title: BIRCH: A New Data Clustering Algorithm and Its Applications

abstract: Data clustering is an important technique for exploratory data analysis, and has been studied for several years. It has been shown to be useful in many practical domains such as data classification and image processing. Recently, there has been a growing emphasis on exploratory analysis of very large datasets to discover useful patterns and/or correlations among attributes. This is called data mining, and data clustering is regarded as a particular branch. However existing data clustering methods do not adequately address the problem of processing large datasets with a limited amount of resources (e.g., memory and cpu cycles). So as the dataset size increases, they do not scale up well in terms of memory requirement, running time, and result quality. ::: ::: In this paper, an efficient and scalable data clustering method is proposed, based on a new in-memory data structure called CF-tree, which serves as an in-memory summary of the data distribution. We have implemented it in a system called BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and studied its performance extensively in terms of memory requirements, running time, clustering quality, stability and scalability; we also compare it with other available methods. Finally, BIRCH is applied to solve two real-life problems: one is building an iterative and interactive pixel classification tool, and the other is generating the initial codebook for image compression.


title: Analyzing the Performance of an Anycast CDN

abstract: Content delivery networks must balance a number of trade-offs when deciding how to direct a client to a CDN server. Whereas DNS-based redirection requires a complex global traffic manager, anycast depends on BGP to direct a client to a CDN front-end. Anycast is simple to operate, scalable, and naturally resilient to DDoS attacks. This simplicity, however, comes at the cost of precise control of client redirection. We examine the performance implications of using anycast in a global, latency-sensitive, CDN. We analyze millions of client-side measurements from the Bing search service to capture anycast versus unicast performance to nearby front-ends. We find that anycast usually performs well despite the lack of precise control but that it directs roughly 20% of clients to a suboptimal front-end. We also show that the performance of these clients can be improved through a simple history-based prediction scheme.


title: Midas: fabricating custom capacitive touch sensors to prototype interactive objects

abstract: An increasing number of consumer products include user interfaces that rely on touch input. While digital fabrication techniques such as 3D printing make it easier to prototype the shape of custom devices, adding interactivity to such prototypes remains a challenge for many designers. We introduce Midas, a software and hardware toolkit to support the design, fabrication, and programming of flexible capacitive touch sensors for interactive objects. With Midas, designers first define the desired shape, layout, and type of touch sensitive areas, as well as routing obstacles, in a sensor editor. From this high-level specification, Midas automatically generates layout files with appropriate sensor pads and routed connections. These files are then used to fabricate sensors using digital fabrication processes, e.g., vinyl cutters and conductive ink printers. Using step-by-step assembly instructions generated by Midas, designers connect these sensors to the Midas microcontroller, which detects touch events. Once the prototype is assembled, designers can define interactivity for their sensors: Midas supports both record-and-replay actions for controlling existing local applications and WebSocket-based event output for controlling novel or remote applications. In a first-use study with three participants, users successfully prototyped media players. We also demonstrate how Midas can be used to create a number of touch-sensitive interfaces.


title: An Overview of MIT-Olin’s Approach in the AUVSI RobotX Competition

abstract: The inaugural RobotX competition was held in Singapore in Oct. 2014. The purpose of the competition was to challenge teams to develop new strategies for tackling unique and important problems in marine robotics. The joint team from Massachusetts Institute of Technology (MIT) and Olin College was chosen as one of 15 competing teams from five nations (USA, South Korea, Japan, Singapore and Australia). The team received the surface vehicle platform, the WAM-V (Fig. 1) in Nov. 2013 and spent a year building the propulsion, electronic, sensing, and algorithmic capabilities required to complete the five tasks that included navigation, underwater pinger localization, docking, light sequence detection, and obstacle avoidance. Ultimately the MIT/Olin team narrowly won first place in a competitive field. This paper summarizes our approach to the tasks, as well as some lessons learned in the process. As a result of the competition, we have developed a new suite of open-source tools for feature detection and tracking, realtime shape detection from imagery, bearing-only target localization, and obstacle avoidance.


title: Distributed Simulation Platform for Autonomous Driving

abstract: Autonomous vehicle safety and reliability are the paramount requirements when developing autonomous vehicles. These requirements are guaranteed by massive functional and performance tests. Conducting these tests on real vehicles is extremely expensive and time consuming, and thus it is imperative to develop a simulation platform to perform these tasks. For simulation, we can utilize the Robot Operating System (ROS) for data playback to test newly developed algorithms. However, due to the massive amount of simulation data, performing simulation on single machines is not practical. Hence, a high-performance distributed simulation platform is a critical piece in autonomous driving development. In this paper we present our experiences of building a production distributed autonomous driving simulation platform. This platform is built upon Spark distributed framework, for distributed computing management, and ROS, for data playback simulations.


title: Molecular sequence data of Thecaphora frezii affecting peanut crops in Argentina

abstract: In Argentina, Thecaphora frezii is the causal agent of peanut smut, causing severe yield losses in peanut growing areas. In this work sequence data of the large ribosomal subunit (28S rDNA) of four T. frezii specimens from different production regions are provided. A fragment of about 650 bp was amplified by PCR from each of the isolates analyzed. The analysis of the sequences demonstrated 100 % identity among the four specimens and 92–96.3 % identity with sequences of other species of Thecaphora. This is the first study on molecular sequences of this pathogen.


title: Cleanix: A Big Data Cleaning Parfait

abstract: In this demo, we present Cleanix, a prototype system for cleaning relational Big Data. Cleanix takes data integrated from multiple data sources and cleans them on a shared-nothing machine cluster. The backend system is built on-top-of an extensible and flexible data-parallel substrate - the Hyracks framework. Cleanix supports various data cleaning tasks such as abnormal value detection and correction, incomplete data filling, de-duplication, and conflict resolution. We demonstrate that Cleanix is a practical tool that supports effective and efficient data cleaning at the large scale.


title: Support vector machine with adaptive parameters in financial time series forecasting

abstract: A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.


title: Amniotic fluid RNA gene expression profiling provides insights into the phenotype of Turner syndrome

abstract: Turner syndrome is a sex chromosome aneuploidy with characteristic malformations. Amniotic fluid, a complex biological material, could contribute to the understanding of Turner syndrome pathogenesis. In this pilot study, global gene expression analysis of cell-free RNA in amniotic fluid supernatant was utilized to identify specific genes/organ systems that may play a role in Turner syndrome pathophysiology. Cell-free RNA from amniotic fluid of five mid-trimester Turner syndrome fetuses and five euploid female fetuses matched for gestational age was extracted, amplified, and hybridized onto Affymetrix(®) U133 Plus 2.0 arrays. Significantly differentially regulated genes were identified using paired t tests. Biological interpretation was performed using Ingenuity Pathway Analysis and BioGPS gene expression atlas. There were 470 statistically significantly differentially expressed genes identified. They were widely distributed across the genome. XIST was significantly down-regulated (p < 0.0001); SHOX was not differentially expressed. One of the most highly represented organ systems was the hematologic/immune system, distinguishing the Turner syndrome transcriptome from other aneuploidies we previously studied. Manual curation of the differentially expressed gene list identified genes of possible pathologic significance, including NFATC3, IGFBP5, and LDLR. Transcriptomic differences in the amniotic fluid of Turner syndrome fetuses are due to genome-wide dysregulation. The hematologic/immune system differences may play a role in early-onset autoimmune dysfunction. Other genes identified with possible pathologic significance are associated with cardiac and skeletal systems, which are known to be affected in females with Turner syndrome. The discovery-driven approach described here may be useful in elucidating novel mechanisms of disease in Turner syndrome.


title: Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network

abstract: Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word representations (word2vec). Next, we make use of the distributed word representations learned to train a deep feed forward neural network. We test our deep neural network by recommending hashtags for tweets with user-assigned hashtags, using Mean Squared Error (MSE) as the objective function. We also test our deep neural network by recommending hash tags for tweets without user-assigned hashtags. Our experimental results show that the proposed approach recommends hashtags that are specific to the semantics of the tweets and that preserve the linguistic regularity of the tweets. In addition, our experimental results show that the proposed approach is capable of generating hash tags that have not been seen before.


title: Angel: a new large-scale machine learning system

abstract: Machine Learning (ML) techniques now are ubiquitous tools to extract structural information from data collections. With the increasing volume of data, large-scale ML applications require an efficient implementation to accelerate the performance. Existing systems parallelize algorithms through either data parallelism or model parallelism. But data parallelism cannot obtain good statistical efficiency due to the conflicting updates to parameters while the performance is damaged by global barriers in model parallel methods. In this paper, we propose a new system, named Angel, to facilitate the development of large-scale ML applications in production environment. By allowing concurrent updates to model across different groups and scheduling the updates in each group, Angel can achieve a good balance between hardware efficiency and statistical efficiency. Besides, Angel reduces the network latency by overlapping the parameter pulling and update computing and also utilizes the sparseness of data to avoid the pulling of unnecessary parameters. We also enhance the usability of Angel by providing a set of efficient tools to integrate with application pipelines and provisioning efficient fault tolerance mechanisms. We conduct extensive experiments to demonstrate the superiority of Angel.


title: Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation

abstract: This paper describes a complementary Kalman filter design to estimate orientation of human body segments by fusing gyroscope, accelerometer, and magnetometer signals from miniature sensors. Ferromagnetic materials or other magnetic fields near the sensor module disturb the local earth magnetic field and, therefore, the orientation estimation, which impedes many (ambulatory) applications. In the filter, the gyroscope bias error, orientation error, and magnetic disturbance error are estimated. The filter was tested under quasi-static and dynamic conditions with ferromagnetic materials close to the sensor module. The quasi-static experiments implied static positions and rotations around the three axes. In the dynamic experiments, three-dimensional rotations were performed near a metal tool case. The orientation estimated by the filter was compared with the orientation obtained with an optical reference system Vicon. Results show accurate and drift-free orientation estimates. The compensation results in a significant difference (p < 0.01) between the orientation estimates with compensation of magnetic disturbances in comparison to no compensation or only gyroscopes. The average static error was 1.4 degrees (standard deviation 0.4) in the magnetically disturbed experiments. The dynamic error was 2.6 degrees root means square.


title: A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity

abstract: The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three orthogonally mounted uniaxial piezoresistive accelerometers and can be used to register accelerations covering the amplitude and frequency ranges of human body acceleration. Interinstrument and test-retest experiments showed that the offset and the sensitivity of the TA were equal for each measurement direction and remained constant on two measurement days. Transverse sensitivity was significantly different for each measurement direction, but did not influence accelerometer output (<3% of the sensitivity along the main axis). The data unit enables the on-line processing of accelerometer output to a reliable estimator of physical activity over eight-day periods. Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity (r=0.89). Shortcomings of the system are its low sensitivity to sedentary activities and the inability to register static exercise. The validity of the system for the assessment of normal daily physical activity and specific activities outside the laboratory should be studied in free-living subjects.


title: A Phantom-Sensation Based Paradigm for Continuous Vibrotactile Wrist Guidance in Two-Dimensional Space

abstract: Vibrotactile feedback has long been used to improve the human motor learning process through information transfer. However, in most of the approaches described in the literature, only the joint angles of the limb are guided. To guide motions that involve multiple degrees of freedom (DOFs), the user needs to interpret multiple simultaneous vibrotactile cues (at least one cue per joint), which is difficult. In this letter, we guide the position of a user's wrist in space by producing a vibrotactile cue at any place around the wrist by using a vibrotactile illusion known as “Phantom Sensation” (PS). In a user study that involved 10 male participants, the vibrotactile cues produced by PS were reasonably accurately located with an average error of $\boldsymbol{7.9^\circ }$ . To maintain consistency between the vibrotactile cue location and the global frame, we measured the user's wrist rotation and adjusted the position of the vibrotactile cue accordingly. When rotating their hand, the participants recognized the location of the vibrotactile cue with an average error of $\boldsymbol{9.3^\circ }$ . By using the proposed feedback paradigm, we guided users’ wrists in space under two conceptual mappings (“push” and “pull”). This experiment examined the participants’ motion path efficiency (ME), which measures the directness with which the user's wrist moves toward the desired position. The participants reached the desired positions under the vibrotactile feedback alone. The average MEs of the “push” and “pull” mappings were 44.3% and 52.4%, respectively. We will apply the proposed method under the “pull” mapping to guide real life tasks, such as book classification or rehabilitation.


title: A simple geometric model for elastic deformations

abstract: We advocate a simple geometric model for elasticity: distance between the differential of a deformation and the rotation group. It comes with rigorous differential geometric underpinnings, both smooth and discrete, and is computationally almost as simple and efficient as linear elasticity. Owing to its geometric non-linearity, though, it does not suffer from the usual linearization artifacts. A material model with standard elastic moduli (Lamé parameters) falls out naturally, and a minimizer for static problems is easily augmented to construct a fully variational 2nd order time integrator. It has excellent conservation properties even for very coarse simulations, making it very robust. ::: Our analysis was motivated by a number of heuristic, physics-like algorithms from geometry processing (editing, morphing, parameterization, and simulation). Starting with a continuous energy formulation and taking the underlying geometry into account, we simplify and accelerate these algorithms while avoiding common pitfalls. Through the connection with the Biot strain of mechanics, the intuition of previous work that these ideas are "like" elasticity is shown to be spot on.


title: Apache hadoop goes realtime at Facebook

abstract: Facebook recently deployed Facebook Messages, its first ever user-facing application built on the Apache Hadoop platform. Apache HBase is a database-like layer built on Hadoop designed to support billions of messages per day. This paper describes the reasons why Facebook chose Hadoop and HBase over other systems such as Apache Cassandra and Voldemort and discusses the application's requirements for consistency, availability, partition tolerance, data model and scalability. We explore the enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the system, and how this solution has significant advantages over the sharded MySQL database scheme used in other applications at Facebook and many other web-scale companies. We discuss the motivations behind our design choices, the challenges that we face in day-to-day operations, and future capabilities and improvements still under development. We offer these observations on the deployment as a model for other companies who are contemplating a Hadoop-based solution over traditional sharded RDBMS deployments.


title: Person Search via A Mask-Guided Two-Stream CNN Model

abstract: In this work, we tackle the problem of person search, which is a challenging task consisted of pedestrian detection and person re-identification (re-ID). Instead of sharing representations in a single joint model, we find that separating detector and re-ID feature extraction yields better performance. In order to extract more representative features for each identity, we propose a simple yet effective re-ID method, which models foreground person and original image patches individually, and obtains enriched representations from two separate CNN streams. On the standard person search benchmark datasets, we achieve mAP of \(83.0\%\) and \(32.6\%\) respectively for CUHK-SYSU and PRW, surpassing the state of the art by a large margin (more than 5 pp).


title: Asynchronous Advantage Actor-Critic Agent for Starcraft II

abstract: Deep reinforcement learning, and especially the Asynchronous Advantage Actor-Critic algorithm, has been successfully used to achieve super-human performance in a variety of video games. Starcraft II is a new challenge for the reinforcement learning community with the release of pysc2 learning environment proposed by Google Deepmind and Blizzard Entertainment. Despite being a target for several AI developers, few have achieved human level performance. In this project we explain the complexities of this environment and discuss the results from our experiments on the environment. We have compared various architectures and have proved that transfer learning can be an effective paradigm in reinforcement learning research for complex scenarios requiring skill transfer.


title: The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis

abstract: Abstract This individual differences study examined the separability of three often postulated executive functions—mental set shifting (“Shifting”), information updating and monitoring (“Updating”), and inhibition of prepotent responses (“Inhibition”)—and their roles in complex “frontal lobe” or “executive” tasks. One hundred thirty-seven college students performed a set of relatively simple experimental tasks that are considered to predominantly tap each target executive function as well as a set of frequently used executive tasks: the Wisconsin Card Sorting Test (WCST), Tower of Hanoi (TOH), random number generation (RNG), operation span, and dual tasking. Confirmatory factor analysis indicated that the three target executive functions are moderately correlated with one another, but are clearly separable. Moreover, structural equation modeling suggested that the three functions contribute differentially to performance on complex executive tasks. Specifically, WCST performance was related most strongly to Shifting, TOH to Inhibition, RNG to Inhibition and Updating, and operation span to Updating. Dual task performance was not related to any of the three target functions. These results suggest that it is important to recognize both the unity and diversity of executive functions and that latent variable analysis is a useful approach to studying the organization and roles of executive functions.


title: A Review on Business Intelligence and Big Data

abstract: Improvement of data generating, processing, storing and networking technologies has made storing, capturing and sharing of data easier and cheaper than before and has enabled organizations to handle huge volume of data at high velocity and variety, named as big data. Big data offers many opportunities when the associated difficulties are addressed properly. Business Intelligence (BI) basically focuses on transforming raw data into usable, valuable and actionable information for decision-making. It can be classified as a kind of data-driven decision support system. Although big data related papers have increased for last fifteen years, there are not sufficient papers that directly overviews big data impact on BI. As data is growing exponentially, storage, process and analytics tools and technologies become more important for BI solutions. With the advent of big data, BI’s concept, architecture and capabilities are meant to be changed. Unlike a decades before, BI now is to be extract value from huge data ocean by using big data tools as well as classical ones. So, an interclusion has emerged between big data and BI. This paper overviews the current state of the art of BI and big data, and discuss how big data era affects BI solutions in general context.


title: Pose tracking by efficiently exploiting global features

abstract: Typical pose tracking algorithms first obtain a set of plausible pose hypotheses in all image frames of a video and subsequently stitch compatible detections across time to form a pose-track. This approach to tracking is commonly termed tracking-by-detections, and has been very successful in other areas such as multiple object tracking, video segmentation using object proposals. Often models in this category can only incorporate local spatio-temporal evidence due to exponentially increased cost when using global information. Local spatio-temporal evidence can be ambiguous, thus leading to an inferior objective modeling. To deal with ambiguities in local information it is necessary to incorporate global information over multiple frames into a model. Based on the recent advances in generating multiple solutions from a probabilistic model, we first generate multiple plausible pose-track hypotheses, and subsequently employ a mixture of local and global features to express the quality of these solutions with high fidelity. We perform extensive experiments and competitive results across varied datasets demonstrate the robustness of our approach.


title: SnapVideo: Personalized Video Generation for a Sightseeing Trip

abstract: Leisure tourism is an indispensable activity in urban people’s life. Due to the popularity of intelligent mobile devices, a large number of photos and videos are recorded during a trip. Therefore, the ability to vividly and interestingly display these media data is a useful technique. In this paper, we propose SnapVideo, a new method that intelligently converts a personal album describing of a trip into a comprehensive, aesthetically pleasing, and coherent video clip. The proposed framework contains three main components. The scenic spot identification model first personalizes the video clips based on multiple prespecified audience classes. We then search for some auxiliary related videos from YouTube 1 according to the selected photos. To comprehensively describe a scenery, the view generation module clusters the crawled video frames into a number of views. Finally, a probabilistic model is developed to fit the frames from multiple views into an aesthetically pleasing and coherent video clip, which optimally captures the semantics of a sightseeing trip. Extensive user studies demonstrated the competitiveness of our method from an aesthetic point of view. Moreover, quantitative analysis reflects that semantically important spots are well preserved in the final video clip. 1 https://www.youtube.com/


title: Attendance and Information System using RFID and Web-Based Application for Academic Sector

abstract: Recently, students attendance have been considered as one of the crucial elements or issues that reflects the academic achievements and the performance contributed to any university compared to the traditional methods that impose time-consuming and inefficiency. Diverse automatic identification technologies have been more in vogue such as Radio Frequency Identification (RFID). An extensive research and several applications are produced to take maximum advantage of this technology and bring about some concerns. RFID is a wireless technology which uses to a purpose of identifying and tracking an object via radio waves to transfer data from an electronic tag, called RFID tag or label to send data to RFID reader. The current study focuses on proposing an RFID based Attendance Management System (AMS) and also information service system for an academic domain by using RFID technology in addition to the programmable Logic Circuit (such as Arduino), and web-based application. The proposed system aims to manage student’s attendance recording and provides the capabilities of tracking student absentee as well, supporting information services include students grading marks, daily timetable, lectures time and classroom numbers, and other student-related instructions provided by faculty department staff. Based on the results, the proposed attendance and information system is time-effective and it reduces the documentation efforts as well as, it does not have any power consumption. Besides, students attendance RFID based systems that have been proposed are also analyzed and criticized respect to systems functionalities and main findings. Future directions for further researchers are focused and identified.


title: On the security of modern Single Sign-On Protocols: OpenID Connect 1.0.

abstract: OpenID Connect is a new Single Sign-On authentication protocol, which is becoming increasingly important since its publication in February 2014. OpenID Connect relies on the OAuth protocol, which currently is the de facto standard for delegated authorization in the modern web and is supported by leading companies like, e.g., Google, Facebook and Twitter. ::: An important limitation of OAuth is the fact that it was designed for authorization and not for authentication -- it introduces a concept that allows a third party, e.g., a mobile App or a web application, to only access a subset of resources belonging to a user. However, it does not provide a secure means to uniquely identify the user. Thus, recent research revealed existing problems in case that OAuth is used for authentication nonetheless. These problems result in severe security vulnerabilities. ::: To fill this gap, OpenID Connect was created. It provides federated identity management and authentication by adding authentication capabilities on top of the OAuth protocol. % Although OpenID Connect is a very new standard, companies like Google, Microsoft, AOL and PayPal, who were also involved in the development, use it already. ::: In this paper we describe the OpenID Connect protocol and provide the first in-depth analysis of one of the key features of OpenID Connect, the \emph{Discovery} and the \emph{Dynamic Registration} extensions. We show that the usage of these extensions can compromise the security of the entire protocol. We develop a new attack called \emph{Malicious Endpoints} attack, evaluate it against an existing implementation, and propose countermeasures to fix the presented issues.


title: On the uniform decay in viscoelastic problems in Rn

abstract: In this paper we consider a linear Cauchy viscoelastic problem. We show that, for compactly supported initial data and for an exponentially decaying relaxation function, the decay of the first energy of solution is polynomial. The finite-speed propagation is used to compensate for the lack of Poincare's inequality in R^n.


title: Game theory in communications: motivation, explanation, and application to power control

abstract: Game theory is a set of tools developed to model interactions between agents with conflicting interests, and is thus well-suited to address some problems in communications systems. We present some of the basic concepts of game theory and show why it is an appropriate tool for analyzing some communication problems and providing insights into how communication systems should be designed. We then provided a detailed example in which game theory is applied to the power control problem in a CDMA-like system.


title: Virtual reality induced symptoms and effects (VRISE) : Comparison of head mounted display (HMD), desktop and projection display systems

abstract: Abstract Virtual reality (VR) systems are used in a variety of applications within industry, education, public and domestic settings. Research assessing reported symptoms and side effects of using VR systems indicates that these factors combine to influence user experiences of virtual reality induced symptoms and effects (VRISE). Three experiments were conducted to assess prevalence and severity of sickness symptoms experienced in each of four VR display conditions; head mounted display (HMD), desktop, projection screen and reality theatre, with controlled examination of two additional aspects of viewing (active vs. passive viewing and light vs. dark conditions). Results indicate 60–70% participants experience an increase in symptoms pre–post exposure for HMD, projection screen and reality theatre viewing and found higher reported symptoms in HMD compared with desktop viewing (nausea symptoms) and in HMD compared with reality theatre viewing (nausea, oculomotor and disorientation symptoms). No effect of lighting condition was found. Higher levels of symptoms were reported in passive viewing compared to active control over movement in the VE. However, the most notable finding was that of high inter- and intra-participant variability. As this supports other findings of individual susceptibility to VRISE, recommendations are offered concerning design and use of VR systems in order to minimise VRISE.


title: Depth Separation for Neural Networks

abstract: Let $f:\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}\to\mathbb{S}$ be a function of the form $f(\mathbf{x},\mathbf{x}') = g(\langle\mathbf{x},\mathbf{x}'\rangle)$ for $g:[-1,1]\to \mathbb{R}$. We give a simple proof that shows that poly-size depth two neural networks with (exponentially) bounded weights cannot approximate $f$ whenever $g$ cannot be approximated by a low degree polynomial. Moreover, for many $g$'s, such as $g(x)=\sin(\pi d^3x)$, the number of neurons must be $2^{\Omega\left(d\log(d)\right)}$. Furthermore, the result holds w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$. As many functions of the above form can be well approximated by poly-size depth three networks with poly-bounded weights, this establishes a separation between depth two and depth three networks w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$.


title: Intertemporal Pricing with Strategic Customer Behavior

abstract: This paper develops a model of dynamic pricing with endogenous intertemporal demand. In the model, there is a monopolist who sells a finite inventory over a finite time horizon. The seller adjusts prices dynamically to maximize revenue. Customers arrive continually over the duration of the selling season. At each point in time, customers may purchase the product at current prices, remain in the market at a cost to purchase later, or exit, and they wish to maximize individual utility. The customer population is heterogeneous along two dimensions: they may have different valuations for the product and different degrees of patience (waiting costs). ::: ::: We demonstrate that heterogeneity in both valuation and patience is important because they jointly determine the structure of optimal pricing policies. In particular, when high-value customers are proportionately less patient, markdown pricing policies are effective because the high-value customers would buy early at high prices while the low-value customers are willing to wait (i.e., they are not lost). On the other hand, when the high-value customers are more patient than the low-value customers, prices should increase over time to discourage inefficient waiting. Contrary to intuition, we find that strategic waiting by customers may sometimes benefit the seller: when low-value customers wait, they compete for availability with high-value customers and thus increase their willingness to pay. Our results also shed light on how the composition of the customer population affects optimal revenue, consumer surplus, and social welfare. Finally, we consider the long-run problem of selecting the optimal initial stocking quantity.


title: Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations

abstract: How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group? How can we train such a classifier when data on the protected group is difficult to attain? In many settings, finding out the sensitive input attribute can be prohibitively expensive even during model training, and sometimes impossible during model serving. For example, in recommender systems, if we want to predict if a user will click on a given recommendation, we often do not know many attributes of the user, e.g., race or age, and many attributes of the content are hard to determine, e.g., the language or topic. Thus, it is not feasible to use a different classifier calibrated based on knowledge of the sensitive attribute. ::: Here, we use an adversarial training procedure to remove information about the sensitive attribute from the latent representation learned by a neural network. In particular, we study how the choice of data for the adversarial training effects the resulting fairness properties. We find two interesting results: a small amount of data is needed to train these adversarial models, and the data distribution empirically drives the adversary's notion of fairness.


title: Video Rain Streak Removal by Multiscale Convolutional Sparse Coding

abstract: Videos captured by outdoor surveillance equipments sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks. Rain streak removal from a video is thus an important topic in recent computer vision research. In this paper, we raise two intrinsic characteristics specifically possessed by rain streaks. Firstly, the rain streaks in a video contain repetitive local patterns sparsely scattered over different positions of the video. Secondly, the rain streaks are with multiscale configurations due to their occurrence on positions with different distances to the cameras. Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task. Specifically, we use multiple convolutional filters convolved on the sparse feature maps to deliver the former characteristic, and further use multiscale filters to represent different scales of rain streaks. Such a new encoding manner makes the proposed method capable of properly extracting rain streaks from videos, thus getting fine video deraining effects. Experiments implemented on synthetic and real videos verify the superiority of the proposed method, as compared with the state-of-the-art ones along this research line, both visually and quantitatively.


title: Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter

abstract: The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS’ unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and “lone wolf” attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS’ sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group’s propaganda dissemination through retweets.


title: Biometric identification of identical twins: A survey

abstract: The ability of biometric techniques to distinguish between identical twins is of interest for multiple reasons. The research literature touching on this topic is spread across a variety of areas. This survey pulls together the literature to date in this area, identifies available datasets for research, points out topics of uncertainty and suggests possible future research.


title: Integrating Topics and Syntax

abstract: Statistical approaches to language learning typically focus on either short-range syntactic dependencies or long-range semantic dependencies between words. We present a generative model that uses both kinds of dependencies, and can be used to simultaneously find syntactic classes and semantic topics despite having no representation of syntax or semantics beyond statistical dependency. This model is competitive on tasks like part-of-speech tagging and document classification with models that exclusively use short- and long-range dependencies respectively.


title: Indoor Mobile Localization System and Stabilization of Localization Performance using Pre-filtering

abstract: In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practical and efficient. Unfortunately, a number of localization systems for indoor space do not have sufficient accuracy to establish any special task such as precise position control of a moving target even though they require comparatively high developmental cost. Therefore, we developed an Indoor Mobile Localization System having high localization performance; specifically, the Unscented Kalman Filter is applied for improving the localization accuracy. In addition, we also present the additive filter named 'Pre-filtering' to compensate the performance of the estimation algorithm. Pre-filtering has been developed to overcome negative effects from unexpected external noise so that localization through the Unscented Kalman Filter has come to be stable. Moreover, we tried to demonstrate the performance comparison of the Unscented Kalman Filter and another estimation algorithm, such as the Unscented Particle Filter (UPF), through simulation for our system.


title: Power-steering control architecture for automatic driving

abstract: The unmanned control of the steering wheel is, at present, one of the most important challenges facing researchers in autonomous vehicles within the field of intelligent transportation systems (ITSs). In this paper, we present a two-layer control architecture for automatically moving the steering wheel of a mass-produced vehicle. The first layer is designed to calculate the target position of the steering wheel at any time and is based on fuzzy logic. The second is a classic control layer that moves the steering bar by means of an actuator to achieve the position targeted by the first layer. Real-time kinematic differential global positioning system (RTK-DGPS) equipment is the main sensor input for positioning. It is accurate to about 1 cm and can finely locate the vehicle trajectory. The developed systems are installed on a Citroe/spl uml/n Berlingo van, which is used as a testbed vehicle. Once this control architecture has been implemented, installed, and tuned, the resulting steering maneuvering is very similar to human driving, and the trajectory errors from the reference route are reduced to a minimum. The experimental results show that the combination of GPS and artificial-intelligence-based techniques behaves outstandingly. We can also draw other important conclusions regarding the design of a control system derived from human driving experience, providing an alternative mathematical formalism for computation, human reasoning, and integration of qualitative and quantitative information.


title: An empirical resource for discovering cognitive principles of discourse organisation: the ANNODIS corpus

abstract: This paper describes the ANNODIS resource, a discourse-level annotated corpus for French. The corpus combines two perspectives on discourse: a bottom-up approach and a top-down approach. The bottom-up view incrementally builds a structure from elementary discourse units, while the top-down view focuses on the selective annotation of multi-level discourse structures. The corpus is composed of texts that are diversified with respect to genre, length and type of discursive organisation. The methodology followed here involves an iterative design of annotation guidelines in order to reach satisfactory inter-annotator agreement levels. This allows us to raise a few issues relevant for the comparison of such complex objects as discourse structures. The corpus also serves as a source of empirical evidence for discourse theories. We present here two first analyses taking advantage of this new annotated corpus --one that tested hypotheses on constraints governing discourse structure, and another that studied the variations in composition and signalling of multi-level discourse structures.


title: Single Viewpoint Catadioptric Cameras

abstract: Many applications in computational vision require that a large field of view is imaged. Examples include surveillance, teleconferencing, and model acquisition for virtual reality. A number of other applications, such as egomotion estimation and tracking, would also benefit from enhanced fields of view. Unfortunately, conventional imaging systems are severely limited in their fields of view. Both researchers and practitioners have therefore had to resort to using either multiple or rotating cameras in order to image the entire scene.


title: Calibration of RGBD camera and cone-beam CT for 3D intra-operative mixed reality visualization

abstract: Purpose ::: This work proposes a novel algorithm to register cone-beam computed tomography (CBCT) volumes and 3D optical (RGBD) camera views. The co-registered real-time RGBD camera and CBCT imaging enable a novel augmented reality solution for orthopedic surgeries, which allows arbitrary views using digitally reconstructed radiographs overlaid on the reconstructed patient’s surface without the need to move the C-arm.


Penetration of 6061-T 6511 aluminum targets by ogive-nosed VAR 4340 steel projectiles at oblique angles : experiments and simulations


Implementation of Image Registration Techniques and its Applications in Medical Image Analysis


title: Multiclass Learning With Partially Corrupted Labels

abstract: Traditional classification systems rely heavily on sufficient training data with accurate labels. However, the quality of the collected data depends on the labelers, among which inexperienced labelers may exist and produce unexpected labels that may degrade the performance of a learning system. In this paper, we investigate the multiclass classification problem where a certain amount of training examples are randomly labeled. Specifically, we show that this issue can be formulated as a label noise problem. To perform multiclass classification, we employ the widely used importance reweighting strategy to enable the learning on noisy data to more closely reflect the results on noise-free data. We illustrate the applicability of this strategy to any surrogate loss functions and to different classification settings. The proportion of randomly labeled examples is proved to be upper bounded and can be estimated under a mild condition. The convergence analysis ensures the consistency of the learned classifier to the optimal classifier with respect to clean data. Two instantiations of the proposed strategy are also introduced. Experiments on synthetic and real data verify that our approach yields improvements over the traditional classifiers as well as the robust classifiers. Moreover, we empirically demonstrate that the proposed strategy is effective even on asymmetrically noisy data.


title: Compact Combinatorial Maps: a Volume Mesh Data Structure

abstract: We propose a compact data structure for volumetric meshes of arbitrary topology and bounded valence that offers cell-face, face-edge, and edge-vertex incidence queries in constant time. Our structure is simple to implement, easy to use, and allows for arbitrary, user-defined 3-cells such as prisms and hexahedra, while remaining very efficient in memory usage compared to previous work. Its time complexity for commonly-used incidence and adjacency queries such as vertex and dart one-rings is analyzed.


title: Enhancement of Sentence-Generation Based Summarization Method By Modelling Inter-Sentential Consequent-Relationships

abstract: In the text summarization field, based on how to create the summary, there are two main theoretic approaches (E. Lloret [22], K. Jezek and J. Steinberger [13]): the trend based on "extraction" in which the most important sentences in the source text will be chosen; the trend based on "abstraction" in which a new reduced grammatical and meaningful text will be created based on understanding the original text. In this paper, we focus on "abstraction" approach to summarize Vietnamese texts having only two sentences. We also restrict this research to consider verbs representing "actions" and "states", which appear in both two sentences of source text. Our method is based on modeling and processing Consequent-Relations between the verbs in the first sentence and the second sentence. The aim of this research is to generate new Vietnamese meaning-summarizing sentences which satisfy two requirements: (i) summarize the true meaning of the source pair of sentences; (ii) have natural expression for summarization. We also experiment and assess our approach by proposing a new quantitative evaluation method that suitable for the research approach.


title: Robust High Dynamic Range Imaging by Rank Minimization

abstract: This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples.


title: An inset fed patch antenna with a modified slot for WLAN and WiMAX applications

abstract: This paper gives the prototype of a microstrip antenna with an inset feed, at two different frequencies of 2.2 GHz and 5.2 GHz. The bandwidth of these antennas has been enhanced, by introducing slots in it; and has been made suitable for WLAN applications. On successful simulation of the antennas, the values of directivity and gain were observed. Gain and Directivity for the antennas operating at 2.2GHz was found to be 7.699dB and 8.480dBi. The value of Gain and Directivity for the antenna operating at 5.2GHz (Without Slots) was found to be 6.187dB and 5.199dBi. On introduction of slots in this antenna, the values achieved were 6.452dB and 12.94dBi. The results are analyzed and discussed in terms of return loss, gain, directivity.. The simulation of the antennas is done on CST Microwave Studio.


title: A formal approach towards measuring trust in distributed systems

abstract: Emerging digital environments and infrastructures, such as distributed security services and distributed computing services, have generated new options of communication, information sharing, and resource utilization in past years. However, when distributed services are used, the question arises of to what extent we can trust service providers to not violate security requirements, whether in isolation or jointly. Answering this question is crucial for designing trustworthy distributed systems and selecting trustworthy service providers. This paper presents a novel trust measurement method for distributed systems, and makes use of propositional logic and probability theory. The results of the qualitative part include the specification of a formal trust language and the representation of its terms by means of propositional logic formulas. Based on these formulas, the quantitative part returns trust metrics for the determination of trustworthiness with which given distributed systems are assumed to fulfill a particular security requirement.


title: Analysis and operation of an integrated battery charger using the EV traction motor as filter

abstract: This paper presents an operation mode for an onboard integrated battery charger (IBC) which uses the electric vehicle (EV) traction motor as input filter in the battery charging mode. The studied charger adds few components to the already existing power electronics employed for propulsion. By connecting the single-phase mains to the traction inverter, only two mechanical switches or solid state relays are necessary to configure the same converter to operate as a battery charger. The proposed operation mode for this IBC reduce and distribute the inverter losses during charging operation mode. Furthermore, a machine analysis is presented to characterize the machine behavior considering the single-phase input current flowing through the three-phase traction motor. Although the IBC is best suited for an induction motor (IM) application, an analysis of the feasibility of using a permanent magnet (PM) motor is realized. Simulation results are presented which validates the functionality of the proposed charger operation.


title: Can seeking happiness make people unhappy? Paradoxical effects of valuing happiness.

abstract: Happiness is a key ingredient of well-being. It is thus reasonable to expect that valuing happiness will have beneficial outcomes. We argue that this may not always be the case. Instead, valuing happiness could be self-defeating, because the more people value happiness, the more likely they will feel disappointed. This should apply particularly in positive situations, in which people have every reason to be happy. Two studies support this hypothesis. In Study 1, female participants who valued happiness more (vs. less) reported lower happiness when under conditions of low, but not high, life stress. In Study 2, compared to a control group, female participants who were experimentally induced to value happiness reacted less positively to a happy, but not a sad, emotion induction. This effect was mediated by participants’ disappointment at their own feelings. Paradoxically, therefore, valuing happiness may lead people to be less happy just when happiness is within reach.


title: Unsupervised Feature Selection with Heterogeneous Side Information

abstract: Compared to supervised feature selection, unsupervised feature selection tends to be more challenging due to the lack of guidance from class labels. Along with the increasing variety of data sources, many datasets are also equipped with certain side information of heterogeneous structure. Such side information can be critical for feature selection when class labels are unavailable. In this paper, we propose a new feature selection method, SideFS, to exploit such rich side information. We model the complex side information as a heterogeneous network and derive instance correlations to guide subsequent feature selection. Representations are learned from the side information network and the feature selection is performed in a unified framework. Experimental results show that the proposed method can effectively enhance the quality of selected features by incorporating heterogeneous side information.


title: A Hierarchical Spatio-Temporal Model for Human Activity Recognition

abstract: There are two key issues in human activity recognition: spatial dependencies and temporal dependencies. Most recent methods focus on only one of them, and thus do not have sufficient descriptive power to recognize complex activity. In this paper, we propose a hierarchical spatio-temporal model (HSTM) to solve the problem by modeling spatial and temporal constraints simultaneously. The new HSTM is a two-layer hidden conditional random field (HCRF), where the bottom-layer HCRF aims at describing spatial relations in each frame and learning more discriminative representations, and the top-layer HCRF utilizes these high-level features to characterize temporal relations in the whole video sequence. The new HSTM takes advantage of the bottom layer as the building blocks for the top layer and it aggregates evidence from local to global level. A novel learning algorithm is derived to train all model parameters efficiently and its effectiveness is validated theoretically. Experimental results show that the HSTM can successfully classify human activities with higher accuracies on single-person actions (UCF) than other existing methods. More importantly, the HSTM also achieves superior performance on more practical interactions, including human–human interactional activities (UT-Interaction, BIT-Interaction, and CASIA) and human–object interactional activities (Gupta video dataset).


title: Causality in Thought

abstract: Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning—the role of mechanism, narrative, and mental simulation—all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.


title: Synthetic data generation for end-to-end thermal infrared tracking

abstract: The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved the performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state-of-the-art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge, we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on the VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features, we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers.


title: Microstrip Lowpass Filters with Reduced Size and Improved Stopband Characteristics

abstract: Novel microstrip lowpass filters are developed with reduced size and significantly improved stopband characteristics. After introducing quarter-wavelength open stubs, we get one or two transmission zeros in the stopband. By folding the high impedance microstrip lines, we reduce the size of the filter. Three-pole and five-pole lowpass filters are designed, and their measured frequency responses agree well with theoretical predictions.


title: Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey

abstract: We present a study of using game theory for protecting wireless sensor networks (WSNs) from selfish behavior or malicious nodes. Due to scalability, low complexity and disseminated nature of WSNs, malicious attacks can be modeled effectively using game theory. In this study, we survey the different game-theoretic defense strategies for WSNs. We present a taxonomy of the game theory approaches based on the nature of the attack, whether it is caused by an external attacker or it is the result of an internal node acting selfishly or maliciously. We also present a general trust model using game theory for decision making. We, finally, identify the significant role of evolutionary games for WSNs security against intelligent attacks; then, we list several prospect applications of game theory to enhance the data trustworthiness and node cooperation in different WSNs.


title: Finding centric local outliers in categorical/numerical spaces

abstract: Outlier detection techniques are widely used in many applications such as credit-card fraud detection, monitoring criminal activities in electronic commerce, etc. These applications attempt to identify outliers as noises, exceptions, or objects around the border. The existing density-based local outlier detection assigns the degree to which an object is an outlier in a numerical space. In this paper, we propose a novel mutual-reinforcement-based local outlier detection approach. Instead of detecting local outliers as noise, we attempt to identify local outliers in the center, where they are similar to some clusters of objects on one hand, and are unique on the other. Our technique can be used for bank investment to identify a unique body, similar to many good competitors, in which to invest. We attempt to detect local outliers in categorical, ordinal as well as numerical data. In categorical data, the challenge is that there are many similar but different ways to specify relationships among the data items. Our mutual-reinforcement-based approach is stable, with similar but different user-defined relationships. Our technique can reduce the burden for users to determine the relationships among data items, and find the explanations why the outliers are found. We conducted extensive experimental studies using real datasets.


title: Evolutionary Visual Art and Design

abstract: This chapter presents an introduction to the different artistic design domains that make use of interactive evolutionary design approaches, the techniques they use, and many of the challenges arising. After a brief introduction to concepts and terminology common to most artificial genetic design, there is a survey of artistic evolutionary systems and related research for evolving images and forms. While the focus is primarily on purely aesthetic fitness landscapes, the survey also ventures into areas such as product design and architecture. The overview shifts from technique to application as organizational strategies, as appropriate. After briefly surveying additional information sources, the chapter concludes with a discussion of major topics of relevance to evolutionary system designers, providing context for the following chapters. It is hoped that this snapshot of the state of the field will increase exposure to projects and issues, discussion amongst participants, and ultimately the accessibility of these techniques and approaches.


title: Toward Marker-Free 3D Pose Estimation in Lifting: A Deep Multi-View Solution

abstract: Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks, which requires very accurate 3D pose. Existing approaches mainly utilize marker-based sensors to collect 3D information. However, these methods are usually expensive to setup, time-consuming in process, and sensitive to the surrounding environment. In this study, we propose a multi-view based deep perceptron approach to address aforementioned limitations. Our approach consists of two modules: a"view-specific perceptron"network extracts rich information independently from the image of view, which includes both 2D shape and hierarchical texture information; while a"multi-view integration"network synthesizes information from all available views to predict accurate 3D pose. To fully evaluate our approach, we carried out comprehensive experiments to compare different variants of our design. The results prove that our approach achieves comparable performance with former marker-based methods, i.e. an average error of $14.72 \pm 2.96$ mm on the lifting dataset. The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.


title: The Geometry of Information Retrieval

abstract: Preface Prologue 1. Introduction 2. On sets and kinds in IR 3. Vector and Hilbert spaces 4. Linear transformations, operators and matrices 5. Conditional logic in IR 6. The geometry of IR Appendix I. Linear algebra Appendix II. Quantum mechanics Appendix III. Probability Bibliography Index.


title: Aggregate G-Buffer Anti-Aliasing -Extended Version-

abstract: We present Aggregate G-Buffer Anti-Aliasing (AGAA), a new technique for efficient anti-aliased deferred rendering of complex geometry using modern graphics hardware. In geometrically complex situations where many surfaces intersect a pixel, current rendering systems shade each contributing surface at least once per pixel. As the sample density and geometric complexity increase, the shading cost becomes prohibitive for real-time rendering. Under deferred shading, so does the required framebuffer memory. Our goal is to make high per-pixel sampling rates practical for real-time applications by substantially reducing shading costs and per-pixel storage compared to traditional deferred shading. AGAA uses the rasterization pipeline to generate a compact, pre-filtered geometric representation inside each pixel. We shade this representation at a fixed rate, independent of geometric complexity. By decoupling shading rate from geometric sampling rate, the algorithm reduces the storage and bandwidth costs of a geometry buffer, and allows scaling to high visibility sampling rates for anti-aliasing. AGAA with two aggregates per-pixel generates results comparable to 32 × MSAA, but requires 54 percent less memory and is up to 2.6 × faster ( -30 percent memory and 1.7 × faster for 8 × MSAA).


title: Reliability challenges for electric vehicles: from devices to architecture and systems software

abstract: Today, modern high-end cars have close to 100 electronic control units (ECUs) that are used to implement a variety of applications ranging from safety-critical control to driver assistance and comfort-related functionalities. The total sum of these applications is several million lines of software code. The ECUs are connected to different sensors and actuators and communicate via a variety of communication buses like CAN, FlexRay and now also Ethernet. In the case of electric vehicles, both the amount and the importance of such electronics and software are even higher. Here, a number of hydraulic or pneumatic controls are replaced by corresponding software-implemented controllers in order to reduce the overall weight of the car and hence to improve its driving range. Until recently, most of the software and system design in the automotive domain -- as in many other domains -- relied on an always correctly functioning or a zero-defect hardware implementation platform. However, as the device geometries of integrated circuits continue to shrink, this assumption is increasingly not true. Incorporating large safety margins in the design process results in very pessimistic design and expensive processors. Further, the processors in cars -- in contrast to those in many consumer electronics devices like mobile phones -- are exposed to harsh environments, extreme temperature variations, and often, strong electromagnetic fields. Hence, their reliability is even more questionable and must be explicitly accounted for in all layers of design abstraction -- starting from circuit design to architecture design, to software design and runtime management and monitoring. In this paper we outline some of these issues, currently followed practices, and the challenges that lie ahead of us in the automotive and electric vehicles domain.


title: Quality management on Amazon Mechanical Turk

abstract: Crowdsourcing services, such as Amazon Mechanical Turk, allow for easy distribution of small tasks to a large number of workers. Unfortunately, since manually verifying the quality of the submitted results is hard, malicious workers often take advantage of the verification difficulty and submit answers of low quality. Currently, most requesters rely on redundancy to identify the correct answers. However, redundancy is not a panacea. Massive redundancy is expensive, increasing significantly the cost of crowdsourced solutions. Therefore, we need techniques that will accurately estimate the quality of the workers, allowing for the rejection and blocking of the low-performing workers and spammers. However, existing techniques cannot separate the true (unrecoverable) error rate from the (recoverable) biases that some workers exhibit. This lack of separation leads to incorrect assessments of a worker's quality. We present algorithms that improve the existing state-of-the-art techniques, enabling the separation of bias and error. Our algorithm generates a scalar score representing the inherent quality of each worker. We illustrate how to incorporate cost-sensitive classification errors in the overall framework and how to seamlessly integrate unsupervised and supervised techniques for inferring the quality of the workers. We present experimental results demonstrating the performance of the proposed algorithm under a variety of settings.


title: CudaChain: A Practical GPU-accelerated 2D Convex Hull Algorithm

abstract: This paper presents a practical GPU-accelerated convex hull algorithm and a novel Sorting-based Preprocessing Approach (SPA) for planar point sets. The proposed algorithm consists of two stages: (1) two rounds of preprocessing performed on the GPU and (2) the finalization of calculating the expected convex hull on the CPU. We first discard the interior points that locate inside a quadrilateral formed by four extreme points, and then distribute the remaining points into several (typically four) sub regions. For each subset of points, we first sort them in parallel, then perform the second round of discarding using SPA, and finally form a simple chain for the current remaining points. A simple polygon can be easily generated by directly connecting all the chains in sub regions. We at last obtain the expected convex hull of the input points by calculating the convex hull of the simple polygon. We use the library Thrust to realize the parallel sorting, reduction, and partitioning for better efficiency and simplicity. Experimental results show that our algorithm achieves 5x ~ 6x speedups over the Qhull implementation for 20M points. Thus, this algorithm is competitive in practical applications for its simplicity and satisfied efficiency.


title: Generating Chinese Classical Poems with RNN Encoder-Decoder

abstract: We take the generation of Chinese classical poem lines as a sequence-to-sequence learning problem, and build a novel system based on the RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a topic word as input. Our system can jointly learn semantic meaning within a single line, semantic relevance among lines in a poem, and the use of structural, rhythmical and tonal patterns, without utilizing any constraint templates. Experimental results show that our system outperforms other competitive systems. We also find that the attention mechanism can capture the word associations in Chinese classical poetry and inverting target lines in training can improve


title: Adaptive Object Detection Using Adjacency and Zoom Prediction

abstract: State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.


title: Participation in a mobile health intervention trial to improve retention in HIV care: does gender matter?

abstract: Background To be consistent with the United Nations' sustainable development goals on gender equality, mobile health (mHealth) programmes should aim to use communications technology to promote the empowerment of women. We conducted a pre-trial analysis of data from the WelTel Retain study on retention in HIV care to assess gender-based differences in phone access, phone sharing and concerns about receiving text messages from a healthcare provider. Methods Between April 2013-June 2015, HIV-positive adults were screened for trial participation at two clinics in urban slums in Nairobi, Kenya. Proportions of men and women excluded from the trial due to phone-related criteria were compared using a chi-square test. Gender-based differences in phone sharing patterns and concerns among trial participants were similarly compared. Results Of 1068 individuals screened, there was no difference in the proportion of men ( n = 39/378, 10.3%) and women ( n = 71/690, 10.3%) excluded because of phone-related criteria ( p-value = 0.989). Among those who shared their phone, women ( n = 52/108, 48.1%) were more likely than men ( n = 6/60, 10.0%) to share with other non-household and household members ( p < 0.001). Few participants had concerns about receiving text messages from their healthcare provider; those with concerns were all women ( n = 6/700). Discussion In this study, men and women were equally able to participate in a trial of an mHealth intervention. Equitable access in these urban slums may indicate the 'gender digital divide' is narrowing in some settings; however, gender-specific phone sharing patterns and concerns regarding privacy must be fully considered in the development and scale-up of mHealth programmes.


title: Automatic ranking of swear words using word embeddings and pseudo-relevance feedback

abstract: This paper describes a method for automatically ranking a dictionary of swear words based on their level of rudeness. The final ranking is generated by combining two baseline rankings: 1) using the normalized accumulated cosine similarity between the word embeddings of the swear word and the n-best list of closest neighborhoods, and 2) using a pseudo-relevance feedback and bootstrapping algorithm. The proposed methods are trained using dialogues extracted from movies scripts and evaluated against a list of swear words ranked manually in 5 categories by four different annotators. The Spearman correlation coefficient between the rankings generated by the proposed system and a consolidated gold standard reaches a similar value to the ones obtained among the different human annotators, proving that the proposed method is a good alternative to the manual process.


Steviol Glycosides from Stevia rebaudiana Bertoni : Functional Properties , Safety and Application in Food Industry


title: Optique: Towards OBDA Systems for Industry

abstract: The recently started EU FP7-funded project Optique will develop an end-to-end OBDA system providing scalable end-user access to industrial Big Data stores. This paper presents an initial architectural specification for the Optique system along with the individual system components.


title: SPEECH EMOTION RECOGNITION USING SUPPORT VECTOR MACHINE

abstract: Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with wide range of applications. The speech features such as, Mel Frequency cepstrum coefficients (MFCC) and Mel Energy Spectrum Dynamic Coefficients (MEDC) are extracted from speech utterance. The Support Vector Machine (SVM) is used as classifier to classify different emotional states such as anger, happiness, sadness, neutral, fear, from Berlin emotional database. The LIBSVM is used for classification of emotions. It gives 93.75% classification accuracy for Gender independent case 94.73% for male and 100% for female speech.


A Comparison of Parallel Graph Coloring Algorithms


title: Predicting Human Trustfulness from Facebook Language

abstract: Trustfulness -- one's general tendency to have confidence in unknown people or situations -- predicts many important real-world outcomes such as mental health and likelihood to cooperate with others such as clinicians. While data-driven measures of interpersonal trust have previously been introduced, here, we develop the first language-based assessment of the personality trait of trustfulness by fitting one's language to an accepted questionnaire-based trust score. Further, using trustfulness as a type of case study, we explore the role of questionnaire size as well as word count in developing language-based predictive models of users' psychological traits. We find that leveraging a longer questionnaire can yield greater test set accuracy, while, for training, we find it beneficial to include users who took smaller questionnaires which offers more observations for training. Similarly, after noting a decrease in individual prediction error as word count increased, we found a word count-weighted training scheme was helpful when there were very few users in the first place.


title: Efficient Client-Side Deduplication of Encrypted Data With Public Auditing in Cloud Storage

abstract: At present, there is a considerable increase in the amount of data stored in storage services, along with dramatic evolution of networking techniques. In storage services with huge data, the storage servers may want to reduce the volume of stored data, and the clients may want to monitor the integrity of their data with a low cost, since the cost of the functions related to data storage increase in proportion to the size of the data. To achieve these goals, secure deduplication and integrity auditing delegation techniques have been studied, which can reduce the volume of data stored in storage by eliminating duplicated copies and permit clients to efficiently verify the integrity of stored files by delegating costly operations to a trusted party, respectively. So far many studies have been conducted on each topic, separately, whereas relatively few combined schemes, which support the two functions simultaneously, have been researched. In this paper, we design a combined technique, which performs both secure deduplication of encrypted data and public integrity auditing of data. To support the two functions, the proposed scheme performs challenge-response protocols using the BLS signature-based homomorphic linear authenticator. We utilize a third party auditor for performing public audit, in order to help low-powered clients. The proposed scheme satisfies all the fundamental security requirements. We also propose two variances that provide higher security and better performance.


title: Multiple-Parameter Discrete Fractional Transform and its Applications

abstract: In recent years, several special multiple-parameter discrete fractional transforms (MPDFRTs) have been proposed, and their advantages have been demonstrated in the fields of communication systems and information security. However, the general theoretical framework of MPDFRTs has not yet been established. In this paper, we propose two separate theoretical frameworks called the type I and II MPDFRT that can include existing multiple-parameter transforms as special cases. The properties of the type I and II MPDFRT have been analyzed in detail and their high-dimensional operators have been defined. Under the theoretical frameworks, we can construct new types of transforms that may be useful in signal processing and information security. Finally, we perform two applications about image encryption and image feature extraction in the type I and II MPDFRT domain. The simulation results demonstrate that the typical transforms constructed under the proposed theoretical frameworks yield promising results in these applications.


title: Approximating meta-heuristics with homotopic recurrent neural networks

abstract: Much combinatorial optimisation problems constitute a non-polynomial (NP) hard optimisation problem, i.e., they can not be solved in polynomial time. One such problem is finding the shortest route between two nodes on a graph. Meta-heuristic algorithms such as $A^{*}$ along with mixed-integer programming (MIP) methods are often employed for these problems. Our work demonstrates that it is possible to approximate solutions generated by a meta-heuristic algorithm using a deep recurrent neural network. We compare different methodologies based on reinforcement learning (RL) and recurrent neural networks (RNN) to gauge their respective quality of approximation. We show the viability of recurrent neural network solutions on a graph that has over 300 nodes and argue that a sequence-to-sequence network rather than other recurrent networks has improved approximation quality. Additionally, we argue that homotopy continuation -- that increases chances of hitting an extremum -- further improves the estimate generated by a vanilla RNN.


title: Exploring Optical and Vibrational Properties of the Uranium Carbonate Andersonite with Spectroscopy and First-Principles Calculations

abstract: Original periodic first-principles calculations based on the generalized gradient approximation combined with several analyses in microspectroscopy are presented for a hydrated uranium carbonate crystal, andersonite, possessing a specific channel structure. Infrared and ultraviolet–visible absorption, Raman scattering, and steady-state and time-resolved photoluminescence spectroscopy are used to address the atomic vibrations of water, uranyl, and carbonate ions, to determine the fluorescence decay time (around 220 μs) and to estimate the amplitude of the optical gap (close to 3 eV). The role of structural water for andersonite stability is discussed by also performing calculations on a dehydrated model structure. Experiments and calculations address both the intrachannel and extra channel possibilities for the water molecules. The current research is a detailed study of a water-containing channel uranium system using a combined infrared/Raman treatment coupled with density functional theory calculation, p...


title: WMR Control Via Dynamic Feedback Linearization: Design, Implementation, and Experimental Validation

abstract: The subject of the paper is the motion control problem of wheeled mobile robots (WMRs) in environments without obstacles. With reference to the popular unicycle kinematics, it is shown that dynamic feedback linearization is an efficient design tool leading to a solution simultaneously valid for both trajectory tracking and setpoint regulation problems. The implementation of this approach on the laboratory prototype SuperMARIO, a two-wheel differentially driven mobile robot, is described in detail. To assess the quality of the proposed controller, we compare its performance with that of several existing control techniques in a number of experiments. The obtained results provide useful guidelines for WMR control designers.


title: Self-paced learning for matrix factorization

abstract: Matrix factorization (MF) has been attracting much attention due to its wide applications. However, since MF models are generally non-convex, most of the existing methods are easily stuck into bad local minima, especially in the presence of outliers and missing data. To alleviate this deficiency, in this study we present a new MF learning methodology by gradually including matrix elements into MF training from easy to complex. This corresponds to a recently proposed learning fashion called self-paced learning (SPL), which has been demonstrated to be beneficial in avoiding bad local minima. We also generalize the conventional binary (hard) weighting scheme for SPL to a more effective real-valued (soft) weighting manner. The effectiveness of the proposed self-paced MF method is substantiated by a series of experiments on synthetic, structure from motion and background subtraction data.


title: Design of a Robust Stair Climbing Compliant Modular Robot to Tackle Overhang on Stairs

abstract: This paper discusses the concept and parameter design of a Robust Stair Climbing Compliant Modular Robot, capable of tackling stairs with overhangs. Modifying the geometry of the periphery of the wheels of our robot helps in tackling overhangs. Along with establishing a concept design, robust design parameters are set to minimize performance variation. The Grey-based Taguchi Method is adopted for providing an optimal setting for the design parameters of the robot. The robot prototype is shown to have successfully scaled stairs of varying dimensions, with overhang, thus corroborating the analysis performed.


title: Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur Studies of Successful Aging.

abstract: Objectives. Social support and social networks have been shown to exert significant effects on health and functioning among elderly persons. Although theorists have speculated that the strength of these effects may differ as a function of sociodemographic characteristics and prior health status, few studies have directly tested the moderating effects of these variables. Methods. Longitudinal data from the MacArthur Study of Successful Aging were used to examine the effects of structural and functional social support on changes in physical functioning over a 7-year period, measured by the Nagi scale, in a sample of initially high-functioning men and women aged 70 to 79 years. Multiple regression analyses were used to test the main effects of social support and social network variables, as well as their interactions with gender, income, and baseline physical performance. Results. After controlling for potential confounding effects, respondents with more social ties showed less functional decline. The beneficial effects of social ties were stronger for respondents who were male or had lower levels of baseline physical performance. Discussion. The effects of social support and social networks may vary according to the individual's gender and baseline physical capabilities. Studies of functional decline among elderly persons should not ignore this population variation in the effects of social networks.


title: Improved Alpha-Beta Pruning of Heuristic Search in Game-Playing Tree

abstract: The game playing is an import domain of heuristic search, and its procedure is represented by a special and/or tree. Alpha-beta pruning is always used for problem solving by searching the game-playing-tree. In this paper, the plan which child nodes are inserted into game-playing-tree from large value of estimation function to small one when the node of no receiving fixed ply depth is expand is proposed based on alpha-beta pruning. It improves effect of search


title: Robotic agents capable of natural and safe physical interaction with human co-workers

abstract: Many future application scenarios of robotics envision robotic agents to be in close physical interaction with humans: On the factory floor, robotic agents shall support their human co-workers with the dull and health threatening parts of their jobs. In their homes, robotic agents shall enable people to stay independent, even if they have disabilities that require physical help in their daily life - a pressing need for our aging societies. A key requirement for such robotic agents is that they are safety-aware, that is, that they know when actions may hurt or threaten humans and actively refrain from performing them.


Two-Phase Microchannel Heat Sinks: Theory, Applications, and Limitations


Dermatitis from chlorphenesin in a deodorant


title: Lavinia: An Audit-Payment Protocol for Censorship-Resistant Storage

abstract: As distributed storage systems grow in popularity, there is now a demand for a reliable incentive and payment system to guarantee and reward the pristine storage of documents. However, many existing proof-of-retrieval and micropayment protocols are not secure in a censorship resistance setting, in which powerful adversaries may infiltrate a system or coerce the original publisher to remove content. Additionally, most existing censorship resistance systems lack a rigorous game-theoretic analysis. We propose Lavinia, an audit and payment protocol for censorship-resistant storage. Lavinia incentivizes document availability by providing micropayments to participating servers in exchange for honestly storing and serving content. Our protocol enables the implementation of a digital printing press as described in Anderson’s Eternity Service: allowing the publisher, as opposed to public interest or an appointed editorial board, to decide whether a document is worth storing, and for how long. In addition to proving the security of our protocol, we provide an in-depth game-theoretic analysis and show that self-interested participants of our system will faithfully implement the desired behaviour and continue to store documents until their expiration date.


title: An Approach for Distribution Transformer Management With a Multiagent System

abstract: The objective of this paper is to propose an approach for smart distribution transformer (TR) management to help implement emergency demand response (DR) at a TR. In this paper, a DR algorithm embedded in a multiagent system is presented. The proposed algorithm ensures that an instantaneous power demand at a TR is kept below a certain demand limit during a DR event while the impacts of demand restrike are minimized. At the same time, homeowners' goals of securing critical loads, mitigating comfort level violation, and minimizing appliance's compensation time during a DR event are taken into account. To validate and evaluate the effectiveness of the proposed DR algorithm, a case study of a TR serving three homes is demonstrated.


title: Unsupervised auxiliary visual words discovery for large-scale image object retrieval

abstract: Image object retrieval–locating image occurrences of specific objects in large-scale image collections–is essential for manipulating the sheer amount of photos. Current solutions, mostly based on bags-of-words model, suffer from low recall rate and do not resist noises caused by the changes in lighting, viewpoints, and even occlusions. We propose to augment each image with auxiliary visual words (AVWs), semantically relevant to the search targets. The AVWs are automatically discovered by feature propagation and selection in textual and visual image graphs in an unsupervised manner. We investigate variant optimization methods for effectiveness and scalability in large-scale image collections. Experimenting in the large-scale consumer photos, we found that the the proposed method significantly improves the traditional bag-of-words (111% relatively). Meanwhile, the selection process can also notably reduce the number of features (to 1.4%) and can further facilitate indexing in large-scale image object retrieval.


title: Identification and characterization of an anaerobic ethanol-producing cellulolytic bacterial consortium from Great Basin hot springs with agricultural residues and energy crops.

abstract: In order to obtain the cellulolytic bacterial consortia, sediments from Great Basin hot springs (Nevada, USA) were sampled and enriched with cellulosic biomass as the sole carbon source. The bacterial composition of the resulting anaerobic ethanol-producing celluloytic bacterial consortium, named SV79, was analyzed. With methods of the full-length 16S rRNA librarybased analysis and denaturing gradient gel electrophoresis, 21 bacteria belonging to eight genera were detected from this consortium. Clones with closest relation to the genera Acetivibrio, Clostridium, Cellulosilyticum, Ruminococcus, and Sporomusa were predominant. The cellulase activities and ethanol productions of consortium SV79 using different agricultural residues (sugarcane bagasse and spent mushroom substrate) and energy crops (Spartina anglica, Miscanthus floridulus, and Pennisetum sinese Roxb) were studied. During cultivation, consortium SV79 produced the maximum filter paper activity (FPase, 9.41 U/ml), carboxymethylcellulase activity (CMCase, 6.35 U/ml), and xylanase activity (4.28 U/ml) with sugarcane bagasse, spent mushroom substrate, and S. anglica, respectively. The ethanol production using M. floridulus as substrate was up to 2.63 mM ethanol/g using gas chromatography analysis. It has high potential to be a new candidate for producing ethanol with cellulosic biomass under anoxic conditions in natural environments.


title: A microstrip-line-fed tri-band monopole antenna for WLAN/WiMAX applications

abstract: A tri-band microstrip-line-fed patch antenna for wireless local area network (WLAN) and worldwide interoperability of microwave access (WIMAX) application is presented. The proposed antenna consists of an asymmetric V-shaped patch and a hook-like patch in the middle, there is a 60° angle between each branch and the ground plane in order to radiate more energy into the free space so that we can improve the far field performance of the antenna. The asymmetric V-shaped patch excites the 5.2 GHz-band by the left branch and 3.5 GHz-band by the right one due to the different length between them. Furthermore, the 2.4 GHz-band is excited by adding the middle patch, the length is about half guided wavelength at 2.45 GHz, the the middle patch was bent into a hook-liked structure to realize a compact design, the overall size of the antenna is 24 * 30 * 0.8mm3. Key parameters were studied and it has been found that the three bands can be tuned by different branches, respectively, it has minor effect on other bands when one band is tuned, according to the numerical results, a method of tuning the three operating bands has been summarized. The proposed antenna has been fabricated and measured, the −10-dB (S 11 < −10 dB) bands are 2.4 GHz–2.56 GHz, 3.35 GHz–3.95 GHz, 5.05 GHz–5.85 GHz, which can cover the WLAN (2.4–2.484 GHz/5.15–5.35 GHz/5.725–5.825 GHz) bands and WiMAX (3.4–3.69 GHz/5.25–5.85 GHz) bands, it has a monopole-liked radiation characteristic and the peak gain is good according to the far field results. Hence, with the compact simple structure, minor effect on adjacent bands when tuning one band characteristic, and the summarized tuning method, such design can be a good candidate for wireless applications.


title: Risk Factors in Distributed Projects

abstract: In this manuscript we develop a conceptual understanding of how key risk factors associated with Information Technology development projects are magnified or multiplied when dealing with distributed project teams as might be seen in global corporations or with the use of outsourced development resources. The analysis goes beyond the typical cultural or time zone view to include additional dimensions of project team distribution such as perceived distance (spatial and temporal), national culture, systems methodologies, task structure, and organizational distance. How these dimensions interact with key project risk factors is explored through the lens of agency theory and transaction cost theory and further developed providing useful insight on the challenge of managing distributed project teams. Data from several prior research studies by the authors and extant published data is presented in support of the key arguments presented.