title: The influence of implicit and explicit biofeedback in first-person shooter games

abstract: To understand how implicit and explicit biofeedback work in games, we developed a first-person shooter (FPS) game to experiment with different biofeedback techniques. While this area has seen plenty of discussion, there is little rigorous experimentation addressing how biofeedback can enhance human-computer interaction. In our two-part study, (N=36) subjects first played eight different game stages with two implicit biofeedback conditions, with two simulation-based comparison and repetition rounds, then repeated the two biofeedback stages when given explicit information on the biofeedback. The biofeedback conditions were respiration and skin-conductance (EDA) adaptations. Adaptation targets were four balanced player avatar attributes. We collected data with psycho¬physiological measures (electromyography, respiration, and EDA), a game experience questionnaire, and game-play measures. According to our experiment, implicit biofeedback does not produce significant effects in player experience in an FPS game. In the explicit biofeedback conditions, players were more immersed and positively affected, and they were able to manipulate the game play with the biosignal interface. We recommend exploring the possibilities of using explicit biofeedback interaction in commercial games.


title: Super-Resolution Stereo- and Multi-View Synthesis from Monocular Video Sequences

abstract: This paper presents a new approach for generation of super-resolution stereoscopic and multi-view video from monocular video. Such multi-view video is used for instance with multi-user 3D displays or auto-stereoscopic displays with head-tracking to create a depth impression of the observed scenery. Our approach is an extension of the realistic stereo view synthesis (RSVS) approach which is based on structure from motion techniques and image-based rendering to generate the desired stereoscopic views for each point in time. The extension relies on an additional super- resolution mode which utilizes a number of frames of the original video sequence to generate a virtual stereo frame with higher resolution. The algorithm is tested on several TV broadcast videos, as well as on sequences captured with a single handheld camera and sequences from the well known BBC documentation "Planet Earth". Finally, some simulation results will show that RSVS is quite suitable for super-resolution 2D-3D conversion.


title: Hydromagnetic Pulsating Flow of Blood in a Constricted Porous Channel: A Theoretical Study

abstract: A theoretical study of pulsatile blood flow through a constricted porous channel in the presence of an external magnetic field by considering the incompressible Newtonian fluid model is investigated. The influence of magnetic field on the flow is studied using the dimensionless magnetic parameter M and a Darcian linear impedance for low Reynolds number is taken into account in the transformed momentum equation. A perturbation method is employed to solve the governing differential equations by using a small perturbation parameter ϵ (such that 0 < ϵ << 1), which is incorporated in the time dependent transpiration velocity (suction/ injection). Using appropriate boundary conditions analytical expressions for the velocity distribution, volumetric flow rate and wall shear stress have been derived and the numerical results are presented graphically for different values of the physical parameters of interest. system depends upon the pumping action of the heart, which in turn produces a pulsatile pressure gradient throughout the system. (26) theoretically analyzed the pulsatile flow of blood in a stenosed artery, where the non-Newtonian behavior of blood was taken to be of Herschel-Bulkley type. Some excellent studies on pulsatile blood flow have made by (29). (30) studied the fluid dynamics of pulsatile flow past a single cylinder for a non-Newtonian Casson fluid. (10) carried out the pulsatile flow of blood in an artery by considering the effects of body acceleration. (23) modeled the blood flow through arterial stenosis by treating blood as a couple stress fluid. (22) proposed a mathematical model for pulsatile blood flow in a constricted tube using the Power-law fluid. The effect of externally imposed body acceleration and magnetic field on peristaltic flow of blood through an arterial segment having stenosis has been investigated by (25). Their studies pertains to a situation in which blood obeying micropolar fluid model, where the effect of heat transfer phenomena has been taken into account.


title: A Survey On Video Forgery Detection

abstract: The Digital Forgeries though not visibly identifiable to human perception it may alter or meddle with underlying natural statistics of digital content. Tampering involves fiddling with video content in order to cause damage or make unauthorized alteration/modification. Tampering detection in video is cumbersome compared to image when considering the properties of the video. Tampering impacts need to be studied and the applied technique/method is used to establish the factual information for legal course in judiciary. In this paper we give an overview of the prior literature and challenges involved in video forgery detection where passive approach is found.


title: Fast 6D pose from a single RGB image using Cascaded Forests Templates

abstract: This paper presents a method for 6D pose estimation from a single RGB image for complex texture-less objects. This class of objects are common in any environment but still challenging to deal with. This is due to the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. Here we propose a novel part-based method using an efficient template matching approach where each template independently encodes the similarity function using a Forest trained over the templates. Moreover, accuracy is even more incremented by using a cascade of the learned forest. These templates forests together with the simplicity of the computed image features allow a quick estimate of the pose achieving real-time performance. Performance are demonstrated both on synthetic and real images with known ground truth.


title: Justifying Shadow IT Usage

abstract: Employees and/or functional managers increasingly adopt and use IT systems and services that the IS management of the organization does neither provide nor approve. To effectively counteract such shadow IT in organizations, the understanding of employees’ motivations and drivers is necessary. However, the scant literature on this topic primarily focused on various governance approaches at firm level. With the objective to open the black box of shadow IT usage at the individual unit of analysis, we develop a research model and propose a laboratory experiment to examine users’ justifications for violating implicit and explicit IT usage restrictions based on neutralization theory. To be precise, in this research-in-progress, we posit positive associations between shadow IT usage and human tendencies to downplay such kind of rule-breaking behaviors due to necessity, no injury, and injustice. We expect a lower impact of these neutralization effects in the presence of behavioral IT guidelines that explicitly prohibit users to employ exactly those shadow IT systems.


Cancer RNA-Seq Nexus: a database of phenotype-specific transcriptome profiling in cancer cells


title: ApproxHadoop: Bringing Approximations to MapReduce Frameworks

abstract: We propose and evaluate a framework for creating and running approximation-enabled MapReduce programs. Specifically, we propose approximation mechanisms that fit naturally into the MapReduce paradigm, including input data sampling, task dropping, and accepting and running a precise and a user-defined approximate version of the MapReduce code. We then show how to leverage statistical theories to compute error bounds for popular classes of MapReduce programs when approximating with input data sampling and/or task dropping. We implement the proposed mechanisms and error bound estimations in a prototype system called ApproxHadoop. Our evaluation uses MapReduce applications from different domains, including data analytics, scientific computing, video encoding, and machine learning. Our results show that ApproxHadoop can significantly reduce application execution time and/or energy consumption when the user is willing to tolerate small errors. For example, ApproxHadoop can reduce runtimes by up to 32x when the user can tolerate an error of 1% with 95% confidence. We conclude that our framework and system can make approximation easily accessible to many application domains using the MapReduce model.


title: Memory-based face recognition for visitor identification

abstract: We show that a simple, memory-based technique for appearance-based face recognition, motivated by the real-world task of visitor identification, can outperform more sophisticated algorithms that use principal components analysis (PCA) and neural networks. This technique is closely related to correlation templates; however, we show that the use of novel similarity measures greatly improves performance. We also show that augmenting the memory base with additional, synthetic face images results in further improvements in performance. Results of extensive empirical testing on two standard face recognition datasets are presented, and direct comparisons with published work show that our algorithm achieves comparable (or superior) results. Our system is incorporated into an automated visitor identification system that has been operating successfully in an outdoor environment since January 1999.


title: Prognostic models to detect and monitor the near-term risk of suicide: state of the science.

abstract: Aspirational Goal 3 of the National Action Alliance for Suicide Prevention’s Research Prioritization Task Force research agenda is to “find ways to assess who is at risk for attempting suicide in the immediate future.” Suicide risk assessment is the practice of detecting patient-level conditions that may rapidly progress toward suicidal acts. With hundreds of thousands of risk assessments occurring every year, this single activity arguably represents the most broadly implemented, sustained suicide prevention activity practiced in the U.S. Given this scope of practice, accurate and reliable risk assessment capabilities hold a central and irreplaceable position among interventions mounted as part of any public health approach to suicide prevention. Development of more reliable methods to detect and measure the likelihood of impending suicidal behaviors, therefore, represents one of the more substantial advancements possible in suicide prevention science today. Although past “second-generation” risk models using largely static risk factors failed to show predictive capabilities, the current “third-generation” dynamic risk prognostic models have shown initial promise. Methodologic improvements to these models include the advent of real-time, in vivo data collection processes, common data elements across studies and data sharing to build knowledge around key factors, and analytic methods designed to address rare event outcomes. Given the critical need for improved risk detection, these promising recent developments may well foreshadow advancement toward eventual achievement of this Aspirational Goal.


Theoretical and numerical studies of chaotic mixing


title: Review of job recommender system using big data analytics

abstract: The war on talent, supply demand issues, shifting growth paradigms across different sectors form part of the ecosystem in which we live and operate, which is constantly evolving, adaptating and increasing the use of advanced technologies to solve problems across the Talent supply chain domain. Innovations and value added extensions and services are required at a corporate and individual level. At present, we have number of online services which impact our lives in different ways. E-recruitment is one of the known and popular online service for the job seekers as well as for the employers. Past proposed job recommender systems worked with small and structured data sets. But today we have large amount of heterogeneous and unstructured data in comparison to structured data which is growing at tremendous speed. Past proposed techniques of job recommender system does not work adequately on Big Data. Hence, we need more advanced techniques for handling Big data. In this paper, we explored recent work on job recommender system and the research gap between current abilities and new requirements were identified. We proposes a new job recommendation framework which handle Big data effectively.


title: Are abstract action words embodied? An fMRI investigation at the interface between language and motor cognition

abstract: The cognitive and neural representation of abstract words is still an open question for theories of embodied cognition. Generally, it is proposed that abstract words are grounded in the activation of sensorimotor or at least experiential properties, exactly as concrete words. Further behavioral theories propose multiple representations evoked by abstract and concrete words. We conducted a functional magnetic resonance imaging study to investigate the neural correlates of concrete and abstract multi-word expressions in an action context. Participants were required to read simple sentences which combined each concrete noun with an adequate concrete verb and an adequate abstract verb, as well as an adequate abstract noun with either kind of verbs previously used. Thus, our experimental design included a continuum from pure concreteness to mere abstractness. As expected, comprehension of both concrete and abstract language content activated the core areas of the sensorimotor neural network namely the left lateral (precentral gyrus) and medial (supplementary motor area) premotor cortex. While the purely concrete multi-word expressions elicited activations within the left inferior frontal gyrus (pars triangularis) and two foci within the left inferior parietal cortex, the purely abstract multi-word expressions were represented in the anterior part of left middle temporal gyrus that is part of the language processing system. Although the sensorimotor neural network is engaged in both concrete and abstract language contents, the present findings show that concrete multi-word processing relies more on the sensorimotor system, and abstract multi-word processing relies more on the linguistic system.


title: On Loss Functions for Deep Neural Networks in Classification

abstract: Deep neural networks are currently among the most commonly used classifiers. Despite easily achieving very good performance, one of the best selling points of these models is their modular design - one can conveniently adapt their architecture to specific needs, change connectivity patterns, attach specialised layers, experiment with a large amount of activation functions, normalisation schemes and many others. While one can find impressively wide spread of various configurations of almost every aspect of the deep nets, one element is, in authors' opinion, underrepresented - while solving classification problems, vast majority of papers and applications simply use log loss. In this paper we try to investigate how particular choices of loss functions affect deep models and their learning dynamics, as well as resulting classifiers robustness to various effects. We perform experiments on classical datasets, as well as provide some additional, theoretical insights into the problem. In particular we show that L1 and L2 losses are, quite surprisingly, justified classification objectives for deep nets, by providing probabilistic interpretation in terms of expected misclassification. We also introduce two losses which are not typically used as deep nets objectives and show that they are viable alternatives to the existing ones.


title: Constraint-based causal discovery: conflict resolution with answer set programming

abstract: Recent approaches to causal discovery based on Boolean satisfiability solvers have opened new opportunities to consider search spaces for causal models with both feedback cycles and unmeasured confounders. However, the available methods have so far not been able to provide a principled account of how to handle conflicting constraints that arise from statistical variability. Here we present a new approach that preserves the versatility of Boolean constraint solving and attains a high accuracy despite the presence of statistical errors. We develop a new logical encoding of (in)dependence constraints that is both well suited for the domain and allows for faster solving. We represent this encoding in Answer Set Programming (ASP), and apply a state-of-the-art ASP solver for the optimization task. Based on different theoretical motivations, we explore a variety of methods to handle statistical errors. Our approach currently scales to cyclic latent variable models with up to seven observed variables and outperforms the available constraint-based methods in accuracy.


title: A vision for GPU-accelerated parallel computation on geo-spatial datasets

abstract: We summarize the need and present our vision for accelerating geo-spatial computations and analytics using a combination of shared and distributed memory parallel platforms, with general-purpose Graphics Processing Units (GPUs) with 100s to 1000s of processing cores in a single chip forming a key architecture to parallelize over. A GPU can yield one-to-two orders of magnitude speedups and will become increasingly more affordable and energy efficient due to mass marketing for gaming. We also survey the current landscape of representative geo-spatial problems and their parallel, GPU-based solutions.


title: A low-complexity energy disaggregation method: Performance and robustness

abstract: Disaggregating total household's energy data down to individual appliances via non-intrusive appliance load monitoring (NALM) has generated renewed interest with ongoing or planned large-scale smart meter deployments worldwide. Of special interest are NALM algorithms that are of low complexity and operate in near real time, supporting emerging applications such as in-home displays, remote appliance scheduling and home automation, and use low sampling rates data from commercial smart meters. NALM methods, based on Hidden Markov Model (HMM) and its variations, have become the state of the art due to their high performance, but suffer from high computational cost. In this paper, we develop an alternative approach based on support vector machine (SVM) and k-means, where k-means is used to reduce the SVM training set size by identifying only the representative subset of the original dataset for the SVM training. The resulting scheme outperforms individual k-means and SVM classifiers and shows competitive performance to the state-of-the-art HMM-based NALM method with up to 45 times lower execution time (including training and testing).


title: Trust in close relationships.

abstract: A theoretical model describing interpersonal trust in close relationships is presented. Three dimensions of trust are identified, based on the type of attributions drawn about a partner's motives. These dimensions are also characterized by a developmental progression in the relationship. The validity of this theoretical perspective was examined through evidence obtained from a survey of a heterogeneous sample of established couples. An analysis of the Trust Scale in this sample was consistent with the notion that the predictability, dependability, and faith components represent distinct and coherent dimensions. A scale to measure interpersonal motives was also developed. The perception of intrinsic motives in a partner emerged as a dimension, as did instrumental and extrinsic motives. As expected, love and happiness were closely tied to feelings of faith and the attribution of intrinsic motivation to both self and partner. Women appeared to have more integrated, complex views of their relationships than men: All three forms of trust were strongly related and attributions of instrumental motives in their partners seemed to be self-affirming. Finally, there was a tendency for people to view their own motives as less self-centered and more exclusively intrinsic in flavor than their partner's motives.


title: Inner Plant Values: Diversity, Colonization and Benefits from Endophytic Bacteria

abstract: One of the most exciting scientific advances in recent decades has been the realization that the diverse and immensely active microbial communities are not only ‘passengers’ with plants, but instead play an important role in plant growth, development and resistance to biotic and abiotic stresses. A picture is emerging where plant roots act as ‘gatekeepers’ to screen soil bacteria from the rhizosphere and rhizoplane. This typically results in root endophytic microbiome dominated by Proteobacteria, Actinobacteria and to a lesser extent Bacteroidetes and Firmicutes, but Acidobacteria and Gemmatimonadetes being almost depleted. A synthesis of available data suggest that motility, plant cell-wall degradation ability and reactive oxygen species scavenging seem to be crucial traits for successful endophytic colonization and establishment of bacteria. Recent studies provide solid evidence that these bacteria serve host functions such as improving of plant nutrients through acquisition of nutrients from soil and nitrogen fixation in leaves. Additionally, some endophytes can engage ‘priming’ plants which elicit a faster and stronger plant defense once pathogens attack. Due to these plant growth-promoting effects, endophytic bacteria are being widely explored for their use in the improvement of crop performance. Updating the insights into the mechanism of endophytic bacterial colonization and interactions with plants is an important step in potentially manipulating endophytic bacteria/microbiome for viable strategies to improve agricultural production.


title: Behavioral and policy issues in information systems security: the insider threat

abstract: Modern global economic and political conditions, technological infrastructure, and socio-cultural developments all contribute to an increasingly turbulent and dynamic environment for organizations, which maintain information systems (IS) for use in business, government, and other domains. As our institutions (economic, political, military, legal, social) become increasingly global and inter-connected; as we rely more on automated control systems to provide us with energy and services; and as we establish internet-based mechanisms for coordinating this global interaction, we introduce greater vulnerability to our systems and processes. This increased dependence on cyberspace also inflates our vulnerability – isolation is no longer an option. Perhaps no aspect of this phenomenon is as alarming and challenging as the need to understand and address the various risks to the security of the IS on which we depend.


title: Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network

abstract: To assess health conditions of rotating machinery efficiently, multiple accelerometers are mounted on different locations to acquire a variety of possible faults signals. The statistical features are extracted from these signals to identify the running status of a machine. However, the acquired vibration signals are different due to sensor’s arrangement and environmental interference, which may lead to different diagnostic results. In order to improve the fault diagnosis reliability, a new multisensor data fusion technique is proposed. First, time-domain and frequency-domain features are extracted from the different sensor signals, and then these features are input into multiple two-layer sparse autoencoder (SAE) neural networks for feature fusion. Finally, fused feature vectors can be regarded as the machine health indicators, and be used to train deep belief network (DBN) for further classification. To verify the effectiveness of the proposed SAE-DBN scheme, the bearing fault experiments were conducted on a bearing test platform, and the vibration data sets under different running speeds were collected for algorithm validation. For comparison, different feature fusion methods were also applied to multisensor fusion in the experiments. Experimental results demonstrated that the proposed approach can effectively identify the machine running conditions and significantly outperform other fusion methods.


title: Protein Adsorption on Chemically Modified Block Copolymer Nanodomains: Influence of Charge and Flow.

abstract: Understanding the interactions of biomacromolecules with nanoengineered surfaces is vital for assessing material biocompatibility. This study focuses on the dynamics of protein adsorption on nanopatterned block copolymers (BCPs). Poly(styrene)-block-poly(1,2-butadiene) BCPs functionalized with an acid, amine, amide, or captopril moieties were processed to produce nanopatterned films. These films were characterized using water contact angle measurements and atomic force microscopy in air and liquid to determine how the modification process affected. wettability and swelling. Protein adsorption experiments were conducted under static and dynamic conditions via a quartz crystal microbalance with dissipation. Proteins of various size, charge, and stability were investigated to determine whether their physical characteristics affected adsorption. Significantly decreased contact angles were caused by selective swelling of modified BCP domains. The results indicate that nanopatterned chemistry and experimental conditions strongly impact adsorption dynamics. Depending on the structural stability of the protein, polyelectrolyte surfaces significantly increased adsorption over controls. Further analysis suggested that protein stability may correlate with dissipation versus frequency plots.


title: Implementation of Vector Field Histogram based obstacle avoidance wheeled robot

abstract: This paper describes the navigation of mobile robots in a dynamic environment with obstacles, providing guaranteed reach to the target. Ultrasonic Sensor mounted on the Mobile Robot is used for obstacle detection. VFH (Vector Field Histogram method is used to avoid obstacles and make the navigation of the robot smoother. Spartan 3 FPGA is used for controlling the robot. The FPGA is used because it can operate with low power and it can provide parallelism operation, so the robot can do its work in a faster manner. Experimental results show the robot navigation and obstacle detection implemented in the FPGA processor.


title: Selection for mastitis resistance in dairy cattle: a review with focus on the situation in the Nordic countries.

abstract: Abstract The literature concerning selection for mastitis resistance in dairy cattle is reviewed and the reasons for including mastitis resistance in dairy cattle breeding programs are described. The current situation in Denmark, Finland, Norway and Sweden is described with emphasis on the data recording schemes and the choice of models used for breeding value estimation. The use of clinical mastitis data and somatic cell counts in selection for mastitis resistance as well as implications and prospects for the future are discussed.


title: BHP UNIVERSALITY HYPOTHESIS VERIFICATION FOR BET-FI INDEX OF BUCHAREST STOCK EXCHANGE

abstract: In this paper we show the assumption of BHP universality BET -FI index from BVB (Bucharest Exchange Stock) on the assumption of universality issued paper [4] on the Dow Jones index composition (DJIA30) and Standard & Poors 100 (S & P100) by Goncalves and Pinto. BHP (S. T. Bramwell, P. C. W. Holdsworth, J. F. Pinton) distribution is a non-parametric probability distribution from GHT (Gumbel-Fisher-Tippett) distributions classes discovered from a magnetization problem. Universality of this distribution was shown in electricity, hidrology, stocks. This paper extend results from [9]-[10] recently submitted in spirit of [4] using some software tools developped.


title: Control of quadrotor aerial vehicles equipped with a robotic arm

abstract: In this paper a novel hierarchical motion control scheme for quadrotor aerial vehicles equipped with a manipulator is proposed. The controller is organized into two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the top layer. A simulation case study is developed to demonstrate the effectiveness of the approach in the presence of disturbances and unmodeled dynamics.


title: An EMI robust LIN driver with low electromagnetic emission

abstract: This paper describes a LIN (Local Interconnect Network) Transmitter designed in a BCD HV technology. The key design target is to comply with EMI (electromagnetic interference) specification limits. The two main aspects are low EME (electromagnetic emission) and sufficient immunity against RF disturbance. A gate driver is proposed which uses a certain current summation network for lowering the slew rate on the one hand and being reliable against radio frequency (RF) disturbances within the automotive environment on the other hand. Nowadays the low cost single wire LIN Bus is used for establishing communication between sensors, actuators and other components.


title: 2002b) Damned if you don’t, damned if you do: Crime mapping and its implications in the real world, Policing and Society

abstract: A small but growing North American trend is the publication of maps of crime on the Internet. A number of web sites allow observers to view the spatial distribution of crime in various American cities, often to a considerable resolution, and increasingly in an interactive format. The use of Geographical Information Systems (GIS) technology to map crime is a rapidly expanding field that is, as this paper will explain, still in a developmental stage, and a number of technical and ethical issues remain to be resolved. The public right to information about local crime has to be balanced by a respect for the privacy of crime victims. Various techniques are being developed to assist crime mappers to aggregate spatial data, both to make their product easier to comprehend and to protect identification of the addresses of crime victims. These data aggregation techniques, while preventing identification of individuals, may also be inadvertently producingmaps with the appearance of ‘greater risk’ in low crime areas. When some types of crime mapping have the potential to cause fallinghouse prices, increasinginsurance premiums or business abandonment, conflicts may exist between providinga public service and protectingthe individual, leavingthe cartographer vulnerable to litigation.


title: Role of Dopamine D2 Receptors in Human Reinforcement Learning

abstract: Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well.


title: Abstractive Summarization of Reddit Posts with Multi-level Memory Networks

abstract: We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. Thus, our dataset could less suffer from some biases that key sentences usually locate at the beginning of the text and favorable summary candidates are already inside the text in similar forms. Second, we propose a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly abstractive and the MMN outperforms the state-of-the-art summarization models.


title: Hydrogen Generation through Solar Photocatalytic Processes: A Review of the Configuration and the Properties of Effective Metal-Based Semiconductor Nanomaterials

abstract: Photocatalytic water splitting and organic reforming based on nano-sized composites are gaining increasing interest due to the possibility of generating hydrogen by employing solar energy with low environmental impact. Although great efforts in developing materials ensuring high specific photoactivity have been recently recorded in the literature survey, the solar-to-hydrogen energy conversion efficiencies are currently still far from meeting the minimum requirements for real solar applications. This review aims at reporting the most significant results recently collected in the field of hydrogen generation through photocatalytic water splitting and organic reforming, with specific focus on metal-based semiconductor nanomaterials (e.g., metal oxides, metal (oxy)nitrides and metal (oxy)sulfides) used as photocatalysts under UVA or visible light irradiation. Recent developments for improving the photoefficiency for hydrogen generation of most used metal-based composites are pointed out. The main synthesis and operating variables affecting photocatalytic water splitting and organic reforming over metal-based nanocomposites are critically evaluated.


title: Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method

abstract: News items have a significant impact on stock markets but the ways are obscure. Many previous works have aimed at finding accurate stock market forecasting models. In this paper, we use text mining and sentiment analysis on Chinese online financial news, to predict Chinese stock tendency and stock prices based on support vector machine (SVM). Firstly, we collect 2,302,692 news items, which date from 1/1/2008 to 1/1/2015. Secondly, based on this dataset, a specific domain stop-word dictionary and a precise sentiment dictionary are formed. Thirdly, we propose a forecasting model using SVM. On the algorithm of SVM implementation, we also propose two-parameter optimization algorithms to search for the best initial parameter setting. The result shows that parameter G has the main effect, while parameter C's effect is not obvious. Furthermore, support vector regression (SVR) models for different Chinese stocks are similar whereas in support vector classification (SVC) models best parameters are quite differential. Series of contrast experiments show that: a) News has significant influence on stock market; b) Expansion input vector for additional situations when that day has no news data is better than normal input in SVR, yet is worse in SVC; c) SVR shows a fantastic degree of fitting in predicting stock fluctuation while such result has some time lag; d) News effect time lag for stock market is less than two days; e) In SVC, historic stock data has a most efficient time lag which is about 10 days, whereas in SVR this effect is not obvious. Besides, based on the special structure of the input vector, we also design a method to calculate the financial source impact factor. Result suggests that the news quality and audience number both have a significant effect on the source impact factor. Besides, for Chinese investors, traditional media has more influence than digital media.


title: Built-in Self-Test and Fault Diagnosis for Lab-on-Chip Using Digital Microfluidic Logic Gates

abstract: Dependability is an important system attribute for microfluidic lab-on-chip. Structural test and functional test are needed to detect defects and malfunctions, respectively. Previously proposed techniques for reading test outcomes and for pulse-sequence analysis are cumbersome and error-prone. We present a built-in self-test (BIST) method for digital microfluidic lab-on-chip. This method utilizes digital microfluidic logic gates to implement the BIST architecture; AND, OR and NOT gates are used to compress test-outcome droplets into one droplet signature. This approach obviates the need for capacitive sensing test-outcome circuits for analysis. An efficient diagnosis method based on a microfluidic encoder is also proposed to locate a single defective electrode in a microfluidic array. Finally, reconfiguration is used during test and diagnosis to enable BIST with low area overhead.


title: Applying Machine Learning to Text Segmentation for Information Retrieval

abstract: We propose a self-supervised word segmentation technique for text segmentation in Chinese information retrieval. This method combines the advantages of traditional dictionary based, character based and mutual information based approaches, while overcoming many of their shortcomings. Experiments on TREC data show this method is promising. Our method is completely language independent and unsupervised, which provides a promising avenue for constructing accurate multi-lingual or cross-lingual information retrieval systems that are flexible and adaptive. We find that although the segmentation accuracy of self-supervised segmentation is not as high as some other segmentation methods, it is enough to give good retrieval performance. It is commonly believed that word segmentation accuracy is monotonically related to retrieval performance in Chinese information retrieval. However, for Chinese, we find that the relationship between segmentation and retrieval performance is in fact nonmonotonics that is, at around 70% word segmentation accuracy an over-segmentation phenomenon begins to occur which leads to a reduction in information retrieval performance. We demonstrate this effect by presenting an empirical investigation of information retrieval on Chinese TREC data, using a wide variety of word segmentation algorithms with word segmentation accuracies ranging from 44% to 95%, including 70% word segmentation accuracy from our self-supervised word-segmentation approach. It appears that the main reason for the drop in retrieval performance is that correct compounds and collocations are preserved by accurate segmenters, while they are broken up by less accurate (but reasonable) segmenters, to a surprising advantage. This suggests that words themselves might be too broad a notion to conveniently capture the general semantic meaning of Chinese text. Our research suggests machine learning techniques can play an important role in building adaptable information retrieval systems and different evaluation standards for word segmentation should be given to different applications.


title: SMARTMUSEUM: A mobile recommender system for the Web of Data

abstract: Semantic and context knowledge has been envisioned as an appropriate solution for addressing the content heterogeneity and information overload in mobile Web information access, but few have explored their full potential in mobile scenarios, where information objects refer to their physical counterparts, and retrieval is context-aware and personalized for users. We present SMARTMUSEUM, a mobile ubiquitous recommender system for the Web of Data, and its application to information needs of tourists in context-aware on-site access to cultural heritage. The SMARTMUSEUM system utilizes Semantic Web languages as the form of data representation. Ontologies are used to bridge the semantic gap between heterogeneous content descriptions, sensor inputs, and user profiles. The system makes use of an information retrieval framework wherein context data and search result clustering are used in recommendation of suitable content for mobile users. Results from laboratory experiments demonstrate that ontology-based reasoning, query expansion, search result clustering, and context knowledge lead to significant improvement in recommendation performance. The results from field trials show that the usability of the system meets users' expectations in real-world use. The results indicate that semantic content representation and retrieval can significantly improve the performance of mobile recommender systems in knowledge-rich domains.


title: A simple but effective method to incorporate trusted neighbors in recommender systems

abstract: Providing high quality recommendations is important for online systems to assist users who face a vast number of choices in making effective selection decisions. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users. But it suffers from several issues like data sparsity and cold start. To address these issues, in this paper, we propose a simple but effective method, namely "Merge", to incorporate social trust information (i.e. trusted neighbors explicitly specified by users) in providing recommendations. More specifically, ratings of a user's trusted neighbors are merged to represent the preference of the user and to find similar other users for generating recommendations. Experimental results based on three real data sets demonstrate that our method is more effective than other approaches, both in accuracy and coverage of recommendations.


title: Power to the people? The evolving recognition of human aspects of security

abstract: It is perhaps unsurprising to find much of the focus in IT and computer security being drawn towards the technical aspects of the discipline. However, it is increasingly recognised that technology alone cannot deliver a complete solution, and there is also a tangible need to address human aspects. At the core, people must understand the threats they face and be able to use the protection available to them, and although this has not been entirely ignored, it has not received the level of attention that it merits either. Indeed, security surveys commonly reveal that the more directly user-facing aspects such as policy, training and education are prone to receiving significantly less attention than technical controls such as firewalls, antivirus and intrusion detection. The underlying reason for such disparity is that the human aspects are in many ways a more challenging problem to approach, not least because they cannot be easily targeted with a product-based solution. There is also a direct overlap into the technical area, with issues such as the usability and acceptability of technology solutions having a direct impact upon the actual protection that they are able to deliver. This paper explores these themes, highlighting the need for human aspects to form part of a holistic security strategy alongside the necessary technologies. Taking the specific examples of security awareness and two user-facing technical controls (user authentication and antivirus), the discussion examines how things have evolved to the present day and considers how they need to be positioned for the future.


title: Visual Affordance and Function Understanding: A Survey

abstract: Nowadays, robots are dominating the manufacturing, entertainment and healthcare industries. Robot vision aims to equip robots with the ability to discover information, understand it and interact with the environment. These capabilities require an agent to effectively understand object affordances and functionalities in complex visual domains. In this literature survey, we first focus on Visual affordances and summarize the state of the art as well as open problems and research gaps. Specifically, we discuss sub-problems such as affordance detection, categorization, segmentation and high-level reasoning. Furthermore, we cover functional scene understanding and the prevalent functional descriptors used in the literature. The survey also provides necessary background to the problem, sheds light on its significance and highlights the existing challenges for affordance and functionality learning.


title: Sensor-Based Mobile Web Cross-Site Input Inference Attacks and Defenses

abstract: In this paper, we investigate the accelerometer and gyroscope motion sensor-based cross-site input inference attacks that may compromise the security of many mobile Web users, and quantify the extent to which they can be effective. We formulate our attacks as a typical multi-class classification problem and build an inference framework that trains a classifier in the training phase and predicts the user’s new inputs in the attacking phase. To make our attacks effective and realistic, we design unique techniques and address major data quality and data segmentation challenges. We intensively evaluate the effectiveness of our attacks using 98 691 keystrokes collected from 20 participants. Overall, our attacks are effective, for example, they are about 10.8 times more effective than the random guessing attacks regarding inferring letters. We also perform experiments to evaluate the effect of using the data perturbation defense techniques on decreasing the accuracy of our input inference attacks. Our results demonstrate that researchers, smartphone vendors, and app developers should pay serious attention to the motion sensor-based cross-site input inference attacks that can be pervasively performed, and start to design and deploy effective defense techniques.


title: Virtual Reality and Exposure Group Therapy for Social Anxiety Disorder: Results from a 4–6 Year Follow-Up

abstract: This is the first long-term follow-up study of virtual reality exposure therapy and exposure group therapy for social anxiety disorder—completed 6 years, on average, after treatment completion. Participants (N = 28) had a clinical diagnosis of social anxiety disorder that included public speaking fears and had previously completed 8 sessions of either virtual reality exposure therapy or exposure group therapy delivered according to a treatment manual. The final sample was ethnically diverse, middle-aged (mean age = 42 years) and mostly female (71%). Participants completed standardized self-report measures of public speaking anxiety and fear of negative evaluation, a behavioral speech task, a diagnostic interview, and ratings of global improvement. Participants showed statistically significant improvement on all self-report measures from pre-treatment to follow-up. All participants completed the speech task, with two exceptions: one participant declined to do the speech task and one person completed the follow-up assessment by phone. The majority of participants no longer met diagnostic criteria for the disorder (54%) and reported themselves “very much” or “much” improved (68%). With one exception, there were no differences between treatments at follow-up across self-report, clinician-rated, and behavioral data. Virtual reality exposure therapy and exposure group therapy for social anxiety disorder produce long-lasting benefits, consistent with research on a variety of forms of short-term cognitive behavioral therapy for social anxiety disorder.


title: Enhancing Reinforcement Learning With Metacognitive Monitoring and Control for Improved Perturbation Tolerance

abstract: Maintaining adequate performance in dynamic and uncertain settings has been a perennial stumbling block for intelligent systems. Nevertheless, any system intended for real-world deployment must be able to accommodate unexpected change—that is, it must be perturbation tolerant. We have found that metacognitive monitoring and control—the ability of a system to self-monitor its own decision-making processes and ongoing performance, and to make targeted changes to its beliefs and action-determining components—can play an important role in helping intelligent systems cope with the perturbations that are the inevitable result of real-world deployment. In this article we present the results of several experiments demonstrating the efficacy of metacognition in improving the perturbation tolerance of reinforcement learners, and discuss a general theory of metacognitive monitoring and control, in a form we call the metacognitive loop. ||This research is supported in part by the AFOSR and ONR.


title: Accurate and Fast Asymmetric Locality-Sensitive Hashing Scheme for Maximum Inner Product Search

abstract: The problem of Approximate Maximum Inner Product (AMIP) search has received increasing attention due to its wide applications. Interestingly, based on asymmetric transformation, the problem can be reduced to the Approximate Nearest Neighbor (ANN) search, and hence leverage Locality-Sensitive Hashing (LSH) to find solution. However, existing asymmetric transformations such as L2-ALSH and XBOX, suffer from large distortion error in reducing AMIP search to ANN search, such that the results of AMIP search can be arbitrarily bad. In this paper, we propose a novel Asymmetric LSH scheme based on Homocentric Hypersphere partition (H2-ALSH) for high-dimensional AMIP search. On the one hand, we propose a novel Query Normalized First (QNF) transformation to significantly reduce the distortion error. On the other hand, by adopting the homocentric hypersphere partition strategy, we can not only improve the search efficiency with early stop pruning, but also get higher search accuracy by further reducing the distortion error with limited data range. Our theoretical studies show that H2-ALSH enjoys a guarantee on search accuracy. Experimental results over four real datasets demonstrate that H2-ALSH significantly outperforms the state-of-the-art schemes.


title: An Experimental Comparison of Classification Algorithms for the Hierarchical Prediction of Protein Function

abstract: 1 Computing Laboratory and Centre for BioMedical Informatics, University of Kent, Canterbury, CT2 7NF, UK Email: {a.d.secker, a.a.freitas}@kent.ac.uk 2 Edward Jenner Institute, Compton, Newbury, Berkshire, RG20 7NN, UK Email: m.davies@mail.cryst.bbk.ac.uk, darren.flower@jenner.ac.uk 3 Departments of Computer Science and Electronics, University of York, Heslington, YO10 5DD, UK Email: {jtimmis, miguel}@cs.york.ac.uk


title: A 12.1mW, 60dB SNR, 8-channel beamforming embedded SAR ADC for ultrasound imaging systems

abstract: A power-efficient analog beamforming embedded SAR ADC for ultrasound imaging systems is presented. It is constructed from multiple sub-beamforming SAR ADCs, which sequentially perform analog beamforming and analog-to-digital conversion for an assigned focal point on a scan-line. Power is saved because these operations are carried out in the charge domain without a summing op-amp. This is realized by a capacitor beamforming digital-to-analog converter, which acts as both an analog memory cell and a digital-to-analog converter. The proposed analog beamformer was implemented in a 0.18pm CMOS process, and its power consumption is 12.1mW. The delay resolution is 4.17ns and the maximum achievable delay is 292ns. The sampling frequency is 20MS/s and the SNR of the prototype is 60dB. The circuit occupies an active area of 2.2mm2.


title: Kinematics, dynamics and dimensional synthesis of a novel 2-DoF translational manipulator

abstract: In the past few years, parallel manipulators have become increasingly popular in industry, especially, in the field of machine tools. In this paper, a novel 2-degree-of-freedom (DoF) parallel manipulator, which has two translational DoFs, is proposed. It is characterized by the fact that the output of the manipulator is two planar DoFs of a rigid body, while its orientation remains constant. The inverse and forward kinematics can be described in closed form. The velocity equation, singularity, and workspace of the manipulator are presented. In addition the inverse dynamics problem of the device is investigated employing the Lagrange multipliers approach. The dimensional synthesis based on the workspace and conditioning indices is presented. The proposed manipulator can be applied to the field of machine tools or used as the mobile base for a spatial manipulator. The results of the paper are very useful for the design and application of the new manipulator.


title: Title extraction from bodies of HTML documents and its application to web page retrieval

abstract: This paper is concerned with automatic extraction of titles from the bodies of HTML documents. Titles of HTML documents should be correctly defined in the title fields; however, in reality HTML titles are often bogus. It is desirable to conduct automatic extraction of titles from the bodies of HTML documents. This is an issue which does not seem to have been investigated previously. In this paper, we take a supervised machine learning approach to address the problem. We propose a specification on HTML titles. We utilize format information such as font size, position, and font weight as features in title extraction. Our method significantly outperforms the baseline method of using the lines in largest font size as title (20.9%-32.6% improvement in F1 score). As application, we consider web page retrieval. We use the TREC Web Track data for evaluation. We propose a new method for HTML documents retrieval using extracted titles. Experimental results indicate that the use of both extracted titles and title fields is almost always better than the use of title fields alone; the use of extracted titles is particularly helpful in the task of named page finding (23.1% -29.0% improvements).


title: Performance tradeoffs in dynamic time warping algorithms for isolated word recognition

abstract: The technique of dynamic programming for the time registration of a reference and a test pattern has found widespread use in the area of isolated word recognition. Recently, a number of variations on the basic time warping algorithm have been proposed by Sakoe and Chiba, and Rabiner, Rosenberg, and Levinson. These algorithms all assume that the test input is the time pattern of a feature vector from an isolated word whose endpoints are known (at least approximately). The major differences in the methods are the global path constraints (i.e., the region of possible warping paths), the local continuity constraints on the path, and the distance weighting and normalization used to give the overall minimum distance. The purpose of this investigation is to study the effects of such variations on the performance of different dynamic time warping algorithms for a realistic speech database. The performance measures that were used include: speed of operation, memory requirements, and recognition accuracy. The results show that both axis orientation and relative length of the reference and the test patterns are important factors in recognition accuracy. Our results suggest a new approach to dynamic time warping for isolated words in which both the reference and test patterns are linearly warped to a fixed length, and then a simplified dynamic time warping algorithm is used to handle the nonlinear component of the time alignment. Results with this new algorithm show performance comparable to or better than that of all other dynamic time warping algorithms that were studied.


title: Q-rater: A collaborative reputation system based on source credibility theory

abstract: The consumer is an important information source and the after-transaction-feedback is used as the quality indicator for trust building in e-commerce. However, the possibility of incorrect information from unreliable users and declining trust in the electronic market looms large. Hence, there is a need for a fairer and more objective reputation mechanism. Most existing reputation systems focus on the reputation of users than items and they rely on explicit feedback information. As a solution to these problems, we propose a reputation system (''Q-rater'') suitable for B2C e-commerce. We adopt the source credibility theory of consumer psychology and the basic mechanism of collaborative filtering methods for the overall process of the proposed reputation system. We also evaluate performance of the Q-rater experimentally by comparing it with the other benchmark systems and observing the performance changes with variations in the size of rater group and item rating aggregation mechanism.


title: Cloudlet-based cyber-foraging for mobile systems in resource-constrained edge environments

abstract: First responders and others operating in crisis environments increasingly make use of handheld devices to help with tasks such as face recognition, language translation, decision-making and mission planning. These resource-constrained edge environments are characterized by dynamic context, limited computing resources, high levels of stress, and intermittent network connectivity. Cyber-foraging is the leverage of external resource-rich surrogates to augment the capabilities of resource-limited devices. In cloudlet-based cyber-foraging, resource-intensive computation is offloaded to cloudlets – discoverable, generic servers located in single-hop proximity of mobile devices. This paper presents several strategies for cloudlet-based cyber-foraging and encourages research in this area to consider a tradeoff space beyond energy, performance and fidelity of results.


title: Colour image segmentation using the self-organizing map and adaptive resonance theory

abstract: We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of Kohonen, overcomes the limitations of (i) the stability-plasticity trade-offs in neural architectures that employ ART; and (ii) the lack of on-line learning property in the SOM. In order to explore the generation of a growing feature map using ART and to motivate the main contribution, we first present a preliminary experimental model, SOMART, based on Fuzzy ART. Then we propose the new model, SmART, that utilizes a novel lateral control of plasticity to resolve the stability-plasticity problem. SmART has been experimentally found to perform well in RGB colour space, and is believed to be more coherent than Fuzzy ART.


title: Road Geometry Classification by Adaptive Shape Models

abstract: Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.


Human-Robot Teaming using Shared Mental Models


title: Can Qualitative Content Analysis be Adapted for use by Social Informaticians to Study Social Media Discourse? A Position Paper

abstract: IS research on social media (e.g. Facebook, Twitter, blogs) has so far used user surveys or quantitative content analysis (QuantCA) research methods almost exclusively. There is considerable potential for social informatics research to use qualitative content analysis (QualCA) to explore social media discourse and its appropriation by people “in situ”. This paper presents the position that QualCA offers researchers the flexibility to identify emergent research questions and units of analysis which they may not have preconceived. This is likely to be important for IS research because of the infancy and evolving nature of social media discourse. The paper puts forward suggestions on how the QualCA research method can be adapted for this type of research.


title: Link Prediction with Cardinality Constraint

abstract: Inferring the links among entities in networks is an important research problem for various disciplines. Depending on the specific application settings, the links to be inferred are usually subject to different cardinality constraints, like one-to-one, one-to-many and many-to-many. However, most existing research works on link prediction problems fail to consider such a kind of constraint. In this paper, we propose to study the link prediction problem with general cardinality constraints, which is formally defined as the CLP (Cardinality Constrained Link Prediction) problem. By minimizing the projection loss of links from feature vectors to labels, the CLP problem is formulated as an optimization problem involving multiple variables, where the cardinality constraints are modeled as mathematical constraints on node degrees. The objective function is shown to be not jointly convex and the optimal solution subject to the cardinality constraints can be very time-consuming to achieve. To solve the optimization problem, an iterative variable updating based link prediction framework ITERCLIPS (Iterative Constrained Link Prediction & Selection) is introduced in this paper, which involves the steps on link updating and selection alternatively. To overcome the high time cost problem, a greedy link selection step is introduced in this paper, which picks links greedily while preserving the link cardinality constraints simultaneously. Meanwhile, to ensure the effectiveness of ITERCLIPS on large-scale networks, a distributed implementation of ITERCLIPS is further presented as a scalable solution to the CLP problem. Extensive experiments have been done on three real-world network datasets with different types of cardinality constraints, and the experimental results achieved by ITERCLIPS on all these datasets can demonstrate the effectiveness and advantages of ITERCLIPS in solving the CLP problem.


title: Dynamic metabolic exchange governs a marine algal-bacterial interaction

abstract: Microscopic algae that live in the ocean release countless tons of oxygen into the atmosphere each year. Widespread algae – known as coccolithophores – surround their little plant-like body with a mineral shell made of a material similar to chalk. These microscopic algae form seasonal blooms. Over several weeks in early summer, the algae grow to enormous numbers and cover hundreds of thousands of square kilometers in the ocean. These blooms become so vast that satellites can detect them. However, suddenly the blooms collapse; the algae die and their chalky shells sink to the bottom of the ocean where they have been accumulating for millions of years. More and more evidence suggests that these tiny algae interact with bacteria in various ways. However, so far, no one had documented a direct interaction between bacteria and a member of this key group of algae. Now, in a controlled laboratory environment, Segev et al. show that marine bacteria from the Roseobacter group physically attach onto a tiny coccolithophore alga called Emiliania huxleyi. While the bacteria are attached to their algal host, they enjoy a supply of nutrients that trickles from the algal cell. Unexpectedly, Segev et al. also discovered that the algae grow better in the presence of the bacteria. It turns out that the bacteria use a molecule that they obtain from their algal hosts to produce a small hormone-like molecule that in turn enhances the growth of the algae. However, after three weeks of growing together, the bacteria produce so much of the growth-enhancing molecule – which is harmful in higher concentrations – that they actually kill their algal host. These findings suggest that the bacteria first promote the alga’s growth to boost their supply of nutrients. But as algae grow older, the bacteria harvest the algae to enjoy a last pulse of nutrients and allow their offspring to swim away and attach to younger algae. The next challenge will be to link these laboratory observations to the actual microbial interactions in the ocean. It will also be important to explore whether other algae and bacteria interact in similar ways and if bacteria contribute to the sudden collapse of algal blooms by killing the algae.


Noise Reduction Based on Robust Principal Component Analysis


title: A New Battery/UltraCapacitor Hybrid Energy Storage System for Electric, Hybrid, and Plug-In Hybrid Electric Vehicles

abstract: In this paper, a new battery/ultracapacitor hybrid energy storage system (HESS) is proposed for electric drive vehicles including electric, hybrid electric, and plug-in hybrid electric vehicles. Compared to the conventional HESS design, which uses a larger dc/dc converter to interface between the ultracapacitor and the battery/dc link to satisfy the real-time peak power demands, the proposed design uses a much smaller dc/dc converter working as a controlled energy pump to maintain the voltage of the ultracapacitor at a value higher than the battery voltage for the most city driving conditions. The battery will only provide power directly when the ultracapacitor voltage drops below the battery voltage. Therefore, a relatively constant load profile is created for the battery. In addition, the battery is not used to directly harvest energy from the regenerative braking; thus, the battery is isolated from frequent charges, which will increase the life of the battery. Simulation and experimental results are presented to verify the proposed system.


title: Active Learning for Networked Data

abstract: We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the labels of linked nodes are correlated, and the goal is to exploit these dependencies and accurately label the nodes. This problem arises in many domains, including social and biological network analysis and document classification, and there has been much recent interest in methods that collectively classify the nodes in the network. While in many cases labeled examples are expensive, often network information is available. We show how an active learning algorithm can take advantage of network structure. Our algorithm effectively exploits the links between instances and the interaction between the local and collective aspects of a classifier to improve the accuracy of learning from fewer labeled examples. We experiment with two real-world benchmark collective classification domains, and show that we are able to achieve extremely accurate results even when only a small fraction of the data is labeled.


title: Central nervous system stem cell transplantation for children with neuronal ceroid lipofuscinosis.

abstract: OBJECT ::: Infantile and late-infantile neuronal ceroid lipofuscinoses (NCLs) are invariably fatal lysosomal storage diseases associated with defects in lysosomal enzyme palmitoyl-protein thioesterase 1 (PPT-1) or tripeptidyl peptidase 1 (TPP1) activity. Previous preclinical studies have demonstrated that human CNS stem cells (HuCNS-SCs) produce both PPT-1 and TPP1 and result in donor cell engraftment and reduced accumulation of storage material in the brain when tested in an NCL mouse model. ::: ::: ::: METHODS ::: HuCNS-SC transplantation was tested in an open-label dose-escalation Phase I clinical trial as a potential treatment for infantile and late-infantile NCL. Study design included direct neurosurgical transplantation of allogeneic HuCNS-SCs into the cerebral hemispheres and lateral ventricles accompanied by 12 months of immunosuppression. ::: ::: ::: RESULTS ::: Six children with either the infantile or late-infantile forms of NCL underwent low- (3 patients) and high- (3 patients) dose transplantation of HuCNS-SCs followed by immunosuppression. The surgery, immunosuppression, and cell transplantation were well tolerated. Adverse events following transplantation were consistent with the underlying disease, and none were directly attributed to the donor cells. Observations regarding efficacy of the intervention were limited by the enrollment criteria requiring that patients be in advanced stages of disease. ::: ::: ::: CONCLUSIONS ::: This study represents the first-in-human clinical trial involving transplantation of a purified population of human neural stem cells for a neurodegenerative disorder. The feasibility of this approach and absence of transplantation-related serious adverse events support further exploration of HuCNS-SC transplantation as a potential treatment for select subtypes of NCL, and possibly for other neurodegenerative disorders.


Ambient RF Energy Harvesting Technology and its Applications


Evolution of Low Drop Out Voltage Regulator in CMOS Technologies


title: Simulating quantum computers with probabilistic methods

abstract: We investigate the boundary between classical and quantum computational power. This work consists of two parts. First we develop new classical simulation algorithms that are centered on sampling methods. Using these techniques we generate new classes of classically simulatable quantum circuits where standard techniques relying on the exact computation of measurement probabilities fail to provide efficient simulations. For example, we show how various concatenations of matchgate, Toffoli, Clifford, bounded-depth, Fourier transform and other circuits are classically simulatable. We also prove that sparse quantum circuits as well as circuits composed of CNOT and exp[itheta;X] gates can be simulated classically. In a second part, we apply our results to the simulation of quantum algorithms. It is shown that a recent quantum algorithm, concerned with the estimation of Potts model partition functions, can be simulated efficiently classically. Finally, we show that the exponential speed-ups of Simon's and Shor's algorithms crucially depend on the very last stage in these algorithms, dealing with the classical postprocessing of the measurement outcomes. Specifically, we prove that both algorithms would be classically simulatable if the function classically computed in this step had a sufficiently peaked Fourier spectrum.


title: A continuous-time analog adaptive biquadratic filter : design, construction, and characterization

abstract: This thesis presents the design, layout, construction and characterization of a continuoustime analog adaptive biquadratic filter. After reviewing the Wiener-Hopf formula and the Least Mean Square (LMS) algorithm to derive the update formulas, this thesis describes the design process by which the transfer function and update formulas were realized using discrete operational amplifiers, analog multipliers, capacitors and resistors. Block diagrams as well as the full schematics and layout drawings are included. The circuit was built up onto a tinned copper-clad ground plane. The adaptive filter's performance was characterized using a two-channel synthesizer and a noise generator for the input stimulus, and monitoring the output with an oscilloscope and spectrum analyzer. Performance was excellent when the filter was required to follow a single-tone's amplitude and phase by adapting the zeroes of its transfer function. When adaptation of poles was required, the filter sometimes had trouble when there were not enough constraints. I determined that the circuitry adapted as well as it could, but would reach a local minimum, where the error was orthogonal to the signal. This is a common problem with IIR filters. The filter was able to duplicate the magnitude response of a reference filter when both were driven by a random noise source. 1.0 INTRODUCTION Adaptive filters are now a common building block in electronic signal processing systems. Digital adaptive filters of the finite impulse response (FIR) variety are dependable, robust, always stable, and not surprisingly the most frequently chosen. Infinite impulse response (UR) filters have a size advantage because they able to meet filter requirements with less stages than FIR filters, but require cautious application because they are not inherently stable. Analog signal processing is chosen over digital signal processing wherever its advantages of low power, low complexity, and high frequency may be put to use, including in adaptive filtering. Analog FIR filters in discrete as well as continuous time are now well known signal processing components. IIR continuous time analog adaptive filters have been simulated, but do not yet see widespread use. This thesis is about a continuous time analog IIR adaptive filter which uses the Modified Equation Error Algorithm as its adaptation process. This architecture has been simulated by James Little [1] and looks promising. My purpose in selecting this topic was to choose a learning vehicle with which I could tie in knowledge from previous engineering courses and take the learning process still further. The objective was to design, build, and characterize an IIR, continuous-time, analog adaptive filter with biquadratic transfer function:


Teaching Children to Respect Diversity


title: The Scientific Model of Causality 1

abstract: Causality is a very intuitive notion that is difficult to make precise without lapsing into tautology. Two ingredients are central to any definition: (1) a set of possible outcomes (counterfactuals) generated by a function of a set of ‘‘factors’’ or ‘‘determinants’’ and (2) a manipulation where one (or more) of the ‘‘factors’’ or ‘‘determinants’’ is changed. An effect is realized as a change in the argument of a stable function that produces the same change in the outcome for a class of interventions that change the ‘‘factors’’ by the same amount. The outcomes are compared at different levels of the factors or generating variables. Holding all factors save one at a constant level, the change in the outcome associated with manipulation of the varied factor is called a causal effect of the manipulated factor. This definition, or some version of it, goes back to Mill (1848) and Marshall (1890). Haavelmo’s (1943) made it more precise within the context of linear equations models. The phrase ‘ceteris paribus’ (everything else held constant) is a mainstay of economic analysis


title: Approach and avoidance achievement goals and intrinsic motivation: A mediational analysis.

abstract: nMost contemporary achievement goal conceptualizations consist of a performance goal versus mastery goal dichotomy. The present research offers an alternative framework by partitioning the performance goal orientation into independent approach and avoidance motivational orientations. Two experiments investigated the predictive utility of the proposed approach-avoidance achievement goal conceptualization in the intrinsic motivation domain. Results from both experiments supported the proposed framework; only performance goals grounded in the avoidance of failure undermined intrinsic motivation. Task involvement was validated as a mediator of the observed effects on intrinsic motivation. Ramifications for the achievement goal approach to achievement motivation and future research avenues are discussed. Achievement motivation theorists focus their research attention on a particular class of behaviors, those involving competence. Individuals may aspire to attain competence or may strive to avoid incompetence, and this approach-avoidance distinction was explicitly incorporated into the earliest achievement motivation conceptualizations. Two independent motivational orientations, the desire for success and the desire to avoid failure, were identified by Lewin and colleagues as critical determinants of aspiration behavior (Hoppe, cited in Lewin, Dembo, Festinger, & Sears, 1944). In his nascent achievement motivation theory, McClelland ( 1951 ) proposed that "there are at least two kinds of achievement motivation, one of which appears to be oriented around avoiding failure and the other around the more positive goal of attaining success" (p. 202).


title: High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method

abstract: Here we describe a high-efficiency version of the lithium acetate/single-stranded carrier DNA/PEG method of transformation of Saccharomyces cerevisiae. This method currently gives the highest efficiency and yield of transformants, although a faster protocol is available for small number of transformations. The procedure takes up to 1.5 h, depending on the length of heat shock, once the yeast culture has been grown. This method is useful for most transformation requirements.


title: FPGA implementation of an advanced encoding and decoding architecture of polar codes

abstract: Polar code, newly formulated by Erdal Arikan, has got a wide recognition from the information theory community. Polar code achieves the capacity of the class of symmetric binary memory less channels. In this paper, we propose efficient hardware architecture on a FPGA platform using Xilinx Virtex VI for implementing the advanced encoding and decoding schemes. The performance of the proposed architecture out performs the existing techniques such as: successive cancellation decoder, list successive cancellation, belief propagation etc; with respect to bit error rate and resource utilization.


title: A stream function solver for liquid simulations

abstract: This paper presents a liquid simulation technique that enforces the incompressibility condition using a stream function solve instead of a pressure projection. Previous methods have used stream function techniques for the simulation of detailed single-phase flows, but a formulation for liquid simulation has proved elusive in part due to the free surface boundary conditions. In this paper, we introduce a stream function approach to liquid simulations with novel boundary conditions for free surfaces, solid obstacles, and solid-fluid coupling. ::: Although our approach increases the dimension of the linear system necessary to enforce incompressibility, it provides interesting and surprising benefits. First, the resulting flow is guaranteed to be divergence-free regardless of the accuracy of the solve. Second, our free-surface boundary conditions guarantee divergence-free motion even in the un-simulated air phase, which enables two-phase flow simulation by only computing a single phase. We implemented this method using a variant of FLIP simulation which only samples particles within a narrow band of the liquid surface, and we illustrate the effectiveness of our method for detailed two-phase flow simulations with complex boundaries, detailed bubble interactions, and two-way solid-fluid coupling.


title: Self-awareness, self-regulation, and self-transcendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness

abstract: Mindfulness - as a state, trait, process, type of meditation, and intervention has proven to be beneficial across a diverse group of psychological disorders as well as for general stress reduction. Yet, there remains a lack of clarity in the operationalization of this construct, and underlying mechanisms. Here, we provide an integrative theoretical framework and systems-based neurobiological model that explains the mechanisms by which mindfulness reduces biases related to self-processing and creates a sustainable healthy mind. Mindfulness is described through systematic mental training that develops meta-awareness (self-awareness), an ability to effectively modulate one’s behavior (self-regulation), and the development of a positive relationship between self and other that transcends self-focused needs and increases prosocial characteristics (self-transcendence). This framework of self-awareness, regulation, and transcendence (S-ART) illustrates a method for becoming aware of the conditions that cause (and remove) distortions or biases. The development of S-ART through meditation is proposed to modulate self-specifying and narrative self-networks through an integrative fronto-parietal control network. Relevant perceptual, cognitive, emotional, and behavioral neuropsychological processes are highlighted, including intention and motivation, attention regulation, emotion regulation, extinction and reconsolidation, prosociality, non-attachment and decentering. The S-ART framework and neurobiological model is based on our growing understanding of the mechanisms for neurocognition, empirical literature, and through dismantling the specific meditation practices thought to cultivate mindfulness. The proposed framework will inform future research in the contemplative sciences and target specific areas for development in the treatment of psychological disorders.


title: A Single-Channel, 1.25-GS/s, 6-bit, 6.08-mW Asynchronous Successive-Approximation ADC With Improved Feedback Delay in 40-nm CMOS

abstract: A single-channel, asynchronous successive-approximation (SA) ADC with improved feedback delay is fabricated in 40 nm CMOS. Compared with a conventional SAR structure that employs a single quantizer controlled by a digital feedback logic loop, the proposed SAR-ADC employs multiple quantizers for each conversion bit, clocked by an asynchronous ripple clock that is generated after each quantization. Hence, the sampling rate of the 6-bit ADC is limited only by the six delays of the Capacitive-DAC settling and each comparator's quantization delay, as the digital logic delay is eliminated. Measurement results of the 40 nm-CMOS SAR-ADC achieves a peak SNDR of 32.9 dB and 30.5 dB, at 1 GS/s and 1.25 GS/s, consuming 5.28 mW and 6.08 mW, leading to a FoM of 148 fJ/conv-step and 178 fJ/conv-step, respectively, in a core area less than 170 um by 85 um.


title: Game-Based Extraction of Web Users' Personality Factors for Personalization

abstract: The volume of information users are exposed to on the web is overwhelming. To increase effectiveness of information delivery to users, providers employ personalization strategies. In a highly competitive environment, simplistic strategies do not suffice, and high-quality personalization is required. These can be based on users' decision making models. To build such models, we need to extract factors of direct influence on users' decision making. Personality factors are known to have this direct influence. They are stable over time and across situations, and they assist in predicting future behavior of individuals in a scientific way. In this paper, we introduce a novel methodology for extracting users' personality factors without holding any prior information on the users' behavior and, notably, without administering any psychological questionnaires. This allows us to build a designated model for each user or users' group, and in turn facilitates effective personalized information delivery.


title: Novel Flux-Weakening Control of an IPMSM for Quasi-Six-Step Operation

abstract: This paper proposes a novel flux-weakening control algorithm of an interior permanent magnet synchronous motor for 'quasi' six-step operation. The proposed method is composed of a feed forward and a feedback path. The feedforward path consists of 1-dimensional look-up table, and the feedback is based on the difference between the reference voltage updated by current regulator and the output voltage limited by the over- modulation. Using this method, the flux-weakening and the anti- windup control can be achieved simultaneously. And also, the quasi six-step operation can be obtained. That is, the available maximum output torque in the flux-weakening region is close to that in the six-step operation while the ability of the current control is maintained. The effectiveness of this method is proved by the experimental results.


title: A method of small object detection and tracking based on particle filters

abstract: In this paper an efficient method of small object localization is proposed that integrates detection and tracking. The system is initialized using a strong detector and then it locates the object over time using a weak detector and a temporal tracker. Both of strong and weak detectors are based on foreground-background segmentation. The strong detector is created from shape analysis of foreground blobs and used to trigger the object tracker. The weak detector is built with outputs from the foreground detection likelihood and integrated into the trackerpsilas observation likelihood. In the particle filter-based object tracker, motion estimation is embedded to generate a better proposal distribution and a mixture model is tailored to handle the ambiguity of template matching due to cluttered background. As a case study, the proposed scheme is applied to ball detection and tracking in soccer game videos. Promising results are presented to illustrate the proposed method effectively handles heavy clutter, occlusion and motion blur.


title: Malware detection using machine learning based analysis of virtual memory access patterns

abstract: Malicious software, referred to as malware, continues to grow in sophistication. Past proposals for malware detection have primarily focused on software-based detectors which are vulnerable to being compromised. Thus, recent work has proposed hardware-assisted malware detection. In this paper, we introduce a new framework for hardware-assisted malware detection based on monitoring and classifying memory access patterns using machine learning. This provides for increased automation and coverage through reducing user input on specific malware signatures. The key insight underlying our work is that malware must change control flow and/or data structures, which leaves fingerprints on program memory accesses. Building on this, we propose an online framework for detecting malware that uses machine learning to classify malicious behavior based on virtual memory access patterns. Novel aspects of the framework include techniques for collecting and summarizing per-function/system-call memory access patterns, and a two-level classification architecture. Our experimental evaluation focuses on two important classes of malware (i) kernel rootkits and (ii) memory corruption attacks on user programs. The framework has a detection rate of 99.0% with less than 5% false positives and outperforms previous proposals for hardware-assisted malware detection.


Aggression Detection on Social Media Text Using Deep Neural Networks


title: Batched Stochastic Gradient Descent with Weighted Sampling

abstract: We analyze a batched variant of Stochastic gradient descent (SGD) with weighted sampling distribution for smooth and non-smooth objective functions. We show that by distributing the batches computationally, a significant speedup in the convergence rate is provably possible compared to either batched sampling or weighted sampling alone. We propose several computationally efficient schemes to approximate the optimal weights and compute proposed sampling distributions explicitly for the least squares and hinge loss problems. We show both analytically and experimentally that substantial gains can be obtained.


title: A 0.8-V, 8-/spl mu/W, CMOS OTA with 50-dB gain and 1.2-MHz GBW in 18-pF load

abstract: A low-power low-voltage OTA with rail-to-rail output is introduced. The proposed topology is based on the common current mirror OTA topology and provide gain enhancement without extra power consumption. Implemented in a standard 0.25/spl mu/m CMOS technology, the proposed OTA achieves 50 dB DC gain in 0.8 V supply voltage. The GBW is 1.2MHz and the static power consumption is 8/spl mu/W while driving 18pF load. The class AB operation increases the slew rate and still maintains low static biasing current. This topology is suitable for low-power low-voltage switched-capacitor application.


A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing


title: The Relationship between Nomophobia and Loneliness among Turkish Adolescents

abstract: This study looks into the relationship between nomophobia and loneliness, and the effects of smartphone and mobile internet use among adolescents. A total of 301 adolescents were employed for the study and the data were collected via Nomophobia (NMP-Q) and UCLA Loneliness Short-Form (ULS-8) Scales. The study was conducted in a relational survey model using descriptive statistics, ANOVA, Pearson correlation, and linear regression. According to the findings the levels of nomophobic behaviors of adolescents were at a moderate level. While there was not a statistically significant correlation in terms of the duration of smartphone ownership and monthly mobile internet GSM quota, a significant difference was found in terms of the duration of mobile internet ownership, the duration of daily mobile internet use, and daily smartphone checking time. Finally, there was a statistically significant relationship between nomophobia and loneliness, and it can be ascertained that loneliness of adolescents predicts their nomophobia levels to a certain extent.


Cross-Gender and Cross-Dialect Tone Recognition for Vietnamese.


title: Vision-based autonomous landing of an unmanned aerial vehicle

abstract: We present the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. The helicopter is required to navigate from an initial position to a final position in a partially known environment based on GPS and vision, locate a landing target (a helipad of a known shape) and land on it. We use vision for precise target detection and recognition. The helicopter updates its landing target parameters based on vision and uses an on-board behavior-based controller to follow a path to the landing site. We present results from flight trials in the field which demonstrate that our detection, recognition and control algorithms are accurate and repeatable.


title: Extended iterative maintenance life cycle using eXtreme programming

abstract: Software maintenance is the continuous process of enhancing the operational life of software. The existing approaches to software maintenance, derived from the traditional approaches to development, are unable to resolve the problems of unstructured code, team morale, poor visibility of the project, lack of communication, and lack of proper test suites. Alternatively, extreme programming practices such as test driven development, refactoring, pair programming, continuous integration, small releases, and collective ownership help to resolve the aforesaid problems. In this paper, a process model is proposed for software maintenance using extreme programming practices to resolve maintenance issues in an improved manner. The proposed approach speeds up the maintenance process and produces more maintainable code with less effort for future maintenance and evolution. The proposed model is validated by applying it on several maintenance projects in an academic environment. It has been observed that the approach provides higher quality code. The proposed model based on extreme programming enhances both learning and productivity of the team by improving the morale, courage, and confidence of the team, which supports higher motivation during maintenance.


title: Novel dry cleaning machine for photovoltaic and solar panels

abstract: Accumulation of dust (also known as soiling) on the surface of solar panels decreases the amount of sunlight reaching the solar cells underneath and thus the efficiency of the solar panel is severely impacted. To harness their designed capacity to its fullest, they need to be cleaned periodically, usually with water. The Middle East region is a very suitable region for harvesting solar energy due to abundance of sunlight, but there is also a profusion of sand and dust. Due to water scarcity in this area, cleaning becomes difficult, challenging and subsequently costly. Here, a novel four-stage automated ‘dry cleaning’ method is reported for solar panels. The proposed cleaning process is carried out in four stages with no involvement of liquids. The cleaning process involves compressed air spray, followed by cleaning through a foam roller and a polywool synthetic duster. An electronically controlled mechanical assembly holds the rollers and guides them along the solar panels. A set of servo motors and a stepper motor is installed on the assembly to rotate and guide the cleaning structure. The system is very useful for small and large installations alike, especially in dry areas where there is little or no rain throughout the year.


title: Vertical ridge augmentation in the esthetic zone

abstract: The reconstruction of deficient alveolar ridges using vertical and/or horizontal guided bone regeneration techniques allows for ideal implant placement, which is crucial for function and also for esthetically successful outcomes. Unlike in the past, when meeting a patient's functional demands was sufficient, many patients now have greater expectations from their implant restoration. Hence, it is no longer enough simply to restore the edentulous space with a functioning tooth or teeth. It has been suggested that patients now measure their final restoration using the contralateral natural tooth as the gold standard. Both subjective and objective levels of patient information on dental implants have increased significantly in the last decade. As a result of this demand, implant literature has inherited and developed specific esthetic parameters and patient-centered outcomes from studies in the restorative field. Unfortunately, studies reporting on guided bone regeneration in the esthetic zone entirely lack such parameters and outcomes. Currently, there is a strong need for a consensus on objective and well-defined parameters to assess the esthetics in bone regeneration and subsequently on implant dentistry.


title: Escapes of fishes from Norwegian sea-cage aquaculture: causes, consequences and prevention

abstract: The escape of fish from aquaculture is perceived as a threat to wild fish populations. The escapes problem is largely caused by technical and operational failures of fish farming equipment. In Norway, 3.93 million Atlantic salmon Salmo salar, 0.98 million rainbow trout Oncorhynchus mykiss and 1.05 million Atlantic cod Gadus morhua escaped from 2001 to 2009. Salmonids primarily escape after structural failures of containment equipment, while a far greater percentage of cod than salmon escape through holes in the nets. Correlative evidence suggests that after the Norwegian technical standard (NS 9415) for sea-cage farms took effect in 2004, the total number of escaped Atlantic salmon declined from >600 000 (2001 to 2006) to <200 000 fish yr -1 (2007 to 2009), despite the total number of salmon held in sea-cages increasing by 44% during this period. No similar decrease in escaped cod has occurred, suggesting that other measures, such as improved netting materials for sea-cages, are required. In addition to escaping as juveniles or adults, cod may reproduce in sea- cages, and thus fertilised eggs escape to the environment. The ecological effects of 'escape through spawning' are unclear, and methods to inhibit escape by this mechanism are being explored. To pre- vent escapes of juvenile and adult fish as sea-cage aquaculture industries develop, we recommend that policy-makers implement a 5 component strategy: (1) establish mandatory reporting of all escape incidents; (2) establish a mechanism to analyse and learn from the mandatory reporting; (3) conduct mandatory, rapid, technical assessments to determine the causes of escape incidents involving more than 10 000 fish; (4) introduce a technical standard for sea-cage aquaculture equipment coupled with an independent mechanism to enforce the standard; and (5) conduct mandatory training of fish farm staff in escape-critical operations and techniques.


title: Automatic data warehouse conceptual design

abstract: This paper presents a methodology to develop a dimensional data warehouse by integrating all three development approaches such as supply-driven, goal-driven and demand-driven. By having the combination of all three approaches, the final design will ensure that user requirements, company interest and existing source of data are included in the model. We proposed an automatic system using ontology as the knowledge domain. Starting from operational ER-D (Entity Relationship-Diagram), the selection of facts table, verification of terms and consistency checking will utilize domain ontology. The model will also be verified against user and company requirements. Any discrepancy in the final design requires designer and user intervention. The proposed methodology is supported by a prototype using a business data warehouse example.


Fake Review Detection : Classification and Analysis of Real and Pseudo Reviews