title: Agile Development Iterations and UI Design

abstract: Many agile projects require user interaction (UI) design, but the integration of UI design into agile development is not well understood. This is because both agile development and UI design are iterative - but while agile methods iterate on code with iterations lasting weeks, UI designs typically iterate only on the user interface using low technology prototypes with iterations lasting hours or days. Similarly, both agile development and UI design emphasise testing, but agile development involves automated code testing, while UI must done by expert inspectors or ideally potential end users. We report on a qualitative grounded theory study of real agile projects involving significant UI design. The key results from our study are that agile iterations facilitates usability testing; allows software developers to incorporate results of those tests into subsequent iterations; and crucially, can significantly improve the quality of the relationship between UI designers and software developers.


title: An efficient real-time implementation of the Wigner-Ville distribution

abstract: The Wigner-Ville distribution (WVD) is a valuable tool for time-frequency signal analysis. In order to implement the WVD in real time, an efficient algorithm and architecture have been developed which may be implemented with commercial components. This algorithm successively computes the analytic signal corresponding to the input signal, forms a weighted kernel function, and analyzes the kernel via a discrete Fourier transform (DFT). To evaluate the analytic signal required by the algorithm, it is shown that the time domain definition implemented as a finite impulse response (FIR) filter is practical and more efficient than the frequency domain definition of the analytic signal. The windowed resolution of the WVD in the frequency domain is shown to be similar to the resolution of a windowed Fourier transform. A real-time signal processor has been designed for evalution of the WVD analysis system. The system is easily paralleled and can be configured to meet a variety of frequency and time resolutions. The arithmetic unit is based on a pair of high-speed VLSI floating-point multiplier and adder chips.


title: Prefrontal organization of cognitive control according to levels of abstraction

abstract: The prefrontal cortex (PFC) plays a crucial role in cognitive control and higher mental functions by maintaining working memory representations of currently relevant information, thereby inducing a mindset that facilitates the processing of such information. Using fMRI, we examined how the human PFC implements mindsets for information at varying levels of abstraction. Subjects solved anagrams grouped into three kinds of blocks (concrete, moderately abstract, and highly abstract) according to the degree of abstraction of their solutions. Mindsets were induced by cuing subjects at the beginning of every block as to the degree of abstraction of solutions they should look for. Different levels of abstraction were matched for accuracy and reaction time, allowing us to examine the effects of varying abstraction in the absence of variations in cognitive complexity. Mindsets for concrete, moderately abstract, and highly abstract information were associated with stronger relative recruitment of ventrolateral, dorsolateral, and rostrolateral PFC regions, respectively, suggesting a functional topography whereby increasingly anterior regions are preferentially associated with representations of increasing abstraction. Rather than being a structural property of the neurons in different prefrontal subregions, this relative specialization may reflect one of the principles according to which lateral PFC adaptively codes and organizes task-relevant information.


title: Active Deep Networks for Semi-Supervised Sentiment Classification

abstract: This paper presents a novel semi-supervised learning algorithm called Active Deep Networks (ADN), to address the semi-supervised sentiment classification problem with active learning. First, we propose the semi-supervised learning method of ADN. ADN is constructed by Restricted Boltzmann Machines (RBM) with unsupervised learning using labeled data and abundant of unlabeled data. Then the constructed structure is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Second, we apply active learning in the semi-supervised learning framework to identify reviews that should be labeled as training data. Then ADN architecture is trained by the selected labeled data and all unlabeled data. Experiments on five sentiment classification datasets show that ADN outperforms the semi-supervised learning algorithm and deep learning techniques applied for sentiment classification.


title: Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising

abstract: In display advertising, click through rate (CTR) prediction is the problem of estimating the probability that an advertisement (ad) is clicked when displayed to a user in a specific context. Due to its easy implementation and promising performance, logistic regression (LR) model has been widely used for CTR prediction, especially in industrial systems. However, it is not easy for LR to capture the nonlinear information, such as the conjunction information, from user features and ad features. In this paper, we propose a novel model, called coupled group lasso (CGL), for CTR prediction in display advertising. CGL can seamlessly integrate the conjunction information from user features and ad features for modeling. Furthermore, CGL can automatically eliminate useless features for both users and ads, which may facilitate fast online prediction. Scalability of CGL is ensured through feature hashing and distributed implementation. Experimental results on real-world data sets show that our CGL model can achieve state-of-the-art performance on webscale CTR prediction tasks.


title: Design WSN Node for Protection of Forest Trees Against Poaching Based MSP430

abstract: Smuggling/theft of trees in forests, acts a serious trouble to forest capital, causes significant economic damage and eventually has quite a shocking effect on the environment over the all creation. Smuggling of trees has run to increased risk of Natural Resource getting smothered. Animals are losing their Natural Environment, thus using total imbalance in Nature. Proposed work on a micro-controller based anti-poaching system provide work on Wireless Sensor Networks(WSNs) technology., which is accomplished of sensing theft by monitoring the signal produced by the cutting of trees using a 3 axis accelerometer. A low power MSP430 microcontroller is used along with RF modules. WSN is widely cast-off technology in monitoring and controlling for the remote applications. The system architecture and the hardware designs are designated in detail.


abstract: Abstract Introduction Inflatable penile prosthesis (IPP) is an established treatment option for men with erectile dysfunction (ED) refractory to medical therapy. Standardization of surgical technique and improvements in device construction have reduced all-cause complication rates to less than 5% in recent reports. Nonetheless, complications do exist, and can strongly impact morbidity and the quality of life of patients. Prosthetic urologists must be aware of the constellation of complications that can arise during or after IPP placement. Aim To provide a comprehensive review of penile prosthesis complications and discuss preventative strategies, as well as proper preoperative, intraoperative, and postoperative decision making. Methods A review of the available literature from 1973 to 2018 was performed using PubMed with regard to IPP complications. Main Outcome Measures We reviewed publications that outlined preoperative planning strategies and the following IPP complications: hematoma, floppy glans, corporal fibrosis, corporal perforation and crossover, urethral injury, infection, impending erosion, and glandular ischemia. Results Careful patient and device selection, setting realistic expectations of postsurgical outcomes, and adherence to a perioperative checklist is essential in the preoperative period. Intraoperatively, anticipate corporal fibrosis situations and always dilate laterally during corporal passage to reduce the risk of crossover and urethral injury. Limit perioperative antiplatelet therapy, apply compressive dressing, use a closed suction drain if indicated, and leave the device partially inflated postoperatively to reduce risk of hematoma. After surgery, monitor patients for potential complications that may warrant device explantation or salvage: IPP infection, glans ischemia, and impending erosion. Conclusions By using evidence and expert opinion-based decision-making strategies in the preoperative, intraoperative, and postoperative period of IPP placement, surgeons can reduce the risk of complications and dissatisfaction, even in ED patients with multiple comorbid conditions. Scherzer ND, Dick B, Gabrielson AT, et al. Penile Prosthesis Complications: Planning, Prevention, and Decision Making. Sex Med Rev 2019;7:349–359.


title: Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation

abstract: The Hough transformation employing a normal line-to-point parameterization is widely applied in digital image processing for feature detection. In this paper, we demonstrate how this same transformation can be adapted to classify defect signatures on semiconductor wafers as an aid to visual defect metrology. Given a rectilinear grid of die centers on a wafer, we demonstrate an efficient and effective procedure for classifying defect clusters composed of lines at angles of 0deg, 45deg, 90deg, and 135deg from the horizontal, as well as adjacent compositions of such lines. Included are defect clusters representing stripes, scratches at arbitrary angles, and center and edge defects. The principle advantage of the procedure over current industrial practice is that it can be fully automated to screen wafers for further engineering analysis.


title: A scale model of mixed reality

abstract: Augmented Reality (AR) is becoming more interesting to a variety of research communities beyond the small group of researchers that have traditionally studied AR technology. From its earliest years, AR has been presented as a subset of Mixed Reality (MR), and both have been conceived of in terms of the technologies they use. However, while the definition of AR has been fixed for a number of years, MR is much more ambiguous. Through a re-examination of the accepted definition of AR, we derive a new definition of MR that centers on human experience rather than technology. Then, through discussion of a number of paradigmatic examples that fit this new definition, we generate a classification system for MR experiences based on the concept of spatial scale and its associated cognitive processes. Finally, we discuss how this new "scale model" of MR helps to identify key concepts that can be used in the design process of future MR experiences.


title: On the Capacity of MIMO Systems with Magnitude Knowledge and Phase Uncertainty

abstract: In this work, we study different capacity formulations in multi-input multi-output channels where the transmitter is only informed with the magnitude of the complex channel matrix coefficients. Perfect channel knowledge is assumed at the receiver side. First, we give the expressions for the ergodic, compound, and outage capacities for our particular model of channel state information. Next, focusing on the compound formulation, we find that the optimal transmitter strategy consists in independent signaling through the transmit dimensions. Finally, we present some results on the optimal power allocation for the maximization of the outage mutual information for a simple MIMO system where the transmitter is equipped with only two antennas.


title: NUClear: A Loosely Coupled Software Architecture for Humanoid Robot Systems

abstract: This paper discusses the design and interface of NUClear, a new hybrid message-passing architecture for embodied humanoid robotics. NUClear is modular, low latency and promotes functional and expandable software design. It greatly reduces the latency for messages passed between modules as the messages routes are established at compile time. It also reduces the number of functions that must be written using a system called co-messages which aids in dealing with multiple simultaneous data. NUClear has primarily been evaluated on a humanoid robotic soccer platform and on a robotic boat platform, with evaluations showing that NUClear requires fewer callbacks and cache variables over existing message-passing architectures. NUClear does have limitations when applying these techniques on multi-processed systems. It performs best in lower power systems where computational resources are limited. Future work will focus on applying the architecture to new platforms, including a larger form humanoid platform and a virtual reality platform and further evaluating the impact of the novel techniques introduced.


title: Privacy-Preserving Face Recognition

abstract: Face recognition is increasingly deployed as a means to unobtrusively verify the identity of people. The widespread use of biometrics raises important privacy concerns, in particular if the biometric matching process is performed at a central or untrusted server, and calls for the implementation of Privacy-Enhancing Technologies. In this paper we propose for the first time a strongly privacy-enhanced face recognition system, which allows to efficiently hide both the biometrics and the result from the server that performs the matching operation, by using techniques from secure multiparty computation. We consider a scenario where one party provides a face image, while another party has access to a database of facial templates. Our protocol allows to jointly run the standard Eigenfaces recognition algorithm in such a way that the first party cannot learn from the execution of the protocol more than basic parameters of the database, while the second party does not learn the input image or the result of the recognition process. At the core of our protocol lies an efficient protocol for securely comparing two Pailler-encrypted numbers. We show through extensive experiments that the system can be run efficiently on conventional hardware.


title: Learning Abduction under Partial Observability

abstract: Juba recently proposed a formulation of learning abductive reasoning from examples, in which both the relative plausibility of various explanations, as well as which explanations are valid, are learned directly from data. The main shortcoming of this formulation of the task is that it assumes access to full-information (i.e., fully specified) examples; relatedly, it offers no role for declarative background knowledge, as such knowledge is rendered redundant in the abduction task by complete information. In this work, we extend the formulation to utilize such partially specified examples, along with declarative background knowledge about the missing data. We show that it is possible to use implicitly learned rules together with the explicitly given declarative knowledge to support hypotheses in the course of abduction. We also show how to use knowledge in the form of graphical causal models to refine the proposed hypotheses. Finally, we observe that when a small explanation exists, it is possible to obtain a much- improved guarantee in the challenging exception-tolerant setting. Such small, human-understandable explanations are of particular interest for potential applications of the task.


title: An Open-Source Dialog System with Real-Time Engagement Tracking for Job Interview Training Applications

abstract: In complex conversation tasks, people react to their interlocutor’s state, such as uncertainty and engagement to improve conversation effectiveness Forbes-Riley and Litman (Adapting to student uncertainty improves tutoring dialogues, pp 33–40, 2009 [2]). If a conversational system reacts to a user’s state, would that lead to a better conversation experience? To test this hypothesis, we designed and implemented a dialog system that tracks and reacts to a user’s state, such as engagement, in real time. We designed and implemented a conversational job interview task based on the proposed framework. The system acts as an interviewer and reacts to user’s disengagement in real-time with positive feedback strategies designed to re-engage the user in the job interview process. Experiments suggest that users speak more while interacting with the engagement-coordinated version of the system as compared to a non-coordinated version. Users also reported the former system as being more engaging and providing a better user experience.


title: Is the Most Effective Team Leadership Shared? The Impact of Shared Leadership, Age Diversity, and Coordination on Team Performance

abstract: In the present paper we examine the moderating effects of age diversity and team coordination on the relationship between shared leadership and team performance. Using a field sample of 96 individuals in 26 consulting project teams, team members assessed their team’s shared leadership and coordination. Six to eight weeks later, supervisors rated their teams’ performance. Results indicated that shared leadership predicted team performance and both age diversity and coordination moderated the impact of shared leadership on team performance. Thereby shared leadership was positively related to team performance when age diversity and coordination were low, whereas higher levels of age diversity and coordination appeared to compensate for lower levels of shared leadership effectiveness. In particular strong effects of shared leadership on team performance were evident when both age diversity and coordination were low, whereas shared leadership was not related to team performance when both age diversity and coor...


title: A Content-Based Recommendation Approach Using Semantic User Profile in E-recruitment

abstract: In this paper, we propose a content-based recommendation approach in the domain of e-recruitment to recommend users with job offers that suit the most their profile and learned preferences. In order to present the best offers, we construct a semantic vocabulary of the domain from the job offers corpus and initialize a profile for each user based on his Curriculum Vitae. Our method is enriching the user profiles using triggers and statistical methods following his actions regarding the job offers. The approach we propose presents to the users job offers that are the closest to their learned needs and interests which also can be updated based on his daily actions regarding these offers.


title: Blended Learning or E-learning?

abstract: ICT or Information and Communication Technology has pervaded the fields of education. In recent years the term “e-learning” has emerged as a result of the integration of ICT in the education fields. Following the application this technology into teaching, some pitfalls have been identified and this have led to the “Blended learning” phenomenon. However, the preference on this new method has been debated quite extensively. The aim of this paper is to investigate the advantages of blended learning over face-to-face instruction through reviews of related literature. The present survey revealed that blended learning is more favorable than pure e-learning and offers many advantages for learners like producing a sense of community or belonging. This study concludes that blended learning can be considered as an efficient approach of distance learning in terms of students’ learning experience, student-student interaction as well as student-instructor interaction and is likely to emerge as the predominant education model in the future.


title: Measuring usability of educational computer games based on the user success rate

abstract: Children learn through play [1]. According to Crawford [2] educational game is the children's first contact with the computer. Therefore, it is important that the computer games for children are well designed and usable. Usability testing can also be adopted to assess learner's motivation towards learning. Assessing the usability with children requires special consideration. Children must be allowed to play freely and expressed their opinion while being observed. This study explores the measuring consideration for the usability of an educational game called Jelajah. Jelajah, was designed to teach pre-schools children to learn the Malay words and was developed based on a solid research on educational approaches. Five pre-school children were selected for this study. During the usability testing session, children are observed and their behaviors are recorded in an observation checklist. The session was video-taped for further observation. A post test was administered after the session to seek the children's satisfaction in using the game for learning. Data collected were quantitative and qualitative data. Data were later quantified to measure the effectiveness, efficiency and satisfaction towards the software. Based on the analysis, children showed interest and were highly motivated to learn when using educational game for learning.


title: Unresponsive or un-noticed?: Cyberbystander intervention in an experimental cyberbullying context

abstract: We establish an experimental design to test cyber-bystander behavior.Noticing cyberbullying is a predictor of cyber-bystander intervention.Various online distractions do not appear to affect cyber-bystander intervention.Cyberbystanders intervene indirectly more often than directly in cyberbullying.Cyberbystanders choose to intervene directly more often with the cyberbully. With increasing reliance on computer-mediated communication, emergencies and negative communication will also increase. Nearly one-fifth of adolescents report being cyberbullied, and over 25% of those report multiple occurrences. Though important gains have been made to understand the adverse effects and possible risk factors of cyberbullying for victims and cyberbullies, most individuals (70%; Pew Research Center, 2014) online fall into a third group-cyberbystanders. This experiment tests the first step (i.e., cyberbystanders notice the cyberbullying incident) of the five-step Bystander Intervention Model in a virtual environment. Data were analyzed from 221 cyberbystanders who witnessed in real time multiple episodes of cyberbullying. Results confirm that noticing cyberbullying significantly predicts intervention, indirect or direct. Nearly 68% of participants noticed the cyberbullying, but only 10% directly intervened by engaging with the bully. Most participants (68%) intervened indirectly after the incident and threat were removed. Further research is necessary to understand other boundary conditions, and to test the remaining steps of the Bystander Intervention Model in a virtual environment. This model has been very effective in understanding and increasing bystander intervention in the real world. We hope that the model will have similar effects on understanding and increasing cyberbystander intervention in the virtual world.


title: Classification Model of Prediction for Placement of Students

abstract: Data mining methodology can analyze relevant information results and produce different perspectives to understand more about the students' activities. When designing an educational environment, applying data mining techniques discovers useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for taking important decisions. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system. In this study, we collected the student's data that have different information about their previous and current academics records and then apply different classification algorithm using Data Mining tools (WEKA) for analysis the student's academics performance for Training and placement. This study presents a proposed model based on classification approach to find an enhanced evaluation method for predicting the placement for students. This model can determine the relations between academic achievement of students and their placement in campus selection.


Facial Expression Recognition with Convolutional Neural Networks


title: Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries using Sparse Linear Regression

abstract: Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called "errors-in-variables". Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct "keystone species", Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.


title: 3D Volumetric Modeling with Introspective Neural Networks

abstract: In this paper, we study the 3D volumetric modeling problem by adopting the Wasserstein introspective neural networks method (WINN) that was previously applied to 2D static images. We name our algorithm 3DWINN which enjoys the same properties as WINN in the 2D case: being simultaneously generative and discriminative. Compared to the existing 3D volumetric modeling approaches, 3DWINN demonstrates competitive results on several benchmarks in both the generation and the classification tasks. In addition to the standard inception score, the Frechet Inception Distance (FID) metric is´ also adopted to measure the quality of 3D volumetric generations. In addition, we study adversarial attacks for volumetric data and demonstrate the robustness of 3DWINN against adversarial examples while achieving appealing results in both classification and generation within a single model. 3DWINN is a general framework and it can be applied to the emerging tasks for 3D object and scene modeling.1


title: Radar cross section measurements of the human body for UWB radar applications

abstract: An experimental set-up for mono-static radar cross section measurements in the 1 – 10 GHz frequency band, employing an anechoic chamber, is presented and validated. The test range is calibrated by means of a readily available aluminum flat panel and its performance is tested against canonical metallic and lossy dielectric targets. The set-up is then employed to carry out investigations on the radar cross section of the human body. Initial findings show a complex resonating pattern, highly influenced by even slight subject movements, with a distinct spectral signature for each subject. The final aim is the achievement of realistic scattering data of human subjects for the purpose of designing a ultra wideband (UWB) radar system for breath activity monitoring.


title: Swarm Control of UAVs for Cooperative Hunting with DDDAS

abstract: Swarm control is a problem of increasing importance with technological advancements. Recently, governments have begun employing UAVs for reconnaissance, including swarms of drones searching for evasive targets. An agent-based simulation for dynamic cooperative cleaning is augmented with additional behaviors and implemented into a Dynamic Data-Driven Application System (DDDAS) framework for dynamic swarm control.


title: Gender Bias in Neural Natural Language Processing

abstract: We examine whether neural natural language processing (NLP) systems reflect historical biases in training data. We define a general benchmark to quantify gender bias in a variety of neural NLP tasks. Our empirical evaluation with state-of-the-art neural coreference resolution and textbook RNN-based language models trained on benchmark datasets finds significant gender bias in how models view occupations. We then mitigate bias with CDA: a generic methodology for corpus augmentation via causal interventions that breaks associations between gendered and gender-neutral words. We empirically show that CDA effectively decreases gender bias while preserving accuracy. We also explore the space of mitigation strategies with CDA, a prior approach to word embedding debiasing (WED), and their compositions. We show that CDA outperforms WED, drastically so when word embeddings are trained. For pre-trained embeddings, the two methods can be effectively composed. We also find that as training proceeds on the original data set with gradient descent the gender bias grows as the loss reduces, indicating that the optimization encourages bias; CDA mitigates this behavior.


title: The VIS-AD Data Model: Integrating Metadata and Polymorphic Display with a Scientific Programming Language

abstract: The VIS-AD data model integrates metadata about the precision of values, including missing data indicators and the way that arrays sample continuous functions, with the data objects of a scientific programming language. The data objects of this data model form a lattice, ordered by the precision with which they approximate mathematical objects. We define a similar lattice of displays and study visualization processes as functions from data lattices to display lattices. Such functions can be applied to visualize data objects of all data types and are thus polymorphic.


title: Efficient grasping from RGBD images: Learning using a new rectangle representation

abstract: Given an image and an aligned depth map of an object, our goal is to estimate the full 7-dimensional gripper configuration—its 3D location, 3D orientation and the gripper opening width. Recently, learning algorithms have been successfully applied to grasp novel objects—ones not seen by the robot before. While these approaches use low-dimensional representations such as a ‘grasping point’ or a ‘pair of points’ that are perhaps easier to learn, they only partly represent the gripper configuration and hence are sub-optimal. We propose to learn a new ‘grasping rectangle’ representation: an oriented rectangle in the image plane. It takes into account the location, the orientation as well as the gripper opening width. However, inference with such a representation is computationally expensive. In this work, we present a two step process in which the first step prunes the search space efficiently using certain features that are fast to compute. For the remaining few cases, the second step uses advanced features to accurately select a good grasp. In our extensive experiments, we show that our robot successfully uses our algorithm to pick up a variety of novel objects.


title: Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by

abstract: The PCR is used widely for the study of rRNA genes amplified from mixed microbial populations. These studies resemble quantitative applications of PCR in that the templates are mixtures of homologs and the relative abundance of amplicons is thought to provide some measure of the gene ratios in the starting mixture. Although such studies have established the presence of novel rRNA genes in many natural ecosystems, inferences about gene abundance have been limited by uncertainties about the relative efficiency of gene amplification in the PCR. To address this question, three rRNA gene standards were prepared by PCR, mixed in known proportions, and amplified a second time by using primer pairs in which one primer was labeled with a fluorescent nucleotide derivative. The PCR products were digested with restriction endonucleases, and the frequencies of genes in the products were determined by electrophoresis on an Applied Biosystems 373A automated DNA sequencer in Genescan mode. Mixtures of two templates amplified with the 519F-1406R primer pair yielded products in the predicted proportions. A second primer pair (27F-338R) resulted in strong bias towards 1:1 mixtures of genes in final products, regardless of the initial proportions of the templates. This bias was strongly dependent on the number of cycles of replication. The results fit a kinetic model in which the reannealing of genes progressively inhibits the formation of template-primer hybrids.


title: A Survey on Eye-Gaze Tracking Techniques

abstract: Study of eye-movement is being employed in Human Computer Interaction (HCI) research. Eye - gaze tracking is one of the most challenging problems in the area of computer vision. The goal of this paper is to present a review of latest research in this continued growth of remote eye-gaze tracking. This overview includes the basic definitions and terminologies, recent advances in the field and finally the need of future development in the field.


title: Estimation of ECG parameters using photoplethysmography

abstract: Regular ECG check up is a good practice for cardiac patients as well as elderly people. In this paper we propose a low cost methodology to coarsely estimate the range of some important parameters of ECG using Photoplethysmography (PPG). PPG is easy to measure (even with a smart phone) and strongly related to human cardio-vascular system. The proposed methodology extracts a set of time domain features from PPG signal. A statistical analysis is performed to select the most relevant set of PPG features for the ECG parameters. Training model for the ECG parameters are created based on those selected features. Both artificial neural network and support vector machine based supervised learning approach is used for performance comparison. Experimental results, performed on benchmark dataset shows that good accuracy in the estimation of ECG parameters can be achieved in our proposed methodology. Results also show that the overall performance improves in using feature selection technique rather than using all the PPG features for classification.


FACE IMAGE RECOGNITION BASED ON PARTIAL FACE MATCHING USING GENETIC ALGORITHM


title: Social skills groups for people aged 6 to 21 with autism spectrum disorders (ASD).

abstract: BACKGROUND ::: Since autism was first described, major difficulties in social interaction have been a defining feature of individuals with autism spectrum disorders (ASD). Social skills groups are a common intervention for individuals with ASD. Although a frequently recommended practice, the few studies that have addressed the efficacy of social skills groups have shown mixed results. ::: ::: ::: OBJECTIVES ::: To determine the effectiveness of social skills groups for improving social competence, social communication, and quality of life for people with ASD who are six to 21 years of age. ::: ::: ::: SEARCH METHODS ::: We searched the following databases in December 2011: CENTRAL (2011 Issue 4), MEDLINE (1948 to November Week 3, 2011), EMBASE (1980 to Week 50, 2011), PsycINFO (1887 to December Week 2, 2011), CINAHL (1937 to current), ERIC (1966 to current), Sociological Abstracts (1952 to current), OCLC WorldCat (12 December 2011), Social Science Citation Index (1970 to 16 December 2011), and the metaRegister of Controlled Trials (20 December 2011). We also searched the reference lists of published papers. ::: ::: ::: SELECTION CRITERIA ::: Randomized control trials (RCTs) comparing treatment (social skills groups) with a control group who were not receiving the treatment for participants aged six to 21 years with ASD. The control group could be no intervention, wait list, or treatment as usual. Outcomes sought were standardized measures of social competence, social communication, quality of life, emotion recognition, and any other specific behaviors. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Two review authors independently selected and appraised studies for inclusion and assessed the risk of bias in each included study. All outcome data were continuous and standardized mean difference effect sizes (ES) with small sample correction were calculated. We conducted random-effects meta-analysis where possible. ::: ::: ::: MAIN RESULTS ::: We included five RCTs evaluating the effects of social skills groups in 196 participants with ASD aged 6 to 21 years old. The results show there is some evidence that social skills groups improve overall social competence (ES = 0.47, 95% confidence interval (CI) 0.16 to 0.78, P = 0.003) and friendship quality (ES = 0.41, 95% CI 0.02 to 0.81, P = 0.04) for this population. No differences were found between treatment and control groups in relation to emotional recognition (ES = 0.34, 95% CI -0.20 to 0.88, P = 0.21) assessed in two studies or social communication as related to the understanding of idioms (ES = 0.05, 95% CI -0.63 to 0.72, P = 0.89), which was assessed in only one study. Two additional quality of life outcomes were evaluated, with results of single studies suggesting decreases in loneliness (ES = -0.66, 95% CI -1.15 to -0.17) but no effect on child or parental depression. No adverse events were reported.Given the nature of the intervention and the selected outcome measures, the risk of performance and detection bias are high. There is limited generalizability from the studies as they were all conducted in the US; they focused mainly on children aged 7 to 12, and the participants were all of average or above average intelligence. ::: ::: ::: AUTHORS' CONCLUSIONS ::: There is some evidence that social skills groups can improve social competence for some children and adolescents with ASD. More research is needed to draw more robust conclusions, especially with respect to improvements in quality of life.


title: High Technology and Barriers to Innovation: from Globalization to relocalization

abstract: We live in an era when a remarkable number of new information and other technologies are successfully bypassing the main obstacle to technological advance: technology support network (TSN). Technology support net, when fully established and fixed, erects significant barriers to innovation. Innovation is not free and autonomous process of applied creativity, but is technically, economically and politically subservient to the "holders and owners" of the support net. We present operational definitions of technology and high technology which explain past and current barriers to innovation shifts from one technological paradigm to another. The TSN-bypass technologies are causing the process of globalization to slow down and revert itself into a process of relocalization, bringing forth the economy of autonomous, self-reliant and self-sustainable individuals, communities, localities and regions.


Calibration for 3D Reconstruction of Thermal Images


title: Can Google nowcast the market trend of Iranian mobile games?

abstract: The Internet has become an integral part of everyday life. In this paper, we investigate if the Internet behavior of consumers correlates with their actual behavior in computer games market. In particular, we aim to investigate to what extent Web search data can be exploited to predict the ranking of mobile games in the world. Based on our findings that show the existence of such correlations, we use web search data (from Google) about mobile games in Iran to nowcast1 (predict the present status of) the ranking of Iranian mobile games.


title: A 42 Gbps VCSEL driver with adjustable 2-tap feed-forward equalizer in 14 nm SOI CMOS

abstract: In this paper the design and measurement results of a 42 Gbps vertical-cavity surface-emitting laser (VCSEL) driver is presented. A 2-tap feed-forward equalizer (FFE) architecture with adjustable delay is chosen and optimized to decrease the inter-symbol interference of the VCSEL data transmission. Circuit realizations of different blocks are presented as well. The chip was fabricated in a 14 nm SOI CMOS technology and bonded to a common-cathode 20 GHz VCSEL. Optical measurements show that error-free data transmission up to a data rate of 42 Gbps was possible. The total power dissipation, including the one of the VCSEL, is 117 mW, which provides a power efficiency of 2.8 pJ/bit. To the best of the author's knowledge, this is fastest VCSEL driver circuit in a CMOS technology presented so far.


title: Dropout with Expectation-linear Regularization

abstract: Dropout, a simple and effective way to train deep neural networks, has led to a number of impressive empirical successes and spawned many recent theoretical investigations. However, the gap between dropout's training and inference phases, introduced due to tractability considerations, has largely remained under-appreciated. In this work, we first formulate dropout as a tractable approximation of some latent variable model, leading to a clean view of parameter sharing and enabling further theoretical analysis. Then, we introduce (approximate) expectation-linear dropout neural networks, whose inference gap we are able to formally characterize. Algorithmically, we show that our proposed measure of the inference gap can be used to regularize the standard dropout training objective, resulting in an \emph{explicit} control of the gap. Our method is as simple and efficient as standard dropout. We further prove the upper bounds on the loss in accuracy due to expectation-linearization, describe classes of input distributions that expectation-linearize easily. Experiments on three image classification benchmark datasets demonstrate that reducing the inference gap can indeed improve the performance consistently.


title: Campaign War Chests, Entry Deterrence, and Voter Rationality

abstract: It is often claimed that the accumulation of "war chests" by incumbents deters entry by high-quality challengers in Congressional elections. This paper presents a game-theoretic analysis of the interaction between an incumbent, potential challengers, an interest group, and a representative (rational) voter, where the incumbent's "quality" (or "legislative effectiveness") is known to the interest group, but not to the voter or to potential challengers. Under certain conditions, a perfectly revealing equilibrium exists; the incumbent signals her quality by raising funds from the interest group to accumulate a war chest. The entry deterrence effect thus operates solely through the role of war chests in signaling incumbent quality. Copyright Blackwell Publishers Ltd 2002.


title: Neural Attentional Rating Regression with Review-level Explanations

abstract: Reviews information is dominant for users to make online purchasing decisions in e-commerces. However, the usefulness of reviews is varied. We argue that less-useful reviews hurt model’s performance, and are also less meaningful for user’s reference. While some existing models utilize reviews for improving the performance of recommender systems, few of them consider the usefulness of reviews for recommendation quality. In this paper, we introduce a novel attention mechanism to explore the usefulness of reviews, and propose a Neural Attentional Regression model with Review-level Explanations (NARRE) for recommendation. Specifically, NARRE can not only predict precise ratings, but also learn the usefulness of each review simultaneously. Therefore, the highly-useful reviews are obtained which provide review-level explanations to help users make better and faster decisions. Extensive experiments on benchmark datasets of Amazon and Yelp on different domains show that the proposed NARRE model consistently outperforms the state-of-the-art recommendation approaches, including PMF, NMF, SVD++, HFT, and DeepCoNN in terms of rating prediction, by the proposed attention model that takes review usefulness into consideration. Furthermore, the selected reviews are shown to be effective when taking existing review-usefulness ratings in the system as ground truth. Besides, crowd-sourcing based evaluations reveal that in most cases, NARRE achieves equal or even better performances than system’s usefulness rating method in selecting reviews. And it is flexible to offer great help on the dominant cases in real e-commerce scenarios when the ratings on review-usefulness are not available in the system.


title: The university of Florida sparse matrix collection

abstract: We describe the University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments: robust because performance results with artificially generated matrices can be misleading, and repeatable because matrices are curated and made publicly available in many formats. Its matrices cover a wide spectrum of domains, include those arising from problems with underlying 2D or 3D geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, power networks, and other networks and graphs). We provide software for accessing and managing the Collection, from MATLAB™, Mathematica™, Fortran, and C, as well as an online search capability. Graph visualization of the matrices is provided, and a new multilevel coarsening scheme is proposed to facilitate this task.


title: Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set

abstract: We address a cross-scenario clothing retrieval problem- given a daily human photo captured in general environment, e.g., on street, finding similar clothing in online shops, where the photos are captured more professionally and with clean background. There are large discrepancies between daily photo scenario and online shopping scenario. We first propose to alleviate the human pose discrepancy by locating 30 human parts detected by a well trained human detector. Then, founded on part features, we propose a two-step calculation to obtain more reliable one-to-many similarities between the query daily photo and online shopping photos: 1) the within-scenario one-to-many similarities between a query daily photo and an extra auxiliary set are derived by direct sparse reconstruction; 2) by a cross-scenario many-to-many similarity transfer matrix inferred offline from the auxiliary set and the online shopping set, the reliable cross-scenario one-to-many similarities between the query daily photo and all online shopping photos are obtained.


title: Everyday Digital Entrepreneurship: The Inception, Shifts, and Scaling of Future Shaping Practices.

abstract: Digitization has created extremely favourable conditions for digital innovation, initiating shifts in the organizing logic of products, services, and various aspects of organizational practices. Current literature has eagerly explored this digitally enabled diversification. However, it has paid little attention to the digitiz ation of everyday practices and how forms of entrepreneurship emerge from such practices. In this paper we propose that digital technology instigates a new type of digital innovation – future-shaping practices, and will attempt to exemplify and explore the forces behind such newly emerging everyday practices. Our research draws on a multimethod research design based on four case studies that represent future shaping practices in transportation, healthcare, education, and sustainability. Drawing on document analysis and semi-structured interviews, we report on our research-in-progress that seeks to determine the generative mechanisms that enable the inception, shift, and scaling of future shaping practices.


title: Chronic disease management: what will it take to improve care for chronic illness?

abstract: M eeting the complex needs of patients with chronic illness or impairment is the single greatest challenge facing organized medical practice. Usual care is not doing the job; dozens of surveys and audits have revealed that sizable proportions of chronically ill patients are not receiving effective therapy, have poor disease con- trol, and are unhappy with their care (1). Results of randomized trials also show that effective disease management programs can achieve substantially better outcomes than usual care, the control intervention. These trials, along with the ideas and efforts for improvement discussed in this issue, show that we can improve care and outcomes. As the articles suggest, these improvements will not come easily. If we are to improve care for most patients with chronic illness, the evidence strongly suggests that we reshape our ambulatory care systems for this purpose. Pri- mary care practice was largely designed to provide ready access and care to patients with acute, varied problems, with an emphasis on triage and patient flow; short appointments; diagnosis and treatment of symptoms and signs; reliance on laborato- ry investigations and prescriptions; brief, didactic patient education; and patient- initiated follow-up. Patients and families struggling with chronic illness have differ- ent needs, and these needs are unlikely to be met by an acute care organization and culture. They require planned, regular interactions with their caregivers, with a focus on function and prevention of exacerbations and complications. This interac- tion includes systematic assessments, attention to treatment guidelines, and behav- iorally sophisticated support for the patient's role as self-manager. These interactions must be linked through time by clinically relevant information systems and continu- ing follow-up initiated by the medical practice. Comprehensive System Change


title: Skip-Thought Memory Networks

abstract: Question Answering (QA) is fundamental to natural language processing in that most nlp problems can be phrased as QA (Kumar et al., 2015). Current weakly supervised memory network models that have been proposed so far struggle at answering questions that involve relations among multiple entities (such as facebook's bAbi qa5-three-arg-relations in (Weston et al., 2015)). To address this problem of learning multi-argument multi-hop semantic relations for the purpose of QA, we propose a method that combines the jointly learned long-term read-write memory and attentive inference components of end-to-end memory networks (MemN2N) (Sukhbaatar et al., 2015) with distributed sentence vector representations encoded by a Skip-Thought model (Kiros et al., 2015). This choice to append Skip-Thought Vectors to the existing MemN2N framework is motivated by the fact that Skip-Thought Vectors have been shown to accurately model multi-argument semantic relations (Kiros et al., 2015).


title: Marked point processes for crowd counting

abstract: A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placement of individuals with a conditional mark process for selecting body shape. We automatically learn the mark (shape) process from training video by estimating a mixture of Bernoulli shape prototypes along with an extrinsic shape distribution describing the orientation and scaling of these shapes for any given image location. The reversible jump Markov Chain Monte Carlo framework is used to efficiently search for the maximum a posteriori configuration of shapes, leading to an estimate of the count, location and pose of each person in the scene. Quantitative results of crowd counting are presented for two publicly available datasets with known ground truth.


title: User oriented tweet ranking: a filtering approach to microblogs

abstract: The increasing volume of streaming data on microblogs has re-introduced the necessity of effective filtering mechanisms for such media. Microblog users are overwhelmed with mostly uninteresting pieces of text in order to access information of value. In this paper, we propose a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward. In addition, we also investigate how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets. Finally, conducting a pilot user study, we analyze how retweet likelihood correlates with the interestingness of the tweets.


title: Shared-Memory Optimizations for Inter-Virtual-Machine Communication

abstract: Virtual machines (VMs) and virtualization are one of the core computing technologies today. Inter-VM communication is not only prevalent but also one of the leading costs for data-intensive systems and applications in most data centers and cloud computing environments. One way to improve inter-VM communication efficiency is to support coresident VM communication using shared-memory-based methods and resort to the traditional TCP/IP for communications between VMs that are located on different physical machines. In recent years, several independent kernel development efforts have been dedicated to improving communication efficiency between coresident VMs using shared-memory channels, and the development efforts differ from one another in terms of where and how the shared-memory channel is established. In this article, we provide a comprehensive overview of the design choices and techniques for performance optimization of coresident inter-VM communication. We examine the key issues for improving inter-VM communication using shared-memory-based mechanisms, such as implementation choices in the software stack, seamless agility for dynamic addition or removal of coresident VMs, and multilevel transparency, as well as advanced requirements in reliability, security, and stability. An in-depth comparison of state-of-the-art research efforts, implementation techniques, evaluation methods, and performance is conducted. We conjecture that this comprehensive survey will not only provide the foundation for developing the next generation of inter-VM communication optimization mechanisms but also offers opportunities to both cloud infrastructure providers and cloud service providers and consumers for improving communication efficiency between coresident VMs in virtualized computing platforms.


title: An interactive design system for pop-up cards with a physical simulation

abstract: We present an interactive system that allows users to design original pop-up cards. A pop-up card is an interesting form of papercraft consisting of folded paper that forms a three-dimensional structure when opened. However, it is very difficult for the average person to design pop-up cards from scratch because it is necessary to understand the mechanism and determine the positions of objects so that pop-up parts do not collide with each other or protrude from the card. In the proposed system, the user interactively sets and edits primitives that are predefined in the system. The system simulates folding and opening of the pop-up card using a mass–spring model that can simply simulate the physical movement of the card. This simulation detects collisions and protrusions and illustrates the movement of the pop-up card. The results of the present study reveal that the user can design a wide range of pop-up cards using the proposed system.


title: Communicating Algorithmic Process in Online Behavioral Advertising

abstract: Advertisers develop algorithms to select the most relevant advertisements for users. However, the opacity of these algorithms, along with their potential for violating user privacy, has decreased user trust and preference in behavioral advertising. To mitigate this, advertisers have started to communicate algorithmic processes in behavioral advertising. However, how revealing parts of the algorithmic process affects users' perceptions towards ads and platforms is still an open question. To investigate this, we exposed 32 users to why an ad is shown to them, what advertising algorithms infer about them, and how advertisers use this information. Users preferred interpretable, non-creepy explanations about why an ad is presented, along with a recognizable link to their identity. We further found that exposing users to their algorithmically-derived attributes led to algorithm disillusionment---users found that advertising algorithms they thought were perfect were far from it. We propose design implications to effectively communicate information about advertising algorithms.


title: Eye-q: Eyeglass Peripheral Display for Subtle Intimate Notifications

abstract: Mobile devices are generally used in public, where the user is surrounded by others not involved in the interaction. Audible notification cues are often a cause of unnecessary disruption and distraction both for co-located people and even for the user to whom they are directed. We present a wearable peripheral display embedded in eyeglasses that delivers subtle, discreet and unobtrusive cues. The display is personal and intimate; it delivers visual cues in the wearers' periphery without disrupting their immediate environment. A user study conducted to validate the design reveals that the display is effective and subtle in notifying users. Experimental results show, with significance, that the cues can be designed to meet specific levels of visibility and disruption for the wearer, so that some cues are less noticeable when the user is not under high workload, which is highly desirable in many practical circumstances. Hence, peripheral notification displays can provide an effective solution for designing socially acceptable notification displays, unobtrusive to the user and the immediate environment.


title: Entertainment as Pleasurable and Meaningful: Identifying Hedonic and Eudaimonic Motivations for Entertainment Consumption

abstract: The purpose of this research is to broaden the conceptualization of entertainment selection to identify not only pleasure-seeking (hedonic concerns) as a motivator, but to also recognize that individuals may choose media as a means of ‘‘truth-seeking’’ (eudaimonic concerns). This article conceptualized and developed measures to illustrate that entertainment can be used as a means of experiencing not only enjoyment, but also as a means of grappling with questions such as life’s purpose and human meaningfulness. Four studies were conducted in the development of these measures, providing evidence for their validity in terms of entertainment preference and individual differences, and illustrating how these motivations predict preferences for entertainment that elicits unique affective experiences.


title: Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues

abstract: Automatically constructed Knowledge Bases (KBs) are often incomplete and there is a genuine need to improve their coverage. Path Ranking Algorithm (PRA) is a recently proposed method which aims to improve KB coverage by performing inference directly over the KB graph. For the first time, we demonstrate that addition of edges labeled with latent features mined from a large dependency parsed corpus of 500 million Web documents can significantly outperform previous PRAbased approaches on the KB inference task. We present extensive experimental results validating this finding. The resources presented in this paper are publicly available.


Reconstructing the Giant: Automating the Categorization of Scientific Articles with Deep Learning Techniques


title: RTSeg: Real-Time Semantic Segmentation Comparative Study

abstract: Semantic segmentation benefits robotics related applications, especially autonomous driving. Most of the research on semantic segmentation only focuses on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. The few work conducted in this direction does not provide principled methods to evaluate the different design choices for segmentation. In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods. The framework is comprised of different network architectures for feature extraction such as VGG16, Resnet18, MobileNet, and ShuffleNet. It is also comprised of multiple meta-architectures for segmentation that define the decoding methodology. These include SkipNet, UNet, and Dilation Frontend. Experimental results are presented on the Cityscapes dataset for urban scenes. The modular design allows novel architectures to emerge, that lead to 143x GFLOPs reduction in comparison to SegNet. This benchmarking framework is publicly available at 11https://github.com/MSiam/TFSegmentation.


title: Supervised Matrix Factorization for Cross-Modality Hashing

abstract: Matrix factorization has been recently utilized for the task of multi-modal hashing for cross-modality visual search, where basis functions are learned to map data from different modalities to the same Hamming embedding. In this paper, we propose a novel cross-modality hashing algorithm termed Supervised Matrix Factorization Hashing (SMFH) which tackles the multi-modal hashing problem with a collective non-matrix factorization across the different modalities. In particular, SMFH employs a well-designed binary code learning algorithm to preserve the similarities among multi-modal original features through a graph regularization. At the same time, semantic labels, when available, are incorporated into the learning procedure. We conjecture that all these would facilitate to preserve the most relevant information during the binary quantization process, and hence improve the retrieval accuracy. We demonstrate the superior performance of SMFH on three cross-modality visual search benchmarks, i.e., the PASCAL-Sentence, Wiki, and NUS-WIDE, with quantitative comparison to various state-of-the-art methods


title: Learning to decompose for object detection and instance segmentation

abstract: Although deep convolutional neural networks(CNNs) have achieved remarkable results on object detection and segmentation, pre- and post-processing steps such as region proposals and non-maximum suppression(NMS), have been required. These steps result in high computational complexity and sensitivity to hyperparameters, e.g. thresholds for NMS. In this work, we propose a novel end-to-end trainable deep neural network architecture, which consists of convolutional and recurrent layers, that generates the correct number of object instances and their bounding boxes (or segmentation masks) given an image, using only a single network evaluation without any pre- or post-processing steps. We have tested on detecting digits in multi-digit images synthesized using MNIST, automatically segmenting digits in these images, and detecting cars in the KITTI benchmark dataset. The proposed approach outperforms a strong CNN baseline on the synthesized digits datasets and shows promising results on KITTI car detection.


title: Exploring controls education: A re-configurable ball and plate platform kit

abstract: The kit is an expanded version of: ::: Bay, C.J. and Rasmussen, B.P., 2016, "Exploring Controls Education: A Re-Configurable Ball and Plate Platform Kit", American Control Conference (ACC) 2016, Boston, MA, USA. ::: ::: It includes a manual, the software code needed to operate the kit, .stl files for the 3-D printed parts, and a supply list.


title: Spectrum Survey in Singapore: Occupancy Measurements and Analyses

abstract: We study the 24-hour spectrum usage pattern in Singapore in the frequency bands ranging from 80 MHz to 5850 MHz. The objectives are to find how the scarce radio spectrum allocated to different services is utilized in Singapore and identify the bands that could be accessed for future opportunistic use due to their low or no active utilization. The results from the spectrum measurements taken over 12 weekday periods reveal that a significant amount of spectrum in Singapore has very low occupancy all the time. The occupancy is quantified as the amount of spectrum detected above a certain received power threshold. The outcome of this study suggests that Singapore has a great potential for employing emerging spectrum sharing technology such as the cognitive radio technology to accommodate enormous demands for future wireless services. However, this study of spectrum survey is preliminary in its nature and future long term studies need to be performed to determine any potential secondary usage on those channels that have low or no active utilization.


title: Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training

abstract: Although great progresses have been made in automatic speech recognition (ASR), significant performance degradation is still observed when recognizing multi-talker mixed speech. In this paper, we propose and evaluate several architectures to address this problem under the assumption that only a single channel of mixed signal is available. Our technique extends permutation invariant training (PIT) by introducing the front-end feature separation module with the minimum mean square error (MSE) criterion and the back-end recognition module with the minimum cross entropy (CE) criterion. More specifically, during training we compute the average MSE or CE over the whole utterance for each possible utterance-level output-target assignment, pick the one with the minimum MSE or CE, and optimize for that assignment. This strategy elegantly solves the label permutation problem observed in the deep learning based multi-talker mixed speech separation and recognition systems. The proposed architectures are evaluated and compared on an artificially mixed AMI dataset with both two- and three-talker mixed speech. The experimental results indicate that our proposed architectures can cut the word error rate (WER) by 45.0% and 25.0% relatively against the state-of-the-art single-talker speech recognition system across all speakers when their energies are comparable, for two- and three-talker mixed speech, respectively. To our knowledge, this is the first work on the multi-talker mixed speech recognition on the challenging speaker-independent spontaneous large vocabulary continuous speech task.


title: Student Perceptions of Cloud Computing Effectiveness in Higher Education

abstract: Cloud computing technology use in higher education institutions is rapidly expanding and becoming a more integral part of the collegiate experience. This expansion of the use of cloud applications comes with some significant learning and adoption challenges to the end users along with the technical advantages that allow greater accessibility and ease of use. The influence of individual user perceptions toward these technologies is an issue that merits further investigation. Several technology acceptance models have demonstrated some degree of success in predicting adoption behaviors utilizing individual user traits and beliefs. However, there have been limited investigations of variables after the technology has been implemented and used. This paper focuses on the associations between variables identified in the literature that are considered to be influencing use and perceptions of technology for undergraduate students of technology at a mid-sized university in Southeast Michigan. The results statistically present significant correlations between each measured variable and support the interaction between perceived ease of use, computer anxiety, computer self-efficacy, and internet self-efficacy with the perceived usefulness of cloud applications in a higher education setting.


title: Behind the War on Terror: Western Secret Strategy and the Struggle for Iraq

abstract: Following the U.S. declaration of a war on terror, Washington hawks were quick to label Iraq part of an axis of evil. After a tense build-up, in March 2003 the United States and Britain invaded Iraq, purportedly to protect Western publics from weapons of mass destruction (WMD). But was this the real reason, or simply a convenient pretext to veil a covert agenda? Using official sources, Ahmed investigates U.S. and British claims about Iraq's WMD programs and in the process reveals the hidden motives behind the 2003 invasion and the grand strategy of which it is a part. He shows that the true goals of U.S.-British policy in the Middle East are camouflaged by spin, P.R. declarations and seemingly noble words. The reality can only be comprehended through knowledge of the history of Western intervention in the region. Ahmed demonstrates that such intervention has been dictated ruthlessly by economic and political interests, with little regard for human rights. He traces events of the past decades, beginning with the West's support for the highly repressive Shah of Iran, his subsequent usurpation by the Ayatollah's Islamist regime and the West's resultant backing of Saddam Hussein. The sponsorship of Saddam's tyranny-a self-serving tactic intended to strategically counterbalance Iran-included the supply of technology to build WMD, as well as tacit complicity in their use against Iranians and Kurds. Ahmed's meticulous research into the secret history of Western maneuverings in the Middle East since the collapse of the Ottoman Empire reveals the actual causes of the first Gulf War, the humanitarian catastrophe created by the 12-year sanctions policy against Iraq, and the consistentobstructions of the Oil for Food program. He also provides information on the West's own widespread use of WMD, and the likely culprits of the 2001 anthrax attacks in the U.S. Nafeez Mosaddeq Ahmed is a human rights activist and political analyst specializing in the study of conflicts. The executive director of the Institute for Policy Research & Development, he is the author of a variety of reports on human rights practices, as well as the best-selling book, The War on Freedom: How and Why America was Attacked, S


title: Memory systems of the brain: A brief history and current perspective

abstract: The idea that memory is composed of distinct systems has a long history but became a topic of experimental inquiry only after the middle of the 20th century. Beginning about 1980, evidence from normal subjects, amnesic patients, and experimental animals converged on the view that a fundamental distinction could be drawn between a kind of memory that is accessible to conscious recollection and another kind that is not. Subsequent work shifted thinking beyond dichotomies to a view, grounded in biology, that memory is composed of multiple separate systems supported, for example, by the hippocampus and related structures, the amygdala, the neostriatum, and the cerebellum. This article traces the development of these ideas and provides a current perspective on how these brain systems operate to support behavior.


title: Systematic Review of Effect Size in Software Engineering Experiments

abstract: An effect size quantifies the effects of an experimental treatment. Conclusions drawn from hypothesis testing results might be erroneous if effect sizes are not judged in addition to statistical significance. This paper reports a systematic review of 92 controlled experiments published in 12 major software engineering journals and conference proceedings in the decade 1993-2002. The review investigates the practice of effect size reporting, summarizes standardized effect sizes detected in the experiments, discusses the results and gives advice for improvements. Standardized and/or unstandardized effect sizes were reported in 29% of the experiments. Interpretations of the effect sizes in terms of practical importance were not discussed beyond references to standard conventions. The standardized effect sizes computed from the reviewed experiments were equal to observations in psychology studies and slightly larger than standard conventions in behavioral science.


title: Hybrid algorithm for indoor positioning using wireless LAN

abstract: Locating an indoor mobile station based on a wireless communication infrastructure, has practical applications. The most widely employed methods today use an RF propagation loss (PL) model or location fingerprinting (LF). The PL method is known to perform poorly compared to LF. But LF requires an extensive training dataset and cannot adapt well to configuration changes or receiver breakdown. In this paper, we develop a hybrid method that combines the strength of these two methods. It first formulates the RF PL in a nonlinear, censored regression model and adjusts the regression function to the observed signal strength in the fingerprint dataset. In the absence of a training dataset, the hybrid method coincides with the PL method, and, as the spatial granularity of the training dataset increases, the result of the algorithm approaches the result of the LF method. It balances the flexibility and accuracy of the two traditional methods, makes intelligent use of missing values, produces error bounds, and can be made dynamic. We evaluate the performance of the algorithm by applying it to a real site and observe satisfactory positioning accuracy.


title: A 1.8-GHz 33-dBm $P$ 0.1-dB CMOS T/R Switch Using Stacked FETs With Feed-Forward Capacitors in a Floated Well Structure

abstract: A 33-dBm P 0.1-dB single-pole double-throw antenna switch is designed and implemented using a standard 0.18-mum CMOS process at 1.8 GHz. An analysis shows a relation between parasitic junction capacitors and substrate resistance for low insertion loss (IL). The power-handling capability of the switch was also investigated through the voltage dividing mechanism through the substrate in the case of an ON-insertion loss-state NMOS switch implemented in a triple-well structure. A multistacked field-effect transistor (FET) structure with feed-forward capacitors in an Rx switch was chosen as the method of designing an antenna switch with high power-handling capability. Low IL of the switch in the multistacked FET structure is achieved by the optimization of layout and minimization of junction capacitors through the deep N-well bias. Allowance of a negative voltage swing at either the source or drain port is ensured by a floated well structure with a negatively biased P-well for each switch device of the multistacked FET structure. Intentional unequal division of the voltage swing level at each NMOS device by feed-forward capacitors with negative biases of the off-state switches helps to prevent channel formation in the off-state device. Experimental data shows that the proposed design achieves a 0.1-dB compression point at 33-dBm input power at 1.8 GHz with a negative bias supply to control the voltage at the off-state switches and the P -well of each device. The IL of the Tx switch is 0.5 and 0.73 dB at 900 MHz and 1.8 GHz, respectively. The Rx switch has 0.7- and 1.1-dB IL at 900 MHz and 1.8 GHz, respectively. In addition, a reliability issue related to antenna load mismatch was tested using a load-pull measurement setup.


title: Dynamic syslog mining for network failure monitoring

abstract: Syslog monitoring technologies have recently received vast attentions in the areas of network management and network monitoring. They are used to address a wide range of important issues including network failure symptom detection and event correlation discovery. Syslogs are intrinsically dynamic in the sense that they form a time series and that their behavior may change over time. This paper proposes a new methodology of dynamic syslog mining in order to detect failure symptoms with higher confidence and to discover sequential alarm patterns among computer devices. The key ideas of dynamic syslog mining are 1) to represent syslog behavior using a mixture of Hidden Markov Models, 2) to adaptively learn the model using an on-line discounting learning algorithm in combination with dynamic selection of the optimal number of mixture components, and 3) to give anomaly scores using universal test statistics with a dynamically optimized threshold. Using real syslog data we demonstrate the validity of our methodology in the scenarios of failure symptom detection, emerging pattern identification, and correlation discovery.


title: Self-organizing spatio-temporal pattern formation in two-dimensional daisyworld

abstract: Watson and Lovelock's daisyworld model [1] was devised to demonstrate how the biota of a world could stabilise it, driving it to a temperature regime that favoured survival of the biota. The subsequent studies have focused on the behaviour of daisyworld in various fields. This study looks at the emergent patterns that arise in 2D daisyworlds at different parameter settings, demonstrating that a wide range of patterns can be observed. Selecting from an immense range of tested parameter settings, we present the emergence of complex patterns, Turing-like structures, cyclic patterns, random patterns and uniform dispersed patterns, corresponding to different kinds of possible worlds. The emergence of such complex behaviours from a simple, abstract model serve to illuminate the complex mosaic of patterns that we observe in real-world biosystems.


title: From Backup to Hot Standby: High Availability for HDFS

abstract: Cluster-based distributed file systems generally have a single master to service clients and manage the namespace. Although simple and efficient, that design compromises availability, because the failure of the master takes the entire system down. Before version 2.0.0-alpha, the Hadoop Distributed File System (HDFS) -- an open-source storage, widely used by applications that operate over large datasets, such as MapReduce, and for which an uptime of 24x7 is becoming essential -- was an example of such systems. Given that scenario, this paper proposes a hot standby for the master of HDFS achieved by (i) extending the master's state replication performed by its check pointer helper, the Backup Node, and by (ii) introducing an automatic fail over mechanism. The step (i) took advantage of the message duplication technique developed by other high availability solution for HDFS named Avatar Nodes. The step (ii) employed another Hadoop software: ZooKeeper, a distributed coordination service. That approach resulted in small code changes, 1373 lines, not requiring external components to the Hadoop project. Thus, easing the maintenance and deployment of the file system. Compared to HDFS 0.21, tests showed that both in loads dominated by metadata operations or I/O operations, the reduction of data throughput is no more than 15% on average, and the time to switch the hot standby to active is less than 100 ms. Those results demonstrate the applicability of our solution to real systems. We also present related work on high availability for other file systems and HDFS, including the official solution, recently included in HDFS 2.0.0-alpha.


title: A robust adaptive neural control scheme to drive an actuated orthosis for assistance of knee movements

abstract: Abstract In this paper, we present an actuated orthosis intended to assist the subject׳s movements for flexion/extension of the knee. The equivalent system “subject׳s lower limb-actuated orthosis” is considered as black-box and is driven by a Multi-Layer Perceptron Neural Network (MLPNN) controller. This controller is adaptive, does not require the dynamic model of the system and is able to take into account all its uncertainties. The latter include the nonlinearities due to the subject׳s lower limb/actuated orthosis coupling, the modeling and identification errors as well as the parameter uncertainties resulting from the system׳s dynamics. Stability of the “subject׳s lower limb-actuated orthosis” system, using the proposed approach, is mathematically proved based on the Lyapunov theory. Performances of the proposed MLPNN controller are compared to those of the PID (Proportional Integrator Derivative) controller for the track of desired position and velocity trajectories. These comparisons include the trajectories errors, the capacity of each controller to assist the torque produced by the subject and the robustness of the system against external disturbances. To illustrate the efficiency of the proposed controller, real-time experiments were conducted on five voluntary subjects.


title: Performance Analysis of Software-Defined Networking (SDN)

abstract: Software-Defined Networking (SDN) approaches were introduced as early as the mid-1990s, but just recently became a well-established industry standard. Many network architectures and systems adopted SDN, and vendors are choosing SDN as an alternative to the fixed, predefined, and inflexible protocol stack. SDN offers flexible, dynamic, and programmable functionality of network systems, as well as many other advantages such as centralized control, reduced complexity, better user experience, and a dramatic decrease in network systems and equipment costs. However, SDN characterization and capabilities, as well as workload of the network traffic that the SDN-based systems handle, determine the level of these advantages. Moreover, the enabled flexibility of SDN-based systems comes with a performance penalty. The design and capabilities of the underlying SDN infrastructure influence the performance of common network tasks, compared to a dedicated solution. In this paper we analyze two issues: a) the impact of SDN on raw performance (in terms of throughput and latency) under various workloads, and b) whether there is an inherent performance penalty for a complex, more functional, SDN infrastructure. Our results indicate that SDN does have a performance penalty, however, it is not necessarily related to the complexity level of the underlying SDN infrastructure.


title: Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged poincare plots

abstract: Designing an efficient automatic emotion recognition system based on physiological signals has attracted great interests within the research of human–machine interactions. This study was aimed to classify emotional responses by means of a simple dynamic signal processing technique and fusion frameworks. The electrocardiogram and finger pulse activity of 35 participants were recorded during rest condition and when subjects were listening to music intended to stimulate certain emotions. Four emotion categories, including happiness, sadness, peacefulness, and fear were chosen. Estimating heart rate variability (HRV) and pulse rate variability (PRV), 4 Poincare indices in 10 lags were extracted. The support vector machine classifier was used for emotion classification. Both feature level (FL) and decision level (DL) fusion schemes were examined. Significant differences have been observed between lag 1 Poincare plot indices and the other lagged measures. The mean accuracies of 84.1, 82.9, 79.68, and 76.05% were obtained for PRV, DL, FL, and HRV measures, respectively. However, DL outperformed others in discriminating sadness and peacefulness, using SD1 and total features, correspondingly. In both cases, the classification rates improved up to 92% (with the sensitivity of 95% and specificity of 83.33%). Totally, DL resulted in better performances compared to FL. In addition, the impact of the fusion rules on the classification performances has been confirmed.


title: Active learning for text classification with reusability

abstract: We investigate the reusability problem in active learning for text classification.The reusability problem affects active learning systems for text classification.If the consumer classifier type is known, it should be used for the selector.Local and global classifiers do not mix well with regard to reusability.To maximize reusability support vector machines should be used as selectors. Where active learning with uncertainty sampling is used to generate training sets for classification applications, it is sensible to use the same type of classifier to select the most informative training examples as the type of classifier that will be used in the final classification application. There are scenarios, however, where this might not be possible, for example due to computational complexity. Such scenarios give rise to the reusability problem-are the training examples deemed most informative by one classifier type necessarily as informative for a different classifier types? This paper describes a novel exploration of the reusability problem in text classification scenarios. We measure the impact of using different classifier types in the active learning process and in the classification applications that use the results of active learning. We perform experiments on four different text classification problems, using the three classifier types most commonly used for text classification. We find that the reusability problem is a significant issue in text classification; that, if possible, the same classifier type should be used both in the application and during the active learning process; and that, if the ultimate classifier type is unknown, support vector machines should be used in active learning to maximise reusability.


Crossmodal Language Grounding, Learning, and Teaching


title: ClickNP: Highly flexible and High-performance Network Processing with Reconfigurable Hardware

abstract: Highly flexible software network functions (NFs) are crucial components to enable multi-tenancy in the clouds. However, software packet processing on a commodity server has limited capacity and induces high latency. While software NFs could scale out using more servers, doing so adds significant cost. This paper focuses on accelerating NFs with programmable hardware, i.e., FPGA, which is now a mature technology and inexpensive for datacenters. However, FPGA is predominately programmed using low-level hardware description languages (HDLs), which are hard to code and difficult to debug. More importantly, HDLs are almost inaccessible for most software programmers. This paper presents ClickNP, a FPGA-accelerated platform for highly flexible and high-performance NFs with commodity servers. ClickNP is highly flexible as it is completely programmable using high-level C-like languages, and exposes a modular programming abstraction that resembles Click Modular Router. ClickNP is also high performance. Our prototype NFs show that they can process traffic at up to 200 million packets per second with ultra-low latency ($


title: Examining the Effects of New York Experience Corps ® Program on Young Readers

abstract: There are hundreds of tutoring programs that utilize community volunteers being implemented across the country; however, there are few rigorous efforts to evaluate their effectiveness. This article presents findings on reading achievement from an evaluation of the New York City Experience Corps®, a program that uses older volunteers to work with students in public elementary schools. Two hundred and eighty-eight first- and second-grade students participated in a pre-test/post-test two group design with randomization to assess the impact of the program on their reading abilities. Reading was assessed using the Phonological Awareness Literacy Screening (PALS) and Early Childhood Literary Assessment System (ECLAS). Results indicate that Experience Corps is effective at improving reading scores. This study provides evidence that older volunteers can be successfully utilized to tutor young readers.


title: Toward a computer vision-based wayfinding aid for blind persons to access unfamiliar indoor environments

abstract: Independent travel is a well known challenge for blind and visually impaired persons. In this paper, we propose a proof-of-concept computer vision-based wayfinding aid for blind people to independently access unfamiliar indoor environments. In order to find different rooms (e.g. an office, a lab, or a bathroom) and other building amenities (e.g. an exit or an elevator), we incorporate object detection with text recognition. First we develop a robust and efficient algorithm to detect doors, elevators, and cabinets based on their general geometric shape, by combining edges and corners. The algorithm is general enough to handle large intra-class variations of objects with different appearances among different indoor environments, as well as small inter-class differences between different objects such as doors and door-like cabinets. Next, in order to distinguish intra-class objects (e.g. an office door from a bathroom door), we extract and recognize text information associated with the detected objects. For text recognition, we first extract text regions from signs with multiple colors and possibly complex backgrounds, and then apply character localization and topological analysis to filter out background interference. The extracted text is recognized using off-the-shelf optical character recognition (OCR) software products. The object type, orientation, location, and text information are presented to the blind traveler as speech.


title: Automatic tuning matrix multiplication performance on graphics hardware

abstract: In order to utilize the tremendous computing power of graphics hardware and to automatically adapt to the fast and frequent changes in its architecture and performance characteristics, this paper implements an automatic tuning system to generate high-performance matrix-multiplication implementation on graphics hardware. The automatic tuning system uses a parameterized code generator to generate multiple versions of matrix multiplication, whose performances are empirically evaluated by actual execution on the target platform. An ad-hoc search engine is employed to search over the implementation space for the version that yields the best performance. In contrast to similar systems on CPUs, which utilize cache blocking, register tiling, instruction scheduling tuning strategies, this paper identifies and exploits several tuning strategies that are unique for graphics hardware. These tuning strategies include optimizing for multiple-render-targets, SIMD instructions with data packing, overcoming limitations on instruction count and dynamic branch instruction. The generated implementations have comparable performance with expert manually tuned version in spite of the significant overhead incurred due to the use of the high-level BrookGPU language.


title: Generative Mixology: An Engine for Creating Cocktails

abstract: This paper details an expert cocktail generation system. After using expert knowledge to break down cocktails into eight categories, the system generates cocktails from a particular category using a context-free stochastic grammar. These cocktails were then evaluated by human participants in a research setting. Participants evaluated the cocktails on the basis of quality, novelty and typicality to check the creative potential of the generator’s output.


title: The potential of Virtual Reality as anxiety management tool: a randomized controlled study in a sample of patients affected by Generalized Anxiety Disorder

abstract: BackgroundGeneralized anxiety disorder (GAD) is a psychiatric disorder characterized by a constant and unspecific anxiety that interferes with daily-life activities. Its high prevalence in general population and the severe limitations it causes, point out the necessity to find new efficient strategies to treat it. Together with the cognitive-behavioural treatments, relaxation represents a useful approach for the treatment of GAD, but it has the limitation that it is hard to be learned. To overcome this limitation we propose the use of virtual reality (VR) to facilitate the relaxation process by visually presenting key relaxing images to the subjects. The visual presentation of a virtual calm scenario can facilitate patients' practice and mastery of relaxation, making the experience more vivid and real than the one that most subjects can create using their own imagination and memory, and triggering a broad empowerment process within the experience induced by a high sense of presence.According to these premises, the aim of the present study is to investigate the advantages of using a VR-based relaxation protocol in reducing anxiety in patients affected by GAD.Methods/DesignThe trial is based on a randomized controlled study, including three groups of 25 patients each (for a total of 75 patients): (1) the VR group, (2) the non-VR group and (3) the waiting list (WL) group. Patients in the VR group will be taught to relax using a VR relaxing environment and audio-visual mobile narratives; patients in the non-VR group will be taught to relax using the same relaxing narratives proposed to the VR group, but without the VR support, and patients in the WL group will not receive any kind of relaxation training. Psychometric and psychophysiological outcomes will serve as quantitative dependent variables, while subjective reports of participants will be used as qualitative dependent variables.ConclusionWe argue that the use of VR for relaxation represents a promising approach in the treatment of GAD since it enhances the quality of the relaxing experience through the elicitation of the sense of presence. This controlled trial will be able to evaluate the effects of the use of VR in relaxation while preserving the benefits of randomization to reduce bias.Trial RegistrationNCT00602212 (ClinicalTrials.gov)


DRIVERS AND IMPEDIMENTS TO E-COMMERCE IN MALAYSIA


title: Terahertz Light-Field Imaging

abstract: Light-field imaging is a highly versatile computational method that has been traditionally limited to the visible frequency range of the electromagnetic spectrum. This paper presents an implementation of the light-field technique in terahertz (THz) radiation for the first time. A silicon hyper-hemispherical lens-integrated 32 × 32 pixel CMOS camera was used as the THz light-field sampler and it was scanned across an image plane to record the complete static light-field from a point THz source. Two experiments have been performed to demonstrate the feasibility of light-fields at THz frequencies. In the first experiment, the light-field from a 16-pixel lens-coupled power source module operating at 0.53 THz was recorded and its radiation pattern was computationally rendered. In the other, light-field based transmission mode imaging was demonstrated for a ×48 multiplier based 25-dBi 0.65-THz source without the use of an external optical train.


title: Focal adhesion targeting of v‐Crk is essential for FAK phosphorylation and cell migration in mouse embryo fibroblasts deficient src family kinases or p130CAS

abstract: We examined the consequences of v-Crk expression in mouse embryo fibroblasts deficient Src family kinases or p130CAS. We found that Src kinases are essential for p130CAS/v-Crk signaling leading to FAK phosphorylation and cell migration in which Src is likely to mediate the focal adhesion targeting of v-Crk. SYF cells showed only low levels of FAK phosphorylation and cell migration, even in the presence of v-Crk. Expression of v-Crk restored migration of p130CAS-deficient cells to the level of wild-type cells, most likely through the targeting of v-Crk to focal adhesions by cSrc. In addition, we identified a new v-Crk-interacting protein that mediates v-Crk signaling in p130CAS-deficient cells. Using RT-PCR and caspase cleavage assays, we confirmed that this protein is not p130CAS and is responsible for maintaining v-Crk/Src signaling and migration in these. These findings suggest that focal adhesion targeting of v-Crk is essential in v-Crk-mediated cellular signaling and that v-Crk must form a complex with p130CAS or a p130CAS substitute to transduce signaling from the extracellular matrix. J. Cell. Physiol. 214: 604–613, 2008. © 2007 Wiley-Liss, Inc.


title: VAGAL TONE AND THE PHYSIOLOGICAL REGULATION OF EMOTION

abstract: Because emotions are psychological processes, the experience and regulation of emotion should be functionally dependent on the state of the nervous system. If a major source of emotion variation is dependent on the nervous system, how would this be evaluated? The goal of this essay is to address this problem by introducing vagal tone as a measurable organismic variable that contributes to individual and developmental differences in the expression and regulation of emotion. We propose that understanding the mechanisms determining individual and developmental differences in emotion expression and regulation might provide a rationale for identifying subjects who differentially express the ability to regulate emotion. Thus, there is the possibility that individual differences in the nervous system might mediate the expression and regulation of emotion. Most research on the autonomic correlates of emotion has