title: Pattern-based reverse-engineering of design components

abstract: Many reverse-engineering tools have been developed to derive abstract representations from source code. Yet, most of these tools completely ignore recovery of the all-important rationale behind the design decisions that have lead to its physical shape. Design patterns capture the rationale behind proven design solutions and discuss the trade-offs among their alternatives. We argue that it is these patterns of thought that are at the root of many of the key elements of large-scale software systems, and that, in order to comprehend these systems, we need to recover and understand the patterns on which they were built. In this paper, we present our environment for the reverse engineering of design components based on the structural descriptions of design patterns. We give an overview of the environment, explain three case studies, and discuss how pattern-based reverse-engineering helped gain insight into the design rationale of some of the pieces of three large-scale C++ software systems.


title: A comprehensive people, process and technology (PPT) application model for Information Systems (IS) risk management in small/medium enterprises (SME)

abstract: With advent of modern era. Information Technology (IT) has turned out to be very critical in operations, management and growth of an enterprise. Now, Information Systems (IS) are shaping the existing corporate strategies and creating new avenues for all size enterprises. At the same time, they are subject to numerous threats. Previously, executive management of enterprises didn't involve themselves in the information technology affairs and used to leave these threats to the operational management. However, increased dependencies of businesses on information systems have changed these dimensions, as these unmanaged threats results into a number of business risks which can even threaten business existence. In order to deal with these risks, a number of risk management standards have been developed but most of them focus on large organizations, who have well-structured business processes and some IT risk management expertise is already there. Secondly, either these standards are specific to some particular business domain or provide generic guidelines at strategic level with missing operational level details, which needs to be integrated and customized before its application to a particular enterprise business processes and environment. Therefore, IT risk management in small and medium enterprises is still a challenge. In case of small or medium size enterprises, application of these standards is difficult, primarily due to lack of budget and expertise. Furthermore, selection of appropriate standard among wide spectrum of risk management standards, remains a weak link. Therefore the main question analyzed in this research paper is: How to Apply Risk Management in information systems at operational level and integration of various risk management frameworks within enterprise context? In order to answer these questions, widely accepted risk management frameworks and tools have been analyzed, challenges have been identified and solution is proposed by developing a risk management application model with the aim that it can even be used by small enterprises.


title: Detecting and Analyzing Urban Regions with High Impact of Weather Change on Transport

abstract: In this work, we focus on two fundamental questions that are unprecedentedly important to urban planners to understand the functional characteristics of various urban regions throughout a city, namely, (i) how to identify regional weather-traffic sensitivity index throughout a city, that indicates the degree to which the region traffic in a city is impacted by weather changes; (ii) among complex regional features, such as road structure and population density, how to dissect the most influential regional features that drive the urban region traffic to be more vulnerable to weather changes. However, these two questions are nontrivial to answer, because urban traffic changes dynamically over time and is essentially affected by many other factors, which may dominate the overall impact. We make the first study on these questions, by developing a weather-traffic index (WTI) system. The system includes two main components: weather-traffic index establishment and key factor analysis. Using the proposed system, we conducted comprehensive empirical study in Shanghai, and the weather-traffic indices extracted have been validated to be surprisingly consistent with real world observations. Further regional key factor analysis yields interesting results. For example, house age has significant impact on the weather-traffic index, which sheds light on future urban planning and reconstruction.


Predicting the Price of Used Cars using Machine Learning Techniques


title: Revisiting Reweighted Wake-Sleep

abstract: Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn. Sampling discrete latent variables can result in high-variance gradient estimators for two primary reasons: 1. branching on the samples within the model, and 2. the lack of a pathwise derivative for the samples. While current state-of-the-art methods employ control-variate schemes for the former and continuous-relaxation methods for the latter, their utility is limited by the complexities of implementing and training effective control-variate schemes and the necessity of evaluating (potentially exponentially) many branch paths in the model. Here, we revisit the reweighted wake-sleep (RWS) (Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it circumvents both these issues, outperforming current state-of-the-art methods in learning discrete latent-variable models. Moreover, we observe that, unlike the importance weighted autoencoder, RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent-variable models as well. Our results suggest that RWS is a competitive, often preferable, alternative for learning deep generative models.


title: Unsupervised Traffic Flow Classification Using a Neural Autoencoder

abstract: To cope with the varying delay and bandwidth requirements of today’s mobile applications, mobile wireless networks can profit from classifying and predicting mobile application traffic. State-of-the-art traffic classification approaches have various disadvantages: port-based classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic flow classification using statistical properties of flows and clustering based on a neural autoencoder. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic flow classification independent of known traffic classes. An experimental evaluation on real data captured over a period of four months is presented. The obtained results show that 7 different classes of mobile traffic flows are detected with an average precision of 80% and an average recall of 75%.


title: Multispectral Infrared Face Recognition: a comparative study

abstract: Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear dimensionality reduction techniques like PCA and LDA. In the thermal infrared spectrum, variations can occur between face images of the same individual due to pose, metabolic, time changes, etc. In this work we introduce the use of non linear dimensionality reduction techniques and a probabilistic Bayesian technique for infrared face recognition. These techniques permit to reduce intrapersonal variation, thus making them very interesting for infrared face recognition. A comparative study is conducted in order to evaluate the performance of the proposed techniques for infrared face recognition. Experimental results show that the non linear and probabilistic techniques are promising and lead to interesting results in the infrared spectrum.


MAKInG MEAnInG WITh GRAMMAR : A reperto re of poss b l t


title: Suggesting meaningful variable names for decompiled code: a machine translation approach

abstract: When code is compiled, information is lost, including some of the structure of the original source code as well as local identifier names. Existing decompilers can reconstruct much of the original source code, but typically use meaningless placeholder variables for identifier names. Using variable names which are more natural in the given context can make the code much easier to interpret, despite the fact that variable names have no effect on the execution of the program. In theory, it is impossible to recover the original identifier names since that information has been lost. However, most code is natural: it is highly repetitive and predictable based on the context. In this paper we propose a technique that assigns variables meaningful names by taking advantage of this naturalness property. We consider decompiler output to be a noisy distortion of the original source code, where the original source code is transformed into the decompiler output. Using this noisy channel model, we apply standard statistical machine translation approaches to choose natural identifiers, combining a translation model trained on a parallel corpus with a language model trained on unmodified C code. We generate a large parallel corpus from 1.2 TB of C source code obtained from GitHub. Under the most conservative assumptions, our technique is still able to recover the original variable names up to 16.2% of the time, which represents a lower bound for performance.


R 1 2 A Model of Heuristic Judgment


title: Categorizing Binary Topological Relations Between Regions , Lines , and Points in

abstract: This research was partially funded by NSF grant No. IRI-9309230 and grants from Intergraph Corporation. Additional support from NSF for the NCGIA under No. SBR-9204141 is gratefully acknowledged. Max J. Egenhofer University of Maine, National Center for Geographic Information and Analysis and Department of Surveying Engineering, Department of Computer Science, University of Maine, Orono, ME 04469-5711, max@mecan1.maine.edu


title: Complete coverage path planning and guidance for cleaning robots

abstract: This paper describes a complete coverage path planning and guidance methodology for a mobile robot, having the automatic floor cleaning of large industrial areas as a target application. The proposed algorithms rely on the a priori knowledge of a 2D map of the environment and cope with unexpected obstacles not represented on the map. A template based approach is used to control the path execution, thus incorporating, in a natural way, the kinematic and the geometric model of the mobile robot on the path planning procedure. The novelty of the proposed approach is the capability of the path planner to deal with a priori mapped or unexpected obstacles in the middle of the working space. If unmapped obstacles permanently block the planned trajectory, the path tracking control avoids these obstacles. The paper presents experimental results with a LABMATE mobile robot, confirming the feasibility of the total coverage path and the robustness of the path tracking behaviour based control.


title: Throughput Estimation for Short Lived TCP Cubic Flows

abstract: Mobile devices are increasingly becoming the dominant device for Internet access. The network throughput achieved by a mobile device directly affects the performance and user experience. Throughput measurement techniques thus play an important role in predicting expected performance. Measurement techniques that require the transfer of large amounts of data can provide higher accuracy but incur large overhead. Further, since most mobile cellular plans impose usage quota, the overhead of such measurements over cellular networks can become quite high. Smaller data transfers have also been used to measure the throughput. Due to the conservative TCP slow start behaviour, however, these measurements often underestimate the achievable throughput. Considering these weaknesses in existing throughput measurement techniques, we propose a throughput estimation technique for TCP Cubic that uses 1 MB of data transfer to predict the throughput for prevalent large transfer sizes in mobile traffic such as 5 MB, 10 MB and 20 MB. Our evaluation shows that our approach can achieve high accuracy with low overhead, in predicting the achievable throughput.


title: Autonomous flight in unstructured and unknown indoor environments

abstract: This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms developed for ground robots. We then describe our solutions to the key problems, including a multi-level sensing and control hierarchy, a high-speed laser scan-matching algorithm, EKF data fusion, and a high-level SLAM implementation. Finally, we show experimental results that illustrate the helicopter’s ability to navigate accurately and autonomously in unknown environments.


title: Tangible views for information visualization

abstract: In information visualization, interaction is commonly carried out by using traditional input devices, and visual feedback is usually given on desktop displays. By contrast, recent advances in interactive surface technology suggest combining interaction and display functionality in a single device for a more direct interaction. With our work, we contribute to the seamless integration of interaction and display devices and introduce new ways of visualizing and directly interacting with information. Rather than restricting the interaction to the display surface alone, we explicitly use the physical three-dimensional space above it for natural interaction with multiple displays. For this purpose, we introduce tangible views as spatially aware lightweight displays that can be interacted with by moving them through the physical space on or above a tabletop display's surface. Tracking the 3D movement of tangible views allows us to control various parameters of a visualization with more degrees of freedom. Tangible views also facilitate making multiple -- previously virtual -- views physically "graspable". In this paper, we introduce a number of interaction and visualization patterns for tangible views that constitute the vocabulary for performing a variety of common visualization tasks. Several implemented case studies demonstrate the usefulness of tangible views for widely used information visualization approaches and suggest the high potential of this novel approach to support interaction with complex visualizations.


title: CIO roles and responsibilities: Twenty-five years of evolution and change

abstract: We investigated how the CIO's job has changed over the past years and found that the CIOs' role has evolved to reflect both the firm's IS infrastructure and strategy. This has led to two versions of the role: an executive-level manager focused on the firm's strategy and processes, and a technical manager focused on minimizing costs by rationalizing and leveraging the existing IS infrastructure. The degree to which a firm has standardized its IS architecture infrastructure, and the degree to which IS enables core products, services, processes, or competitive advantage of the firm impact the nature of the role.


title: Regression techniques for the prediction of stock price trend

abstract: This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pre-transformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. The outcomes of both processes are examined and appraised. The primary design is based on regression analysis from WEKA machine learning software. The stock price movement in Bursa Malaysia is used as our research setting. The data sources are corporate annual reports which included balance sheet, income statement and cash flow statement. The variables included in the data set were formed based on stock market trading fundamental analysis approach. Classifiers in WEKA were used as algorithms to produce the outcomes. This study showed that the outcomes of regression techniques can be improved for the prediction of stock price trend by using a dataset in standardized ordinal data format.


title: Development of short-range ground surveillance radar for moving target detection

abstract: In this paper, we proposed a short-range ground surveillance FMCW (Frequency Modulated Continuous Wave) radar. For this work, we employed a 2D-FFT algorithm to detect moving targets, and a windowing scheme to reduce the distortion generated by frequency transition. Moreover, we designed a moving target finding method to extract moving targets from the remaining distortions. We developed a 24GHz FMCW radar system comprised of front-end and back-end modules. A newly designed algorithm was implemented in a micro-processor of the back-end module. When we tested the function of the developed radar system in the field, the range and velocity profiles of a moving car occupying the detection zone were extracted without distortion.


title: Evaluation of 3D printing and its potential impact on biotechnology and the chemical sciences.

abstract: Nearing 30 years since its introduction, 3D printing technology is set to revolutionize research and teaching laboratories. This feature encompasses the history of 3D printing, reviews various printing methods, and presents current applications. The authors offer an appraisal of the future direction and impact this technology will have on laboratory settings as 3D printers become more accessible.


title: Convolutional Neural Network Committees for Handwritten Character Classification

abstract: In 2010, after many years of stagnation, the MNIST handwriting recognition benchmark record dropped from 0.40% error rate to 0.35%. Here we report 0.27% for a committee of seven deep CNNs trained on graphics cards, narrowing the gap to human performance. We also apply the same architecture to NIST SD 19, a more challenging dataset including lower and upper case letters. A committee of seven CNNs obtains the best results published so far for both NIST digits and NIST letters. The robustness of our method is verified by analyzing 78125 different 7-net committees.


title: Tailigator: Cooperative System for Safety Distance Observance

abstract: A driver's inattention to or disregard for the minimum safety distance creates a hazardous situation in which avoiding a rear-end collision is nearly impossible. In a joint effort to implement safety and decision-making processes at an individual level, we present in this paper a cooperative approach to increase the driver's visual awareness of safe distances. Our Tailigator system garners information through the stereoscopic capturing and processing of images by rear cameras to calculate the distance from the leading to a following vehicle. Visual data related to the safety distance is provided to the rear vehicle in real-time, relying on an asynchronous collaborative process. Results from the system qualitative evaluation are discussed.


title: Massive MIMO Antenna Selection: Switching Architectures, Capacity Bounds, and Optimal Antenna Selection Algorithms

abstract: Antenna selection is a multiple-input multiple-output (MIMO) technology, which uses radio frequency (RF) switches to select a good subset of antennas. Antenna selection can alleviate the requirement on the number of RF transceivers, thus being attractive for massive MIMO systems. In massive MIMO antenna selection systems, RF switching architectures need to be carefully considered. In this paper, we examine two switching architectures, i.e., full-array and sub-array. By assuming independent and identically distributed Rayleigh flat fading channels, we use asymptotic theory on order statistics to derive the asymptotic upper capacity bounds of massive MIMO channels with antenna selection for the both switching architectures in the large-scale limit. We also use the derived bounds to further derive the upper bounds of the ergodic achievable spectral efficiency considering the channel state information (CSI) acquisition. It is also showed that the ergodic capacity of sub-array antenna selection system scales no faster than double logarithmic rate. In addition, optimal antenna selection algorithms based on branch-and-bound are proposed for both switching architectures. Our results show that the derived asymptotic bounds are effective and also apply to the finite-dimensional MIMO. The CSI acquisition is one of the main limits for the massive MIMO antenna selection systems in the time-variant channels. The proposed optimal antenna selection algorithms are much faster than the exhaustive-search-based antenna selection, e.g., 1000 × speedup observed in the large-scale system. Interestingly, the full-array and sub-array systems have very close performance, which is validated by their exact capacities and their close upper bounds on capacity.


title: Neural Basis of Endogenous and Exogenous Spatial Orienting: A Functional MRI Study

abstract: Whole-brain functional magnetic resonance imaging (MRI) was used to examine the neural substrates of internally (endogenous) and externally (exogenous) induced covert shifts of attention. Thirteen normal subjects performed three orienting conditions: endogenous (location of peripheral target predicted by a central arrow 80p of the time), exogenous (peripheral target preceded by a noninformative peripheral cue), and control (peripheral target preceded by noninformative central cue). Behavioral results indicated faster reaction times (RTs) for valid than for invalid trials for the endogenous condition but slower RTs for valid than for invalid trials for the exogenous condition (inhibition of return). The spatial extent and intensity of activation was greatest for the endogenous condition, consistent with the hypothesis that endogenous orienting is more effortful (less automatic) than exogenous orienting. Overall, we did not observe distinctly separable neural systems associated with the endogenous and exogenous orienting conditions. Both exogenous and endogenous orienting, but not the control condition, activated bilateral parietal and dorsal premotor regions, including the frontal eye fields. These results suggest a specific role for these regions in preparatory responding to peripheral stimuli. The right dorsolateral prefrontal cortex (BA 46) was activated selectively by the endogenous condition. This finding suggests that voluntary, but not reflexive, shifts of attention engage working memory systems.


title: AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games

abstract: Evaluating agent performance when outcomes are stochastic and agents use randomized strategies can be challenging when there is limited data available. The variance of sampled outcomes may make the simple approach of Monte Carlo sampling inadequate. This is the case for agents playing heads-up no-limit Texas hold'em poker, where man-machine competitions have involved multiple days of consistent play and still not resulted in statistically significant conclusions even when the winner's margin is substantial. In this paper, we introduce AIVAT, a low variance, provably unbiased value assessment tool that uses an arbitrary heuristic estimate of state value, as well as the explicit strategy of a subset of the agents. Unlike existing techniques which reduce the variance from chance events, or only consider game ending actions, AIVAT reduces the variance both from choices by nature and by players with a known strategy. The resulting estimator in no-limit poker can reduce the number of hands needed to draw statistical conclusions by more than a factor of 10.


title: Continuous Auditing in Big Data Computing Environments: Towards an Integrated Audit Approach by Using CAATTs

abstract: Through the risen operating of autonomous and automated Accounting Information System (AIS), enterprises are increasingly faced with the growing size of accounting-relevant data. Therefore, the audit’s purpose, which is namely to examine the true and fair view of financial statements, is heavily increasing in complexity. To cover the requirements to AIS in Big Data Computing Environments, we strive for the development of a Continuous Auditing (CA) approach. Since, the implementation of CA is a recognized challenge among researchers and practitioners, and traditional audit tools and techniques neglect the potential of Big Data Analytics, we strive for the development of appropriate computer-assisted audit tools and techniques (CAATTs). Therefore, we analyzed established CAATTs considering the dimensions of the Big Data paradigm in this paper. To do so, we analyzed relevant literature and viewpoints of occupational organizations. This led us to a first proposal of a conceptual architecture for the integrated audit approach. We conclude this paper with a critical discussion and emphasize implications for further work.


title: Using clustering analysis to improve semi-supervised classification

abstract: Semi-supervised classification has become an active topic recently and a number of algorithms, such as Self-training, have been proposed to improve the performance of supervised classification using unlabeled data. In this paper, we propose a semi-supervised learning framework which combines clustering and classification. Our motivation is that clustering analysis is a powerful knowledge-discovery tool and it may reveal the underlying data space structure from unlabeled data. In our framework, semi-supervised clustering is integrated into Self-training classification to help train a better classifier. In particular, the semi-supervised fuzzy c-means algorithm and support vector machines are used for clustering and classification, respectively. Experimental results on artificial and real datasets demonstrate the advantages of the proposed framework.


title: Terrain synthesis by-example

abstract: Synthesizing terrain or adding detail to terrains manually is a long and tedious process. With procedural synthesis methods this process is faster but more difficult to control. This paper presents a new technique of terrain synthesis that uses an existing terrain to synthesize new terrain. To do this we use multi-resolution analysis to extract the high-resolution details from existing models and apply them to increase the resolution of terrain. Our synthesized terrains are more heterogeneous than procedural results, are superior to terrains created by texture transfer, and retain the large-scale characteristics of the original terrain.


title: Successful Nash Equilibrium Agent for a 3-Player Imperfect-Information Game

abstract: Creating strong agents for games with more than two players is a major open problem in AI. Common approaches are based on approximating game-theoretic solution concepts such as Nash equilibrium, which have strong theoretical guarantees in two-player zero-sum games, but no guarantees in non-zero-sum games or in games with more than two players. We describe an agent that is able to defeat a variety of realistic opponents using an exact Nash equilibrium strategy in a 3-player imperfect-information game. This shows that, despite a lack of theoretical guarantees, agents based on Nash equilibrium strategies can be successful in multiplayer games after all.


title: A General Evaluation Framework for Text Based Conversational Agent

abstract: This paper details the development of a new evaluation framework for a text based Conversational Agent (CA). A CA is an intelligent system that handle spoken or/and text based conversations between machine and human. Generally, the lack of evaluation frameworks for CAs effects its development. The idea behind any system’s evaluation is to make sure about the system’s functionalities and to continue development on it. A specific CA has been chosen to test the proposed framework on it; namely ArabChat. The ArabChat is a rule based CA and uses pattern matching technique to handle user’s Arabic text based conversations. The proposed and developed evaluation framework in this paper is natural language independent. The proposed framework is based on the exchange of specific information between ArabChat and user called “Information Requirements”. This information are tagged for each rule in the applied domain and should be exist in a user’s utterance (conversation). A real experiment has been done in Applied Science University in Jordan as an information point advisor for their native Arabic students to evaluate the ArabChat and then evaluating the proposed evaluation framework.


title: Car Number Plate Recognition (CNPR) system using multiple template matching

abstract: Image processing algorithms are used for converting textual image to editable text. One application could be a car number plate recognition (CNPR) system where these type of algorithms automatically detect the car registration number by capturing an image. In this paper, we improve one of the existing CNPR algorithms. The contributions include: multiple template matching; considering light intensity and recognizing the car number plate even in low intense light and independence of distance of car number plate to camera. The involvement of different conditions and external factors actually improve the CNPR system efficiency. We run different experiments on different car number plates. Our proposed improvements yield better results in terms of false positive and false negative values for CNPR.


i FAST TRANSIENT SIMULATION OF IEEE RECOMMENDED SURGE ARRESTER MODEL ON A TRANSMISSION LINE USING ALTERNATIVE TRANSIENT


title: From enterprise architecture to business models and back

abstract: In this study, we argue that important IT change processes affecting an organization's enterprise architecture are also mirrored by a change in the organization's business model. An analysis of the business model may establish whether the architecture change has value for the business. Therefore, in order to facilitate such analyses, we propose an approach to relate enterprise models specified in ArchiMate to business models, modeled using Osterwalder's Business Model Canvas. Our approach is accompanied by a method that supports business model-driven migration from a baseline architecture to a target architecture and is demonstrated by means of a case study.


title: Beyond dominant resource fairness: extensions, limitations, and indivisibilities

abstract: We study the problem of allocating multiple resources to agents with heterogeneous demands. Technological advances such as cloud computing and data centers provide a new impetus for investigating this problem under the assumption that agents demand the resources in fixed proportions, known in economics as Leontief preferences. In a recent paper, Ghodsi et al. [2011] introduced the dominant resource fairness (DRF) mechanism, which was shown to possess highly desirable theoretical properties under Leontief preferences. We extend their results in three directions. First, we show that DRF generalizes to more expressive settings, and leverage a new technical framework to formally extend its guarantees. Second, we study the relation between social welfare and properties such as truthfulness; DRF performs poorly in terms of social welfare, but we show that this is an unavoidable shortcoming that is shared by every mechanism that satisfies one of three basic properties. Third, and most importantly, we study a realistic setting that involves indivisibilities. We chart the boundaries of the possible in this setting, contributing a new relaxed notion of fairness and providing both possibility and impossibility results.


Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings.


title: An AIDS prevention program for homeless youth.

abstract: Larkin Street Youth Center (LSYC) is a multidisciplinary service center for homeless youth in San Francisco. This article describes the strategies developed by the medical clinic at LSYC for the prevention of human immunodeficiency virus infection.


title: EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation

abstract: During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence however is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The 2-layer architecture was investigated on a large dataset of 63,558 enzymes from the Protein Data Bank and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.


0 SleepMonitor : Monitoring Respiratory Rate and Body Position During Sleep Using


title: Perceptual Quality Assessment of Immersive Images Considering Peripheral Vision Impact

abstract: Conventional images/videos are often rendered within the central vision area of the human visual system (HVS) with uniform quality. Recent virtual reality (VR) device with head mounted display (HMD) extends the field of view (FoV) significantly to include both central and peripheral vision areas. It exhibits the unequal image quality sensation among these areas because of the non-uniform distribution of photoreceptors on our retina. We propose to study the sensation impact on the image subjective quality with respect to the eccentric angle $\theta$ across different vision areas. Often times, image quality is controlled by the quantization stepsize $q$ and spatial resolution $s$, separately and jointly. Therefore, the sensation impact can be understood by exploring the $q$ and/or $s$ in terms of the $\theta$, resulting in self-adaptive analytical models that have shown quite impressive accuracy through independent cross validations. These models can further be applied to give different quality weights at different regions, so as to significantly reduce the transmission data size but without subjective quality loss. As demonstrated in a gigapixel imaging system, we have shown that the image rendering can be speed up about 10$\times$ with the model guided unequal quality scales, in comparison to the the legacy scheme with uniform quality scales everywhere.


title: Hydraulic Autonomous Soft Robotic Fish for 3D Swimming

abstract: This work presents an autonomous soft-bodied robotic fish that is hydraulically actuated and capable of sustained swimming in three dimensions. The design of a fish-like soft body has been extended to deform under hydraulic instead of pneumatic power. Moreover, a new closed-circuit drive system that uses water as a transmission fluid is used to actuate the soft body. Circulation of water through internal body channels provides control over the fish’s caudal fin propulsion and yaw motion. A new fabrication technique for the soft body is described, which allows for arbitrary internal fluidic channels, enabling a wide-range of continuous body deformations. Furthermore, dynamic diving capabilities are introduced through pectoral fins as dive planes. These innovations enable prolonged fish-like locomotion in three dimensions.


title: Long-Term Potentiation (LTP) in the Central Amygdala (CeA) Is Enhanced After Prolonged Withdrawal From Chronic Cocaine and Requires CRF1 Receptors

abstract: The amygdala is part of the brain reward circuitry that plays a role in cocaine-seeking and abstinence in animals and cocaine craving and relapse in humans. Cocaine-seeking is elicited by cocaine-associated cues, and the basolateral amygdala (BLA) and CeA are essential in forming and communicating drug-related associations that are thought to be critical in long-lasting relapse risk associated with drug addiction. Here we simulated a cue stimulus with high-frequency stimulation (HFS) of the BLA–CeA pathway to examine mechanisms that may contribute to drug-related associations. We found enhanced long-term potentiation (LTP) after 14-day but not 1-day withdrawal from 7-day cocaine treatment mediated through N -methyl-d-aspartate (NMDA) receptors (NRs), L-type voltage-gated calcium channels (L-VGCCs), and corticotropin-releasing factor (CRF)1 receptors; this was accompanied by increased phosphorylated NR1 and CRF1 protein not associated with changes in NMDA/AMPA ratios in amygdalae from cocaine-treated animals. We suggest that these signaling mechanisms may provide therapeutic targets for the treatment of cocaine cravings.


title: Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

abstract: Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.


title: An introduction to hidden Markov models

abstract: The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.


title: Recovering Semantics of Tables on the Web

abstract: The Web offers a corpus of over 100 million tables [6], but the meaning of each table is rarely explicit from the table itself. Header rows exist in few cases and even when they do, the attribute names are typically useless. We describe a system that attempts to recover the semantics of tables by enriching the table with additional annotations. Our annotations facilitate operations such as searching for tables and finding related tables. ::: ::: To recover semantics of tables, we leverage a database of class labels and relationships automatically extracted from the Web. The database of classes and relationships has very wide coverage, but is also noisy. We attach a class label to a column if a sufficient number of the values in the column are identified with that label in the database of class labels, and analogously for binary relationships. We describe a formal model for reasoning about when we have seen sufficient evidence for a label, and show that it performs substantially better than a simple majority scheme. We describe a set of experiments that illustrate the utility of the recovered semantics for table search and show that it performs substantially better than previous approaches. In addition, we characterize what fraction of tables on the Web can be annotated using our approach.


DF2Net: Discriminative Feature Learning and Fusion Network for RGB-D Indoor Scene Classification


An investigation of some properties of an ant algorithm


title: Scaffold: Quantum Programming Language

abstract: Abstract : For quantum computing to become potentially viable however, research must also make inroads regarding the implementation, compilation, and architectural issues that lie between high-level mathematical algorithm expressions and low-level physical implementations. In particular, we need tools and analysis techniques that can--for a given algorithm and potential physical implementation technology--answer questions like: how much would it cost (i.e., how much resource in terms of qubits, gates, time are required?) to implement the algorithm in this technology? What is its performance potential? Is it scalable? Are there more algorithms that offer such speedups over classical computers? Our work is building a language and toolflow to answer such questions. This document describes the Scaffold programming language, its design goals, and related tools. Scaffold is a programming language for expressing quantum algorithms. A quantum algorithm can consist of a wide variety of components (including classical and quantum routines) which will be defined using different coding techniques. As a quantum programming language (QPL), Scaffold was formulated to make it easy to express an algorithm with so many disparate components in a clean and efficient manner. It is from this notion of putting things together that Scaffold derives its name.


title: Biometric Authentication: A Review

abstract: Advances in the field of Information Technology also make Information Security an inseparable part of it. In order to deal with security, Authentication plays an important role. This paper presents a review on the biometric authentication techniques and some future possibilities in this field. In biometrics, a human being needs to be identified based on some characteristic physiological parameters. A wide variety of systems require reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user, and not anyone else. By using biometrics it is possible to confirm or establish an individual’s identity. The position of biometrics in the current field of Security has been depicted in this work. We have also outlined opinions about the usability of biometric authentication systems, comparison between different techniques and their advantages and disadvantages in this paper.


title: Performance and Design Considerations for Dissipative Semi-Active Suspension Systems for Automobiles

abstract: Following previous work aimed at determining the relative performances of automobile suspension systems of passive, active and semi-active types by calculation, similar theoretical methods are applied here to evaluate a class of semi-active system that contains a passive spring of fixed stifness in parallel with the active damper. The static attitude control of the vehicle derives from the springs and in the cases considered the spring stiffness is adequate from this point of view. Systems based on two alternative forms of control law are studied. Results which show the best systems to offer particularly good performance for rough road conditions are presented. By adaptation of the damper control law using continuously updated information some capacity to obtain good performance under moderate conditions is shown to exist, but performance comparable with that obtainable from a purpose designed or fully adaptive system on average main road and motorway conditions is not possible.


title: CAD modeling of coplanar waveguide interdigital capacitor

abstract: A lumped-element circuit is proposed to model a coplanar waveguide (CPW) interdigital capacitor (IDC). Closed-form expressions suitable for CAD purposes are given for each element in the circuit. The obtained results for the series capacitance are in good agreement with those available in the literature. In addition, the scattering parameters obtained from the circuit model are compared with those obtained using the full-wave method of moments (MoM) and good agreement is obtained. Moreover, a multilayer feed-forward artificial neural network (ANN) is developed to model the capacitance of the CPW IDC. It is shown that the developed ANN has successfully learned the required task of evaluating the capacitance of the IDC. © 2005 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005.


title: Root canal anatomy of the human permanent teeth

abstract: Two thousand four hundred human permanent teeth were decalcified, injected with dye, and cleared in order to determine the number of root canals and their different types, the ramifications of the main root canals, the location of apical foramina and transverse anastomoses, and the frequency of apical deltas.


title: The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data

abstract: Abstract Background Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. New methods We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps − from flexible read-in of data formats to GLM regressor/contrast creation − without any manual intervention. Results We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N = 35). Comparison with existing methods The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. Conclusions Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.


Fashion Forecasting : An Exploration of Tweets regarding Trend Identification for Fashion Forecasting


title: Nonextraction treatment with temporary skeletal anchorage devices to correct a Class II Division 2 malocclusion with excessive gingival display.

abstract: The patient was a 22-year-old Japanese woman who complained of a gummy smile. She had several other orthodontic problems, including crowding of the maxillary anterior teeth, retroclination of the maxillary central incisors, excessive maxillary incisor display, a deep overbite, Class II dental relationships, a Class II profile, and a long face. Two options for the correction of these problems were proposed. The first option was to extract the maxillary first premolars to correct the Class II relationship and implant a miniscrew to correct the gingival display; the second option was to place 2 miniplates for distalization of the maxillary molars and a miniscrew to correct the gingival smile without premolar extractions. The patient chose the second option. After placing a preadjusted bracketed system, 2 miniplates were placed in the zygomatic buttresses bilaterally with monocortical screws, and 1 miniscrew was fixed between the root apices of the maxillary central incisors. Distalization and intrusion of the maxillary molars and intrusion of the maxillary incisors were simultaneously started with those temporary skeletal anchorage devices functioning as absolute orthodontic anchors. The total treatment period was approximately 22 months. Her orthodontic problems were corrected. According to the cephalometric evaluation, the entire maxillary dentition was significantly distalized, and her maxillary incisors were successfully intruded, with the mandible showing a slight counterclockwise rotation. Thanks to the temporary anchorage devices combined with miniplates and a miniscrew, we were able to predictably achieve her treatment goals without premolar extractions, orthognathic surgery, and the need for patient compliance.


Opaque Control-Flow Integrity


title: Correcting sample selection bias by unlabeled data

abstract: We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.


title: Dynamic Label Graph Matching for Unsupervised Video Re-identification

abstract: Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in each camera, and then graph matching scheme is introduced for cross-camera labeling association. While labels directly output from existing graph matching methods may be noisy and inaccurate due to significant cross-camera variations, this paper proposes a dynamic graph matching (DGM) method. DGM iteratively updates the image graph and the label estimation process by learning a better feature space with intermediate estimated labels. DGM is advantageous in two aspects: 1) the accuracy of estimated labels is improved significantly with the iterations; 2) DGM is robust to noisy initial training data. Extensive experiments conducted on three benchmarks including the large-scale MARS dataset show that DGM yields competitive performance to fully supervised baselines, and outperforms competing unsupervised learning methods.


title: Lifetime Amelioration for an AMOLED Pixel Circuit by Using a Novel AC Driving Scheme

abstract: This paper describes how a reversed-bias voltage affects organic light-emitting diodes (OLEDs) with green emitting Alq3:C545T (40 nm). The luminance efficiency and the active area of the test OLED devices are 8-11 cd/A and 2.5 mm × 2.5 mm, respectively. The OLED lifetime under an ac driving scheme can be apparently improved, in contrast with that under a dc driving scheme. Experimental results indicate that adjusting the duty ratio of the ac driving scheme can ameliorate the brightness uniformity; meanwhile, the normalized luminance of a 20% duty ratio after a 3600-s stress is 94.3%, and that of dc stress is 76.4%. Therefore, a novel pixel circuit with three p-type thin-film transistors (TFTs) for an active-matrix OLED application is proposed to ameliorate the brightness uniformity. Variations of the threshold voltage of the driving TFT are compensated for, and the performance and lifetime of the OLED are improved by using an ac driving method. Simulation results indicate that the output current is almost constant over the data range. Furthermore, the current error rate of the proposed pixel circuit is less than 6.5%.


title: Ultra-Wideband (UWB) Antenna Sensor Based Microwave Breast Imaging: A Review

abstract: Globally, breast cancer is reported as a primary cause of death in women. More than 1.8 million new breast cancer cases are diagnosed every year. Because of the current limitations on clinical imaging, researchers are motivated to investigate complementary tools and alternatives to available techniques for detecting breast cancer in earlier stages. This article presents a review of concepts and electromagnetic techniques for microwave breast imaging. More specifically, this work reviews ultra-wideband (UWB) antenna sensors and their current applications in medical imaging, leading to breast imaging. We review the use of UWB sensor based microwave energy in various imaging applications for breast tumor related diseases, tumor detection, and breast tumor detection. In microwave imaging, the back-scattered signals radiating by sensors from a human body are analyzed for changes in the electrical properties of tissues. Tumorous cells exhibit higher dielectric constants because of their high water content. The goal of this article is to provide microwave researchers with in-depth information on electromagnetic techniques for microwave imaging sensors and describe recent developments in these techniques.


title: Using Mise-En-Sc\`ene Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation

abstract: Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the crowd (e.g., tag), and as such, they are prone to noise and are expensive to collect. Moreover, these features are often rare or absent for new items, making it difficult or even impossible to provide good quality recommendations. ::: In this paper, we show that user's preferences on movies can be better described in terms of the mise-en-sc\`ene features, i.e., the visual aspects of a movie that characterize design, aesthetics and style (e.g., colors, textures). We use both MPEG-7 visual descriptors and Deep Learning hidden layers as example of mise-en-sc\`ene features that can visually describe movies. Interestingly, mise-en-sc\`ene features can be computed automatically from video files or even from trailers, offering more flexibility in handling new items, avoiding the need for costly and error-prone human-based tagging, and providing good scalability. ::: We have conducted a set of experiments on a large catalogue of 4K movies. Results show that recommendations based on mise-en-sc\`ene features consistently provide the best performance with respect to richer sets of more traditional features, such as genre and tag.


title: Research on the cable-pulley underactuated lower limb exoskeleton

abstract: A wide variety of lower limb exoskeletons have been applied in the fields of load-carrying augmentation, walking assistance, and rehabilitation training. The traditional exoskeleton based on fully actuated joints can provide powerful assistance for human motion, while it is bulky requiring a large energy consumption, which limits its application. The passive and quasi-passive exoskeletons are weak in the load-carrying condition due to the lack of active actuators. The traditional underactuated exoskeleton with only one joint actuated generally has limited power assistance. As for soft exoskeleton, uncomfortable feeling resulting from the pressure generated by cable tension on muscles are unavoidable. To compensate these drawbacks, this paper presents a lower limb exoskeleton based on the cable-pulley underactuated principle. By studying the synergies of torque and power assistance at the joints and designing the control strategy, a single cable will be able to drive the hip joint and the knee joint simultaneously, which can reduce the number of actuators. Considering the human-exoskeleton interactive dynamics, a biological method is utilized to evaluate the performance of the exoskeleton. Finally, the contrast simulation results demonstrate that the exoskeleton proposed in this paper can effectively provide power augmentation in load-carrying walking without interfering the human motion and the control strategy is feasible.


title: Nonintrusive Remote Monitoring of Sleep in Home-Based Situation

abstract: Sleep deprivation can lead to loss of concentration, and risky decision-making. Nevertheless, some people may underestimate the importance of getting quality sleep. The standard health care systems might not be suitable for long-term monitoring of sleep. As an example, the polysomnography, i.e., the gold standard for assessing sleep disorders is cumbersome, expensive, and time-consuming. As a result, portable, nonintrusive and inexpensive systems for monitoring quality of sleep are greatly needed. This paper demonstrates a novel nonintrusive system for monitoring quality of sleep using an optical fiber embedded sensor mat. The proposed system is deployed in real-life conditions over a one-month period. Three senior female residents were enrolled for the study, where the sensor mat is placed under the bed mattress. Sleep quality is assessed based on several parameters, such as duration of sleep, sleep interruption, vital signs (heart rate and respiration). The proposed system shows an agreement with a user's survey collected before the study. Furthermore, the system is integrated within an existing ambient assisted living platform with a user-friendly interface to make it more convenient for the caregivers to follow-up the sleep parameters of the residents.


title: Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks

abstract: Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For multi-contrast synthesis, the current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can, in turn, suffer from the loss of structural details in synthesized images. Here, in this paper, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves intermediate-to-high frequency details via an adversarial loss, and it offers enhanced synthesis performance via pixel-wise and perceptual losses for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improve synthesis quality. Demonstrations on T1- and T2- weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to the previous state-of-the-art methods. Our synthesis approach can help improve the quality and versatility of the multi-contrast MRI exams without the need for prolonged or repeated examinations.


title: Antisense-fos RNA causes partial reversion of the transformed phenotypes induced by the c-Ha-ras oncogene.

abstract: Abstract ::: Several lines of evidence have suggested that c-fos may act downstream from c-Ha-ras in a growth-regulatory signal transduction pathway. We used antisense RNA to inhibit c-fos gene expression and investigated the effects of diminished c-fos expression on the phenotypes induced by the EJ c-Ha-ras oncogene in NIH 3T3 cells. Immunofluorescent staining demonstrated that the antisense RNA caused a marked reduction in the amount of c-fos protein expressed following serum stimulation. EJ cells containing antisense-fos RNA continued to overexpress ras and remained capable of proliferating in vitro. However, the antisense-fos RNA caused a partial reversion of the major transformed phenotypes of EJ cells, including a restoration of both density-dependent growth arrest and the ability to be rendered quiescent by serum deprivation, a reversion to a flat morphology, inhibition of anchorage-independent growth, and inhibition of tumorigenicity in nude mice. Our results indicate that inhibition of c-fos expression, to a level still supporting in vitro proliferation, prevents the transforming effects of the ras oncogene; they thus provide additional evidence for the participation of c-fos in ras-regulated signal transduction pathways.


title: Interconnected Personal Health Record Ecosystem Using IoT Cloud Platform and HL7 FHIR

abstract: Personal health record (PHR) systems have yet to reach mass adoption despite years of varied implementations. In this paper, we examine the limitations of current PHRs and propose an interconnected PHR system built in adherence with healthcare data communication standards and an IoT Cloud platform. The new ecosystem includes: an interoperable hospital information system to store electronic medical records (EMRs) and transfer them to a PHR system via email; a public cloud that supports encrypted data sharing for big data analysis services; a PHR gateway repository based on an IoT module which communicates with hospitals and public clouds; and a mobile application to manage and view PHR. This system can store and share raw EMR and life log data based on the healthcare communication standard "HL7 FHIR". To validate the usability of the interconnected PHR system, a clinical trial is underway to develop an obesity management model from individuals' hospital-provided genomic data, on the one hand, and diet and exercise logs gathered by the mobile application, on the other. We demonstrate how the proposed IoT Cloud based PHR ecosystem is interoperable and practical for promoting healthcare big data analysis services.


title: Development of an FMCW LADAR Source Chip using MEMS-Electronic-Photonic Heterogeneous Integration

abstract: We present a modular integration platform, combining active and passive photonic devices with CMOS integrated electronics. Based on this integration platform, an integrated Frequency Modulated Continuous Wave (FMCW) Laser Detection and Ranging (LADAR) source chip is being developed. Such a LADAR source chip can be used in miniaturized 3D imaging systems for defense and consumer electronics applications. In this paper, we discuss the integration approach, the performance of select individual components, and experimental results on a bench top LADAR model system.


title: Extraction of Generalized Coupling Coefficients for Inline Extracted Pole Filters With Nonresonating Nodes

abstract: This paper presents the procedure for extracting the generalized coupling coefficients between resonating and nonresonating nodes of extracted pole sections. The extraction allows obtaining key design parameters for inline extracted pole filters directly from results of full-wave simulations. A third-order waveguide E-plane bandpass filters with λ/4-wavelength resonators is designed to prove feasibility of the proposed concept. The bandpass filter with a generalized Chebyshev response has 300-MHz bandwidth at 9.45 GHz and reveals wide stopband due to three transmission zeros arranged at predicted frequencies. Performance of the fabricated filter is evaluated through measurements, the experimental results obtained show very good agreement with simulations.


On the genetic evolution of a perfect Tic-tac-toe strategy


Knowledge Base Completion using Compositional Vector Space Models


title: Imaging sonar-aided navigation for autonomous underwater harbor surveillance

abstract: In this paper we address the problem of drift-free navigation for underwater vehicles performing harbor surveillance and ship hull inspection. Maintaining accurate localization for the duration of a mission is important for a variety of tasks, such as planning the vehicle trajectory and ensuring coverage of the area to be inspected. Our approach only uses onboard sensors in a simultaneous localization and mapping setting and removes the need for any external infrastructure like acoustic beacons. We extract dense features from a forward-looking imaging sonar and apply pair-wise registration between sonar frames. The registrations are combined with onboard velocity, attitude and acceleration sensors to obtain an improved estimate of the vehicle trajectory. We show results from several experiments that demonstrate drift-free navigation in various underwater environments.


title: Power Control Strategies of On-Road Charging for Electric Vehicles

abstract: On-road charging systems for electric vehicles (EVs) have shown revolutionary potential in extending driving range and reducing battery capacities. The optimal equivalent load resistances to maximize receiving power of each EV according to different EV amounts are investigated. This paper introduces a typical on-road charging system with a single transmitting coil and multiple receiving coils. The equivalent circuit models according to different numbers of EVs are built. Power control strategies with regard to a varying number of EVs are then presented. Specifically, self-adaptive source voltage based on primary current detection is utilized to charge EVs, while the source can support enough EVs by providing the rated power. Otherwise, the source voltage is regulated to its maximum value and the charging energy of each EV is suggested to be controlled by adjusting the individual driving speed. A remarkable feature of the power control strategies is that the charging power for each EV is stable and can compensate for energy losses efficiently. As for urgent power demand from a particular EV with a low battery capacity, the adjustment of the corresponding load resistance is applied to alter the power distribution. The proposed technique has been verified in an experimental prototype.


title: A Novel Attack Graph Posterior Inference Model Based on Bayesian Network

abstract: Network attack graphs are originally used to evaluate what the worst security state is when a concerned net-work is under attack. Combined with intrusion evidence such like IDS alerts, attack graphs can be further used to perform security state posterior inference (i.e. inference based on observation experience). In this area, Bayesian network is an ideal mathematic tool, however it can not be directly applied for the following three reasons: 1) in a network attack graph, there may exist directed cycles which are never permitted in a Bayesian network, 2) there may exist temporal partial ordering relations among intrusion evidence that can-not be easily modeled in a Bayesian network, and 3) just one Bayesian network cannot be used to infer both the current and the future security state of a network. In this work, we improve an approximate Bayesian posterior inference algorithm–the likelihood-weighting algorithm to resolve the above obstacles. We give out all the pseudocodes of the algorithm and use several examples to demonstrate its benefit. Based on this, we further propose a network security assessment and enhancement method along with a small network scenario to exemplify its usage.


Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System


title: Optimal Control of Nonlinear Systems with Temporal Logic Specifications

abstract: We present a mathematical programming-based method for optimal control of nonlinear systems subject to temporal logic task specifications. We specify tasks using a fragment of linear temporal logic (LTL) that allows both finite- and infinite-horizon properties to be specified, including tasks such as surveillance, periodic motion, repeated assembly, and environmental monitoring. Our method directly encodes an LTL formula as mixed-integer linear constraints on the system variables, avoiding the computationally expensive process of creating a finite abstraction. Our approach is efficient; for common tasks our formulation uses significantly fewer binary variables than related approaches and gives the tightest possible convex relaxation. We apply our method on piecewise affine systems and certain classes of differentially flat systems. In numerical experiments, we solve temporal logic motion planning tasks for high-dimensional (10\(+\) continuous state) systems.


title: Pages 1–2 Yale Image Finder (YIF): a new search engine for retrieving biomedical images

abstract: Summary: Yale Image Finder (YIF) is a publicly accessible search engine featuring a new way of retrieving biomedical images and associated papers based on the text carried inside the images. Image queries can also be issued against the image caption, as well as words in the associated paper abstract and title. A typical search scenario using YIF is as follows: a user provides few search keywords and the most relevant images are returned and presented in the form of thumbnails. Users can click on the image of interest to retrieve the high resolution image. In addition, the search engine will provide two types of related images: those that appear in the same paper, and those from other papers with similar image content. Retrieved images link back to their source papers, allowing users to find related papers starting with an image of interest. Currently, YIF has indexed over 140 000 images from over 34 000 open access biomedical journal papers. ::: ::: Availability: http://krauthammerlab.med.yale.edu/imagefinder/ ::: ::: Contact: michael.krauthammer@yale.edu


title: Drive circuits for ultra-fast and reliable actuation of Thomson coil actuators used in hybrid AC and DC circuit breakers

abstract: Thomson coil actuators (also known as repulsion coil actuators) are well suited for vacuum circuit breakers when fast operation is desired such as for hybrid AC and DC circuit breaker applications. This paper presents investigations on how the actuator drive circuit configurations as well as their discharging pulse patterns affect the magnetic force and therefore the acceleration, as well as the mechanical robustness of these actuators. Comprehensive multi-physics finite-element simulations of the Thomson coil actuated fast mechanical switch are carried out to study the operation transients and how to maximize the actuation speed. Different drive circuits are compared: three single switch circuits are evaluated; the pulse pattern of a typical pulse forming network circuit is studied, concerning both actuation speed and maximum stress; a two stage drive circuit is also investigated. A 630 A, 15 kV / 1 ms prototype employing a vacuum interrupter with 6 mm maximum open gap was developed and tested. The total moving mass accelerated by the actuator is about 1.2 kg. The measured results match well with simulated results in the FEA study.


title: A Pneumatic Artificial Muscle Manufactured Out of Self-Healing Polymers That Can Repair Macroscopic Damages

abstract: Pneumatic muscles are mostly constructed out of flexible membranes and are implemented in a wide range of robotic applications. In these soft materials, perforations are often caused by overpressuring of the muscles and wear. In many cases, this leads to limited numbers of actuation cycles. In this letter, pleated pneumatic artificial muscles were developed, whose membrane is constructed out of self-healing polymer. This allows for the complete healing of microscopic and macroscopic damages through a mild heat treatment. Two prototypes were designed, manufactured, and experimentally characterized. The self-healing was validated by repairing large realistic cuts in the muscle membrane completely. Even when repeatedly damaging and healing the muscles, the mechanical properties were recovered in a reversible and repeatable fashion through the self-healing procedure.


title: Multi-view Subspace Clustering

abstract: For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.


title: The happiness-income paradox revisited.

abstract: The striking thing about the happiness-income paradox is that over the long-term – usually a period of 10 y or more – happiness does not increase as a country's income rises. Heretofore the evidence for this was limited to developed countries. This article presents evidence that the long term nil relationship between happiness and income holds also for a number of developing countries, the eastern European countries transitioning from socialism to capitalism, and an even wider sample of developed countries than previously studied. It also finds that in the short-term in all three groups of countries, happiness and income go together, i.e., happiness tends to fall in economic contractions and rise in expansions. Recent critiques of the paradox, claiming the time series relationship between happiness and income is positive, are the result either of a statistical artifact or a confusion of the short-term relationship with the long-term one.


title: Hybrid Model with Margin-Based Real-Time Face Detection and Tracking

abstract: Face detection and tracking algorithms mainly suffer from low accuracy, slow processing speed, and poor robustness when meet with real-time setup. The problem becomes crucial in real-time situations such as in human robot interactions (HRI) or video analysis. A margin-based region of interest (ROI) hybrid approach that combines Haar cascade and template matching for face detection and tracking is proposed in this paper to improve the detection accuracy and processing speed. To speed up the processing time, region of interests (ROIs) with fixed and dynamic margin concepts are used. A dataset comprising of ten RGB video streams of fifteen seconds have been created from real-life videos containing a person in lecture delivering environment. In each video, there exists person’s movement, face turning and camera movements. An accuracy of 97.96% with processing time of 10.76 ms per frame has been achieved. The proposed algorithm can detect and track faces in sideway orientation apart from frontal face. The proposed approach can process the video streams at the speed above 90 frames per second (FPS). The proposed approach reduces processing time by ten times and with a boost to accuracy in comparison to the conventional full frame scanning techniques.


Investigation of the Ætiology of Typhus Fever, especially undertaken for the Egyptian Government in the Public Health Laboratories, Cairo.


Self-Conscious Emotions: Where Self and Emotion Meet


title: Safety verification of hybrid systems by constraint propagation-based abstraction refinement

abstract: This paper deals with the problem of safety verification of nonlinear hybrid systems. We start from a classical method that uses interval arithmetic to check whether trajectories can move over the boundaries in a rectangular grid. We put this method into an abstraction refinement framework and improve it by developing an additional refinement step that employs interval-constraint propagation to add information to the abstraction without introducing new grid elements. Moreover, the resulting method allows switching conditions, initial states, and unsafe states to be described by complex constraints, instead of sets that correspond to grid elements. Nevertheless, the method can be easily implemented, since it is based on a well-defined set of constraints, on which one can run any constraint propagation-based solver. Tests of such an implementation are promising.


title: Implementing publish/subscribe pattern for CoAP in fog computing environment

abstract: As more and more Internet of Things (IoT) devices are connected to the cloud services, fog computing has emerged as a new computing model to provide analytic and intelligent services to the end users for fast response time. Among the application layer protocols used for IoT, MQTT and CoAP are two well-known lightweight protocols that run on top of TCP and UDP, respectively. MQTT supports publish/subscribe pattern, while CoAP adopts request/response pattern. In IoT applications, publish/subscribe model was thought as a better model due to lower network bandwidth and less message processing which in turn extending the lifetime of battery-run devices. In this paper, we implement the publish/subscribe pattern in fog computing to preserve the privacy of customer energy usage data in smart grid environment using CoAP as the underlying application layer protocol. The performance of CoAP vs. MQTT in this model is also reported.


title: Compact Zigzag-Shaped-Slit Microstrip Antenna With Circular Defected Ground Structure for Wireless Applications

abstract: In this letter, a compact zigzag-shaped-slit rectangular microstrip patch antenna with circular defected ground structure (DGS) is designed for wireless applications. The probe-fed antenna consisting of a zigzag-shaped slit, dual T-shaped slits on either sides of a rectangular patch, and circular dumbbell-shaped defected ground plane is optimized. The antenna was able to generate three separate resonances to cover both the 2.45/5.28-GHz WLAN bands and the 3.5-GHz WiMAX bands while maintaining a small overall size of $40 \times 28 \times 3.175~\hbox{mm}^3$ . The return-loss impedance bandwidth values are enhanced significantly for three resonant frequencies. The designed antenna is characterized with better radiation patterns and potentially stable gain around 4–6 dBi over the working bands. Good agreement was obtained between measurements and simulations.


Clickstream data warehousing for web crawlers profiling


title: A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

abstract: Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1 the acoustic impulse response model, 2 the spatial filter design criterion, 3 the parameter estimation algorithm, and 4 optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.


title: Supporting Semantic Interoperability in Conservation-Restoration Domain: The PARCOURS Project

abstract: In this article, we present a semantic-based approach for dealing with the interoperability issue in the conservation-restoration domain. We first evaluate the context and our observations confirm the critical need for a data integration system taking advantage of data semantics. Our solution consists in: (1) building a domain-specific ontology, to rely on a unified understanding of the conservation-restoration data; (2) mapping the shared ontology to each data source, allowing each participating source to manage its own semantic database, consisting of its original data now associated to the semantic level; and (3) integrating all sources’ data, for querying them in the same homogeneous way. The presented achievements have been conducted as part of the PARCOURS project, whose aim is to develop an information system able to provide a unified access to distinct information sources, related to the cultural heritage field in general and the conservation-restoration processes in particular.


title: Cuckoo Optimization Algorithm

abstract: In this paper a novel evolutionary algorithm, suitable for continuous nonlinear optimization problems, is introduced. This optimization algorithm is inspired by the life of a bird family, called Cuckoo. Special lifestyle of these birds and their characteristics in egg laying and breeding has been the basic motivation for development of this new evolutionary optimization algorithm. Similar to other evolutionary methods, Cuckoo Optimization Algorithm (COA) starts with an initial population. The cuckoo population, in different societies, is in two types: mature cuckoos and eggs. The effort to survive among cuckoos constitutes the basis of Cuckoo Optimization Algorithm. During the survival competition some of the cuckoos or their eggs, demise. The survived cuckoo societies immigrate to a better environment and start reproducing and laying eggs. Cuckoos' survival effort hopefully converges to a state that there is only one cuckoo society, all with the same profit values. Application of the proposed algorithm to some benchmark functions and a real problem has proven its capability to deal with difficult optimization problems.


title: More than just skin deep? Personality information influences men's ratings of the attractiveness of women's body sizes.

abstract: This study examined the influence of personality information on perceptions of the physical attractiveness of a range of female body sizes. A sample of 2,157 male university students were randomly assigned to one of 10 groups in which they received personality information about women they were rating, or a control group in which they received no personality information. Controlling for participants' age and body mass index, results showed no significant between-group differences in the body size that participants found most attractive. However, participants provided with positive personality information perceived a wider range of body sizes as physically attractive compared with the control group, whereas participants provided with negative personality information perceived a narrower range of body sizes as attractive. Correlations showed that participants' own Extraversion was associated with their body size ratings. These results suggest that non-physical cues have an influence on the perception of physical beauty.


title: Novel algorithm for segmentation and automatic identification of pests on plants using image processing

abstract: Enormous agricultural yield is lost every year, due to rapid infestation by pests and insects. A lot of research is being carried out worldwide to identify scientific methodologies for early detection/identification of these bio-aggressors. In the recent past, several approaches based on automation and image processing have come to light to address this issue. Most of the algorithms concentrate on pest identification and detection, limited to a greenhouse environment. Also, they involve several complex calculations to achieve the same. In this paper, we propose a novel and unique algorithm to segregate and detect pests using image processing. The proposed methodology involves reduced computational complexity and aims at pest detection not only in a greenhouse environment but also in a farm environment as well. The whitefly, a bio-aggressor which poses a threat to a multitude of crops, was chosen as the pest of interest in this paper. The algorithm was tested for several whiteflies affecting different leaves and an accuracy of 96% of whitefly detection was achieved. The algorithm was developed and implemented using MATLAB programming language on MATLAB 7.1 build 2011a.


title: Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue

abstract: In this paper, we study the effects of three different kinds of search engine rankings on consumer behavior and search engine revenues: direct ranking effect, interaction effect between ranking and product ratings, and personalized ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: 1 A consumer-utility-based ranking mechanism can lead to a significant increase in overall search engine revenue. 2 Significant interplay occurs between search engine ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their ranking algorithms. 3 Our randomized experiments also reveal that an “active” personalized ranking system wherein users can interact with and customize the ranking algorithm leads to higher clicks but lower purchase propensities and lower search engine revenue compared with a “passive” personalized ranking system wherein users cannot interact with the ranking algorithm. This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized ranking systems by default. Overall, our study unravels the economic impact of ranking and its interaction with social media on product search engines. ::: ::: This paper was accepted by Lorin Hitt, information systems.


title: A CirCuit for All SeASonS

abstract: Digital-to-analog converters (DACs) find application in many systems, including communication transmitters and consumer electronics. Among various DAC realizations, the current-steering topology offers the highest speed and becomes the de facto solution at gigahertz frequencies, especially if the analog output must be delivered to a resistive load. In this article, we study this DAC's design principles.


title: Digital Mont’e Prama: Exploring Large Collections of Detailed 3D Models of Sculptures

abstract: We present and evaluate a scalable interactive system for the exploration of large collections of detailed three-dimensional digital models of sculptures. The system has been applied to the valorization of the Mont’e Prama complex, an extraordinary collection of protostoric Mediterranean sculptures, which depict models of cone-shaped stone towers, as well as larger-than-life human figures. The software architecture is based on scalable components for efficient distribution and adaptive rendering of extremely detailed surface meshes with overlaid information. The user interface, based on a simple and effective interactive camera controller tailored for touch interaction, has been designed for targeting both small screens and large display systems. The system components have been integrated in different interactive applications, ranging from large-screen museum setups and low-end mobile devices both with very high visual quality. The large-scale system has been installed in a variety of temporal and permanent exhibitions and has been extensively used by tens of thousands of visitors. We provide an early analysis in this article of the data gathered during a 20-month period in the National Archaeological Museum in Cagliari and a 6-month period in the Civic Museum in Cabras, for a total of over 67,000 exploration sessions.


title: Guide to measuring privacy concern: Review of survey and observational instruments

abstract: The debate about online privacy gives testimony of Web users' concerns. Privacy concerns make consumers adopt data protection features, guide their appreciation for existing features, and can steer their consumption choices amongst competing businesses. However, approaches to measure privacy concern are fragmented and often ad-hoc, at the detriment of reliable results. The need for measurement instruments for privacy concern is twofold. First, attitudes and opinions about data protection cannot be established and compared without reliable mechanisms. Second, behavioural studies, notably in technology acceptance and the behavioural economics of privacy require measures for concern as a moderating factor. In its first part, this paper provides a comprehensive review of existing survey instruments for measuring privacy concerns. The second part focuses on revealed preferences that can be used for opportunistically measuring privacy concerns in the wild or for scale validation. Recommendations for scale selection and reuse are provided.


title: A tomographic formulation of spotlight-mode synthetic aperture radar

abstract: Spotlight-mode synthetic aperture radar (spotlight-mode SAR) synthesizes high-resolution terrain maps using data gathered from multiple observation angles. This paper shows that spotlight-mode SAR can be interpreted as a tomographic reeonstrution problem and analyzed using the projection-slice theorem from computer-aided tomograpy (CAT). The signal recorded at each SAR transmission point is modeled as a portion of the Fourier transform of a central projection of the imaged ground area. Reconstruction of a SAR image may then be accomplished using algorithms from CAT. This model permits a simple understanding of SAR imaging, not based on Doppler shifts. Resolution, sampling rates, waveform curvature, the Doppler effect, and other issues are also discussed within the context of this interpretation of SAR.


BUSINESS FAILURE PREDICTION WITH SUPPORT VECTOR MACHINES AND NEURAL NETWORKS: A COMPARATIVE STUDY *


title: Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation

abstract: We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.


title: A combinatorial approach to graphlet counting

abstract: Motivation: Small-induced subgraphs called graphlets are emerging as a possible tool for exploration of global and local structure of networks and for analysis of roles of individual nodes. One of the obstacles to their wider use is the computational complexity of algorithms for their discovery and counting. Results: We propose a new combinatorial method for counting graphlets and orbit signatures of network nodes. The algorithm builds a system of equations that connect counts of orbits from graphlets with up to five nodes, which allows to compute all orbit counts by enumerating just a single one. This reduces its practical time complexity in sparse graphs by an order of magnitude as compared with the existing pure enumeration-based algorithms. Availability and implementation: Source code is available freely at http://www.biolab.si/supp/orca/orca.html.


title: Optimizing the Census Transform on CUDA enabled GPUs

abstract: The Census Transform is one of the most widely used matching metrics in problems that involve correspondence search such as stereo reconstruction and optical flow. Graphic processing units (GPUs) have become popular platforms for such computation intensive applications that expose a high degree of data parallelism. Their evolution as a platform for general purpose computing by continuously adding new hardware features has improved performance for many applications but it has also expanded the set of possible implementations choices up to the point where guidelines alone are not sufficient for optimum performance. What is the best implementation in the case of the Census Transform? This paper will answer that question by benchmarking all major possible implementations. Its aim is to provide an optimal implementation of the Census Transform on a current generation graphics processing unit using the Compute Unified Device Architecture (CUDA). The results have value reaching far beyond the Census Transform and provide insight for applications where non-separable 2D convolutions are present.


title: The Berkeley FrameNet Project

abstract: FrameNet is a three-year NSF-supported project in corpus-based computational lexicography, now in its second year (NSF IRI-9618838, "Tools for Lexicon Building"). The project's key features are (a) a commitment to corpus evidence for semantic and syntactic generalizations, and (b) the representation of the valences of its target words (mostly nouns, adjectives, and verbs) in which the semantic portion makes use of frame semantics. The resulting database will contain (a) descriptions of the semantic frames underlying the meanings of the words described, and (b) the valence representation (semantic and syntactic) of several thousand words and phrases, each accompanied by (c) a representative collection of annotated corpus attestations, which jointly exemplify the observed linkings between "frame elements" and their syntactic realizations (e.g. grammatical function, phrase type, and other syntactic traits). This report will present the project's goals and workflow, and information about the computational tools that have been adapted or created in-house for this work.


title: Eye In-Painting with Exemplar Generative Adversarial Networks

abstract: This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in-painting results. We propose using exemplar information in the form of a reference image of the region to in-paint, or a perceptual code describing that object. Unlike previous conditional GAN formulations, this extra information can be inserted at multiple points within the adversarial network, thus increasing its descriptive power. We show that ExGANs can produce photo-realistic personalized in-painting results that are both perceptually and semantically plausible by applying them to the task of closed-to-open eye in-painting in natural pictures. A new benchmark dataset is also introduced for the task of eye inpainting for future comparisons.