title: Multiple description-DASH: Pragmatic video streaming maximizing end-users' quality of experience

abstract: Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.


title: Security Analysis of Contiki IoT Operating System

abstract: The Internet of Things (IoT) has introduced a myriad of ways in which devices can interact with each other. The IoT concept provides opportunities for novel and useful applications but at the same time, concerns have been raised over potential security issues caused by buggy IoT software. It is therefore imperative to detect and fix these bugs in order to minimise the risk of IoT devices becoming the target or source of attacks. In this paper, we focus our investigation on the underlying IoT operating system (OS), which is critical for the overall security of IoT devices. We picked Contiki as our case study since it is a very popular IoT OS and we have access to part of the development team, allowing us to discuss potential vulnerabilities with them so that fixes can be implemented quickly. Using static program analysis tools and techniques, we are able to scan the source code of the Contiki OS systematically in order to identify, analyse and patch vulnerabilities. Our main contribution is a holistic and systematic analysis of Contiki, starting with an exploration of its metrics, fundamental architecture, and finally some of its vulnerabilities. Our analysis produced relevant data on the number of unsafe functions in use, as well as the bug density; both of which provide an indication of the overall security of the inspected system. Our effort led to the finding of two major issues, described in two Common Vulnerabilities and Exposures (CVE) reports.


title: Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network

abstract: Abstract Precise liver segmentation in abdominal MRI images is one of the most important steps for the computer-aided diagnosis of liver pathology. The first and essential step for diagnosis is automatic liver segmentation, and this process remains challenging. Extensive research has examined liver segmentation; however, it is challenging to distinguish which algorithm produces more precise segmentation results that are applicable to various medical imaging techniques. In this paper, we present a new automatic system for liver segmentation in abdominal MRI images. The system includes several successive steps. Preprocessing is applied to enhance the image (edge-preserved noise reduction) by using mathematical morphology. The proposed algorithm for liver region extraction is a combined algorithm that utilizes MLP neural networks and watershed algorithm. The traditional watershed transformation generally results in oversegmentation when directly applied to medical image segmentation. Therefore, we use trained neural networks to extract features of the liver region. The extracted features are used to monitor the quality of the segmentation using the watershed transform and adjust the required parameters automatically. The process of adjusting parameters is performed sequentially in several iterations. The proposed algorithm extracts liver region in one slice of the MRI images and the boundary tracking algorithm is suggested to extract the liver region in other slices, which is left as our future work. This system was applied to a series of test images to extract the liver region. Experimental results showed positive results for the proposed algorithm.


title: A Case-Based Reasoning Framework for Developing Agents Using Learning by Observation

abstract: Most realistic environments are complex, partially observable and impose real-time constraints on agents operating within them. This paper describes a framework that allows agents to learn by observation in such environments. When learning by observation, agents observe an expert performing a task and learn to perform the same task based on those observations. Our framework aims to allow agents to learn in a variety of domains (physical or virtual) regardless of the behaviour or goals of the observed expert. To achieve this we ensure that there is a clear separation between the central reasoning system and any domain-specific information. We present case studies in the domains of obstacle avoidance, robotic arm control, simulated soccer and Tetris.


title: WebSOM - self-organizing maps of document collections

abstract: Abstract With the WEBSOM method a textual document collection may be organized onto a graphical map display that provides an overview of the collection and facilitates interactive browsing. Interesting documents can be located on the map using a content-directed search. Each document is encoded as a histogram of word categories which are formed by the self-organizing map (SOM) algorithm based on the similarities in the contexts of the words. The encoded documents are organized on another self-organizing map, a document map, on which nearby locations contain similar documents. Special consideration is given to the computation of very large document maps which is possible with general-purpose computers if the dimensionality of the word category histograms is first reduced with a random mapping method and if computationally efficient algorithms are used in computing the SOMs.


title: Learning Graph Convolution Filters from Data Manifold

abstract: Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood. In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation. Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains.


title: Look Ma, No Hands: Analyzing the Monotonic Feature Abstraction for Text Classification

abstract: Is accurate classification possible in the absence of hand-labeled data? This paper introduces the Monotonic Feature (MF) abstraction—where the probability of class membership increases monotonically with the MF's value. The paper proves that when an MF is given, PAC learning is possible with no hand-labeled data under certain assumptions. ::: ::: We argue that MFs arise naturally in a broad range of textual classification applications. On the classic "20 Newsgroups" data set, a learner given an MF and unlabeled data achieves classification accuracy equal to that of a state-of-the-art semi-supervised learner relying on 160 hand-labeled examples. Even when MFs are not given as input, their presence or absence can be determined from a small amount of hand-labeled data, which yields a new semi-supervised learning method that reduces error by 15% on the 20 Newsgroups data.


title: What Can Be Predicted from Six Seconds of Driver Glances?

abstract: We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.


title: Data quality and data cleaning: an overview

abstract: Data quality is a serious concern in any data-driven enterprise, often creating misleading findings during data mining, and causing process disruptions in operational databases. The manifestations of data quality problems can be very expensive- "losing" customers, "misplacing" billions of dollars worth of equipment, misallocated resources due to glitched forecasts, and so on. Solving data quality problems typically requires a very large investment of time and energy -- often 80% to 90% of a data analysis project is spent in making the data reliable enough that the results can be trusted.In this tutorial, we present a multi disciplinary approach to data quality problems. We start by discussing the meaning of data quality and the sources of data quality problems. We show how these problems can be addressed by a multidisciplinary approach, combining techniques from management science, statistics, database research, and metadata management. Next, we present an updated definition of data quality metrics, and illustrate their application with a case study. We conclude with a survey of recent database research that is relevant to data quality problems, and suggest directions for future research.


title: Cinnamon improves metabolic factors without detectable effects on adiponectin in women with polycystic ovary syndrome.

abstract: BACKGROUND AND OBJECTIVES ::: The objectives of this study were to investigate the effects of cinnamon supplementation on glycemic indices, serum lipids and adiponectin in patients with polycystic ovary syndrome (PCOS). ::: ::: ::: METHODS AND STUDY DESIGN ::: This double-blind randomized controlled clinical trial was conducted on 84 overweight or obese PCOS patients. Subjects in cinnamon (n=42) and placebo (n=42) groups were given 3 cinnamon capsules (each one contained 500 mg cinnamon) or placebo daily for 8 weeks. Fasting blood samples, anthropometric measurements and dietary intake data were collected at the baseline and at the end of the trial. Data were analyzed by independent t test, paired t test and analysis of covariance. ::: ::: ::: RESULTS ::: Cinnamon significantly decreased serum fasting blood glucose, insulin, homeostatic model assessment for insulin resistance, total cholesterol and low-density lipoprotein cholesterol and weight and increased high-density lipoprotein cholesterol compared with placebo (all p<0.05). Serum triglyceride and body mass index significantly decreased in the cinnamon group, in comparison with baseline values (p=0.001 and p=0.002, respectively). No significant changes were seen in serum adiponectin in either group. ::: ::: ::: CONCLUSIONS ::: Short term supplementation of cinnamon had some favourable effects on metabolic risk factors of women with PCOS and may be useful in management of PCOS complications.


title: Rescue Robots and Systems in Japan

abstract: Intelligent rescue systems with high information and robot technology have been expected to mitigate disaster damages, especially in Japan after the 1995 Hanshin-Awaji earthquake. In development of robots for search and rescue tasks it is important to develop a robot which can actually work in a real disaster site. Several robots were used for the search and detection operation in the collapsed World Trade Center building in September 2001. From 2002 "special project for earthquake disaster mitigation in urban areas" (5 years project) launched by Ministry of Education, Culture, Sports, Science and Technology, Japan. It aims at significant mitigation the earthquake disaster damage on the scale of the great Hanshin earthquake, in big city regions such as Tokyo metropolitan area and Keihanshin area. In this paper, the problem domain of earthquake disasters and search and rescue processes is clarified with introducing an overview of this project


title: Genetic learning of fuzzy cognitive maps

abstract: Fuzzy cognitive maps (FCMs) are a very convenient, simple, and powerful tool for simulation and analysis of dynamic systems. They were originally developed in 1980 by Kosko, and since then successfully applied to numerous domains, such as engineering, medicine, control, and political affairs. Their popularity stems from simplicity and transparency of the underlying model. At the same time FCMs are hindered by necessity of involving domain experts to develop the model. Since human experts are subjective and can handle only relatively simple networks (maps), there is an urgent need to develop methods for automated generation of FCM models. This study proposes a novel learning method that is able to generate FCM models from input historical data, and without human intervention. The proposed method is based on genetic algorithms, and requires only a single state vector sequence as an input. The paper proposes and experimentally compares several different design alternatives of genetic optimization and thoroughly tests and discusses the best design. Extensive benchmarking tests, which involve 200 FCMs with varying size and density of connections, performed on both synthetic and real-life data quantifies the performance of the development method and emphasizes its suitability.


USE OF INTERNET FOR ACADEMIC PURPOSES AMONG STUDENTS IN MALAYSIAN INSTITUTIONS OF HIGHER EDUCATION


title: UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSs

abstract: The evaluation of algorithms and techniques to implement intrusion detection systems heavily rely on the existence of well designed datasets. In the last years, a lot of efforts have been done towards building these datasets. Yet, there is still room to improve. In this paper, a comprehensive review of existing datasets is first done, making emphasis on their main shortcomings. Then, we present a new dataset that is built with real traffic and up-to-date attacks. The main advantage of this dataset over previous ones is its usefulness for evaluating IDSs that consider long-term evolution and traffic periodicity. Models that consider differences in daytime/night or weekdays/weekends can also be trained and evaluated with it. We discuss all the requirements for a modern IDS evaluation dataset and analyze how the one presented here meets the different needs.


title: An SiC-Based Half-Bridge Module With an Improved Hybrid Packaging Method for High Power Density Applications

abstract: SiC devices have the potential to structure high power density converters; however, SiC devices have high d i /d t during switching. Therefore, the parasitic inductances in the power loop and gating loop must be reduced to restrain the induced voltage. This paper proposes a SiC-based, half-bridge (HB) module with a hybrid packaging method. Such a module is based on printed circuit board (PCB) plus direct bonding copper (DBC) structure. The DBC provides part of power loops, and the SiC bare dies can be directly soldered on these loops; the PCB provides another part of the power loops, as well as the gating loops. Such a design allows the power loops on the PCB to be soldered with those on the DBC directly; in the meantime, the SiC bare dies soldered on the DBC can be connected to the PCB with bounding wires. Such a structure provides extra degrees of freedom for design so that the parasitic inductances can be minimized by optimizing the current communication loops, driver locations, and the gating connections, and the heat can also be dissipated through the direct cooling structure easily. A 1200 V/24 A SiC HB module is fabricated with the proposed method in the laboratory, and based on the fabricated module, a synchronous buck converter with power density of 379.3 W/in3 is also structured. The double pulse test shows that the fabricated module can be switched within 10 ns and that the drain-source voltage overshoot is only 2.5%. The highest efficiency of the converter based on the fabricated module is up to 99.46%.


title: Data-Driven Threat Hunting Using Sysmon

abstract: Threat actors can be persistent, motivated and agile, and they leverage a diversified and extensive set of tactics, techniques, and procedures to attain their goals. In response to that, organizations establish threat intelligence programs to improve their defense capabilities and mitigate risk. Actionable threat intelligence is integrated into security information and event management systems (SIEM) forming a threat intelligence platform. A threat intelligence platform aggregates log data from multiple disparate sources by deploying numerous collection agents and provides centralized analysis and reporting of an organization's security events for identifying malicious activity. Sysmon logs is a data source that has received considerable attention for endpoint visibility. Approaches for threat detection using Sysmon have been proposed mainly focusing on search engines (NoSQL database systems). This paper presents a new automated threat assessment system that relies on the analysis of continuous incoming feeds of Sysmon logs. The system is based on a cyber threat intelligence ontology and analyses Sysmon logs to classify software in different threat levels and augment cyber defensive capabilities through situational awareness, prediction, and automated courses of action.


title: Teaching Life-Saving Manoeuvres in Primary School

abstract: Introduction. In the event of sudden cardiac arrest (SCA) early intervention provided by a layperson can be life-saving. Teaching first aid in primary school may increase the lifelong ability and motivation of young people to take action in an emergency. Objective. The aim of this article is to report a training experience on BLSD (Basic Life Support and Defibrillation) designed for a group of pupils in an Italian primary school, with assessment of its effectiveness at a distance. Methods. The assessment was carried out using a multiple choice questionnaire on a sample of 130 pupils aged 11-12, 62 trained in BLSD and 68 as a control group. The trained group also performed an emergency simulation to assess their learning of practical skills. Results. Using the test, significant differences emerged in the questionnaire scores between the case-control group. The results of the skill test were positive, even for the most difficult manoeuvres such as opening airways, assessing breathing, or using an AED (Automated External Defibrillator). Conclusion. Although there are still some open questions regarding the ability to retain these skills in the medium/long term, the study shows that life-saving manoeuvres can be effectively taught to primary school pupils.


title: Probability estimation for pedestrian crossing intention at signalized crosswalks

abstract: With the rapid development of the techniques for autonomous driving and ADAS in the last decade, more advanced methods to understand pedestrian behavior are required. Crosswalks at intersections are the one of most hazardous where many accidents between turning-vehicles and pedestrians occur. In this paper, we present a method for estimating the pedestrian’s intention to cross a signalized crosswalk or stop in front of it. The intention is crucial to not only the collision avoidance but also smooth traffic in the context of autonomous driving by reducing unnecessary risk margins. Regarding the behavioral flow of pedestrian: assessment, decision-making and physical movement, as a stochastic process, we construct a probabilistic model with the Dynamic Bayesian Network. It takes account of not only pedestrian physical states but also contextual information and integrates the relationship among them. By employing the particle filter as a Bayesian filtering framework, the model estimates the pedestrian state from signal information and pedestrian position measurements. Evaluation using experimental data collected in real traffic scene shows that the proposed model has an ability to detect the pedestrian intention to cross a crosswalk even when he/she is far from it.


title: Geometrization of deep networks for the interpretability of deep learning systems

abstract: How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical world. By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.


title: A Tandem Algorithm for Singing Pitch Extraction and Voice Separation From Music Accompaniment

abstract: Singing pitch estimation and singing voice separation are challenging due to the presence of music accompaniments that are often nonstationary and harmonic. Inspired by computational auditory scene analysis (CASA), this paper investigates a tandem algorithm that estimates the singing pitch and separates the singing voice jointly and iteratively. Rough pitches are first estimated and then used to separate the target singer by considering harmonicity and temporal continuity. The separated singing voice and estimated pitches are used to improve each other iteratively. To enhance the performance of the tandem algorithm for dealing with musical recordings, we propose a trend estimation algorithm to detect the pitch ranges of a singing voice in each time frame. The detected trend substantially reduces the difficulty of singing pitch detection by removing a large number of wrong pitch candidates either produced by musical instruments or the overtones of the singing voice. Systematic evaluation shows that the tandem algorithm outperforms previous systems for pitch extraction and singing voice separation.


title: Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

abstract: The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.


A Comparison of Software Architectural Styles Using Aspects.


title: Sparse probabilistic matrix factorization by Llaplace distribution for collaborative filtering

abstract: In recommendation systems, probabilistic matrix factorization (PMF) is a state-of-the-art collaborative filtering method by determining the latent features to represent users and items. However, two major issues limiting the usefulness of PMF are the sparsity problem and long-tail distribution. Sparsity refers to the situation that the observed rating data are sparse, which results in that only part of latent features are informative for describing each item/user. Long tail distribution implies that a large fraction of items have few ratings. In this work, we propose a sparse probabilistic matrix factorization method (SPMF) by utilizing a Laplacian distribution to model the item/user factor vector. Laplacian distribution has ability to generate sparse coding, which is beneficial for SPMF to distinguish the relevant and irrelevant latent features with respect to each item/user. Meanwhile, the tails in Laplacian distribution are comparatively heavy, which is rewarding for SPMF to recommend the tail items. Furthermore, a distributed Gibbs sampling algorithm is developed to efficiently train the proposed sparse probabilistic model. A series of experiments on Netflix and Movielens datasets have been conducted to demonstrate that SPMF outperforms the existing PMF and its extended version Bayesian PMF (BPMF), especially for the recommendation of tail items.


title: Position and Speed Control of Brushless DC Motors Using Sensorless Techniques and Application Trends

abstract: This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks.


title: Phantom vibrations among undergraduates: Prevalence and associated psychological characteristics

abstract: 'Phantom vibration syndrome,' or perceived vibrations from a device that is not really vibrating, is a recent psychological phenomenon that has attracted the attention of the media and medical community. Most (89%) of the 290 undergraduates in our sample had experienced phantom vibrations, and they experienced them about once every two weeks, on average. However, few found them bothersome. Those higher in conscientiousness experienced phantom vibrations less frequently, and those who had strong reactions to text messages (higher in the emotional reaction subscale of text message dependence) were more bothered by phantom vibrations. These findings suggest that targeting individuals' emotional reactions to text messages might be helpful in combating the negative consequences of both text message dependency and phantom vibrations. However, because few young adults were bothered by these phantom vibrations or made attempts to stop them, interventions aimed at this population may be unnecessary.


title: The personality of popular facebook users

abstract: We study the relationship between Facebook popularity (number of contacts) and personality traits on a large number of subjects. We test to which extent two prevalent viewpoints hold. That is, popular users (those with many social contacts) are the ones whose personality traits either predict many offline (real world) friends or predict propensity to maintain superficial relationships. We find that the predictor for number of friends in the real world (Extraversion) is also a predictor for number of Facebook contacts. We then test whether people who have many social contacts on Facebook are the ones who are able to adapt themselves to new forms of communication, present themselves in likable ways, and have propensity to maintain superficial relationships. We show that there is no statistical evidence to support such a conjecture.


title: High Gain and Low Cost Differentially Fed Circularly Polarized Planar Aperture Antenna for Broadband Millimeter-Wave Applications

abstract: This paper presents a novel circularly polarized (CP) planar aperture antenna for cost-sensitive millimeter-wave, particularly fifth generation (5G), applications. The proposed antenna, which is a differential one, has high gain and wide bandwidth and can be built on a single-layer laminate using standard printed-circuit board (PCB) technology. This makes the antenna both low cost and easy to integrate with common differential circuits. The antenna consists of an opening cavity formed by metalized vias, which encloses a set of loop-like strips that are fed differentially. The strips carry traveling wave current which together with the aperture’s E-fields distribution give rise to CP radiation. The aperture size of the antenna can be extended for higher gain by adopting a dual- or quadruple-loop-like strips configuration enclosed by a larger opening cavity. The fabricated quadruple-loop-like strips-based antenna prototype designed to operate at 60-GHz band could achieve 3-dB axial ratio (AR) bandwidth of 16.7% (56–66.2 GHz) within its $-10$ -dB impedance bandwidth and stable right-handed CP (RHCP) gain throughout the operating bandwidth with a peak of 14.6 dBi.


title: Rule Extraction : Using Neural Networks or For Neural Networks ?

abstract: In the research of rule extraction from neural networks, fidelity describes how well the rules mimic the behavior of a neural network while accuracy describes how well the rules can be generalized. This paper identifies the fidelity-accuracy dilemma. It argues to distinguish rule extraction using neural networks and rule extraction for neural networks according to their different goals, where fidelity and accuracy should be excluded from the rule quality evaluation framework, respectively.


title: Design space exploration of FPGA-based Deep Convolutional Neural Networks

abstract: Deep Convolutional Neural Networks (DCNN) have proven to be very effective in many pattern recognition applications, such as image classification and speech recognition. Due to their computational complexity, DCNNs demand implementations that utilize custom hardware accelerators to meet performance and energy-efficiency constraints. In this paper we propose an FPGA-based accelerator architecture which leverages all sources of parallelism in DCNNs. We develop analytical feasibility and performance estimation models that take into account various design and platform parameters. We also present a design space exploration algorithm for obtaining the implementation with the highest performance on a given platform. Simulation results with a real-life DCNN demonstrate that our accelerator outperforms other competing approaches, which disregard some sources of parallelism in the application. Most notably, our accelerator runs 1.9× faster than the state-of-the-art DCNN accelerator on the same FPGA device.


ExpoDB : An Exploratory Data Science Platform


title: Rise of the planet of the apps: a systematic study of the mobile app ecosystem

abstract: Mobile applications (apps) have been gaining rising popularity due to the advances in mobile technologies and the large increase in the number of mobile users. Consequently, several app distribution platforms, which provide a new way for developing, downloading, and updating software applications in modern mobile devices, have recently emerged. To better understand the download patterns, popularity trends, and development strategies in this rapidly evolving mobile app ecosystem, we systematically monitored and analyzed four popular third-party Android app marketplaces. Our study focuses on measuring, analyzing, and modeling the app popularity distribution, and explores how pricing and revenue strategies affect app popularity and developers' income. Our results indicate that unlike web and peer-to-peer file sharing workloads, the app popularity distribution deviates from commonly observed Zipf-like models. We verify that these deviations can be mainly attributed to a new download pattern, to which we refer as the clustering effect. We validate the existence of this effect by revealing a strong temporal affinity of user downloads to app categories. Based on these observations, we propose a new formal clustering model for the distribution of app downloads, and demonstrate that it closely fits measured data. Moreover, we observe that paid apps follow a different popularity distribution than free apps, and show how free apps with an ad-based revenue strategy may result in higher financial benefits than paid apps. We believe that this study can be useful to appstore designers for improving content delivery and recommendation systems, as well as to app developers for selecting proper pricing policies to increase their income.


title: European S1 guideline for the treatment of hidradenitis suppurativa/acne inversa

abstract: Hidradenitis suppurativa/acne inversa (HS) is a chronic, inflammatory, recurrent, debilitating skin disease of the hair follicle that usually presents after puberty with painful, deep-seated, inflamed lesions in the apocrine gland-bearing areas of the body, most commonly the axillae, inguinal and anogenital regions. A mean disease incidence of 6.0 per 100 000 person-years and an average prevalence of 1% has been reported in Europe. HS has the highest impact on patients’ quality of life among all assessed dermatological diseases. HS is associated with a variety of concomitant and secondary diseases, such as obesity, metabolic syndrome, inflammatory bowel disease, e.g. Crohn’s disease, spondyloarthropathy, follicular occlusion syndrome and other hyperergic diseases. The central pathogenic event in HS is believed to be the occlusion of the upper part of the hair follicle leading to a perifollicular lympho-histiocytic inflammation. A highly significant association between the prevalence of HS and current smoking (Odds ratio 12.55) and overweight (Odds ratio 1.1 for each body mass index unit) has been documented. The European S1 HS guideline suggests that the disease should be treated based on its individual subjective impact and objective severity. Locally recurring lesions can be treated by classical surgery or LASER techniques, whereas medical treatment either as monotherapy or in combination with radical surgery is more appropriate for widely spread lesions. Medical therapy may include antibiotics (clindamycin plus rifampicine, tetracyclines), acitretin and biologics (adalimumab, infliximab). A Hurley severity grade-relevant treatment of HS is recommended by the expert group following a treatment algorithm. Adjuvant measurements, such as pain management, treatment of superinfections, weight loss and tobacco abstinence have to be considered.


title: Automatic Analysis of Facial Actions: A Survey

abstract: As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has recently received significant attention. Over the past 30 years, extensive research has been conducted by psychologists and neuroscientists on various aspects of facial expression analysis using FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Such an automated process can also potentially increase the reliability, precision and temporal resolution of coding. This paper provides a comprehensive survey of research into machine analysis of facial actions. We systematically review all components of such systems: pre-processing, feature extraction and machine coding of facial actions. In addition, the existing FACS-coded facial expression databases are summarised. Finally, challenges that have to be addressed to make automatic facial action analysis applicable in real-life situations are extensively discussed. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the future of machine recognition of facial actions: what are the challenges and opportunities that researchers in the field face.


title: Design of a Traveling-Wave Slot Array Power Divider Using the Method of Moments and a Genetic Algorithm

abstract: A 1-to-25-way uniform amplitude and linear phase power divider, using slotted rectangular waveguides in the traveling-wave mode, is designed to cover the 71–77-GHz band. The design employs the scattering parameters of three-port couplers computed from the method of moments (MoM) solution to the integral equation of the slot aperture field. Subsequent genetic algorithm (GA) optimization is aimed at maximizing the return loss and combining efficiency and flatness of coupling amplitudes in the whole frequency band. GA utilizes the MoM solution to the coupled integral equations of slot apertures in the entire power divider structure. Simulations indicate the optimized power divider yielding better than 26-dB return loss and greater than 86% efficiency in the 71–77-GHz band for the power divider combiner connected back to back. Computed MoM results have been validated by the commercial code HFSS and also by experimentally measured results. The measured insertion loss of two back-to-back divider–combiners is about 2 dB.


title: Automation Failures on Tasks Easily Performed by Operators Undermine Trust in Automated Aids

abstract: Objective: We tested the hypothesis that automation errors on tasks easily performed by humans undermine trust in automation. Background: Research has revealed that the reliability of imperfect automation is frequently misperceived. We examined the manner in which the easiness and type of imperfect automation errors affect trust and dependence. Method: Participants performed a target detection task utilizing an automated aid. In Study 1, the aid missed targets either on easy trials (easy miss group) or on difficult trials (difficult miss group). In Study 2, we manipulated both easiness and type of error (miss vs. false alarm). The aid erred on either difficult trials alone (difficult errors group) or on difficult and easy trials (easy miss group; easy false alarm group). Results: In both experiments, easy errors led to participants mistrusting and disagreeing more with the aid on difficult trials, as compared with those using aids that generated only difficult errors. This resulted in a downward shift in ...


title: A mobile crowd sensing ecosystem enabled by CUPUS : Cloud-based publish / subscribe middleware for the Internet of Things

abstract: Mobile crowd sensing (MCS) is a novel class of mobile Internet of Things (IoT) applications for community sensing where sensors and mobile devices jointly collect and share data of interest to observe phenomena over a large geographic area. The inherent device mobility and high sensing frequency has the capacity to produce dense and rich spatiotemporal information about our environment, but also creates new challenges due to device dynamicity and energy constraints, as well as large volumes of generated raw sensor data which need to be processed and analyzed to extract useful information for end users. The paper presents an ecosystem for mobile crowd sensing which relies on the CloUd-based PUblish/Subscribe middleware (CUPUS) to acquire sensor data from mobile devices in a flexible and energy-efficient manner and to perform near real-time processing of Big Data streams. CUPUS has unique features compared to other MCS platforms: It enables management of mobile sensor resources within the cloud, supports filtering and aggregation of sensor data on mobile devices prior to its transmission into the cloud based on global data requirements, and can push information of interest from the cloud to user devices in near real-time. We present our experience with implementation and deployment of an MCS application for air quality monitoring built on top of the CUPUS middleware. Our experimental evaluation shows that CUPUS offers scalable processing performance, both on mobile devices and within the cloud, while its data propagation delay is mainly affected by transmission delay on wireless links. A generic ecosystem for MCS services based on a publish/subscribe middleware.High-performance publish/subscribe processing middleware.Selective and data-driven acquisition of sensor data on mobile devices.Evaluation based on an MCS air quality monitoring campaign.


title: Improved Hessian approximations for the limited memory BFGS method

abstract: This paper considers simple modifications of the limited memory BFGS (L-BFGS) method for large scale optimization. It describes algorithms in which alternating ways of re-using a given set of stored difference vectors are outlined. The proposed algorithms resemble the L-BFGS method, except that the initial Hessian approximation is defined implicitly like the L-BFGS Hessian in terms of some stored vectors rather than the usual choice of a multiple of the unit matrix. Numerical experiments show that the new algorithms yield desirable improvement over the L-BFGS method.


title: Comparametric Equations with Practical Applications in Quantigraphic Image Processing

abstract: It is argued that, hidden within the flow of signals from typical cameras, through image processing, to display media, is a homomorphic filter. While homomorphic filtering is often desirable, there are some occasions where it is not. Thus, cancellation of this implicit homomorphic filter is proposed, through the introduction of an antihomomorphic filter. This concept gives rise to the principle of quantigraphic image processing, wherein it is argued that most cameras can be modeled as an array of idealized light meters each linearly responsive to a semi-monotonic function of the quantity of light received, integrated over a fixed spectral response profile. This quantity depends only on the spectral response of the sensor elements in the camera. A particular class of functional equations, called comparametric equations, is introduced as a basis for quantigraphic image processing. These are fundamental to the analysis and processing of multiple images differing only in exposure. The "gamma correction" of an image is presented as a simple example of a comparametric equation, for which it is shown that the underlying quantigraphic function does not pass through the origin. Thus, it is argued that exposure adjustment by gamma correction is inherently flawed, and alternatives are provided. These alternatives, when applied to a plurality of images that differ only in exposure, give rise to a new kind of processing in the "amplitude domain". The theoretical framework presented in this paper is applicable to the processing of images from nearly all types of modern cameras. This paper is a much revised draft of a 1992 peer-reviewed but unpublished report by the author, entitled "Lightspace and the Wyckoff principle."


title: An Evaluation of The Neocognitron

abstract: We describe a sequence of experiments investigating the strengths and limitations of Fukushima's neocognitron as a handwritten digit classifier. Using the results of these experiments as a foundation, we propose and evaluate improvements to Fukushima's original network in an effort to obtain higher recognition performance. The neocognitron performance is shown to be strongly dependent on the choice of selectivity parameters and we present two methods to adjust these variables. Performance of the network under the more effective of the two new selectivity adjustment techniques suggests that the network fails to exploit the features that distinguish different classes of input data. To avoid this shortcoming, the network's final layer cells were replaced by a nonlinear classifier (a multilayer perceptron) to create a hybrid architecture. Tests of Fukushima's original system and the novel systems proposed in this paper suggest that it may be difficult for the neocognitron to achieve the performance of existing digit classifiers due to its reliance upon the supervisor's choice of selectivity parameters and training data.


ONE DAY INTERNATIONAL CRICKET MATCHES USING MACHINE LEARNING


title: CPU-assisted GPGPU on fused CPU-GPU architectures

abstract: This paper presents a novel approach to utilize the CPU resource to facilitate the execution of GPGPU programs on fused CPU-GPU architectures. In our model of fused architectures, the GPU and the CPU are integrated on the same die and share the on-chip L3 cache and off-chip memory, similar to the latest Intel Sandy Bridge and AMD accelerated processing unit (APU) platforms. In our proposed CPU-assisted GPGPU, after the CPU launches a GPU program, it executes a pre-execution program, which is generated automatically from the GPU kernel using our proposed compiler algorithms and contains memory access instructions of the GPU kernel for multiple thread-blocks. The CPU pre-execution program runs ahead of GPU threads because (1) the CPU pre-execution thread only contains memory fetch instructions from GPU kernels and not floating-point computations, and (2) the CPU runs at higher frequencies and exploits higher degrees of instruction-level parallelism than GPU scalar cores. We also leverage the prefetcher at the L2-cache on the CPU side to increase the memory traffic from CPU. As a result, the memory accesses of GPU threads hit in the L3 cache and their latency can be drastically reduced. Since our pre-execution is directly controlled by user-level applications, it enjoys both high accuracy and flexibility. Our experiments on a set of benchmarks show that our proposed pre-execution improves the performance by up to 113% and 21.4% on average.


title: Integrating process and work breakdown structure with design structure matrix

abstract: ∗Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan E-mail: {yjlee, jass, wtlee, jielee}@selab.csie.ncu.edu.tw ∗∗Department of Computer and Information Science, National Taichung University, Taichung, Taiwan E-mail: glenn@mail.ntcu.edu.tw ∗∗∗Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan E-mail: albert@mail.ntou.edu.tw


title: A $Ku$ -Band Two-Antenna Four-Simultaneous Beams SiGe BiCMOS Phased Array Receiver

abstract: This paper presents a Ku-band SiGe BiCMOS phased array receive chip capable of forming four-simultaneous beams from two antenna inputs. The design is based on the all-RF architecture with 4-bit active phase shifters and 4-bit variable gain amplifiers in each channel. The four-beam chip results in a gain of 4-6 dB per channel at 13-15 GHz, a noise figure of 10-11 dB, a worst case input P1 dB of -14.3 dBm per channel (input third-order intercept point of -7 dBm), and an rms phase and gain error of 40 dB at 13-15 GHz. The chips can operate over an instantaneous bandwidth of > 1 GHz at any frequency from 13 to 15 GHz, and the four beams can be at the same frequency if required. With all digital control circuitry and electrostatic discharge protection for all I/O pads, the chip occupies an area of 2.4 × 4.3 mm2 and consumes 520 mA at 3.5-V supply voltage. To our knowledge, this is the first demonstration of an all-RF phased array silicon chip capable of producing four-simultaneous beams from two different antennas or four-simultaneous beams of different polarizations from a dual polarization antenna. The application areas are in satellite communications and defense systems.


title: Why it can matter more than IQ

abstract: In an apparatus for the gasification of a biomass in an aqueous medium in the presence of organic substances degradable by the biomass, comprising a gasification tank, and a clearing chamber for the gasified water-containing biomass provided concentrically around the gasification tank, the gasification tank communicating with the clearing chamber through an inlet in the latter, the improvement wherein A. THE GASIFICATION TANK IS FROM ABOUT 10 TO 32 METERS HIGH, HAS A HEIGHT/DIAMETER RATIO BETWEEN APPROXIMATELY 40 AND 0.2, IS PROVIDED ADJACENT ITS FLOOR WITH GAS INLET POINTS, AND IS CONNECTED VIA INLET PIPES TO AT LEAST ONE GAS REMOVAL AND FLOCCULATION CYCLONE; B. THE SAME LIQUID LEVEL IN ALL THE CLEARING CHAMBERS IS MAINTAINED BY MEANS OF AN OVERFLOW CHANNEL AT ABOUT 0.1 TO 2 METERS BELOW THE LIQUID LEVEL OF THE GASIFICATION TANK; AND C. A SLUDGE REMOVAL PIPE CONNECTS EACH CLEARING CHAMBER WITH A COLLECTOR PIPE.


Session 42: Preimplantation Genetic Diagnosis


Airway Changes During Labor and Delivery


title: Social Security and Social Welfare Data Mining: An Overview

abstract: The importance of social security and social welfare business has been increasingly recognized in more and more countries. It impinges on a large proportion of the population and affects government service policies and people's life quality. Typical welfare countries, such as Australia and Canada, have accumulated a huge amount of social security and social welfare data. Emerging business issues such as fraudulent outlays, and customer service and performance improvements challenge existing policies, as well as techniques and systems including data matching and business intelligence reporting systems. The need for a deep understanding of customers and customer-government interactions through advanced data analytics has been increasingly recognized by the community at large. So far, however, no substantial work on the mining of social security and social welfare data has been reported. For the first time in data mining and machine learning, and to the best of our knowledge, this paper draws a comprehensive overall picture and summarizes the corresponding techniques and illustrations to analyze social security/welfare data, namely, social security data mining (SSDM), based on a thorough review of a large number of related references from the past half century. In particular, we introduce an SSDM framework, including business and research issues, social security/welfare services and data, as well as challenges, goals, and tasks in mining social security/welfare data. A summary of SSDM case studies is also presented with substantial citations that direct readers to more specific techniques and practices about SSDM.


title: Quantum Radiation from a 5-Dimensional Rotating

abstract: We study a massless scalar field propagating in the background of a five-dimensional rotating black hole. We show that in the Myers-Perry metric describing such a black hole the massless field equation allows the separation of variables. The obtained angular equation is a generalization of the equation for spheroidal functions. The radial equation is similar to the radial Teukolsky equation for the 4-dimensional Kerr metric. We use these results to quantize the massless scalar field in the space-time of the 5-dimensional rotating black hole and to derive expressions for energy and angular momentum fluxes from such a black hole.


title: Pixel-Wise Attentional Gating for Scene Parsing

abstract: To achieve dynamic inference in pixel labeling tasks, we propose Pixel-wise Attentional Gating (PAG), which learns to selectively process a subset of spatial locations at each layer of a deep convolutional network. PAG is a generic, architecture-independent, problem-agnostic mechanism that can be readily "plugged in" to an existing model with fine-tuning. We utilize PAG in two ways: 1) learning spatially varying pooling fields that improve model performance without the extra computation cost associated with multi-scale pooling, and 2) learning a dynamic computation policy for each pixel to decrease total computation (FLOPs) while maintaining accuracy. We extensively evaluate PAG on a variety of per-pixel labeling tasks, including semantic segmentation, boundary detection, monocular depth and surface normal estimation. We demonstrate that PAG allows competitive or state-of-the-art performance on these tasks. Our experiments show that PAG learns dynamic spatial allocation of computation over the input image which provides better performance trade-offs compared to related approaches (e.g., truncating deep models or dynamically skipping whole layers). Generally, we observe PAG can reduce computation by 10% without noticeable loss in accuracy and performance degrades gracefully when imposing stronger computational constraints.


title: Design and evaluation of a 1.2 kVA single phase rotary transformer

abstract: The doubly fed induction generator (DFIG) is the most popular choice for wind energy generation systems. The cost and difficulty incurred in the maintenance of the slip rings has led to the development of the brushless DFIG. Amongst other design options, the brushless DFIG can be realized through the use of rotary transformers (RTs). Due to the low operating frequency of the rotor currents, a RT used in place of slip rings in a conventional DFIG configuration, can be bulky. However, by utilizing a rotor tied to the grid DFIG system configuration, the RT's operating frequency can be increased to the grid's frequency. In this paper a single phase 1.2 kVA RT is designed, using a previously proposed optimal design method, and developed. Three of these RTs can be used in place of the slip rings in DFIGs. Remarkable measurement results as well as simulation results are also presented.


title: KANE ’ S EQUATIONS FOR HAPTIC DISPLAY OF MULTIBODY SYSTEMS

abstract: The design of a general-purpose multibody simulator that runs in real-time and features haptic display is presented. The repertoire of this simulator includes systems subject to holonomic constraints, nonholonomic constraints, and notably, systems subject to changing constraints. In contrast to the use of generic equations of motion, Kane’s method is used by a computerized symbolic manipulator to produce custom-built, compact, and computationally efficient ordinary differential equations for each virtual system to be simulated. A method is presented whereby new equations, reflecting the presence of a transient constraint, may be formulated on-line by a recombination of terms comprising the nominal equations. This paper also serves as a tutorial introduction to Kane’s method and includes a detailed derivation of Kane’s equations with a geometric interpretation. An illustrative example featuring changing constraint conditions is developed in the body of the paper while two more examples, chosen to highlight certain features within Kane’s method, are presented at the conclusion.


title: Stochastic Optimization with Importance Sampling for Regularized Loss Minimization

abstract: Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Mirror Descent (prox-SMD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimate of the corresponding true quantity, the resulting estimator may have a rather high variance, which negatively affects the convergence of the underlying optimization procedure. In this paper we study stochastic optimization, including prox-SMD and prox-SDCA, with importance sampling, which improves the convergence rate by reducing the stochastic variance. We theoretically analyze the algorithms and empirically validate their effectiveness.


title: Comparing Events Coverage in Online News and Social Media: The Case of Climate Change

abstract: Social media is becoming more and more integrated in the distribution and consumption of news. How is news in social media different from mainstream news? This paper presents a comparative analysis covering a span of 17 months and hun- dreds of news events, using a method that combines auto- matic and manual annotations. We focus on climate change, a topic that is frequently present in the news through a number of arguments, from current practices and causes (e.g. frack- ing, CO2 emissions) to consequences and solutions (e.g. ex- treme weather, electric cars). The coverage that these dif- ferent aspects receive is often dependent on how they are framed—typically by mainstream media. Yet, evidence sug- gests an existing gap between what the news media pub- lishes online and what the general public shares in social media. Through the analysis of a series of events, including awareness campaigns, natural disasters, governmental meet- ings and publications, among others, we uncover differences in terms of the triggers, actions, and news values that are prevalent in both types of media. This methodology can be extended to other important topics present in the news.


title: Past, Present and Future of Ambient Intelligence and Smart Environments

abstract: We are gradually making a transition to a new era where computers become truly intertwined with our daily lives. Up to not so long ago, we were able to know clearly where computers were and in which way they affected our lives. This has been gradually blurred and now computing devices of various types are all around us, embedded in different objects we interact with and in that way they influence our lives. There are indications that this trend is irreversible and that computing and society will now interact with each other in far richer ways than before, to the point that computing will become transparent to humans and still intrinsically involved in our daily living. This paper provides a brief overview of the evolution of these fields, describes some of the current developments, and points at some of the immediate challenges that researchers in these area face.


title: A Survey of Security Attacks in Information-Centric Networking

abstract: Information-centric networking (ICN) is a new communication paradigm that focuses on content retrieval from a network regardless of the storage location or physical representation of this content. In ICN, securing the content itself is much more important than securing the infrastructure or the endpoints. To achieve the security goals in this new paradigm, it is crucial to have a comprehensive understanding of ICN attacks, their classification, and proposed solutions. In this paper, we provide a survey of attacks unique to ICN architectures and other generic attacks that have an impact on ICN. It also provides a taxonomy of these attacks in ICN, which are classified into four main categories, i.e., naming, routing, caching, and other miscellaneous related attacks. Furthermore, this paper shows the relation between ICN attacks and unique ICN attributes, and that between ICN attacks and security requirements, i.e., confidentiality, integrity, availability, and privacy. Finally, this paper presents the severity levels of ICN attacks and discusses the existing ICN security solutions.


title: Improved Flexible Sensing Cable System for Leak Detection and Localization

abstract: The accuracy of leak localization of conventional sensing cable systems is related to the current stability of constant current source. To improve the performance of sensing cable systems, a novel integrated method is proposed for distributed liquid leak detection and localization, which includes an online self-test and parametric self-calibration, adaptive threshold leak detection and alarm generation, accurate leak localization based on share voltage compensation, and rough estimation of leak contaminant size. With the proposed method and algorithms, an improved flexible sensing cable system employing a standard programmable controller is developed; it performs real time and reliable leakage incident monitoring. The leak detection sensitivity, leak localization accuracy, and effectiveness of the developed system for extendable sensing cable are demonstrated by means of experimental results and simulations.


title: Use of the gram stain in microbiology.

abstract: The Gram stain differentiates bacteria into two fundamental varieties of cells. Bacteria that retain the initial crystal violet stain (purple) are said to be "gram-positive," whereas those that are decolorized and stain red with carbol fuchsin (or safranin) are said to be "gram-negative." This staining response is based on the chemical and structural makeup of the cell walls of both varieties of bacteria. Gram-positives have a thick, relatively impermeable wall that resists decolorization and is composed of peptidoglycan and secondary polymers. Gram-negatives have a thin peptidoglycan layer plus an overlying lipid-protein bilayer known as the outer membrane, which can be disrupted by decolorization. Some bacteria have walls of intermediate structure and, although they are officially classified as gram-positives because of their linage, they stain in a variable manner. One prokaryote domain, the Archaea, have such variability of wall structure that the Gram stain is not a useful differentiating tool.


title: Mixed Valence in YBaFe2O5

abstract: YBaFe2O5 has been synthesized by heating a nanoscale citrate precursor in a carefully controlled reducing environment. Successful synthesis of a single-phase sample can only be achieved in a narrow window of oxygen partial pressures and temperatures. YBaFe2O5 adopts an oxygen-deficient perovskite-type structure, which contains double layers of corner sharing FeO5 square pyramids separated by Y3+ ions. At TN ≅ 430 K, tetragonal (P4/mmm) and paramagnetic YBaFe2O5 orders antiferromagnetically (AFM) experiencing a slight orthorhombic distortion (Pmmm). Around this temperature, it can be characterized as a class-III mixed valence (MV) compound, where all iron atoms exist as equivalent MV Fe2.5+ ions. The magnetic structure is characterized by AFM Fe−O−Fe superexchange coupling within the double layers and a ferromagnetic Fe−Fe direct-exchange coupling between neighboring double layers. Upon cooling below ∼335 K, a premonitory charge ordering (2Fe2.5+ → Fe2.5+δ + Fe2.5-δ) into a class-II MV phase takes place. T...


title: www.elsevier.com/locate/eswa A personalized English learning recommender system for ESL students

abstract: This paper has developed an online personalized English learning recommender system capable of providing ESL students with reading lessons that suit their different interests and therefore increase the motivation to learn. The system, using content-based analysis, collaborative filtering, and data mining techniques, analyzes real students' reading data and generates recommender scores, based on which to help select appropriate lessons for respective students. Its performance having been tracked over a period of one year, this recommender system has proved to be very useful in heightening ESL learners' motivation and interest in reading.


title: An ant algorithm for the graph matching problem

abstract: This paper describes a new Ant Colony Optimization (ACO) algorithm for solving Graph Matching Problems, the goal of which is to find the best matching between vertices of multi-labeled graphs. This new ACO algorithm is experimentally compared with greedy and reactive tabu approaches on subgraph isomorphism problems and on multivalent graph matching problems.


title: End-User Development: An Emerging Paradigm

abstract: We think that over the next few years, the goal of interactive systems and services will evolve from just making systems easy to use (even though that goal has not yet been completely achieved) to making systems that are easy to develop by end users. By now, most people have become familiar with the basic functionality and interfaces of computers, but they are not able to manage any programming language. Therefore, they cannot develop new applications or modify current ones according to their needs. In order to address such challenges it is necessary a new paradigm, based on a multidisciplinary approach involving several types of expertise, such as software engineering, human-computer inter- action, CSCW, which are now rather fragmented and with little interaction. The resulting methods and tools can provide results useful across many application domains, such as ERP, multi-device services (accessible through both mobile and stationary devices), and professional applications.


title: Scene completion using millions of photographs

abstract: What can you do with a million images? In this paper, we present a new image completion algorithm powered by a huge database of photographs gathered from the Web. The algorithm patches up holes in images by finding similar image regions in the database that are not only seamless, but also semantically valid. Our chief insight is that while the space of images is effectively infinite, the space of semantically differentiable scenes is actually not that large. For many image completion tasks, we are able to find similar scenes which contain image fragments that will convincingly complete the image. Our algorithm is entirely data driven, requiring no annotations or labeling by the user. Unlike existing image completion methods, our algorithm can generate a diverse set of image completions and we allow users to select among them. We demonstrate the superiority of our algorithm over existing image completion approaches.


title: Using the text to evaluate short answers for reading comprehension exercises

abstract: Short answer questions for reading comprehension are a common task in foreign language learning. Automatic short answer scoring is the task of automatically assessing the semantic content of a student’s answer, marking it e.g. as correct or incorrect. While previous approaches mainly focused on comparing a learner answer to some reference answer provided by the teacher, we explore the use of the underlying reading texts as additional evidence for the classification. First, we conduct a corpus study targeting the links between sentences in reading texts for learners of German and answers to reading comprehension questions based on those texts. Second, we use the reading text directly for classification, considering three different models: an answer-based classifier extended with textual features, a simple text-based classifier, and a model that combines the two according to confidence of the text-based classification. The most promising approach is the first one, results for which show that textual features improve classification accuracy. While the other two models do not improve classification accuracy, they do investigate the role of the text and suggest possibilities for developing automatic answer scoring systems with less supervision needed from instructors.


title: Why and How to Perform Fraud Experiments

abstract: Fraud isn't new, but in the eyes of many experts, phishing and crimeware threaten to topple society's overall stability because they erode trust in its underlying computational infrastructure. Most people agree that phishing and crimeware must be fought, but to do so effectively, we must fully understand both types of threat; that starts by quantifying how and when people fall for deceit. In this article, we look closer at how to perform fraud experiments. Researchers typically use three approaches to quantify fraud: surveys, in-lab experiments, and naturalistic experiments.


title: INTEX: A Syntactic Role Driven Protein-Protein Interaction Extractor For Bio-Medical Text

abstract: In this paper, we present a fully automated extraction system, named IntEx, to identify gene and protein interactions in biomedical text. Our approach is based on first splitting complex sentences into simple clausal structures made up of syntactic roles. Then, tagging biological entities with the help of biomedical and linguistic ontologies. Finally, extracting complete interactions by analyzing the matching contents of syntactic roles and their linguistically significant combinations. Our extraction system handles complex sentences and extracts multiple and nested interactions specified in a sentence. Experimental evaluations with two other state of the art extraction systems indicate that the IntEx system achieves better performance without the labor intensive pattern engineering requirement.


title: Predictor-Estimator: Neural Quality Estimation Based on Target Word Prediction for Machine Translation

abstract: Recently, quality estimation has been attracting increasing interest from machine translation researchers, aiming at finding a good estimator for the “quality” of machine translation output. The common approach for quality estimation is to treat the problem as a supervised regression/classification task using a quality-annotated noisy parallel corpus, called quality estimation data, as training data. However, the available size of quality estimation data remains small, due to the too-expensive cost of creating such data. In addition, most conventional quality estimation approaches rely on manually designed features to model nonlinear relationships between feature vectors and corresponding quality labels. To overcome these problems, this article proposes a novel neural network architecture for quality estimation task—called the predictor-estimator—that considers word prediction as an additional pre-task. The major component of the proposed neural architecture is a word prediction model based on a modified neural machine translation model—a probabilistic model for predicting a target word conditioned on all the other source and target contexts. The underlying assumption is that the word prediction model is highly related to quality estimation models and is therefore able to transfer useful knowledge to quality estimation tasks. Our proposed quality estimation method sequentially trains the following two types of neural models: (1) Predictor: a neural word prediction model trained from parallel corpora and (2) Estimator: a neural quality estimation model trained from quality estimation data. To transfer word a prediction task to a quality estimation task, we generate quality estimation feature vectors from the word prediction model and feed them into the quality estimation model. The experimental results on WMT15 and 16 quality estimation datasets show that our proposed method has great potential in the various sub-challenges.


title: A Multiple Lyapunov Function Approach to Distributed Synchronization Control of Multi-Agent Systems With Switching Directed Communication Topologies and Unknown Nonlinearities

abstract: This paper investigates the distributed adaptive control problem for synchronization of multi-agent systems where the dynamics of the agents are nonlinear, nonidentical, unknown and subject to external disturbances. In our recent work, we solved this problem for general higher order systems under two types of communication topologies, represented, respectively, by a fixed strongly connected directed graph and by a switching connected undirected graph. A common Lyapunov function technique is employed to establish the results. In this paper, we solve the problem for a more general communication topology which is represented by a switching strongly-connected directed graph. We construct a sequence of different Lyapunov functions and use them to establish our results. Simulation study verifies the effectiveness of our theoretical results.


title: CAP1 (Cyclase-Associated Protein 1) Exerts Distinct Functions in the Proliferation and Metastatic Potential of Breast Cancer Cells Mediated by ERK

abstract: The actin-regulating protein CAP1 is implicated in the invasiveness of human cancers. However, the exact role remains elusive and controversial given lines of conflicting evidence. Moreover, a potential role in the proliferative transformation has largely been overlooked. Further establishing the role and dissecting underlying mechanisms are imperative before targeting CAP1 can become a possibility for cancer treatment. Here we report our findings that CAP1 exerts cell type-dependent functions in the invasiveness of breast cancer cells. Depletion of CAP1 in the metastatic MDA-MB-231 and BT-549 cancer cells stimulated the metastatic potential while it actually inhibited it in the non-metastatic MCF-7 cancer cells or in normal cells. Moreover, we demonstrate functions for CAP1 in cancer cell proliferation and anchorage-independent growth, again in a cell context-dependent manner. Importantly, we identify pivotal roles for the ERK-centered signaling in mediating both CAP1 functions. Phosphor mutants of CAP1 at the S307/S309 regulatory site had compromised rescue effects for both the invasiveness and proliferation in CAP1-knockdown cells, suggesting that CAP1 likely mediates upstream cell signals to control both functions. These novel mechanistic insights may ultimately open up avenues for strategies targeting CAP1 in the treatment of breast cancer, tailored for specific types of the highly diverse disease.


title: The neural organization of language: evidence from sign language aphasia

abstract: Abstract To what extent is the neural organization of language dependent on factors specific to the modalities in which language is perceived and through which it is produced? That is, is the left-hemisphere dominance for language a function of a linguistic specialization or a function of some domain-general specialization(s), such as temporal processing or motor planning? Investigations of the neurobiology of signed language can help answer these questions. As with spoken languages, signed languages of the deaf display complex grammatical structure but are perceived and produced via radically different modalities. Thus, by mapping out the neurological similarities and differences between signed and spoken language, it is possible to identify modality-specific contributions to brain organization for language. Research to date has shown a significant degree of similarity in the neurobiology of signed and spoken languages, suggesting that the neural organization of language is largely modality-independent.


title: A collaborative approach for research paper recommender system

abstract: Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.


title: CUSVM: A CUDA IMPLEMENTATION OF SUPPORT VECTOR CLASSIFICATION AND REGRESSION

abstract: This paper presents cuSVM, a software package for high-speed Support Vector Machine (SVM) training and prediction that exploits the mas- sively parallel processing power of Graphics Processors (GPUs). cuSVM is written in NVIDIA's CUDA C-language GPU programming environment, in- cludes implementations of both classication and regression, and performs SVM training (prediction) at 13-73 (22-172) times the rate of state of the art CPU software.


title: ADAPTIVE EXPERIMENTATION IN INTERACTIVE MARKETING: THE CASE OF VIRAL MARKETING AT PLAXO

abstract: This case study shows how interactive marketing campaigns can be systematically fine-tuned and made more productive through adaptive experimentation. It details the use of adaptive experimentation in a viral marketing campaign at Plaxo, Inc., a company that provides Internet-based updating of personal contact information. The experiences of Plaxo highlight that even for a product that is amenable to viral marketing, growth is not guaranteed. To achieve a desired level of growth, Plaxo identified the product features that contributed to greater adoption conversion of recipients of its marketing message and improved them through continuous experimentation. To overcome potential negative side effects of aggressive viral growth, the company used a carefully crafted feedback loop via internet alert services that tapped into the blogging community. This practice allowed management to better understand negative perceptions of the product and take timely corrective actions.


Complexity Management for Projects, Programmes, and Portfolios: An Engineering Systems Perspective


title: Hierarchical Spatial Sum–Product Networks for Action Recognition in Still Images

abstract: Recognizing actions from still images is popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial SPN (Sum-Product Networks). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside sub-images and models the correlation of sub-images via extra layers of SPN. Our method is shown to be effective on two benchmark datasets.


title: Sensor networks data acquisition and task management for decision support of smart farming

abstract: Agricultural sector has been facing great challenges in order to feed the increasing number of population living in the world. In the future, it will be very difficult to rely on traditional farming to produce food. Some researchers and industry experts have begun developing smart farming by utilizing information technology. This paper presents the conceptual model and system design for decision support of smart farming with network sensor applications in order to perform necessary tasks required for farmers. We propose a comprehensive model using Internet of Things (IoT) approach which will be applied to agriculture. Data acquisition via sensors, control and tasks management, and data analysis are considered in the development of model and system design. In this system, we propose a solution to help farmers facing problems of tasks management and planning, environment factors measurements, and information distribution.


title: UbiHeld: ubiquitous healthcare monitoring system for elderly and chronic patients

abstract: Once the person's identity is established, the most important aspects of ubiquitous healthcare monitoring of elderly and chronic patients are location, activity, physiological and psychological parameters. Since smartphones have become the most pervasive computing platform today, it is only a logical extension to use the same in healthcare domain for bringing ubiquity. Besides smartphone, skeleton based activity detection and localization using depth sensor like Kinect make ubiquitous monitoring effective without compromising privacy to a large extent. Finally sensing mental condition is made possible by analysis of the subject's social network feed. This paper presents an end-to-end healthcare monitoring system code named UbiHeld (Ubiquitous Healthcare for Elderly) using the techniques mentioned above and an IoT (Internet of Things) based back-end platform.


title: Will Online Chat Help Alleviate Mood Loneliness?

abstract: Abstract The present study examines the relationship between social Internet use and loneliness and reviews the studies about this topic from both social psychology and computer-mediated communication literature, as a response to the call for interdisciplinary research from scholars in these two areas. Two hundred thirty-four people participated in both the survey testing trait loneliness and a 5-condition (face-to-face chatting, instant message chatting, watching video, writing assignments, and “do nothing”) experiment. Participants reported increase of mood loneliness after chatting online. The level of mood loneliness after online chat was higher than that in face-to-face communication. For people with high trait loneliness, the mood loneliness increase in the computer-mediated communication condition was significantly higher than in the face-to-face communication condition. The author of the current study hopes to help clarify the mixed research findings in previous social Internet use literature abou...


title: Signed Support Recovery for Single Index Models in High-Dimensions

abstract: In this paper we study the support recovery problem for single index models $Y=f(\boldsymbol{X}^{\intercal} \boldsymbol{\beta},\varepsilon)$, where $f$ is an unknown link function, $\boldsymbol{X}\sim N_p(0,\mathbb{I}_{p})$ and $\boldsymbol{\beta}$ is an $s$-sparse unit vector such that $\boldsymbol{\beta}_{i}\in \{\pm\frac{1}{\sqrt{s}},0\}$. In particular, we look into the performance of two computationally inexpensive algorithms: (a) the diagonal thresholding sliced inverse regression (DT-SIR) introduced by Lin et al. (2015); and (b) a semi-definite programming (SDP) approach inspired by Amini&Wainwright (2008). When $s=O(p^{1-\delta})$ for some $\delta>0$, we demonstrate that both procedures can succeed in recovering the support of $\boldsymbol{\beta}$ as long as the rescaled sample size $\kappa=\frac{n}{s\log(p-s)}$ is larger than a certain critical threshold. On the other hand, when $\kappa$ is smaller than a critical value, any algorithm fails to recover the support with probability at least $\frac{1}{2}$ asymptotically. In other words, we demonstrate that both DT-SIR and the SDP approach are optimal (up to a scalar) for recovering the support of $\boldsymbol{\beta}$ in terms of sample size. We provide extensive simulations, as well as a real dataset application to help verify our theoretical observations.


title: Isolation of Endophytic Plant Growth-Promoting Bacteria Associated with the Halophyte Salicornia europaea and Evaluation of their Promoting Activity Under Salt Stress

abstract: Several reports have highlighted that many plant growth-promoting endophytic bacteria (PGPE) can assist their host plants in coping with various biotic and abiotic stresses. However, information about the PGPE colonizing in the halophytes is still scarce. This study was designed to isolate and characterize PGPE from salt-accumulating halophyte Salicornia europaea grown under extreme salinity and to evaluate in vitro the bacterial mechanisms related to plant growth promotion. A total of 105 isolates were obtained from the surface-sterilized roots, stems, and assimilation twigs of S. europaea. Thirty-two isolates were initially selected for their ability to produce 1-aminocyclopropane-1-carboxylate deaminase as well as other properties such as production of indole-3-acetic acid and phosphate-solubilizing activities. The 16S rRNA gene-sequencing analysis revealed that these isolates belong to 13 different genera and 19 bacterial species. For these 32 strains, seed germination and seedling growth in axenically grown S. europaea seedlings at different NaCl concentrations (50-500 mM) were quantified. Five isolates possessing significant stimulation of the host plant growth were obtained. The five isolates were identified as Bacillus endophyticus, Bacillus tequilensis, Planococcus rifietoensis, Variovorax paradoxus, and Arthrobacter agilis. All the five strains could colonize and can be reisolated from the host plant interior tissues. These results demonstrate that habitat-adapted PGPE isolated from halophyte could enhance plant growth under saline stress conditions.


The Ensemble Kalman Filter: theoretical formulation And Practical Implementation


title: Robustness to lighting variations: An RGB-D indoor visual odometry using line segments

abstract: Large lighting variation challenges all visual odometry methods, even with RGB-D cameras. Here we propose a line segment-based RGB-D indoor odometry algorithm robust to lighting variation. We know line segments are abundant indoors and less sensitive to lighting change than point features. However, depth data are often noisy, corrupted or even missing for line segments which are often found on object boundaries where significant depth discontinuities occur. Our algorithm samples depth data along line segments, and uses a random sample consensus approach to identify correct depth and estimate 3D line segments. We analyze 3D line segment uncertainties and estimate camera motion by minimizing the Mahalanobis distance. In experiments we compare our method with two state-of-the-art methods including a keypoint-based approach and a dense visual odometry algorithm, under both constant and varying lighting. Our method demonstrates superior robustness to lighting change by outperforming the competing methods on 6 out of 8 long indoor sequences under varying lighting. Meanwhile our method also achieves improved accuracy even under constant lighting when tested using public data.


title: Friends FTW! Friendship, Collaboration and Competition in Halo: Reach

abstract: How important are friendships in determining success by individuals and teams in complex collaborative environments? By combining a novel data set containing the dynamics of millions of ad hoc teams from the popular multiplayer online first person shooter Halo: Reach with survey data on player demographics, play style, psychometrics and friendships derived from an anonymous online survey, we investigate the impact of friendship on collaborative and competitive performance. In addition to finding significant differences in player behavior across these variables, we find that friendships exert a strong influence, leading to both improved individual and team performance - even after controlling for the overall expertise of the team - and increased pro-social behaviors. Players also structure their in-game activities around social opportunities, and as a result hidden friendship ties can be accurately inferred directly from behavioral time series. Virtual environments that enable such friendship effects will thus likely see improved collaboration and competition.


title: Bottom-up strategy of materials fabrication: a new trend in nanotechnology of soft materials

abstract: Recent progresses in nanometer-scale molecular self-organization and mesoscopic pattern formation are reviewed from the view point of nanotechnology of bottom-up materials fabrication. Nanometer-scale layer-by-layer self-assemblies on nanoparticles will provide wide applications in many fields. The micro-contact printing technique is effectively used for up-sizing the nanostructured molecular assemblies as submicrometer- and micrometer-scale patterns. Dissipative structures formed in non-equilibrium systems as self-organized spatio-temporal structures are newly employed for the mesoscopic patterning of the nanostructured molecular assemblies.


title: VERY HIGH RESOLUTION SAR TOMOGRAPHY VIA COMPRESSIVE SENSING

abstract: By using multi-pass SAR acquisitions, SAR tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. Since the orbits of modern space-borne SAR systems, like TerraSAR-X, are tightly controlled, the elevation resolution (depending on the elevation aperture size) is at least an order of magnitude lower than in range and azimuth. Hence, superresolution algorithms are desired. The high anisotropic 3D resolution element renders the signals sparse in elevation. This property suggests using compressive sensing (CS) methods. The paper presents the theory of 4D (i.e. space-time) CS TomoSAR and compares it with classical tomographic methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented. In addition, the model based time warp method for differential tomographic non-linear motion monitoring is proposed and validated by reconstructing seasonal motion (caused by thermal expansion) of a building complex.


title: Comparing Yoga, Exercise, and a Self-Care Book for Chronic Low Back Pain

abstract: BACKGROUND ::: Chronic low back pain is a common problem that has only modestly effective treatment options. ::: ::: ::: OBJECTIVE ::: To determine whether yoga is more effective than conventional therapeutic exercise or a self-care book for patients with chronic low back pain. ::: ::: ::: DESIGN ::: Randomized, controlled trial. ::: ::: ::: SETTING ::: A nonprofit, integrated health care system. ::: ::: ::: PATIENTS ::: 101 adults with chronic low back pain. ::: ::: ::: INTERVENTION ::: 12-week sessions of yoga or conventional therapeutic exercise classes or a self-care book. ::: ::: ::: MEASUREMENTS ::: Primary outcomes were back-related functional status (modified 24-point Roland Disability Scale) and "bothersomeness" of pain (11-point numerical scale). The primary time point was 12 weeks. Clinically significant change was considered to be 2.5 points on the functional status scale and 1.5 points on the bothersomeness scale. Secondary outcomes were days of restricted activity, general health status, and medication use. ::: ::: ::: RESULTS ::: After adjustment for baseline values, back-related function in the yoga group was superior to the book and exercise groups at 12 weeks (yoga vs. book: mean difference, -3.4 [95% CI, -5.1 to - 1.6] [P < 0.001]; yoga vs. exercise: mean difference, -1.8 [CI, -3.5 to - 0.1] [P = 0.034]). No significant differences in symptom bothersomeness were found between any 2 groups at 12 weeks; at 26 weeks, the yoga group was superior to the book group with respect to this measure (mean difference, -2.2 [CI, -3.2 to - 1.2]; P < 0.001). At 26 weeks, back-related function in the yoga group was superior to the book group (mean difference, -3.6 [CI, -5.4 to - 1.8]; P < 0.001). ::: ::: ::: LIMITATIONS ::: Participants in this study were followed for only 26 weeks after randomization. Only 1 instructor delivered each intervention. ::: ::: ::: CONCLUSIONS ::: Yoga was more effective than a self-care book for improving function and reducing chronic low back pain, and the benefits persisted for at least several months.


title: Towards the Extraction of Customer-to-Customer Suggestions from Reviews

abstract: State of the art in opinion mining mainly focuses on positive and negative sentiment summarisation of online customer reviews. We observe that reviewers tend to provide advice, recommendations and tips to the fellow customers on a variety of points of interest. In this work, we target the automatic detection of suggestion expressing sentences in customer reviews. This is a novel problem, and therefore to begin with, requires a well formed problem definition and benchmark dataset. This work provides a 3- fold contribution, namely, problem definition, benchmark dataset, and an approach for detection of suggestions for the customers. The problem is framed as a sentence classification problem, and a set of linguistically motivated features are proposed. Analysis of the nature of suggestions, and classification errors, highlight challenges and research opportunities associated with this problem.


and Generalized Singular Value Decomposition


title: Towards building a hybrid model for predicting stock indexes

abstract: Predicting stock prices using computer generated models has been a popular research topic and has also been widely explored. However, the connectivity of the global financial market, availability of big data in multiple domains that influence the financial market, accessibility of information in real time and the demand for fast analytics continue to offer new research challenges. One of the complexities stems from the numerous ways in which we seek to set prediction parameters, whether it is the difference in an individual stocks' growth pattern or the time frame in which the predictions occur. The level of complexity has created a trend towards more advanced techniques in this field namely, the research into developing hybrid models that are composed of multiple prediction models with a view to yield more accurate results. The Proposed Hybrid Model (PHM) used in this paper is a combination of an Exponential Smoothing Model (ESM), an Auto Regressive Integrated Moving Average (ARIMA) model, and a Back-propagation Neural Network (BPNN) model. PHM combines the predictions of each of the component model based on weights assigned by a genetic algorithm, which is designed to provide an optimum output. In this paper, we seek to use the S&P 400 and 500 indexes to train and test the PHM to find daily closing values. For comparison of the results, Directional Accuracy (DA) is used as a metric. It was found that the results for the ARIMA and ESM on daily stock index data were far less accurate than that of the BPNN, which received comparable results to the baseline. However, due to the poor results of the ARIMA and ESM the hybrid model showed no significant results for the data and was inferior to the baseline.


title: Differences in gut microbiota profile between women with active lifestyle and sedentary women

abstract: Physical exercise is a tool to prevent and treat some of the chronic diseases affecting the world’s population. A mechanism through which exercise could exert beneficial effects in the body is by provoking alterations to the gut microbiota, an environmental factor that in recent years has been associated with numerous chronic diseases. Here we show that physical exercise performed by women to at least the degree recommended by the World Health Organization can modify the composition of gut microbiota. Using high-throughput sequencing of the 16s rRNA gene, eleven genera were found to be significantly different between active and sedentary women. Quantitative PCR analysis revealed higher abundance of health-promoting bacterial species in active women, including Faecalibacterium prausnitzii, Roseburia hominis and Akkermansia muciniphila. Moreover, body fat percentage, muscular mass and physical activity significantly correlated with several bacterial populations. In summary, we provide the first demonstration of interdependence between some bacterial genera and sedentary behavior parameters, and show that not only does the dose and type of exercise influence the composition of gut microbiota, but also the breaking of sedentary behavior.


Visual Chess Recognition


title: Optimizing F-Measures by Cost-Sensitive Classification

abstract: We present a theoretical analysis of F-measures for binary, multiclass and multilabel classification. These performance measures are non-linear, but in many scenarios they are pseudo-linear functions of the per-class false negative/false positive rate. Based on this observation, we present a general reduction of F-measure maximization to cost-sensitive classification with unknown costs. We then propose an algorithm with provable guarantees to obtain an approximately optimal classifier for the F-measure by solving a series of cost-sensitive classification problems. The strength of our analysis is to be valid on any dataset and any class of classifiers, extending the existing theoretical results on F-measures, which are asymptotic in nature. We present numerical experiments to illustrate the relative importance of cost asymmetry and thresholding when learning linear classifiers on various F-measure optimization tasks.


title: Modeling embodied visual behaviors

abstract: To make progess in understanding human visuomotor behavior, we will need to understand its basic components at an abstract level. One way to achieve such an understanding would be to create a model of a human that has a sufficient amount of complexity so as to be capable of generating such behaviors. Recent technological advances have been made that allow progress to be made in this direction. Graphics models that simulate extensive human capabilities can be used as platforms from which to develop synthetic models of visuomotor behavior. Currently, such models can capture only a small portion of a full behavioral repertoire, but for the behaviors that they do model, they can describe complete visuomotor subsystems at a useful level of detail. The value in doing so is that the body's elaborate visuomotor structures greatly simplify the specification of the abstract behaviors that guide them. The net result is that, essentially, one is faced with proposing an embodied “operating system” model for picking the right set of abstract behaviors at each instant. This paper outlines one such model. A centerpiece of the model uses vision to aid the behavior that has the most to gain from taking environmental measurements. Preliminary tests of the model against human performance in realistic VR environments show that main features of the model show up in human behavior.


Virtual Reality Motion Parallax with the Facebook Surround-360


title: Automatic Layer Separation using Light Field Imaging

abstract: We propose a novel approach that jointly removes reflection or translucent layer from a scene and estimates scene depth. The input data are captured via light field imaging. The problem is couched as minimizing the rank of the transmitted scene layer via Robust Principle Component Analysis (RPCA). We also impose regularization based on piecewise smoothness, gradient sparsity, and layer independence to simultaneously recover 3D geometry of the transmitted layer. Experimental results on synthetic and real data show that our technique is robust and reliable, and can handle a broad range of layer separation problems.