title: High frequency modeling for cable and induction motor overvoltage studies in long cable drives

abstract: High-frequency simulation models for power cables and motors are key tools to aid a better understanding of the overvoltage problem in pulsewidth modulation drives with long feeders. In this paper, frequency responses of the cable characteristic and the motor input impedances are obtained experimentally and suitable models are developed to match the experimental results. Several lumped segments incorporating a lossy representation of the line are used to model the cable. The cable and induction motor models may be implemented using a computational tool such as MATLAB, thereby providing a convenient method to analyze the overvoltage phenomena. Simulation and experimental results are presented for a typical 3-hp induction motor, showing the suitability of the developed simulation models. The most promising dv/dt filter networks are also investigated through simulation analysis, and a design approach based on a tradeoff between filter losses and motor peak voltage is proposed. Experimental results of an RC filter placed at the motor terminals demonstrate the validity of the simulation models.


title: Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces

abstract: Many different algorithms have been developed in the last few decades for solving complex real-world search and optimization problems. The main focus in this research has been on the development of a single universal genetic operator for population evolution that is always efficient for a diverse set of optimization problems. In this paper, we argue that significant advances to the field of evolutionary computation can be made if we embrace a concept of self-adaptive multimethod optimization in which multiple different search algorithms are run concurrently, and learn from each other through information exchange using a common population of points. We present an evolutionary algorithm, entitled A Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO), that implements this concept of self adaptive multimethod search. This method simultaneously merges the strengths of the covariance matrix adaptation (CMA) evolution strategy, genetic algorithm (GA), and particle swarm optimizer (PSO) for population evolution and implements a self-adaptive learning strategy to automatically tune the number of offspring these three individual algorithms are allowed to contribute during each generation. Benchmark results in 10, 30, and 50 dimensions using synthetic functions from the special session on real-parameter optimization of CEC 2005 show that AMALGAM-SO obtains similar efficiencies as existing algorithms on relatively simple unimodal problems, but is superior for more complex higher dimensional multimodal optimization problems. The new search method scales well with increasing number of dimensions, converges in the close proximity of the global minimum for functions with noise induced multimodality, and is designed to take full advantage of the power of distributed computer networks.


title: Unconstrained scene text and video text recognition for Arabic script

abstract: Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.


title: The influence of color on emotional perception of natural scenes.

abstract: Is color a critical factor when processing the emotional content of natural scenes? Under challenging perceptual conditions, such as when pictures are briefly presented, color might facilitate scene segmentation and/or function as a semantic cue via association with scene-relevant concepts (e.g., red and blood/injury). To clarify the influence of color on affective picture perception, we compared the late positive potentials (LPP) to color versus grayscale pictures, presented for very brief (24 ms) and longer (6 s) exposure durations. Results indicated that removing color information had no effect on the affective modulation of the LPP, regardless of exposure duration. These findings imply that the recognition of the emotional content of scenes, even when presented very briefly, does not critically rely on color information. Language: en


title: Modeling Short-Channel Effect of Elliptical Gate-All-Around MOSFET by Effective Radius

abstract: Gate-all-around (GAA) MOSFETs with elliptical cross section are studied in this letter. From the solution of the Poisson equation, an effective radius concept (Reff) is proposed to convert elliptical GAA MOSFETs into equivalent circular GAA MOSFETs for the study of short-channel effect. The Reff approach is used to estimate the subthreshold swing, drain-induced barrier lowering, and Qon/Qoff ratio of elliptical GAA MOSFETs. The predictions are validated by numerical 3-D TCAD simulations, and good agreement is achieved. Reff is also simplified to an area-to-perimeter ratio of the channel under some special conditions, and its physical meaning is explained.


title: A System Based on Intrinsic Features for Fraudulent Document Detection

abstract: Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer from a lack of security. However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one. In this paper, we present an automatic forgery detection method based on document's intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set is computed for all characters. Then, based on a distance between characters of the same class, the character is classified as a genuine one or a fake one.


title: Neural reuse of action perception circuits for language, concepts and communication

abstract: Abstract Neurocognitive and neurolinguistics theories make explicit statements relating specialized cognitive and linguistic processes to specific brain loci. These linking hypotheses are in need of neurobiological justification and explanation. Recent mathematical models of human language mechanisms constrained by fundamental neuroscience principles and established knowledge about comparative neuroanatomy offer explanations for where, when and how language is processed in the human brain. In these models, network structure and connectivity along with action- and perception-induced correlation of neuronal activity co-determine neurocognitive mechanisms. Language learning leads to the formation of action perception circuits (APCs) with specific distributions across cortical areas. Cognitive and linguistic processes such as speech production, comprehension, verbal working memory and prediction are modelled by activity dynamics in these APCs, and combinatorial and communicative-interactive knowledge is organized in the dynamics within, and connections between APCs. The network models and, in particular, the concept of distributionally-specific circuits, can account for some previously not well understood facts about the cortical ‘hubs’ for semantic processing and the motor system’s role in language understanding and speech sound recognition. A review of experimental data evaluates predictions of the APC model and alternative theories, also providing detailed discussion of some seemingly contradictory findings. Throughout, recent disputes about the role of mirror neurons and grounded cognition in language and communication are assessed critically.


title: Crime Mapping through Geo-Spatial Social Media Activity

abstract: The presence of crime is one of the major challenges for societies all over the World, especially in metropolitan areas. As indicated by prior research, Information Systems can contribute greatly to cope with the complex factors that influence the emergence and location of delinquencies. In this work, we combine commonly used approaches of static environmental characteristics with Social Media. We expect that blending in such dynamic information of public behavior is a valuable addition to explain and predict criminal activity. Consequently, we employ Zero-Inflated Poisson Regressions and Geographically Weighted Regressions to examine how suitable Social Media data actually is for this purpose. Our results unveil geographic variation of explanatory power throughout a metropolitan area. Furthermore, we find that Social Media works exceptionally well for description of certain crime types and thus is also likely to enhance the accuracy of delinquency prediction.


title: On the identification of sales forecasting models in the presence of promotions

abstract: Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.


title: A survey of modern optical character recognition techniques

abstract: This report explores the latest advances in the field of digital document recognition. With the focus on printed document imagery, we discuss the major developments in optical character recognition (OCR) and document image enhancement/restoration in application to Latin and non-Latin scripts. In addition, we review and discuss the available technologies for hand-written document recognition. In this report, we also provide some company-accumulated benchmark results on available OCR engines.


title: Radius aware probabilistic testing of deadlocks with guarantees

abstract: Concurrency bugs only occur under certain interleaving. Existing randomized techniques are usually ineffective. PCT innovatively generates scheduling, before executing a program, based on priori-ties and priority change points. Hence, it provides a probabilistic guarantee to trigger concurrency bugs. PCT randomly selects prior-ity change points among all events, which might be effective for non-deadlock concurrency bugs. However, deadlocks usually in-volve two or more threads and locks, and require more ordering constraints to be triggered. We interestingly observe that, every two events of a deadlock usually occur within a short range. We gener-ally formulate this range as the bug Radius, to denote the max dis-tance of every two events of a concurrency bug. Based on the bug radius, we propose RPro (Radius aware Probabilistic testing) for triggering deadlocks. Unlike PCT, RPro selects priority change points within the radius of the targeted deadlocks but not among all events. Hence, it guarantees larger probabilities to trigger dead-locks. We have implemented RPro and PCT and evaluated them on a set of real-world benchmarks containing 10 unique deadlocks. The experimental results show that RPro triggered all deadlocks with higher probabilities (i.e., >7.7x times larger on average) than that by PCT. We also evaluated RPro with radius varying from 1 to 150 (or 300). The result shows that the radius of a deadlock is much smaller (i.e., from 2 to 114 in our experiment) than the num-ber of all events. This further confirms our observation and makes RPro meaningful in practice.


title: Pose-conditioned Spatio-Temporal Attention for Human Action Recognition

abstract: We address human action recognition from multi-modal video data involving articulated pose and RGB frames and propose a two-stream approach. The pose stream is processed with a convolutional model taking as input a 3D tensor holding data from a sub-sequence. A specific joint ordering, which respects the topology of the human body, ensures that different convolutional layers correspond to meaningful levels of abstraction. The raw RGB stream is handled by a spatio-temporal soft-attention mechanism conditioned on features from the pose network. An LSTM network receives input from a set of image locations at each instant. A trainable glimpse sensor extracts features on a set of predefined locations specified by the pose stream, namely the 4 hands of the two people involved in the activity. Appearance features give important cues on hand motion and on objects held in each hand. We show that it is of high interest to shift the attention to different hands at different time steps depending on the activity itself. Finally a temporal attention mechanism learns how to fuse LSTM features over time. We evaluate the method on 3 datasets. State-of-the-art results are achieved on the largest dataset for human activity recognition, namely NTU-RGB+D, as well as on the SBU Kinect Interaction dataset. Performance close to state-of-the-art is achieved on the smaller MSR Daily Activity 3D dataset.


title: Subjective Assessment of Speech-System Interface Usability

abstract: Methods for the evaluation of the efficiency and effectiveness of speech input / output systems are well established. However, user subjective reactions to speech interfaces may well be a more important predictor of real world success. A review of existing subjective measures of speech system usability reveals a number of limitations in the approaches taken. This paper then summarises the work we have conducted in developing a new measure for the subjective assessment of speech system interfaces (SASSI).


title: Fetal spinal anomalies in a first-trimester sonographic screening program for aneuploidy.

abstract: OBJECTIVES ::: To review the sonographic features of spinal anomalies in first-trimester fetuses presenting for screening for chromosomal abnormalities. ::: ::: ::: METHODS ::: Fetuses with a spinal abnormality diagnosed prenatally or postnatally that underwent first-trimester sonographic evaluation at our institution had their clinical information retrieved and their sonograms reviewed. ::: ::: ::: RESULTS ::: A total of 21 fetuses complied with the entry criteria including eight with body stalk anomaly, seven with spina bifida, two with Vertebral, Anal, Cardiac, Tracheal, Esophageal, Renal, and Limb (VACTERL) association, and one case each of isolated kyphoscoliosis, tethered cord, iniencephaly, and sacrococcygeal teratoma. One fetus with body stalk anomaly and another with VACTERL association also had a myelomeningocele, making a total of nine cases of spina bifida in our series. Five of the nine (56%) cases with spina bifida, one of the two cases with VACTERL association, and the cases with tethered cord and sacrococcygeal teratoma were undiagnosed in the first trimester. Although increased nuchal translucency was found in seven (33%) cases, chromosomal analysis revealed only one case of aneuploidy in this series. ::: ::: ::: CONCLUSIONS ::: Fetal spinal abnormalities diagnosed in the first trimester are usually severe and frequently associated with other major defects. The diagnosis of small defects is difficult and a second-trimester scan is still necessary to detect most cases of spina bifida.


title: Augmented state Kalman filtering for AUV navigation

abstract: Addresses the problem of estimating the motion of an autonomous underwater vehicle (AUV), while it constructs a visual map ("mosaic" image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position.


title: Store Location Selection via Mining Search Query Logs of Baidu Maps

abstract: Choosing a good location when opening a new store is crucial for the future success of a business. Traditional methods include offline manual survey, which is very time consuming, and analytic models based on census data, which are un- able to adapt to the dynamic market. The rapid increase of the availability of big data from various types of mobile devices, such as online query data and offline positioning data, provides us with the possibility to develop automatic and accurate data-driven prediction models for business store placement. In this paper, we propose a Demand Distribution Driven Store Placement (D3SP) framework for business store placement by mining search query data from Baidu Maps. D3SP first detects the spatial-temporal distributions of customer demands on different business services via query data from Baidu Maps, the largest online map search engine in China, and detects the gaps between demand and sup- ply. Then we determine candidate locations via clustering such gaps. In the final stage, we solve the location optimization problem by predicting and ranking the number of customers. We not only deploy supervised regression models to predict the number of customers, but also learn to rank models to directly rank the locations. We evaluate our framework on various types of businesses in real-world cases, and the experiments results demonstrate the effectiveness of our methods. D3SP as the core function for store placement has already been implemented as a core component of our business analytics platform and could be potentially used by chain store merchants on Baidu Nuomi.


title: Chord-PKI: A distributed trust infrastructure based on P2P networks

abstract: Many P2P applications require security services such as privacy, anonymity, authentication, and non-repudiation. Such services could be provided through a hierarchical Public Key Infrastructure. However, P2P networks are usually Internet-scale distributed systems comprised of nodes with an undetermined trust level, thus making hierarchical solutions unrealistic. In this paper, we propose Chord-PKI, a distributed PKI architecture which is build upon the Chord overlay network, in order to provide security services for P2P applications. Our solution distributes the functionality of a PKI across the peers by using threshold cryptography and proactive updating. We analyze the security of the proposed infrastructure and through simulations we evaluate its performance for various scenarios of untrusted node distributions.


Collective Entity Resolution with Multi-Focal Attention


title: Choosing the right crowd: expert finding in social networks

abstract: Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks. This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users. We consider the following problem: given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need? We considers social networks both as a source of expertise information and as a route to reach expert users, and define models and methods for evaluating people's expertise by considering their profiles and by tracing their activities in social networks. For matching queries to social resources, we use both text analysis and semantic annotation. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.


title: Using author-specified keywords in building an initial reading list of research papers in scientific paper retrieval and recommender systems

abstract: The requirements for the task of building an initial reading list in literature review are re-conceptualized and a novel retrieval technique centered on author-specified keywords of papers is proposed for this task.The HITS variant of the proposed technique best satisfies the requirements of the task in an offline evaluation experiment.The proposed technique is evaluated by 132 researchers using 14 evaluation measures in a user evaluation study.Relevance, Recency and Usefulness were identified as the measures with high agreement percentages from participants.Students group were more satisfied with the results than staff group.Three predictors for user satisfaction were identified through the evaluation study. An initial reading list is prepared by researchers at the start of literature review for getting an overview of the research performed in a particular area. Prior studies have taken the approach of merely recommending seminal or popular papers to aid researchers in such a task. In this paper, we present an alternative technique called the AKR (Author-specified Keywords based Retrieval) technique for providing popular, recent, survey and a diverse set of papers as a part of the initial reading list. The AKR technique is based on a novel coverage value that has its calculation centered on author-specified keywords. We performed an offline evaluation experiment with four variants of the AKR technique along with three state-of-the-art approaches involving collaborative filtering and graph ranking algorithms. Findings show that the Hyperlink-Induced Topic Search (HITS) enhanced variant of the AKR technique performs better than other techniques, satisfying most requirements for a reading list. A user evaluation study was conducted with 132 researchers to gauge user interest on the proposed technique using 14 evaluation measures. Results show that (i) students group are more satisfied with the recommended papers than staff group, (ii) popularity measure is strongly correlated with the output quality measures and (iii) the measures familiarity, usefulness and agreeability on a good list were found to be strong predictors for user satisfaction. The AKR technique provides scope for extension in future information retrieval (IR) and content-based recommender systems (RS) studies.


title: Image quality assessment software of security screening system

abstract: Security screening systems based on imaging technology, like X-ray and gamma-ray based screening systems, are widely used in the field of aviation security. The image quality of the systems reflects detection performance directly. In the paper a set of image quality assessment software based-on human visual sensitivity (HVS) has been developed. The software integrated HVS method in the objective method and overcomes the shortage of both objective and subjective method of image processing. Experiments show that the assessment results are consistent with the perception of operators. With the help of the image quality assessment software, the image quality assessment of the security screening system can be finished justly and efficiently, which contributes the safety of aviation transportation.


title: 2.5D building modeling by discovering global regularities

abstract: We introduce global regularities in the 2.5D building modeling problem, to reflect the orientation and placement similarities between planar elements in building structures. Given a 2.5D point cloud scan, we present an automatic approach that simultaneously detects locally fitted plane primitives and global regularities. While global regularities are extracted by analyzing the plane primitives, they adjust the planes in return and effectively correct local fitting errors. We explore a broad variety of global regularities between 2.5D planar elements including both planer roof patches and planar facade patches. By aligning planar elements to global regularities, our method significantly improves the model quality in terms of both geometry and human judgement.


title: Down to the bare metal: using processor features for binary analysis

abstract: A detailed understanding of the behavior of exploits and malicious software is necessary to obtain a comprehensive overview of vulnerabilities in operating systems or client applications, and to develop protection techniques and tools. To this end, a lot of research has been done in the last few years on binary analysis techniques to efficiently and precisely analyze code. Most of the common analysis frameworks are based on software emulators since such tools offer a fine-grained control over the execution of a given program. Naturally, this leads to an arms race where the attackers are constantly searching for new methods to detect such analysis frameworks in order to successfully evade analysis. In this paper, we focus on two aspects. As a first contribution, we introduce several novel mechanisms by which an attacker can delude an emulator. In contrast to existing detection approaches that perform a dedicated test on the environment and combine the test with an explicit conditional branch, our detection mechanisms introduce code sequences that have an implicitly different behavior on a native machine when compared to an emulator. Such differences in behavior are caused by the side-effects of the particular operations and imperfections in the emulation process that cannot be mitigated easily. Motivated by these findings, we introduce a novel approach to generate execution traces. We propose to utilize the processor itself to generate such traces. Mores precisely, we propose to use a hardware feature called branch tracing available on commodity x86 processors in which the log of all branches taken during code execution is generated directly by the processor. Effectively, the logging is thus performed at the lowest level possible. We evaluate the practical viability of this approach.


title: Parallel discrete event simulation

abstract: Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see [59]), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit [14]. A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc. We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem. This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems. Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.


title: Exploring game design for cybersecurity training

abstract: Cybersecurity awareness and cyber skills training are vitally important and challenging. A huge number of attacks against everyday users occur routinely. Prevention techniques and responses are wide ranging but are only effective if used effectively. The objective of this research is to teach everyday users the requisite cybersecurity skills through gaming, beyond the current state of practice. Because the skill level of the trainees is also wide ranging, from causal computer users to software engineers to system administrators to managers, the games must also be capable of training this wide range of computer users. Computer games can provide a media for delivering training in an engaging format at levels appropriate for the individual trainees. In this paper we (1) describe the state of practice by describing the gaming tool used in most cyber challenges at high schools and colleges in the U.S, i.e., the cybersecurity gaming tool CyberNEXS™ (Science Applications International Corporation), (2) outline some of the additional topics that should be addressed in cybersecurity training and (3) note some other approaches to game design that might prove useful for future cybersecurity training game development beyond CyberNEXS.


title: Python implementation of local intervoxel-texture operators in neuroimaging using Anaconda and 3D Slicer environments

abstract: In neuroimaging, magnetic resonance images can be used to locate and obtain various parameters in order to find a wide range of pathologies, improving diagnosis and hence early treatment. Since images of the brain are volumetric, they are treated volumetrically in voxels, rather than planarly in pixels. We present an alternative implementation of local neighborhood-based texture parameters that have been recently shown to improve the detection of differences in the brain between healthy patients and those with Alzheimer's disease using diffusion tensor imaging [1]. We implemented the method (1) in Python using the Anaconda environment and the PyCharm compiler and (2) in 3D Slicer environment, as it is widely used by the neurology community, like the National Alliance for Medical Image Computing, among others. Weighted rotational invariant local operators were used in the calculus, namely average, standard deviation, coefficient of variation, normalized skewness, median, inter-quartile range and quartile coefficient of variation. Comparison between the implementations has been measured with normalized root-mean-square error. No differences have been observed for the non-linear parameters based on quartiles and errors smaller than 0.5% have been observed for operators that used Fast Fourier Transform based convolution instead of the explicit method.


Directed Graph Embedding


title: ProB: A Model Checker for B

abstract: We present PROB, an animation and model checking tool for the B method. PROB’s animation facilities allow users to gain confidence in their specifications, and unlike the animator provided by the B-Toolkit, the user does not have to guess the right values for the operation arguments or choice variables. PROB contains a model checker and a constraint-based checker, both of which can be used to detect various errors in B specifications. We present our first experiences in using PROB on several case studies, highlighting that PROB enables users to uncover errors that are not easily discovered by existing tools.


title: A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data

abstract: Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users' efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. TRIPS is validated using a real, large-scale dataset and showcases its effectiveness on the ridesharing task.


title: Learning Morphology with Morfette

abstract: Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the predictions of the Maximum-Entropy models and outputs a probability distribution over tag-lemma pair sequences. The lemmatization module exploits the idea of recasting lemmatization as a classification task by using class labels which encode mappings from wordforms to lemmas. Experimental evaluation results and error analysis on three morphologically rich languages show that the system achieves high accuracy with no language-specific ::: feature engineering or additional resources.


title: Getting More Out Of Syntax with PropS

abstract: Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dependency trees, often leading to further ad-hoc heuristic post-processing or to information loss. To directly address the needs of semantic applications, we present PropS -- an output representation designed to explicitly and uniformly express much of the proposition structure which is implied from syntax, and an associated tool for extracting it from dependency trees.


title: Improving reliability of jamming attack detection in ad hoc networks

abstract: Defense against denial of service (DoS) attacks is a critical component of any security system as these attacks can affect the availability of a node or an entire network. In this work, we focus on jamming type DoS attacks at the physical a nd MAC layers in 802.11 based ad hoc networks. Collisions in wirel ess networks occur due to varying factors such as jamming attack s, hidden terminal interferences and network congestion. We p resent a probabilistic analysis to show that collision occur rence alone cannot be used to conclusively determine jamming attacks i n wireless channel. To increase the reliability of attack dete ction, it is necessary to provide enhanced detection mechanisms that can determine the actual cause of channel collisions. T o address this, we first investigate the problem of diagnosing the pres ence of jamming in ad hoc networks. We then evaluate the detection mechanism using cross-layer information obtained from physica l and link layers to differentiate between jamming and congest ed network scenarios. By correlating the cross-layer data with collision detection metrics, we can distinguish attack scenar ios from the impact of traffic load on network behavior. Through simulation results we demonstrate the effectiveness of our sch eme in detecting jamming with improved precision.


title: Impedance-based tissue discrimination for needle guidance

abstract: Measurement of electrical impedance can discriminate between tissues of different electrical properties. A measurement system with adequate spatial resolution focused on a volume around the tip of a needle or other invasive clinical equipment can be used to determine in which type of tissue the tip is positioned. We have measured the sensitivity zone of a needle electrode with an active electrode area of 0.3 mm2, and measured impedance spectra in porcine tissue in vivo. Small electrode impedance data will be influenced by electrode polarization impedance (EPI) at low frequencies. To refine existing methods for needle guidance with higher spatial resolution, we have used multivariate analysis and new interpretations of EPI, and tissue data gathered with selected needle electrodes. The focus of this study is on discrimination between muscle and fat/subdermis for drug administration, but our results also indicate that these refinements will facilitate new clinical applications for impedance-based needle guidance in general.


title: Auto-adjusting camera exposure for outdoor robotics using gradient information

abstract: We present a new method to auto-adjust camera exposure for outdoor robotics. In outdoor environments, scene dynamic range may be wider than the dynamic range of the cameras due to sunlight and skylight. This can results in failures of vision-based algorithms because important image features are missing due to under-/over-saturation. To solve the problem, we adjust camera exposure to maximize image features in the gradient domain. By exploiting the gradient domain, our method naturally determines the proper exposure needed to capture important image features in a manner that is robust against illumination conditions. The proposed method is implemented using an off-the-shelf machine vision camera and is evaluated using outdoor robotics applications. Experimental results demonstrate the effectiveness of our method, which improves the performance of robot vision algorithms.


title: Quantum nonlocality without entanglement

abstract: We exhibit an orthogonal set of product states of two three-state particles that nevertheless cannot be reliably distinguished by a pair of separated observers ignorant of which of the states has been presented to them, even if the observers are allowed any sequence of local operations and classical communication between the separate observers. It is proved that there is a finite gap between the mutual information obtainable by a joint measurement on these states and a measurement in which only local actions are permitted. This result implies the existence of separable superoperators that cannot be implemented locally. A set of states are found involving three two-state particles that also appear to be nonmeasurable locally. These and other multipartite states are classified according to the entropy and entanglement costs of preparing and measuring them by local operations.


title: Vizster: visualizing online social networks

abstract: Recent years have witnessed the dramatic popularity of online social networking services, in which millions of members publicly articulate mutual "friendship" relations. Guided by ethnographic research of these online communities, we have designed and implemented a visualization system for playful end-user exploration and navigation of large scale online social networks. Our design builds upon familiar node link network layouts to contribute customized techniques for exploring connectivity in large graph structures, supporting visual search and analysis, and automatically identifying and visualizing community structures. Both public installation and controlled studies of the system provide evidence of the system's usability, capacity for facilitating discovery, and potential for fun and engaged social activity


title: Dermoscopy of Lymphomatoid Papulosis

abstract: L YMPHOMATOID PAPULOSIS (LYP) IS CHARACterized by papular, papulonecrotic, and/or nodular skin lesions at different stages of development, predominantly on the trunk and limbs. We prospectively evaluated 8 patients using dermoscopy and histopathologic correlations. The aim of this pilot study was to define dermoscopic criteria associated with the different stages of the disease (erythematous papule, ulcerated and/or necrotic papule, and cicatricial lesion). The dermoscopic pattern was similar in all patients and varied with the different stages of the disease. The initial inflammatory papular lesion (Figure 1A) showed essentially a vascular pattern of tortuous irregular vessels radiating from the center to the periphery of the lesion (Figure 1B). A white structureless area was seen around the vessels (Figure 1B, inset). On histopathologic evaluation, dilated vessels were observed on the upper dermis (Figure 1C and D, arrow) among the tumoral dense infiltrate of large CD30 cells (for type A LyP). Intravascular thrombosis was noted on the small vessels, usually at the beginning of the ischemic process (Figure 1E, arrow). On more mature lesions (hyperkeratotic papules), a whitish structureless area was seen at the center of the papule (Figure 2A). The vessel pattern was still observed, but it spared the middle of the lesion (Figure 2B). This central pattern was correlated with the onset of a hyperkeratosis on histologic samples and the onset of fibrinoid necrosis (Figure 2C, arrow) associated with intravascular thrombosis on the upper dermis. In the third stage, the necrotic ulceration replaced the whitish structureless area (Figure 3A). We observed centrally a brown-gray structureless area corresponding to the fibrinocruoric material; the vessel pattern was strictly limited to the extreme periphery of the lesion (Figure 3B). On histopathologic analysis (Figure 3C), a crust was seen on the center of the lesion, while the tumoral infiltrate and vessels were only seen deeper and at the periphery. Finally, in the cicatricial phase (Figure 4A), we observed no more residual vessel pattern; only a browngray structureless mark suggesting postinflammatory pigmentation persisted (Figure 4B). Histopathologically, findings corresponded to the dermal fibrosis noted in scarring LyP but without residual tumoral infiltrate (Figure 4C). As LyP lesions mature, their histopathologic changes can be well correlated with morphologic features seen on dermoscopy.


title: Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm

abstract: With the continual growth of mobile devices, they become a universal portable platform for effective business and personal communication. They enable a plethora of textual communication modes including electronic mails, instant messaging, and short messaging services. A downside of such great technology is the alarming rate of spam messages that are not only annoying to end-users but raises security concerns as well. This paper presents an intelligent framework for filtering multimodal textual communication including emails and short messages. We explore a novel methodology for information fusion inspired by the human immune system and hybrid approaches of machines learning. We study a number of methods to extract and select more relevant features to reduce the complexity of the proposed model to suite mobile applications while preserving good performance. The proposed framework is intensively evaluated on a number of benchmark datasets with remarkable results achieved. Proposed an intelligent framework for multimodal textual spam filtering for mobile devices.A novel hybrid machine learning approach and fusion with dendritic cell algorithm.Analyzed the discrimination of a rich set of content and style related features that can be easily extracted from received messages.Rigorously evaluated and benchmarked models on five email and SMS datasets using a variety of performance measures.Reduce complexity for feature extraction while preserving good performance.


Non-surgical treatment of pain associated with posterior tibial tendon dysfunction: study protocol for a randomised clinical trial


title: Towards Bandwidth Efficient Adaptive Streaming of Omnidirectional Video over HTTP: Design, Implementation, and Evaluation

abstract: Real-time entertainment services such as streaming audiovisual content deployed over the open, unmanaged Internet account now for more than 70% during peak periods. More and more such bandwidth hungry applications and services are proposed like immersive media services such as virtual reality and, specifically omnidirectional/360-degree videos. The adaptive streaming of omnidirectional video over HTTP imposes an important challenge on today's video delivery infrastructures which calls for dedicated, thoroughly designed techniques for content generation, delivery, and consumption.; AB@This paper describes the usage of tiles --- as specified within modern video codecs such HEVC/H.265 and VP9 --- enabling bandwidth efficient adaptive streaming of omnidirectional video over HTTP and we define various streaming strategies. Therefore, the parameters and characteristics of a dataset for omnidirectional video are proposed and exemplary instantiated to evaluate various aspects of such an ecosystem, namely bitrate overhead, bandwidth requirements, and quality aspects in terms of viewport PSNR. The results indicate bitrate savings from 40% (in a realistic scenario with recorded head movements from real users) up to 65% (in an ideal scenario with a centered/fixed viewport) and serve as a baseline and guidelines for advanced techniques including the outline of a research roadmap for the near future.


title: See all by looking at a few: Sparse modeling for finding representative objects

abstract: We consider the problem of finding a few representatives for a dataset, i.e., a subset of data points that efficiently describes the entire dataset. We assume that each data point can be expressed as a linear combination of the representatives and formulate the problem of finding the representatives as a sparse multiple measurement vector problem. In our formulation, both the dictionary and the measurements are given by the data matrix, and the unknown sparse codes select the representatives via convex optimization. In general, we do not assume that the data are low-rank or distributed around cluster centers. When the data do come from a collection of low-rank models, we show that our method automatically selects a few representatives from each low-rank model. We also analyze the geometry of the representatives and discuss their relationship to the vertices of the convex hull of the data. We show that our framework can be extended to detect and reject outliers in datasets, and to efficiently deal with new observations and large datasets. The proposed framework and theoretical foundations are illustrated with examples in video summarization and image classification using representatives.


title: Explicit Torque and Back EMF Expressions for Slotless Surface Permanent Magnet Machines With Different Magnetization Patterns

abstract: Slotless permanent magnet machines are attractive in some modern drive and power generation fields, where the cogging torque and additional losses need to be minimized or removed. The stator slotless design can be combined with different surface permanent magnet (SPM) rotor topologies. In this paper, explicit analytical expressions are derived to analytically compute the slotless machine torque and no-load back Electro-Motive Force in the case of segmented SPM rotor with parallel, radial, or Halbach-array magnetization patterns. The expressions are found by solving the magnetic field due to the slotless stator winding and to the permanent magnet blocks; the latter modeled through equivalent surface current densities. The accuracy of the method is successfully assessed by comparison with the finite-element analysis (FEA). The proposed formulas are an effective alternative to the FEA to quickly compare different design solutions as well as to optimize them. Application examples are provided in which the presented method is adopted to define the machine cross section that maximizes the torque density.


title: Mining Personal Context-Aware Preferences for Mobile Users

abstract: In this paper, we illustrate how to extract personal context-aware preferences from the context-rich device logs (i.e., context logs) for building novel personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his/her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context independent and context dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world data set show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.


title: Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines

abstract: Early detection of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is crucial for the success of the defibrillation therapy. A wide variety of detection algorithms have been proposed based on temporal, spectral, or complexity parameters extracted from the ECG. However, these algorithms are mostly constructed by considering each parameter individually. In this study, we present a novel life-threatening arrhythmias detection algorithm that combines a number of previously proposed ECG parameters by using support vector machines classifiers. A total of 13 parameters were computed accounting for temporal (morphological), spectral, and complexity features of the ECG signal. A filter-type feature selection (FS) procedure was proposed to analyze the relevance of the computed parameters and how they affect the detection performance. The proposed methodology was evaluated in two different binary detection scenarios: shockable (FV plus VT) versus nonshockable arrhythmias, and VF versus nonVF rhythms, using the information contained in the medical imaging technology database, the Creighton University ventricular tachycardia database, and the ventricular arrhythmia database. sensitivity (SE) and specificity (SP) analysis on the out of sample test data showed values of SE=95%, SP=99%, and SE=92% , SP=97% in the case of shockable and VF scenarios, respectively. Our algorithm was benchmarked against individual detection schemes, significantly improving their performance. Our results demonstrate that the combination of ECG parameters using statistical learning algorithms improves the efficiency for the detection of life-threatening arrhythmias.


The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0


title: Network science of biological systems at different scales: A review

abstract: Abstract Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present research highlights ranging from determination of the molecular interaction network within a cell to studies of architectural and functional properties of brain networks and biological transportation networks. Second, we focus on synergies between network science and data analysis, which enable us to determine functional connectivity patterns in multicellular systems. Until now, this intermediate scale of biological organization received the least attention from the network perspective. As an example, we review the methodology for the extraction of functional beta cell networks in pancreatic islets of Langerhans by means of advanced imaging techniques. Third, we concentrate on the emerging field of multilayer networks and review the first endeavors and novel perspectives offered by this framework in exploring biological complexity. We conclude by outlining challenges and directions for future research that encompass utilization of the multilayer network formalism in exploring intercellular communication patterns in tissues, and we advocate for network science being one of the key pillars for assessing physiological function of complex biological systems—from organelles to organs—in health and disease.


title: Disentangling factors of variation in deep representations using adversarial training

abstract: We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified variability. During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class. Examples of such observations include images of a set of labeled objects captured at different viewpoints, or recordings of set of speakers dictating multiple phrases. In both instances, the intra-class diversity is the source of the unspecified factors of variation: each object is observed at multiple viewpoints, and each speaker dictates multiple phrases. Learning to disentangle the specified factors from the unspecified ones becomes easier when strong supervision is possible. Suppose that during training, we have access to pairs of images, where each pair shows two different objects captured from the same viewpoint. This source of alignment allows us to solve our task using existing methods. However, labels for the unspecified factors are usually unavailable in realistic scenarios where data acquisition is not strictly controlled. We address the problem of disentaglement in this more general setting by combining deep convolutional autoencoders with a form of adversarial training. Both factors of variation are implicitly captured in the organization of the learned embedding space, and can be used for solving single-image analogies. Experimental results on synthetic and real datasets show that the proposed method is capable of generalizing to unseen classes and intra-class variabilities.


title: Rough terrain locomotion of a leg-wheel hybrid quadruped robot

abstract: This paper proposes a new mechanism and control method of 4-leg-wheel hybrid locomotion. Many previous hybrid vehicles execute wheel-mode and leg-mode alternatively. However, to overcome higher obstacle with sufficient velocity, a wheel locomotion should continuously be executed. In this paper, suitable load distribution control is proposed considering a contact force to the ground and step edge, a wheel driving torque, and a friction coefficient of the wheel and terrain. In a proposed new leg-wheel hybrid mechanism, load distribution control is realized by a torque of twisting joint at the center of the body, and a forward/backward shift of the body. The validity of this control and mechanism is confirmed by some experiments, where vehicle can run over a 105 mm step by 90 mm radius wheels with a good continuous movement. With an adjustment control of the approach angle, a robot can also ride over a 230 mm step


title: Service Performance and Analysis in Cloud Computing

abstract: Cloud computing is a new cost-efficient computing paradigm in which information and computer power can be accessed from aWeb browser by customers. Understanding the characteristics of computer service performance has become critical for service applications in cloud computing. For the commercial success of this new computing paradigm, the ability to deliver Quality of Services (QoS) guaranteed services is crucial. In this paper, we present an approach for studying computer service performance in cloud computing. Specifically, in an effort to deliver QoS guaranteed services in such a computing environment, we find the relationship among the maximal number of customers, the minimal service resources and the highest level of services. The obtained results provide the guidelines of computer service performance in cloud computing that would be greatly useful in the design of this new computing paradigm.


title: Portraying Collective Spatial Attention in Twitter

abstract: Microblogging platforms such as Twitter have been recently frequently used for detecting real-time events. The spatial component, as reflected by user location, usually plays a key role in such systems. However, an often neglected source of spatial information are location mentions expressed in tweet contents. In this paper we demonstrate a novel visualization system for analyzing how Twitter users collectively talk about space and for uncovering correlations between geographical locations of Twitter users and the locations they tweet about. Our exploratory analysis is based on the development of a model of spatial information extraction and representation that allows building effective visual analytics framework for large scale datasets. We show visualization results based on half a year long dataset of Japanese tweets and a four months long collection of tweets from USA. The proposed system allows observing many space related aspects of tweet messages including the average scope of spatial attention of social media users and variances in spatial interest over time. The analytical framework we provide and the findings we outline can be valuable for scientists from diverse research areas and for any users interested in geographical and social aspects of shared online data.


title: Adopting agile practices when developing software for use in the medical domain

abstract: Non-safety critical software developers have been reaping the benefits of adopting agile practices for a number of years. However, developers of safety critical software often have concerns about adopting agile practices. Through performing a literature review, this research has identified the perceived barriers to following agile practices when developing medical device software. A questionnaire-based survey was also conducted with medical device software developers in Ireland to determine the barriers to adopting agile practices. The survey revealed that half of the respondents develop software in accordance with a plan-driven software development lifecycle and that they believe that there are a number of perceived barriers to adopting agile practices when developing regulatory compliant software such as being contradictory to regulatory requirements, insufficient coverage of risk management activities and the lack of up-front planning. In addition, a comparison is performed between the perceived and actual barriers. Based upon the findings of the literature review and survey, it emerged that no external barriers exist to adopting agile practices when developing medical device software and the barriers that do exists are internal barriers such as getting stakeholder buy in. Copyright © 2013 John Wiley & Sons, Ltd.


title: Networked privacy: How teenagers negotiate context in social media

abstract: While much attention is given to young people’s online privacy practices on sites like Facebook, current theories of privacy fail to account for the ways in which social media alter practices of information-sharing and visibility. Traditional models of privacy are individualistic, but the realities of privacy reflect the location of individuals in contexts and networks. The affordances of social technologies, which enable people to share information about others, further preclude individual control over privacy. Despite this, social media technologies primarily follow technical models of privacy that presume individual information control. We argue that the dynamics of sites like Facebook have forced teens to alter their conceptions of privacy to account for the networked nature of social media. Drawing on their practices and experiences, we offer a model of networked privacy to explain how privacy is achieved in networked publics.


title: Biomechanically Driven Registration of Pre- to Intra-Operative 3D Images for Laparoscopic Surgery

abstract: Minimally invasive laparoscopic surgery is widely used for the treatment of cancer and other diseases. During the procedure, gas insufflation is used to create space for laparoscopic tools and operation. Insufflation causes the organs and abdominal wall to deform significantly. Due to this large deformation, the benefit of surgical plans, which are typically based on pre-operative images, is limited for real time navigation. In some recent work, intra-operative images, such as cone-beam CT or interventional CT, are introduced to provide updated volumetric information after insufflation. Other works in this area have focused on simulation of gas insufflation and exploited only the pre-operative images to estimate deformation. This paper proposes a novel registration method for pre- and intra-operative 3D image fusion for laparoscopic surgery. In this approach, the deformation of pre-operative images is driven by a biomechanical model of the insufflation process. The proposed method was validated by five synthetic data sets generated from clinical images and three pairs of in vivo CT scans acquired from two pigs, before and after insufflation. The results show the proposed method achieved high accuracy for both the synthetic and real insufflation data.


title: The orbitofrontal – amygdala circuit and self-regulation of social – emotional behavior in autism

abstract: Individuals with an autistic spectrum disorder are impaired not only in understanding others' mental states, but also in self-regulation of social–emotional behavior. Therefore, a model of the brain in autism must encompass not only those brain systems that subserve social–cognitive and emotional functioning, but also those that subserve the self-regulation of behavior in response to a changing social environment. We present evidence to support the hypothesis that developmental dysfunction of the orbitofrontal–amygdala circuit of the brain is a critical factor in the development of autism and that some of the characteristic deficits of persons with autism in socio-emotional cognition and behavioral self-regulation are related to early dysfunction of different components of this circuit. A secondary hypothesis posits that the degree of intellectual impairment present in individuals with autism is directly related to the integrity of the dorsolateral prefrontal–hippocampal circuit of the brain. Together, these hypotheses have the potential to help explain the neurodevelopmental basis of some of the primary manifestations of autism as well as the heterogeneity of outcomes.


title: New cache designs for thwarting software cache-based side channel attacks

abstract: Software cache-based side channel attacks are a serious new class of threats for computers. Unlike physical side channel attacks that mostly target embedded cryptographic devices, cache-based side channel attacks can also undermine general purpose systems. The attacks are easy to perform, effective on most platforms, and do not require special instruments or excessive computation power. In recently demonstrated attacks on software implementations of ciphers like AES and RSA, the full key can be recovered by an unprivileged user program performing simple timing measurements based on cache misses. We first analyze these attacks, identifying cache interference as the root cause of these attacks. We identify two basic mitigation approaches: the partition-based approach eliminates cache interference whereas the randomization-based approach randomizes cache interference so that zero information can be inferred. We present new security-aware cache designs, the Partition-Locked cache (PLcache) and Random Permutation cache (RPcache), analyze and prove their security, and evaluate their performance. Our results show that our new cache designs with built-in security can defend against cache-based side channel attacks in general-rather than only specific attacks on a given cryptographic algorithm-with very little performance degradation and hardware cost.


title: Symbol-Level Multiuser MISO Precoding for Multi-Level Adaptive Modulation

abstract: Symbol-level precoding is a new paradigm for multiuser multiple-antenna downlink systems aimed at creating constructive interference among transmitted data streams. This can be enabled by designing the precoded signal of the multiantenna transmitter on a symbol level, taking into account both channel state information and data symbols. Previous literature has studied this paradigm for Mary phase shift keying modulations by addressing various performance metrics, such as power minimization and maximization of the minimum rate. In this paper, we extend this to generic multi-level modulations, i.e., Mary quadrature amplitude modulation by establishing connection to PHY layer multicasting with phase constraints. Furthermore, we address the adaptive modulation schemes which are crucial in enabling the throughput scaling of symbol-level precoded systems. In this direction, we design the signal processing algorithms for minimizing the required power under per-user signal to interference noise ratio or goodput constraints. Extensive numerical results show that the proposed algorithm provides considerable power and energy efficiency gains, while adapting the employed modulation scheme to match the requested data rate.


title: A piece of the action: Modulation of sensory-motor regions by action idioms and metaphors

abstract: The idea that the conceptual system draws on sensory and motor systems has received considerable experimental support in recent years. Whether the tight coupling between sensory-motor and conceptual systems is modulated by factors such as context or task demands is a matter of controversy. Here, we tested the context sensitivity of this coupling by using action verbs in three different types of sentences in an fMRI study: literal action, apt but non-idiomatic action metaphors, and action idioms. Abstract sentences served as a baseline. The result showed involvement of sensory-motor areas for literal and metaphoric action sentences, but not for idiomatic ones. A trend of increasing sensory-motor activation from abstract to idiomatic to metaphoric to literal sentences was seen. These results support a gradual abstraction process whereby the reliance on sensory-motor systems is reduced as the abstractness of meaning as well as conventionalization is increased, highlighting the context sensitive nature of semantic processing.


title: A Virtual Reality Driving Environment for Training Safe Gaze Patterns: Application in Individuals with ASD

abstract: It has been well established that adolescents with Autism Spectrum Disorders (ASD) present social and behavioral characteristics that differ significantly from those of their peers without ASD. A growing number of recent studies have begun to look closely at automobile operation characteristics in individuals diagnosed with ASD. Some of this work has suggested that certain driving behaviors demonstrated by those with ASD may pose significant safety concerns to both themselves and other drivers. Expanding on previous work, we designed and tested a gaze-contingent driving intervention system in which drivers were required to not only perform well, but also to look at key regions of interest in the environment such as traffic lights, stop signs, pedestrians and side-view mirrors. We present preliminary results from a study comparing performance outcomes and eye gaze patterns in a group using the gaze-contingent system and a group using a gaze-insensitive, performance-based system.


A Walk with SGD: How SGD Explores Regions of Deep Network Loss?


title: 'Distance education' and 'e-learning': Not the same thing

abstract: This article examines the distinct differences between ‘distance education’ and ‘e-learning’ in higher education settings. Since the emergence of the new information and communication technologies (ICT), many have related to them as the new generation of distance education, and some have referred to their implementation in academia as challenging the very existence of campus-based universities. Many policy makers, scholars and practitioners in higher education use these two terms interchangeably as synonyms. But the fact is that distance education in most higher education systems is not delivered through the new electronic media, and vice versa – e-learning in most universities and colleges all over the world is not used for distance education purposes. ‘Distance education’ and ‘e-learning’ do overlap in some cases, but are by no means identical. The lack of distinction between ‘e-learning’ and ‘distance education’ accounts for much of the misunderstanding of the ICT roles in higher education, and for the wide gap between the rhetoric in the literature describing the future sweeping effects of the ICT on educational environments and their actual implementation. The article examines the erroneous assumptions on which many exaggerated predictions as to the future impact of the ICT were based upon, and it concludes with highlighting the future trends of ‘distance education’ and ‘e-learning’ in academia.


title: Lexicalization in natural language generation: A survey

abstract: In natural language generation, a meaning representation of some kind is successively transformed into a sentence or a text. Naturally, a central subtask of this problem is the choice of words, orlexicalization. In this paper, we propose four major issues that determine how a generator tackles lexicalization, and survey the contributions that researchers have made to them. Open problems are identified, and a possible direction for future research is sketched.


title: Patterns of expression of intermediate filaments in ameloblastoma and human fetal tooth germ

abstract: Monoclonal antibodies (Mab) were used to study the expression of cytokeratins and vimentin in various histological types of ameloblastoma and in human fetal tooth germ. The ameloblastoma and the tooth germ epithelia showed characteristics of both simple glandular and stratified squamous epithelial cells. Cytokeratin No. 18 was detected focally in most ameloblastomas studied but not in fetal odontogenic epithelia. Cytokeratins Nos. 8 and 19 were expressed in all epithelial elements of ameloblastomas and tooth germs. Only two tumors showed focally characteristics of keratinizing epithelia also seen in dental lamina but not in the enamel organ. All tumors except the granular cell ameloblastoma showed a variable coexpression of vimentin and cytokeratins in their neoplastic epithelia. A similar coexpression was detected in the stellate reticulum cells of the developing tooth. Ameloblastoma and human tooth germ epithelia share complex pattern of cytokeratin polypeptides together with coexpression of vimentin. The results strongly support the theory that ameloblastomas are of odontogenic origin and not direct derivatives of basal cells of oral epithelium or epidermis.


title: Hardware architecture design of face recognition system based on FPGA

abstract: A novel hardware architecture for face-recognition system has been proposed in this paper. In order to make the system cost effective a simple yet efficient algorithm of face-recognition system has been used. We have designed, implemented and verified the algorithm in a cyclone III Field Programmable Gate Array (FPGA) chip. Altera DE0 development board which contains a cyclone III chip on it has been used for debugging purpose. We have also ensured for low power consumption such that the chip could be used universally in a wide range of security systems. To develop a simple yet efficient face recognition algorithm (such as PCA, FFT etc.) on digital hardware, we have researched on various face recognition algorithms using MATLAB codes and studied their detection efficiency under various posture and background and also the complexity of the algorithm. To save hardware resource and at the same time to obtain an acceptable level of recognition we have chosen to use Fast Fourier Transform (FFT).


title: A Pilot Study Comparing the Effects of Mindfulness-Based and Cognitive-Behavioral Stress Reduction

abstract: OBJECTIVES ::: The objective of this pilot study was to compare the effects of two mind-body interventions: mindfulness-based stress reduction (MBSR) and cognitive-behavioral stress reduction (CBSR). ::: ::: ::: SUBJECTS ::: Fifty (50) subjects were recruited from the community and took part in MBSR (n = 36) and CBSR (n = 14) courses. Participants self-selected into MBSR or CBSR courses taught at different times. There were no initial differences between the MBSR and CBSR subjects on demographics, including age, gender, education, and income. ::: ::: ::: INTERVENTION ::: MBSR was an 8-week course using meditation, gentle yoga, and body scanning exercises to increase mindfulness. CBSR was an 8-week course using cognitive and behavioral techniques to change thinking and reduce distress. ::: ::: ::: DESIGN ::: Perceived stress, depression, psychological well-being, neuroticism, binge eating, energy, pain, and mindfulness were assessed before and after each course. Pre-post scores for each intervention were compared by using paired t tests. Pre-post scores across interventions were compared by using a general linear model with repeated measures. SETTINGS/LOCATIONS: Weekly meetings for both courses were held in a large room on a university medical center campus. ::: ::: ::: RESULTS ::: MBSR subjects improved on all eight outcomes, with all of the differences being significant. CBSR subjects improved on six of eight outcomes, with significant improvements on well-being, perceived stress, and depression. Multivariate analyses showed that the MBSR subjects had better outcomes across all variables, when compared with the CBSR subjects. Univariate analyses showed that MBSR subjects had better outcomes with regard to mindfulness, energy, pain, and a trend for binge eating. ::: ::: ::: CONCLUSIONS ::: While MBSR and CBSR may both be effective in reducing perceived stress and depression, MBSR may be more effective in increasing mindfulness and energy and reducing pain. Future studies should continue to examine the differential effects of cognitive behavioral and mindfulness-based interventions and attempt to explain the reasons for the differences.


title: Constant ON-time 3-level buck converter for low power applications

abstract: Car black boxes operate mostly in sleep mode, which requires high efficiency for light load. We propose two schemes to improve light load efficiency of the 3-level buck converter proposed by Reusch. The first scheme is to adopt constant ON-time control with DCM for light load. The second scheme is to shut down the gate drivers for the two bottom MOSFETs allowing Schottky diodes in parallel to conduct the inductor current, in the very light load condition. The proposed converter is prototyped using off-the-shelf components. The efficiency of the proposed 3-level buck converter ranges from 82% to 95% at 100 mW and 5 W, respectively.


title: Effectiveness of Occupational Therapy Interventions for Adults With Fibromyalgia: A Systematic Review

abstract: This systematic review addresses the effectiveness of occupational therapy-related interventions for adults with fibromyalgia.We examined the literature published between January 2000 and June 2014. A total of 322 abstracts from five databases were reviewed. Forty-two Level I studies met the inclusion criteria. Studies were evaluated primarily with regard to the following outcomes: daily activities, pain, depressive symptoms, fatigue, and sleep.Strong evidence was found for interventions categorized for this review as cognitive-behavioral interventions; relaxation and stress management; emotional disclosure; physical activity; and multidisciplinary interventions for improving daily living, pain, depressive symptoms, and fatigue. There was limited to no evidence for self-management, and few interventions resulted in better sleep.Although the evidence supports interventions within the scope of occupational therapy practice for people with fibromyalgia, few interventions were occupation based.


Unintended Consequences of Machine Learning in Medicine


title: Deeper Inside PageRank

abstract: This paper serves as a companion or extension to the "Inside PageRank" paper by Bianchini et al. [Bianchini et al. 03]. It is a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, suggested alternatives to the traditional solution methods, sensitivity and conditioning, and finally the updating problem. We introduce a few new results, provide an extensive reference list, and speculate about exciting areas of future research.


title: Control strategy applied on double flying capacitor multi-cell inverter for increasing number of generated voltage levels

abstract: A 15-level flying capacitor multi-cell based inverter with new control method is proposed. The main benefit of this topology is minimising the number of components (switches, IGBTs Q1 , diodes, flying capacitors and DC sources with minimum variety number) that are used for producing the same number of output voltage levels. The proposed novel control strategy is capable of regulating voltage of flying capacitors on desi red values, which results in increased number of output voltage levels. Another feature of the proposed control strategy is robustness against the load changes. Moreover, a new algorithm is presented for determining the voltage rating of flying capacitors. Simulations carried out in PSCAD/EMTDC software package and the experimental data were validated against numerical results. In this paper, a 15-level FCM based Inverter with new control strategy is proposed. Application of proposed new control strategy results in a reduction in number of IGBTs, diodes, DC voltage sources and its variety, compared with the previously presented FCMs, DFCMs, IDFCMs and SM inverters. The proposed control strategy is capable of keeping the voltage of flying capacitors on the specified rating values. This topology is composed of one DC voltage source, eight power switches and two flying capacitors. Moreover, a new algorithm is presented to determine the voltage ratings of used flying capacitors. Rather complicated charging and discharging strategy of flying capacitors, is the main drawback of proposed control strategy. Following this section, the proposed topology is introduced. The proposed new algorithm for determination of voltage ratings of flying capacitors comes in Section 3. Section 4 contains a brief description of applied control strategy. In Section 5, sizing of flying capacitors are investigated. The proposed topology is compared with some selected novel CMLIs and flying capacitor multi-cell based inverters (FCMIs) to evaluate its advantages and disadvantages at Section 6. At Section 7, simulation and experimental results are presented for proposed 15-level inverter and a conclusion comes in Section 8.


Did Elvis Presley Die of Boredom?


title: DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification

abstract: The accuracy of deep learning (e.g., convolutional neural networks) for an image classification task critically relies on the amount of labeled training data. Aiming to solve an image classification task on a new domain that lacks labeled data but gains access to cheaply available unlabeled data, unsupervised domain adaptation is a promising technique to boost the performance without incurring extra labeling cost, by assuming images from different domains share some invariant characteristics. In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks. In contrast to the existing unsupervised domain adaption approaches, the proposed DART not only learns domain-invariant features via adversarial training, but also achieves robust domain-adaptive classification via a residual-transfer strategy, all in an end-to-end training framework. We evaluate the performance of the proposed method for cross-domain image classification tasks on several well-known benchmark data sets, in which our method clearly outperforms the state-of-the-art approaches.


title: Sliding Mode Measurement Feedback Control for Antilock Braking Systems

abstract: We describe a nonlinear observer-based design for control of vehicle traction that is important in providing safety and obtaining desired longitudinal vehicle motion. First, a robust sliding mode controller is designed to maintain the wheel slip at any given value. Simulations show that longitudinal traction controller is capable of controlling the vehicle with parameter deviations and disturbances. The direct state feedback is then replaced with nonlinear observers to estimate the vehicle velocity from the output of the system (i.e., wheel velocity). The nonlinear model of the system is shown locally observable. The effects and drawbacks of the extended Kalman filters and sliding observers are shown via simulations. The sliding observer is found promising while the extended Kalman filter is unsatisfactory due to unpredictable changes in the road conditions.


title: Field measurement and analysis on the prestress loss of a long-span PC box Girder Bridge

abstract: The crack and large deflection of long-span PC box Girder Bridge is common in their service stage. The lower estimation for the loss of prestress is one of possible reasons. Combined with a long-span prestressed concrete box girder bridges, the loss of longitudinal and vertical prestress of a long-span PC box girder bridge is measured during the construction and service stage. Based on the measured results, the loss of the longitudinal and vertical prestress of the long-span PC box girder bridge is analyzed by GA-ANN method and the code of JTG D62-2004, respectively. The results demonstrate that the instantaneous loss of the longitudinal prestress is more than 40% of the designed value, and the total loss of the vertical prestress is about 40% more than the designed one as well. The value of the prestress loss is much higher than the expected one of the design. The reason lies in the inappropriate construction technology, such as not tension the prestress to the designed value, tension at an early age, and excessive different of the tube wobble and so on. Therefore, the engineering quality control must be strengthened during the construction stage of the long-span PC box girder bridge for avoiding excessive prestress loss.


title: Learning the Higher-Order Structure of a Natural Sound

abstract: Unsupervised learning algorithms paying attention only to second-order statistics ignore the phase structure (higher-order statistics) of signals, which contains all the informative temporal and spatial coincidences which we think of as ‘features’. Here we discuss how an Independent Component Analysis (ICA) algorithm may be used to elucidate the higher-order structure of natural signals, yielding their independent basis functions. This is illustrated with the ICA transform of the sound of a fingernail tapping musically on a tooth. The resulting independent basis functions look like the sounds themselves, having similar temporal envelopes and the same musical pitches. Thus they reflect both the phase and frequency information inherent in the data.*This paper was presented at the Workshop on Information Theory and the Brain, held at the University of Stirling, UK, on 4—5 September 1995


title: Medical Concept Embeddings via Labeled Background Corpora.

abstract: In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures.


title: CONSTRUCTIVE NEURAL NETWORKS: A REVIEW

abstract: In conventional neural networks, we have to define the architecture prior to training but in constructive neural networks the network architecture is constructed during the training process. In this paper, we review constructive neural network algorithms that constructing feedforward architecture for regression problems. Cascade-Correlation algorithm (CCA) is a well-known and widely used constructive algorithm. Cascade 2 algorithm is a variant of CCA that is found to be more suitable for regression problems and is reviewed in this paper. We review our recently proposed two constructive algorithms that emphasize on architectural adaptation and functional adaptation during training. To achieve functional adaptation, the slope of the sigmoidal function is adapted during learning. The algorithm determines not only the optimum number of hidden layer nodes, as also the optimum value of the slope parameter of sigmoidal function. The role of adaptive sigmoidal activation function has been verified in constructive neural networks for better generalization performance and lesser training time.


title: A trajectory planning method based on forward path generation and backward tracking algorithm for Automatic Parking Systems

abstract: This paper proposes a planning method based on forward path generation and backward tracking algorithm for Automatic Parking Systems, especially suitable for backward parking situations. The algorithm is based on the steering property that backward moving trajectory coincides with the forward moving trajectory for the identical steering angle. The basic path planning is divided into two segments: a collision-free locating segment and an entering segment that considers the continuous steering angles for connecting the two paths. MATLAB simulations were conducted, along with experiments involving parallel and perpendicular situations.


title: A flexible auto white balance based on histogram overlap

abstract: Auto white-balance plays a very important role in computer vision, and also is a prerequisite of color processing algorithms. For keeping the color constancy in the real-time outdoor environment, a simple and flexible auto white balance algorithm based on the color histogram overlap of the image is presented in this paper. After looking at a numerous images under different illuminance, an essential characteristic of the white-balance, the color histogram coincidence, is generalized as the basic criterion. Furthermore the overlap area of the color histogram directly reflects this coincidence, namely, when the overlap area of the color histogram reaches the maximum, the respective gain coefficients of color channels can be derived to achieve the white-balance of the camera. Through the subjective and objective evaluations based on the processing of real world images, the proposed histogram overlap algorithm can not only flexibly implement the auto white-balance of the camera but also achieve the outstanding performance in the real-time outdoor condition.


title: Looking at pictures: Affective, facial, visceral, and behavioral reactions

abstract: Colored photographic pictures that varied widely across the affective dimensions of valence (pleasant-unpleasant) and arousal (excited-calm) were each viewed for a 6-s period while facial electromyographic (zygomatic and corrugator muscle activity) and visceral (heart rate and skin conductance) reactions were measured. Judgments relating to pleasure, arousal, interest, and emotional state were measured, as was choice viewing time. Significant covariation was obtained between (a) facial expression and affective valence judgments and (b) skin conductance magnitude and arousal ratings. Interest ratings and viewing time were also associated with arousal. Although differences due to the subject's gender and cognitive style were obtained, affective responses were largely independent of the personality factors investigated. Response specificity, particularly facial expressiveness, supported the view that specific affects have unique patterns of reactivity. The consistency of the dimensional relationships between evaluative judgments (i.e., pleasure and arousal) and physiological response, however, emphasizes that emotion is fundamentally organized by these motivational parameters.


title: About pyramid structure in convolutional neural networks

abstract: Deep convolutional neural networks (CNN) brought revolution without any doubt to various challenging tasks, mainly in computer vision. However, their model designing still requires attention to reduce number of learnable parameters, with no meaningful reduction in performance. In this paper we investigate to what extend CNN may take advantage of pyramid structure typical of biological neurons. A generalized statement over convolutional layers from input till fully connected layer is introduced that helps further in understanding and designing a successful deep network. It reduces ambiguity, number of parameters, and their size on disk without degrading overall accuracy. Performance are shown on state-of-the-art models for MNIST, Cifar-10, Cifar-100, and ImageNet-12 datasets. Despite more than 80% reduction in parameters for Caffe_LENET, challenging results are obtained. Further, despite 10-20% reduction in training data along with 10-40% reduction in parameters for AlexNet model and its variations, competitive results are achieved when compared to similar well-engineered deeper architectures.


The necessary and sufficient conditions of therapeutic personality change


title: Seeking academic information on the Internet: Doctoral students' Internet self-efficacy and emotions

abstract: The main purpose of this proposed study is to understand the relationship between doctoral students' Internet self-efficacy and their affections when seeking academic information on the Internet. A structured questionnaire will be designed and conducted for 300 doctoral students. Participants' demographic information, information seeking behavior, Internet self-efficacy, and emotions will be measured in this questionnaire by using Information-Seeking Behavior Scale (ISBS), Internet Self-efficacy Scale (ISS), and the Positive and Negative Affect Schedule (PANAS). For the predicted outcomes, doctoral students' academic information seeking behaviors will be analyzed. Moreover, the interventions among doctoral students' Internet self-efficacy, emotions, and their information seeking behaviors will be analyzed and addressed. In the future, this proposed study will be conducted practically. The expected findings will be proposed for doctoral students so that they can based on the provided information to build up their suitable information seeking styles. Furthermore, the expected findings can also be presented for professionals, scholars, or even doctoral students' academic advisors who mainly do research and seek academic information online to have a better understanding of doctoral students' mental and psychical statuses on conducting academic information seeking activity on the Internet. To sum up, the results will not only reflect doctoral students' information seeking behaviors but also provide helpful suggestions for further researches.


title: SelCSP: A Framework to Facilitate Selection of Cloud Service Providers

abstract: With rapid technological advancements, cloud marketplace witnessed frequent emergence of new service providers with similar offerings. However, service level agreements (SLAs) , which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services to store and process their clients’ data. If loss of data occurs due to an outage, the customer’s business gets affected. Therefore, the major challenge for a customer is to select an appropriate service provider to ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP , which combines trustworthiness and competence to estimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider’s SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms.


title: A Scalable Global Model for Summarization

abstract: We present an Integer Linear Program for exact inference under a maximum coverage model for automatic summarization. We compare our model, which operates at the sub-sentence or "concept-level, to a sentence-level model, previously solved with an ILP. Our model scales more efficiently to larger problems because it does not require a quadratic number of variables to address redundancy in pairs of selected sentences. We also show how to include sentence compression in the ILP formulation, which has the desirable property of performing compression and sentence selection simultaneously. The resulting system performs at least as well as the best systems participating in the recent Text Analysis Conference, as judged by a variety of automatic and manual content-based metrics.