title: Assistive Robotics and an Ecology of Elders Living Independently in Their Homes

abstract: For elders who remain independent in their homes, the home becomes more than just a place to eat and sleep. The home becomes a place where people care for each other, and it gradually subsumes all activities. This article reports on an ethnographic study of aging adults who live independently in their homes. Seventeen elders aged 60 through 90 were interviewed and observed in their homes in 2 Midwestern cities. The goal is to understand how robotic products might assist these people, helping them to stay independent and active longer. The experience of aging is described as an ecology of aging made up of people, products, and activities taking place in a local environment of the home and the surrounding community. In this environment, product successes and failures often have a dramatic impact on the ecology, throwing off a delicate balance. When a breakdown occurs, family members and other caregivers have to intervene, threatening elders' independence and identity. This article highlights the interest in how the elder ecology can be supported by new robotic products that are conceived of as a part of this interdependent system. It is recommended that the design of these products fit the ecology as part of the system, support elders' values, and adapt to all of the members of the ecology who will interact with them.


title: Using Failure Modes, Mechanisms, and Effects Analysis in Medical Device Adverse Event Investigations

abstract: In the United States, when medical devices are associated with adverse events that result in death or serious injury, or have malfunctions that could lead to death/serious injury, these events must be reported to the Food and Drug Administration’s Center for Devices and Radiologic Health by device manufacturers and user facilities. However, the defects in the medical device evaluation process (e.g., failing to identify the failure mechanisms), can result in assessment risks and reoccurrences of adverse events. This paper presents an approach for medical device evaluation by using failure modes, mechanisms, and effects analysis to identify the root causes and failure mechanisms, which can improve the designs and reliability of medical devices. This method can also help medical device manufacturers to generate an internal evaluation reports for medical device evaluation, which can improve the reporting process to Food and Drug Administration.


Augmented Reality for the Study of Human Heart Anatomy


title: A Look at Motion Planning for Autonomous Vehicles at an Intersection

abstract: Autonomous Vehicles are currently being tested in a variety of scenarios. As we move towards Autonomous Vehicles, how should intersections look? To answer that question, we break down an intersection management into the different conundrums and scenarios involved in the trajectory planning and current approaches to solve them. Then, a brief analysis of current works in autonomous intersection is conducted. With a critical eye, we try to delve into the discrepancies of existing solutions while presenting some critical and important factors that have been addressed. Furthermore, open issues that have to be addressed are also emphasized. We also try to answer the question of how to benchmark intersection management algorithms by providing some factors that impact autonomous navigation at intersection.


title: Detecting and interpreting myocardial infarction using fully convolutional neural networks

abstract: Objective: We aim to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data without any preprocessing and to investigate its decision criteria. Approach: We train an ensemble of fully convolutional neural networks on the PTB ECG dataset and apply state-of-the-art attribution methods. Main results: Our classifier reaches 93.3% sensitivity and 89.7% specificity evaluated using 10-fold cross-validation with sampling based on patients. The presented method outperforms state-of-the-art approaches and reaches the performance level of human cardiologists for detection of myocardial infarction. We are able to discriminate channel-specific regions that contribute most significantly to the neural network's decision. Interestingly, the network's decision is influenced by signs also recognized by human cardiologists as indicative of myocardial infarction. Significance: Our results demonstrate the high prospects of algorithmic ECG analysis for future clinical applications considering both its quantitative performance as well as the possibility of assessing decision criteria on a per-example basis, which enhances the comprehensibility of the approach.


title: Pallet Recognition and Localization Method for Vision Guided Forklift

abstract: For navigating automatic forklift, we proposed an method for locating of the forklift. To improve efficiently of image processing and robust of object identification, the color image is transformed from RGB space into HSV and YUV space. Afterwards, we find out the mid-point of the pallets, calculate position of pallets relative to forklift by camera space model which builds the relationship between image space and real world space. In order to improve system speed, we use the Kalman filter to decrease the processing data. Experiment results indicate that our method has good performance in improving accuracy and efficiency of localization.


title: Sparse Bayesian learning for efficient visual tracking

abstract: This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well-known. This is addressed here by using a fully probabilistic relevance vector machine (RVM) to generate observations with Gaussian distributions that can be fused over time. Rather than adapting a recognizer, we build a displacement expert which directly estimates displacement from the target region. An object detector is used in tandem, for object verification, providing the capability for automatic initialization and recovery. This approach is demonstrated in real-time tracking systems where the sparsity of the RVM means that only a fraction of CPU time is required to track at frame rate. An experimental evaluation compares this approach to the state of the art showing it to be a viable method for long-term region tracking.


BackPropagation Through Time


title: Snubberless Bidirectional DC–DC Converter With New CLLC Resonant Tank Featuring Minimized Switching Loss

abstract: A bidirectional DC-DC converter (BDC) with a new CLLC-type resonant tank, which features zero-voltage switching (ZVS) for the input inverting choppers and zero-current switching (ZCS) for the output rectifier switches, regardless of the direction of the power flow, is proposed in this paper. Possessing the very optimal ZVS +ZCS soft-switching feature, this proposed converter will have a minimized switching loss if all of the main switches are implemented with metal-oxide-semiconductor field-effect transistors, and thereby, the proposed converter is fully soft switched and totally snubberless. The detail operation principles, as well as the design considerations, are presented. The methodologies to develop a unidirectional ZVS+ZCS dc-dc converter for the corresponding pulsewidth modulation and frequency modulation converters are proposed. The approach on how to construct a fully soft-switched BDC has also been proposed and analyzed. Finally, a topology extension is made, and another fully soft-switched BDC is derived. A prototype, which interfaces the 400-48-V dc buses for the uninterrupted power supply system with a power rating of 500 VA, was developed to verify the validity and applicability of this proposed converter. The highest applicable conversion efficiencies for the bidirectional operational modes are exceeding 96%.


AQUSA: The Automatic Quality User Story Artisan for Agile Software Development


title: Investigating the Intersection of Science Fiction, Human-Computer Interaction and Computer Science Research

abstract: This paper outlines ongoing dissertation research located in the intersection of science fiction, human-computer interaction and computer science. Through an interdisciplinary perspective, drawing from fields such as human-computer interaction, film theory and studies of science and technology, qualitative and quantitative content analysis techniques are used to contextually analyze expressions of science fiction in peer-reviewed computer science research repositories, such as the ACM or IEEE Xplore Digital Libraries. This paper concisely summarizes and introduces the relationship of science fiction and computer science research and presents the research questions, aims and implications in addition to prior work and study methodology. In the latter part of this work-in-progress report, preliminary results, current limitations, future work and a post-dissertation trajectory are outlined.


title: Augmented reality in science laboratories: The effects of augmented reality on university students' laboratory skills and attitudes toward science laboratories

abstract: This study investigated the effects of the use of augmented reality (AR) technologies in science laboratories on university students' laboratory skills and attitudes towards laboratories. A quasi-experimental pre-test/post-test control group design was employed. The participants were 76 first-year university students, aged 18-20 years old. They were assigned to either an experimental or a control group. Qualitative and quantitative data collection tools were used. The experimental results obtained following the 5-week application revealed that the AR technology significantly enhanced the development of the university students' laboratory skills. AR technology both improved the students' laboratory skills and helped them to build positive attitudes towards physics laboratories. The statements of the students and the instructor regarding other effects of AR technology on science laboratories, both negative and positive, are also discussed. This study explored the effects of the use of AR in science laboratories.A quasi-experimental pre-test/post-test control group design was employed.AR technology enhanced the development of the university students' laboratory skills.AR technology helped them to build positive attitudes towards physics laboratories.


title: The Mediterranean Diet: Socio-cultural Relevance for Contemporary Health Promotion

abstract: In biomedical literature, The Mediterranean Diet describes a healthy eating model, based on epidemiological findings on the predominant eating practices in Crete and Southern Italy in the 1960s. At the time, the level of life expectancy in this region was amongst the highest worldwide and rates of cardiovascular disease were amongst the lowest. Medical research has since given increasing attention to this dietary pattern and its potential health benefits. The various components of The Mediterranean Diet are fast becoming a paradigm for healthier lifestyles as well as potential model for weight loss. In 2010 UNESCO recognised The Mediterranean Diet as an intangible cultural heritage of Italy, Spain, Greece and Morocco and the diet has moved away from a uniquely biomedical model to a cultural representation. This has led to increased recognition of the importance of social and cultural context in the dietary model, particularly the idea of conviviality, the pleasure of shared meals. It has also brought to light the debate over the cultural legitimacy of The Mediterranean Diet and its ability to represent the cultural diversity of the region. This literature review consolidates interdisciplinary perspectives on the cultural context of the Mediterranean Diet. A literature search was conducted using both biomedical and social science databases to reflect the interdisciplinary nature of the article. This review explores the relevance of


title: Addressing the Rare Word Problem in Neural Machine Translation

abstract: Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing the NMT system to emit, for each OOV word in the target sentence, the position of its corresponding word in the source sentence. This information is later utilized in a post-processing step that translates every OOV word using a dictionary. Our experiments on the WMT’14 English to French translation task show that this method provides a substantial improvement of up to 2.8 BLEU points over an equivalent NMT system that does not use this technique. With 37.5 BLEU points, our NMT system is the first to surpass the best result achieved on a WMT’14 contest task.


title: Interactions between natural organic matter and organic pollutants as revealed by NMR spectroscopy

abstract: Natural organic matter (NOM) plays a critical role in regulating the transport and the fate of organic contaminants in the environment. NMR spectroscopy is a powerful technique for the investigation of the sorption and binding mechanisms between NOM and pollutants, as well as their mutual chemical transformations. Despite NMR relatively low sensibility but due to its wide versatility to investigating samples in the liquid, gel, and solid phases, NMR application to environmental NOM-pollutants relations enables the achievement of specific and complementary molecular information. This report is a brief outline of the potentialities of the different NMR techniques and pulse sequences to elucidate the interactions between NOM and organic pollutants, with and without their labeling with nuclei that enhance NMR sensitivity.


title: Composite texture descriptions

abstract: Textures can often more easily be described as a composition of subtextures than as a single texture. The paper proposes a way to model and synthesize such "composite textures", where the layout of the different subtextures is itself modeled as a texture, which can be generated automatically. Examples are shown for building materials with an intricate structure and for the automatic creation of landscape textures. First, a model of the composite texture is generated. This procedure comprises manual or unsupervised texture segmentation to learn the spatial layout of the composite texture and the extraction of models for each of the subtextures. Synthesis of a composite texture includes the generation of a layout texture, which is subsequently filled in with the appropriate subtextures. This scheme is refined further by also including interactions between neighboring subtextures.


title: An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

abstract: The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.


title: A Kalman filter based methodology for EEG spike enhancement

abstract: In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.


title: Active noise control: a tutorial review

abstract: Active noise control (ANC) is achieved by introducing a cancelling "antinoise" wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.


title: NVMalloc: Exposing an Aggregate SSD Store as a Memory Partition in Extreme-Scale Machines

abstract: DRAM is a precious resource in extreme-scale machines and is increasingly becoming scarce, mainly due to the growing number of cores per node. On future multi-peta flop and exa flop machines, the memory pressure is likely to be so severe that we need to rethink our memory usage models. Fortunately, the advent of non-volatile memory (NVM) offers a unique opportunity in this space. Current NVM offerings possess several desirable properties, such as low cost and power efficiency, but suffer from high latency and lifetime issues. We need rich techniques to be able to use them alongside DRAM. In this paper, we propose a novel approach for exploiting NVM as a secondary memory partition so that applications can explicitly allocate and manipulate memory regions therein. More specifically, we propose an NVMalloc library with a suite of services that enables applications to access a distributed NVM storage system. We have devised ways within NVMalloc so that the storage system, built from compute node-local NVM devices, can be accessed in a byte-addressable fashion using the memory mapped I/O interface. Our approach has the potential to re-energize out-of-core computations on large-scale machines by having applications allocate certain variables through NVMalloc, thereby increasing the overall memory capacity available. Our evaluation on a 128-core cluster shows that NVMalloc enables applications to compute problem sizes larger than the physical memory in a cost-effective manner. It can bring more performance/efficiency gain with increased computation time between NVM memory accesses or increased data access locality. In addition, our results suggest that while NVMalloc enables transparent access to NVM-resident variables, the explicit control it provides is crucial to optimize application performance.


title: Analysis of a Wavelength-Scaled Array (WSA) Architecture

abstract: Wavelength-scaled array architectures use scaled elements to achieve ultrawideband performance with significantly fewer overall radiators than traditional ultrawideband arrays based on a single element type. Compared to a conventional ultrawideband array with 8:1 bandwidth, a wavelength-scaled array that uses elements of three different sizes creates an aperture with fewer than 16% of the original element count, i.e., 6.4-times fewer elements, and by extension a comparable reduction in electronics required to feed the array. In this paper, a study of an asymmetric wavelength-scaled array architecture is presented for finite arrays of offset-centered dual-polarized flared-notch radiators. The unique element transitions within the finite array structure are modeled via a non-matching grid Domain Decomposition-Finite Element Method that allows for rigorous impedance and radiation pattern prediction of full-sized wavelength-scaled arrays. This design study shows that the wavelength-scaled array has comparable performance to traditional ultrawideband arrays in terms of VSWR, radiation patterns, array mismatch efficiency, and cross-polarization levels.


TCS-TRA-1260 TCS Technical Report ZDD-Based Computation of the Number of Paths in a Graph by


title: A persistent lock-free queue for non-volatile memory

abstract: Non-volatile memory is expected to coexist with (or even displace) volatile DRAM for main memory in upcoming architectures. This has led to increasing interest in the problem of designing and specifying durable data structures that can recover from system crashes. Data structures may be designed to satisfy stricter or weaker durability guarantees to provide a balance between the strength of the provided guarantees and performance overhead. This paper proposes three novel implementations of a concurrent lock-free queue. These implementations illustrate algorithmic challenges in building persistent lock-free data structures with different levels of durability guarantees. In presenting these challenges, the proposed algorithmic designs, and the different durability guarantees, we hope to shed light on ways to build a wide variety of durable data structures. We implemented the various designs and compared their performance overhead to a simple queue design for standard (volatile) memory.


title: Generating 3D faces using Convolutional Mesh Autoencoders

abstract: Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http://github.com/anuragranj/coma


title: Smart Home Based on WiFi Sensing: A Survey

abstract: Conventional sensing methodologies for smart home are known to be labor-intensive and complicated for practical deployment. Thus, researchers are resorting to alternative sensing mechanisms. Wi-Fi is one of the key technologies that enable connectivity for smart home services. Apart from its primary use for communication, Wi-Fi signal has now been widely leveraged for various sensing tasks, such as gesture recognition and fall detection, due to its sensitivity to environmental dynamics. Building smart home based on Wi-Fi sensing is cost-effective, non-invasive, and enjoys convenient deployment. In this paper, we survey the recent advances in the smart home systems based on the Wi-Fi sensing, mainly in such areas as health monitoring, gesture recognition, contextual information acquisition, and authentication.


title: Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language

abstract: This paper introduces the task of questionanswer driven semantic role labeling (QA-SRL), where question-answer pairs are used to represent predicate-argument structure. For example, the verb “introduce” in the previous sentence would be labeled with the questions “What is introduced?”, and “What introduces something?”, each paired with the phrase from the sentence that gives the correct answer. Posing the problem this way allows the questions themselves to define the set of possible roles, without the need for predefined frame or thematic role ontologies. It also allows for scalable data collection by annotators with very little training and no linguistic expertise. We gather data in two domains, newswire text and Wikipedia articles, and introduce simple classifierbased models for predicting which questions to ask and what their answers should be. Our results show that non-expert annotators can produce high quality QA-SRL data, and also establish baseline performance levels for future work on this task.


title: Learning in Nonstationary Environments: A Survey

abstract: The prevalence of mobile phones, the internet-of-things technology, and networks of sensors has led to an enormous and ever increasing amount of data that are now more commonly available in a streaming fashion [1]-[5]. Often, it is assumed - either implicitly or explicitly - that the process generating such a stream of data is stationary, that is, the data are drawn from a fixed, albeit unknown probability distribution. In many real-world scenarios, however, such an assumption is simply not true, and the underlying process generating the data stream is characterized by an intrinsic nonstationary (or evolving or drifting) phenomenon. The nonstationarity can be due, for example, to seasonality or periodicity effects, changes in the users' habits or preferences, hardware or software faults affecting a cyber-physical system, thermal drifts or aging effects in sensors. In such nonstationary environments, where the probabilistic properties of the data change over time, a non-adaptive model trained under the false stationarity assumption is bound to become obsolete in time, and perform sub-optimally at best, or fail catastrophically at worst.


title: Sprites: Payment Channels that Go Faster than Lightning

abstract: It is well known that Bitcoin, Ethereum, and other blockchain-based cryptocurrencies are facing hurdles in scaling to meet user demand. One of the most promising approaches is to form a network of"off-chain payment channels,"which are backed by on-chain currency but support rapid, optimistic transactions and use the blockchain only in case of disputes. We develop a novel construction for payment channels that reduces the worst-case"collateral cost"for off- chain payments. In existing proposals, particularly the Lightning Network, a payment across a path of $\ell$ channels requires locking up collateral for $O(\ell \Delta)$ time, where $\Delta$ is the time to commit a on-chain transaction. Our construction reduces this cost to $O(\ell + \Delta)$. We formalize our construction in the simulation-based security model, and provide an implementation as an Ethereum smart contract. Our construction relies on a general purpose primitive called a"state channel,"which is of independent interest.


title: New approach for clustering of big data: DisK-means

abstract: The exponential growth in the amount of data gathered from various sources has resulted in the need for more efficient algorithms to quickly analyze large datasets. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results. K-means++ initialization algorithm provides solution based on providing an initial set of centres to the K-Means algorithm. However, its inherent sequential nature makes it suffer from various limitations when applied to large datasets. For instance, it makes k iterations to find k centres. In this paper, we present an algorithm that attempts to overcome the drawbacks of previous algorithms. Our work provides a method to select a good initial seeding in less time, facilitating fast and accurate cluster analysis over large datasets.


title: Privacy in Interaction: Exploring Disclosure and Social Capital in Facebook

abstract: In this paper, we explore the relationship between Facebook users’ privacy concerns, relationship maintenance strategies, and social capital outcomes. Previous research has found a positive relationship between various measures of Facebook use and perceptions of social capital, i.e., one’s access to social and information-based resources. Other research has found that social network site users with high privacy concerns modify their disclosures on the site. However, no research to date has empirically tested how privacy concerns and disclosure strategies interact to influence social capital outcomes. To address this gap in the literature, we explored these questions with survey data (N=230). Findings indicate that privacy concerns and behaviors predict disclosures on Facebook, but not perceptions of social capital. In addition, when looking at predictors of social capital, we identify interaction effects between users’ network composition and their use of privacy features.


title: Bracketing Compound Nouns for Logic Form Derivation

abstract: We present here a method for bracketing coordinated compound nouns in the larger context of deriving first order logic forms. The method consists of two phases: (1) detection - extract candidate compound nouns from a parse tree and (2) interpretation - provide a bracketing solution for each candidate. Bracketing performance on 525 coordinations is 87.42%. This compares favorably with a 52.35% baseline when a default split by the coordination is applied.


title: Posterior Consistency via Precision Operators for Bayesian Nonparametric Drift Estimation in SDEs

abstract: We study a Bayesian approach to nonparametric estimation of the periodic drift function of a one-dimensional diffusion from continuous-time data. Rewriting the likelihood in terms of local time of the process, and specifying a Gaussian prior with precision operator of differential form, we show that the posterior is also Gaussian with the precision operator also of differential form. The resulting expressions are explicit and lead to algorithms which are readily implementable. Using new functional limit theorems for the local time of diffusions on the circle, we bound the rate at which the posterior contracts around the true drift function.


title: A mixed-signal X-band SiGe multi-function control MMIC for phased array radar applications

abstract: In this paper is reported the design, fabrication and test of a mixed-signal SiGe X-band multi-function control MMIC for phased array radar applications. Said MMIC, fabricated, with the ST-Microelectronics BiCMOS7RF SiGe technology, comprises a 5-bit phase shifter, 5-bit attenuator, SPDT switches, several gain amplifiers and a digital serial to parallel converter to reduce the number of MMIC I/O control lines. The gain amplifiers are implemented using SiGe HBTs, while phase shifter, attenuator and SPDT switches are based on CMOS transistors. The measurement results show a return loss better than 15 dB and a gain of 17 dB, equal in both RX and TX state. In RX mode the obtained noise figure is lower than 10 dB, while in TX mode the output P 1dB is higher than 12 dBm. The achieved RF performance, the low power consumption and associated low cost, make this SiGe control-chip an attractive solution for high performance/low cost Tx/Rx components for phased array radar applications.


title: Choosing Between the One-Way or Two-Way Street An Exploration of Relationship Promotion by Professional Athletes on Twitter

abstract: The purpose of this study was to explore the relationships promoted by professional athletes on Twitter utilizing the theoretical framework of parasocial interaction (PSI). Specifically, this study was a content analysis that examined professional athlete tweets in order to determine whether they predominately promoted social or parasocial relationships. The study also explored with whom athletes were engaging in social interaction as well as the topic of each tweet. The data revealed that professional athletes promoted both parasocial and social relationships equally. When they chose to be social, athletes were communicating with lay people and other professional and college athletes. Most athlete tweets were either general statements or insights into their personal lives. The implications of these and other findings will be discussed further.


title: Design of Embedded Systems : Formal Models , Validation , and Synthesis

abstract: This paper addresses the design of reactive real-time embedded systems. Such systems are often heterogeneous in implementation technologies and design styles, for example by combining hardware application-specific integrated circuits (ASICs) with embedded software. The concurrent design process for such embedded systems involves solving the specification, validation, and synthesis problems. We review the variety of approaches to these problems that have been taken.


title: I/O performance challenges at leadership scale

abstract: Today's top high performance computing systems run applications with hundreds of thousands of processes, contain hundreds of storage nodes, and must meet massive I/O requirements for capacity and performance. These leadership-class systems face daunting challenges to deploying scalable I/O systems. In this paper we present a case study of the I/O challenges to performance and scalability on Intrepid, the IBM Blue Gene/P system at the Argonne Leadership Computing Facility. Listed in the top 5 fastest supercomputers of 2008, Intrepid runs computational science applications with intensive demands on the I/O system. We show that Intrepid's file and storage system sustain high performance under varying workloads as the applications scale with the number of processes.


title: A position and stiffness control strategy for variable stiffness actuators

abstract: Variable stiffness actuators (VSAs) have been introduced to improve, at the design level, the safety and the energy efficiency of the new generation of robots that have to interact closely with humans. A wide variety of design solutions have recently been proposed, and a common factor in most of the VSAs is the introduction of a flexible transmission with varying stiffness. This, from the control perspective, usually implies a nonlinear actuation plant with varying dynamics following time-varying parameters, which requires more complex control strategies with respect to those developed for flexible joints with a constant stiffness. For this reason, this paper proposes an approach for controlling the link position and stiffness of a VSA. The link positioning relies on a LQR-based gain scheduling approach useful for continuously adjusting the control effort based on the current stiffness of the flexible transmission. The stiffness perceived at the output link is adjusted to match the varying task requirements through the combination of the positioning gains and the mechanical stiffness. The stability of the overall strategy is briefly discussed. The effectiveness of the controller in terms of tracking performance and stiffness adjustment is verified through experiments on the Actuator with Adjustable Stiffness (AwAS).


A FRAMEWORK FOR DETERMINING CRITICAL SUCCESS FACTORS INFLUENCING CONSTRUCTION BUSINESS PERFORMANCE


title: The Role of Feedback Types on False Memory in Children and Adolescents

abstract: Despite the large number of studies published in the area of false memory, not many have looked into the malleability of a witness’s confidence levels in relation to the susceptibility of false memories to feedback. This experiment aimed to examine the effects of postidentification feedback on witness confidence levels and retrospective memory evaluations in children aged 8 years old (N=30) and adolescents aged between18 to 19 years old (N=60). Each participant is asked to watch a crime scene video reconstruction of a theft. They are, then, asked to identify the suspect. Participants are given either positive, negative, or no feedback after the suspect identification. Results show that there are significant effects from feedback on the participants’ levels of confidence and retrospective memory evaluations. The age factor also has a significant effect on participants’ recall of the clarity of detail of a suspect’s face and their attention while viewing the video. There is also a significant interaction between the factors of feedback and age. Children are influenced by both types of feedback, positive and negative. While the levels of confidence and retrospective memory evaluations of adolescents are influenced only by positive feedback. This experiment demonstrates that post-identification feedback influences witnesses’ confidence levels and evaluations of an event. Therefore, it is essential to ensure that appropriate precautions are taken during the examination of witnesses, especially child witnesses.


title: Minerva: a scalable OWL ontology storage and inference system

abstract: With the increasing use of ontologies in Semantic Web and enterprise knowledge management, it is critical to develop scalable and efficient ontology management systems In this paper, we present Minerva, a storage and inference system for large-scale OWL ontologies on top of relational databases It aims to meet scalability requirements of real applications and provide practical reasoning capability as well as high query performance The method combines Description Logic reasoners for the TBox inference with logic rules for the ABox inference Furthermore, it customizes the database schema based on inference requirements User queries are answered by directly retrieving materialized results from the back-end database The effective integration of ontology inference and storage is expected to improve reasoning efficiency, while querying without runtime inference guarantees satisfactory response time Extensive experiments on University Ontology Benchmark show the high efficiency and scalability of Minerva system.


title: Computing Lexical Contrast

abstract: Knowing the degree of semantic contrast between words has widespread application in natural language processing, including machine translation, information retrieval, and dialogue systems. Manually created lexicons focus on opposites, such as hot and cold. Opposites are of many kinds such as antipodals, complementaries, and gradable. Existing lexicons often do not classify opposites into the different kinds, however. They also do not explicitly list word pairs that are not opposites but yet have some degree of contrast in meaning, such as warm and cold or tropical and freezing. We propose an automatic method to identify contrasting word pairs that is based on the hypothesis that if a pair of words, A and B, are contrasting, then there is a pair of opposites, C and D, such that A and C are strongly related and B and D are strongly related. (For example, there exists the pair of opposites hot and cold such that tropical is related to hot, and freezing is related to cold.) We will call this the contrast hypo...


Interference in the 2 . 4 GHz ISM Band : Challenges and Solutions


title: Convolutional Neural Network based sentiment analysis using Adaboost combination

abstract: Sentimental polarity detection has long been a hot task in natural language processing since its applications range from product feedback analysis to user statement understanding. Recently a lot of machine learning approaches have been proposed in the literature, e.g., SVM, Naive Bayes, recursive neural network, auto-encoders and etc. Among these different models, Convolutional Neural Network (CNN) architecture have also demonstrated profound efficiency in NLP tasks including sentiment classification. In CNN, the width of convolutional filter functions alike number N in N-grams model. Thus, different filter lengths may influence the performance of CNN classifier. In this paper, we want to study the possibility of leveraging the contribution of different filter lengths and grasp their potential in the final polarity of the sentence. We then use Adaboost to combine different classifiers with respective filter sizes. The experimental study on commonly used datasets has shown its potential in identifying the different roles of specific N-grams in a sentence respectively and merging their contribution in a weighted classifier.


title: Sensors of Infection: Viral Nucleic Acid PRRs in Fish

abstract: Viruses produce nucleic acids during their replication, either during genomic replication or transcription. These nucleic acids are present in the cytoplasm or endosome of an infected cell, or in the extracellular space to be sensed by neighboring cells during lytic infections. Cells have mechanisms of sensing virus-generated nucleic acids; these nucleic acids act as flags to the cell, indicating an infection requiring defense mechanisms. The viral nucleic acids are called pathogen-associated molecular patterns (PAMPs) and the sensors that bind them are called pattern recognition receptors (PRRs). This review article focuses on the most recent findings regarding nucleic acids PRRs in fish, including: Toll-like receptors (TLRs), RIG-I-like receptors (RLRs), cytoplasmic DNA sensors (CDSs) and class A scavenger receptors (SR-As). It also discusses what is currently known of the downstream signaling molecules for each PRR family and the resulting antiviral response, either type I interferons (IFNs) or pro-inflammatory cytokine production. The review highlights what is known but also defines what still requires elucidation in this economically important animal. Understanding innate immune systems to virus infections will aid in the development of better antiviral therapies and vaccines for the future.


title: Wideband Microstrip Ring Resonator Bandpass Filters Under Multiple Resonances

abstract: A new class of wideband microstrip bandpass filters is proposed under multiple resonances of an asymmetric ring resonator. Two capacitive coupling elements are placed at two perpendicular positions of a squared ring, whereas a pair of open-circuited stubs is formed in the symmetrical plane of these two excited ports similar to a dual-mode ring filter in shape. By stretching the paired stubs close to one-eighth of a wavelength, the first two even-order resonances move down to be quasi-equally located at two sides of the first odd-order resonance, thus forming a triple-resonance ring resonator. As interdigital coupled lines are installed at two ports instead of lumped capacitors, two extra resonances can be moved into passband, thereby making up a quintuple-resonance ring resonator. To provide an insight into their operating mechanism, these ring resonators are characterized in terms of transmission line theory. Afterwards, various wideband microstrip ring resonator bandpass filters with one or two asymmetric ring resonators are optimally designed and fabricated. Simulated results are confirmed via experiment, showing good wideband filtering performance with widened/deepened upper stopband and sharpened rejection skirts outside the wide passband.


title: The effect of fasting and vitamin E on insulin action in obese type 2 diabetes mellitus.

abstract: The influence of either short-term fasting or vitamin E administration on insulin action was studied in two groups of obese Type 2 diabetic patients. Twelve patients underwent 7 days of fasting (group A), whereas 600 mg of vitamin E was administered daily during 3 months in 9 diabetic patients (group B). Insulin action was examined by using hyperinsulinemic isoglycemic clamps (insulin infusion rate, 1.0 mU/kg/min) and insulin receptors on erythrocytes before and after respective regimens. An increase of glucose disposal rate (29.5 +/- 8.9 vs. 24.0 +/- 7.5 mumol/kg/min, p < 0.01) and an increase of metabolic clearance rate of glucose (4.0 +/- 2.5 vs. 2.3 +/- 0.9 mL/kg/min, p < 0.01) were observed in group A after fasting. On the contrary, decreases of glucose disposal rate (21.3 +/- 8.5 vs. 26.6 +/- 9.8 mumol/kg/min, p < 0.02), metabolic clearance rate of glucose (2.9 +/- 0.8 vs. 3.7 +/- 1.7 mL/kg/min, p < 0.05), and insulin receptor number (p < 0.01) were found after vitamin E administration as compared with pretreated values. A worsening of diabetes control as observed by an increase of HbA1C (p < 0.01) was present in the latter group. In summary, we found an improvement of insulin action after short-term fasting in contrast with the worsening of metabolic parameters after vitamin E administration in obese Type 2 diabetic patients.


title: Classification of Emg Signals Using Neuro-Fuzzy System and Diagnosis of Neuromuscular Diseases

abstract: This work investigates the performance of neuro-fuzzy system for analyzing and classifying EMG signals recorded from normal, neuropathy, and myopathy subjects. EMG signals were obtained from 177 subjects, 60 of them had suffered from neuropathy disorder, 60 of them had suffered from myopathy disorder, and rest of them had been normal. Coefficients that were obtained from the EMG signals using Autoregressive (AR) analysis was applied to neuro-fuzzy system. The classification performance of the feature sets was investigated for three classes.


title: A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

abstract: We introduce a novel embedding method for knowledge base completion task. Our approach advances state-of-the-art (SOTA) by employing a convolutional neural network (CNN) for the task which can capture global relationships and transitional characteristics. We represent each triple (head entity, relation, tail entity) as a 3-column matrix which is the input for the convolution layer. Different filters having a same shape of 1x3 are operated over the input matrix to produce different feature maps which are then concatenated into a single feature vector. This vector is used to return a score for the triple via a dot product. The returned score is used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction results than previous SOTA models on two current benchmark datasets WN18RR and FB15k-237.


title: Fast removal of non-uniform camera shake

abstract: Camera shake leads to non-uniform image blurs. State-of-the-art methods for removing camera shake model the blur as a linear combination of homographically transformed versions of the true image. While this is conceptually interesting, the resulting algorithms are computationally demanding. In this paper we develop a forward model based on the efficient filter flow framework, incorporating the particularities of camera shake, and show how an efficient algorithm for blur removal can be obtained. Comprehensive comparisons on a number of real-world blurry images show that our approach is not only substantially faster, but it also leads to better deblurring results.


title: Siphon-based deadlock prevention for a class of S4PR generalized Petri nets

abstract: The paper proposes a new technique for deadlock control for a class of generalized Petri net (PN) with S4PR net, from the concept of control siphon. One important property of PN is to design structure, in terms of siphons, in order to the characterization of the deadlock prevent, and analytic structure of the synchronization subsystem which is needed to control place in a system. An efficient siphon method provides a solution to calculating minimal siphons, which is lead to constructing an optimal supervisor, ensuring that the system live state is preserved. The experimental results show the illustration of the proposed methods by using an S4PR net with an example is represented in FMS. The simulation result is also provided as a Petri nets the conception as well as the integration with MATLAB.


title: Wildlife road-crossing monitoring system: Advances and test-site validation

abstract: The design and validation of a wireless monitoring system for dealing with wildlife road crossing problems is addressed. The wildlife detection procedure is based on the Doppler radar technology integrated in wireless sensor network devices. Such a solution tries to overcome the so-called habit effect arising with standard alert road-systems (e.g., static or flashing road signs) introducing the principle of real-time and event-based driver notification. To this end, the radar signal is locally processed by the wireless node to infer the target presence close to roadsides. In case of radar detection, the wireless node promptly transmits the collected information to the control unit, for data storage and further statistics. A prototype of the system has been deployed in a real test-site in Alps region for performance assessment. A selected set of preliminary results are here presented and discussed to show the capabilities of the proposed solution.


title: The parcellation-based connectome: Limitations and extensions

abstract: The human connectome is an intricate system of interconnected elements, providing the basis for integrative brain function. An essential step in the macroscopic mapping and examination of this network of structural and functional interactions is the subdivision of the brain into large-scale regions. Parcellation approaches used for the formation of macroscopic brain networks include application of predefined anatomical templates, randomly generated templates and voxel-based divisions. In this review, we discuss the use of such parcellation approaches for the examination of connectome characteristics. We specifically address the impact of the choice of parcellation scheme and resolution on the estimation of the brain's topological and spatial network features. Although organizational principles of functional and structural brain networks appear to be largely independent of the adopted parcellation approach, quantitative measures of these principles may be significantly modulated. Future parcellation-based connectome studies might benefit from the adoption of novel network tools and promising advances in connectivity-based parcellation approaches.


Evaluating Neural Network Predictors by Bootstrapping


title: Steroidogenic factor-1 (SF-1, NR5A1) and human disease

abstract: Steroidogenic factor-1 (SF-1, Ad4BP, encoded by NR5A1) is a key regulator of adrenal and reproductive development and function. Based upon the features found in Nr5a1 null mice, initial attempts to identify SF-1 changes in humans focused on those rare individuals with primary adrenal failure, a 46,XY karyotype, complete gonadal dysgenesis and Mullerian structures. Although alterations affecting DNA-binding of SF-1 were found in two such cases, disruption of SF-1 is not commonly found in patients with adrenal failure. In contrast, it is emerging that variations in SF-1 can be found in association with a range of human reproductive phenotypes such as 46,XY disorders of sex development (DSD), hypospadias, anorchia, male factor infertility, or primary ovarian insufficiency in women. Overexpression or overactivity of SF-1 is also reported in some adrenal tumors or endometriosis. Therefore, the clinical spectrum of phenotypes associated with variations in SF-1 is expanding and the importance of this nuclear receptor in human endocrine disease is now firmly established.


title: On the Origins of Memes by Means of Fringe Web Communities

abstract: Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation of memes across multiple Web communities, using a processing pipeline based on perceptual hashing and clustering techniques, and a dataset of 160M images from 2.6B posts gathered from Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab, over the course of 13 months. We group the images posted on fringe Web communities (/pol/, Gab, and The_Donald subreddit) into clusters, annotate them using meme metadata obtained from Know Your Meme, and also map images from mainstream communities (Twitter and Reddit) to the clusters. ::: Our analysis provides an assessment of the popularity and diversity of memes in the context of each community, showing, e.g., that racist memes are extremely common in fringe Web communities. We also find a substantial number of politics-related memes on both mainstream and fringe Web communities, supporting media reports that memes might be used to enhance or harm politicians. Finally, we use Hawkes processes to model the interplay between Web communities and quantify their reciprocal influence, finding that /pol/ substantially influences the meme ecosystem with the number of memes it produces, while \td has a higher success rate in pushing them to other communities.


title: Parallel consensual neural networks

abstract: A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.


title: "Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention

abstract: Social media platforms are increasingly being used to share and seek advice on mental health issues. In particular, Reddit users freely discuss such issues on various subreddits, whose structure and content can be leveraged to formally interpret and relate subreddits and their posts in terms of mental health diagnostic categories. There is prior research on the extraction of mental health-related information, including symptoms, diagnosis, and treatments from social media; however, our approach can additionally provide actionable information to clinicians about the mental health of a patient in diagnostic terms for web-based intervention. Specifically, we provide a detailed analysis of the nature of subreddit content from domain expert's perspective and introduce a novel approach to map each subreddit to the best matching DSM-5 (Diagnostic and Statistical Manual of Mental Disorders - 5th Edition) category using multi-class classifier. Our classification algorithm analyzes all the posts of a subreddit by adapting topic modeling and word-embedding techniques, and utilizing curated medical knowledge bases to quantify relationship to DSM-5 categories. Our semantic encoding-decoding optimization approach reduces the false-alarm-rate from 30% to 2.5% over a comparable heuristic baseline, and our mapping results have been verified by domain experts achieving a kappa score of 0.84.


title: Psychosocial risks: Can their effects on the security of information systems really be ignored?

abstract: Purpose ::: ::: ::: ::: – The purpose of this paper is to highlight the relation of psychosocial risks to information security (IS). Although psychosocial risks at the workplace have been extensively researched from a managerial point of view, their effect on IS has not been formally studied to the extent required by the gravity of the topic. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: – Based on existing research on psychosocial risks, their potential effects on IS are examined. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: – It is shown that as psychosocial risks affect people at the workplace, they diminish their ability to defend IS. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: – Psychosocial risks are identified as a factor in IS breakdown. Future research should be directed towards assessing the significance of the effects of various psychosocial risks on IS, creating an assessment methodology for the resulting IS posture of the organisation and devising mitigation methodologies. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: – The proposed approach will provide a significant part of the answer to the question of why IS fails when all prescribed measures and controls are in place and active. More effective controls for psychosocial risks at the workplace can be created as the incentive of upholding IS will be added to the equation of their mitigation. ::: ::: ::: ::: ::: Social implications ::: ::: ::: ::: – The organisational environment in which human beings are called upon to function in a secure manner will be redefined, along with what constitutes a “reasonable request” from human operators in the context of IS. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: – Bringing together psychosocial risks and IS in research will provide a better understanding of the shortcomings of human nature with respect to IS. Organisations and employees will benefit from the resulting psychosocial risk mitigation.


title: HARD-PnP: PnP Optimization Using a Hybrid Approximate Representation

abstract: This paper proposes a Hybrid Approximate Representation (HAR) based on unifying several efficient approximations of the generalized reprojection error (which is known as the gold standard for multiview geometry). The HAR is an over-parameterization scheme where the approximation is applied simultaneously in multiple parameter spaces. A joint minimization scheme “HAR-Descent” can then solve the PnP problem efficiently, while remaining robust to approximation errors and local minima. The technique is evaluated extensively, including numerous synthetic benchmark protocols and the real-world data evaluations used in previous works. The proposed technique was found to have runtime complexity comparable to the fastest $O(n)$ techniques, and up to 10 times faster than current state of the art minimization approaches. In addition, the accuracy exceeds that of all 9 previous techniques tested, providing definitive state of the art performance on the benchmarks, across all 90 of the experiments in the paper and supplementary material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPAMI.2018.2806446 .


title: Shedding Light on a New Treatment for Diabetic Wound Healing: A Review on Phototherapy

abstract: Impaired wound healing is a common complication associated with diabetes with complex pathophysiological underlying mechanisms and often necessitates amputation. With the advancement in laser technology, irradiation of these wounds with low-intensity laser irradiation (LILI) or phototherapy, has shown a vast improvement in wound healing. At the correct laser parameters, LILI has shown to increase migration, viability, and proliferation of diabetic cells in vitro; there is a stimulatory effect on the mitochondria with a resulting increase in adenosine triphosphate (ATP). In addition, LILI also has an anti-inflammatory and protective effect on these cells. In light of the ever present threat of diabetic foot ulcers, infection, and amputation, new improved therapies and the fortification of wound healing research deserves better prioritization. In this review we look at the complications associated with diabetic wound healing and the effect of laser irradiation both in vitro and in vivo in diabetic wound healing.


title: Sleep function and synaptic homeostasis

abstract: Summary This paper reviews a novel hypothesis about the functions of slow wave sleep—the synaptic homeostasis hypothesis. According to the hypothesis, plastic processes occurring during wakefulness result in a net increase in synaptic strength in many brain circuits. The role of sleep is to downscale synaptic strength to a baseline level that is energetically sustainable, makes efficient use of gray matter space, and is beneficial for learning and memory. Thus, sleep is the price we have to pay for plasticity, and its goal is the homeostatic regulation of the total synaptic weight impinging on neurons. The hypothesis accounts for a large number of experimental facts, makes several specific predictions, and has implications for both sleep and mood disorders.


title: Transsexualism: A Different Viewpoint to Brain Changes

abstract: Transsexualism refers to a condition or belief which results in gender dysphoria in individuals and makes them insist that their biological gender is different from their psychological and experienced gender. Although the etiology of gender dysphoria (or transsexualism) is still unknown, different neuroimaging studies show that structural and functional changes of the brain result from this sexual incongruence. The question here is whether these reported changes form part of the etiology of transsexualism or themselves result from transsexualism culture, behaviors and lifestyle. Responding to this question can be more precise by consideration of cultural neuroscience concepts, particularly the culture-behavior-brain (CBB) loop model and the interactions between behavior, culture and brain. In this article, we first review the studies on the brain of transgender people and then we will discuss the validity of this claim based on the CBB loop model. In summary, transgender individuals experience change in lifestyle, context of beliefs and concepts and, as a result, their culture and behaviors. Given the close relationship and interaction between culture, behavior and brain, the individual's brain adapts itself to the new condition (culture) and concepts and starts to alter its function and structure.


title: Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network

abstract: Device-to-device (D2D) communication is becoming an increasingly important technology in future networks with the climbing demand for local services. For instance, resource sharing in the D2D network features ubiquitous availability, flexibility, low latency and low cost. However, these features also bring along challenges when building a satisfactory resource sharing system in the D2D network. Specifically, user mobility is one of the top concerns for designing a cooperative D2D computational resource sharing system since mutual communication may not be stably available due to user mobility. A previous endeavour has demonstrated and proven how connectivity can be incorporated into cooperative task scheduling among users in the D2D network to effectively lower average task execution time. There are doubts about whether this type of task scheduling scheme, though effective, presents fairness among users. In other words, it can be unfair for users who contribute many computational resources while receiving little when in need. In this paper, we propose a novel blockchain-based credit system that can be incorporated into the connectivity-aware task scheduling scheme to enforce fairness among users in the D2D network. Users’ computational task cooperation will be recorded on the public blockchain ledger in the system as transactions, and each user’s credit balance can be easily accessible from the ledger. A supernode at the base station is responsible for scheduling cooperative computational tasks based on user mobility and user credit balance. We investigated the performance of the credit system, and simulation results showed that with a minor sacrifice of average task execution time, the level of fairness can obtain a major enhancement.


title: Robust visual robot localization across seasons using network flows

abstract: Image-based localization is an important problem in robotics and an integral part of visual mapping and navigation systems. An approach to robustly match images to previously recorded ones must be able to cope with seasonal changes especially when it is supposed to work reliably over long periods of time. In this paper, we present a novel approach to visual localization of mobile robots in outdoor environments, which is able to deal with substantial seasonal changes. We formulate image matching as a minimum cost flow problem in a data association graph to effectively exploit sequence information. This allows us to deal with nonmatching image sequences that result from temporal occlusions or from visiting new places. We present extensive experimental evaluations under substantial seasonal changes. Our approach achieves accurate matching across seasons and outperforms existing state-of-the-art methods such as FABMAP2 and SeqSLAM.


title: INRIASAC: Simple Hypernym Extraction Methods

abstract: Given a set of terms from a given domain, how can we structure them into a taxonomy without manual intervention? This is the task 17 of SemEval 2015. Here we present our simple taxonomy structuring techniques which, despite their simplicity, ranked first in this 2015 benchmark. We use large quantities of text (English Wikipedia) and simple heuristics such as term overlap and document and sentence co-occurrence to produce hypernym lists. We describe these techniques and pre-sent an initial evaluation of results.


title: Photoplethysmogram intensity ratio: A potential indicator for improving the accuracy of PTT-based cuffless blood pressure estimation

abstract: The most commonly used method for cuffless blood pressure (BP) measurement is using pulse transit time (PTT), which is based on Moens-Korteweg (M-K) equation underlying the assumption that arterial geometries such as the arterial diameter keep unchanged. However, the arterial diameter is dynamic which varies over the cardiac cycle, and it is regulated through the contraction or relaxation of the vascular smooth muscle innervated primarily by the sympathetic nervous system. This may be one of the main reasons that impair the BP estimation accuracy. In this paper, we propose a novel indicator, the photoplethysmogram (PPG) intensity ratio (PIR), to evaluate the arterial diameter change. The deep breathing (DB) maneuver and Valsalva maneuver (VM) were performed on five healthy subjects for assessing parasympathetic and sympathetic nervous activities, respectively. Heart rate (HR), PTT, PIR and BP were measured from the simultaneously recorded electrocardiogram (ECG), PPG, and continuous BP. It was found that PIR increased significantly from inspiration to expiration during DB, whilst BP dipped correspondingly. Nevertheless, PIR changed positively with BP during VM. In addition, the spectral analysis revealed that the dominant frequency component of PIR, HR and SBP, shifted significantly from high frequency (HF) to low frequency (LF), but not obvious in that of PTT. These results demonstrated that PIR can be potentially used to evaluate the smooth muscle tone which modulates arterial BP in the LF range. The PTT-based BP measurement that take into account the PIR could therefore improve its estimation accuracy.


title: Neural Correlates of True Memory, False Memory, and Deception

abstract: We used functional magnetic resonance imaging (fMRI) to determine whether neural activity can differentiate between true memory, false memory, and deception. Subjects heard a series of semantically related words and were later asked to make a recognition judgment of old words, semantically related nonstudied words (lures for false recognition), and unrelated new words. They were also asked to make a deceptive response to half of the old and unrelated new words. There were 3 main findings. First, consistent with the notion that executive function supports deception, 2 types of deception (pretending to know and pretending not to know) recruited prefrontal activity. Second, consistent with the sensory reactivation hypothesis, the difference between true recognition and false recognition was found in the left temporoparietal regions probably engaged in the encoding of auditorily presented words. Third, the left prefrontal cortex was activated during pretending to know relative to correct rejection and false recognition, whereas the right anterior hippocampus was activated during false recognition relative to correct rejection and pretending to know. These findings indicate that fMRI can detect the difference in brain activity between deception and false memory despite the fact that subjects respond with “I know” to novel events in both processes.


title: Views: a way for pattern matching to cohabit with data abstraction

abstract: Pattern matching and data abstraction are important concepts in designing programs, but they do not fit well together. Pattern matching depends on making public a free data type representation, while data abstraction depends on hiding the representation. This paper proposes the views mechanism as a means of reconciling this conflict. A view allows any type to be viewed as a free data type, thus combining the clarity of pattern matching with the efficiency of data abstraction.


title: Efficient feature selection filters for high-dimensional data

abstract: Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be computationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10^5 features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.


title: Online Learning Of Approximate Dependency Parsing Algorithms

abstract: In this paper we extend the maximum spanning tree (MST) dependency parsing framework of McDonald et al. (2005c) to incorporate higher-order feature representations and allow dependency structures with multiple parents per word. We show that those extensions can make the MST framework computationally intractable, but that the intractability can be circumvented with new approximate parsing algorithms. We conclude with experiments showing that discriminative online learning using those approximate algorithms achieves the best reported parsing accuracy for Czech and Danish.


title: Instantaneous Decentralized Poker

abstract: We present efficient protocols for amortized secure multiparty computation with penalties and secure cash distribution, of which poker is a prime example. Our protocols have an initial phase where the parties interact with a cryptocurrency network, that then enables them to interact only among themselves over the course of playing many poker games in which money changes hands. The high efficiency of our protocols is achieved by harnessing the power of stateful contracts. Compared to the limited expressive power of Bitcoin scripts, stateful contracts enable richer forms of interaction between standard secure computation and a cryptocurrency. We formalize the stateful contract model and the security notions that our protocols accomplish, and provide proofs using the simulation paradigm. Moreover, we provide a reference implementation in Ethereum/Solidity for the stateful contracts that our protocols are based on. We also adopt our off-chain cash distribution protocols to the special case of stateful duplex micropayment channels, which are of independent interest. In comparison to Bitcoin based payment channels, our duplex channel implementation is more efficient and has additional features.


title: Multipath Sparse Coding Using Hierarchical Matching Pursuit

abstract: Complex real-world signals, such as images, contain discriminative structures that differ in many aspects including scale, invariance, and data channel. While progress in deep learning shows the importance of learning features through multiple layers, it is equally important to learn features through multiple paths. We propose Multipath Hierarchical Matching Pursuit (M-HMP), a novel feature learning architecture that combines a collection of hierarchical sparse features for image classification to capture multiple aspects of discriminative structures. Our building blocks are MI-KSVD, a codebook learning algorithm that balances the reconstruction error and the mutual incoherence of the codebook, and batch orthogonal matching pursuit (OMP), we apply them recursively at varying layers and scales. The result is a highly discriminative image representation that leads to large improvements to the state-of-the-art on many standard benchmarks, e.g., Caltech-101, Caltech-256, MITScenes, Oxford-IIIT Pet and Caltech-UCSD Bird-200.


title: Data rate bound for mmWave hybrid beamforming systems with subarrays

abstract: In this paper, we consider single-link mmWave transmission. We investigate the usual hybrid beamforming architecture under an additional constraint on the analog precoding stage. Namely, for practical reasons only partial interconnection between the antennas and the RF chains is allowed. We present a novel upper bound for spectral efficiency under this architecture constraint. We evaluate the tightness of the bound by comparing it to the rate achievable with a proposed practical scheme. Finally, we compare the bound with the channel capacity.


title: License Plate Extraction in Low Resolution Video

abstract: Extensive study has been conducted in the detection of license plate for the applications in intelligent transportation system (ITS). However, these results are all based on images acquired at a resolution of 640 times 480. In this paper, a new method is proposed to extract license plate from the surveillance video which is shot at lower resolution (320 times 240) as well as degraded by video compression. Morphological operations of bottom-hat and morphology gradient are utilized to detect the LP candidates, and effective schemes are applied to select the correct one. The average rates of correct extraction and false alarms are 96.62% and 1.77%, respectively, based on the experiments using more than four hours of video. The experimental results demonstrate the effectiveness and robustness of the proposed method


Deep-dense Conditional Random Fields for Object Co-segmentation


title: Filling holes in complex surfaces using volumetric diffusion

abstract: We address the problem of building watertight 3D models from surfaces that contain holes - for example, sets of range scans that observe most but not all of a surface. We specifically address situations in which the holes are too geometrically and topologically complex to fill using triangulation algorithms. Our solution begins by constructing a signed distance function, the zero set of which defines the surface. Initially, this function is defined only in the vicinity of observed surfaces. We then apply a diffusion process to extend this function through the volume until its zero set bridges whatever holes may be present. If additional information is available, such as known-empty regions of space inferred from the lines of sight to a 3D scanner, it can be incorporated into the diffusion process. Our algorithm is simple to implement, is guaranteed to produce manifold non-interpenetrating surfaces, and is efficient to run on large datasets because computation is limited to areas near holes.


title: Robust regression methods for computer vision: A review

abstract: Regression analysis (fitting a model to noisy data) is a basic technique in computer vision, Robust regression methods that remain reliable in the presence of various types of noise are therefore of considerable importance. We review several robust estimation techniques and describe in detail the least-median-of-squares (LMedS) method. The method yields the correct result even when half of the data is severely corrupted. Its efficiency in the presence of Gaussian noise can be improved by complementing it with a weighted least-squares-based procedure. The high time-complexity of the LMedS algorithm can be reduced by a Monte Carlo type speed-up technique. We discuss the relationship of LMedS with the RANSAC paradigm and its limitations in the presence of noise corrupting all the data, and we compare its performance with the class of robust M-estimators. References to published applications of robust techniques in computer vision are also given.


title: On Random Weights and Unsupervised Feature Learning

abstract: Recently two anomalous results in the literature have shown that certain feature learning architectures can yield useful features for object recognition tasks even with untrained, random weights. In this paper we pose the question: why do random weights sometimes do so well? Our answer is that certain convolutional pooling architectures can be inherently frequency selective and translation invariant, even with random weights. Based on this we demonstrate the viability of extremely fast architecture search by using random weights to evaluate candidate architectures, thereby sidestepping the time-consuming learning process. We then show that a surprising fraction of the performance of certain state-of-the-art methods can be attributed to the architecture alone.


title: Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps

abstract: Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator.


title: TrainingPal: An Algorithm for Recognition and Counting Popular Exercises Using Smartphone Sensors

abstract: Nowadays, most of the smartphones come with 3D-accelerometer, magnetometer, and gyroscope. There is a growing interest in utilizing these sensors for monitoring and tracking exercises. In this paper, we proposed a framework, called TrainingPal, for recognizing five types of cardio exercises (i.e. walking, running, using elliptical machine, rowing, and jumping jack) and five types of resistance exercises (i.e. squat, lunge, situp, push-up, and bench dip). The TrainingPal is also capable of counting number of repetition of each exercise. To train and test the TrainingPal, data was collected by utilizing the built-in accelerometer, magnetometer and gyroscope of Samsung Galaxy S7 edge. Using an armband, the smartphone was attached to the outer side of arm approximately 10 to 12 cm below the shoulder. Leave-one-subject-out cross validation was used to avoid overfitting of the TrainingPal to the study participants. For recognition of different types of exercises, an overall accuracy of 91.71% was achieved. The accuracy was the highest for the recognition of push-ups (100%) and the lowest for the recognition of bench dips (60.33%). For counting the repetitions of the exercises, the TrainingPal achieved an accuracy above 90% for all types of exercises. In conclusion, the proposed framework can be used for tracking and recognizing the popular exercises included in this study. The framework could be potentially extended to other types of exercises and the data collected using other wearable devices.


title: Introduction to Industrial Control Networks

abstract: An industrial control network is a system of interconnected equipment used to monitor and control physical equipment in industrial environments. These networks differ quite significantly from traditional enterprise networks due to the specific requirements of their operation. Despite the functional differences between industrial and enterprise networks, a growing integration between the two has been observed. The technology in use in industrial networks is also beginning to display a greater reliance on Ethernet and web standards, especially at higher levels of the network architecture. This has resulted in a situation where engineers involved in the design and maintenance of control networks must be familiar with both traditional enterprise concerns, such as network security, as well as traditional industrial concerns such as determinism and response time. This paper highlights some of the differences between enterprise and industrial networks, presents a brief history of industrial networking, gives a high level explanation of some operations specific to industrial networks, provides an overview of the popular protocols in use and describes current research topics. The purpose of this paper is to serve as an introduction to industrial control networks, aimed specifically at those who have had minimal exposure to the field, but have some familiarity with conventional computer networks.


Dynamic and integrative approaches to understanding pathogen spillover


title: Population, economy and society in the Chassey-Cortaillod-Lagozza cultures

abstract: Abstract This paper estimates numbers of inhabitants for 36 sites of the Chassey‐Cortaillod‐Lagozza cultures, from a variety of data, of which house size is felt to be the most useful. A majority of the sites were probably occupied by 10 to 250 persons. Faunal remains are used both to calculate possible population size and to give information about the economy. The importance of domesticated animals is most marked in the Chasseen area, with either sheep or cattle being most abundant. Cortaillod sites give the richest information about cereals and plants used. The variety of economic responses and the relatively small populations suggest that pre‐tribal and tribal types of social organization may have been present in Southern France, Switzerland and North Italy from about 3800 to 2500 B.C. (radiocarbon dating).


title: A Study of Cavitation Activity in Ex vivo Tissue Exposed to High Intensity Focused Ultrasound

abstract: Cavitation is often avoided in Focused Ultrasound Surgery (FUS or HIFU) because it can render lesion formation unpredictable. However, cavitation is known to enhance heating. Emissions used as indicators for cavitation activity in ex vivo tissue are not fully understood. This study investigates a wide range of simultaneous acoustic emissions and other potential indicators of cavitation activity.A high frequency (⩽50MHz) data acquisition system is used to detect cavitation ex vivo. The passive cavitation detector (PCD) used is a broadband (0.1–10MHz) cavitation sensor. Its broadband nature allows simultaneous measurement of subharmonics, superharmonics and broadband emissions, all potential indicators of either inertial or both types of cavitation. The electrical impedance change of the transducer (1.69MHz, 15cm focal length, 1.79f‐number), caused by backscattered ultrasound, has been monitored. Low frequency acoustic signals (<100kHz) have been recorded using a hydrophone (Reson TC4013, 1Hz–170kHz).The ul...


title: Real-time edge segment detection with Edge Drawing algorithm

abstract: Edge Drawing (ED) is an edge detection method that works by computing a set of anchor points, which are most likely to be edge elements, and linking them with a predefined set of rules which we call smart routing. ED can thus obtain one-pixel wide, contiguous and smooth edges in real-time. In most cases, obtaining the edges in segment form (i.e. blob of connected edgels) is desired. Because edge segments also include the connectivity information among the pixels, they contain useful features about the geometric shape of the detected edges. In contrast to conventional detectors, ED is able to output the resulting edge map as vectorial data, i.e., array of edge segments instead of a 2D binary image. In this work, this capability of ED is explained and discussed with examples.


title: Opening the black box - data driven visualization of neural networks

abstract: Artificial neural networks are computer software or hardware models inspired by the structure and behavior of neurons in the human nervous system. As a powerful learning tool, increasingly neural networks have been adopted by many large-scale information processing applications but there is no a set of well defined criteria for choosing a neural network. The user mostly treats a neural network as a black box and cannot explain how learning from input data was done nor how performance can be consistently ensured. We have experimented with several information visualization designs aiming to open the black box to possibly uncover underlying dependencies between the input data and the output data of a neural network. In this paper, we present our designs and show that the visualizations not only help us design more efficient neural networks, but also assist us in the process of using neural networks for problem solving such as performing a classification task.


title: Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm

abstract: The real and the virtual worlds are growing speedily and closely to form the Internet of Things (IoT). In fact, IoT has stimulated the factories and the governments to launch an evolutionary journey toward the fourth industrial revolution called Industry 4.0. Industrial production of the new era will be highly flexible in production volume and customization, extensive integration between customers, companies, and suppliers, and above all sustainable. Reviewing and analyzing the current initiatives and related studies of the smart factories/Industry 4.0, this paper presents a reference architecture for IoT-based smart factories, defines the main characteristics of such factories with a focus on the sustainability perspectives. And then it proposes an approach for energy management in smart factories based on the IoT paradigm: a guideline and expected benefits are discussed and presented.


Efficient Mining of Regional Movement Patterns in Semantic Trajectories


title: Hyperdimensional Computing for Blind and One-Shot Classification of EEG Error-Related Potentials

abstract: The mathematical properties of high-dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred to as “hypervectors,” is a brain-inspired alternative to computing with numbers. HD computing is characterized by generality, scalability, robustness, and fast learning, making it a prime candidate for utilization in application domains such as brain–computer interfaces. We describe the use of HD computing to classify electroencephalography (EEG) error-related potentials for noninvasive brain–computer interfaces. Our algorithm naturally encodes neural activity recorded from 64 EEG electrodes to a single temporal–spatial hypervector without requiring any electrode selection process. This hypervector represents the event of interest, can be analyzed to identify the most discriminative electrodes, and is used for recognition of the subject’s intentions. Using the full set of training trials, HD computing achieves on average 5% higher single-trial classification accuracy compared to a conventional machine learning method on this task (74.5% vs. 69.5%) and offers further advantages: (1) Our algorithm learns fast: using only 34% of training trials it achieves an average accuracy of 70.5%, surpassing the conventional method. (2) Conventional method requires prior domain expert knowledge, or a separate process, to carefully select a subset of electrodes for a subsequent preprocessor and classifier, whereas our algorithm blindly uses all 64 electrodes, tolerates noises in data, and the resulting hypervector is intrinsically clustered into HD space; in addition, most preprocessing of the electrode signal can be eliminated while maintaining an average accuracy of 71.7%.


title: A novel and robust method for automatic license plate recognition system based on pattern recognition

abstract: In this paper, for finding the place of plate, a real time and fast method is expressed. In our suggested method, the image is taken to HSV color space; then, it is broken into blocks in a stable size. In frequent process, each block, in special pattern is probed. With the appearance of pattern, its neighboring blocks according to geometry of plate as a candidate are considered and increase blocks, are omitted. This operation is done for all of the uncontrolled blocks of images. First, all of the probable candidates are exploited; then, the place of plate is obtained among exploited candidates as density and geometry rate. In probing every block, only its lip pixel is studied which consists 23.44% of block area. From the features of suggestive method, we can mention the lack of use of expensive operation in image process and its low dynamic that it increases image process speed. This method is examined on the group of picture in background, distance and point of view. The rate of exploited plate reached at 99.33% and character recognition rate achieved 97%.


Booted : An Analysis of a Payment Intervention on a DDoS-for-Hire Service


title: An Ultrawideband Conformal Capsule Antenna With Stable Impedance Matching

abstract: An ultrawideband conformal capsule slot antenna, which has a simple configuration and a stable impedance matching characteristic, is described in this paper. In the past, wideband outer-wall antennas have been proposed for capsule-type applications. However, this paper shows that the outer wall is not a good choice for placing capsule antennas, since such a choice exhibits a high specific absorption ratio, low gain, and low efficiency. Instead, the antenna proposed in this paper is conformal to the inner wall of a capsule shell, and it provides a wide impedance bandwidth ranging from 1.64 to 5.95 GHz (113.6%). Furthermore, the impedance matching remains stable even with a change in the operating environment. Since in a typical application scenario a capsule will move through the digestive system, and thus experience varying environments, a wide bandwidth and stable performance are both very desirable attributes of the antenna, whose design is discussed in this paper. Moreover, the proposed antenna continues to maintain a stable impedance match even when a battery is added inside the capsule, or when there is a change in the battery size and/or its position. Given these advantages, we argue that slot antennas are well suited for incorporation into capsule antennas for the present application.


title: BLAST-SSAHA Hybridization for Credit Card Fraud Detection

abstract: A phenomenal growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. In real life, fraudulent transactions are interspersed with genuine transactions and simple pattern matching is not often sufficient to detect them accurately. Thus, there is a need for combining both anomaly detection as well as misuse detection techniques. In this paper, we propose to use two-stage sequence alignment in which a profile analyzer (PA) first determines the similarity of an incoming sequence of transactions on a given credit card with the genuine cardholder's past spending sequences. The unusual transactions traced by the profile analyzer are next passed on to a deviation analyzer (DA) for possible alignment with past fraudulent behavior. The final decision about the nature of a transaction is taken on the basis of the observations by these two analyzers. In order to achieve online response time for both PA and DA, we suggest a new approach for combining two sequence alignment algorithms BLAST and SSAHA.


title: Focal Loss for Dense Object Detection

abstract: The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Code is at: https://github.com/facebookresearch/Detectron .


title: Data Clustering: 50 Years Beyond K-Means

abstract: The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms in to taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes cluster analysis (unsupervised learning) from discriminant analysis (supervised learning). The objective of cluster analysis is to simply find a convenient and valid organization of the data, not to establish rules for separating future data into categories.


title: Kernel Association for Classification and Prediction: A Survey

abstract: Kernel association (KA) in statistical pattern recognition used for classification and prediction have recently emerged in a machine learning and signal processing context. This survey outlines the latest trends and innovations of a kernel framework for big data analysis. KA topics include offline learning, distributed database, online learning, and its prediction. The structural presentation and the comprehensive list of references are geared to provide a useful overview of this evolving field for both specialists and relevant scholars.


title: Knowledge-sharing motivations affecting R&D employees' acceptance of electronic knowledge repository

abstract: Why would RD (2) altruism was also found to be an important antecedent to perceived ease of use; (3) reputation was the most influential factor of perceived usefulness, and another influential factor of perceived usefulness was reciprocity. Three knowledge-sharing motivations that significantly affect the perceived ease of use were listed as reciprocity, altruism, and reputation, according to the relative importance; (4) altruism plays an important role in explaining the EKR usage intentions for knowledge-sharing both directly and indirectly; and (5) the results were consistent with the propositions of TAM. This study contributes theoretically and empirically to the body of EKR usage research and also has practical implications.