title: Animal-assisted activity and emotional status of patients with Alzheimer's disease in day care.

abstract: Background: Preliminary studies suggest beneficial effects of animal-assisted activities (AAA) on behavioral and psychological symptoms of dementia (BPSD), but data are inconsistent. This study aimed to assess the effect of AAA with dogs on cognition, BPSD, emotional status and motor activity in severe Alzheimer's disease (AD). Methods: Ten patients attending an Alzheimer Day Care Center (ADCC) participated in a repeated measures study, which included: two weeks' pre-intervention, three weeks' control activity with plush dogs (CA), and three weeks' AAA. Cognitive function (Severe Impairment Battery), mood (Cornell Scale for Depression in Dementia; CSDD), BPSD (Neuropsychiatric Inventory; NPI) and agitation (Cohen-Mansfield Agitation Inventory; CMAI) were assessed at baseline and after each period. Observed Emotion Rating Scale (OERS) for emotional status, Agitated Behavior Mapping Instrument (ABMI) and a checklist for motor activity were completed across the study periods, both during intervention sessions and after three hours. Results: Cognition and NPI were unchanged across the study. Declines in the CMAI and CSDD scores after AAA were not significant, while the NPI anxiety item score decreased in comparison with CA (CA 3.1±2.3, AAA 1.5±2.7, p = 0.04). OERS “sadness” decreased (p = 0.002), while “pleasure” (p = 0.016) and “general alertness” (p = 0.003) increased during AAA compared with CA sessions, and observed sadness remained lower after three hours (p = 0.002). Motor activity increased significantly during AAA. Conclusion: In this sample of severe AD patients in ADCC, AAA was associated with a decrease in anxiety and sadness and an increase in positive emotions and motor activity in comparison with a control activity.


title: Product codebook feedback for massive MIMO with cross-polarized 2D antenna arrays

abstract: Massive MIMO involves the use of large scale antenna arrays for high-gain adaptive beamforming and high-order spatial multiplexing. An important design challenge in Massive MIMO systems is the acquisition of channel state information at the transmit array, where accurate channel knowledge is critical for obtaining the best performance with Multi-User MIMO transmission on the downlink. In this paper, we explore the use of a product codebook feedback methodology for two-dimensional antenna arrays where the codebook feedback strategy is decomposed into two separate feedback processes, one for azimuth and one for elevation. We specifically address the case where the transmit array consists of cross-polarized antennas and show how two separate codebook feedback processes can reduce reference signal overhead and simplify the mobile complexity while providing significant gains in performance over existing LTE configurations.


Close-range camera calibration


title: Richard Thaler and the Rise of Behavioral Economics

abstract: Richard Thaler was awarded the 2017 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for his contributions to behavioral economics. In this article, I review and discuss these contributions.


title: A Forensic Investigation of Robot Operating System

abstract: The Robot Operating System (ROS) is a framework that is mostly used in industrial applications such as automotive, healthcare and manufacturing and it is not immune from potential future hacking. By carrying out various types of cyber-attacks, hackers can disrupt the normal operation of a robot. It is easy to get control of communication between a robot and a human due to the open communication network after carrying out malicious attacks to jam the network. As a result, hackers can change commands which are sent by an operator to the robot, making usual activities impossible. For instance, in a case of ROS hacking, man-in-the-middle attacks, Trojans, backdoor attacks, and so on, can change the behaviour of robots to something completely different than expected.Therefore, forensic analysts require a specific method to forensically investigate ROS. This is a new area in the computer forensics field. Therefore, it is proposed to create an analytical framework to facilitate the forensic investigation of the Robot Operating System and methodologies and standards for acquiring related digital evidence using forensic tools. This study addresses a formalized and structured methodology that would assist the forensic investigation approach.This research will help to enhance the gathering, identification and preservation of evidence related to forensics investigations of the Robot Operating System. The forensic analysts could adapt the examination procedure of hacked ROS with a focused, crime-specific, forensics framework.


title: Healthcare Can Change from Within: Sustained Improvement in the Healthcare Response to Intimate Partner Violence

abstract: There is a great need to demonstrate sustained improvement in healthcare-based inquiry, intervention, and prevention provided to patients exposed to intimate partner violence (IPV). We evaluated implementation of the Healthcare Can Change from Within model (HCCW) in three primary care clinics and an emergency department within a large healthcare system, using two other primary care clinics for a usual-care comparison on selected variables. Outcome measures included individual-level variables (staff knowledge and attitudes) and system characteristics (clinic policies, procedures, patient education materials, and IPV documentation in patient records). Doctors and nurses reported increased self-efficacy, understanding of referral resources, and understanding of legal issues; IPV knowledge was unchanged. Intervention clinics implemented new policies and procedures, increased patient education, and increased documentation of IPV screening, an improvement which was sustained at 2-year follow-up. Results suggest HCCW is a promising practice for improving the healthcare response to IPV.


title: Female-to-Male Transsexual Individuals Demonstrate Different Own Body Identification.

abstract: Transsexualism is characterized by feelings of incongruity between one’s natal sex and one’s gender identity. It is unclear whether transsexual individuals have a body image that is more congruent with their gender identity than their sex assigned at birth (natal sex) and, if so, whether there are contributions from perceptual dysfunctions. We compared 16 pre-hormone treatment female-to-male transsexual (FtM) individuals to 20 heterosexual female and 20 heterosexual male controls on a visual identification task. Participants viewed photographs of their own body that were morphed by different degrees to bodies of other females or males, and were instructed to rate “To what degree is this picture you?” We also tested global vs. local visual processing using the inverted faces task. FtM differed from both control groups in demonstrating higher self-identification ratings for bodies morphed to the sex congruent with their gender identity, and across a broad range of morph percentages. This difference was more pronounced for longer viewing durations. FtM showed reduced accuracy for upright faces compared with female controls for short duration stimuli, but no advantage for inverted faces. These results suggest different own body identification in FtM, consisting of a relatively diffuse identification with body images congruent with their gender identity. This is more likely accounted for by conscious, cognitive factors than perceptual differences.


title: Effective Clipart Image Vectorization Through Direct Optimization of Bezigons

abstract: Bezigons, i.e., closed paths composed of Bezier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due to accumulated errors, fitting ambiguities, and a lack of curve priors, especially for low-resolution images. In this paper, we describe a novel method for vectorizing clipart images. In contrast to previous methods, we directly optimize the bezigons rather than using other intermediate representations; therefore, the resultant bezigons are not only of higher fidelity compared with the original raster image but also more reasonable because they were traced by a proficient expert. To enable such optimization, we have overcome several challenges and have devised a differentiable data energy as well as several curve-based prior terms. To improve the efficiency of the optimization, we also take advantage of the local control property of bezigons and adopt an overlapped piecewise optimization strategy. The experimental results show that our method outperforms both the current state-of-the-art method and commonly used commercial software in terms of bezigon quality.


title: GQBE: Querying knowledge graphs by example entity tuples

abstract: We present GQBE, a system that presents a simple and intuitive mechanism to query large knowledge graphs. Answers to tasks such as “list university professors who have designed some programming languages and also won an award in Computer Science” are best found in knowledge graphs that record entities and their relationships. Real-world knowledge graphs are difficult to use due to their sheer size and complexity and the challenging task of writing complex structured graph queries. Toward better usability of query systems over knowledge graphs, GQBE allows users to query knowledge graphs by example entity tuples without writing complex queries. In this demo we present: 1) a detailed description of the various features and user-friendly GUI of GQBE, 2) a brief description of the system architecture, and 3) a demonstration scenario that we intend to show the audience.


title: Evaluation of the Standard i-Vectors Based Speaker Verification Systems on Limited Data

abstract: It is known that the performance of the i-vectors/PLDA based speaker verification systems is affected in the cases of short utterances and limited training data. The performance degradation appears because the shorter the utterance, the less reliable the extracted i-vector is, and because the total variability covariance matrix and the underlying PLDA matrices need a significant amount of data to be robustly estimated. Considering the “MIT Mobile Device Speaker Verification Corpus” (MIT-MDSVC) as a representative dataset for robust speaker verification tasks on limited amount of training data, this paper investigates which configuration and which parameters lead to the best performance of an i-vectors/PLDA based speaker verification. The i-vectors/PLDA based system achieved good performance only when the total variability matrix and the underlying PLDA matrices were trained with data belonging to the enrolled speakers. This way of training means that the system should be fully retrained when new enrolled speakers were added. The performance of the system was more sensitive to the amount of training data of the underlying PLDA matrices than to the amount of training data of the total variability matrix. Overall, the Equal Error Rate performance of the i-vectors/PLDA based system was around 1% below the performance of a GMM-UBM system on the chosen dataset. The paper presents at the end some preliminary experiments in which the utterances comprised in the CSTR VCTK corpus were used besides utterances from MIT-MDSVC for training the total variability covariance matrix and the underlying PLDA matrices.


title: Baseline Mechanisms for Enterprise Governance of IT in SMEs

abstract: Information Technology (IT) has become fundamental for most organizations since it is vital to their sustainability, development and success. This pervasive use led organizations to a critical dependency on IT. Despite the benefits, it exposes organizations to several risks. Hence, a significant focus on Enterprise Governance of IT (EGIT) is required. EGIT involve the definition and implementation of processes, structures and relational mechanisms to support the business/IT alignment and the creation of business value from IT investments. These mechanisms act as a mean to direct and operationalize IT-related decision-making. However, identifying the appropriate mechanisms is a complex task since there are internal and external contingency factors that influence the design and implementation of EGIT. Small and Medium Enterprises (SMEs) are considered key elements to promote economic growth, job creation, social integration and innovation. The use of IT in these organizations can imply severe consequences on survival and growth in highly competitive markets, becoming critical in this globalization era. Several studies were developed to investigate and identify EGIT mechanisms in different contingencies but very few focused on the organization' size criterion. However, SMEs have particular characteristics that require further investigation. Therefore, our goal in this research is to evaluate EGIT mechanisms in a SME's context and identify a baseline of mechanisms for SMEs using semi-structured interviews with IT experts that have experience in SMEs. The article ends by presenting contributions, limitations and future work.


title: Classification of Artefacts in EEG Signal Recordings and Overview of Removing Techniques

abstract: EEG is a record of brain activity from various sites of the brain. Artefacts are unwanted noise signals in an EEG record. Classification of artefacts based on source of its generation like physiological artefacts and external artefacts. Body of the subjects are main source of Physiological artifacts, while external artefacts are from outside the body due to the environment or measuring devices. Recognition, identification and elimination of artifacts is an important process to minimize the chance of misinterpretation of EEG, not only for clinical and non-clinical fields such as brain computer interface, intelligent control system robotics etc. This paper classifies the artefacts from the database collected at Dr. R. N. Cooper Mun. General Hospital Mumbai India.


title: Evolution of Trajectories: A Novel Representation for Deep Action Recognition

abstract: Achieving high classification accuracy remains a challenge for human action recognition approaches that are based on convolutional neural networks (CNN). CNN-based action recognition methods on resource-constrained edge systems are currently unable to train on whole videos due to infeasible computational and memory requirements. On the other hand, approaches that utilize video-level supervision with sparse-sampling designs run the risk of learning local features shared with multiple similar classes. Additionally, features captured by motion estimation algorithms for temporal stream CNN's are already reduced in dimension, increasing the possibility of label mismatch in methods that rely on short-term features. To address the aforementioned points, we design a novel temporal representation to capture a 1) long-term interval of motion and 2) integrate the trajectory of motion captured therein. We compare our approach with current motion representations and demonstrate its efficacy for examples containing local features with high inter-class similarity. We implement our representation as part of two and three-stream CNN's and conduct experiments on one of the popular and most difficult action recognition datasets: HMDB51. Our results show that, for methods employing sparse-sampling designs, our approach surpasses the current state-of-the-art approaches achieving 71.76% on HMDB51.


title: Ffsck: The Fast File-System Checker

abstract: Crash failures, hardware errors, and file system bugs can corrupt file systems and cause data loss, despite the presence of journals and similar preventive techniques. While consistency checkers such as fsck can detect this corruption and restore a damaged image to a usable state, they are generally created as an afterthought, to be run only at rare intervals. Thus, checkers operate slowly, causing significant downtime for large scale storage systems when they are needed. ::: ::: We address this dilemma by treating the checker as a key component of the overall file system (and not merely a peripheral add-on). To this end, we present a modified ext3 file system, rext3, to directly support the fast file system checker, ffsck. The rext3 file system co-locates and self-identifies its metadata blocks, removing the need for costly seeks and tree traversals during checking. These modifications to the file system allow ffsck to scan and repair the file system at rates approaching the full sequential bandwidth of the underlying device. In addition, we demonstrate that rext3 performs competitively with ext3 in most cases and exceeds it in handling random reads and large writes.


title: The psychology behind QR codes: User experience perspective

abstract: QR codes, 2-D bar codes that can be read by special software on a smartphone, have been undergoing rapid development. This study examines how a Technology Acceptance Model could predict users' intentions to continue using QR codes by integrating interactivity and quality motivations as primary determining factors. Although studies have shown interactivity is important to users, not many study have analyzed the mediating influence of interactivity within interactive marketing context. Therefore, the original TAM was expanded to encompass user perceptions of interactivity and quality. The results of this study show that user intentions and behaviors are largely influenced by the perception of the quality of QR codes. In addition, the results of the model show that interactivity is a key behavioral antecedent to the use of QR codes. In the extended model, the moderating and mediating effects of the perceived interactivity on relationships among the variables were found to be significant. The new set of variables is QR code-specific, acting as factors that enhance attitudes and behavioral intentions. Implications of the findings are discussed in terms of construction of a theory of interactivity and provision of practical insights into development of user-centered QR codes.


title: How power users help and hinder open bug reporting

abstract: Many power users that contribute to open source projects have no intention of becoming regular contributors; they just want a bug fixed or a feature implemented. How often do these users participate in open source projects and what do they contribute? To investigate these questions, we analyzed the reports of Mozilla contributors who reported problems but were never assigned problems to fix. These analyses revealed that over 11 years and millions of reports, most of these 150,000 users reported non-issues that devolved into technical support, redundant reports with little new information, or narrow, expert feature requests. Reports that did lead to changes were reported by a comparably small group of experienced, frequent reporters, mostly before the release of Firefox 1. These results suggest that the primary value of open bug reporting is in recruiting talented reporters, and not in deriving value from the masses.


title: Set-Based Design: Case Study on Innovative Hospital Design

abstract: This paper describes collaboration efforts of a project team that implemented lean concepts in the course of structural system selection during the design phase of a hospital project. Out-of-the-box thinking, contractual incentives for team work, early collaboration, and a set-based design approach led to the development of an innovative and cost-effective structural system that may set precedent for other medical facilities to be constructed in seismically active zones. The structural design team on this project rigorously explored the design space and tested design alternatives against project value propositions. When pushed by the owner to think more broadly, the structural engineer proposed using a new technology, namely viscous damping walls. This concept was developed in Japan but has not yet been tried on projects in the United States. Because it is a first, this solution requires not only rigorous analysis and testing by the structural engineer but also detailed investigation by the state's regulatory agency that issues building permits. This paper describes the team's efforts at defining the design space and the set-based design approach they used. A key lesson from this case study is that teams have a lot to learn about how to make requests and commitments while pursuing setbased design to be lean.


title: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Bayesian Nonparametric Feature Construction for Inverse Reinforcement Learning

abstract: Most of the algorithms for inverse reinforcement learning (IRL) assume that the reward function is a linear function of the pre-defined state and action features. However, it is often difficult to manually specify the set of features that can make the true reward function representable as a linear function. We propose a Bayesian nonparametric approach to identifying useful composite features for learning the reward function. The composite features are assumed to be the logical conjunctions of the pre-defined atomic features so that we can represent the reward function as a linear function of the composite features. We empirically show that our approach is able to learn composite features that capture important aspects of the reward function on synthetic domains, and predict taxi drivers' behaviour with high accuracy on a real GPS trace dataset.


title: Intrusion Detection System Methodologies Based on Data Analysis

abstract: With the rapidly growing and wide spread use of computer networks the number of new threats has grown extensively. Intrusion and detection system can only identifying and protecting the attacks successfully. In this paper we focuses on detailed study of different types of attacks using in KDD99CUP Data Set and classification of IDS are also presented. They are Anomaly Detection System, Misuse Detection Systems. Different Data Analysis Methodologies also explained for IDS. To identify eleven data computing techniques associated with IDS are divided groups into categories. Some of those methods are based on computation such as Fuzzy logic and Bayesian networks, some are Artificial Intelligence such as Expert Systems, agents and neural networks some other are biological concepts such as Genetics and Immune systems.


title: Big data Analytics in the Education Sector: Needs, Opportunities and Challenges

abstract: Academic institutions are now drawing attention in finding methods for making effective learning process, for identifying learner’s achievements and weakness, for tracing academic progress and also for predicting future performance. People’s increased expectation for accountability and transparency makes it necessary to implement big data analytics in the educational institution. But not all the educationalist and administrators are ready to take the challenge. So, it is now obvious to know about the necessity and opportunity as well as challenges of implementing big data analytics. This paper will describe the needs, opportunities and challenges of implementing big data analytics in the education sector.


title: Neural Reranking for Named Entity Recognition

abstract: We propose a neural reranking system for named entity recognition (NER). The basic idea is to leverage recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as \textit{Barack Obama}, into their entity types, such as \textit{PER}. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, "PER was born in LOC" can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.


Harvesting strategies of fuelwood and kraalwood users at Machibi : identifying the driving factors and feedbacks


in a Digital Age Modeling Internet Effects on Civic Participation


title: The evolutionary history of vertebrate cranial placodes II. Evolution of ectodermal patterning

abstract: Cranial placodes are evolutionary innovations of vertebrates. However, they most likely evolved by redeployment, rewiring and diversification of preexisting cell types and patterning mechanisms. In the second part of this review we compare vertebrates with other animal groups to elucidate the evolutionary history of ectodermal patterning. We show that several transcription factors have ancient bilaterian roles in dorsoventral and anteroposterior regionalisation of the ectoderm. Evidence from amphioxus suggests that ancestral chordates then concentrated neurosecretory cells in the anteriormost non-neural ectoderm. This anterior proto-placodal domain subsequently gave rise to the oral siphon primordia in tunicates (with neurosecretory cells being lost) and anterior (adenohypophyseal, olfactory, and lens) placodes of vertebrates. Likewise, tunicate atrial siphon primordia and posterior (otic, lateral line, and epibranchial) placodes of vertebrates probably evolved from a posterior proto-placodal region in the tunicate-vertebrate ancestor. Since both siphon primordia in tunicates give rise to sparse populations of sensory cells, both proto-placodal domains probably also gave rise to some sensory receptors in the tunicate-vertebrate ancestor. However, proper cranial placodes, which give rise to high density arrays of specialised sensory receptors and neurons, evolved from these domains only in the vertebrate lineage. We propose that this may have involved rewiring of the regulatory network upstream and downstream of Six1/2 and Six4/5 transcription factors and their Eya family cofactors. These proteins, which play ancient roles in neuronal differentiation were first recruited to the dorsal non-neural ectoderm in the tunicate-vertebrate ancestor but subsequently probably acquired new target genes in the vertebrate lineage, allowing them to adopt new functions in regulating proliferation and patterning of neuronal progenitors.


title: End-to-End Learning of Driving Models from Large-Scale Video Datasets

abstract: Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm. We provide a novel large-scale dataset of crowd-sourced driving behavior suitable for training our model, and report results predicting the driver action on held out sequences across diverse conditions.


title: Pink and blue: the color of gender

abstract: Assigning color to gender is mostly a twentieth-centurytrait. It should be noted that it is a practice limited mostoften to Western Europe and the Americas. It would alsoseem that the effect of color-coded gender differences (pinkfor girls, blue for boys) existed oppositely initially [10].In fact, this reversal of what we consider “normal” wasconsidered conventional, even in the early twentiethcentury. The debate of when and why pink and blue cameinto fashion to designate gender rages on, but almost everyargument alludes to a passage in the novel Little Women,published in 1868: “Amy ties a pink bow and a blue bowon Meg’s twins Daisy and Demi, so people will know thedifference between the girl and the boy.” This is said to bedone in the “French style,” suggesting that it might havebeen possible in France that pink and blue were alreadygender-specific.However, there is evidence that this practice was notalways common or always done throughout much ofEurope. In fact, in the nineteenth century, parents dressedinfants in white dresses, suggesting that color and dresseswere not used to distinguish between girls and boys [3].At one point, pink was considered more of a boy’s color,as a watered-down, bold, dramatic red, which is a fiercecolor. Instead, blue was considered more for girls. Probablythis choice was affected by the fact that blue, especiallydark blue, was associated with the Virgin Mary in ChristianEurope. In fact, painters often mixed lapis lazuli into paintsto depict what was considered the most sacred feminineicon.The Sunday Sentinal, an American newspaper, in 1914advised mothers: “If you like the color note on the littleone’s garments, use pink for the boy and blue for the girl, ifyou are a follower of convention” (March 29, 1914).Similarly, Ladies Home Journal informed: “There has beena great diversity of opinion on the subject, but the generallyaccepted rule is pink for the boy and blue for the girl. Thereason is that pink being a more decided and stronger coloris more suitable for the boy, while blue, which is moredelicate and dainty, is prettier for the girl” (June, 1918).The current pink for girls and blue for boys was notuniform until the 1950s [11]. It would seem that NaziGermany had something to do with the association of pinkwith femininity: catholic traditions in Germany andneighboring countries reverse the current color coding,because of the strong association of blue with the VirginMary; the Nazis in their concentration camps use a pinktriangle to identify homosexuals. The Nazi’s choice of pinksuggests that, by the 1930s, it was a color that in Germanyhad become associate with girls.Thus, Dress Maker Magazine stated: “The preferredcolor to dress young boys in was pink! Blue was reservedfor girls as it was considered the paler, more dainty of thetwo colors, and pink was thought to be the stronger (akin tored). It was not until WWII that the colors were reversedand pink was used for girls and blue for boys...”After World War II, blue was used extensively for men’suniforms. Therefore, blue became associated as more of amasculine color. From the 1940s onward, pink was pushedas a woman’s color. “Think pink” was the marketing sloganto convince women to embrace their femininity.The 1950s featured a virtual color explosion, not only inclothing, but also in things like appliances and furniture.Dressing children in pink and blue to specifically denotegender suggested the rising middle class and above. In


title: The Similarity for Nominal Variables Based on F-Divergence

abstract: Measuring the similarity between nominal variables is an important problem in data mining. It's the base to measure the similarity of data objects which contain nominal variables. There are two kinds of traditional methods for this task, the first one simply distinguish variables by same or not same while the second one measures the similarity based on co-occurrence with variables of other attributes. Though they perform well in some conditions, but are still not enough in accuracy. This paper proposes an algorithm to measure the similarity between nominal variables of the same attribute based on the fact that the similarity between nominal variables depends on the relationship between subsets which hold them in the same dataset. This algorithm use the difference of the distribution which is quantified by f-divergence to form feature vector of nominal variables. The theoretical analysis helps to choose the best metric from four most common used forms of f-divergence. Time complexity of the method is linear with the size of dataset and it makes this method suitable for processing the large-scale data. The experiments which use the derived similarity metrics with K-modes on extensive UCI datasets demonstrate the effectiveness of our proposed method.


title: An Overview of Automated Scoring of Essays

abstract: Automated Essay Scoring (AES) is defined as the computer technology that evaluates and scores the written prose (Shermis & Barrera, 2002; Shermis & Burstein, 2003; Shermis, Raymat, & Barrera, 2003). AES systems are mainly used to overcome time, cost, reliability, and generalizability issues in writing assessment (Bereiter, 2003; Burstein, 2003; Chung & O’Neil, 1997; Hamp-Lyons, 2001; Myers, 2003; Page, 2003; Rudner & Gagne, 2001; Rudner & Liang, 2002; Sireci & Rizavi, 1999; http://people.emich.edu). AES continue attracting the attention of public schools, universities, testing companies, researchers and educators (Burstein, Kukich, Wolff, Lu, & Chodorow, 1998; Shermis & Burstein, 2003; Sireci & Rizavi, 1999). The main purpose of this article is to provide an overview of current approaches to AES. After describing the most widely used AES systems (i.e., Project Essay Grader (PEG), Intelligent Essay Assessor (IEA), E-rater and Criterion, IntelliMetric and MY Access, and Bayesian Essay Test Scoring System (BETSY)), main characteristics of these systems will be discussed and current issues regarding the use of them both in low-stakes assessment (in classrooms) and high-stakes assessment (as standardized tests) will be discussed in this article.


Alpha and the History of Digital Compositing


title: Potent Antibacterial Peptides Generated by Pepsin Digestion of Bovine Lactoferrin

abstract: Abstract The antibacterial properties of enzymatic hydrolysates of bovine lactoferrin were examined to determine whether active peptides are produced from this protein. Hydrolysates prepared by cleavage of lactoferrin with porcine pepsin, cod pepsin, or acid protease from Penicillium duponti showed strong activity against Escherichia coli O111, whereas hydrolysates produced by trypsin, papain, or other neutral proteases were much less active. Low molecular weight peptides generated by porcine pepsin cleavage of lactoferrin showed broad-spectrum antibacterial activity, inhibiting the growth of a number of Gram-negative and Gram-positive species, including strains that were resistant to native lactoferrin. The antibacterial potency of the hydrolysate was at least eightfold greater than that of undigested lactoferrin with all strains tested. The active peptides retained their activity in the presence of added iron, unlike native lactoferrin. The effect of the hydrolysate was bactericidal as indicated by a rapid loss of viability of E. coli O111. The lactoferrin hydrolysate described in the present study has commercial value as a natural preservative agent for use in foods and cosmetics, and as a functional component of new clinical foods for prevention or treatment of gastrointestinal disease.


title: Optimized Adaptive Perturb and Observe Maximum Power Point Tracking Control for Photovoltaic Generation

abstract: The power extracted from PV arrays is usually maximized using maximum power point tracking algorithms. One of the most widely used techniques is the perturb & observe algorithm, which periodically perturbs the operating point of the PV array, sometime with an adaptive perturbation step, and compares the PV power before and after the perturbation. This paper analyses the most suitable perturbation step to optimize maximum power point tracking performance and suggests a design criterion to select the parameters of the controller. Using this proposed adaptive step, the MPPT perturb & observe algorithm achieves an excellent dynamic response by adapting the perturbation step to the actual operating conditions of the PV array. The proposed algorithm has been validated and tested in a laboratory using a dual input inductor push-pull converter. This particular converter topology is an efficient interface to boost the low voltage of PV arrays and effectively control the power flow when input or output voltages are variable. The experimental results have proved the superiority of the proposed algorithm in comparison of traditional perturb & observe and incremental conductance techniques.


title: Visual Analogies between Atari Games for Studying Transfer Learning in RL

abstract: In this work, we ask the following question: Can visual analogies, learned in an unsupervised way, be used in order to transfer knowledge between pairs of games and even play one game using an agent trained for another game? We attempt to answer this research question by creating visual analogies between a pair of games: a source game and a target game. For example, given a video frame in the target game, we map it to an analogous state in the source game and then attempt to play using a trained policy learned for the source game. We demonstrate convincing visual mapping between four pairs of games (eight mappings), which are used to evaluate three transfer learning approaches.


Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design


A Multi-Entity Page Rank Algorithm.


title: Applications of the potential algebras of the two-dimensional Dirac-like operators

abstract: Abstract Potential algebras can be used effectively in the analysis of the quantum systems. In the article, we focus on the systems described by a separable, 2×2 matrix Hamiltonian of the first order in derivatives. We find integrals of motion of the Hamiltonian that close centrally extended s o ( 3 ) , s o ( 2 , 1 ) or oscillator algebra. The algebraic framework is used in construction of physically interesting solvable models described by the ( 2 + 1 ) dimensional Dirac equation. It is applied in description of open-cage fullerenes where the energies and wave functions of low-energy charge-carriers are computed. The potential algebras are also used in construction of shape-invariant, one-dimensional Dirac operators. We show that shape-invariance of the first-order operators is associated with the N = 4 nonlinear supersymmetry which is represented by both local and nonlocal supercharges. The relation to the shape-invariant non-relativistic systems is discussed as well.


title: Three-dimensional plotted scaffolds with controlled pore size gradients: Effect of scaffold geometry on mechanical performance and cell seeding efficiency

abstract: Abstract Scaffolds produced by rapid prototyping (RP) techniques have proved their value for tissue engineering applications, due to their ability to produce predetermined forms and structures featuring fully interconnected pore architectures. Nevertheless, low cell seeding efficiency and non-uniform distribution of cells remain major limitations when using such types of scaffold. This can be mainly attributed to the inadequate pore architecture of scaffolds produced by RP and the limited efficiency of cell seeding techniques normally adopted. In this study we aimed at producing scaffolds with pore size gradients to enhance cell seeding efficiency and control the spatial organization of cells within the scaffold. Scaffolds based on blends of starch with poly(e-caprolactone) featuring both homogeneously spaced pores (based on pore sizes of 0.75 and 0.1 mm) and pore size gradients (based on pore sizes of 0.1–0.75–0.1 and 0.75–0.1–0.75 mm) were designed and produced by three-dimensional plotting. The mechanical performance of the scaffolds was characterized using dynamic mechanical analysis (DMA) and conventional compression testing under wet conditions and subsequently characterized using scanning electron microscopy and micro-computed tomography. Osteoblast-like cells were seeded onto such scaffolds to investigate cell seeding efficiency and the ability to control the zonal distribution of cells upon seeding. Scaffolds featuring continuous pore size gradients were originally produced. These scaffolds were shown to have intermediate mechanical and morphological properties compared with homogenous pore size scaffolds. The pore size gradient scaffolds improved seeding efficiency from ∼35% in homogeneous scaffolds to ∼70% under static culture conditions. Fluorescence images of cross-sections of the scaffolds revealed that scaffolds with pore size gradients induce a more homogeneous distribution of cells within the scaffold.


title: Interventions for preventing infectious complications in haemodialysis patients with central venous catheters

abstract: Background ::: Central venous catheters (CVC) continue to play a prominent role in haemodialysis vascular access with 46% to 70% of patients commencing haemodialysis via a CVC. CVC access is associated with catheter-related infections, increased patient hospitalisations and death due to infection. A variety of interventions are used to prevent CVC infection. ::: ::: Objectives ::: To evaluate the benefits and harms of prophylactic topical antimicrobials, topical antiseptics, medicated and non-medicated dressings on infectious complications among haemodialysis patients with CVC. ::: ::: Search methods ::: We searched the Cochrane Renal Group's specialised register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE and reference lists of articles without language restriction. ::: ::: Selection criteria ::: We included randomised controlled trials (RCTs) and quasi-RCTs investigating any intervention that prevented infectious complications among haemodialysis patients with CVC. We excluded antimicrobial impregnated CVC or CVC using locking solutions with antimicrobial properties. ::: ::: Data collection and analysis ::: Two authors assessed study quality and extracted data. Dichotomous outcomes were expressed as risk ratios (RR) with 95% confidence intervals (CI) and continuous outcomes as mean differences (MD). ::: ::: Main results ::: Ten studies (786 patients) were included. Mupirocin ointment reduced the risk of catheter-related bacteraemia (RR 0.17, 95%CI 0.07 to 0.43) and had a significant effect on catheter-related infections caused by S. aureus. The risk of catheter-related bacteraemia was reduced by polysporin (RR 0.40, 95%CI 0.19 to 0.86) and povidone-iodine ointment (RR 0.10, 95%CI 0.01 to 0.72). Subgroup analysis suggested mupirocin (RR 0.12, 95%CI 0.01 to 2.13) and povidone-iodine ointment (RR 0.84, 95%CI 0.24 to 2.98) had no effect on all-cause mortality while polysporin ointment showed a significant reduction (RR 0.22, 95%CI 0.07 to 0.74). Mortality related to infection was not reduced by mupirocin, polysporin or povidone-iodine ointment. Topical honey did not reduce the risk of exit site infection (RR 0.45, 95%CI 0.10 to 2.11) or catheter-related bacteraemia (RR 0.80, 95%CI 0.37 to 1.73). Transparent polyurethane dressing compared to dry gauze dressing did not reduce the risk of CVC or exit site infection, or catheter-related bacteraemia. ::: ::: Authors' conclusions ::: Mupirocin ointment appears effective in reducing the risk of catheter-related bacteraemia. Insufficient reporting on mupirocin resistance was noted and needs to be considered in future studies. A lack of high quality data on the routine use of povidone-iodine ointment, polysporin ointment and topical honey warrant larger RCTs. Insufficient data were available to determine which dressing type (transparent polyurethane or dry gauze dressing) has the lowest risk of catheter-related infections.


title: Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

abstract: Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS) data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost) instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines) to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity.


title: Understanding latency variations of black box services

abstract: Data centers run many services that impact millions of users daily. In reality, the latency of each service varies from one request to another. Existing tools allow to monitor services for performance glitches or service disruptions, but typically they do not help understanding the variations in latency. We propose a general framework for understanding performance of arbitrary black box services. We consider a stream of requests to a given service with their monitored attributes, as well as latencies of serving each request. We propose what we call the multi-dimensional f-measure, that helps for a given interval to identify the subset of monitored attributes that explains it. We design algorithms that use this measure not only for a fixed latency interval, but also to explain the entire range of latencies of the service by segmenting it into smaller intervals. We perform a detailed experimental study with synthetic data, as well as real data from a large search engine. Our experiments show that our methods automatically identify significant latency intervals together with request attributes that explain them, and are robust.


Concepts for Modeling Enterprise Architectures


title: Networks and the Epidemiology of Infectious Disease

abstract: The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues.


title: 12 APPLYING MACHINE LEARNING TECHNIQUES TO ANALYSIS OF GENE EXPRESSION DATA: CANCER DIAGNOSIS

abstract: Classification of patient samples is a crucial aspect of cancer diagnosis. DNA hybridization arrays simultaneously measure the expression levels of thousands of genes and it has been suggested that gene expression may provide the additional information needed to improve cancer classification and diagnosis. This paper presents methods for analyzing gene expression data to classify cancer types. Machine learning techniques, such as Bayesian networks, neural trees, and radial basis function (RBF) networks, are used for the analysis of the CAMDA Data Set 2. These techniques have their own properties including the ability of finding important genes for cancer classification, revealing relationships among genes, and classifying cancer. This paper reports on comparative evaluation of the experimental results of these methods.


title: Electrospun Tropoelastin for Delivery of Therapeutic Adipose-Derived Stem Cells to Full-Thickness Dermal Wounds

abstract: Objective: To evaluate the physiological effects of electrospun tropoelastin scaffolds as therapeutic adipose-derived stem cell (ADSC) delivery vehicles for the treatment of full-thickness dermal wounds. Approach: Using the process of electrospinning, several prototype microfiber scaffolds were created with tropoelastin. Initial testing of scaffold biocompatibility was performed in vitro through ADSC culture, followed by scanning electron microscopy (SEM) for assessment of ADSC attachment, morphology, and new extracellular matrix (ECM) deposition. The wound healing effects of ADSC-seeded scaffolds were then evaluated in a murine dermal excisional wound model. Results: For the in vitro study, SEM revealed exceptional biocompatibility of electrospun tropoelastin for ADSCs. In the wound-healing study, ADSC-treated groups demonstrated significantly enhanced wound closure and epithelial thickness compared to controls. Innovation: This is the first report on the use of tropoelastin-based biomaterials as delivery vehicles for therapeutic ADSCs. Conclusion: We have demonstrated that tropoelastin-based ADSC delivery vehicles significantly accelerate wound healing compared to controls that represent the current clinical standard of care. Furthermore, the unique mechanical and biochemical characteristics of tropoelastin may favor its use over other biological or synthetic scaffolds for the treatment of certain pathologies due to its unique intrinsic mechanical properties.


title: Working Memory: A View from Neuroimaging

abstract: Abstract We have used neuroimaging techniques, mainly positron emission tomography (PET), to study cognitively driven issues about working memory. Two kinds of experiments are described. In the first kind, we employ standard subtraction logic to uncover the basic components of working memory. These studies indicate that: (a) there are different working-memory systems for spatial, object, and verbal information (with the spatial system localized more in the right hemisphere, and the verbal system more in the left hemisphere); (b) within at least the spatial and verbal systems, separable components seem to be responsible for the passive storage of information and the active maintenance of information (with the storage component being localized more in the back of the brain, and the maintenance component in the front); and (c) there may be separate components responsible for processing the contents of working memory (localized in prefrontal cortex). In our second kind of experiment we have focused on verbal working memory and incrementally varied one task parameter—memory load—in an effort to obtain a more fine-grained analysis of the system's operations. The results indicate that all relevant components of the system show some increase in activity with increasing memory load (e.g., the frontal regions responsible for verbal rehearsal show incremental increases in activation with increasing memory load). In contrast, brain regions that are not part of the working-memory system show no effect of memory load. Furthermore, the time courses of activation may differ for regions that are sensitive to load versus those that are not. Taken together, our results provide support for certain cognitive models of working memory (e.g., Baddeley, 1992) and also suggest some distinctions that these models have not emphasized. And more fundamentally, the results provide a neural base for cognitive models of working memory.


title: The GuideCane-a computerized travel aid for the active guidance of blind pedestrians

abstract: This paper introduces the GuideCane, a novel device designed to help blind or visually impaired travellers to navigate safely and quickly among obstacles and other hazards faced by blind pedestrians. The GuideCane, currently under development at the University of Michigan's Mobile Robotics Lab, comprises of a long handle and a "sensor head" unit that is attached at the distal end of the handle. The sensor head is mounted on a steerable but unpowered two-wheeled steering axle. During operation, the user pushes the lightweight GuideCane ahead of him/her. Ultrasonic sensors mounted on the sensor head detect obstacles and steer the device around it. The user feels the steering command as a very noticeable physical force through the handle and is able to follow the GuideCane's path easily and without any conscious effort.


title: Chapter 4 USING ONTOLOGIES IN THE SEMANTIC WEB: A SURVEY

abstract: The Semantic Web is well recognized as an effective infrastructure to enhance visibility of knowledge on the Web. The core of the Semantic Web is “ontology”, which is used to explicitly represent our conceptualizations. Ontology engineering in the Semantic Web is primarily supported by languages such as RDF, RDFS and OWL. This chapter discusses the requirements of ontology in the context of the Web, compares the above three languages with existing knowledge representation formalisms, and surveys tools for managing and applying ontology. Advantages of using ontology in both knowledge-base-style and database-style applications are demonstrated using three real world applications.


title: A Calculator for Pareto Points

abstract: This paper presents the Pareto calculator, a tool for compositional computation of Pareto points, based on the algebra of Pareto points. The tool is a useful instrument for multidimensional optimisation problems, design-space exploration and development of quality management and control strategies. Implementations and their complexity of the operations of the algebra are discussed. In particular, a generalisation of the well-known divide-and-conquer algorithm was discussed to compute the Pareto points (optimal solutions) from a set of possible configurations, also known as the maximal vector or skyline problem. The generalisation lies in the fact that we allow for partially ordered domains instead of only totally ordered ones. The calculator is available through the following url: http://www.es.ele.tue.nl/pareto


title: Matching events in a content-based subscription system

abstract: Content-based subscription systems are an emerging alternative to traditional publish-subscribe systems, because they permit more flexible subscriptions along multiple dimensions. In these systems, each subscription is a predicate which may test arbitrary attributes within an event. However, the matching problem for content-based systems — determining for each event the subset of all subscriptions whose predicates match the event — is still an open problem. We present an efficient, scalable solution to the matching problem. Our solution has an expected time complexity that is sub-linear in the number of subscriptions, and it has a space complexity that is linear. Specifically, we prove that for predicates reducible to conjunctions of elementary tests, the expected time to match a random event is no greater than O(N 1 ) where N is the number of subscriptions, and is a closed-form expression that depends on the number and type of attributes (in some cases, 1=2). We present some optimizations to our algorithms that improve the search time. We also present the results of simulations that validate the theoretical bounds and that show acceptable performance levels for tens of thousands of subscriptions. Department of Computer Science, Cornell University, Ithaca, N.Y. 14853-7501, aguilera@cs.cornell.edu IBM T.J. Watson Research Center, Yorktown Heights, N.Y. 10598, fstrom, sturman, tusharg@watson.ibm.com Department of Computer Science, University of Illinois at Urbana-Champaign, 1304 W. Springfield Ave, Urbana, I.L. 61801, astley@cs.uiuc.edu


title: RASSH - Reinforced adaptive SSH honeypot

abstract: The wide spread of cyber-attacks made the need of gathering as much information as possible about them, a real demand in nowadays global context. The honeypot systems have become a powerful tool on the way to accomplish that. Researchers have already focused on the development of various honeypot systems but the fact that their administration is time consuming made clear the need of self-adaptive honeypot system capable of learning from their interaction with attackers. This paper presents a self-adaptive honeypot system we are developing that tries to overlap some of the disadvantaged that existing systems have. The proposed honeypot is a medium interaction system developed using Python and it emulates a SSH (Secure Shell) server. The system is capable of interacting with the attackers by means of reinforcement learning algorithms.


title: SiC power devices with low on-resistance for fast switching applications

abstract: Silicon carbide switching devices exhibit superior properties compared to silicon devices. Low specific on-resistance for high breakdown voltages is believed to be the most outstanding feature of SiC power switching devices. In this paper, MOSFETs and JFETs capable to block 1800 V with a specific on-resistance of 47 m/spl Omega/ cm/sup 2/ and 14.5 m/spl Omega/ cm/sup 2/, resp., are discussed. However, there are additional advantages making SiC devices attractive for the system designer. The authors present fast recovery of the 6H-SiC MOSFET reverse diode (Q/sub rr/ 30 nC, t/sub rr/ 20 ns) and fast switching as well as short circuit capability (1 ms) of vertical VJFETs. Finally, a short outlook to future SiC switching devices is given.


title: Cooperative output regulation of multi-agent systems

abstract: In this paper, we will describe the cooperative output regulation problem for multi-agent systems. The formulation of this problem generalizes the leader-following consensus problem in the sense that the problem simultaneously addresses asymptotic tracking and disturbance rejection, accounts for model uncertainty, and accommodates a general leader system. Like the classical output regulation problem, this problem can be handled by the feedforward control approach and the internal model approach. These two approaches can also be used to handle other cooperative control problems of multi-agent systems such as formation, rendezvous, and flocking. We will also briefly overview some recent results on this problem, and point out some possible new avenues of this research problem.


title: IoT-academia architecture: A profound approach

abstract: The advent of a new era of smart objects and networks of things inspired us to present a unified proposal for a model of a smart academic forum. It's an IoT architectural model that spans almost all vital divisions of the academic forum. We present IoT-architecture showing how academic services as well as data records could be delivered in an automated way to make academic life easier. This work elucidates the process of deriving the system concrete architectural model from IoT-A an outcome of European IoT-A project. A context view along with its IoT model is also presented. Finally, a connectivity model is described to show the interactivity among the main suggested architecture components.


title: Orbitocraniobasal Approach for Anterior Communicating Artery Aneurysms

abstract: We describe an orbitofrontotemporobasal craniotomy technique that allows excellent access to anterior communicating artery aneurysms. This orbitocraniobasal approach is particularly useful for the surgical treatment of ruptured aneurysms in the acute stage of subarachnoid hemorrhage, when retraction of the brain needs to be kept to a minimum. With this approach, retraction of the orbital contents decreases the amount of retraction of the brain to such an extent that a brain spatula is not necessary for access to the anterior communicating artery complex. The procedure is described, as is a modification of the approach for removal of large tumors on the skull base.


title: Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation

abstract: Due to the expensive and time-consuming annotations (e.g., segmentation) for real-world images, recent works in computer vision resort to synthetic data. However, the performance on the real image often drops significantly because of the domain shift between the synthetic data and the real images. In this setting, domain adaptation brings an appealing option. The effective approaches of domain adaptation shape the representations that (1) are discriminative for the main task and (2) have good generalization capability for domain shift. To this end, we propose a novel loss function, i.e., Conservative Loss, which penalizes the extreme good and bad cases while encouraging the moderate examples. More specifically, it enables the network to learn features that are discriminative by gradient descent and are invariant to the change of domains via gradient ascend method. Extensive experiments on synthetic to real segmentation adaptation show our proposed method achieves state of the art results. Ablation studies give more insights into properties of the Conservative Loss. Exploratory experiments and discussion demonstrate that our Conservative Loss has good flexibility rather than restricting an exact form.


CORR Insights®: What Are the MCIDs for PROMIS, NDI, and ODI Instruments Among Patients With Spinal Conditions?


title: Assessment of lexical semantic judgment abilities in alcohol-dependent subjects: An fMRI study

abstract: Neuropsychological studies have shown that alcohol dependence is associated with neurocognitive deficits in tasks requiring memory, perceptual motor skills, abstraction and problem solving, whereas language skills are relatively spared in alcoholics despite structural abnormalities in the language-related brain regions. To investigate the preserved mechanisms of language processing in alcohol-dependents, functional brain imaging was undertaken in healthy controls (n=18) and alcohol-dependents (n=16) while completing a lexical semantic judgment task in a 3 T MR scanner. Behavioural data indicated that alcohol-dependents took more time than controls for performing the task but there was no significant difference in their response accuracy. fMRI data analysis revealed that while performing the task, the alcoholics showed enhanced activations in left supramarginal gyrus, precuneus bilaterally, left angular gyrus, and left middle temporal gyrus as compared to control subjects. The extensive activations observed in alcoholics as compared to controls suggest that alcoholics recruit additional brain areas to meet the behavioural demands for equivalent task performance. The results are consistent with previous fMRI studies suggesting compensatory mechanisms for the execution of task for showing an equivalent performance or decreased neural efficiency of relevant brain networks. However, on direct comparison of the two groups, the results did not survive correction for multiple comparisons; therefore, the present findings need further exploration.


title: Examining the Contribution of Information Technology Toward Productivity and Profitability in U.S. Retail Banking

abstract: There has been much debate on whether or not the investment in Information Technology (IT) provides improvements in productivity and business efficiency. Several studies both at the industry-level and at the firm-level have contributed differing understandings of this phenomenon. Of late, however, firm-level studies, primarily in the manufacturing sector, have shown that there are significant positive contributions from IT investments toward productivity. This study examines the effect of IT investment on both productivity and profitability in the retail banking sector. Using data collected through a major study of retail banking institutions in the United States, this paper concludes that additional investment in IT capital may have no real benefits and may be more of a strategic necessity to stay even with the competition. However, the results indicate that there are substantially high returns to increase in investment in IT labor, and that retail banks need to shift their emphasis in IT investment from capital to labor.


title: A survey on automated dynamic malware-analysis techniques and tools

abstract: Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.


title: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU

abstract: Abstract For large-scale sparse matrices, SpMV cannot be processed on GPU using the common storage formats because of the memory limitation. In addition, the parallel effect is poor using general formats for the sparse matrices with extremely uneven distribution of non-zero elements, which leads to performance deterioration. This paper presents an optimal partitioning strategy based on the distribution of non-zero elements in a sparse matrix to improve the performance of SpMV, and uses a hybrid format, which mixes CSR and ELL formats, to store the blocks partitioned from the sparse matrix. The hybrid blocked format has better compression effect and more uniform distribution of non-zero elements, which can be suitable for more types of sparse matrices. Our partitioning strategy is proven to be optimal, which can yield the minimum parallel execution time on GPU.


title: Self-disclosure on social networking sites, positive feedback, and social capital among Chinese college students

abstract: Drawing on social capital theory, this study examined whether college students' self-disclosure on a social networking site was directly associated with social capital, or related indirectly through the degree of positive feedback students got from Internet friends. Structural equation models applied to anonymous, self-report survey data from 264 first-year students at 3 universities in Beijing, China, indicated direct effects on bridging social capital and indirect effects on bonding social capital. Effects remained significant, though modest in magnitude, after controlling for social skills level. Findings suggest ways in which social networking sites can foster social adjustment as an adolescent transition to residential college environments.


title: A toolkit for managing user attention in peripheral displays

abstract: Traditionally, computer interfaces have been confined to conventional displays and focused activities. However, as displays become embedded throughout our environment and daily lives, increasing numbers of them must operate on the periphery of our attention. Peripheral displays can allow a person to be aware of information while she is attending to some other primary task or activity. We present the Peripheral Displays Toolkit (PTK), a toolkit that provides structured support for managing user attention in the development of peripheral displays. Our goal is to enable designers to explore different approaches to managing user attention. The PTK supports three issues specific to conveying information on the periphery of human attention. These issues are abstraction of raw input, rules for assigning notification levels to input, and transitions for updating a display when input arrives. Our contribution is the investigation of issues specific to attention in peripheral display design and a toolkit that encapsulates support for these issues. We describe our toolkit architecture and present five sample peripheral displays demonstrating our toolkit's capabilities.


title: HeteroMF: recommendation in heterogeneous information networks using context dependent factor models

abstract: With the growing amount of information available online, recommender systems are starting to provide a viable alternative and complement to search engines, in helping users to find objects of interest. Methods based on Matrix Factorization (MF) models are the state-of-the-art in recommender systems. The input to MF is user feedback, in the form of a rating matrix. However, users can be engaged in interactions with multiple types of entities across different contexts, leading to multiple rating matrices. In other words, users can have interactions in a heterogeneous information network. Generally, in a heterogeneous network, entities from any two entity types can have interactions with a weight (rating) indicating the level of endorsement. Collective Matrix Factorization (CMF) has been proposed to address the recommendation problem in heterogeneous networks. However, a main issue with CMF is that entities share the same latent factor across different contexts. This is particularly problematic in two cases: Latent factors for entities that are cold-start in a context will be learnt mainly based on the data from other contexts where these entities are not cold-start, and therefore the factors are not properly learned for the cold-start context. Also, if a context has more data compared to another context, then the dominant context will dominate the learning process for the latent factors for entities shared in these two contexts. In this paper, we propose a context-dependent matrix factorization model, HeteroMF, that considers a general latent factor for entities of every entity type and context-dependent latent factors for every context in which the entities are involved. We learn a general latent factor for every entity and transfer matrices for every context to convert the general latent factors into a context-dependent latent factor. Experiments on two real life datasets from Epinions and Flixster demonstrate that HeteroMF substantially outperforms CMF, particularly for cold-start entities and for contexts where interactions in one contexts are dominated by other contexts.


title: Immunopathogenesis of IBD: current state of the art

abstract: IBD is a chronic inflammatory condition of the gastrointestinal tract encompassing two main clinical entities: Crohn's disease and ulcerative colitis. Although Crohn's disease and ulcerative colitis have historically been studied together because they share common features (such as symptoms, structural damage and therapy), it is now clear that they represent two distinct pathophysiological entities. Both Crohn's disease and ulcerative colitis are associated with multiple pathogenic factors including environmental changes, an array of susceptibility gene variants, a qualitatively and quantitatively abnormal gut microbiota and a broadly dysregulated immune response. In spite of this realization and the identification of seemingly pertinent environmental, genetic, microbial and immune factors, a full understanding of IBD pathogenesis is still out of reach and, consequently, treatment is far from optimal. An important reason for this unsatisfactory situation is the currently limited comprehension of what are the truly relevant components of IBD immunopathogenesis. This article will comprehensively review current knowledge of the classic immune components and will expand the concept of IBD immunopathogenesis to include various cells, mediators and pathways that have not been traditionally associated with disease mechanisms, but that profoundly affect the overall intestinal inflammatory process.


title: Predicting links in ego-networks using temporal information

abstract: Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network. In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships. As the structural information is very poor, we rely on another source of information to predict links among egos' neighbors: the timing of interactions. We define several features to capture different kinds of temporal information and apply machine learning methods to combine these various features and improve the quality of the prediction. We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features which prove themselves to perform well in this context, in particular the temporal profile of interactions and elapsed time between contacts.


title: Reliable detection and skew correction method of license plate for PTZ camera-based license plate recognition system

abstract: The skewness of license plates affects negatively all processing steps of license plate recognition including character segmentation and character classification. The skewness of the license plate in the captured image may appear severely under PTZ camera environments since license plate appears to have various postures in the captured image due to panning and tilting positions of PTZ cameras. Thus far, most of previous works on this subject have mainly resolved skewness problem due to rotation in plane, but not rotation in depth. In this paper, by utilizing planar projective transformation (planar homography), we propose a reliable skew correction method of license plates which handles also the skewness due to rotation in depth. For more reliable deskewing process, the license plate needs to be detected more reliably so that this paper also proposes a reliable license plate detection method. The effectiveness of the proposed skew correction method as well as the proposed license plate detection method is verified though experiments.


title: Mapping data into colour gamuts: Using interaction to increase usability and reduce complexity

abstract: Abstract An interactive system for mapping scientific data into the perceptual gamuts of colour devices is presented. Interaction provides the critical feedback that allows the user to concentrate on the problem domain, rather than be distracted by the combined complexities of the data mapping and realisation problems. By careful interface design, the combined problems of choosing appropriate colour representations, and of achieving colour-consistent visualisation and reproduction, are reduced to an intuitively straightforward set of operations. The interaction draws the user into understanding the limitations of specific colour devices within a device-independent representation, a spatially-represented perceptually uniform colour space. Colour-corrected feedback allows the user to see directly the implications of data mapping choices and strategies for dealing with problems imposed by device limitations, such as out-of-gamut points.


title: Bottom-Up Segmentation for Top-Down Detection

abstract: In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those regions. Our approach allows every detection hypothesis to select a segment (including void), and scores each box in the image using both the traditional HOG filters as well as a set of novel segmentation features. Thus our model ``blends'' between the detector and segmentation models. Since our features can be computed very efficiently given the segments, we maintain the same complexity as the original DPM. We demonstrate the effectiveness of our approach in PASCAL VOC 2010, and show that when employing only a root filter our approach outperforms Dalal & Triggs detector on all classes, achieving 13% higher average AP. When employing the parts, we outperform the original DPM in $19$ out of $20$ classes, achieving an improvement of 8% AP. Furthermore, we outperform the previous state-of-the-art on VOC 2010 test by 4%.


title: Spatial Resolution Effect of Precipitation Data on SWAT Calibration and Performance: Implications for CEAP

abstract: Precipitation data sets representing four spatial resolutions were used to evaluate the performance of the Soil and Water Assessment Tool (SWAT) on the basis of reproducing measured streamflow, and to show differences in model parameters when different precipitation data sets are used to calibrate the model. The experiment was conducted on the 786 km 2 Ft. Cobb Reservoir experimental watershed (FCREW) in southwestern Oklahoma. Precipitation data sets included the National Weather Service (NWS) cooperative weather network (Co-op), NWS next-generation radar precipitation estimates (NEXRAD), the University of Oklahoma and Oklahoma State University's joint state-wide weather station network (Mesonet), and the USDA-ARS weather station network (Micronet) deployed in the FCREW. The FCREW was divided into three main subwatersheds (Cobb, Lake, and Willow Creeks), with SWAT calibrated for each subwatershed using each precipitation data set. Model simulations were generally "good" to "very good" at both the daily and monthly time steps for all precipitation data sets, except in the Willow Creek subwatershed, which scored "satisfactory" at the monthly time step and "unsatisfactory" at the daily time step when the Co-op data were used. Calibrated parameter values within the Cobb Creek subwatershed changed little across precipitation data sets. In the Lake Creek and Willow Creek subwatersheds, the deep recharge calibration parameter values varied greatly with respect to precipitation data source. Such variation could inappropriately affect, for example, model assessments of conservation practices designed to ameliorate the movement of agro-chemicals from the surface to lower positions in the soil profile and eventually into the groundwater.


title: Automatic Hotspots Detection for Intracellular Calcium Analysis in Fluorescence Microscopic Videos

abstract: In recent years, life-cell imaging techniques and their software applications have become powerful tools to investigate complex biological mechanisms such as calcium signalling. In this paper, we propose an automated framework to detect areas inside cells that show changes in their calcium concentration i.e. the regions of interests or hotspots, based on videos taken after loading living mouse cardiomyocytes with fluorescent calcium reporter dyes. The proposed system allows an objective and efficient analysis through the following four key stages: (1) Pre-processing to enhance video quality, (2) First level segmentation to detect candidate hotspots based on adaptive thresholding on the frame level, (3) Second-level segmentation to fuse and identify the best hotspots from the entire video by proposing the concept of calcium fluorescence hit-ratio, and (4) Extraction of the changes of calcium fluorescence over time per hotspot. From the extracted signals, different measurements are calculated such as maximum peak amplitude, area under the curve, peak frequency, and inter-spike interval of calcium changes. The system was tested using calcium imaging data collected from Heart muscle cells. The paper argues that the automated proposal offers biologists a tool to speed up the processing time and mitigate the consequences of inter-intra observer variability.


title: Hybrid Architectures With Few-Bit ADC Receivers: Achievable Rates and Energy-Rate Tradeoffs

abstract: Hybrid analog/digital architectures and receivers with low-resolution analog-to-digital converters (ADCs) are two low power solutions for wireless systems with large antenna arrays, such as millimeter wave and massive MIMO systems. Most prior work represents two extreme cases in which either a small number of RF chains with full-resolution ADCs, or low resolution ADC with a number of RF chains equal to the number of antennas is assumed. In this paper, a generalized hybrid architecture with a small number of RF chains and finite number of ADC bits is proposed. For this architecture, achievable rates with channel inversion and SVD based transmission methods are derived. Results show that the achievable rate is comparable to that obtained by full-precision ADC receivers at low and medium SNRs. A trade-off between the achievable rate and power consumption for different numbers of bits and RF chains is devised. This enables us to draw some conclusions on the number of ADC bits needed to maximize the system energy efficiency. Numerical simulations show that coarse ADC quantization is optimal under various system configurations. This means that hybrid combining with coarse quantization achieves better energy-rate trade-off compared to both hybrid combining with full-resolutions ADCs and 1-bit ADC combining.


Implementation of Real Time Driver Drowsiness Detection System


title: So far away and yet so close: augmenting toponym disambiguation and similarity with text-based networks

abstract: Place similarity has a central role in geographic information retrieval and geographic information systems, where spatial proximity is frequently just a poor substitute for semantic relatedness. For applications such as toponym disambiguation, alternative measures are thus required to answer the non-trivial question of place similarity in a given context. In this paper, we discuss a novel approach to the construction of a network of locations from unstructured text data. By deriving similarity scores based on the textual distance of toponyms, we obtain a kind of relatedness that encodes the importance of the co-occurrences of place mentions. Based on the text of the English Wikipedia, we construct and provide such a network of place similarities, including entity linking to Wikidata as an augmentation of the contained information. In an analysis of centrality, we explore the networks capability of capturing the similarity between places. An evaluation of the network for the task of toponym disambiguation on the AIDA CoNLL-YAGO dataset reveals a performance that is in line with state-of-the-art methods.


title: The Disposition toward Critical Thinking.

abstract: There is a set of characterological attributes thought to be associated with developing success at critical thinking (CT). This paper explores the disposition toward CT theoretically , and then as it appears to be manifest in college students. Factor analytic research grounded in a consensus-base d conceptual analysis of CT described seven aspects of the overall disposition toward CT: truth-seeking, open-mindedness, analyticity, systematicity, CTconfidence, inquisitiveness, and cognitive maturity. The California Critical Thinking Disposition Inventory (CCTDI), developed in 1992, was used to sample college students at two comprehensive universities. Entering college freshman students showed strengths in openmindedness and inquisitiveness, weaknesses in systematicity and opposition to truth-seeking. Additional research indicates the disposition toward CT is highly correlated with the psychological constructs of absorption and openness to experience, and strongly predictive of ego-resiliency. A preliminary study explores the interesting and potentially complex interrelationship between the disposition toward CT and CT abilities. In addition to the significance of this work for psychological studies of human development, empirical research on the disposition toward CT promises important implications for all levels of education.


title: Low-Profile Folded Monopoles With Embedded Planar Metamaterial Phase-Shifting Lines

abstract: This paper presents the analysis, design and measurement of novel, low-profile, small-footprint folded monopoles employing planar metamaterial phase-shifting lines. These lines are composed of fully-printed spiral elements, that are inductively coupled and hence exhibit an effective high- mu property. An equivalent circuit for the proposed structure is presented, validating the operating principles of the antenna and the metamaterial line. The impact of the antenna profile and the ground plane size on the antenna performance is investigated using accurate full-wave simulations. A lambda/9 antenna prototype, designed to operate at 2.36 GHz, is fabricated and tested on both electrically large and small ground planes, achieving on average 80% radiation efficiency, 5% (110 MHz) and 2.5% (55 MHz) -10 dB measured bandwidths, respectively, and fully omnidirectional, vertically polarized, monopole-type radiation patterns.


title: Force feedback based gripper control on a robotic arm

abstract: Telemanipulation (master-slave operation) was developed in order to replace human being in hazardous environment, with using a slave device (e.g. robot). In this paper many kind of master devices are studied, for the precise control. Remote control needs much information about remote environment for good and safe human-machine interaction. Existing master devices with vision system or audio feedback lacks most of such information, therefor force feedback, called haptics become significant recently. In this paper we propose a new concept of force feedback. This system can overcome the bottlenecks of other feedback system in a user friendly way. Force sensor and laser distance sensor communicates the information from the gripper's status to the teleoperator by using force feedback module on a glove. Pneumatic pressure gives the operator distance information, while a Magnetorheological Fluid (MR-Fluid) based actuator presents the gripper's force. The experiment result shows the possibility of usage of such force feedback glove in combination with a robotic arm.


title: 77 GHz ACC radar simulation platform

abstract: The development of a system simulation platform for Adaptive Cruise Control (ACC) radar working at 77 GHz is presented. The simulation platform allows us to test different radar architectures, modulation formats and detection algorithms as well as to simulate different scenarios, which improves the decision-making before and during the hardware development.


title: Indoor localization system using rssi measurement of wireless sensor network based on zigbee standard

abstract: To verify the validity of our previously reported autonomous indoor localization system in an actual environment, we implemented it in a wireless sensor network based on the ZigBee standard. The system automatically estimates the distance between sensor nodes by measuring the RSSI (received signal strength indicator) at an appropriate number of sensor nodes. Through experiments, we clarified the validity of our data collection and position estimation techniques. The results show that when the deployment density of sensor nodes was set to 0.27 nodes/ , the position estimation error was reduced to 1.5-2 m.


title: Using Big Data as a window into consumers’ psychology

abstract: The rise of ‘Big Data’ had a big impact on marketing research and practice. In this article, we first highlight sources of useful consumer information that are now available at large scale and very little or no cost. We subsequently discuss how this information – with the help of new analytical techniques – can be translated into valuable insights on consumers’ psychological states and traits that can, in turn, be used to inform marketing strategy. Finally, we discuss opportunities and challenges related to the use of Big Data as a window into consumers’ psychology, and provide recommendations for how to implement related technologies in a way that benefits both businesses and consumers.


title: A wireless home safety gas leakage detection system

abstract: A wireless safety device for gas leakage detection is proposed. The device is intended for use in household safety where appliances and heaters that use natural gas and liquid petroleum gas (LPG) may be a source of risk. The system also can be used for other applications in the industry or plants that depend on LPG and natural gas in their operations. The system design consists of two main modules: the detection and transmission module, and the receiving module. The detection and transmitting module detects the change of gas concentration using a special sensing circuit built for this purpose. This module checks if a change in concentration of gas(es) has exceeded a certain pre-determined threshold. If the sensor detects a change in gas concentration, it activates and audiovisual alarm and sends a signal to the receiver module. The receiver module acts as a mobile alarm device to allow the mobility within the house premises. The system was tested using LPG and the alarm was activated as a result of change in concentration.


title: Brain plasticity through the life span: learning to learn and action video games

abstract: The ability of the human brain to learn is exceptional. Yet, learning is typically quite specific to the exact task used during training, a limiting factor for practical applications such as rehabilitation, workforce training, or education. The possibility of identifying training regimens that have a broad enough impact to transfer to a variety of tasks is thus highly appealing. This work reviews how complex training environments such as action video game play may actually foster brain plasticity and learning. This enhanced learning capacity, termed learning to learn, is considered in light of its computational requirements and putative neural mechanisms.


title: Interplay of curvature-induced micro- and nanodomain structures in multicomponent lipid bilayers

abstract: We discuss different mechanisms for curvature-induced domain formation in multicomponent lipid membranes and present a theoretical model that allows us to study the interplay between the domains. The model represents the membrane by two coupled monolayers, which each carry an additional order parameter field describing the local lipid composition. The spontaneous curvature of each monolayer is coupled to the local composition; moreover, the lipid compositions on opposing monolayers are coupled to each other. Using this model, we calculate the phase behavior of the bilayer in mean-field approximation. The resulting phase diagrams are surprisingly complex and reveal a variety of phases and phase transitions, including a decorated microdomain phase where nanodomains are aligned along the microdomain boundaries. Our results suggest that external membrane tension can be used to control the lateral organization of nanodomains (which might be associated with lipid “rafts”) in a multicomponent lipid bilayer.


title: Automatic attendance recording system using mobile telephone

abstract: These days, instructors in universities and colleges take the attendance manually either by calling out individual's name or by passing around an attendance sheet for student's signature to confirm his/her presence. Using these methods is both cumbersome and time-consuming. Therefore a method of taking attendance using instructor's mobile telephone has been presented in this paper which is paperless, quick, and accurate. An application software installed in the instructor's mobile telephone enables it to query students' mobile telephone via Bluetooth connection and, through transfer of students' mobile telephones' Media Access Control (MAC) addresses to the instructor's mobile telephone, presence of the student can be confirmed. Moreover, detailed record of a student's attendance can also be generated for printing and filing, if needed.


Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization


title: MetaSpace: Full-body Tracking for Immersive Multiperson Virtual Reality

abstract: Most current virtual reality (VR) interactions are mediated by hand-held input devices or hand gestures and they usually display only a partial representation of the user in the synthetic environment. We believe, representing the user as a full avatar that is controlled by natural movements of the person in the real world will lead to a greater sense of presence in VR. Possible applications exist in various domains such as entertainment, therapy, travel, real estate, education, social interaction and professional assistance. In this demo, we present MetaSpace, a virtual reality system that allows co-located users to explore a VR world together by walking around in physical space. Each user's body is represented by an avatar that is dynamically controlled by their body movements. We achieve this by tracking each user's body with a Kinect device such that their physical movements are mirrored in the virtual world. Users can see their own avatar and the other person's avatar allowing them to perceive and act intuitively in the virtual environment.


title: Linked Data: Evolving the Web Into a Global Data Space

abstract: The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study.


title: Machine Recognition of Music Emotion: A Review

abstract: The proliferation of MP3 players and the exploding amount of digital music content call for novel ways of music organization and retrieval to meet the ever-increasing demand for easy and effective information access. As almost every music piece is created to convey emotion, music organization and retrieval by emotion is a reasonable way of accessing music information. A good deal of effort has been made in the music information retrieval community to train a machine to automatically recognize the emotion of a music signal. A central issue of machine recognition of music emotion is the conceptualization of emotion and the associated emotion taxonomy. Different viewpoints on this issue have led to the proposal of different ways of emotion annotation, model training, and result visualization. This article provides a comprehensive review of the methods that have been proposed for music emotion recognition. Moreover, as music emotion recognition is still in its infancy, there are many open issues. We review the solutions that have been proposed to address these issues and conclude with suggestions for further research.


title: Reliability-Based Local Features Aggregation for Image Segmentation

abstract: Local features are used for describing the visual information in a local neighborhood of image pixels. Although using various types of local features can provide complementary information about the pixels, effective integration of these features has remained as a challenging issue. In this paper, we propose a novel segmentation algorithm which aggregates the information obtained from different local features. Starting with an over-segmentation of the input image, local features are fed into a factorization-based framework to construct multiple new representations. We then introduce a novel aggregation model to integrate the new representations. Our proposed model jointly learns the reliability of representations and infers final representation. Final segmentation is obtained by applying post-processing steps on the inferred final representation. Experimental results demonstrate the effectiveness of our algorithm on the Berkeley Segmentation Dataset.


Engineering Record and Replay for Deployability