title: An Integer Programming Approach to Generating Airline Crew Pairings

abstract: The ability to generate crew pairings quickly is essential to solving the airline crew scheduling problem. Although techniques for doing so are well-established, they are also highly customized and require significant implementation efforts. This greatly impedes researchers studying important problems such as robust planning, integrated planning, and automated recovery, all of which also require the generating of crew pairings. As an alternative, we present an integer programming (IP) approach to generating crew pairings, which can be solved via traditional methods such as branch-and-bound using off-the-shelf commercial solvers. This greatly facilitates the prototyping and testing of new research ideas. In addition, we suggest that our modeling approach, which uses both connection variables and marker variables to capture the non-linear cost function and constraints of the crew scheduling problem, can be applicable in other scheduling contexts as well. Computational results using data from a major US hub-and-spoke carrier demonstrate the performance of our approach.


title: Improvement of Face Recognition by Eyeglass Removal

abstract: In this paper, we present a method based on Active Appearance Model (AAM) to remove eyeglasses from face images. The occluded regions are first roughly detected by the AAM search. Then, an ellipse model is used to fit the eyes’ position. After eliminating the eye blocks, residual regions are further enhanced and extracted more precisely by the adaptive threshold method. Finally, the synthesized image is generated by iterative error compensation. Experimental results show that the proposed method provides an effective solution to the recognition of faces occluded by thick-rimmed eyeglasses.


title: Extended Isolation Forest

abstract: We present an extension to the model-free anomaly detection algorithm, Isolation Forest. This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points. We motivate the problem using heat maps for anomaly scores. These maps suffer from artifacts generated by the criteria for branching operation of the binary tree. We explain this problem in detail and demonstrate the mechanism by which it occurs visually. We then propose two different approaches for improving the situation. First we propose transforming the data randomly before creation of each tree, which results in averaging out the bias. Second, which is the preferred way, is to allow the slicing of the data to use hyperplanes with random slopes. This approach results in remedying the artifact seen in the anomaly score heat maps. We show that the robustness of the algorithm is much improved using this method by looking at the variance of scores of data points distributed along constant level sets. We report AUROC and AUPRC for our synthetic datasets, along with real-world benchmark datasets. We find no appreciable difference in the rate of convergence nor in computation time between the standard Isolation Forest and EIF.


title: Relationship between emotional maturity, self-esteem and life-satisfaction: A study on traditional dancers of Odisha region

abstract: AbstractDance is a dynamic root point to develop emotional maturity, self-esteem and life satisfaction. The present study aims to examine emotional maturity towards dancer’s life satisfaction through the presence of self-esteem. The study intentionally portrays co-association between study variables like, emotional maturity, self-esteem and life satisfaction through SEM. It divulges direct and indirect model with elucidating path relationship by using structural equation model. Data were collected from traditional dancers (176 Odissi and 116 folk dancers) at Odisha state in India by administering Rosenberg’s self-esteem scale, emotional maturity scale, life satisfaction scale and demographic information profile. The results reveal that all the variables of factor loading are greater than 0.40 based on EFA and CFA. The multivariate analysis technique used to perform the structural model of variables and their inter-relationship. Furthermore, the indirect model declared satisfactory model that the magnitude...


title: Load balancing in heterogeneous networks based on distributed learning in potential games

abstract: We present a novel approach for distributive load balancing in heterogeneous networks that use cell range expansion (CRE) for user association. First, we formulate the problem as a minimisation of an α-fairness objective function. Depending on α, different objectives in terms of network performance or fairness can be achieved. Next, we model the interactions among the base stations for load balancing as a potential game, in which the potential function is the α-fairness function. The optimal Nash equilibrium of the game is found by using distributed learning algorithms. We use log-linear and binary log-linear learning algorithms for complete and partial information settings, respectively. By running extensive simulations, we show that the proposed algorithms converge within a few tens of iterations. The convergence speed in the case of partial information setting is comparable to that of the complete information setting. We also show that the best response algorithm does not necessarily converge to the optimal Nash equilibrium.


freedom . js : an Architecture for Serverless Web Applications


Evolution of Metacognition


title: A case-control study of anatomic changes resulting from sexual abuse.

abstract: OBJECTIVE ::: Our goal was to identify vulvar and hymenal characteristics associated with sexual abuse among female children between the ages of 3 and 8 years. ::: ::: ::: STUDY DESIGN ::: Using a case-control study design, we examined and photographed the external genitalia of 192 prepubertal children with a history of penetration and 200 children who denied prior abuse. Bivariate analyses were conducted by chi(2), the Fisher exact test, and the Student t test to assess differences in vulvar and hymenal features between groups. ::: ::: ::: RESULTS ::: Vaginal discharge was observed more frequently in abused children (P =.01). No difference was noted in the percentage of abused versus nonabused children with labial agglutination, increased vascularity, linea vestibularis, friability, a perineal depression, or a hymenal bump, tag, longitudinal intravaginal ridge, external ridge, band, or superficial notch. Furthermore, the mean number of each of these features per child did not differ between groups. A hymenal transection, perforation, or deep notch was observed in 4 children, all of whom were abused. ::: ::: ::: CONCLUSION ::: The genital examination of the abused child rarely differs from that of the nonabused child. Thus legal experts should focus on the child's history as the primary evidence of abuse.


title: SLANT+: A Nonlinear Model for Opinion Dynamics in Social Networks

abstract: Online Social Networks (OSNs) have emerged as a global media for forming and shaping opinions on a broad spectrum of topics like politics, e-commerce, sports, etc. So, research on understanding and predicting opinion dynamics in OSNs, especially using a tractable linear model, has abound in literature. However, these linear models are too simple to uncover the actual complex dynamics of opinion flow in social networks. In this paper, we propose SLANT+, a novel nonlinear generative model for opinion dynamics, by extending our earlier linear opinion model SLANT [7]. To design this model, we rely on a network-guided recurrent neural network architecture which learns a proper temporal representation of the messages as well as the underlying network. Furthermore, we probe various signals from the real life datasets and offer a conceptually interpretable nonlinear function that not only provides concrete clues of the opinion exchange process, but also captures the coupled dynamics of message timings and opinion flow. As a result, with five real-life datasets crawled from Twitter, our proposal gives significant accuracy boost over six state-of-the-art baselines.


A Conceptual Model of DI Diesel Combustion Based on Laser-Sheet Imaging*


REACH : Automated Database Design Integrating First-Order Theories , Reconstructive Expertise , and Implementation Heuristics for Accounting Information Systems


title: Pairing flavours and the temporal order of tasting

abstract: There can be little doubt that plating food beautifully is becoming ever more important in the world of high-end cuisine. However, there is a very real danger that all the attention to how a dish looks (or photographs) can end up obscuring the importance of temporal order to flavour perception. That is, there is an increasing tendency to prioritize plating elements in a dish so that they please the eyes of the beholder, rather than necessarily because they deliver the optimum balance of sensations to the palate. Relevant here is the fact that certain combinations of ingredients taste better when sampled simultaneously, or in a particular sequence, than when sampled in a different order. In this review, we examine the importance of sequencing and pairing taste/flavour sensations, both in the design of the meal itself, and when attempting to combine (or match) food and drink. We address the chemical, psychological, and computational strategies that have been suggested by those wishing to combine flavours for maximal impact. We evaluate three general principles of flavour matching: similarity—matching components based on common flavour compounds (or similar flavour profiles); contrast—combinations that are purposely chosen because they differ from each other (a strategy that is more common in the cuisine of some countries than others); and synergy (or emergence)—those combinations that together deliver new flavour experiences or else harmonize with one another. We argue that the psychological account (informed by an awareness of cultural differences), and to a lesser extent the chemical account, provides meaningful suggestions as far as effectively combining flavours is concerned.


A Quick Sequential Forward Floating Feature Selection Algorithm for Emotion Detection from Speech


title: Distributions associated with cell populations

abstract: SUMMARY The theoretical equations relating the numbers of cells at various stages of development and division in the mitotic cycle are noted, and used to derive the stationary age and phase distributions. Further sections refer to the effect of synchronization, and to fluctuations. Examples of compound distributions are also discussed, particularly in relation to observed distributions of grain counts for cells incorporating 'labelled' thymidine.


title: Pixel-wise ground truth annotation in videos An semi-automatic approach for pixel-wise and semantic object annotation

abstract: In the last decades, a large diversity of automatic, semi-automatic and manual approaches for video segmentation and knowledge extraction from video-data has been proposed. Due to the high complexity in both the spatial and temporal domain, it continues to be a challenging research area. In order to develop, train, and evaluate new algorithms, ground truth of video-data is crucial. Pixel-wise annotation of ground truth is usually time-consuming, does not contain semantic relations between objects and uses only simple geometric primitives. We provide a brief review of related tools for video annotation, and introduce our novel interactive and semi-automatic segmentation tool iSeg. Extending an earlier implementation, we improved iSeg with a semantic time line, multithreading and the use of ORB features. A performance evaluation of iSeg on four data sets is presented. Finally, we discuss possible opportunities and applications of semantic polygon-shaped video annotation, such as 3D reconstruction and video inpainting.


title: An anonymous electronic voting protocol for voting over the Internet

abstract: We propose a secure electronic voting protocol that is suitable for large scale voting over the Internet. The protocol allows a voter to cast his or her ballot anonymously, by exchanging untraceable yet authentic messages. The protocol ensures that: only eligible voters are able to cast votes; a voter is able to cast only one vote; a voter is able to verify that his or her vote is counted in the final tally; nobody, other than the voter is able to link a cast vote with a voter; and if a voter decides not to cast a vote, nobody is able to cast a fraudulent vote in place of the voter.


title: Optimizing Segmentation Strategies for Simultaneous Speech Translation

abstract: In this paper, we propose new algorithms for learning segmentation strategies for simultaneous speech translation. In contrast to previously proposed heuristic methods, our method finds a segmentation that directly maximizes the performance of the machine translation system. We describe two methods based on greedy search and dynamic programming that search for the optimal segmentation strategy. An experimental evaluation finds that our algorithm is able to segment the input two to three times more frequently than conventional methods in terms of number of words, while maintaining the same score of automatic evaluation. 1


title: I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis

abstract: Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industry-leading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 89% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 18% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website.


title: Semi-active fuzzy control of a wind-excited tall building using multi-objective genetic algorithm

abstract: Abstract In this study, a multi-objective optimal fuzzy control system for the response reduction of a wind-excited tall building has been proposed. A semi-active tuned mass damper (STMD) is used for vibration control of a 76-story benchmark building subjected to wind load. An STMD consists of a 100 kN magnetorheological (MR) damper and its natural period is tuned to the first-mode natural period of vibration of the example building structure. The damping force of the MR damper is controlled by a fuzzy logic controller. A multi-objective genetic algorithm is used for optimization of the fuzzy logic controller. Both the 75th floor acceleration response of the structure and the stroke of the STMD have been used as the objective functions for this multi-objective optimization problem. Because a multi-objective optimization approach provides a set of Pareto-optimal solutions, an engineer is able to select an appropriate design for the specific performance requirement. For a comparative study, a sky–ground hook control algorithm is employed for control of the STMD. Based on numerical results, it has been shown that the proposed control system can effectively reduce the STMD motion as well as building responses compared to the comparative sky–ground hook control algorithm. In addition, the control performance of the STMD controlled by the optimal fuzzy controller is superior to that of the passive TMD and is comparable to an active TMD, but with a significant reduction in power consumption.


Managing Byzantine Robots via Blockchain Technology in a Swarm Robotics Collective Decision Making Scenario


title: IoT system for Human Activity Recognition using BioHarness 3 and Smartphone

abstract: This paper presents an Internet of Things (IoT) approach to Human Activity Recognition (HAR) using remote monitoring of vital signs in the context of a healthcare system for self-managed chronic heart patients. Our goal is to create a HAR-IoT system using learning algorithms to infer the activity done within 4 categories (lie, sit, walk and run) as well as the time consumed performing these activities and, finally giving feedback during and after the activity. Alike in this work, we provide a comprehensive insight on the cloud-based system implemented and the conclusions after implementing two different learning algorithms and the results of the overall system for larger implementations.


On the reuse of past optimal queries


title: Patent Mining: A Survey

abstract: Patent documents are important intellectual resources of protecting interests of individuals, organizations and companies. Different from general web documents, patent documents have a well-defined format including frontpage, description, nclaims, and figures. However, they are lengthy and rich in technical terms, which requires enormous human efforts for analysis. Hence, a new research area, called patent mining, emerges in recent years, aiming to assist patent analysts in investigating, processing, and analyzing patent documents. Despite the recent advances in patent mining, it is still far from being well explored in research communities. To help patent analysts and interested readers obtain a big picture of patent mining, we thus provide a systematic summary of existing research efforts along this direction. In this survey, we first present an overview of the technical trend in patent mining. We then investigate multiple research questions related to patent documents, including patent retrieval, patent classification, and patent visualization, and provide summaries and highlights for each question by delving into the corresponding research efforts.


title: Word Sense Disambiguation Using IndoWordNet

abstract: Word sense disambiguation (WSD) is considered as one of the toughest problems in the field of natural language processing. IndoWordNet is a linked structure of WordNets of major Indian languages. Recently, several IndoWordNet-based WSD approaches have been proposed and implemented for Indian languages. In this chapter, we present the usage of various other features of IndoWordNet in performing WSD. Here, we have used features such as linked WordNets and lexico-semantic relations. We have followed two unsupervised approaches, viz. (1) use of IndoWordNet in bilingual WSD for finding the sense distribution with the help of expectation maximization algorithm and (2) use of IndoWordNet in WSD for finding the most frequent sense using word and sense embeddings. Both these approaches justify the importance of IndoWordNet for word sense disambiguation for Indian languages, as the results are found to be promising and can beat the baselines.


title: Spying on the Smart Home: Privacy Attacks and Defenses on Encrypted IoT Traffic

abstract: The growing market for smart home IoT devices promises new conveniences for consumers while presenting new challenges for preserving privacy within the home. Many smart home devices have always-on sensors that capture users' offline activities in their living spaces and transmit information about these activities on the Internet. In this paper, we demonstrate that an ISP or other network observer can infer privacy sensitive in-home activities by analyzing Internet traffic from smart homes containing commercially-available IoT devices even when the devices use encryption. We evaluate several strategies for mitigating the privacy risks associated with smart home device traffic, including blocking, tunneling, and rate-shaping. Our experiments show that traffic shaping can effectively and practically mitigate many privacy risks associated with smart home IoT devices. We find that 40KB/s extra bandwidth usage is enough to protect user activities from a passive network adversary. This bandwidth cost is well within the Internet speed limits and data caps for many smart homes.


title: Behavioral and demographic predictors of suicide after traumatic spinal cord injury.

abstract: Abstract Among people with spinal cord injuries, death from suicide is two to six times more prevalent than in the general population. To determine if individual characteristics and behaviors present during rehabilitation can identify high-risk individuals, records of 5,200 spinal cord injured patients admitted to the Rocky Mountain Regional Spinal Injury System were reviewed. Of 489 deaths, 9% were due to suicide. They were compared with a control group of equal size, matched on age, gender, and injury level. The two groups differed significantly on postinjury despondency; expressions of shame, apathy, and hopelessness; and preinjury family disruption ( p p


title: Digital watermarking for deep neural networks

abstract: Although deep neural networks have made tremendous progress in the area of multimedia representation, training neural models requires a large amount of data and time. It is well known that utilizing trained models as initial weights often achieves lower training error than neural networks that are not pre-trained. A fine-tuning step helps to both reduce the computational cost and improve the performance. Therefore, sharing trained models has been very important for the rapid progress of research and development. In addition, trained models could be important assets for the owner(s) who trained them; hence, we regard trained models as intellectual property. In this paper, we propose a digital watermarking technology for ownership authorization of deep neural networks. First, we formulate a new problem: embedding watermarks into deep neural networks. We also define requirements, embedding situations, and attack types on watermarking in deep neural networks. Second, we propose a general framework for embedding a watermark in model parameters, using a parameter regularizer. Our approach does not impair the performance of networks into which a watermark is placed because the watermark is embedded while training the host network. Finally, we perform comprehensive experiments to reveal the potential of watermarking deep neural networks as the basis of this new research effort. We show that our framework can embed a watermark during the training of a deep neural network from scratch, and during fine-tuning and distilling, without impairing its performance. The embedded watermark does not disappear even after fine-tuning or parameter pruning; the watermark remains complete even after 65% of parameters are pruned.


title: Mechanisms of cardiac arrhythmias

abstract: Blood circulation is the result of the beating of the heart, which provides the mechanical force to pump oxygenated blood to, and deoxygenated blood away from, the peripheral tissues. This depends critically on the preceding electrical activation. Disruptions in the orderly pattern of this propagating cardiac excitation wave can lead to arrhythmias. Understanding of the mechanisms underlying their generation and maintenance requires knowledge of the ionic contributions to the cardiac action potential, which is discussed in the first part of this review. A brief outline of the different classification systems for arrhythmogenesis is then provided, followed by a detailed discussion for each mechanism in turn, highlighting recent advances in this area.


title: Catharsis and Media Violence: A Conceptual Analysis

abstract: The concept that doing something to “vent” aggression as a method of reducing aggressive feelings and behaviors, such as watching media violence or playing violent video games, continues to enjoy widespread public support despite a lack of empirical support. This article describes the historical origins of the concept and examines how well these conceptions fit with the modern usage of the aggression catharsis hypothesis. It is argued that there are four primary flaws with the catharsis hypothesis. First, the metaphor underlying Freud, Breuer, and Lorenz’s conception of aggression is flawed. Aggression is not a drive. Second, although Aristotle did use the term catharsis with relation to violent media (plays and poetry), he did not mean that viewing media violence can purge the viewer of aggressive feelings or behaviors. Furthermore, he describes several detailed requirements of plot and character that must be followed if his type of catharsis is to be achieved, and modern media violence does not meet these requirements. Third, the empirical support is not only lacking, a large empirical base contradicts the catharsis hypothesis. This is seen both in studies attempting to demonstrate catharsis directly and in the broader media violence literature. Fourth, human neuroscience contradicts the catharsis hypothesis. Learning is not hindered by viewing something one more time—it is improved. Taken together, it appears that there is no possible way that the aggression catharsis hypothesis can be accurate. It nevertheless continues to “feel” correct at a phenomenological level, and the reasons for this are discussed.


title: Automatic Extraction and Classification of Footwear Patterns

abstract: Identification of the footwear traces from crime scenes is an important yet largely forgotten aspect of forensic intelligence and evidence. We present initial results from a developing automatic footwear classification system. The underlying methodology is based on large numbers of localized features located using MSER feature detectors. These features are transformed into robust SIFT or GLOH descriptors with the ranked correspondence between footwear patterns obtained through the use of constrained spectral correspondence methods. For a reference dataset of 368 different footwear patterns, we obtain a first rank performance of 85% for full impressions and 84% for partial impressions.


title: Probabilistic Extraction of Beat Positions from a Beat Activation Function

abstract: We present a probabilistic way to extract beat positions from the output (activations) of the neural network that is at the heart of an existing beat tracker. The method can serve as a replacement for the greedy search the beat tracker currently uses for this purpose. Our experiments show improvement upon the current method for a variety of data sets and quality measures, as well as better results compared to other state-of-the-art algorithms.


title: Identifying Key Features of Student Performance in Educational Video Games and Simulations through Cluster Analysis

abstract: The assessment cycle of evidence-centered design (ECD) provides a framework for treating an educational video game or simulation as an assessment. One of the main steps in the assessment cycle of ECD is the identification of the key features of student performance. While this process is relatively simple for multiple choice tests, when applied to log data from educational video games or simulations it becomes one of the most serious bottlenecks facing researchers interested in implementing ECD. In this paper we examine the utility of cluster analysis as a method of identifying key features of student performance in log data stemming from educational video games or simulations. In our study, cluster analysis was able to consistently identify key features of student performance in the form of solution strategies and error patterns across levels, which contained few extraneous actions and explained a sufficient amount of the data.


title: Selecting Software Test Data Using Data Flow Information

abstract: This paper defines a family of program test data selection criteria derived from data flow analysis techniques similar to those used in compiler optimization. It is argued that currently used path selection criteria, which examine only the control flow of a program, are inadequate quate. Our procedure associates with each point in a program at which a variable is defined, those points at which the value is used. Several test data selection criteria, differing in the type and number of these associations, are defined and compared.


title: From Monolithic to Microservices: An Experience Report from the Banking Domain

abstract: Microservices have seen their popularity blossoming with an explosion of concrete applications in real-life software. Several companies are currently involved in a major refactoring of their back-end systems in order to improve scalability. This article presents an experience report of a real-world case study, from the banking domain, in order to demonstrate how scalability is positively affected by reimplementing a monolithic architecture into microservices. The case study is based on the FX Core system for converting from one currency to another. FX Core is a mission-critical system of Danske Bank, the largest bank in Denmark and one of the leading financial institutions in Northern Europe.


title: Generalized Boosting Algorithms for Convex Optimization

abstract: Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks, we analyze gradient-based descent algorithms for boosting with respect to any convex objective and introduce a new measure of weak learner performance into this setting which generalizes existing work. We present the weak to strong learning guarantees for the existing gradient boosting work for strongly-smooth, strongly-convex objectives under this new measure of performance, and also demonstrate that this work fails for non-smooth objectives. To address this issue, we present new algorithms which extend this boosting approach to arbitrary convex loss functions and give corresponding weak to strong convergence results. In addition, we demonstrate experimental results that support our analysis and demonstrate the need for the new algorithms we present.


title: Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results

abstract: One of the key tasks of sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. In this work, we focus on using supervised sequence labeling as the base approach to performing the task. Although several extraction methods using sequence labeling methods such as Conditional Random Fields (CRF) and Hidden Markov Models (HMM) have been proposed, we show that this supervised approach can be significantly improved by exploiting the idea of concept sharing across multiple domains. For example, "screen" is an aspect in iPhone, but not only iPhone has a screen, many electronic devices have screens too. When "screen" appears in a review of a new domain (or product), it is likely to be an aspect too. Knowing this information enables us to do much better extraction in the new domain. This paper proposes a novel extraction method exploiting this idea in the context of supervised sequence labeling. Experimental results show that it produces markedly better results than without using the past information.


title: Interactive Test Tool for Interoperable C-ITS Development

abstract: This paper presents the architecture of an Interactive Test Tool (ITT) for interoperability testing of Cooperative Intelligent Transport Systems (C-ITS). Cooperative systems are developed by different manufacturers at different locations, which makes interoperability testing a tedious task. Up until now, interoperability testing is performed during physical meetings where the C-ITS devices are placed within range of wireless communication, and messages are exchanged. The ITT allows distributed (e.g. over Internet) interoperability testing starting from the network Transport Layer and all the way up to the Application Layer, e.g. to platooning. ITT clients can be implemented as Hardware-in-the-Loop, thus allowing to combine physical and virtual vehicles. Since the ITT considers each client as a black box, manufacturers can test together without revealing internal implementations to each other. The architecture of the ITT allows users to easily switch between physical wireless networking and virtual ITT networking. Therefore, only one implementation of the ITS communication stack is required for both development and testing, which reduces the work overhead and ensures that the stack that is used during the testing is the one deployed in the real world.


title: Restoration of controllable hysteresis in partially depleted SOI CMOS Schmitt trigger circuits

abstract: This paper presents a new circuit technique to alleviate the uncontrollable floating-body-induced hysteretic component present in the transfer characteristics of voltage-mode CMOS Schmitt trigger circuits in a partially depleted silicon-on-insulator technology. This technique integrates a successive switching threshold shift mechanism with the systematic body contact scheme, resulting in improved noise immunity and well-defined hysteresis behavior for the Schmitt trigger circuit that is suitable for use as a low-noise receiver, level shifter, waveform-reshaping circuit, and delay element in very large-scale integrated applications.


title: Topology, search, and fault tolerance in unstructured P2P networks

abstract: Decentralized and unstructured peer-to-peer networks such as Gnutella are attractive for certain applications because they require no centralized directories and no precise control over network topology or data placement. However, the flooding-based query algorithm used in Gnutella does not scale; each query generates a large amount of traffic and large systems quickly become overwhelmed by the query-induced load. This paper explores, through simulation, various alternatives to Gnutella's query algorithm, data replication strategy, and network topology. We propose a query algorithm based on multiple random walks that resolves queries almost as quickly as Gnutella's flooding method while reducing the network traffic by two orders of magnitude in many cases. We also present simulation results on a distributed replication strategy proposed in [8]. Finally, we find that among the various network topologies we consider, uniform random graphs yield the best performance.


title: Automated microfluidic chromatin immunoprecipitation from 2,000 cells

abstract: Chromatin immunoprecipitation (ChIP) is a powerful assay used to probe DNA-protein interactions. Traditional methods of implementing this assay are lengthy, cumbersome and require a large number of cells, making it difficult to study rare cell types such as certain cancer and stem cells. We have designed a microfluidic device to perform sensitive ChIP analysis on low cell numbers in a rapid, automated fashion while preserving the specificity of the assay. Comparing ChIP results for two modified histone protein targets, we showed our automated microfluidic ChIP (AutoChIP) from 2,000 cells to be comparable to that of conventional ChIP methods using 50,000-500,000 cells. This technology may provide a solution to the need for a high sensitivity, rapid, and automated ChIP assay, and in doing so facilitate the use of ChIP for many interesting and valuable applications.


title: Boids with a fuzzy way of thinking

abstract: The increase of the processing power of personal computers in the last decade resulted in a notable progress of the artificial animal (animat) construction and simulation field. Regardless of the achieved results the coding of an animat’s behavior can, to someone unfamiliar with physics of motion and robotics, seem like pure witchcraft. Not to mention the wealth of ethological knowledge required regarding the behavior of the animal that is being modeled. In this article we suggest the use of fuzzy logic as the basis of an animat’s decision about its next step. We hypothesize that by using linguistic programming based on common sense unclear and even partially contradictory knowledge of the animal’s behavior, we can achieve comparable if not even better simulation results than with the classical crisp implementation. The following article presents an investigation of our theories on the case of a boid – a special type of animat – limiting itself on the boid’s urge of alignment with its flockmates.


title: Modelling pedestrian trajectory patterns with Gaussian processes

abstract: We propose a non-parametric model for pedestrian motion based on Gaussian Process regression, in which trajectory data are modelled by regressing relative motion against current position. We show how the underlying model can be learned in an unsupervised fashion, demonstrating this on two databases collected from static surveillance cameras. We furthermore exemplify the use of model for prediction, comparing the recently proposed GP-Bayesfilters with a Monte Carlo method. We illustrate the benefit of this approach for long term motion prediction where parametric models such as Kalman Filters would perform poorly.


title: Estimating with BIM: A Survey of US Construction Companies

abstract: The use of Building Information Modeling (BIM) in the construction industry is on the rise. It is widely acknowledged that adoption of BIM would cause a seismic shift in the business processes within the construction industry and related fields. Cost estimation is a key aspect in the workflow of a construction project. Processes within estimating, such as quantity survey and pricing, may be automated by using existing BIM software in combination with existing estimating software. The adoption of this combination of technologies is not as widely seen as might be expected. Researchers conducted a survey of construction practitioners to determine the extent to which estimating processes were automated in the conjunction industry, with the data from a BIM model. Survey participants were asked questions about how BIM was used within their organization and how it was used in the various tasks involved in construction cost estimating. The results of the survey data revealed that while most contractors were using BIM, only a small minority were using it to automate estimating processes. Most organizations reported that employees skilled in BIM did not have the estimating experience to produce working estimates from BIM models and vice-versa. The results of the survey are presented and analyzed to determine conditions that would improve the adoption of these new business processes in the construction estimating field.


title: Relational Bayesian Networks

abstract: A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network. By using a powerful way of specifying conditional probability distributions in these networks, the resulting formalism is more expressive than the previous ones. Particularly, it provides for constraints on equalities of events, and it allows to define complex, nested combination functions.


Color Appearance Models


title: Assessing Text Semantic Similarity Using Ontology

abstract: Sentence and document similarity assessment is key to most NLP applications. This paper presents a novel measure of calculating the similarity between sentences or between documents using ontology. The similarity is assessed using sentence or document concept vector forming from finding the linkage between ontology terms and sentence or document content, the linage can be used to generate semantic indexes of sentences or document and apply them to implement highly efficient searching algorithms to compute sentence or document similarity, and the difference between the sentence and document similarity measurement is articulated. Results were verified through experiments. Experiments show that this technique is efficient and compares favorably to other similarity measures, and it is flexible enough to allow the user to make comparisons without any additional dictionary or corpus information. We believe that this method can be applied in a variety of text knowledge representation and discovery applications.


title: Research Commentary - The Digital Transformation of Healthcare: Current Status and the Road Ahead

abstract: As the United States expends extraordinary efforts toward the digitization of its health-care system, and as policy makers across the globe look to information technology (IT) as a means of making health-care systems safer, more affordable, and more accessible, a rare and remarkable opportunity has emerged for the information systems research community to leverage its in-depth knowledge to both advance theory and influence practice and policy. Although health IT (HIT) has tremendous potential to improve quality and reduce costs in healthcare, significant challenges need to be overcome to fully realize this potential. In this commentary, we survey the landscape of existing studies on HIT to provide an overview of the current status of HIT research. We then identify three major areas that warrant further research: (1) HIT design, implementation, and meaningful use; (2) measurement and quantification of HIT payoff and impact; and (3) extending the traditional realm of HIT. We discuss specific research questions in each domain and suggest appropriate methods to approach them. We encourage information systems scholars to become active participants in the global discourse on health-care transformation through IT.


title: Pre-existing brain function predicts subsequent practice of mindfulness and compassion meditation

abstract: Abstract While a variety of meditation techniques are increasingly employed as health interventions, the fact that meditation requires a significant commitment of time and effort may limit its potential widespread utility. In the current study, we ask whether baseline subjective reports or brain activity in response to a “Pain for Self and Others” paradigm predicts subsequent engagement in mindfulness and compassion meditation. The study also investigated whether compassion training would impact neural responses when compared to an active health education control group. Prior to training, activation of the left and right anterior insula, an area thought to be important for empathy, in response to the Other pain task was positively related to engagement with compassion meditation as measured by practice time (n = 13). On the other hand, activity in the left amygdala during the Self pain task was negatively correlated with mindfulness practice time. Following the study intervention, there was no difference between the compassion group (n = 13), and the control group (n = 8), in brain responses to either the Self or Other task. These results are the first to indicate that baseline neural responses may predict engagement with meditation training and suggest that pre-existing neurobiological profiles differentially predispose individuals to engage with disparate meditation techniques.


title: Deep convolutional neural networks for LVCSR

abstract: Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary speech tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is the optimal number of hidden units, what is the best pooling strategy, and the best input feature type for CNNs. We then explore the behavior of neural network features extracted from CNNs on a variety of LVCSR tasks, comparing CNNs to DNNs and GMMs. We find that CNNs offer between a 13-30% relative improvement over GMMs, and a 4-12% relative improvement over DNNs, on a 400-hr Broadcast News and 300-hr Switchboard task.


title: Integrating urban energy simulation in a parametric environment: a Grasshopper interface for CitySim

abstract: The increasing popularity of parametric design tools goes hand in hand with the use of building performance simulation (BPS) tools from the early design phase. However, current methods require a significant computational time and a high number of parameters as input, as they are based on traditional BPS tools conceived for detailed building design phase. Their application to the urban scale is hence difficult. As an alternative to the existing approaches, we developed an interface to CitySim, a validated building simulation tool adapted to urban scale assessments, bundled as a plug-in for Grasshopper, a popular parametric design platform. On the one hand, CitySim allows faster simulations and requires fewer parameters than traditional BPS tools, as it is based on algorithms providing a good trade-off between the simulations requirements and their accuracy at the urban scale; on the other hand, Grasshopper allows the easy manipulation of building masses and energy simulation parameters through semi-automated parametric workflows. In this paper, we present a preliminary version of the developed plug-in and a typical design workflow for the simulation and visualization of building performance at the neighbourhood scale. We conclude by discussing its scalability to larger urban areas using 3D geodata as input and the coupling with Computational Fluid Dynamics (CFD) simulations and optimization algorithms.


title: Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta

abstract: The Atlanta Fire Rescue Department (AFRD), like many municipal fire departments, actively works to reduce fire risk by inspecting commercial properties for potential hazards and fire code violations. However, AFRD's fire inspection practices relied on tradition and intuition, with no existing data-driven process for prioritizing fire inspections or identifying new properties requiring inspection. In collaboration with AFRD, we developed the Firebird framework to help municipal fire departments identify and prioritize commercial property fire inspections, using machine learning, geocoding, and information visualization. Firebird computes fire risk scores for over 5,000 buildings in the city, with true positive rates of up to 71% in predicting fires. It has identified 6,096 new potential commercial properties to inspect, based on AFRD's criteria for inspection. Furthermore, through an interactive map, Firebird integrates and visualizes fire incidents, property information and risk scores to help AFRD make informed decisions about fire inspections. Firebird has already begun to make positive impact at both local and national levels. It is improving AFRD's inspection processes and Atlanta residents' safety, and was highlighted by National Fire Protection Association (NFPA) as a best practice for using data to inform fire inspections.


title: Multi-scale dictionary for single image super-resolution

abstract: Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually.


title: Fetal nasal bone length in the second trimester: comparison between population groups from different ethnic origins

abstract: Abstract Objective: To compare normal ranges of ultrasonographically measured fetal nasal bone length in the second trimester between different ethnic groups. Method: A prospective, non-interventional study in order to establish normal ranges of fetal nasal bone length in the second trimester in a Greek population was conducted in 1220 singleton fetuses between 18 completed weeks and 23 weeks and 6 days of gestation. A literature search followed in order to identify similar studies in different population groups. Fetal nasal bone length mean values and percentiles from different population groups were compared. Results: Analysis of measurements in the Greek population showed a linear association, i.e., increasing nasal bone length with increasing gestational age from 5.73 mm at 18 weeks to 7.63 mm at 23 weeks. Eleven studies establishing normal ranges of fetal nasal bone length in the second trimester were identified. Comparison of fetal nasal bone length mean values between the 12 population groups showed statistically significant differences (P<0.0001). Conclusion: Normal ranges of fetal nasal bone length in the second trimester vary significantly between different ethnic groups. Hence, distinct ethnic nomograms of fetal nasal bone length in the second trimester should be used in a given population rather than an international model.


title: Differentially Private Distributed Constrained Optimization

abstract: Many resource allocation problems can be formulated as an optimization problem whose constraints contain sensitive information about participating users. This paper concerns a class of resource allocation problems whose objective function depends on the aggregate allocation (i.e., the sum of individual allocations); in particular, we investigate distributed algorithmic solutions that preserve the privacy of participating users. Without privacy considerations, existing distributed algorithms normally consist of a central entity computing and broadcasting certain public coordination signals to participating users. However, the coordination signals often depend on user information, so that an adversary who has access to the coordination signals can potentially decode information on individual users and put user privacy at risk. We present a distributed optimization algorithm that preserves differential privacy, which is a strong notion that guarantees user privacy regardless of any auxiliary information an adversary may have. The algorithm achieves privacy by perturbing the public signals with additive noise, whose magnitude is determined by the sensitivity of the projection operation onto user-specified constraints. By viewing the differentially private algorithm as an implementation of stochastic gradient descent, we are able to derive a bound for the suboptimality of the algorithm. We illustrate the implementation of our algorithm via a case study of electric vehicle charging. Specifically, we derive the sensitivity and present numerical simulations for the algorithm. Through numerical simulations, we are able to investigate various aspects of the algorithm when being used in practice, including the choice of step size, number of iterations, and the trade-off between privacy level and suboptimality.


title: Introduction to Neurolinguistics

abstract: This introduction to neurolinguistics is intended for anybody who wants to acquire a grounding in the field. It was written for students of linguistics and communication disorders, but students of psychology, neuroscience and other disciplines will also find it valuable. The introductory section presents the theories, models and frameworks underlying modern neurolinguistics. Then the neurolinguistic aspects of different components of language – phonology, morphology, lexical semantics, and semantics-pragmatics in communication – are discussed. The third section examines reading and writing, bilingualism, the evolution of language, and multimodality. The book also contains three resource chapters, one on techniques for investigating the brain, another on modeling brain functions, and a third that introduces the basic concepts of neuroanatomy and neurophysiology. This text provides an up-to-date linguistic perspective, with a special focus on semantics and pragmatics, evolutionary perspectives, neural network modeling and multimodality, areas that have been less central in earlier introductory works.


title: Evaluation of WiFi-Based Indoor (WBI) Positioning Algorithm

abstract: This paper proposes an indoor positioning algorithm, WBI based on WiFi Received Signal Strength (RSS) technology in conjunction with trilateration techniques. The WBI algorithm estimates the location using RSS values previously collected from within the area of interest, determine whether it falls within the Min-Max bounding box, corrects for non-line-of-sight propagation effects on positioning errors using Kalman filtering, and finally update the location estimation using Least Square Estimation (LSE). The paper analyzes the complexity of the proposed algorithm and compares its performance against existing algorithms. Furthermore, the proposed WBI algorithm was able to achieve an average accuracy of 2.6 m.


title: Improving MQTT by Inclusion of Usage Control

abstract: Due to the increasing pervasiveness of Internet of Things (IoT) and Internet of Everything (IoE) devices, securing both their communications and operations has become of capital importance. Among the several existing IoT protocols, Message Queue Telemetry Transport (MQTT) is a widely-used general purpose one, usable in both constrained and powerful devices, which coordinates data exchanges through a publish/subscribe approach. In this paper, we propose a methodology to increase the security of the MQTT protocol, by including Usage Control in its operative workflow. The inclusion of usage control enables a fine-grained dynamic control of the rights of subscribers to access data and data-streams over time, by monitoring mutable attributes related to the subscriber, the environment or data itself. We will present the architecture and workflow of MQTT enhanced through Usage Control, also presenting a real implementation on Raspberry Pi 3 for performance evaluation.


Language Shapes Thought : Rethinking on Linguistic Relativity


title: Focussing on Consumer Attention: Price-Quality Relationships and Reputation Indicators

abstract: In Germany, the focus of agricultural policy is now "the consumer". In the aftermath of the BSE crisis, farmers are encouraged to produce higher quality "ecological" food for which consumers are willing to pay more. Food is an experience good and quality signals are becoming a more important determinant of the prices received. However, given consumers' cognitive limitations, all signals can not receive equal attention. We argue that consumer attention to product quality signals increases with its producer's quality performance, and given attention spillovers (collective reputation), with the expertise of associated producers. Over time, collective reputations should have an effect on price when attention (or quality performance) is low, but should lose their impact as attention increases. We illustrate our consumer attention argument with an empirical application of wine producers and regions and draw some conclusions for the new consumer oriented agri-food policy in Germany.


title: BPMN vs. UML Activity Diagram for Business Process Modeling

abstract: During the last years, it has been noticed a growing interest of organizations in improving their business processes in order to be more competitive in a globalized economy that passes nowadays through a severe financial crisis with restrictive market conditions and limited profit margins. The first step in achieving this goal is to use an adequate business process modeling language to represent their business processes. For this purpose, an evaluation of the existing business process modeling languages would be very useful in making the right decision. Our research work comes to supplement the previous researches that have evaluated business process modeling languages. The evaluation performed in this paper is focused on the two most widely used graphical notations for business processes: Business Process Modeling and Notation (BPMN) and UML Activity Diagram (UML AD). The evaluation criteria are: capacity of being readily understandable, adequacy of the graphical elements of BPMN and UML AD to represent the real business processes of an organization and mapping to Business Process Execution Languages. The results of evaluating BPMN and UML AD against each of these three criteria are presented in the paper.


title: Area-efficient cross-coupled charge pump for on-chip solar cell

abstract: In this paper, an area-efficient cross-coupled charge pump for standalone chip using on-chip solar cell is proposed. The proposed cross-coupled charge pump outputs higher voltage than conventional cross-coupled charge pump on the same area by adding capacitors to drive MOS transistors. The proposed circuit is fabricated in 0.18um standard CMOS process, and outputs 80mV higher than the conventional circuit with 500mV input and 100kHz clock frequency.


title: Searching models, modeling search: on the synergies of SBSE and MDE

abstract: In the past years, several researchers applied search-based optimization algorithms successfully in the software engineering domain to obtain automatically near-optimal solutions to complex problems posing huge solution spaces. More recently, such algorithms have also been proven useful for solving problems in model engineering. However, applying search-based optimization algorithms to problems in model engineering efficiently and effectively is a challenging endeavor demanding for expertize in both, search-based algorithms as well as model engineering formalisms and techniques. In this paper, we report on our experiences in applying such search-based algorithms for model engineering problems and propose a model-driven approach to ease the adoption of search-based algorithms for the area of model engineering.


title: Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems

abstract: Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated (Cas) systems in bacteria and archaea use RNA-guided nuclease activity to provide adaptive immunity against invading foreign nucleic acids. Here, we report the use of type II bacterial CRISPR-Cas system in Saccharomyces cerevisiae for genome engineering. The CRISPR-Cas components, Cas9 gene and a designer genome targeting CRISPR guide RNA (gRNA), show robust and specific RNA-guided endonuclease activity at targeted endogenous genomic loci in yeast. Using constitutive Cas9 expression and a transient gRNA cassette, we show that targeted double-strand breaks can increase homologous recombination rates of single- and double-stranded oligonucleotide donors by 5-fold and 130-fold, respectively. In addition, co-transformation of a gRNA plasmid and a donor DNA in cells constitutively expressing Cas9 resulted in near 100% donor DNA recombination frequency. Our approach provides foundations for a simple and powerful genome engineering tool for site-specific mutagenesis and allelic replacement in yeast.


title: BRDF Representation and Acquisition

abstract: Photorealistic rendering of real world environments is important in a range of different areas; including Visual Special effects, Interior/Exterior Modelling, Architectural Modelling, Cultural Heritage, Computer Games and Automotive Design. ::: ::: Currently, rendering systems are able to produce photorealistic simulations of the appearance of many real-world materials. In the real world, viewer perception of objects depends on the lighting and object/material/surface characteristics, the way a surface interacts with the light and on how the light is reflected, scattered, absorbed by the surface and the impact these characteristics have on material appearance. In order to re-produce this, it is necessary to understand how materials interact with light. Thus the representation and acquisition of material models has become such an active research area. ::: ::: This survey of the state-of-the-art of BRDF Representation and Acquisition presents an overview of BRDF (Bidirectional Reflectance Distribution Function) models used to represent surface/material reflection characteristics, and describes current acquisition methods for the capture and rendering of photorealistic materials.


title: Animating fluid sediment mixture in particle-laden flows

abstract: We present a mixed explicit and semi-implicit Material Point Method for simulating particle-laden flows, which is capable of reproducing several effects including dune migration, sediment transportation, and sedimentation.


title: Modeling 2D Appearance Evolution for 3D Object Categorization

abstract: 3D object categorization is a non-trivial task in computer vision encompassing many real-world applications. We pose the problem of categorizing 3D polygon meshes as learning appearance evolution from multi-view 2D images. Given a corpus of 3D polygon meshes, we first render the corresponding RGB and depth images from multiple viewpoints on a uniform sphere. Using rank pooling, we propose two methods to learn the appearance evolution of the 2D views. Firstly, we train view-invariant models based on a deep convolutional neural network (CNN) using the rendered RGB-D images and learn to rank the first fully connected layer activations and, therefore, capture the evolution of these extracted features. The parameters learned during this process are used as the 3D shape representations. In the second method, we learn the aggregation of the views from the outset by employing the ranking machine to the rendered RGB- D images directly, which produces aggregated 2D images which we term as ``3D shape images". We then learn CNN models on this novel shape representation for both RGB and depth which encode salient geometrical structure of the polygon. Experiments on the ModelNet40 and ModelNet10 datasets show that the proposed method consistently outperforms existing state-of-the-art algorithms in 3D shape recognition.


title: Differences in Horizontal, Neoclassical Facial Canons in Chinese (Han) and North American Caucasian Populations

abstract: To better our ability to analyze the facial disproportions of patients of Chinese ancestry, we compared the validity of four neoclassical canons of facial proportion in Chinese and North American Caucasians populations. We tested the frequency of four horizontal facial canons and their eight variations in 206 healthy adults (105 males and 101 females, 18-25 years old) belonging to the predominant ethnic group (Han: 400 million) of the Chinese population, and compared them to those of 103 healthy young North American Caucasian adults. The nose width corresponded to one-quarter of the face width (the nasofacial canon) significantly more frequently in Chinese participants (51.5%) than in Caucasian adults (36.9%). The nose was narrower than one-quarter of the face width in 38.8% of North American Caucasians and in 21.8% of Chinese; this difference was also statistically significant. In defiance of the naso-oral canon, the mouths of Chinese people were significantly more often narrower than 1.5 times the nose width (71.8%), while in North American Caucasian ethnics the mouth was significantly more frequently wider (60.2%).


title: Multidisciplinary approach for a patient with dentinogenesis imperfecta and anterior trauma

abstract: Dentinogenesis imperfecta is an inherited dentinal dysplasia involving several risks for orthodontic treatment. This case report describes the multidisciplinary treatment of a 17-year-old girl whose Class II Division 1 malocclusion was complicated by dentinogenesis imperfecta type II and maxillary anterior trauma.


title: Growing and Sustaining an Offshore Scrum Engagement

abstract: From Scrum team formation and team building, to developing effective communication channels, the mechanics of Scrum do not translate easily for many Asian cultures. By taking a straightforward, sensible approach to implementing Scrum along with applying a well-known team building model, Kelley Blue Book (KBB) successfully created offshoring teams in China and India, in a few short months. KBB didnpsilat just want to "add horsepower" to augment the onshore development teams, we wanted to create independent teams to help our product offerings expand. We will illustrate how and why we applied the five dysfunctions of a team model by Patrick Lencioni to provide a foundation for growing and sustaining offshore teams several time zones away. Wepsilall cover examples of how employing this model has improved our offshore engagements and overcome a number of challenges to improve the sustainable delivery of the Scrum teams.


title: Didactic Games For Teaching Information Theory

abstract: We developed a set of didactic games and activities that can be used to illustrate and teach various concepts from Information Theory. For each of the games and activities we list the topics it covers, give its rules and related information, describe our practical experiences and give an overview of its scientific background. We also discuss the proper ways to integrate these games into the knowledge acquisition process.


title: Uses and misuses of Bronfenbrenner's bioecological theory of human development

abstract: This paper evaluates the application of Bronfenbrenner’s bioecological theory as it is represented in empirical work on families and their relationships. We describe the ‘‘mature’’ form of bioecological theory of the mid-1990s and beyond, with its focus on proximal processes at the center of the Process-Person-Context-Time model. We then examine 25 papers published since 2001, all explicitly described as being based on Bronfenbrenner’s theory, and show that all but 4 rely on outmoded versions of the theory, resulting in conceptual confusion and inadequate testing of the theory.


title: Automatic Malware Detection Using Deep Learning Based on Static Analysis

abstract: Malware detection is an important challenge in the field of information security. The paper proposes a novel method using deep learning based on static analysis. Deep learning has stronger nonlinear expression ability than shallow learning, so it has received much attention from scholar and manufacturers. We use static analysis to extract the malware features are mapped into the input of deep learning. The experiments show that the method is suitable for detecting malware.


title: Fisher discriminant analysis with kernels

abstract: A non-linear classification technique based on Fisher's discriminant is proposed. The main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space. The linear classification in feature space corresponds to a (powerful) non-linear decision function in input space. Large scale simulations demonstrate the competitiveness of our approach.


title: Importance of risk factors associated with malaria for Sub-Saharan African children

abstract: Abstract To assess the importance of malaria risk factors for children in sub-Saharan African countries. 61,292 children of 16 countries from DHS and MIS surveys were included in analysis. A regression model with analyse of variance and plots of mean decrease accuracy and mean decrease Gini indices were performed. The most important risk factor was the country and it contributed 52.33 % to the variance of the model. Wealth status of the child’s family was the first socio-economic factor which contributed more to the difference of malaria risk among African children. There was no geographic factor among the five most important variables. Quantity of precipitation was the sixth most important factor. Our study has the potential for driving control effort in the fight against malaria in the continent which represent the majority of global malaria cases. The study indicates that, when implementing health policies, community characteristics must be taken into account.


title: New Energy Efficient Multi-Hop Routing Techniques for Wireless Sensor Networks: Static and Dynamic Techniques

abstract: The performance of Wireless Sensor Networks (WSNs) faces a number of challenges. Of these challenges, energy consumption is considered a hot research area. Most WSN energy is used in transmitting the data from the sensor nodes either among each other or to a Base Station (BS). For this reason, many routing protocols have been developed to facilitate the data dissemination in the WSNs. One of these protocols, Low Energy Adaptive Clustering Hierarchy (LEACH) has provided a distinctive hierarchical approach that efficiently forwards the nodes data to the BS, but it suffers from increased energy consumption and a significant decline in the network performance in the case of large-scale networks. This paper aims to present a new approach for splitting the whole sensor network into several levels. Thus, every node will be acting accordingly on its position and status. Further, two techniques, a static one and a dynamic one, have been developed to route the data between the levels. The simulation results demonstrated that the proposed techniques prolong the lifespan, improve the stability and raise the throughput of the network compared with the LEACH, the Improved MHT-LEACH (IMHT-LEACH), and the Enhancing DMHT-LEACH (EDMHT-LEACH) protocols.


title: Spatial graphlet matching kernel for recognizing aerial image categories

abstract: This paper presents a method for recognizing aerial image categories based on matching graphlets(i.e., small connected subgraphs) extracted from aerial images. By constructing a Region Adjacency Graph (RAG) to encode the geometric property and the color distribution of each aerial image, we cast aerial image category recognition as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by matching all their respective graphlets. Towards an effective graphlet matching process, we develop a manifold embedding algorithm to transfer different-sized graphlets into equal length feature vectors and further integrate these feature vectors into a kernel. This kernel is used to train a SVM [8] classifier for aerial image categories recognition. Experimental results demonstrate our method outperforms several state-of-the-art object/scene recognition models.


title: The Design and Implementation of FFTW3

abstract: FFTW is an implementation of the discrete Fourier transform (DFT) that adapts to the hardware in order to maximize performance. This paper shows that such an approach can yield an implementation that is competitive with hand-optimized libraries, and describes the software structure that makes our current FFTW3 version flexible and adaptive. We further discuss a new algorithm for real-data DFTs of prime size, a new way of implementing DFTs by means of machine-specific single-instruction, multiple-data (SIMD) instructions, and how a special-purpose compiler can derive optimized implementations of the discrete cosine and sine transforms automatically from a DFT algorithm.


title: Modeling of rolling friction by recurrent neural network using LSTM

abstract: The modeling and identification of a mechanical system is the most important issue for many control systems in order to realize the desired control specifications. In particular, the friction characteristics often deteriorate the control performance, such as in the fast and precise positioning performance in industrial robots, the force estimation accuracy based on a disturbance observer, and the posture control performance of an inverted pendulum robot. Rolling friction tends to cause overshoot, undershoot, or limit cycles of the target value in positioning systems. In previous research, some model structures for rolling friction have been proposed to express the hysteresis characteristics in order to overcome these control issues. However, it is difficult to identify the correct parameters for precise modeling. In this paper, the modeling of rolling friction based on a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) is proposed to precisely express the rolling friction characteristics. The initial value design of the RNN during supervised learning is also presented to achieve a better model. The effectiveness of the proposed approach is verified by comparison with conventional friction models using an actual experimental setup.


title: A versatile and user-oriented versioning file system

abstract: File versioning is a useful technique for recording a history of changes. Applications of versioning include backups and disaster recovery, as well as monitoring intruders' activities. Alas, modern systems do not include an automatic and easy-to-use file versioning system. Existing backup solutions are slow and inflexible for users. Even worse, they often lack backups for the most recent day's activities. Online disk snapshotting systems offer more fine-grained versioning, but still do not record the most recent changes to files. Moreover, existing systems also do not give individual users the flexibility to control versioning policies. ::: ::: We designed a lightweight user-oriented versioning file system called Versionfs. Versionfs works with any file system and provides a host of user-configurable policies: versioning by users, groups, processes, or file names and extensions; version retention policies and version storage policies. Versionfs creates file versions automatically, transparently, and in a file-system portable manner--while maintaining Unix semantics. A set of user-level utilities allow administrators to configure and enforce default policies: users can set policies within configured boundaries, as well as view, control, and recover files and their versions. We have implemented the system on Linux. Our performance evaluation demonstrates overheads that are not noticeable by users under normal workloads.


title: Input Switched Affine Networks: An RNN Architecture Designed for Interpretability

abstract: There exist many problem domains where the interpretability of neural network models is essential for deployment. Here we introduce a recurrent architecture composed of input-switched affine transformations - in other words an RNN without any explicit nonlinearities, but with input-dependent recurrent weights. This simple form allows the RNN to be analyzed via straightforward linear methods: we can exactly characterize the linear contribution of each input to the model predictions; we can use a change-of-basis to disentangle input, output, and computational hidden unit subspaces; we can fully reverse-engineer the architecture's solution to a simple task. Despite this ease of interpretation, the input switched affine network achieves reasonable performance on a text modeling tasks, and allows greater computational efficiency than networks with standard nonlinearities.


title: TFHE: Fast Fully Homomorphic Encryption Over the Torus

abstract: This work describes a fast fully homomorphic encryption scheme over the torus (TFHE) that revisits, generalizes and improves the fully homomorphic encryption (FHE) based on GSW and its ring variants. The simplest FHE schemes consist in bootstrapped binary gates. In this gate bootstrapping mode, we show that the scheme FHEW of Ducas and Micciancio (Eurocrypt, 2015) can be expressed only in terms of external product between a GSW and an LWE ciphertext. As a consequence of this result and of other optimizations, we decrease the running time of their bootstrapping from 690 to 13 ms single core, using 16 MB bootstrapping key instead of 1 GB, and preserving the security parameter. In leveled homomorphic mode, we propose two methods to manipulate packed data, in order to decrease the ciphertext expansion and to optimize the evaluation of lookup tables and arbitrary functions in $${\mathrm {RingGSW}}$$-based homomorphic schemes. We also extend the automata logic, introduced in Gama et al. (Eurocrypt, 2016), to the efficient leveled evaluation of weighted automata, and present a new homomorphic counter called $$\mathrm {TBSR}$$, that supports all the elementary operations that occur in a multiplication. These improvements speed up the evaluation of most arithmetic functions in a packed leveled mode, with a noise overhead that remains additive. We finally present a new circuit bootstrapping that converts $$\mathsf {LWE}$$ ciphertexts into low-noise $${\mathrm {RingGSW}}$$ ciphertexts in just 137 ms, which makes the leveled mode of TFHE composable and which is fast enough to speed up arithmetic functions, compared to the gate bootstrapping approach. Finally, we provide an alternative practical analysis of LWE based schemes, which directly relates the security parameter to the error rate of LWE and the entropy of the LWE secret key, and we propose concrete parameter sets and timing comparison for all our constructions.


title: Stationary Graph Processes and Spectral Estimation

abstract: Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios the information of interest resides in more irregular graph domains. This lack of regularity hampers the generalization of the classical notion of stationarity to graph signals. The contribution in this paper is twofold. Firstly, we propose a definition of weak stationarity for random graph signals that takes into account the structure of the graph where the random process takes place, while inheriting many of the meaningful properties of the classical definition in the time domain. Our definition requires that stationary graph processes can be modeled as the output of a linear graph filter applied to a white input. We will show that this is equivalent to requiring the correlation matrix to be diagonalized by the graph Fourier transform. Secondly, we analyze the properties of the power spectral density and propose a number of methods to estimate it. We start with nonparametric approaches, including periodograms, window-based average periodograms, and filter banks. We then shift the focus to parametric approaches, discussing the estimation of moving-average (MA), autoregressive (AR) and ARMA processes. Finally, we illustrate the power spectral density estimation in synthetic and real-world graphs.


title: Challenging the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL): A Survey

abstract: RPL is the IPv6 routing protocol for low-power and lossy networks, standardized by IETF in 2012 as RFC6550. Specifically, RPL is designed to be a simple and inter-operable networking protocol for resource-constrained devices in industrial, home, and urban environments, intended to support the vision of the Internet of Things with thousands of devices interconnected through multihop mesh networks. More than four-years have passed since the standardization of RPL, and we believe that it is time to examine and understand its current state. In this paper, we review the history of research efforts in RPL; what aspects have been (and have not been) investigated and evaluated, how they have been studied, what was (and was not) implemented, and what remains for future investigation. We reviewed over 97 RPL-related academic research papers published by major academic publishers and present a topic-oriented survey for these research efforts. Our survey shows that only 40.2% of the papers evaluate RPL through experiments using implementations on real embedded devices, ContikiOS and TinyOS are the two most popular implementations (92.3%), and TelosB was the most frequently used hardware platform (69%) on testbeds that have average and median size of 49.4 and 30.5 nodes, respectively. Furthermore, unfortunately, despite it being approximately four years since its initial standardization, we are yet to see wide adoption of RPL as part of real-world systems and applications. We present our observations on the reasons behind this and suggest directions on which RPL should evolve.


title: Full Motion Video in a coalition environment

abstract: An overview of practices, lessons learned and challenges associated with the utilization of Full Motion Video (FMV) data in NATO operations is presented in this paper. FMV has become one of the most requested sensor data types in recent operations. The response to these rapidly evolving conditions includes both technical and operational solutions. Current efforts to establish an Alliance Ground Surveillance (AGS) capability within NATO opens new dimensions for disseminating video information in multinational environment.


title: Efficient interactive 3 D Livewire segmentation of complex objects with arbitrary topology

abstract: We present a novel interactive method based on a 3D Livewire approach for segmenting complex objects of arbitrary topologies. Our proposed technique automatically and seamlessly handles objects with branchings, concavities, protrusions, and non-spherical topologies with minimal user-input. Given sparse interactively segmented contours on orthogonal slices, our proposed method determines Livewire seedpoints on all slices in the third orthogonal direction, which are used to mimic user-guided segmentation. In doing so, our method pre-processes these points to increase algorithm robustness, and uses a novel seedpoint sorting method using ideas from L-system’s Turtle algorithm. Moreover, we present a segmentation tool based on our proposed framework and demonstrate the robustness of our approach on real medical data. Results highlight the superior performance of the proposed method with validation tests on synthetic and real MRI and CT data, with segmentation reproducibility exceeding 95% and segmentation task time decreasing to less than 20% when compared to performing 2D Livewire on each volume slice. © 2008 Elsevier Ltd. All rights reserved.


title: Secure kNN computation on encrypted databases

abstract: Service providers like Google and Amazon are moving into the SaaS (Software as a Service) business. They turn their huge infrastructure into a cloud-computing environment and aggressively recruit businesses to run applications on their platforms. To enforce security and privacy on such a service model, we need to protect the data running on the platform. Unfortunately, traditional encryption methods that aim at providing "unbreakable" protection are often not adequate because they do not support the execution of applications such as database queries on the encrypted data. In this paper we discuss the general problem of secure computation on an encrypted database and propose a SCONEDB Secure Computation ON an Encrypted DataBase) model, which captures the execution and security requirements. As a case study, we focus on the problem of k-nearest neighbor (kNN) computation on an encrypted database. We develop a new asymmetric scalar-product-preserving encryption (ASPE) that preserves a special type of scalar product. We use APSE to construct two secure schemes that support kNN computation on encrypted data; each of these schemes is shown to resist practical attacks of a different background knowledge level, at a different overhead cost. Extensive performance studies are carried out to evaluate the overhead and the efficiency of the schemes.


title: Emerging Research on Bone Health Using High-Resolution CT and MRI

abstract: Today's most prevalent bone disease in the western hemisphere is osteoporosis. Predominantly post- menopausal women and older men suffer from bone loss caused by an imbalance in the physiological tissue renewal process between bone formation and resorption. As a result, osteoporosis is associated with fragility fractures, disability, impaired bone regeneration and increased mor- tality. The World Health Organization based the gold standard for diagnosing osteoporosis on bone mineral density (BMD) measurements using dual X-ray absorpti- ometry. However, BMD measurements are limited in dis- criminating subjects with and without osteoporotic fractures and have been shown to only partly reflect suc- cessful treatment of osteoporotic fractures. Bone micro- structure is an integral determinant of bone strength. Today, new high-resolution imaging techniques such as high-resolution peripheral quantitative computed tomog- raphy and high-resolution magnetic resonance imaging make it possible to measure three-dimensional bone mic- roarchitecture and volumetric bone mineral density with high accuracy and a relatively low radiation dose.


title: Local Group Invariant Representations via Orbit Embeddings

abstract: Invariance to nuisance transformations is one of the desirable properties of effective representations. We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations. Locality is achieved by defining a suitable probability distribution over the group which in turn induces distributions in the input feature space. We learn a decision function over these distributions by appealing to the powerful framework of kernel methods and generate local invariant random feature maps via kernel approximations. We show uniform convergence bounds for kernel approximation and provide excess risk bounds for learning with these features. We evaluate our method on three real datasets, including Rotated MNIST and CIFAR-10, and observe that it outperforms competing kernel based approaches. The proposed method also outperforms deep CNN on Rotated-MNIST and performs comparably to the recently proposed group-equivariant CNN.


title: Contextualisation in the revised dual representation theory of PTSD: A response to Pearson and colleagues☆

abstract: Three recent studies (Pearson, 2012; Pearson, Ross, & Webster, 2012) purported to test the revised dual representation theory of posttraumatic stress disorder (Brewin, Gregory, Lipton, & Burgess, 2010) by manipulating the amount of additional information accompanying traumatic stimulus materials and assessing the effect on subsequent intrusive memories. Here we point out that these studies involve a misunderstanding of the meaning of “contextual” within the theory, such that the manipulation would be unlikely to have had the intended effect and the results are ambiguous with respect to the theory. Past and future experimental tests of the theory are discussed.


title: Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering

abstract: Abstract This paper presents a novel rule-based skin detection method that works in the YCbCr color space. The method is based on correlation rules that evaluate the combinations of chrominance values to identify the skin pixels in the YCb and YCr subspaces. The correlation rules depend on the shape and size of dynamically generated skin color clusters, which are computed on a statistical basis in the YCb and YCr subspaces for each single image, and represent the areas that include most of the candidate skin pixels. Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters. Moreover, the qualitative analysis shows that the method achieves satisfactory results also in critical scenarios, including severe variations in illumination conditions.


title: Synthesis of inline filters with arbitrarily placed attenuation poles by using nonresonating nodes

abstract: A general and direct synthesis technique of pseudoelliptic inline filters with arbitrarily placed attenuation poles (APs) (transmission zeros) at real frequencies is presented. The APs are brought about and independently controlled by dedicated resonators, which are coupled to nonresonating nodes. Simple rules to properly determine the phases of the reflection coefficients at the input and output are given. To reduce the effect of roundoff errors, especially for higher order filters, the extraction of the elements of the network is performed from the input and output simultaneously. Multiplicity and scaling properties of the solutions are discussed. Synthesis examples are presented to demonstrate the soundness of the procedure. Theoretical results are compared with measurement to demonstrate the validity of the presented theory.


Machine learning-based sentiment analysis of automatic indonesian translations of english movie reviews


title: An experimental study of shared sensitivity to physical pain and social rejection

abstract: Recent evidence points to a possible overlap in the neural systems underlying the distressing experience that accompanies physical pain and social rejection (Eisenberger et al., 2003). The present study tested two hypotheses that stem from this suggested overlap, namely: (1) that baseline sensitivity to physical pain will predict sensitivity to social rejection and (2) that experiences that heighten social distress will heighten sensitivity to physical pain as well. In the current study, participants’ baseline cutaneous heat pain unpleasantness thresholds were assessed prior to the completion of a task that manipulated feelings of social distress. During this task, participants played a virtual ball-tossing game, allegedly with two other individuals, in which they were either continuously included (social inclusion condition) or they were left out of the game by either never being included or by being overtly excluded (social rejection conditions). At the end of the game, three pain stimuli were delivered and participants rated the unpleasantness of each. Results indicated that greater baseline sensitivity to pain (lower pain unpleasantness thresholds) was associated with greater self-reported social distress in response to the social rejection conditions. Additionally, for those in the social rejection conditions, greater reports of social distress were associated with greater reports of pain unpleasantness to the thermal stimuli delivered at the end of the game. These results provide additional support for the hypothesis that pain distress and social distress share neurocognitive substrates. Implications for clinical populations are discussed. � 2006 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.


title: High accuracy optical character recognition using neural networks with centroid dithering

abstract: Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from 8 to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set. >


title: Managing the water distribution network with a Smart Water Grid

abstract: In Singapore, the implementation of a Smart Water Grid system supports the Public Utilities Board (PUB)’s mission to supply good water 24/7 to its customers. With sensors and analytic tools deployed island-wide to provide a real-time monitoring and decision support system, the Smart Water Grid system enables PUB to manage the water supply network efficiently, ensuring that all Singaporeans will continue to enjoy a reliable and sustainable water supply for generations to come. This paper covers the five key aspects of the Smart Water Grid system, in relation to both the key operational aspects of a water distribution system (asset management, leak management, water quality monitoring) and the customers’ end (automated meter reading and water conservation). For each aspect, the paper focuses on the challenges, current technology, PUB’s experience with technology and identified gaps, and the technology roadmap.


title: Facebook in Higher Education: Students' Use and Perceptions

abstract: Facebook has rapidly become the most popular Social Networking Site (SNS) among faculty and students in higher education institutions in recent years. Due to the various interactive and collaborative features Facebook supports, it offers great opportunities for higher education institutions to support student engagement and improve different aspects of teaching and learning. To understand the social aspects of Facebook use among students and how they perceive using it for academic purposes, an exploratory survey has been distributed to 105 local and international students at a large public technology university in Malaysia. Results reveal consistent patterns of usage compared to what has been reported in literature reviews in relation to the intent of use and the current use for educational purposes. A comparison was conducted of male and female, international and local, postgraduate and undergraduate students respectively, using nonparametric tests. The results indicate that the students’ perception of using Facebook for academic purposes is not significantly related to students’ gender or students’ background; while it is significantly related to study level and students’ experience. Moreover, based on the overall results of the survey and literature reviews, the paper presents recommendations and suggestions for further research of social networking in a higher education context.


title: G.: Adaptive thresholding using the integral image

abstract: Image thresholding is a common task in many computer vision and graphics applications. The goal of thresholding an image is to classify pixels as either "dark" or "light." Adaptive thresholding is a form of thresholding that takes into account spatial variations in illumination. We present a technique for real-time adaptive thresholding using the integral image of the input. Our technique is an extension of a previous method. However, our solution is more robust to illumination changes in the image. Additionally, our method is simple and easy to implement. Our technique is suitable for processing live video streams at a real-time frame-rate, making it a valuable tool for interactive applications such as augmented reality. Source code is available online.


title: Neural data mining for credit card fraud detection

abstract: Due to a rapid advancement in the electronic commerce technology, use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment, credit card frauds are becoming increasingly rampant in recent years. In this paper, we model the sequence of operations in credit card transaction processing using a confidence-based neural network. Receiver operating characteristic (ROC) analysis technology is also introduced to ensure the accuracy and effectiveness of fraud detection. A neural network is initially trained with synthetic data. If an incoming credit card transaction is not accepted by the trained neural network model (NNM) with sufficiently low confidence, it is considered to be fraudulent. This paper shows how confidence value, neural network algorithm and ROC can be combined successfully to perform credit card fraud detection.


title: Computing Morse-Smale Complexes with Accurate Geometry

abstract: Topological techniques have proven highly successful in analyzing and visualizing scientific data. As a result, significant efforts have been made to compute structures like the Morse-Smale complex as robustly and efficiently as possible. However, the resulting algorithms, while topologically consistent, often produce incorrect connectivity as well as poor geometry. These problems may compromise or even invalidate any subsequent analysis. Moreover, such techniques may fail to improve even when the resolution of the domain mesh is increased, thus producing potentially incorrect results even for highly resolved functions. To address these problems we introduce two new algorithms: (i) a randomized algorithm to compute the discrete gradient of a scalar field that converges under refinement; and (ii) a deterministic variant which directly computes accurate geometry and thus correct connectivity of the MS complex. The first algorithm converges in the sense that on average it produces the correct result and its standard deviation approaches zero with increasing mesh resolution. The second algorithm uses two ordered traversals of the function to integrate the probabilities of the first to extract correct (near optimal) geometry and connectivity. We present an extensive empirical study using both synthetic and real-world data and demonstrates the advantages of our algorithms in comparison with several popular approaches.


title: High power AlGaN/GaN HFET with a high breakdown voltage of over 1.8 kV on 4 inch Si substrates and the suppression of current collapse

abstract: In this paper, we successfully demonstrate an AlGaN HFET with a high breakdown voltage of over 1.8 kV on 4 inch Si substrates. In order to obtain the high breakdown voltage and to improve the crystalline quality of GaN layers, a thick GaN epitaxial layer including a buffer layer with a total thickness of over 6 mum was grown. The breakdown voltage and the maximum drain current were achieved to be over 1.8 kV and 120 A, respectively. Furthermore, the suppression of the current collapse phenomenon is examined. The on-resistance is not so significantly increased up to the high drain off-bias-stress of 1.0 kV.


Isolated Bidirectional Full-Bridge DC–DC Converter with a Flyback Snubber