title: Security Strategies for SCADA Networks

abstract: A caster assembly of the type that accommodates swivelling movement of a caster wheel including a U-shaped horn that flanks the sides of the wheel and supports the wheel axle. A swivel plate is rotatably mounted on top of the horn and is adapted to be connected to the underside of the article to be supported. This plate carries a kingpin that extends through the horn and has an annular swivel lock plate fixed to its lower end. A swivel lock assembly is provided for locking the caster selectively in one of four 90 degree positions and it is mounted on rearwardly projecting ears on the U-shaped horn. The swivel lock assembly includes a spring biased axially movable plunger mounted radially with respect to the swivel lock plate and it is selectively engageable with four ninety degree spaced recesses in the swivel lock plate. The plunger is deactivated manually by pulling a ring extending through the rear end of the plunger against the biasing force of a coil spring and rotating the ring 90 degrees and releasing it into holding recesses on a U-shaped bracket fixed to horn ears.


title: Fast online corrections of tripping responses

abstract: Tripping over obstacles is one of the main causes of falls. One potential hazard to actually fall when tripped is inadequate foot landing. Adequate landing is required to control the body’s angular momentum, while avoiding dangerous surfaces (slippery patch, uneven ground). To avoid such dangers, foot trajectory needs to be controlled by inhibiting and adjusting the initiated recovery foot path during a tripping reaction. We investigated whether such adjustments can be made without jeopardizing balance recovery. Sixteen healthy young adults (25.1 ± 3.2 years) walked at their comfortable speed over a walkway equipped with 14 hidden obstacles. Participants were tripped 10 times in between a random number of normal walking trials; five trips included a projection of a forbidden zone (FZ, 30 × 50 cm) at the subject’s preferred landing position. Participants were instructed to land their recovery foot outside the FZ, if the FZ was presented. Responses were evaluated in terms of foot position and body angular momentum at and following recovery foot landing. Participants successfully landed their recovery foot outside the FZ in 80 % of trials, using strategies of either shortening their recovery steps (84 %) or side stepping (16 %). Their performance improved over trials, and some participants switched strategies. Angular momenta of the adjusted steps remained small at and following recovery foot landing. Young adults can quickly change foot trajectory after tripping by using different strategies, and without detrimental consequences on balance recovery, in terms of the angular momentum. These results open possibilities for training of tripping reactions.


title: Synthesis of Symmetrical and Asymmetrical Azines Encompassing Naphtho[2,1-b]furan by a Novel Approach

abstract: The starting material 3-nitro-2-acetylnaphtho[2,1-b]furan (2) was obtained by nitration of 2-acetylnaphtho[2,1-b] furan (1), under mild condition. The compound 1 was synthesized by the reaction of 2-hydroxy-1-naphthaldehyde with chloroacetone, where in both condensation and cyclization took place in single step. The reaction of 3-nitro-2-acetylnaphtho[2,1-b]furan (2) with hydrazine hydrate produced corresponding hydrazone (3) in excellent yield, which on treatment with various aromatic aldehydes under different reaction conditions resulted in the formation of symmetrical azines (4a-e) and unsymmetrical azines (5a-e). All the newly synthesized compounds have been characterized by analytical and spectral studies and were screened for antibacterial antibacterial activity against Bacillus subtilus and Alcaligenes fecalies and antifungal activity against Aspergillus nidulans, Aspergillus parasiticus and Aspergillus terrus. The Second Harmonic Generation (SHG) efficiency of some of the synthesized compounds was measured by powder technique using Nd:YAG laser.


title: Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection

abstract: Background: In public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes. Objective: Our aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter. Methods: The study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines. Results: We analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16%) tweets as evidence and advocacy, and 6244 (2.19%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84%) were found in communities where the majority of tweets were about evidence and advocacy. Conclusions: The use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines. [J Med Internet Res 2016;18(8):e232]


Star Prediction for Yelp Dataset


title: The Stanford CoreNLP Natural Language Processing Toolkit

abstract: We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.


title: Building perceptual textures to visualize multidimensional datasets

abstract: Presents a new method for using texture to visualize multi-dimensional data elements arranged on an underlying 3D height field. We hope to use simple texture patterns in combination with other visual features like hue and intensity to increase the number of attribute values we can display simultaneously. Our technique builds perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in the data element are used to vary the appearance of a corresponding pexel. Texture patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by controlling three separate texture dimensions: height, density and regularity. Results from computer graphics, computer vision and cognitive psychology have identified these dimensions as important for the formation of perceptual texture patterns. We conducted a set of controlled experiments to measure the effectiveness of these dimensions, and to identify any visual interference that may occur when all three are displayed simultaneously at the same spatial location. Results from our experiments show that these dimensions can be used in specific combinations to form perceptual textures for visualizing multidimensional datasets. We demonstrate the effectiveness of our technique by applying it to two real-world visualization environments: tracking typhoon activity in southeast Asia, and analyzing ocean conditions in the northern Pacific.


title: Identification of Cognates and Recurrent Sound Correspondences in Word Lists

abstract: Identification of cognates and recurrent sound corresponde nces is a component of two principal tasks of historical linguistics: demonstrat ing the relatedness of languages, and reconstructing the histories of language families. We prop ose methods for detecting and quan- tifying three characteristics of cognates: recurrent soun d correspondences, phonetic similarity, and semantic affinity. The ultimate goal is to identify cogna tes and correspondences directly from lists of words representing pairs of languages that areknown to be related. The proposed solutions are language independent, and are evaluated agai nst authentic linguistic data. The results of evaluation experiments involving the Indo-Euro pean, Algonquian, and Totonac lan- guage families indicate that our methods are more accurate t han comparable programs, and achieve high precision and recall on various test sets. The r


title: Dependency or Span, End-to-End Uniform Semantic Role Labeling

abstract: Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence. End-to-end SRL without syntactic input has received great attention. However, most of them focus on either span-based or dependency-based semantic representation form and only show specific model optimization respectively. Meanwhile, handling these two SRL tasks uniformly was less successful. This paper presents an end-to-end model for both dependency and span SRL with a unified argument representation to deal with two different types of argument annotations in a uniform fashion. Furthermore, we jointly predict all predicates and arguments, especially including long-term ignored predicate identification subtask. Our single model achieves new state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL 2008, 2009) SRL benchmarks.


title: The Promise of a Qualitative Case Study Approach for Research on Caregiver Involvement in Inpatient Traumatic Brain Injury Rehabilitation

abstract: Purpose: To demonstrate that, theoretically, a qualitative case study approach holds substantial promise for conducting research on caregiver involvement in inpatient traumatic brain injury (TBI) rehabilitation. Methods: Narrative reviews of the TBI rehabilitation and case study literature are presented to explain our rationale for advocating the use of a qualitative case study approach for research in this area. Results: We found that there are four interconnected reasons why this research approach is well suited for investigating caregiver involvement in in-patient TBI rehabilitation. More specifically, a qualitative case study approach allows us to: (a) address the context of inpatient TBI rehabilitation; (b) account for the complexity of caregiver involvement; (c) adapt research to the practical and ethical needs of TBI inpatients and their caregivers; and (d) create research that is congruent with family-centered theory. Conclusion: A qualitative case study approach has the ability to address the complexity and context that surrounds caregiver involvement in inpatient TBI rehabilitation. A case study approach is also congruent with the characteristics of inpatient rehabilitation settings and can take into consideration family-centered theory, which is instrumental to understanding caregiver involvement.


title: Relationship Between Specialty Choice and Medical Student Temperament and Character Assessed With Cloninger Inventory

abstract: Background: Multiple variables affect medical specialty choice, including temperament, sociodemographic factors, and personal experiences. Many studies address specific variables for specific specialties, but few assess the relative impact of each factor. Purpose: To identify the relative influence of temperament in choosing a specialty. Methods: A sociodemographic and personal experiences questionnaire and a 240-question temperament and character inventory was distributed to 682 medical students. Their scores for 6 medical specialties were examined using analyses of variance, multivariate analyses of variance, and discriminant analysis. Results: Students choosing surgery, emergency medicine, and obstetrics and gynecology were higher on novelty seeking than other students. Future surgeons were lower in harm avoidance and reward dependence (RD) than the others. Students choosing primary care specialties, emergency medicine, and obstetrics and gynecology were all high on RD; with pediatrics being highest. S...


title: Distributed Representations in Memory: Insights from Functional Brain Imaging

abstract: Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly utilized powerful analytical tools (e.g., multivoxel pattern analysis) to decode the information represented within distributed functional magnetic resonance imaging activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses.


title: A rate-distortion framework for supervised learning

abstract: An information-theoretic framework is presented for bounding the number of samples needed for supervised learning in a parametric Bayesian setting. This framework is inspired by an analogy with rate-distortion theory, which characterizes tradeoffs in the lossy compression of random sources. In a parametric Bayesian environment, the maximum a posteriori classifier can be viewed as a random function of the model parameters. Labeled training data can be viewed as a finite-rate encoding of that source, and the excess loss due to using the learned classifier instead of the MAP classifier can be viewed as distortion. A strict bound on the loss-measured in terms of the expected total variation-is derived, providing a minimum number of training samples needed to drive the expected total variation to within a specified tolerance. The tightness of this bound is demonstrated on the classification of Gaus-sians, for which one can derive closed-form expressions for the bound.


title: Brief Report: The Use of WAIS-III in Adults with HFA and Asperger Syndrome

abstract: The WAIS III was administered to 16 adults with high functioning autism (HFA) and 27 adults with Asperger syndrome. Differences between Verbal Intelligence (VIQ) and Performance Intelligence (PIQ) were not found. Processing Speed problems in people with HFA appeared. At the subtest level, the Asperger syndrome group performed weak on Digit Span. Comprehension and Block Design were relative strengths. In the HFA group, performance on Digit-Symbol Coding and Symbol Search was relatively poor. Strengths were found on Information and Matrix Reasoning. The results suggest that the VIQ-PIQ difference cannot distinguish between HFA and Asperger syndrome. WAIS III Factor Scale and Subtest patterning provides a more valid indicator.


Approximation by normal derivatives of fundamental solutions of elliptic differential operator systems


1 Introduction Intelligent Control: An Overview of Techniques ∗


title: Learning Student Networks via Feature Embedding

abstract: Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on mobile devices. Knowledge distillation aims to optimize a portable student network by taking the knowledge from a well-trained heavy teacher network. Traditional teacher-student-based methods used to rely on additional fully connected layers to bridge intermediate layers of teacher and student networks, which brings in a large number of auxiliary parameters. In contrast, this article aims to propagate information from teacher to student without introducing new variables that need to be optimized. We regard the teacher-student paradigm from a new perspective of feature embedding. By introducing the locality preserving loss, the student network is encouraged to generate the low-dimensional features that could inherit intrinsic properties of their corresponding high-dimensional features from the teacher network. The resulting portable network, thus, can naturally maintain the performance as that of the teacher network. Theoretical analysis is provided to justify the lower computation complexity of the proposed method. Experiments on benchmark data sets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.


title: LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation

abstract: This work proposes a novel deep network architecture to solve the camera Ego-Motion estimation problem. A motion estimation network generally learns features similar to Optical Flow (OF) fields starting from sequences of images. This OF can be described by a lower dimensional latent space. Previous research has shown how to find linear approximations of this space. We propose to use an Auto-Encoder network to find a non-linear representation of the OF manifold. In addition, we propose to learn the latent space jointly with the estimation task, so that the learned OF features become a more robust description of the OF input. We call this novel architecture LS-VO. The experiments show that LS-VO achieves a considerable increase in performances in respect to baselines, while the number of parameters of the estimation network only slightly increases.


title: Addictive Personality and Problematic Mobile Phone Use

abstract: Abstract Mobile phone use is banned or regulated in some circumstances. Despite recognized safety concerns and legal regulations, some people do not refrain from using mobile phones. Such problematic mobile phone use can be considered to be an addiction-like behavior. To find the potential predictors, we examined the correlation between problematic mobile phone use and personality traits reported in addiction literature, which indicated that problematic mobile phone use was a function of gender, self-monitoring, and approval motivation but not of loneliness. These findings suggest that the measurements of these addictive personality traits would be helpful in the screening and intervention of potential problematic users of mobile phones.


title: Automatically Tracing Dependability Requirements via Term-Based Relevance Feedback

abstract: In many critical industrial information systems, tracking a dependability requirement is instrumental to the verification and validation (V&V) of security, privacy, and other dependability concerns. Automated traceability tools employ information retrieval methods to recover candidate links, which saves much manual effort. Integrating relevance feedback (RF) could potentially improve the retrieval effectiveness by soliciting the relevance judgments on a subset of the retrieval results and then incorporating the feedback into subsequent retrieval. However, little is known about how to use RF to trace dependability requirements. In this paper, we propose a novel term-based RF algorithm that leverages the term usage context to recommend positive and negative feedback. Experiments on two software datasets show that our algorithm significantly outperforms the contemporary link-based RF tracing method. Our work not only contributes a new solution to dependability requirements’ V&V, but also enables further automation to reduce the manual effort in the development life cycle of dependable industrial systems.


title: Many roads lead to Rome: mapping users' problem solving strategies

abstract: Especially in ill-defined problems like complex, real-world tasks more than one way leads to a solution. Until now, the evaluation of information visualizations was often restricted to measuring outcomes only (time and error) or insights into the data set. A more detailed look into the processes which lead to or hinder task completion is provided by analyzing users' problem solving strategies. A study illustrates how they can be assessed and how this knowledge can be used in participatory design to improve a visual analytics tool. In order to provide the users a tool which functions as a real scaffold, it should allow them to choose their own path to Rome. We discuss how evaluation of problem solving strategies can shed more light on the users' "exploratory minds".


title: Learning styles and formative assessment strategy: enhancing student achievement in Web‐based learning

abstract: The purpose of this research was to investigate the effects of formative assessment and learning style on student achievement in a Web-based learning environment. A quasi-experimental research design was used. Participants were 455 seventh grade students from 12 classes of six junior high schools. A Web-based course, named BioCAL, combining three different formative assessment strategies was developed. The formative assessment strategies included Formative Assessment Module of the Web-Based Assessment and Test Analysis system (FAM-WATA) (with six Web-based formative assessment strategies), Normal Module of Web-Based Assessment and Test Analysis system (N-WATA) (only with partial Web-based formative assessment strategy) and Paper and Pencil Test (PPT) (without Web-based formative assessment strategy). Subjects were tested using Kolb’s Learning Style Inventory, and assigned randomly by class into three groups. Each group took Web-based courses using one of the formative assessment strategies. Pre- and post-achievement testing was carried out. A one-way ANCOVA analysis showed that both learning style and formative assessment strategy are significant factors affecting student achievement in a Webbased learning environment. However, there is no interaction between these two factors. A post hoc comparison showed that performances of the FAM-WATA group are higher than the N-WATA and PPT groups. Learners with a ‘Diverger’ learning style performed best followed by, ‘Assimilator’, ‘Accommodator’, and ‘Converger’, respectively. Finally, FAMWATA group students are satisfied with six strategies of the FAM-WATA.


title: A Survey of Language Identification Techniques and Applications

abstract: Language Identification is the process of determining in which natural language the contents of the text is written. Language identification is always been an important research area which has been carried out from early 1970’s. Still it is a fascinating field to be studied due to increased demand of natural language processing applications. In many applications, it works as a primary step of some larger process. In this paper, a number of applications are outlined where language identification is working successfully. Language Identification can be done using two types of techniques: computational techniques and non-computational techniques. Computational techniques are based on statistical methods and requires large set of training data for each of the language while non-computational techniques require that researcher must have extensive knowledge about the language to-be-identified. In this paper, a brief review of the few papers is presented which outlines the various statistical and non-statistical techniques that have been applied by the different researchers for language identification. Besides it, different researchers performed language identification for different type of documents such as monolingual, multilingual, long and short and for a particular set of languages.


title: Reduced Support Vector Machines: A Statistical Theory

abstract: In dealing with large data sets, the reduced support vector machine (RSVM) was proposed for the practical objective to overcome some computational difficulties as well as to reduce the model complexity. In this paper, we study the RSVM from the viewpoint of sampling design, its robustness, and the spectral analysis of the reduced kernel. We consider the nonlinear separating surface as a mixture of kernels. Instead of a full model, the RSVM uses a reduced mixture with kernels sampled from certain candidate set. Our main results center on two major themes. One is the robustness of the random subset mixture model. The other is the spectral analysis of the reduced kernel. The robustness is judged by a few criteria as follows: 1) model variation measure; 2) model bias (deviation) between the reduced model and the full model; and 3) test power in distinguishing the reduced model from the full one. For the spectral analysis, we compare the eigenstructures of the full kernel matrix and the approximation kernel matrix. The approximation kernels are generated by uniform random subsets. The small discrepancies between them indicate that the approximation kernels can retain most of the relevant information for learning tasks in the full kernel. We focus on some statistical theory of the reduced set method mainly in the context of the RSVM. The use of a uniform random subset is not limited to the RSVM. This approach can act as a supplemental algorithm on top of a basic optimization algorithm, wherein the actual optimization takes place on the subset-approximated data. The statistical properties discussed in this paper are still valid


title: How much parallelism is there in irregular applications?

abstract: Irregular programs are programs organized around pointer-based data structures such as trees and graphs. Recent investigations by the Galois project have shown that many irregular programs have a generalized form of data-parallelism called amorphous data-parallelism. However, in many programs, amorphous data-parallelism cannot be uncovered using static techniques, and its exploitation requires runtime strategies such as optimistic parallel execution. This raises a natural question: how much amorphous data-parallelism actually exists in irregular programs? In this paper, we describe the design and implementation of a tool called ParaMeter that produces parallelism profiles for irregular programs. Parallelism profiles are an abstract measure of the amount of amorphous data-parallelism at different points in the execution of an algorithm, independent of implementation-dependent details such as the number of cores, cache sizes, load-balancing, etc. ParaMeter can also generate constrained parallelism profiles for a fixed number of cores. We show parallelism profiles for seven irregular applications, and explain how these profiles provide insight into the behavior of these applications.


title: VALIDATION GUIDELINES FOR IS POSITIVIST RESEARCH

abstract: The issue of whether IS positivist researchers were validating their instruments sufficiently was initially raised fifteen years ago. Rigor in IS research is still one of the critical scientific issues facing the field. Without solid validation of the instruments that are used to gather data on which findings and interpretations are based, the very scientific basis of the profession is threatened. This study builds on four prior retrospectives of IS research that conclude that IS positivist researchers continue to face major barriers in instrument, statistical, and other forms of validation. It goes beyond these studies by offering analyses of the state-of-the-art of research validities and deriving specific heuristics for research practice in the validities. Some of these heuristics will, no doubt, be controversial. But we believe that it is time for the IS academic profession to bring such issues into the open for community debate. This article is a first step in that direction. Based on our interpretation of the importance of a long list of validities, this paper suggests heuristics for reinvigorating the quest for validation in IS research via content/construct validity, reliability, manipulation validity, and statistical conclusion validity. New guidelines for validation and new research directions are offered.


title: Information systems development as a research method

abstract: This paper takes the stance that some cases of information systems development can be considered knowledge creating activities, and, in those cases, information systems development can be a legitimate research method. In these cases not only is knowledge created about the development process itself but also a deeper understanding emerges about the organisational problem that the system is designed to solve. The paper begins with a brief overview of research in the design sciences and a comparison of research methods that are concerned with the design, and use, of information systems. This is followed by an assessment of the way systems development as a research method deals with the scientific research processes of data collection, analysis, synthesis and display. A case study, where the systems development research method was use, is described to illustrate the method and give the reader a better understanding of the approach.


Technology Adaptation: The Case of a Computer-Supported Inter-Organizational Virtual Team


title: SpaceTop: integrating 2D and spatial 3D interactions in a see-through desktop environment

abstract: SpaceTop is a concept that fuses spatial 2D and 3D interactions in a single workspace. It extends the traditional desktop interface with interaction technology and visualization techniques that enable seamless transitions between 2D and 3D manipulations. SpaceTop allows users to type, click, draw in 2D, and directly manipulate interface elements that float in the 3D space above the keyboard. It makes it possible to easily switch from one modality to another, or to simultaneously use two modalities with different hands. We introduce hardware and software configurations for co-locating these various interaction modalities in a unified workspace using depth cameras and a transparent display. We describe new interaction and visualization techniques that allow users to interact with 2D elements floating in 3D space. We present the results from a preliminary user study that indicates the benefit of such hybrid workspaces.


title: Local-Learning-Based Feature Selection for High-Dimensional Data Analysis

abstract: This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature-selection algorithm that addresses several major issues with prior work, including problems with algorithm implementation, computational complexity, and solution accuracy. The key idea is to decompose an arbitrarily complex nonlinear problem into a set of locally linear ones through local learning, and then learn feature relevance globally within the large margin framework. The proposed algorithm is based on well-established machine learning and numerical analysis techniques, without making any assumptions about the underlying data distribution. It is capable of processing many thousands of features within minutes on a personal computer while maintaining a very high accuracy that is nearly insensitive to a growing number of irrelevant features. Theoretical analyses of the algorithm's sample complexity suggest that the algorithm has a logarithmical sample complexity with respect to the number of features. Experiments on 11 synthetic and real-world data sets demonstrate the viability of our formulation of the feature-selection problem for supervised learning and the effectiveness of our algorithm.


title: Manifold Relevance Determination

abstract: In this paper we present a fully Bayesian latent variable model which exploits conditional nonlinear (in)-dependence structures to learn an efficient latent representation. The latent space is factorized to represent shared and private information from multiple views of the data. In contrast to previous approaches, we introduce a relaxation to the discrete segmentation and allow for a "softly" shared latent space. Further, Bayesian techniques allow us to automatically estimate the dimensionality of the latent spaces. The model is capable of capturing structure underlying extremely high dimensional spaces. This is illustrated by modelling unprocessed images with tenths of thousands of pixels. This also allows us to directly generate novel images from the trained model by sampling from the discovered latent spaces. We also demonstrate the model by prediction of human pose in an ambiguous setting. Our Bayesian framework allows us to perform disambiguation in a principled manner by including latent space priors which incorporate the dynamic nature of the data.


title: Embedded Manifold-Based Kernel Fisher Discriminant Analysis for Face Recognition

abstract: Manifold learning algorithms mainly focus on discovering the intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space. Among them, locally linear embedding (LLE) is one of the most promising dimensionality reduction methods. Though LLE holds local neighborhood information, it doesn't fully take the label information and the global structure information into account for classification tasks. To enhance classification performance, this paper proposes a novel dimensionality reduction method for face recognition, termed embedded manifold-based kernel Fisher discriminant analysis, or EMKFDA for short. The goal of EMKFDA is to emphasize the local geometry structure of the data while utilizing the global discriminative structure obtained from linear discriminant analysis, which can maximize the between-class scatter and minimize the within-class scatter. In addition, by optimizing an objective function in a kernel feature space, nonlinear features can be extracted. Thus, EMKFDA, which combines manifold criterion and Fisher criterion, has better discrimination, and is more suitable for recognition tasks. Experiments on the ORL, Yale, and FERET face databases show the impressive performance of the proposed method. Results show that this proposed algorithm exceeds other popular approaches reported in the literature and achieves much higher recognition accuracy.


title: 60 GHz antennas in HTCC and glass technology

abstract: This paper presents a 60 GHz antenna structure built on glass and flip-chipped on a ceramic module. A single antenna and a two antenna array have been fabricated and demonstrated good performances. The single antenna shows a return loss below −10 dB and a gain of 6–7 dBi over a 7 GHz bandwidth. The array shows a gain of 7–8 dBi over a 3 GHz bandwidth.


title: Ninja on a Plane: Automatic Discovery of Physical Planes for Augmented Reality Using Visual SLAM

abstract: Most work in visual augmented reality (AR) employs predefined markers or models that simplify the algorithms needed for sensor positioning and augmentation but at the cost of imposing restrictions on the areas of operation and on interactivity. This paper presents a simple game in which an AR agent has to navigate using real planar surfaces on objects that are dynamically added to an unprepared environment. An extended Kalman filter (EKF) simultaneous localisation and mapping (SLAM) framework with automatic plane discovery is used to enable the player to interactively build a structured map of the game environment using a single, agile camera. By using SLAM, we are able to achieve real-time interactivity and maintain rigorous estimates of the system's uncertainty, which enables the effects of high quality estimates to be propagated to other features (points and planes) even if they are outside the camera's current field of view.


title: Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy

abstract: Apolipoprotein E (Apo-E) is a major cholesterol carrier that supports lipid transport and injury repair in the brain. APOE polymorphic alleles are the main genetic determinants of Alzheimer disease (AD) risk: individuals carrying the ε4 allele are at increased risk of AD compared with those carrying the more common ε3 allele, whereas the ε2 allele decreases risk. Presence of the APOE ε4 allele is also associated with increased risk of cerebral amyloid angiopathy and age-related cognitive decline during normal ageing. Apo-E-lipoproteins bind to several cell-surface receptors to deliver lipids, and also to hydrophobic amyloid-β (Aβ) peptide, which is thought to initiate toxic events that lead to synaptic dysfunction and neurodegeneration in AD. Apo-E isoforms differentially regulate Aβ aggregation and clearance in the brain, and have distinct functions in regulating brain lipid transport, glucose metabolism, neuronal signalling, neuroinflammation, and mitochondrial function. In this Review, we describe current knowledge on Apo-E in the CNS, with a particular emphasis on the clinical and pathological features associated with carriers of different Apo-E isoforms. We also discuss Aβ-dependent and Aβ-independent mechanisms that link Apo-E4 status with AD risk, and consider how to design effective strategies for AD therapy by targeting Apo-E.


title: A natural head pose and eye gaze dataset

abstract: We present a new dataset, ideal for Head Pose and Eye Gaze Estimation algorithm testings. Our dataset was recorded using a monocular system, and no information regarding camera or environment parameters is offered, making the dataset ideal to be tested with algorithms that do not utilize such information and do not require any specific equipment in terms of hardware.


title: Boundary-Integral Approach to the Numerical Solution of the Cauchy Problem for the Laplace Equation

abstract: We present a survey of a direct method of boundary integral equations for the numerical solution of the Cauchy problem for the Laplace equation in doubly connected domains. The domain of solution is located between two closed boundary surfaces (curves in the case of two-dimensional domains). This Cauchy problem is reduced to finding the values of a harmonic function and its normal derivative on one of the two closed parts of the boundary according to the information about these quantities on the other boundary surface. This is an ill-posed problem in which the presence of noise in the input data may completely destroy the procedure of finding the approximate solution. We describe and present the results for a procedure of regularization aimed at the stable determination of the required quantities based on the representation of the solution to the Cauchy problem in the form a single-layer potential. For given data, this representation yields a system of boundary integral equations with two unknown densities. We establish the existence and uniqueness of these densities and propose a method for the numerical discretization in two- and three-dimensional domains. We also consider the cases of simply connected domains of the solution and unbounded domains. Numerical examples are presented both for two- and three-dimensional domains. These numerical results demonstrate that the proposed method gives good accuracy with relatively small amount of computations.


title: Body Composition Changes After Very-Low-Calorie Ketogenic Diet in Obesity Evaluated by 3 Standardized Methods

abstract: Context: A common concern when using low-calorie diets as a treatment for obesity is the reduction in fat-free mass, mostly muscular mass that occurs together with the fat mass loss, and the best methodologies to evaluate body composition changes. Objective: To evaluate the very low-calorie-ketogenic (VLCK) diet-induced changes in body composition of obese patients and to compare three different methodologies employed to evaluate that changes. Design: Twenty obese patients followed a VLCK diet for 4 months. Body composition was performed by dual-energy X-ray absorptiometry (DXA), multifrequency bioelectrical impedance (MF-BIA) and air displacement plethysmography (ADP) techniques. Muscular strength was also assessed. Measurements were performed at four points matched with the ketotic phases (basal, maximum ketosis, ketosis declining and out of ketosis). Results: After 4-months the VLCK diet induced a -20.2±4.5 kg weight loss, at expenses of reductions in fat mass (FM) of -16.5 ± 5.1 kg (DXA), -18.2 ± 5.8 ...


title: Technology Integration Barriers: Urban School Mathematics Teachers Perspectives.

abstract: Despite the promise of technology in education, many practicing teachers face several challenges when trying to effectively integrate technology into their classroom instruction. Additionally, while national statistics cite a remarkable improvement in access to computer technology tools in schools, teacher surveys show consistent declines in the use and integration of computer technology to enhance student learning. This article reports on primary technology integration barriers that mathematics teachers identified when using technology in their classrooms. Suggestions to overcome some of these barriers are also provided.


title: The chemistry of graphene oxide.

abstract: The chemistry of graphene oxide is discussed in this critical review. Particular emphasis is directed toward the synthesis of graphene oxide, as well as its structure. Graphene oxide as a substrate for a variety of chemical transformations, including its reduction to graphene-like materials, is also discussed. This review will be of value to synthetic chemists interested in this emerging field of materials science, as well as those investigating applications of graphene who would find a more thorough treatment of the chemistry of graphene oxide useful in understanding the scope and limitations of current approaches which utilize this material (91 references).


title: A Comparative Study of Simulated Annealing and Genetic Algorithm for Solving the Travelling Salesman Problem

abstract: Metaheuristic algorithms have proved to be good solvers for the traveling salesman problem (TSP). All metaheuristics usually encounter problems on which they perform poorly so the programmer must gain experience on which optimizers work well in different classes of problems. However due to the unique functionality of each type of meta-heuristic, comparison of metaheuristics is in many ways more difficult than other algorithmic comparisons. In this paper, solution to the traveling salesman problem was implemented using genetic algorithm and simulated annealing. These algorithms were compared based on performance and results using several benchmarks. It was observed that Simulated Annealing runs faster than Genetic Algorithm and runtime of Genetic Algorithm increases exponentially with number of cities. However, in terms of solution quality Genetic Algorithm is better than Simulated Annealing.


title: Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks

abstract: We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms. We propose micromanagement tasks, which present the problem of the short-term, low-level control of army members during a battle. From a reinforcement learning point of view, these scenarios are challenging because the state-action space is very large, and because there is no obvious feature representation for the state-action evaluation function. We describe our approach to tackle the micromanagement scenarios with deep neural network controllers from raw state features given by the game engine. In addition, we present a heuristic reinforcement learning algorithm which combines direct exploration in the policy space and backpropagation. This algorithm allows for the collection of traces for learning using deterministic policies, which appears much more efficient than, for example, {\epsilon}-greedy exploration. Experiments show that with this algorithm, we successfully learn non-trivial strategies for scenarios with armies of up to 15 agents, where both Q-learning and REINFORCE struggle.


title: Music Composition with Interactive Evolutionary Computation

abstract: Interactive Evolutionary Computation (IEC), i.e., Evolutionary Computation whose fitness function is provided by a user his/herself, has been applied to esthetic areas, such as art, design and music. We cannot necessarily define fitness functions explicitly in these areas. With IEC, however, we can embed the user's implicit preference into the optimization system. This paper describes a new approach to music composition, more precisely the composition of rhythms, by means of IEC. The main feature of our method is to combine Genetic Algorithms (GA) and Genetic Programming (GP). In our system, GA individuals represent short pieces of rhythmic patterns, while GP individuals express how these patterns are arranged in terms of their functions. Both populations are evolved interactively through the user's evaluation. The integration of interactive GA and GP makes it possible to search for musical structures effectively in the vast search space. In this paper, we show how successfully our proposed method can generate attractive musical rhythms. The effectiveness of our system is demonstrated by the evolved rhythm phrases, which are available from our web site as sound files.


Fall Detection from Thermal Camera Using Convolutional LSTM Autoencoder


title: A new control plane for 5G network architecture with a case study on unified handoff, mobility, and routing management

abstract: The tremendous growth in wireless Internet use is showing no signs of slowing down. Existing cellular networks are starting to be insufficient in meeting this demand, in part due to their inflexible and expensive equipment as well as complex and non-agile control plane. Software-defined networking is emerging as a natural solution for next generation cellular networks as it enables further network function virtualization opportunities and network programmability. In this article, we advocate an all-SDN network architecture with hierarchical network control capabilities to allow for different grades of performance and complexity in offering core network services and provide service differentiation for 5G systems. As a showcase of this architecture, we introduce a unified approach to mobility, handoff, and routing management and offer connectivity management as a service (CMaaS). CMaaS is offered to application developers and over-the-top service providers to provide a range of options in protecting their flows against subscriber mobility at different price levels.


title: A method for designing marker-based tracking probes

abstract: Many tracking systems utilize collections of fiducial markers arranged in rigid configurations, called tracking probes, to determine the pose of objects within an environment. In this paper, we present a technique for designing tracking probes called the viewpoints algorithm. The algorithm is generally applicable to tracking systems that use at least three fiduciary marks to determine the pose of an object. The algorithm is used to create an integrated, head-mounted display tracking probe. The predicted accuracy of this probe was 0.032 /spl plusmn/ 0.02 degrees in orientation and 0.09 /spl plusmn/ 0.07 mm in position. The measured accuracy of the probe was 0.028 /spl plusmn/ 0.01 degrees in orientation and 0.11 /spl plusmn/ 0.01 mm in position. These results translate to a predicted, static positional overlay error of a virtual object presented at 1m of less than 0.5 mm. The algorithm is part of a larger framework for designing tracking probes based upon performance goals and environmental constraints.


title: Frequency Reconfigurable U-Slot Microstrip Patch Antenna

abstract: A frequency reconfigurable microstrip patch antenna is presented. It is found that the incorporation of a U-slot in the patch can provide a flat input resistance and a linear input reactance across a wider bandwidth than the conventional patch antenna. By placing a variable capacitor and an inductor at the antenna input, the impedance matching frequency of the antenna can be varied. The fabricated prototype antenna attains a tunable frequency range from 2.6 to 3.35 GHz. The proposed antenna is simple in structure, and the control circuitry is placed underneath the ground plane. It is suitable for use in reducing the crosstalk from adjacent channels in multichannel system.


title: Evaluating and Improving Penetration Testing in Web Services

abstract: Developers often rely on penetration testing tools to detect vulnerabilities in web services, although frequently without really knowing their effectiveness. In fact, the lack of information on the internal state of the tested services and the complexity and variability of the responses analyzed, limits the effectiveness of such technique, highlighting the importance of evaluating and improving existing tools. The goal of this paper is to investigate if attack signatures and interface monitoring can be an effective mean to assess and improve the performance of penetration testing tools in web services environments. In practice, attacks performed by such tools are signed and the interfaces between the target application and external resources are monitored (e.g., between services and a database server), allowing gathering additional information on existing vulnerabilities. A prototype was implemented focusing on SQL injection vulnerabilities. The experimental evaluation results clearly show that the proposed approach can be used in real scenarios.


title: Dataless text classification with descriptive LDA

abstract: Manually labeling documents for training a text classifier is expensive and time-consuming. Moreover, a classifier trained on labeled documents may suffer from overfitting and adaptability problems. Dataless text classification (DLTC) has been proposed as a solution to these problems, since it does not require labeled documents. Previous research in DLTC has used explicit semantic analysis of Wikipedia content to measure semantic distance between documents, which is in turn used to classify test documents based on nearest neighbours. The semantic-based DLTC method has a major drawback in that it relies on a large-scale, finely-compiled semantic knowledge base, which is difficult to obtain in many scenarios. In this paper we propose a novel kind of model, descriptive LDA (DescLDA), which performs DLTC with only category description words and unlabeled documents. In DescLDA, the LDA model is assembled with a describing device to infer Dirichlet priors from prior descriptive documents created with category description words. The Dirichlet priors are then used by LDA to induce category-aware latent topics from un-labeled documents. Experimental results with the 20 News-groups and RCV1 datasets show that: (1) our DLTC method is more effective than the semantic-based DLTC baseline method; and (2) the accuracy of our DLTC method is very close to state-of-the-art supervised text classification methods. As neither external knowledge resources nor labeled documents are required, our DLTC method is applicable to a wider range of scenarios.


title: The VideoMob Interactive Art Installation Connecting Strangers through Inclusive Digital Crowds

abstract: VideoMob is an interactive video platform and an artwork that enables strangers visiting different installation locations to interact across time and space through a computer interface that detects their presence, video-records their actions while automatically removing the video background through computer vision, and co-situates visitors as part of the same digital environment. Through the combination of individual user videos to form a digital crowd, strangers are connected through the graphic display. Our work is inspired by the way distant people can interact with each other through technology and influenced by artists working in the realm of interactive art. We deployed VideoMob in a variety of settings, locations, and contexts to observe hundreds of visitors’ reactions. By analyzing behavioral data collected through depth cameras from our 1,068 recordings across eight venues, we studied how participants behave when given the opportunity to record their own video portrait into the artwork. We report the specific activity performed in front of the camera and the influences that existing crowds impose on new participants. Our analysis informs the integration of a series of possible novel interaction paradigms based on real-time analysis of the visitors’ behavior through specific computer vision and machine learning techniques that have the potential to increase the engagement of the artwork's visitors and to impact user experience.


title: Learning Image Representations Tied to Ego-Motion

abstract: Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose to exploit proprioceptive motor signals to provide unsupervised regularization in convolutional neural networks to learn visual representations from egocentric video. Specifically, we enforce that our learned features exhibit equivariance i.e. they respond predictably to transformations associated with distinct ego-motions. With three datasets, we show that our unsupervised feature learning approach significantly outperforms previous approaches on visual recognition and next-best-view prediction tasks. In the most challenging test, we show that features learned from video captured on an autonomous driving platform improve large-scale scene recognition in static images from a disjoint domain.


title: Looking at Pedestrians at Different Scales: A Multiresolution Approach and Evaluations

abstract: Typically, in a detector framework, the model size is fixed at the size of the smallest object to be detected, and larger objects are detected by scaling the input image. The information lost due to scaling could be vital for accurately detecting large objects, which is an essential task for vision-based driver-assistance systems. To this end, we evaluate a multiresolution detector framework by training models at different sizes and demonstrate its effectiveness on a state-of-the-art pedestrian detector. Our comprehensive evaluation demonstrates meaningful improvement in detector performance. On the KITTI dataset under moderate difficulty settings, we achieve a 6% increase in the detector's average precision over the baseline single-resolution result on the KITTI benchmark. Further insights into the detector's improvements are provided using a fine-grained analysis of the detector's performance at various threshold settings.


title: Prediction of Bitcoin Price using Data Mining

abstract: Bitcoin is a computerized digital money and exchange network, represents an essential change in financial sectors, an interesting number of customers and excellent evaluation of channel inspection. In this research, dataset related to ten cryptocurrencies are used and created a new dataset by taking the closing price of each cryptocurrency for the research goal to ascertain how the direction and accuracy of price of the Bitcoin can be predicted by using data mining methods. Features engineering evaluated that all the ten cryptocurrencies are strongly correlated with each other. The task is achieved by implementation of supervised learning method in which random forest, support vector classifier, gradient boosting classifier, and neural network classifier are used under classification category and linear regression, recurrent neural network, gradient boosting regressor are used under regression category. In the classification category, support vector classifier achieved the highest accuracy of 62.31% and precision value 0.77. In regression category, gradient boosting regressor got the highest R-squared value 0.99.


James Watson ’ s most inconvenient truth : Race realism and the moralistic fallacy


title: Some Improvements on Deep Convolutional Neural Network Based Image Classification

abstract: Abstract: We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techiques include adding more image transformations to training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images. This paper summarizes our entry in the Imagenet Large Scale Visual Recognition Challenge 2013. Our system achieved a top 5 classification error rate of 13.55% using no external data which is over a 20% relative improvement on the previous year's winner.


title: CIO − Working Paper 5/2006Solving the Variable Size Bin Packing Problem with Discretized Formulations

abstract: In this paper we study the use of a discretized formulation for solving the variable size bin packing problem (VSBPP). The VSBPP is a generalization of the bin packing problem where bins of different capacities (and different costs) are available for packing a set of items. The objective is to pack all the items minimizing the total cost associated with the bins. We start by presenting a straightforward integer programming formulation to the problem and later on, propose a less straightforward formulation obtained by using a so-called discretized model reformulation technique proposed for other problems (see [Gouveia L. A 2n constraint formulation for the capacitated minimal spanning tree problem. Operations Research 1995; 43:130-141; Gouveia L, Saldanha-da-Gama F. On the capacitated concentrator location problem: a reformulation by discretization. Computers and Operations Research 2006; 33:1242-1258]). New valid inequalities suggested by the variables of the discretized model are also proposed to strengthen the original linear relaxation bounds. Computational results (see Section 4) with up to 1000 items show that these valid inequalities not only enhance the linear programming relaxation bound but may also be extremely helpful when using a commercial package for solving optimally VSBPP.


title: Body shape model, physical activity and eating behaviour

abstract: Objectives: Research on the influence of body shape model on adolescent males is scarce. The current study aimed to assess this influence among adult males involved in intense physical activity and to determine its relationship to eating behaviour. Possible variations between 1998 and 2008 were also analysed. Method: A total of 950 males (672 in 1998 and 278 in 2008), all aspiring professional soldiers, were studied using the Questionnaire of Influences on Body Shape Model (CIMEC-V) and the Eating Attitudes Test-40 (EAT-40), as well as by assessing their physical/sporting activity and body mass index (BMI). Results: Scores on the CIMEC-V were significantly correlated with the EAT-40 and BMI. As regards physical activity the only positive correlation referred to gymbased exercise. A cluster analysis revealed two subgroups with respect to physical activity, BMI, and scores on the CIMEC-V and EAT-40. One of them scored higher on these three variables and they also had a BMI > 25. The comparative study of data from 1998 and 2008 showed significant changes in some variables. Conclusions: Generally, the results differ considerably from those reported for younger samples (which would suggest a lower risk of disordered eating behaviour). However, there is a higher risk group in which the influence of body shape models, physical activity and eating behaviour are related to greater body volume. The influence of the body shape model on males has increased, especially as regards the influence of friends and in terms of behaviours aimed at weight loss.


title: Algorithms for hybrid MILP/CP models for a class of optimization problems

abstract: The goal of this paper is to develop models and methods that use complementary strengths of Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques to solve problems that are otherwise intractable if solved using either of the two methods. The class of problems considered in this paper have the characteristic that only a subset of the binary variables have non-zero objective function coefficients if modeled as an MILP. This class of problems is formulated as a hybrid MILP/CP model that involves some of the MILP constraints, a reduced set of the CP constraints, and equivalence relations between the MILP and the CP variables. An MILP/CP based decomposition method and an LP/CP-based branch-and-bound algorithm are proposed to solve these hybrid models. Both these algorithms rely on the same relaxed MILP and feasibility CP problems. An application example is considered in which the least-cost schedule has to be derived for processing a set of orders with release and due dates using a set of dissimilar parallel machines. It is shown that this problem can be modeled as an MILP, a CP, a combined MILP-CP OPL model (Van Hentenryck 1999), and a hybrid MILP/CP model. The computational performance of these models for several sets shows that the hybrid MILP/CP model can achieve two to three orders of magnitude reduction in CPU time.


L G ] 2 6 A pr 2 01 7 Decision Stream : Cultivating Deep Decision Trees


title: Visual Object Tracking Using Positive and Negative Examples

abstract: This paper presents a robust and real-time object tracking method in image sequences. Existing visual object detection and tracking algorithms usually use apparent features of target object as a model. Those algorithms detects objects by thresholding the similarity or the dissimilarity measure with the model. They sometimes fail to detect objects because of inadequate threshold. In this paper, we propose a method for detection and tracking using positive (object) and negative (non-object) models. This method does not require any threshold, because the object region can be detected just by comparing the similarities or dissimilarities with positive and negative models. If we employ distance as a dissimilarity measure, the problem can be formalized as a nearest neighbor (NN) classification problem. We first show an example of NN classification based color object detection, which detects multiple objects in real-time. Next, we extend the method to tracking. For this extension, we have to measure the distinctiveness of the object, because the detected object by the NN classification sometimes disappears. By finding the high distinctiveness image region around the tracked region in the previous image frame, we can keep tracking even if the object cannot be detected by NN classification. We implemented this method on an active stereo camera system and confirmed that video-rate detection and tracking as well as 3D position measurement can be realized just by using standard PC.


title: Planar endfire circularly polarized antenna with unidirectional radiation

abstract: A single-layer single-fed endfire antenna with circular polarization and unidirectional radiation for X-band applications is proposed. The design is based upon two complementary Yagi-Uda arrays and the series feeding technique. Simulated results confirm a usable bandwidth (the common overlapped bandwidth limited by the 3-dB AR and −10-dB |S11|) of 1.0% can be achieved. At 10 GHz, the gain is 7.23 dBic and the front-to-back ratio is 14.45 dB. The overall antenna size is 1.05λ 0 ×0.65λ 0 while the profile is only 0.026λ 0 .


Scoring Disease-Medication Associations using Advanced NLP, Machine Learning, and Multiple Content Sources.


title: Measuring user influence in Twitter: The million follower fallacy

abstract: Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.


title: Sketch-to-Design: Context-based Part Assembly

abstract: Designing 3D objects from scratch is difficult, especially when the user intent is fuzzy and lacks a clear target form. We facilitate design by providing reference and inspiration from existing model contexts. We rethink model design as navigating through different possible combinations of part assemblies based on a large collection of pre-segmented 3D models. We propose an interactive sketch-to-design system, where the user sketches prominent features of parts to combine. The sketched strokes are analysed individually, and more importantly, in context with the other parts to generate relevant shape suggestions via adesign galleryinterface. As a modelling session progresses and more parts get selected, contextual cues become increasingly dominant, and the model quickly converges to a final form. As a key enabler, we use pre-learned part-based contextual information to allow the user to quickly explore different combinations of parts. Our experiments demonstrate the effectiveness of our approach for efficiently designing new variations from existing shape collections.


title: The path inference filter: model-based low-latency map matching of probe vehicle data

abstract: We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.


Semantic Emotion-Topic Model Based Social Emotion Mining


Three-dimensional sonohysterography (SHG) has improved diagnostic accuracy for intrauterine pathology compared to two-dimensional SHG: a prospective pilot study in infertility patients


title: Brain Pharmacokinetics and Tissue Distribution In Vivo of Fluvoxamine and Fluoxetine by Fluorine Magnetic Resonance Spectroscopy

abstract: Abstract This investigation of fluvoxamine and fluoxetine-norfluoxetine distributions in vivo at steady-state and of quantitative kinetics in brain and plasma after drug therapy interruption was performed by fluorine nuclear magnetic resonance spectroscopy ( 19 F MRS), spectroscopic imaging (MRSI), and plasma HPLC on 12 subjects treated for depression. MRSI suggests a homogeneous distribution of 19 F MRS visible fluvoxamine mainly in brain. Fluvoxamine steady-state brain concentrations (12 ± 5 μM; n = 13) and brain-to-plasma concentration ratios (10 ± 2; n = 12) were similar to those of combined fluoxetine-norfluoxetine (CF-norfluoxetine) (13 ± 6 μM; n = 4 and 10 ± 6; n = 4). Fluvoxamine brain elimination half-life (79 ± 24 hours; n = 4) was significantly shorter than that of CF-norfluoxetine (382 ± 48 hours; n = 2). Fluvoxamine brain-to-plasma-half-life-ratio was 2.2 ± 0.3 ( n = 4), contrarily to CF-norfluoxetine (1.0 ± 0.3; n = 2). This study shows that quantitative pharmacokinetics in target organs by 19 F MRS in vivo should prove useful for understanding and investigating outcome of treatment modifications and side effects.


title: Reliable Mining of Automatically Generated Test Cases from Software Requirements Specification (SRS)

abstract: Writing requirements is a two-way process. In this paper we use to classify Functional Requirements (FR) and Non Functional Requirements (NFR) statements from Software Requirements Specification (SRS) documents. This is systematically transformed into state charts considering all relevant information. The current paper outlines how test cases can be automatically generated from these state charts. The application of the states yields the different test cases as solutions to a planning problem. The test cases can be used for automated or manual software testing on system level. And also the paper presents a method for reduction of test suite by using mining methods thereby facilitating the mining and knowledge extraction from test cases.


title: The role of fillers in facial implant surgery.

abstract: Alloplastic facial implants and injectable fillers are currently used for facial rejuvenation and augmentation. Their respective roles in augmentation and volume replacement of the chin and midface are discussed. Treatment goals, patient selection, procedures, and patient recovery are detailed. In addition, there is a segment for surgeons presenting a decision algorithm for selecting surgical versus less-invasive or nonsurgical approaches for midface rejuvenation.


title: A low-cost LoRaWAN testbed for IoT: Implementation and measurements

abstract: One of the challenges in deploying IoT applications is the cost of building and operating the communication infrastructure. This paper studies the feasibility of building a low-cost IoT network based on LoRa, a leading Low-Power Wide-Area Network (LPWAN) technology, using off-the-shelf components and open source software. To this end, we describe our LoRa testbed, which includes gateways, end devices and a variety of sensors. We then present extensive measurement results to characterize the performance of our LoRa network over the 915 MHz unlicensed ISM band in both indoor and outdoor scenarios for various network setups. Our results show that even in a harsh propagation environment, e.g., when the gateway is located inside a concrete building, the low-cost network is able to achieve great coverage. Specifically, we observed that: i) the indoor coverage is sufficient to cover an entire seven-story office building with minimal packet drop, ii) the outdoor coverage is very dependent on the environment, where in our experiments, a communication range of 4.4 km was achieved with only 15% packet drop, iii) network parameters such as spreading factor and packet size greatly affect the coverage; for example, we observed that a payload size of 242 bytes leads to 90% packet drop versus less than 5% drop with a payload size of 1 byte.


title: 3D-printing techniques in a medical setting: a systematic literature review

abstract: BackgroundThree-dimensional (3D) printing has numerous applications and has gained much interest in the medical world. The constantly improving quality of 3D-printing applications has contributed to their increased use on patients. This paper summarizes the literature on surgical 3D-printing applications used on patients, with a focus on reported clinical and economic outcomes.MethodsThree major literature databases were screened for case series (more than three cases described in the same study) and trials of surgical applications of 3D printing in humans.Results227 surgical papers were analyzed and summarized using an evidence table. The papers described the use of 3D printing for surgical guides, anatomical models, and custom implants. 3D printing is used in multiple surgical domains, such as orthopedics, maxillofacial surgery, cranial surgery, and spinal surgery. In general, the advantages of 3D-printed parts are said to include reduced surgical time, improved medical outcome, and decreased radiation exposure. The costs of printing and additional scans generally increase the overall cost of the procedure.Conclusion3D printing is well integrated in surgical practice and research. Applications vary from anatomical models mainly intended for surgical planning to surgical guides and implants. Our research suggests that there are several advantages to 3D-printed applications, but that further research is needed to determine whether the increased intervention costs can be balanced with the observable advantages of this new technology. There is a need for a formal cost–effectiveness analysis.


title: Graph based tweet entity linking using DBpedia

abstract: Twitter has became an invaluable source of information, due to his dynamic nature with more than 400 million tweets posted per day. Determining what an individual post is about can be a non trivial task because his high contextualization and his informal nature. Named Entity Linking (NEL) is a subtask of information extraction that aims to ground entity mentions to their corresponding node in a Knowledge Base (KB), which requires a disambiguation step, because many resources can be matched to the same entity that lead to synonymy and polysemy problems. To overcome these problems, especially in the context of short text, we present a novel system for tweet entity linking based on graph centrality and DBpedia as knowledge base. Our approach relies on the assumption that related entities tend to appear in the same tweet as tweets are topic specific. Also, we address the problem of irregular name mentions. Finally, to show the effectiveness of our system we evaluate it using a real twitter dataset and compare it to a well known state-of-the-art named entity linking system for short text.


title: Tensor-Factorized Neural Networks

abstract: The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations. The classification performance using vectorized NN is constrained, because the temporal or spatial information in neighboring ways is disregarded. More parameters are required to learn the complicated data structure. This paper presents a new tensor-factorized NN (TFNN), which tightly integrates TF and NN for multiway feature extraction and classification under a unified discriminative objective. This TFNN is seen as a generalized NN, where the affine transformation in an NN is replaced by the multilinear and multiway factorization for tensor-based NN. The multiway information is preserved through layerwise factorization. Tucker decomposition and nonlinear activation are performed in each hidden layer. The tensor-factorized error backpropagation is developed to train TFNN with the limited parameter size and computation time. This TFNN can be further extended to realize the convolutional TFNN (CTFNN) by looking at small subtensors through the factorized convolution. Experiments on real-world classification tasks demonstrate that TFNN and CTFNN attain substantial improvement when compared with an NN and a convolutional NN, respectively.


title: Bipedal robot walking pattern generation based on capture point and minimizing falling damage for posture recovery

abstract: Bipedal walking is one of the core issues of humanoid robots. We generated bipedal walking pattern based on the capture point. Capture point is stable criterion so we can generate desired zero moment point by the capture point and generate center of mass trajectory using the preview control. We also generated the safety fall down & stably get up motion when capture point is out of the stable region.


title: Understanding situational online information disclosure as a privacy calculus

abstract: The effect of situational factors is largely ignored by current studies on information privacy. This paper theorized and empirically tested how an individual's decision-making on information disclosure is driven by competing situational benefits and risk factors. The results of this study indicate that, in the context of an e-commerce transaction with an unfamiliar vendor, information disclosure is the result of competing influences of exchange benefits and two types of privacy beliefs (privacy protection belief and privacy risk belief). In addition, the effect of monetary rewards is dependent upon the fairness of information exchange. Monetary rewards could undermine information disclosure when information collected has low relevance to the purpose of the e-commerce transaction.


title: Generating multi-fingered robotic grasps via deep learning

abstract: This paper presents a deep learning architecture for detecting the palm and fingertip positions of stable grasps directly from partial object views. The architecture is trained using RGBD image patches of fingertip and palm positions from grasps computed on complete object models using a grasping simulator. At runtime, the architecture is able to estimate grasp quality metrics without the need to explicitly calculate the given metric. This ability is useful as the exact calculation of these quality functions is impossible from an incomplete view of a novel object without any tactile feedback. This architecture for grasp quality prediction provides a framework for generalizing grasp experience from known to novel objects.


title: The Impact of Deep Hierarchical Discourse Structures in the Evaluation of Text Coherence

abstract: Previous work by Lin et al. (2011) demonstrated the effectiveness of using discourse relations for evaluating text coherence. However, their work was based on discourse relations annotated in accordance with the Penn Discourse Treebank (PDTB) (Prasad et al., 2008), which encodes only very shallow discourse structures; therefore, they cannot capture long-distance discourse dependencies. In this paper, we study the impact of deep discourse structures for the task of coherence evaluation, using two approaches: (1) We compare a model with features derived from discourse relations in the style of Rhetorical Structure Theory (RST) (Mann and Thompson, 1988), which annotate the full hierarchical discourse structure, against our re-implementation of Lin et al.’s model; (2) We compare a model encoded using only shallow RST-style discourse relations, against the one encoded using the complete set of RST-style discourse relations. With an evaluation on two tasks, we show that deep discourse structures are truly useful for better differentiation of text coherence, and in general, RST-style encoding is more powerful than PDTBstyle encoding in these settings.


title: The pole-zero doublet: A cascode operational amplifier with cross coupled capacitor

abstract: A symbolic analysis is presented to investigate the effect of cross coupled capacitor on a cascode operational amplifier. A complete transfer function of the amplifier with cross coupled capacitor is derived and verified through circuit simulations. The modeled transfer function shows presence of a pole-zero doublet in the amplifier's frequency response when the cross-coupled capacitor is connected across it. The analysis further results in a closed form equations to optimize the amplifier based on user defined specifications such as, an open loop dc gain, a unity gain bandwidth and a phase margin. To check the validity of the model, a 50 dB open loop dc gain and 255 MHz unity gain bandwidth cascode amplifier is designed in a standard 90 nm CMOS technology with the supply voltage of 1.5 V. The results obtained from simulation and modeled transfer function show good agreement with each other and have an acceptable average relative error of 3 %.


title: Positive Youth Development, Participation in Community Youth Development Programs, and Community Contributions of Fifth-Grade Adolescents: Findings From the First Wave Of the 4-H Study of Positive Youth Development

abstract: The 4-H Study of Positive Youth Development (PYD), a longitudinal investigation of a diverse sample of 1,700 fifth graders and 1,117 of their parents, tests developmental contextual ideas linking PYD, youth contributions, and participation in community youth development (YD) programs, representing a key ecological asset. Using data from Wave 1 of the study, structural equation modeling procedures provided evidence for five firstorder latent factors representing the “Five Cs” of PYD (competence, confidence, connection, character, and caring) and for their convergence on a second-order PYD latent construct. A theoretical construct, youth contribution, was also created and examined. Both PYD and YD program participation independently related to contribution. The importance of longitudinal analyses for extending the present results is discussed.


title: Incidence of diastasis recti abdominis during the childbearing year.

abstract: This study was conducted to determine 1) the incidence of diastasis recti abdominis among women during the childbearing year and 2) the location of the condition along the linea alba. Clinicians have long noted its presence, prenatally and postnatally, but the magnitude of the problem is currently unknown. A cross-sectional design was used to test 71 primiparous women placed in one of five groups, based on placement within the childbearing year. A commonly accepted test for diastasis recti abdominis was performed. Palpation for diastasis recti abdominis at the linea alba was performed 4.5 cm above, 4.5 cm below, and at the umbilicus. Diastases were observed at all three places, but most often at the umbilicus. A significant relationship (p less than .05) was found between a woman's placement in her childbearing year and the presence or absence of the condition. Diastasis recti abdominis was observed initially in the women in the second trimester group. Its incidence peaked in the third trimester group; remained high in the women in the immediate postpartum group; and declined, but did not disappear, in the later postpartum group. These findings demonstrate the importance of testing for diastasis recti abdominis above, below, and at the umbilicus throughout and after the childbearing year.


title: Design, calibration and preliminary testing of a robotic telemanipulator for OCT guided retinal surgery

abstract: This paper presents an experimental system for demonstrating a new concept for retinal micro-vascular surgery. This concept involves the use of stents to maintain the structural integrity in artery/vein crossings. A design of an 11 degree of freedom robot that includes a 6 DoF Stewart-Gough platform, a two DoF differential wrist, and a three DoF actuator for deployment of stent and bridge vessel separators is proposed as a validated robotic system for ophthalmic microsurgery. The robot also allows for quick exchange of surgical graspers and the integration of a custom made B-mode OCT probe. The paper presents the kinematic modeling and calibration of the robot for demonstration of ocular and intraocular manipulation. The system telemanipulation framework is constructed and experimental evaluations of stent deployment and membrane peeling are shown with a verification of results using OCT probe images. We believe these preliminary results demonstrate new technology that may enable micro-vascular stenting for treatment of branch retinal vein occlusion while offering a general platform for dexterous retinal surgery.


title: A Mobile Decision Support System for Dynamic Group Decision-Making Problems

abstract: The aim of this paper is to present a decision support system model with two important characteristic: 1) mobile technologies are applied in the decision process and 2) the set of alternatives is not fixed over time to address dynamic decision situations in which the set of solution alternatives could change throughout the decision-making process. We implement a prototype of such mobile decision support system in which experts use mobile phones to provide their preferences anywhere and anytime. To get a general system, experts' preferences are assumed to be represented by different preference representations: 1) fuzzy preference relations; 2) orderings; 3) utility functions; and 4) multiplicative preference relations. Because this prototype incorporates both selection and consensus processes, it allows us to model group decision-making situations. The prototype incorporates a tool for managing the changes on the set of feasible alternatives that could happen throughout the decision process. This way, the prototype provides a new approach to deal with dynamic group decision-making situations to help make decisions anywhere and anytime.


Stereotypes, Role Models, and the Formation of Beliefs


title: Nematodes from the squeaker fishes Synodontis nigromaculatus and S. vanderwaali from the Okavango River, Botswana, including three new species

abstract: Five nematode species were recorded from the stomach and rectum of the spotted squeaker Synodontis nigromaculatus Boulenger or the finetooth squeaker S. vanderwaali Skelton & White (Mochokidae, Siluriformes) from the Okavango River, Botswana: Falcaustra similis n. sp. (Kathlaniidae), Labeonema africanum n. sp. (Cosmocercidae), Synodontisia okavangoensis n. sp. (Pharyngodonidae), Procamallanus (Procamallanus) laeviconchus (Wedl, 1861) (Camallanidae) and Spinitectus sp. (Cystidicolidae) (only a single female). F. similis (type-host Synodontisnigromaculatus) differs from the similar species F. straeleni Campana-Rouget, 1961 mainly by the number and disposition of the male caudal papillae and the structure of the mouth; L. africanum (type-host S. nigromaculatus) differs from its congeners in having distinctly longer spicules (105-120 μm), a relatively shorter gubernaculum (30-36 μm) and in the number and arrangement of the male caudal papillae; Synodontisiaokavangoensis (type-host Synodontisnigromaculatus) is characterised principally by crescent-shaped cephalic papillae, length (87 μm) and shape of the spicule as well as large eggs (0.156-0.180 × 69-102 μm). Synodontisia moraveci Anderson & Lim, 1996 is transferred to a newly established genus, Royandersonia n. g., (as its type-species) as R. moraveci (Anderson & Lim, 1996) n. comb. Some taxonomic problems concerning Procamallanus (P.) laeviconchus are discussed. All species were studied by scanning electron microscopy for the first time.


Dynamics of interfaces and layers in a stratified turbulent fluid


title: The social neuroscience of empathy

abstract: The phenomenon of empathy entails the ability to share the affective experiences of others. In recent years social neuroscience made considerable progress in revealing the mechanisms that enable a person to feel what another is feeling. The present review provides an in-depth and critical discussion of these findings. Consistent evidence shows that sharing the emotions of others is associated with activation in neural structures that are also active during the first-hand experience of that emotion. Part of the neural activation shared between self- and other-related experiences seems to be rather automatically activated. However, recent studies also show that empathy is a highly flexible phenomenon, and that vicarious responses are malleable with respect to a number of factors—such as contextual appraisal, the interpersonal relationship between empathizer and other, or the perspective adopted during observation of the other. Future investigations are needed to provide more detailed insights into these factors and their neural underpinnings. Questions such as whether individual differences in empathy can be explained by stable personality traits, whether we can train ourselves to be more empathic, and how empathy relates to prosocial behavior are of utmost relevance for both science and society.


title: Systematic review of big data analytics in governance

abstract: With advent of technology, data is increasing abruptly day by day. Traditional database systems are not capable of processing and handling such a voluminous data. Big data analytics has the capability of processing, handling and analyzing the large datasets or stream of data. Big data analytics play important role in fields such as healthcare, agriculture, smart grid and policy making. Big data analytics with governance improves planning and decision making phases for government projects. It helps to improve the quality of government services. This paper focuses on the different application areas of governance in which big data analytics play a role and tools used to handle big data management problem. The existing work is classified into different categories and is presented using visualizations. This paper also deals with challenges related to governance field.


Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators


title: A Deep Look into Logarithmic Quantization of Model Parameters in Neural Networks

abstract: Based on the fact that parameters of pre-trained neural networks naturally have non-uniform distributions, logarithmic quantization of network parameters achieves better classification results than linear quantization of the same resolution. In our practice, we found that the logarithmic quantization suffers huge accuracy decrease on small size neural networks. This is because the parameters of trained small neural networks are not highly concentrated around 0. In this paper, we analyse in depth the attributes of logarithmic quantization. In addition, existing compression algorithms highly rely on retraining which requires heavy computational power. In such a situation, we propose a new logarithmic quantization algorithm to mitigate the deterioration on neural networks which contain layers of small size. As the result, our method achieves the minimum accuracy loss on GoogLeNet after direct quantization compared to quantized counterparts.


title: Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions.

abstract: On a gambling task that models real-life decisions, patients with bilateral lesions of the ventromedial prefrontal cortex (VM) opt for choices that yield high immediate gains in spite of higher future losses. In this study, we addressed three possibilities that may account for this behaviour: (i) hypersensitivity to reward; (ii) insensitivity to punishment; and (iii) insensitivity to future consequences, such that behaviour is always guided by immediate prospects. For this purpose, we designed a variant of the original gambling task in which the advantageous decks yielded high immediate punishment but even higher future reward. The disadvantageous decks yielded low immediate punishment but even lower future reward. We measured the skin conductance responses (SCRs) of subjects after they had received a reward or punishment. Patients with VM lesions opted for the disadvantageous decks in both the original and variant versions of the gambling task. The SCRs of VM lesion patients after they had received a reward or punishment were not significantly different from those of controls. In a second experiment, we investigated whether increasing the delayed punishment in the disadvantageous decks of the original task or decreasing the delayed reward in the disadvantageous decks of the variant task would shift the behaviour of VM lesion patients towards an advantageous strategy. Both manipulations failed to shift the behaviour of VM lesion patients away from the disadvantageous decks. These results suggest that patients with VM lesions are insensitive to future consequences, positive or negative, and are primarily guided by immediate prospects. This `myopia for the future' in VM lesion patients persists in the face of severe adverse consequences, i.e. rising future punishment or declining future reward.


title: A Comparison Study between Data Mining Tools over some Classification Methods

abstract: Nowadays, huge amount of data and information are available for everyone, Data can now be stored in many different kinds of databases and information repositories, besides being available on the Internet or in printed form. With such amount of data, there is a need for powerful techniques for better interpretation of these data that exceeds the human's ability for comprehension and making decision in a better way. In order to reveal the best tools for dealing with the classification task that helps in decision making, this paper has conducted a comparative study between a number of some of the free available data mining and knowledge discovery tools and software packages. Results have showed that the performance of the tools for the classification task is affected by the kind of dataset used and by the way the classification algorithms were implemented within the toolkits. For the applicability issue, the WEKA toolkit has achieved the highest applicability followed by Orange, Tanagra, and KNIME respectively. Finally; WEKA toolkit has achieved the highest improvement in classification performance; when moving from the percentage split test mode to the Cross Validation test mode, followed by Orange, KNIME and finally Tanagra respectively.


title: On a New Improvement-Based Acquisition Function for Bayesian Optimization

abstract: Bayesian optimization (BO) is a popular algorithm for solving challenging optimization tasks. It is designed for problems where the objective function is expensive to evaluate, perhaps not available in exact form, without gradient information and possibly returning noisy values. Different versions of the algorithm vary in the choice of the acquisition function, which recommends the point to query the objective at next. Initially, researchers focused on improvement-based acquisitions, while recently the attention has shifted to more computationally expensive information-theoretical measures. In this paper we present two major contributions to the literature. First, we propose a new improvement-based acquisition function that recommends query points where the improvement is expected to be high with high confidence. The proposed algorithm is evaluated on a large set of benchmark functions from the global optimization literature, where it turns out to perform at least as well as current state-of-the-art acquisition functions, and often better. This suggests that it is a powerful default choice for BO. The novel policy is then compared to widely used global optimization solvers in order to confirm that BO methods reduce the computational costs of the optimization by keeping the number of function evaluations small. The second main contribution represents an application to precision medicine, where the interest lies in the estimation of parameters of a partial differential equations model of the human pulmonary blood circulation system. Once inferred, these parameters can help clinicians in diagnosing a patient with pulmonary hypertension without going through the standard invasive procedure of right heart catheterization, which can lead to side effects and complications (e.g. severe pain, internal bleeding, thrombosis).