to object detection performance. Our STSN outperforms the state-of-the-art on the ImageNet VID dataset and compared to prior video object detection methods it uses a simpler design, and does not require optical flow data for training.


title: Multi-sensor context aware clothing

abstract: Inspired by perception in biological systems, distribution of a massive amount of simple sensing devices is gaining more support in detection applications. A focus on fusion of sensor signals instead of strong analysis algorithms, and a scheme to distribute sensors, results in new issues. Especially in wearable computing, where sensor data continuously changes, and clothing provides an ideal supporting structure for simple sensors, this approach may prove to be favourable. Experiments with a body-distributed sensor system investigate the influence of two factors that affect classification of what has been sensed: an increase in sensors enhances recognition, while adding new classes or contexts depreciates the results. Finally, a wearable computing related scenario is discussed that exploits the presence of many sensors.


Neighborhood rough set and SVM based hybrid credit scoring classifier


title: Asthma and allergy: TGFβ — too much of a good thing?

abstract: Enhanced TGFβ receptor signalling may predispose individuals to a range of allergic diseases by skewing regulatory T cell activity.


title: Feature selection for text classification: A review

abstract: Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models.


title: Learning to Diagnose with LSTM Recurrent Neural Networks

abstract: Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab test results are recorded in the patient's Electronic Health Record (EHR). While potentially containing a wealth of insights, the data is difficult to mine effectively, owing to varying length, irregular sampling and missing data. Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for learning from sequence data. They effectively model varying length sequences and capture long range dependencies. We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements. Specifically, we consider multilabel classification of diagnoses, training a model to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements. First, we establish the effectiveness of a simple LSTM network for modeling clinical data. Then we demonstrate a straightforward and effective training strategy in which we replicate targets at each sequence step. Trained only on raw time series, our models outperform several strong baselines, including a multilayer perceptron trained on hand-engineered features.


title: A framework to study requirements-driven collaboration among agile teams: Findings from two case studies

abstract: A framework is proposed to study requirements-driven collaboration among agile teams.Also, it finds the impact of their collaboration patterns on the iteration performance.Collaboration is translated in terms of communication and awareness.Two case studies were conducted to implement the framework.The results aid the project managers to learn about their teams' collaboration health. Stakeholders' collaboration is required to develop requirements in agile software development. Requirements engineering and agile methods share common grounds as they both focus on stakeholder collaboration. The key issue is finding a way to study collaboration driven by requirements in geographically distributed agile teams. In this paper, we aim to propose a framework to study collaboration driven by requirements among agile teams and determine the impact of their collaboration patterns on the iteration performance. We define collaboration in terms of communication as information exchange among members, and awareness knowledge of others. Two case studies were conducted to examine communication and awareness network patterns among distributed agile teams. Data were collected through questionnaires, semi-structured interviews and onsite observation. The findings revealed that the framework aids in determining the core members, collaboration trends, clustering tendency, communication and awareness reciprocity of the teams, small worldliness and centralisation behaviour of the networks and iteration performance of the agile teams. The framework has implications for the industrial practitioners, i.e. managers to learn about their team's collaboration in order to take measures for performance improvement. At the same time, the researchers can use this framework to study other social aspects in variable settings to produce more empirical results.


title: Consequences of Intimate Partner Violence on Substance Use and Depression for Women and Men

abstract: Using data from a nationally representative, longitudinal, and prospective sample of men and women, and controlling for prior involvement in the outcomes and demographic effects, the consequences of Intimate Partner Violence (IPV) on substance use and depression were examined over a 3-year period. The results were unexpected. Men in the sample were more likely than women to report the prevalence of minor and violent IPV victimization, whereas women in the sample were more likely than men to report the prevalence of minor and violent IPV perpetration. Additionally, the consequences of IPV were more apparent for male respondents than for female respondents. Implications of these findings are discussed, as are the study’s limitations, and future research directions.


title: A stepwise regression method and consistent model selection for high-dimensional sparse linear models

abstract: We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ? n, the sample size) sequentially so that the selected variable at each step minimizes the residual sum squares. We derive the convergence rate of OGA and develop a consistent model selection procedure along the OGA path that can adjust for potential spuriousness of the greedily chosen regressors among a large number of candidate variables. The resultant regression estimate is shown to have the oracle property of being equivalent to least squares regression on an asymptotically minimal set of relevant regressors under a strong sparsity condition.


title: A Multiresolution Approach to Model-Based 3-D Surface Quality Inspection

abstract: We propose a novel model-based surface approximation method for three-dimensional (3-D) surface quality inspection that combines a machine learning approach with multiresolution paradigms. Acceptable surface deviations are modeled by learning a number of 3-D measurements of tolerance samples. At the same time, areas with high surface details are hierarchically refined, allowing an improved spatial localization of the surface model. The method is based on a dual eigenvalue decomposition, which leads to fast computation for large datasets of ordered 3-D point clouds. The proposed algorithm is easy to configure and requires few parameters by automatically determining the areas for local refinement. It yields a better defect detection on deformable parts and parts with high tolerance ranges, especially on critical areas with high surface curvature. Experimental results show the effectiveness compared to model-based approaches without multiresolution as well as nonmodel-based methods. Examples are given for successful defect detection where previous methods have failed.


title: WhatsApp Goes to School: Mobile Instant Messaging between Teachers and Students

abstract: WhatsApp is a Smartphone application for instant messaging. Lately the application's popularity has risen. One of the unique features of the application is its ability to enhance communication within a group. Classroom communication between teaching faculty and high school students using WhatsApp has not yet, to our knowledge, been researched thoroughly. Therefore, we have chosen to conduct an exploratory research project employing a qualitative method. Twelve halfstructured interviews were carried out with teachers who use the application in order to communicate with their pupils. It turns out that class WhatsApp groups are used for four main purposes: communicating with students; nurturing the social atmosphere; creating dialogue and encouraging sharing among students; and as a learning platform. The participants mentioned the technical advantages of WhatsApp, such as simple operation, low cost, availability, and immediacy. They also referred to educational advantages, such as the creation of a pleasant environment and an in-depth acquaintance with fellow students, which had a positive influence upon the manner of conversation. The participants also indicated academic advantages such as the accessibility of learning materials, teacher availability, and the continuation of learning beyond class hours. Nevertheless, there are also challenges and problems. Firstly, there is the technical difficulty that not all high school students possess a Smartphone. Secondly, teachers are apt to be annoyed by the flood of irrelevant and nonsensical messages. Also, educational difficulties may arise, such as incompatibility of language between students and the students' assumptions that their teachers should be available on a 24/7 basis.


title: Estimation of post-mortem interval for a drowning case by using flies (Diptera) in Central-South China: Implications for forensic entomology

abstract: The area of forensic entomology has developed to become an increasingly important aspect of forensic science. Insect larvae and adult insects found on human corpses can provide crucial information for the post-mortem interval (PMI) estimation. In July 2010, a decomposing female corpse was found in Xiangjiang River of Changsha section. The main larvae found on the corpse were identified to be Boettcherisca peregrina (Robineau-Desvoidy) by morphologic observation and were then confirmed by mitochondrial DNA sequence. Based on the experimentally obtained entomological evidence, PMI of the female was concluded between 90 and 120 hours. However, eyewitness reports finally confirmed that the elapsed time since death were 128 hours. This case indicates that PMI estimation in terms of the period of insect activity (PIA) is very complicated and concern many factors such as insect species characteristics, weather and seasonality, geographic region. The interaction between forensic entomologists and pathologists in death cases where insects are associated with remains should be encouraged. Key W ords: forensic science, forensic entomology, post-mortem interval, Boettcherisca peregrina, drowning, mitochondrial DNA.


title: Design and implementationof 10W, highly linear, wideband and efficient power amplifier using harmonic termination

abstract: In this paper, the design of a 10W, highly linear, wideband and efficient power amplifier is introduced. The design is based on Cree's CGH40010F GaN HEMT biased in class AB. The amplifier's high linearity is ensured through the use of a fourth order Chebyshev low filter as the output matching network which serves to get rid of higher order harmonics as well as any resulting out of band intermodulation products. At an output power of 40 dBm, the designed amplifier achieves a wide bandwidth extending from 1 to 2.5 GHz (85% fractional bandwidth), while maintaing a high power added efficiency (PAE) ranging from 48–55% across the entire operation bandwidth. Furthermore, the achieved amplifier output IP2 and IP3 are 47 and 76 dBm, respectively.


title: Initial trust and online buyer behaviour

abstract: Purpose – Online trust is one of the key obstacles to vendors succeeding on the internet medium; a lack of trust is likely to discourage online consumers from participating in e‐commerce. This research aims to investigate how online consumers develop their initial trust and purchase intentions. The research in conducted in the context of Taiwanese online bookstores.Design/methodology/approach – The research examines consumers' online initial trust by using four major categories of determinants: perceived technology, perceived risk, company competency, and trust propensity. It also investigates the impacts of both online initial trust and familiarity with online purchasing on purchase intention. The research model is statistically tested using the web sites of four online bookstores in Taiwan. The web site selected by each respondent is unfamiliar.Findings – It is found that perceived usefulness, perceived security, perceived privacy, perceived good reputation, and willingness to customise are the importan...


title: Experimental and Theoretical Assessment of Thin Glass Substrate for Low Warpage

abstract: This paper compares the warpage after lead-free solder assembly of ultrathin glass and organic substrates for microelectronic packages. Smart mobile devices demand packages to be thinner, which exacerbates warpage, which in turn affects interconnect pitch scaling and reliability. Although low-coefficient-of-thermal-expansion (CTE) organic laminates have been developed to reduce the warpage during assembly, glass substrates offer added benefits of much higher modulus and thermo-mechanical stability, with the potential to reduce warpage beyond low-CTE organic materials. The focus of this paper is on modeling and measurements to quantify the warpage of glass and organic laminates at 100- $\mu \text{m}$ core thickness after thermo-compression bonding at 260 °C peak temperature. In the experiments, four-metal-layer glass substrates were fabricated at the panel level, diced into $18.4 \times 18.4$ -mm2 coupons, 10-mm $\times $ 10- $\text {mm}\,\,\times 630-\mu \text{m}$ silicon dies were thermo-compression bonded, and the warpage of the assembled packages was measured using shadow moire interferometry. In parallel to the experiments, numerical models that account for the viscoplastic behavior of the solder as well as the sequential build-up materials and processes have been developed. The predicted warpage from the models was compared to the experimental measurements. It was seen that the low-CTE glass had lower warpage than the organic core of equivalent CTE, while increasing the CTE of the glass increased the warpage. Also, the underfill fillet size was found to influence the warpage for thin substrate packages, and an optimal fillet size has been identified for minimizing warpage. B-staged and capillary underfill materials were compared and found to have similar warpage values.


title: Measurement and analysis of student engagement in university classes where varying levels of PBL methods of instruction are in use

abstract: Students participate more in a classroom and also report a better understanding of course concepts when steps are taken to actively engage them. The Student Engagement (SE) Survey was developed and used in this study for measuring student engagement at the class level and consisted of 14 questions adapted from the original National Survey of Student Engagement (NSSE) survey. The adapted survey examined levels of student engagement in 56 classes at an upper mid‐western university in the USA. Campus‐wide faculty members participated in a program for training them in innovative teaching methods including problem‐based learning (PBL). Results of this study typically showed a higher engagement in higher‐level classes and also those classes with fewer students. In addition, the level of engagement was typically higher in those classrooms with more PBL.


title: Ubiquitous Access to Digital Cultural Heritage

abstract: The digitization initiatives in the past decades have led to a tremendous increase in digitized objects in the cultural heritage domain. Although digitally available, these objects are often not easily accessible for interested users because of the distributed allocation of the content in different repositories and the variety in data structure and standards. When users search for cultural content, they first need to identify the specific repository and then need to know how to search within this platform (e.g., usage of specific vocabulary). The goal of the EEXCESS project is to design and implement an infrastructure that enables ubiquitous access to digital cultural heritage content. Cultural content should be made available in the channels that users habitually visit and be tailored to their current context without the need to manually search multiple portals or content repositories. To realize this goal, open-source software components and services have been developed that can either be used as an integrated infrastructure or as modular components suitable to be integrated in other products and services. The EEXCESS modules and components comprise (i) Web-based context detection, (ii) information retrieval-based, federated content aggregation, (iii) metadata definition and mapping, and (iv) a component responsible for privacy preservation. Various applications have been realized based on these components that bring cultural content to the user in content consumption and content creation scenarios. For example, content consumption is realized by a browser extension generating automatic search queries from the current page context and the focus paragraph and presenting related results aggregated from different data providers. A Google Docs add-on allows retrieval of relevant content aggregated from multiple data providers while collaboratively writing a document. These relevant resources then can be included in the current document either as citation, an image, or a link (with preview) without having to leave disrupt the current writing task for an explicit search in various content providers’ portals.


title: Capturing Design: Improving conceptual ship design through the capture of design rationale

abstract: As the complexity of ship design increases, the knowledge used to resolve these complexities is decreasing due to a loss of intellectual resources. In order to remedy these losses, Naval Architects must capitalize on the knowledge available in design teams. The solution to this quandary involves the effective capturing of design rationale. This thesis focuses on the capture of configuration rationale in complex ship design. It recasts the rationale capture problem as a collection of rationale tasks: express, (re)structure, store and (re)use. The methodology developed in this research, integrated in a Rationale Capture Tool, employs three key features. First, a new rationale elicitation approach, Reactive Knowledge Capturing, is used to capture designer reaction to unorthodox and unconventional designs. Second, expressed configuration rationale is (re)structured using a semi-formal argument based ontology that supports indexing, sorting and conversion from a human to machine understandable form. Finally, a dedicated feedback mechanism, which employs an evolutionary search algorithm, triggers designer expression of missing rationales, thereby improving the overall efficiency of the system. A comprehensive test case, with practicing naval architects, illustrates the potential of this approach. It demonstrated the successful capture of configuration rationale (interactions, proximity rules and their associated rationales). A quantitative and qualitative analysis of expressed rationales shows that they are both diverse and appropriate, i.e., relevant and complete. One of the most promising results of the test case was that the majority of interactions and rationales appeared (re)usable, i.e., the rationale can be used to support other types of ship design. The captured rationale serves to improve the quality of knowledge available in early stages of the design process and complement existing conceptual ship design approaches.


title: The cycloid permanent magnetic gear

abstract: This paper presents a new permanent magnet gear based on the cycloid gearing principle, which normally is characterized by an extreme torque density and a very high gearing ratio. An initial design of the proposed magnetic gear was designed, analyzed, and optimized with an analytical model regarding torque density, which resulted in promising results compared to other high performance magnetic gear designs. A test model was constructed to verify the analytical model


title: genepop’007: a complete re-implementation of the genepop software for Windows and Linux

abstract: This note summarizes developments of the genepop software since its first description in 1995, and in particular those new to version 4.0: an extended input format, several estimators of neighbourhood size under isolation by distance, new estimators and confidence intervals for null allele frequency, and less important extensions to previous options. genepop now runs under Linux as well as under Windows, and can be entirely controlled by batch calls.


title: LiMe: linear methods for pseudo-relevance feedback

abstract: Retrieval effectiveness has been traditionally pursued by improving the ranking models and by enriching the pieces of evidence about the information need beyond the original query. A successful method for producing improved rankings consists in expanding the original query. Pseudo-relevance feedback (PRF) has proved to be an effective method for this task in the absence of explicit user's judgements about the initial ranking. This family of techniques obtains expansion terms using the top retrieved documents yielded by the original query. PRF techniques usually exploit the relationship between terms and documents or terms and queries. In this paper, we explore the use of linear methods for pseudo-relevance feedback. We present a novel formulation of the PRF task as a matrix decomposition problem which we called LiMe. This factorisation involves the computation of an inter-term similarity matrix which is used for expanding the original query. We use linear least squares regression with regularisation to solve the proposed decomposition with non-negativity constraints. We compare LiMe on five datasets against strong state-of-the-art baselines for PRF showing that our novel proposal achieves improvements in terms of MAP, nDCG and robustness index.


title: Styling with Attention to Details

abstract: Fashion as characterized by its nature, is driven by style. In this paper, we propose a method that takes into account the style information to complete a given set of selected fashion items with a complementary fashion item. Complementary items are those items that can be worn along with the selected items according to the style. Addressing this problem facilitates in automatically generating stylish fashion ensembles leading to a richer shopping experience for users. ::: Recently, there has been a surge of online social websites where fashion enthusiasts post the outfit of the day and other users can like and comment on them. These posts contain a gold-mine of information about style. In this paper, we exploit these posts to train a deep neural network which captures style in an automated manner. We pose the problem of predicting complementary fashion items as a sequence to sequence problem where the input is the selected set of fashion items and the output is a complementary fashion item based on the style information learned by the model. We use the encoder decoder architecture to solve this problem of completing the set of fashion items. We evaluate the goodness of the proposed model through a variety of experiments. We empirically observe that our proposed model outperforms competitive baseline like apriori algorithm by ~28 in terms of accuracy for top-1 recommendation to complete the fashion ensemble. We also perform retrieval based experiments to understand the ability of the model to learn style and rank the complementary fashion items and find that using attention in our encoder decoder model helps in improving the mean reciprocal rank by ~24. Qualitatively we find the complementary fashion items generated by our proposed model are richer than the apriori algorithm.


title: Deep Learning for Real-Time Robust Facial Expression Analysis

abstract: The aim of this investigation is to classify real-life facial images into one of six types of emotions. For solving this problem, we propose to use deep machine learning algorithms and convolutional neural network (CNN). CNN is a modern type of neural network, which allows for rapid detection of various objects, as well as to make an effective object classification. For acceleration of CNN learning stage, we use supercomputer NVIDIA DGX-1. This process was implemented in parallel on a large number of independent streams on GPU. Numerical experiments for algorithms were performed on the images of Multi-Pie image database with various lighting of scene and angle rotation of head. For developed models, several metrics of quality were calculated. The designing algorithm was used in real-time video processing in human-computer interaction systems. Moreover, expression recognition can apply in such fields as retail analysis, security, video games, animations, psychiatry, automobile safety, educational software, etc.


title: Learning to be efficient: algorithms for training low-latency, low-compute deep spiking neural networks

abstract: Recent advances have allowed Deep Spiking Neural Networks (SNNs) to perform at the same accuracy levels as Artificial Neural Networks (ANNs), but have also highlighted a unique property of SNNs: whereas in ANNs, every neuron needs to update once before an output can be created, the computational effort in an SNN depends on the number of spikes created in the network. While higher spike rates and longer computing times typically improve classification performance, very good results can already be achieved earlier. Here we investigate how Deep SNNs can be optimized to reach desired high accuracy levels as quickly as possible. Different approaches are compared which either minimize the number of spikes created, or aim at rapid classification by enforcing the learning of feature detectors that respond to few input spikes. A variety of networks with different optimization approaches are trained on the MNIST benchmark to perform at an accuracy level of at least 98%, while monitoring the average number of input spikes and spikes created within the network to reach this level of accuracy. The majority of SNNs required significantly fewer computations than frame-based ANN approaches. The most efficient SNN achieves an answer in less than 42% of the computational steps necessary for the ANN, and the fastest SNN requires only 25% of the original number of input spikes to achieve equal classification accuracy. Our results suggest that SNNs can be optimized to dramatically decrease the latency as well as the computation requirements for Deep Neural Networks, making them particularly attractive for applications like robotics, where real-time restrictions to produce outputs and low energy budgets are common.


title: How Many Topics? Stability Analysis for Topic Models

abstract: Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic. Despite the diversity of topic modeling algorithms that have been proposed, a common challenge in successfully applying these techniques is the selection of an appropriate number of topics for a given corpus. Choosing too few topics will produce results that are overly broad, while choosing too many will result in the "over-clustering" of a corpus into many small, highly-similar topics. In this paper, we propose a term-centric stability analysis strategy to address this issue, the idea being that a model with an appropriate number of topics will be more robust to perturbations in the data. Using a topic modeling approach based on matrix factorization, evaluations performed on a range of corpora show that this strategy can successfully guide the model selection process.


Project milestone report for CS 229 : Blood Pressure detection from PPG signal -


title: The Effect of Contextualized Vocabulary Teaching on Learners' Vocabulary Learning and Retention

abstract: The purpose of this study was to investigate the role of contextualized vocabulary instruction in the vocabulary learning of Iranian EFL learners at intermediate level at Eram English Institute in Gograg, Iran. Also, an attempt was made to explore whether there would be effects on learners vocabulary retention. To do so, the participants were 60 students who were randomly divided into two experimental (30) and control groups (30). The experimental group was exposed to contextualized vocabulary teaching. Conversely, the control group concentrated on traditional definition-based vocabulary teaching. After the treatment, the control and experimental groups' performance were analyzed based on pretest, posttest and retention test data. The two posttest data analyses demonstrated that there was a significant difference between the control and the experimental groups. Principally, the experimental group's scores were statistically more significant than the control group, that is, the results indicated that the contextualized vocabulary instruction may be applied as a useful vocabulary teaching method in language learning classrooms. Also, the analysis of the retention results demonstrated that statistically there were significant differences between both of the experimental and control group learners in terms of their retention of target vocabulary two weeks after conducting post-test.


title: PLAR: Parallel Large-Scale Attribute Reduction on Cloud Systems

abstract: Attribute reduction for big data is viewed as an important preprocessing step in the areas of pattern recognition, machine learning and data mining. In this paper, a novel parallel method based on MapReduce for large-scale attribute reduction is proposed. By using this method, several representative heuristic attribute reduction algorithms in rough set theory have been parallelized. Further, each of the improved parallel algorithms can select the same attribute reduct as its sequential version, therefore, owns the same classification accuracy. An extensive experimental evaluation shows that these parallel algorithms are effective for big data.


title: Raltegravir, tenofovir, and emtricitabine in an HIV-Infected patient with HCV chronic hepatitis, NNRTI intolerance and protease inhibitors-induced severe liver toxicity

abstract: Backgroundin HIV-infected patients with HCV-related chronic hepatitis, liver impairment and drug toxicity may substantially reduce the number of possible therapeutic options.Case Descriptionwe here describe the case of an HCV-HIV coinfected woman who had repeated severe episodes of drug-related liver toxicity with indinavir, saquinavir, fosamprenavir, and darunavir, with minimal further therapeutic options left in this class. Previous treatment-limiting side effects with efavirenz and nevirapine also precluded use of non-nucleoside reverse transcriptase inhibitors. Introduction of an integrase-inhibitor regimen based on raltegravir, tenofovir, and emtricitabine allowed a prompt achievement of undetectable viral load and a substantial rise of CD4 count to high levels, with no subsequent episodes of hepatic toxicity, and no other side effects.Conclusionsgiven the relatively common prevalence of HCV-related chronic hepatitis among people with HIV, raltegravir might represent an important alternative option for a substantial number of patients who cannot be treated with protease inhibitors or NNRTI because of drug-related hepatic toxicity.


title: Where is the child's environment? A group socialization theory of development.

abstract: Do parents have any important long-term effects on the development of their child's personality? This article examines the evidence and concludes that the answer is no. A new theory of development is proposed: that socialization is context-specific and that outside-the-home socialization takes place in the peer groups of childhood and adolescence. Intraand intergroup processes, not dyadic relationships, are responsible for the transmission of culture and for environmental modification of children's personality characteristics. The universality of children's groups explains why development is not derailed by the wide variations in parental behavior found within and between societies.


title: Robust Distributed Spectrum Sensing in Cognitive Radio Networks

abstract: Distributed spectrum sensing (DSS) enables a Cognitive Radio (CR) network to reliably detect licensed users and avoid causing interference to licensed communications. The data fusion technique is a key component of DSS. We discuss the Byzantine failure problem in the context of data fusion, which may be caused by either malfunctioning sensing terminals or Spectrum Sensing Data Falsification (SSDF) attacks. In either case, incorrect spectrum sensing data will be reported to a data collector which can lead to the distortion of data fusion outputs. We investigate various data fusion techniques, focusing on their robustness against Byzantine failures. In contrast to existing data fusion techniques that use a fixed number of samples, we propose a new technique that uses a variable number of samples. The proposed technique, which we call Weighted Sequential Probability Ratio Test (WSPRT), introduces a reputation-based mechanism to the Sequential Probability Ratio Test (SPRT). We evaluate WSPRT by comparing it with a variety of data fusion techniques under various network operating conditions. Our simulation results indicate that WSPRT is the most robust against the Byzantine failure problem among the data fusion techniques that were considered.


title: Lack of quality discrimination in online information markets

abstract: Online social networks are marketplaces in which memes compete for our attention. While one would expect the best ideas to prevail, empirical evidence suggests that high-quality information has no competitive advantage. Here we investigate this puzzling lack of discriminative power through an agent-based model that incorporates behavioral limitations in managing a heavy flow of information and measures the relationship between the quality of an idea and its likelihood to become prevalent at the system level. We find that both information overload and limited attention contribute to a degradation in the market's discriminative power. A good tradeoff between discriminative power and diversity of information is possible according to the model. However, calibration with empirical data characterizing information load and finite attention in real social media reveals a weak correlation between quality and popularity of information. In these realistic conditions, the model provides an interpretation for the high volume of viral misinformation we observe online.


title: A 2×25Gb/s 20mW serializing transmitter with 2.5:1 multiplexers in 40nm technology

abstract: A low-power serializing transmitter is proposed by using a 2.5:1 multiplexer. Owing to its fractional multiplexing operation, only a single clock is needed to simplify the clock generator design as well as hardware cost. Fabricated in a 40 nm CMOS technology, the transmitter can generate 2-channel 25-Gbps signals. The transmitter consumes only 24 mW from a 0.9-V power supply.


title: Generating Maximal Domino Patterns by Cellular Automata Agents

abstract: Considered is a 2D cellular automaton with moving agents. The objective is to find agents controlled by a Finite State Program (FSP) that can form domino patterns. The quality of a formed pattern is measured by the degree of order computed by counting matching \(3 \times 3\) patterns (templates). The class of domino patterns is defined by four templates. An agent reacts on its own color, the color in front, and whether it is blocked or not. It can change the color, move or not, and turn into any direction. Four FSP were evolved for multi-agent systems with 1, 2, 4 agents initially placed in the corners of the field. For a \(12 \times 12\) training field the aimed pattern could be formed with a 100% degree of order. The performance was also high with other field sizes. Livelocks are avoided by using three different variants of the evolved FSP. The degree of order usually fluctuates after reaching a certain threshold, but it can also be stable, and the agents may show the termination by running in a cycle, or by stopping their activity.


title: Saliency detection via Cellular Automata

abstract: In this paper, we introduce Cellular Automata-a dynamic evolution model to intuitively detect the salient object. First, we construct a background-based map using color and space contrast with the clustered boundary seeds. Then, a novel propagation mechanism dependent on Cellular Automata is proposed to exploit the intrinsic relevance of similar regions through interactions with neighbors. Impact factor matrix and coherence matrix are constructed to balance the influential power towards each cell's next state. The saliency values of all cells will be renovated simultaneously according to the proposed updating rule. It's surprising to find out that parallel evolution can improve all the existing methods to a similar level regardless of their original results. Finally, we present an integration algorithm in the Bayesian framework to take advantage of multiple saliency maps. Extensive experiments on six public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods.


Traffic-Related Air Pollution, Noise at School, and Behavioral Problems in Barcelona Schoolchildren: A Cross-Sectional Study


title: Children's conduct of life: Across general school, educational psychology consultation and psychiatry

abstract: The referral and diagnosing system in Denmark is organized as an institutionalized practice consisting of various practices and procedures that frame criteria for normality or deviance, thus enabling intervention. This institutional practice has been criticized for building on a compensatory orientation supported by testing technologies, with a lack of sensitivity to the context which the difficulties can be seen as part of (McDermott, 1996; Allodi and Fishbein, 2000; Storbie, I., Gemmell, M., Moran, E., & Randall, L. (2002). Challenges for educational psychologists and their services: A qualitative analysis. In School Psychology International, 23; Ekstrom, 2004; Ron Larsen, 2012; Kristensen, 2013). Taking its point of departure in Klaus Holzkamp's critique of traditional psychology's structure blindness (Holzkamp, 2013), this article will discuss how the concept of conduct of everyday life may inform and inspire both professional practice and our scientific understandings and developmental possibilities ...


title: Vitamins, minerals, and mood.

abstract: In this article, the authors explore the breadth and depth of published research linking dietary vitamins and minerals (micronutrients) to mood. Since the 1920s, there have been many studies on individual vitamins (especially B vitamins and Vitamins C, D, and E), minerals (calcium, chromium, iron, magnesium, zinc, and selenium), and vitamin-like compounds (choline). Recent investigations with multi-ingredient formulas are especially promising. However, without a reasonable conceptual framework for understanding mechanisms by which micronutrients might influence mood, the published literature is too readily dismissed. Consequently, 4 explanatory models are presented, suggesting that mood symptoms may be expressions of inborn errors of metabolism, manifestations of deficient methylation reactions, alterations of gene expression by nutrient deficiency, and/or long-latency deficiency diseases. These models provide possible explanations for why micronutrient supplementation could ameliorate some mental symptoms.


title: A Miniaturized Dual-Band FSS With Stable Resonance Frequencies of 2.4 GHz/5 GHz for WLAN Applications

abstract: In this letter, we propose an anchor-shaped loop unit-cell structure for compact frequency selective surfaces (FSSs) with dual-bandstop behavior in two wireless local area network (WLAN) frequencies 2.4 and 5.0 GHz. The proposed FSS possesses 230- and 300-MHz bandwidths with insertion loss less than ${-}$ 20 dB around the two central operating frequencies 2.4 and 5.0 GHz, respectively. In addition, the FSS exhibits excellent miniaturization with 0.065 $\lambda \times 0.076 \lambda$ unit cells, where $\lambda$ represents the free-space wavelength. Furthermore, the unit-cell structure provides a stable performance for both TE- and TM- polarizations under incident angles 0 $^{\circ}$ –60 $^{\circ}$ . A prototype of the proposed FSS is fabricated and measured. Good agreement between the simulated and measured results is obtained.


title: Interleaved Boost Converter with Ripple Cancellation Network

abstract: This paper presents an interleaved boost converter (IBC) with ripple cancellation network (RCN). Based on the conventional IBC, two capacitors, two coupled inductors, and two inductors are added as RCN in the proposed converter. Therefore, the proposed converter shares the same advantages of the conventional IBC and achieves input current ripple cancellation without significantly increasing the current stress and loss of the converter. Finally, a 36-V input voltage, 50-V output voltage, and 1-kW output power prototype circuit operating at 100 kHz is implemented in the laboratory to verify the expected performance. The proposed converter can achieve input current ripple cancellation in all power range with the efficiency higher than 96%. The simulation and experimental results show that the proposed IBC with RCN has great potential to be applied in high-power, high-efficiency dc/dc conversion.


title: All-solid-state dye-sensitized solar cells with high efficiency

abstract: A solution-processable inorganic semiconductor is reported that can replace the liquid electrolyte of dye-sensitized solar cells, yielding all-solid-state solar cells with impressive energy conversion efficiencies.


The liprin protein SYD-2 regulates the differentiation of presynaptic termini in C. elegans


Feature selection, perceptron learning, and a usability case study for text categorization


title: IBeaconMap: Automated Indoor Space Representation for Beacon-Based Wayfinding

abstract: Traditionally, there have been few options for navigational aids for the blind and visually impaired (BVI) in large indoor spaces. Some recent indoor navigation systems allow users equipped with smartphones to interact with low cost Bluetoothbased beacons deployed strategically within the indoor space of interest to navigate their surroundings. A major challenge in deploying such beacon-based navigation systems is the need to employ a time and labor-expensive beacon planning process to identify potential beacon placement locations and arrive at a topological structure representing the indoor space. This work presents a technique called IBeaconMap for creating such topological structures to use with beacon-based navigation that only needs the floor plans of the indoor spaces of interest. IBeaconMap employs a combination of computer vision and machine learning techniques to arrive at the required set of beacon locations and a weighted connectivity graph (with directional orientations) for subsequent navigational needs. Evaluations show IBeaconMap to be both fast and reasonably accurate, potentially proving to be an essential tool to be utilized before mass deployments of beacon-based indoor wayfinding systems of the future.


title: Self-reported ability assessment in rock climbing.

abstract: Abstract Level of ability within rock climbing is generally expressed in terms of a “best ascent”, rated using various grading systems within the sport. The most common method of obtaining this information is via self-report. The aim of this study was to examine the validity of self-reported climbing grades. Twenty-nine competitive rock climbers (17 males, 12 females) were first asked to report their current (defined as within the last 12 months) best on-sight lead ascent grade (Aus/NZ). The participants then climbed a specifically designed indoor route, under on-sight conditions (one attempt, no route practice or preview), to obtain an assessed grade. The route increased in difficulty, and was such that the distance achieved by the climber corresponded to a particular grade. The mean (±standard deviation) self-reported and assessed grade was 22.6 ± 3.4 and 22.0 ± 3.0 (Aus/NZ) respectively. Despite slight over- and underestimations in males and females respectively, there was no statistically significant ...


title: Using Big Data to Improve Customer Experience and Business Performance

abstract: In an environment where communications service providers (CSPs) increasingly have the same service offers and devices, offering a superior customer experience is a priority to compete. Solutions that have the ability to highlight what really matters in driving customer satisfaction and deliver actionable insights from their wide-reaching customer, network, and service data are key differentiators for CSPs. This paper explores ways of integrating big data insights with automated and assisted processes related to key customer touchpoints to ultimately improve the customer experience. We show how innovation from Alcatel-Lucent and Bell Labs helps CSPs improve their business performance, using unique methodology designed to select the right key quality indicators, build accurate key business objective “formula,” predict customer behavior, and ultimately understand which factors are influencing the most. This can be used for example to improve the Net Promoter Score (NPS). The net result is a happier customer and a higher customer value.


title: Sentence-Level Polarity Detection in a Computer Corpus

abstract: The paper presents a preliminary research on possible relations between the syntactic structure and the polarity of a Czech sentence by means of the so-called sentiment analysis of a computer corpus. The main goal of sentiment analysis is the detection of a positive or negative polarity, or neutrality of a sentence (or, more broadly, a text). Most often this process takes place by looking for the polarity items, i.e. words or phrases inherently bearing positive or negative values. These words (phrases) are collected in the subjectivity lexicons and implemented into a computer corpus. However, when using sentences as the basic units to which sentiment analysis is applied, it is always important to look at their semantic and morphological analysis, since polarity items may be influenced by their morphological context. It is expected that some syntactic (and hypersyntactic) relations are useful for the identification of sentence polarity, such as negation, discourse relations or the level of embeddedness of the polarity item in the structure. Thus, we will propose such an analysis for a convenient source of data, the richly annotated Prague Dependency Treebank.


title: A State of the Art Survey of Fraud Detection Technology

abstract: With the introduction of IT to conductbusiness we accepted the loss of a human control step.For this reason, the introductionof newIT systemswas accompanied by the development of the authorization concept. But since, in reality, there is no such thing as 100 per cent security; auditors are commissioned to examine all transactions for misconduct. Since the data exists in digital form already, it makes sense to use computer-based processes to analyse it. Such processes allow the auditor to carry out extensive checks within an acceptable timeframe and with reasonable effort. Once the algorithm has been defined, it only takes sufficient computing power to evaluate larger quantities of data. This contribution presents the state of the art for IT-based data analysis processes that can be used to identify fraudulent activities.


title: Minimum-error discrimination of quantum states: New bounds and comparison

abstract: The minimum-error probability of ambiguous discrimination for two quantum states is the well-known {\it Helstrom limit} presented in 1976. Since then, it has been thought of as an intractable problem to obtain the minimum-error probability for ambiguously discriminating arbitrary $m$ quantum states. In this paper, we obtain a new lower bound on the minimum-error probability for ambiguous discrimination and compare this bound with six other bounds in the literature. Moreover, we show that the bound between ambiguous and unambiguous discrimination does not extend to ensembles of more than two states. Specifically, the main technical contributions are described as follows: (1) We derive a new lower bound on the minimum-error probability for ambiguous discrimination among arbitrary $m$ mixed quantum states with given prior probabilities, and we present a necessary and sufficient condition to show that this lower bound is attainable. (2) We compare this new lower bound with six other bounds in the literature in detail, and, in some cases, this bound is optimal. (3) It is known that if $m=2$, the optimal inconclusive probability of unambiguous discrimination $Q_{U}$ and the minimum-error probability of ambiguous discrimination $Q_{E}$ between arbitrary given $m$ mixed quantum states have the relationship $Q_{U}\geq 2Q_{E}$. In this paper, we show that, however, if $m>2$, the relationship $Q_{U}\geq 2Q_{E}$ may not hold again in general, and there may be no supremum of $Q_{U}/Q_{E}$ for more than two states, which may also reflect an essential difference between discrimination for two-states and multi-states. (4) A number of examples are constructed.


title: A three-dimensional view of software ecosystems

abstract: The concept of a Software ecosystem is gaining in popularity amongst large organizations and inherently relies on the adoption of common architectural development for multiple product development. The adoption of these approaches represents a significant shift in traditional software development style and process methodology. Currently several organizations are in practice with this new process model which embraces business, third party involvement and open architecture as its central pillars and these institutions have thrived as a result. The advent of software ecosystems have caused major players in the software industry to rethink their operating practices and engage with third parties, opening their platforms to external entities to attain business objectives. In this paper we present a three dimensional view of the software ecosystem model examine the role played by each of the three central pillars; business; architecture; and social aspects. We further highlight their relationships and conclude that this study will help in further aiding understanding of the overall engineering process of ecosystem software.


title: An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics

abstract: Phylodynamics focuses on the problem of reconstructing past population size dynamics from current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology, but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g.,\ influenza virus. Phylodynamics inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. While this approach is quite powerful, large data sets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand computationally more efficient inference framework. To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm.


title: Social Consequences of the Internet for Adolescents: A Decade of Research

abstract: Adolescents are currently the defining users of the Internet. They spend more time online than adults do, and they use the Internet for social interaction more often than adults do. This article discusses the state of the literature on the consequences of online communication technologies (e.g., instant messaging) for adolescents' social connectedness and well-being. Whereas several studies in the 1990s suggested that Internet use is detrimental, recent studies tend to report opposite effects. We first explain why the results of more recent studies diverge from those of earlier studies. Then, we discuss a viable hypothesis to explain the recent findings: the Internet-enhanced self-disclosure hypothesis. Finally, we discuss some contingent factors that may deserve special attention in future research.


title: PAC-learning in the presence of evasion adversaries

abstract: The existence of evasion attacks during the test phase of machine learning algorithms represents a significant challenge to both their deployment and understanding. These attacks can be carried out by adding imperceptible perturbations to inputs to generate adversarial examples and finding effective defenses and detectors has proven to be difficult. In this paper, we step away from the attack-defense arms race and seek to understand the limits of what can be learned in the presence of an evasion adversary. In particular, we extend the Probably Approximately Correct (PAC)-learning framework to account for the presence of an adversary. We first define corrupted hypothesis classes which arise from standard binary hypothesis classes in the presence of an evasion adversary and derive the Vapnik-Chervonenkis (VC)-dimension for these, denoted as the adversarial VC-dimension. We then show that sample complexity upper bounds from the Fundamental Theorem of Statistical learning can be extended to the case of evasion adversaries, where the sample complexity is controlled by the adversarial VC-dimension. We then explicitly derive the adversarial VC-dimension for halfspace classifiers in the presence of a sample-wise norm-constrained adversary of the type commonly studied for evasion attacks and show that it is the same as the standard VC-dimension, closing an open question. Finally, we prove that the adversarial VC-dimension can be either larger or smaller than the standard VC-dimension depending on the hypothesis class and adversary, making it an interesting object of study in its own right.


title: Time Keeps on Ticking The Experience of Clinical Judgment

abstract: In this rejoinder, the authors respond to reactions by Ridley and Shaw-Ridley and by Lichtenberg to their meta-analysis on the effects of experience on clinical judgment decision making accuracy.


Is a polygenic predictor of antidepressant response a possibility?


title: Pole-zero tracking frequency compensation for low dropout regulator

abstract: Most low dropout regulators (LDRs) have a limited of load current operating range due to stability problems. This paper proposes a new frequency compensation scheme for LDR to optimize the regulator performance over a wide load current range. By introducing a tracking zero to cancel out the regulator output pole, the frequency response of the feedback loop becomes load current independent. The open-loop DC gain is boosted up by a low frequency dominant pole, which increases the regulator accuracy. To demonstrate the feasibility of the proposed scheme, a LDR utilizing the new frequency compensation scheme is designed and fabricated using the TSMC 0.35/spl mu/m digital CMOS process. Simulation results show that with output current from 0 /spl mu/A to 100 mA, the bandwidth variation is only 2.3 times and the minimum DC gain is 72 dB. Measurement of the dynamic response matches well with simulation.


title: Co-ordinate expression of glycine betaine synthesis genes linked by the FMDV 2A region in a single open reading frame in Pichia pastoris

abstract: The genes encoding the two enzymes choline monooxygenase (CMO) and betaine aldehyde dehydrogenase (BADH) of glycine betaine synthesis in Suaeda salsa were cloned and fused with the 2A region of foot-and-mouth disease virus in a single open reading frame. The fused genes were placed under the control of the alcohol oxidase (AOX1) promoter in pPIC3B and transformed into P. pastoris GS115. The expression of the fused genes in P. pastoris and the ability of recombinant yeasts to tolerate environmental stresses were studied. The results showed that induced with 0.5% methanol for 96 h, the maximal activities of CMO and BADH in the tested recombinant yeasts were 45- and 44-fold higher than those in the control yeast transformed empty vector only, respectively; the content of glycine betaine in the recombinant yeasts was 28- to 35-fold higher than that in the control. The fused genes linked by 2A region of foot-and-mouth disease virus were expressed in P. pastoris successfully and the polyprotein was ‘cleaved’ to each functional protein. The yeasts transformed the fused genes, which were more resistant to salt, methanol, and high temperature stresses than the control as result of glycine betaine synthesis genes introduced.


title: Web Page Segmentation Based on Gestalt Theory

abstract: Automatic Web page segmentation is the basis to adaptive Web browsing on mobile devices. It breaks a large page into smaller blocks, in which contents with coherent semantics are keeping together. Then, various adaptations like single column and thumbnail view can be developed. However, page segmentation remains a challenging task, and its poor result directly yields a frustrating user experience. As human usually understand the Web page well, in this paper, we start from Gestalt theory, a psychological theory that can explain human's visual perceptive processes. Four basic laws, proximity, similarity, closure, and simplicity, are drawn from Gestalt theory and then implemented in a program to simulate how human understand the layout of Web pages. The experiments show that this method outperforms existing methods.


title: Improved cepstral mean and variance normalization using Bayesian framework

abstract: Cepstral Mean and Variance Normalization (CMVN) is a computationally efficient normalization technique for noise robust speech recognition. The performance of CMVN is known to degrade for short utterances, due to insufficient data for parameter estimation and loss of discriminable information as all utterances are forced to have zero mean and unit variance. In this work, we propose to use posterior estimates of mean and variance in CMVN, instead of the maximum likelihood estimates. This Bayesian approach, in addition to providing a robust estimate of parameters, is also shown to preserve discriminable information without increase in computational cost, making it particularly relevant for Interactive Voice Response (IVR)-based applications. The relative WER reduction of this approach w.r.t. Cepstral Mean Normalization, CMVN and Histogram Equalization are (i) 40.1%, 27% and 4.3% with the Aurora2 database for all utterances, (ii) 25.7%, 38.6% and 30.4% with the Aurora2 database for short utterances, and (iii) 18.7%, 12.6% and 2.5% with the Aurora4 database.


title: How to do a grounded theory study: a worked example of a study of dental practices

abstract: BackgroundQualitative methodologies are increasingly popular in medical research. Grounded theory is the methodology most-often cited by authors of qualitative studies in medicine, but it has been suggested that many 'grounded theory' studies are not concordant with the methodology. In this paper we provide a worked example of a grounded theory project. Our aim is to provide a model for practice, to connect medical researchers with a useful methodology, and to increase the quality of 'grounded theory' research published in the medical literature.MethodsWe documented a worked example of using grounded theory methodology in practice.ResultsWe describe our sampling, data collection, data analysis and interpretation. We explain how these steps were consistent with grounded theory methodology, and show how they related to one another. Grounded theory methodology assisted us to develop a detailed model of the process of adapting preventive protocols into dental practice, and to analyse variation in this process in different dental practices.ConclusionsBy employing grounded theory methodology rigorously, medical researchers can better design and justify their methods, and produce high-quality findings that will be more useful to patients, professionals and the research community.


title: Re-active learning: active learning with relabeling

abstract: Active learning seeks to train the best classifier at the lowest annotation cost by intelligently picking the best examples to label. Traditional algorithms assume there is a single annotator and disregard the possibility of requesting additional independent annotations for a previously labeled example. However, relabeling examples is important, because all annotators make mistakes — especially crowdsourced workers, who have become a common source of training data. This paper seeks to understand the difference in marginal value between decreasing the noise of the training set via relabeling and increasing the size and diversity of the (noisier) training set by labeling new examples. We use the term re-active learning to denote this generalization of active learning. We show how traditional active learning methods perform poorly at re-active learning, present new algorithms designed for this important problem, formally characterize their behavior, and empirically show that our methods effectively make this tradeoff.


title: POI recommendation with geographical and multi-tag influences

abstract: In this paper, we propose a method for point of interest (POI) recommendation by extracting the multi-tag influence and modeling the geographical influence. First of all, we extract a user-tag matrix from the initial user-POI rating matrix by analyzing the relations between POI and the related bag of tags. Secondly, we use the probabilistic factor model to predict the missing data of the extracted matrix. Thirdly, an effective method to model the geographical influence is proposed by considering the location of user and POI and the related region center. Finally, the multi-tag and geographical influence are fused in the process of making prediction of missing value of every POI. Then we will get a great result for POI recommendation. The experimental analysis on the large dataset Yelp demonstrates that our method outperform the state-of-art methods.


title: Mobile Payments Market: Towards Another Clash of the Titans?

abstract: In this paper we assess developments in the mobile payments in light of new technologies (e.g., NFC) and solution frameworks (e.g., Android). With the recent announcements of Google and other players to enter the mobile payment markets the already dynamic arena of mobile payments is getting more complex and competitive. Everyone is jockeying to new positions by announcing novel payment solutions based e.g. on NFC systems. We argue, however, that significant and recurrent social, institutional, and business challenges remain to be solved for successful mobile payment platforms to emerge. In order to get these multi-sided platforms to diffuse multiple stakeholders have to be simultaneously convinced for related value propositions and more workable economic arrangements need to be forged.


title: Factor analysis and scale revision.

abstract: This article reviews methodological issues that arise in the application of exploratory factor analysis (EFA) to scale revision and refinement. The authors begin by discussing how the appropriate use of EFA in scale revision is influenced by both the hierarchical nature of psychological constructs and the motivations underlying the revision. Then they specifically address (a) important issues that arise prior to data collection (e.g., selecting an appropriate sample), (b) technical aspects of factor analysis (e.g., determining the number of factors to retain), and (c) procedures used to evaluate the outcome of the scale revision (e.g., determining whether the new measure functions equivalently for different populations).


title: Classifying Semantic Relations In Bioscience Texts

abstract: A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities "treatment" and "disease" in bioscience text, and the problem of identifying such entities. We compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy.


title: Disposition of Flavonoids via Enteric Recycling: Enzyme-Transporter Coupling Affects Metabolism of Biochanin A and Formononetin and Excretion of Their Phase II Conjugates

abstract: The purpose of this study was to continue our effort to determine how enzyme-transporter coupling affect disposition of flavonoids. The rat intestinal perfusion and Caco-2 cell models were used together with relevant microsomes. In perfusion model, isoflavone (i.e., formononetin and biochanin A) absorption and subsequent excretion of its metabolites were always site-dependent. Maximal amounts of intestinal and biliary conjugates excreted per 30 min were 31 and 51 nmol for formononetin, more than that for pure biochanin A (12 and 20 nmol). When a standardized red clover extract (biochanin A/formononetin = 10:7) was used, the results indicated that more metabolites of biochanin A than formononetin were found in the perfusate (36.9 versus 22.8 nmol) and bile (78 versus 51 nmol). In metabolism studies, rat intestinal and liver microsomes always glucuronidated biochanin A faster (p < 0.05) than formononetin, whereas intestinal microsomes glucuronidated both isoflavones faster (p < 0.05) than liver microsomes. However, rapid metabolism in the microsomes did not translate into more efficient excretion in either the rat perfusion model as shown previously or in the Caco-2 model. In the Caco-2 model, both isoflavones were rapidly absorbed, efficiently conjugated, and the conjugates excreted apically and basolaterally. More formononetin conjugates were excreted than biochanin A when used alone, but much more biochanin A conjugates were found when using the isoflavone mixture. In conclusion, efficiency of enzyme-transporter coupling controls the amounts of metabolites excreted by the intestine and liver and determines the relative contribution of enteric and enterohepatic recycling to the in vivo disposition of isoflavones.


Report on the 11 th IWSLT Evaluation Campaign , IWSLT 2014


title: Big Data Information of CNC Machine Tool Performed in Position Control Mode

abstract: This paper is investigating the manufacturing information technologies of CNC machine tool, FANUC corporation, based on data estimation for lean information network in manufacturing execution. The response characteristics includes the position loop gain, position feed-forward gain, velocity loop integral gain, velocity loop proportional gain, and velocity feed-forward coefficient. The characteristic responses of the different position loop gains, 10, 30, and 90 1/sec, are investigated in position control mode. The characteristic responses of the different position feed-forward gains, 10, 50, 70, and 100 %, are investigated. The performance characteristics of CNC machine tool was based on tuning operation for big data estimation. The big data establishment of the CNC machine tool was performed in position control mode for transient and steady state responses. The phase cross over frequency and gain margin decreased as the PK1V gain increased. The manufacturing information acquires and processes the big data to report information such as diagnostics or monitors and to optimize the wider intelligent system.


title: Scheduling Stochastic Multi-Stage Jobs to Elastic Hybrid Cloud Resources

abstract: We consider a special workflow scheduling problem in a hybrid-cloud-based workflow management system in which tasks are linearly dependent, compute-intensive, stochastic, deadline-constrained and executed on elastic and distributed cloud resources. This kind of problems closely resemble many real-time and workflow-based applications. Three optimization objectives are explored: number, usage time and utilization of rented VMs. An iterated heuristic framework is presented to schedule jobs event by event which mainly consists of job collecting and event scheduling. Two job collecting strategies are proposed and two timetabling methods are developed. The proposed methods are calibrated through detailed designs of experiments and sound statistical techniques. With the calibrated components and parameters, the proposed algorithm is compared to existing methods for related problems. Experimental results show that the proposal is robust and effective for the problems under study.


title: Ontology-Based Multiple Choice Question Generation

abstract: Multiple choice questions (MCQs) are considered highly useful (being easy to take or mark) but quite difficult to create and large numbers are needed to form valid exams and associated practice materials. The idea of re-using an existing ontology to generate MCQs almost suggests itself and has been explored in various projects. In this project, we are applying suitable educational theory regarding assessments and related methods for their evaluation to ontology-based MCQ generation. In particular, we investigate whether we can measure the similarity of the concepts in an ontology with sufficient reliability so that this measure can be used to control the difficulty of the MCQs generated. In this report, we provide an overview of the background to this research, and describe the main steps taken and insights gained.


title: Pixel estimation based video forgery detection

abstract: In this paper, we propose a novel technique to detect double quantization, which results due to double compression of a tampered video. The proposed algorithm uses principles of estimation theory to detect double quantization. Each pixel of a given frame is estimated from the spatially colocated pixels of all the other frames in a Group of Picture (GOP). The error between the true and estimated value is subjected to a threshold to identify the double compressed frame or frames in a GOP. The advantage of this algorithm is that it can detect tampering of I, P or B frames in a GOP with high accuracy. In addition, the technique can also detect forgery under wide range of double compression bitrates or quantization scale factors. We compare our experimental results against popular video forgery detection techniques and establish the effectiveness of the proposed technique.


title: Velocity-Based Driver Intent Inference at Urban Intersections in the Presence of Preceding Vehicles

abstract: Predicting turn and stop maneuvers of potentially errant drivers is a basic requirement for advanced driver assistance systems for urban intersections. Previous work has shown that an early estimate of the driver's intent can be inferred by evaluating the vehicle's speed during the intersection approach. In the presence of a preceding vehicle, however, the velocity profile might be dictated by car-following behavior rather than by the need to slow down before doing a left or right turn. To infer the driver's intent under such circumstances, a simple, real-time capable approach using a parametric model to represent both car-following and turning behavior is proposed. The performance of two alternative parameterizations based on observations at an individual intersection and a generic curvature-based model is evaluated in combination with two different Bayes net classification algorithms. In addition, the driver model is shown to be capable of predicting the future trajectory of the vehicle.