Information flow and cooperative control of vehicle formations

We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability.


Enhanced Visibility of MoS 2 , MoSe 2 , WSe 2 and Black-Phosphorus : Making Optical Identification of 2 D Semiconductors Easier

We explore the use of Si3N4/Si substrates as a substitute of the standard SiO2/Si substrates employed nowadays to fabricate nanodevices based on 2D materials. We systematically study the visibility of several 2D semiconducting materials that are attracting a great deal of interest in nanoelectronics and optoelectronics: MoS2, MoSe2, WSe2 and black-phosphorus. We find that the use of Si3N4/Si substrates provides an increase of the optical contrast up to a 50%–100% and also the maximum contrast shifts towards wavelength values optimal for human eye detection, making optical identification of 2D semiconductors easier. OPEN ACCESS Electronics 2015, 4 848


Scaling Similarity Joins over Tree-Structured Data

Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-ofthe-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this paper, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph. In order to scale up the join, the computed subgraphs are managed in a two-layer index. Our experimental results on real and synthetic data collections show that our approach outperforms the state-of-the-art methods by up to an order of magnitude.


Design and simulation of a Sierpinski carpet fractal antenna for 5G commercial applications

This article presents the design and simulation of a multi-band microstrip patch antenna, using Sierpinski carpet fractal concept. The proposed antenna is designed on a FR-4 substrate with a relative permittivity of 4.4 and excited using a microstrip feed line at the edge of the patch. Sierpinski carpet fractal with iteration-3 is applied to the radiating patch. After the application of fractal, this patch antenna resonates at 14.80 GHz, 17.92 GHz, 23.12 GHz, and 27.92 GHz with impedance bandwidth of 5800 MHz, 3040 MHz, 2960 MHz, and 3840 MHz respectively. With these four bands, this antenna finds application in commercial 5G wireless communications. To illustrate the effectiveness of the fractal concept, four patch antennas are designed independently at those four resonating frequencies and compared with the single fractal antenna in terms of their size and other performance parameters. All the antennas proposed here, are designed using HFSS15 and results are analyzed in terms of return loss, VSWR, gain, and bandwidth.


OpenCV based disease identification of mango leaves

This paper aims in classifying and identifying the diseases of mango leaves for Indian agriculture. K-means algorithm is chosen for the disease segmentation, and the disease classification and identification is carried out using the SVM classifier. Disease identification based on analysis of patches or discoloring of leaf will hold good for some of the plant diseases, but some other diseases which will deform the leaf shape cannot be identified based on the same method. In this case leaf shape based disease identification has to be performed. Based on this analysis two topics are addressed in this research paper. (1) Disease identification using the OpenCV libraries (2) Leaf shape based disease identification. Keywordk-means,Principal Component Analysis (PCA), feature extraction, shape detection, disease identification, Elliptic fourier analysis, Support Vector Machine(SVM), Artificial Neural Network (ANN)


Assessing the benefits of computational offloading in mobile-cloud applications

This paper presents the results of a formative study conducted to determine the effects of computation offloading in mobile applications by comparing “application performance” (chiefly energy consumption and response time). The study examined two general execution scenarios: (1) computation is performed locally on a mobile device, and (2) when it is offloaded entirely to the cloud. The study also carefully considered the underlying network characteristics as an important factor affecting the performance. More specifically, we refactored two mobile applications to offload their computationally intensive functionality to execute in the cloud. We then profiled these applications under different network conditions, and carefully measured “application performance” in each case. The results were not as conclusive as we had expected. On fast networks, offloading is almost always beneficial. However, on slower networks, the offloading cost-benefit analysis is not as clear cut. The characteristics of the data transferred between the mobile device and the cloud may be a deciding factor in determining whether offloading a computation would improve performance.


A Batteryless Sensor ASIC for Implantable Bio-Impedance Applications

The measurement of the biological tissue's electrical impedance is an active research field that has attracted a lot of attention during the last decades. Bio-impedances are closely related to a large variety of physiological conditions; therefore, they are useful for diagnosis and monitoring in many medical applications. Measuring living tissues, however, is a challenging task that poses countless technical and practical problems, in particular if the tissues need to be measured under the skin. This paper presents a bio-impedance sensor ASIC targeting a battery-free, miniature size, implantable device, which performs accurate 4-point complex impedance extraction in the frequency range from 2 kHz to 2 MHz. The ASIC is fabricated in 150 nm CMOS, has a size of 1.22 mm × 1.22 mm and consumes 165 μA from a 1.8 V power supply. The ASIC is embedded in a prototype which communicates with, and is powered by an external reader device through inductive coupling. The prototype is validated by measuring the impedances of different combinations of discrete components, measuring the electrochemical impedance of physiological solution, and performing ex vivo measurements on animal organs. The proposed ASIC is able to extract complex impedances with around 1 Ω resolution; therefore enabling accurate wireless tissue measurements.


Skeleton-based Adaptive In-between Generation for Hand-drawn Key Frames

For improving the efficiency of 2D animation production, this paper presents a method to create in-between frames based on hand-drawn key-frames. The outlines of characters or objects on two key-frames are used as inputs. First, a skeleton linkage of the target object is automatically constructed by rasterizing each of input key-frames and then applying a pixel-based skeleton extraction method. Secondary, a pair of skeleton linkages having corresponding structure between the current key-frame and the next key-frame is constructed by applying the stroke matching algorithm. After these processes, motion transitions between the skeleton linkages are generated based on our simulation model. When the in-between frames are created only in the 2D plane, the outlines at in-between frames can be generated by a 2D deformation. In case that the in-between transitions are containing a rotation around an axis which is no perpendicular to the drawing plane, however, a 3D structure is required. For achieving such in-between transitions, our method constructs a 3D structure by inflating 2D mesh based on the input outlines. Finally, the contours from the view-point for the created 3D structure are projected onto the 2D plane during in-between transitions. In our method, we adopt the Photic Extremum Lines (PEL) to extract the 2D contours from the obtained 3D shape. In this way, we achieve the in-between creation containing spatial rotation such as hand-flipping, which has not been achieved by general ways of in-between creation method.


Memory-Efficient Adaptive Optimization for Large-Scale Learning

Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each parameter, thus doubling the memory footprint of the optimizer. In behemoth-size applications, this memory overhead restricts the size of the model being used as well as the number of examples in a mini-batch. We describe a novel, simple, and flexible adaptive optimization method with sublinear memory cost that retains the benefits of per-parameter adaptivity while allowing for larger models and mini-batches. We give convergence guarantees for our method and demonstrate its effectiveness in training very large deep models.


Better Approximation of Betweenness Centrality

Centrality indices are used to classify a graph in important and unimportant vertices. A commonly used index is betweenness centrality which is based on shortest paths. For large graphs it is almost impossible to calculate exact betweenness centrality in appropriate time. The best known algorithm today is based on solving n SSSPs and requires O(nm+ n2 log(n)) time. But in most cases, e.g. on a home computer, resources are limited. Current approximation algorithms extrapolate values by solving only k ≪ n SSSPs. Vertices near the source of a SSSP are often overestimated. We introduce improvements which take the distance to the source into account. Euclidean distance between approximation and exact values is reduced by factor 4 with same runtime. Or runtime is 16 times faster with same Euclidean distance in a standard example, the movie actor network. Other real-world networks show similar promising results.


Am I wasting my time organizing email?: a study of email refinding

We all spend time every day looking for information in our email, yet we know little about this refinding process. Some users expend considerable preparatory effort creating complex folder structures to promote effective refinding. However modern email clients provide alternative opportunistic methods for access, such as search and threading, that promise to reduce the need to manually prepare. To compare these different refinding strategies, we instrumented a modern email client that supports search, folders, tagging and threading. We carried out a field study of 345 long-term users who conducted over 85,000 refinding actions. Our data support opportunistic access. People who create complex folders indeed rely on these for retrieval, but these preparatory behaviors are inefficient and do not improve retrieval success. In contrast, both search and threading promote more effective finding. We present design implications: current search-based clients ignore scrolling, the most prevalent refinding behavior, and threading approaches need to be extended.


Partially supervised clustering for image segmentation

All clustering algorithms process unlabeled data and, consequently, suffer from two problems: (P1) choosing and validating the correct number of clusters and (P2) insuring that algorithmic labels correspond to meaningful physical labels. Clustering algorithms such as hard and fuzzy c-means, based on optimizing sums of squared errors objective functions, suffer from a third problem: (P3) a tendency to recommend solutions that equalize cluster populations. The semi-supervised c-means algorithms introduced in this paper attempt to overcome these three problems for problem domains where a few data from each class can be labeled. Segmentation of magnetic resonance images is a problem of this type and we use it to illustrate the new algorithm. Our examples show that the semi-supervised approach provides MRI segmentations that are superior to ordinary fuzzy c-means and to the crisp k-nearest neighbor rule and further, that the new method ameliorates (P1)-(P3). Cluster analysis Fuzzy c-means Partial supervision Image segmentation Magnetic resonance images


Design and Implementation of a Network Security Model for Cooperative Network

In this paper a design and implementation of a network security model was presented, using routers and firewall. Also this paper was conducted the network security weakness in router and firewall network devices, type of threats and responses to those threats, and the method to prevent the attacks and hackers to access the network. Also this paper provides a checklist to use in evaluating whether a network is adhering to best practices in network security and data confidentiality. The main aim of this research is to protect the network from vulnerabilities, threats, attacks, configuration weaknesses and security policy weaknesses. KeywordsNetwork security, network management, threats, security policy Received December 17, 2008; Accepted March 2 2009


Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger questionanswer pair corpus and from pretrained word embeddings.


The Dialog State Tracking Challenge

In a spoken dialog system, dialog state tracking deduces information about the user’s goal as the dialog progresses, synthesizing evidence such as dialog acts over multiple turns with external data sources. Recent approaches have been shown to overcome ASR and SLU errors in some applications. However, there are currently no common testbeds or evaluation measures for this task, hampering progress. The dialog state tracking challenge seeks to address this by providing a heterogeneous corpus of 15K human-computer dialogs in a standard format, along with a suite of 11 evaluation metrics. The challenge received a total of 27 entries from 9 research groups. The results show that the suite of performance metrics cluster into 4 natural groups. Moreover, the dialog systems that benefit most from dialog state tracking are those with less discriminative speech recognition confidence scores. Finally, generalization is a key problem: in 2 of the 4 test sets, fewer than half of the entries out-performed simple baselines. 1 Overview and motivation Spoken dialog systems interact with users via natural language to help them achieve a goal. As the interaction progresses, the dialog manager maintains a representation of the state of the dialog in a process called dialog state tracking (DST). For example, in a bus schedule information system, the dialog state might indicate the user’s desired bus route, origin, and destination. Dialog state tracking is difficult because automatic speech ∗Most of the work for the challenge was performed when the second and third authors were with Honda Research Institute, Mountain View, CA, USA recognition (ASR) and spoken language understanding (SLU) errors are common, and can cause the system to misunderstand the user’s needs. At the same time, state tracking is crucial because the system relies on the estimated dialog state to choose actions – for example, which bus schedule information to present to the user. Most commercial systems use hand-crafted heuristics for state tracking, selecting the SLU result with the highest confidence score, and discarding alternatives. In contrast, statistical approaches compute scores for many hypotheses for the dialog state (Figure 1). By exploiting correlations between turns and information from external data sources – such as maps, bus timetables, or models of past dialogs – statistical approaches can overcome some SLU errors. Numerous techniques for dialog state tracking have been proposed, including heuristic scores (Higashinaka et al., 2003), Bayesian networks (Paek and Horvitz, 2000; Williams and Young, 2007), kernel density estimators (Ma et al., 2012), and discriminative models (Bohus and Rudnicky, 2006). Techniques have been fielded which scale to realistically sized dialog problems and operate in real time (Young et al., 2010; Thomson and Young, 2010; Williams, 2010; Mehta et al., 2010). In end-to-end dialog systems, dialog state tracking has been shown to improve overall system performance (Young et al., 2010; Thomson and Young, 2010). Despite this progress, direct comparisons between methods have not been possible because past studies use different domains and system components, for speech recognition, spoken language understanding, dialog control, etc. Moreover, there is little agreement on how to evaluate dialog state tracking. Together these issues limit progress in this research area. The Dialog State Tracking Challenge (DSTC) provides a first common testbed and evaluation


Partially observable Markov decision processes for spoken dialog systems

In a spoken dialog system, determining which action a machine should take in a given situation is a difficult problem because automatic speech recognition is unreliable and hence the state of the conversation can never be known with certainty. Much of the research in spoken dialog systems centres on mitigating this uncertainty and recent work has focussed on three largely disparate techniques: parallel dialog state hypotheses, local use of confidence scores, and automated planning. While in isolation each of these approaches can improve action selection, taken together they currently lack a unified statistical framework that admits global optimization. In this paper we cast a spoken dialog system as a partially observable Markov decision process (POMDP). We show how this formulation unifies and extends existing techniques to form a single principled framework. A number of illustrations are used to show qualitatively the potential benefits of POMDPs compared to existing techniques, and empirical results from dialog simulations are presented which demonstrate significant quantitative gains. Finally, some of the key challenges to advancing this method – in particular scalability – are briefly outlined. 2006 Elsevier Ltd. All rights reserved.


Human balance and posture control during standing and walking

The fact that we as humans are bipeds and locomote over the ground with one foot in contact (walking), no feet in contact (running), or both feet in contact (standing) creates a major challenge to our balance control system. Because two-thirds of our body mass is located two-thirds of body height above the ground we are an inherently unstable system unless a control system is continuously acting. The purpose of this review is to introduce the reader to the basic anatomy, mechanics, and neuromuscular control that is seen in the current literature related to balance control. The degeneration of the balance control system in the elderly and in many pathologies has forced researchers and clinicians to understand more about how the system works and how to quantify its status at any point in time. With the increase in our ageing population and with increased life expectancy of our elderly the importance of maintaining mobility is becoming ever more critical. Injuries and loss of life due to falls in the elderly is a very major factor facing them. Table 1 compares the deaths due to falls with the deaths due to motor vehicle accidents (MVA) as a function of age as reported by Statistics Canada in 1991. The startling fact is that the deaths due to falls in the SO+ population is almost as high as the MVA deaths in the accident-prone l5-29-year group. However, when we look at the deaths per 100 000 for these two causes the figures are even more startling. The young 15-29-year-old group had 21.5 MVA deaths per 100 000 while the elderly deaths


Math Anxiety : Personal , Educational , and Cognitive Consequences

Highly math-anxious individuals are characterized by a strong tendency to avoid math, which ultimately undercuts their math competence and forecloses important career paths. But timed, on-line tests reveal math-anxiety effects on w h o l e-n u m b e r a r i t h m e t i c p r o b l e m s (e. g. , 4 6 ϩ 2 7) , whereas achievement tests show no competence differences. Math anxiety disrupts cognitive processing by compromising ongoing activity in working memory. Although the causes of math anxiety are undetermined, some teaching styles are implicated as risk factors. We need research on the origins of math anxiety and on its " signature " in brain activity , to examine both its emo-t i o n a l a n d i t s c o g n i t i v e components. My graduate assistant recently told me about a participant he had tested in the lab. She exhibited increasing discomfort and nervousness as the testing session progressed , eventually becoming so distraught that she burst into tears. My assistant remarked that many of our participants show some un-ease or apprehension during test-ing—trembling hands, nervous laughter, and so forth. Many ask, defensively, if their performance says anything about their overall intelligence. These occasionally extreme emotional reactions are not triggered by deliberately provocative procedures—there are no personally sensitive questions or intentional manipulations of stress. Instead, we merely ask college adults to solve elementary-school arithmetic problems, such as 46 ϩ 18 ϭ ? and 34 Ϫ 19 ϭ ? The reactions are obvious symptoms of anxiety, in this case math anxiety induced by ordinary arithmetic problems presented in timed tasks. On the one hand, it is almost unbelievable that tests on such fundamental topics can be so upsetting ; knowing that 15 Ϫ 8 ϭ 7 ought to be as basic as knowing how to spell " cat. " On the other hand, U.S. culture abounds with attitudes that foster math anxiety: Math is thought to be inherently difficult (as Barbie dolls used to say, " Math class is hard "), aptitude is considered far more important than effort (Geary, 1994, chap. 7), and being good at math is considered relatively unimportant, or even optional. In this article, I discuss what has been learned about math anxiety across the past 30 years or so, and suggest …


Enabling Public Verifiability and Data Dynamics for Storage Security

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model [1] by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.


The Effect of Noise on Sofware Engineers' Performance

Background: Noise, dened as an unwanted sound, is one of the commonest factors that could aect people’s performance in their daily work activities. e soware engineering research community has marginally investigated the eects of noise on soware engineers’ performance. Aims: We studied if noise aects soware engineers’ performance in: (i) comprehending functional requirements and (ii) xing faults in source code. Method: We conducted two experiments with nal-year undergraduate students in Computer Science. In the rst experiment, we asked 55 students to comprehend functional requirements exposing them or not to noise, while in the second experiment 42 students were asked to x faults in Java code. Results: e participants in the second experiment, when exposed to noise, had signicantly worse performance in xing faults in source code. On the other hand, we did not observe any statistically signicant dierence in the rst experiment. Conclusions: Fixing faults in source code seems to be more vulnerable to noise than comprehending functional requirements.


Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training

Skill acquisition requires distributed learning both within (online) and across (offline) days to consolidate experiences into newly learned abilities. In particular, piloting an aircraft requires skills developed from extensive training and practice. Here, we tested the hypothesis that transcranial direct current stimulation (tDCS) can modulate neuronal function to improve skill learning and performance during flight simulator training of aircraft landing procedures. Thirty-two right-handed participants consented to participate in four consecutive daily sessions of flight simulation training and received sham or anodal high-definition-tDCS to the right dorsolateral prefrontal cortex (DLPFC) or left motor cortex (M1) in a randomized, double-blind experiment. Continuous electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were collected during flight simulation, n-back working memory, and resting-state assessments. tDCS of the right DLPFC increased midline-frontal theta-band activity in flight and n-back working memory training, confirming tDCS-related modulation of brain processes involved in executive function. This modulation corresponded to a significantly different online and offline learning rates for working memory accuracy and decreased inter-subject behavioral variability in flight and n-back tasks in the DLPFC stimulation group. Additionally, tDCS of left M1 increased parietal alpha power during flight tasks and tDCS to the right DLPFC increased midline frontal theta-band power during n-back and flight tasks. These results demonstrate a modulation of group variance in skill acquisition through an increasing in learned skill consistency in cognitive and real-world tasks with tDCS. Further, tDCS performance improvements corresponded to changes in electrophysiological and blood-oxygenation activity of the DLPFC and motor cortices, providing a stronger link between modulated neuronal function and behavior.


How to gamify software engineering

Software development, like any prolonged and intellectually demanding activity, can negatively affect the motivation of developers. This is especially true in specific areas of software engineering, such as requirements engineering, test-driven development, bug reporting and fixing, where the creative aspects of programming fall short. The developers' engagement might progressively degrade, potentially impacting their work's quality.


A multi-method simulation of a high-frequency bus line

A methodology to model high-frequency bus lines is proposed and a realistic event-simulation model for such a line in the Netherlands is presented. This simulation model helps policy makers to predict changes that have to be made to bus routes and planned travel times before problems occur. With this model, different passenger growth scenarios can be easily evaluated. The model is validated using different key performance indicators, showing that under some model assumptions, it can realistically simulate real-life scenarios. The simulations workings are illustrated by a case study of passenger growth.


The role of mindfulness in a contextual cognitive-behavioral analysis of chronic pain-related suffering and disability

An increasing number of studies consider the specific processes by which distressing sensations, thoughts, and emotional experiences exert their influence on the daily functioning of those who suffer with chronic pain. Clinical methods of mindfulness and the processes that underlie them appear to have clear implications in this area, but have not been systematically investigated to this point in time. The purpose of the present study was to examine mindfulness in relation to the pain, emotional, physical, and social functioning of individuals with chronic pain. The present study included 105 consecutive patients attending a clinical assessment for treatment of chronic pain. Each completed a standardized battery of questionnaires, including a measure of mindfulness, the Mindful Attention Awareness Scale [Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Pers Soc Psychol 2003;84:822-48]. Correlation analyses indicated that mindfulness was unrelated to age, gender, education, or chronicity of pain, but was significantly related to multiple measures of patient functioning. In multiple regression analyses, after controlling for patient background variables, pain intensity, and pain-related acceptance, mindfulness accounted for significant variance in measures of depression, pain-related anxiety; physical, psychosocial, and "other" disability. In each instance greater mindfulness was associated with better functioning. The combined increments of variance explained from acceptance of pain and mindfulness were at least moderate and, in some cases, appeared potentially meaningful. The behavioral processes of mindfulness and their accessibility to scientific study are considered.


Integrative Neuromuscular Training in Youth Athletes. Part II

THE SECOND PART OF THIS REVIEW PROVIDES A FLEXIBLE APPROACH TO INTEGRATIVE NEUROMUSCULAR TRAINING (INT) WITH THE GOALS TO IMPROVE INJURY RESILIENCE AND TO ENHANCE SPORT AND MOTOR PERFORMANCE ABILITIES IN YOUTH POPULATIONS. THE PROPOSED MODEL OF INT IN THIS MANUSCRIPT PRESENTS 6 ESSENTIAL COMPONENTS: DYNAMIC STABILITY (LOWER LIMB AND CORE), STRENGTH, PLYOMETRICS, COORDINATION, SPEED AND AGILITY, AND FATIGUE RESISTANCE. THE DEVELOPMENT OF THESE 6 CAPACITIES ARE INTEGRAL IN ESTABLISHING AN IMPORTANT FOUNDATION BY INITIALLY DEVELOPING FUNDAMENTAL MOVEMENT SKILL COMPETENCY BEFORE BUILDING UPON THESE SKILLS TO ENRICH SPORTS-SPECIFIC AND ACTIVITY-SPECIFIC SKILL SETS. FOR A VIDEO ABSTRACT OF THIS ARTICLE, SEE SUPPLEMENTAL DIGITAL CONTENT 1 (SEE VIDEO, http://links.lww.com/SCJ/A190).


Coping theory and research: past, present, and future.

In this essay in honor of Donald Oken, I emphasize coping as a key concept for theory and research on adaptation and health. My focus will be the contrasts between two approaches to coping, one that emphasizes style—that is, it treats coping as a personality characteristic—and another that emphasizes process—that is, efforts to manage stress that change over time and are shaped by the adaptational context out of which it is generated. I begin with an account of the style and process approaches, discuss their history briefly, set forth the principles of a process approach, describe my own efforts at measurement, and define coping and its functions from a process standpoint. This is followed by a digest of major generalizations that resulted from coping process research. The essay concludes with a discussion of special issues of coping measurement, in particular, the limitations of both coping style and process approaches and how these limitations might be dealt with. There has been a prodigious volume of coping research in the last decade or two, which I can only touch on very selectively. In this essay, I also ignore a host of important developmental issues that have to do with the emergence of coping and its cognitive and motivational bases in infants, as well as a growing literature on whether, how, and why the coping process changes with aging.


Sentiment-specific word embedding for Indonesian sentiment analysis

Word embedding has been proven to improve performance of sentiment classification systems. A new proposed word embedding called sentiment-specific word embedding exploits sentiment information, in order to reach better performance of sentiment analysis systems using word embedding. In this paper, we build Indonesian sentiment-specific word embedding and apply it for sentiment analysis. We compare performance of sentiment classification based on sentiment-specific word embedding with other popular feature representations such as bag of words, Term Frequency-Inverse Document Frequency (TF-IDF), and also generic word embedding. Although our sentiment-specific word embedding models achieve a higher score than the Word2Vec embedding, the lexical representations, bag of words and TF-IDF still gained better performance.


The Airport Ground Movement Problem : Past and Current Research and Future Directions

Determining efficient airport operations is an important and critical problem for airports, airlines, passengers and other stakeholders. Moreover, it is likely to become even more so given the traffic increases which are expected over the next few years. The ground movement problem forms the link between other airside problems, such as arrival sequencing, departure sequencing and gate/stand allocation. This paper provides an overview, categorisation and critical examination of the previous research for ground movement and highlights various important open areas of research. Of particular importance is the question of the integration of various airport operations and their relationships which are considered in this paper.


An Organization Ontology for Enterprise Modelling

The paper presents our exploration into an organization ontology for the TOVE enterprise model. Its primary focus has been in linking structure and behavior through the concept of empowerment. Empowerment is the right of an organization agent to perform status changing actions. This linkage is critical to the unification of enterprise models and their executability.


VL2: a scalable and flexible data center network

To be agile and cost effective, data centers should allow dynamic resource allocation across large server pools. In particular, the data center network should enable any server to be assigned to any service. To meet these goals, we present VL2, a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics. VL2 uses (1) flat addressing to allow service instances to be placed anywhere in the network, (2) Valiant Load Balancing to spread traffic uniformly across network paths, and (3) end-system based address resolution to scale to large server pools, without introducing complexity to the network control plane. VL2's design is driven by detailed measurements of traffic and fault data from a large operational cloud service provider. VL2's implementation leverages proven network technologies, already available at low cost in high-speed hardware implementations, to build a scalable and reliable network architecture. As a result, VL2 networks can be deployed today, and we have built a working prototype. We evaluate the merits of the VL2 design using measurement, analysis, and experiments. Our VL2 prototype shuffles 2.7 TB of data among 75 servers in 395 seconds - sustaining a rate that is 94% of the maximum possible.


Friendlink: Link prediction in social networks via bounded local path traversal

Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the “algorithmic small world hypothesis”. As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our FriendLink algorithm outperforms other approaches in terms of effectiveness and efficiency in both real data sets.


Fast local search and guided local search and their application to British Telecom's workforce scheduling problem

This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecom’s workforce scheduling problem, which is a hard real life problem. The effectiveness of FLS and GLS are demonstrated by the fact that they both out-perform all the methods applied to this problem so far, which include simulated annealing, genetic algorithms and constraint logic programming.


Implementation and Optimization of the Accelerator Based on FPGA Hardware for LSTM Network

Today, artificial neural networks (ANNs) are important machine learning methods which are widely used in a variety of applications. As the emerging field of ANNs, recurrent neural networks (RNNs) are often used for sequencerelated applications. And Long Short-Term Memory (LSTM) is an improved RNN which contains complex computational logic. To achieve high accuracy, researchers always build largescale LSTM networks which are time-consuming and powerconsuming. Thus the acceleration of LSTM networks, low power & energy consumption become the hot issues in today's research. In this paper, we present a hardware accelerator for the LSTM neural network layer based on FPGA Zedboard and use pipeline methods to parallelize the forward computing process. To optimize our implementation, we also use multiple methods including tiled matrix-vector multiplication, binary adder tree, and overlap of computation and data access. Through the acceleration and optimization methods, our accelerator is power-efficient and has a better performance than ARM Cortex A9 processor and Intel Core i5 processor.


Demonstration of MOS capacitor measurement for wafer manufacturing using a Direct Charge Measurement

Direct Charge Measurement (DCM) has a capability to improve the capacitance measurement time in parametric test. Through an actual wafer measurement, we have successfully verified that DCM can measure MOS capacitor much faster than an LCR meter while keeping good correlations for wafer manufacturing.


NB-IoT and LoRA connectivity analysis for M2M/IoT smart grids applications

The evolution of the new M2M/IoT paradigm suggests new applications in several fields. However, the unique characteristics of M2M communications pose different challenges in terms of connectivity and capacity with respect to the conventional telecommunications networks. In particular, the harmful locations where IoT devices could be deployed and the massive number of the devices to be connected require a different approach with respect the traditional wireless communication. In this paper, we analyzed two different technologies expressly devoted the M2M communication: the Long Term Evolution (LTE) based solution, NB-IoT (Narrow Band — Internet of Things), and the LPWAN (Low Power Wide Area Network) based solution, LoRa (Low Range). The analysis has been provided taking into account different propagation scenarios and geographical information of the real MNOs (Mobile Network Operators) sites operating in the territory under test. The aim of this work is to check wireless access method of both cellular and proprietary technologies for the novel IoT Smart Grid applications requirements.


A Survey of Context-Aware Mobile Computing Research

Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity). Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology. Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users. In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context. Through this survey, it is clear that context-aware research is an old but rich area for research. The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality.


Convolutional Neural Networks for Real-Time Beat Tracking: A Dancing Robot Application

In this paper a novel approach that adopts Convolutional Neural Networks (CNN) for the Beat Tracking task is proposed. The proposed architecture involves 2 convolutional layers with the CNN filter dimensions corresponding to time and band frequencies, in order to learn a Beat Activation Function (BAF) from a time-frequency representation. The output of each convolutional layer is computed only over the past values of the previous layer, to enable the computation of the BAF in an online fashion. The output of the CNN is post-processed by a dynamic programming algorithm in combination with a bank of resonators for calculating the salient rhythmic periodicities. The proposed method has been designed to be computational efficient in order to be embedded on a dancing NAO robot application, where the dance moves of the choreography are synchronized with the beat tracking output. The proposed system was submitted to the Signal Processing Cup Challenge 2017 and ranked among the top third algorithms.


Design of a Compact GPS and SDARS Integrated Antenna for Automotive Applications

This letter describes a novel compact solution for integrating a Global Positioning System (GPS) and a Satellite Digital Audio Radio Service (SDARS) antenna in a very small volume to satisfy the requirements of the automotive market. The GPS antenna is a classic small commercial ceramic patch, and the SDARS antenna is designed in order to fit in the reduced space without affecting radiation and band performance of the GPS antenna. The SDARS basic geometry is a square-ring microstrip antenna operating in the frequency range 2.320-2.345 GHz with left-hand circular polarization (LHCP), and it is wrapped around the GPS patch, which receives a right-hand circular polarization (RHCP) signal at 1.575 GHz. The overall volume of the presented integrated antenna solution is only 30 x 30 x 7.6 mm3, rendering it attractive for the automotive market.


A DUAL BAND CIRCULARLY POLARIZED RING ANTENNA BASED ON COMPOSITE RIGHT AND LEFT HANDED METAMATERIALS

Abstract—In this paper a dual band circularly polarized antenna is designed, fabricated, and measured. Based on the concept of composite right and left handed (CRLH) metamaterials, the same phase constants and current distributions on the ring antenna are achieved at two frequency bands. Thus, the antenna has similar radiation patterns at both bands. The circular polarization is implemented by feeding two vertical ports from power dividers that provide equal magnitudes and quadrature phase excitations.


Omnidirectional Circularly Polarized Antenna Utilizing Zeroth-Order Resonance of Epsilon Negative Transmission Line

The omnidirectional circularly polarized (CP) antenna using a circular mushroom structure with curved branches is proposed. The antenna is based on the zeroth-order resonance (ZOR) mode of epsilon negative (ENG) transmission line (TL) to obtain a vertical polarization and an omnidirectional radiation pattern. Also, the horizontal polarization is obtained by the curved branches. The 90° phase difference between two orthogonal polarizations is inherently provided by the zeroth-order resonator. Therefore, the antenna has an omnidirectional CP radiation pattern in the azimuthal plane. In addition, this antenna is planar type and simply designed without a dual feeding structure and 90° phase shifter. The measured average axial ratio and left-hand (LH) CP gain are 2.03 dB and - 0.40 dBic, respectively, in the azimuthal plane.


Gaze Direction based Mobile Application for Quadriplegia Wheelchair Control System

People with quadriplegia recruit the interest of researchers in introducing automated movement systems for adopted special purpose wheelchairs. These systems were introduced for easing the movement of such type of disabled people independently. This paper proposed a comprehensive control system that can control the movement of Quadriplegia wheelchairs using gaze direction and blink detection. The presented system includes two main parts. The first part consists of a smartphone that applies the propose gaze direction detection based mobile application. It then sends the direction commands to the second part via Wi-Fi connection. The second part is a prototype representing the quadriplegia wheelchair that contains robotic car (two-wheel driving car), Raspberry Pi III and ultrasound sensors. The gaze direction commands, sent from the smartphone, are received by the Raspberry Pi for processing and producing the control signals. The ultrasound sensors are fixed at the front and back of the car for performing the emergency stop when obstacles are detected. The proposed system is based on gaze tracking and direction detection without the requirement of calibration with additional sensors and instruments. The obtained results show the superior performance of the proposed system that proves the claim of authors. The accuracy ratio is ranged between 66% and 82% depending on the environment (indoor and outdoor) and surrounding lighting as well as the smart phone type. Keywords—Gaze direction detection; mobile application; obstacle detection; quadriplegia; Raspberry Pi microcomputer


Understanding crowdfunding work: implications for support tools

Crowdfunding is changing the way people realize their work by providing a new way to gain support from a distributed audience. This study seeks to understand the work of crowdfunding project creators in order to inform the design of crowdfunding support tools and systems. We conducted interviews with 30 project creators from three popular crowdfunding platforms in order to understand what tasks are involved and what tools people use to accomplish crowdfunding work. Initial results suggest that project creators carry out three main types of work-preparing the campaign material, marketing the project, and following through with project goals-and have adapted general support tools to facilitate doing this work. From our initial findings, we hope to improve and design future crowdfunding support tools and systems.


Delicate manipulations with compliant mechanism and electrostatic adhesion

Traditional rigid robotic hand manipulator has been used in many field nowadays due to its advantages of large gripping force and stable performance. However, this kind of rigid manipulator is not suitable for gripping fragile objects since it is motorized and force control can be a problem. It is also not suitable to grip object with different shapes since the manipulator is rigid and not compliant. In this study, a novel manipulator with gripping capability is designed and fabricated. The manipulator combines electrostatic adhesion actuation with soft manipulators. The manipulator has high flexibility and can be compliant to different shapes due to the property of the materials. It is very promising to do delicate manipulations in industry field and biomedical field.


A hybrid approach for database intrusion detection at transaction and inter-transaction levels

Nowadays, information plays an important role in the organizations. Sensitive information is often stored within the database. Traditional mechanisms such as encryption, access control, and authentication cannot provide a high level of confidence. Therefore, the existence of Intrusion Detection Systems in the database is a necessity. In this paper, we propose a type of intrusion detection system for detecting attacks in both database transaction level and inter-transaction level (user task level). For this purpose, we propose a detection method at transaction level, which is based on describing the expected transactions within the database applications. Then at inter-transaction level, we propose a detection method that is based on anomaly detection and uses data mining to find dependency and sequence rules. The advantage of this system compared to the previous database intrusion detection systems is that it can detect malicious behaviors in both transaction and inter-transaction levels. Also, it gains advantages of a hybrid method, including specification-based detection and anomaly detection, to minimize both false positive and false negative errors. In order to evaluate the accuracy of the proposed system, some experiments have been done. The experimental evaluation results show high accuracy and effectiveness of the proposed system.


Deep Domain Adaptation in Action Space

In the general settings of supervised learning, human action recognition has been a widely studied topic. The classifiers learned in this setting assume that the training and test data have been sampled from the same underlying probability distribution. However, in most of the practical scenarios, this assumption is not true, resulting in a suboptimal performance of the classifiers. This problem, referred to as Domain Shift, has been extensively studied, but mostly for image/object classification task. In this paper, we investigate the problem of Domain Shift in action videos, an area that has remained under-explored, and propose two new approaches named Action Modeling on Latent Subspace (AMLS) and Deep Adversarial Action Adaptation (DAAA). In the AMLS approach, the action videos in the target domain are modeled as a sequence of points on a latent subspace and adaptive kernels are successively learned between the source domain point and the sequence of target domain points on the manifold. In the DAAA approach, an end-to-end adversarial learning framework is proposed to align the two domains. The action adaptation experiments were conducted using various combinations of multi-domain action datasets, including six common classes of Olympic Sports and UCF50 datasets and all classes of KTH, MSR and our own SonyCam datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.


Parallel Computer Architecture: A Hardware/Software Approach

enter the circuit several times, possibly via different input processors. More elaborate combinatorial considerations than for standard VLSI circuits are needed to show how communication complexity can be used to get lower bounds on multilective VLSI computations. Near the end of the book, Hromkovic presents relations between communication complexity and some complexity measures of sequential computations. The core section introduces a uniform model of one-way communication protocols and shows that the corresponding uniform one-way communication complexity is strongly related to the size of deterministic finite automata. Then he relates communication complexity to the time and space complexity of Turing machines. Finally, he shows how communication complexity can be used to obtain lower bounds on the size and the depth of decisions nees and branching programs.


Sub-Nyquist Radar via Doppler Focusing

We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets sparsely populated in the radar's unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here, we describe a sub-Nyquist sampling and recovery approach called Doppler focusing, which addresses all of these problems: it performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size, which does not increase with increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys a signal-to-noise ratio (SNR) improvement, which scales linearly with P, obtaining good detection performance even at SNR as low as - 25 dB. The recovery is based on the Xampling framework, which allows reduction of the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling radar prototype.


The diffusion of misinformation on social media: Temporal pattern, message, and source

This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temp oral attern, content mutation, and sources of misinformation. We traced the lifecycle of 17 po pular political rumors that circulated on Twitter over 13 months during the 2012 U.S. preside ntial election. Using text analysis based on time series, we found that while false rumors (misi nformation) tend to come back multiple times after the initial publication, true rumors (facts) do not. Rumor resurgence continues, often accompanying textual changes, until the tension aro und the target dissolves. We observed that rumors resurface by partisan news websites that rep ckage the old rumor into news and, gain visibility by influential Twitter users who introdu ce such rumor into the Twittersphere. In this paper, we argue that media scholars should consider the mutability of diffusing information, temporal recurrence of such messages, and the mecha nism by which these messages evolve over time.


Autonomous railway crack detector robot for bangladesh: SCANOBOT

An automatic railway track crack detector system for Bangladesh Railway has been proposed here which aims in building a robot that can detect and analyze any kind of crack on the railway line and send the coordinates of that faulty line to the concerned authority. This robot includes two ultrasonic sensors, GPS, GSM modules, and Arduino Mega based crack detection assembly which is cost effective and robust to facilitate better safety standards in railways. As soon as the robot passed through a crack that might cause the derailment of a train, the ultrasonic sensors sense that and generate a signal. Then this signal is fed into the Arduino Mega. At that point, with the assistance of GSM and GPS modules, an alert SMS consist of the geographic coordinate of that damaged track is sent to the nearby railway authority who can easily take necessary steps to resolve the problem before any major accident occurs. This will save several trains in Bangladesh from an unwanted discontinuity from the rail track.


End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection

Object detection methods usually represent objects through rectangular bounding boxes from which they extract features, regardless of their actual shapes. In this paper, we apply deformations to regions in order to learn representations better fitted to objects. We introduce DP-FCN, a deep model implementing this idea by learning to align parts to discriminative elements of objects in a latent way, i.e. without part annotation. This approach has two main assets: it builds invariance to local transformations, thus improving recognition, and brings geometric information to describe objects more finely, leading to a more accurate localization. We further develop both features in a new model named DP-FCN2.0 by explicitly learning interactions between parts. Alignment is done with an in-network joint optimization of all parts based on a CRF with custom potentials, and deformations are influencing localization through a bilinear product. We validate our models on PASCAL VOC and MS COCO datasets and show significant gains. DP-FCN2.0 achieves state-of-the-art results of 83.3 and 81.2% on VOC 2007 and 2012 with VOC data only.


Suicidal strangulation by double ligature: a case report.

This article reports a self-strangulation suicide in which a double complex group of ligatures was used. Although similar cases have been referenced in forensic literature, this case is notable because of the unusual method used by the victim. A 61-year-old man was found in a locked room with a double elaborate ligature comprising six wire clothes hangers completely encircling the neck and a black rubber band in a double loop. Autopsy also documented parallel superficial cut lesions in proximal forearm interpreted as hesitation marks. It stresses the importance of characteristics such as analysis of number of ligatures, position of the knots, number of knots and turns of ligature marks and the absence of any defense and relevant internal injuries inherent to suicide ligature strangulation cases.


An Evaluation of Distributed Concurrency Control

Increasing transaction volumes have led to a resurgence of interest in distributed transaction processing. In particular, partitioning data across several servers can improve throughput by allowing servers to process transactions in parallel. But executing transactions across servers limits the scalability and performance of these systems. In this paper, we quantify the effects of distribution on concurrency control protocols in a distributed environment. We evaluate six classic and modern protocols in an in-memory distributed database evaluation framework called Deneva, providing an apples-to-apples comparison between each. Our results expose severe limitations of distributed transaction processing engines. Moreover, in our analysis, we identify several protocol-specific scalability bottlenecks. We conclude that to achieve truly scalable operation, distributed concurrency control solutions must seek a tighter coupling with either novel network hardware (in the local area) or applications (via data modeling and semantically-aware execution), or both.


Hierarchical Self-Organization in Genetic programming

This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation onthe-fly. Adaptating the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibilityof approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem’s fitness function. Preliminary empirical results are presented.


Contactless DC Connector Based on GaN LLC Converter for Next-Generation Data Centers

An inductively coupled contactless dc connector has been proposed for the next-generation 380-V dc distribution system in data centers. A LLC resonant dc-dc converter topology with gallium nitride (GaN) power transistors has been applied to realize the short-distance highly efficient contactless power transfer. A prototype of a 1.2-kW 384- to 192-V connector has been fabricated and the conversion efficiency of over 95% with the power density of 8.1 W/cm3 has been confirmed experimentally under 1000-kHz operation. The design consideration has been carried out and the potential to achieve 10.0 W/cm3 has been also shown taking the feature of the GaN power device and the characteristics of the magnetic core material for the transformer into account. The contactless dc connector integrates the functioning of an isolated dc-dc converter into a connector for space saving, and the dc current can be cut off without arc because of the inductive coupling. The proposed connector contributes to realizing a highly efficient, space saving, and reliable future 380-V dc distribution system.


Deep Sentiment Representation Based on CNN and LSTM

Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent neural networks (RNNs) are two mainstream methods of deep learning in document and sentence modeling. In this paper, a model of capturing deep sentiment representation based on CNN and long short-term memory recurrent neural network (LSTM) is proposed. The model uses the pre-trained word vectors as input and employs CNN to gain significant local features of the text, then features are fed to two-layer LSTMs, which can extract context-dependent features and generate sentence representation for sentiment classification. We evaluate the proposed model by conducting a series of experiments on dataset. The experimental results show that the model we designed outperforms existing CNN, LSTM, CNN-LSTM (our implement of one-layer LSTM directly stacked on one-layer CNN) and SVM (support vector machine).


What Do Customers Crave in Mobile 5G?: A survey spotlights four standout factors.

Increasing customer demand coupled with limitations in current fourth-generation (4G) wireless mobile networks and advances toward the next-generation wireless mobile network have all together created a need for us to look at what customers today expect from the upcoming fifth-generation (5G) network. This study was conducted among existing 4G users in the United States, the United Kingdom, Singapore, and Australia. The research was completed in two parts: qualitative and quantitative. The quantitative part was accomplished by performing data collection and selection through surveys and data evaluation by structural equation modeling (SEM) using SMARTPLS.


Multi-target tracking scheme using a track management table for automotive radar systems

In this paper, a multiple target tracking scheme for automotive radars is presented. First, a track table for efficient track management is proposed; then, a track management function is designed using a state machine. The proposed scheme can initiate, confirm, and delete all tracks of multiple targets. Moreover, it can determine whether the detected target is a real target or a ghost track, and a terminated track or a sudden missing track. The simulation results demonstrate that the proposed tracking scheme is processed normally.


Technochange management: using IT to drive organizational change

Using IT in ways that can trigger major organizational changes creates high-risk, potentially high-reward, situations that I call technochange (for technology-driven organizational change). Technochange differs from typical IT projects and from typical organizational change programs and therefore requires a different approach. One major risk in technochange—that people will not use information technology and related work practices—is not thoroughly addressed by the discipline of IT project management, which focuses on project cost, project schedule, and solution functionality. Organizational change management approaches are also generally not effective on their own, because they take as a given the IT ‘‘solutions’’ developed by a technical team. Consequently, the potential for the IT ‘‘solution’’ to be misaligned with important organizational characteristics, such as culture or incentives, is great. Merely combining IT project management and organizational change management approaches does not produce the best results, for two reasons. First, the additive approach does not effectively address the many failure-threatening problems that can arise over the lengthy sequential process of the typical technochange lifecycle. Second, the additive approach is not structured to produce the characteristics of a good technochange solution: a complete intervention consisting of IT and complementary organizational changes, an implementable solution with minimal misfits with the existing organization, and an organization primed to appropriate the potential bene¢ts of the technochange solution. With hard work and care, the combined IT project management plus organizational change approach can be made to work. However, an iterative, incremental approach to implementing technochange can be a better strategy in many situations. The essential characteristic of the technochange prototyping approach is that each phase involves both new IT functionality and related organizational changes, such as redesigned business processes, new performance metrics, and training. Journal of Information Technology (2004) 19, 4–20. doi:10.1057/palgrave.jit.2000002


An efficient FPGA-Based architecture for convolutional neural networks

The goal of this paper is to implement an efficient FPGA-based hardware architectures for the design of fast artificial vision systems. The proposed architecture is capable of performing classification operations of a Convolutional Neural Network (CNN) in realtime. To show the effectiveness of the architecture, some design examples such as hand posture recognition, character recognition, and face recognition are provided. Experimental results show that the proposed architecture is well suited for embedded artificial computer vision systems requiring high portability, high computational speed, and accurate classification.


A BigData approach for classification and prediction of student result using MapReduce

In recent years the amount of data stored in educational database is growing rapidly. The stored database contains hidden information which if used aids improvement of student's performance and behaviour. In this paper predictive modelling approach is used for extracting this hidden information. Data is collected, a predictive model is formulated, predictions are made, and the model is validated as additional data becomes available. The predictive models will help the instructor to understand how well or how poorly the students in his/her class will perform, and hence the instructor can choose proper pedagogical and instructional interventions to enhance student learning outcomes. The implementation is done in Hadoop framework with MapReduce and Revolutionary R Enterprise RRE.


“ Proof-of-Work ” Proves Not to Work version 0 . 1

A frequently proposed method of reducing unsolicited bulk email (“spam”) is for senders to pay for each email they send. Proof-ofwork schemes avoid charging real money by requiring senders to demonstrate that they have expended processing time in solving a cryptographic puzzle. We attempt to determine how difficult that puzzle should be so as to be effective in preventing spam. We analyse this both from an economic perspective, “how can we stop it being cost-effective to send spam”, and from a security perspective, “spammers can access insecure end-user machines and will steal processing cycles to solve puzzles”. Both analyses lead to similar values of puzzle difficulty. Unfortunately, realworld data from a large ISP shows that these difficulty levels would mean that significant numbers of senders of legitimate email would be unable to continue their current levels of activity. We conclude that proof-of-work will not be a solution to the problem of spam.


Strong Injection Locking in Low- $Q$ LC Oscillators: Modeling and Application in a Forwarded-Clock I/O Receiver

A general model for injection-locked LC oscillators (LC-ILOs) is presented that is valid for any tank quality factor and injection strength. Important properties of an ILO such as lock-range, phase shift, bandwidth and response to input jitter are described. An LC-ILO together with a half-rate data sampler is implemented as a forwarded-clock I/O receiver in 45-nm CMOS. A strongly-injected low-Q LC oscillator enables clock deskew across 1UI and rejects high-frequency clock jitter. The complete 27 Gb/s ILO-based data receiver has an overall power efficiency of 1.6 mW/Gb/s.


A 27Gb/s Forwarded-Clock I/O Receiver Using an Injection-Locked LC-DCO in 45nm CMOS

This paper describes a method for both filtering and deskewing a link clock using a differential injection-locked LC-DCO and demonstrates a forwarded-clock data receiver using this technique operating at 27 Gb/s.


Acoustic Modeling for Google Home

This paper describes the technical and system building advances made to the Google Home multichannel speech recognition system, which was launched in November 2016. Technical advances include an adaptive dereverberation frontend, the use of neural network models that do multichannel processing jointly with acoustic modeling, and Grid-LSTMs to model frequency variations. On the system level, improvements include adapting the model using Google Home specific data. We present results on a variety of multichannel sets. The combination of technical and system advances result in a reduction of WER of 8-28% relative compared to the current production system.


Path Reducing Watershed for the GPU

The watershed transform is a popular image segmentation procedure from mathematical morphology used in many applications of computer vision. This paper proposes a novel parallel watershed procedure designed for GPU implementation. Our algorithm constructs paths of steepest descent and reduces these paths into direct pointers to catchment basin minima in logarithmic time, also crucially incorporating successful resolution of plateaux. Three implementation variants and their parameters are analysed through experiments on 2D and 3D images; a comparison against the state-of-the-art shows a runtime improvement of around 30%. For 3D images of 128 megavoxels execution times of approximately 1.5–2 seconds are achieved.


Learning-based approach for online lane change intention prediction

Predicting driver behavior is a key component for Advanced Driver Assistance Systems (ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction. The approach uses the multiclass probabilistic outputs of the Support Vector Machine as an input to the Bayesian filter, and the output of the Bayesian filter is used for the final prediction of lane changes. A lane tracker integrated in a passenger vehicle is used for real-world data collection for the purpose of training and testing. Data from different drivers on different highways were used to evaluate the robustness of the approach. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 1.3 seconds in advance, with a maximum prediction horizon of 3.29 seconds.


On the Role of Seed Lexicons in Learning Bilingual Word Embeddings

A shared bilingual word embedding space (SBWES) is an indispensable resource in a variety of cross-language NLP and IR tasks. A common approach to the SBWES induction is to learn a mapping function between monolingual semantic spaces, where the mapping critically relies on a seed word lexicon used in the learning process. In this work, we analyze the importance and properties of seed lexicons for the SBWES induction across different dimensions (i.e., lexicon source, lexicon size, translation method, translation pair reliability). On the basis of our analysis, we propose a simple but effective hybrid bilingual word embedding (BWE) model. This model (HYBWE) learns the mapping between two monolingual embedding spaces using only highly reliable symmetric translation pairs from a seed document-level embedding space. We perform bilingual lexicon learning (BLL) with 3 language pairs and show that by carefully selecting reliable translation pairs our new HYBWE model outperforms benchmarking BWE learning models, all of which use more expensive bilingual signals. Effectively, we demonstrate that a SBWES may be induced by leveraging only a very weak bilingual signal (document alignments) along with monolingual data.


Beyond traditional DTN routing: social networks for opportunistic communication

This article examines the evolution of routing protocols for intermittently connected ad hoc networks and discusses the trend toward socialbased routing protocols. A survey of current routing solutions is presented, where routing protocols for opportunistic networks are classified based on the network graph employed. The need to capture performance trade-offs from a multi-objective perspective is highlighted.


Modeling and animating eye blinks

Facial animation often falls short in conveying the nuances present in the facial dynamics of humans. In this article, we investigate the subtleties of the spatial and temporal aspects of eye blinks. Conventional methods for eye blink animation generally employ temporally and spatially symmetric sequences; however, naturally occurring blinks in humans show a pronounced asymmetry on both dimensions. We present an analysis of naturally occurring blinks that was performed by tracking data from high-speed video using active appearance models. Based on this analysis, we generate a set of key-frame parameters that closely match naturally occurring blinks. We compare the perceived naturalness of blinks that are animated based on real data to those created using textbook animation curves. The eye blinks are animated on two characters, a photorealistic model and a cartoon model, to determine the influence of character style. We find that the animated blinks generated from the human data model with fully closing eyelids are consistently perceived as more natural than those created using the various types of blink dynamics proposed in animation textbooks.


Emotion Detection From Text Documents

Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for this purpose. At the next step paper also proposed a new architecture for recognizing emotions from text document. Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm. Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.


Honesty on the Streets : A Natural Field Experiment on Newspaper Purchasing

A publisher uses an honor system for selling a newspaper in the street. The customers are supposed to pay, but they can also pay less than the price or not pay at all. We conduct an experiment to study honesty in this market. The results show that appealing to honesty increases payments, whereas reminding the customers of the legal norm has no effect. Furthermore, appealing to honesty does not affect the behavior of the dishonest. These findings suggest that some people have internalized an honesty norm, whereas others have not, and that the willingness to pay to obey the norm differs among individuals. In a follow-up survey study we find that honesty is associated with family characteristics, self-esteem, social connectedness, trust in the legal system, and compliance with tax regulations.


A hybrid edge-cloud architecture for reducing on-demand gaming latency

The cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies.


Explainable , Normative , and Justified Agency

In this paper, we pose a new challenge for AI researchers – to develop intelligent systems that support justified agency. We illustrate this ability with examples and relate it to two more basic topics that are receiving increased attention – agents that explain their decisions and ones that follow societal norms. In each case, we describe the target abilities, consider design alternatives, note some open questions, and review prior research. After this, we return to justified agency, offering a hypothesis about its relation to explanatory and normative behavior. We conclude by proposing testbeds and experiments to evaluate this empirical claim and encouraging other researchers to contribute to this crucial area. 1 Background and Motivation Autonomous artifacts, from self-driving cars to drones to household robots, are becoming more widely adopted, and this trend seems likely to accelerate in the future. The increasing reliance on these devices has raised concerns about our ability to understand their behavior and our capacity to ensure their safety. Before intelligent agents can gain widespred acceptance in society, they must be able to communicate their decision making to humans in ways that convince us they actually share our aims. This challenge involves two distinct but complementary issues. The first is the need for agents to explain the reasons they carried out particular courses of action in terms that we can understand. Langley et al. (2017) have referred to this as explainable agency. The second is the need for assurance that, when agents pursue explicit goals, they will also follow the many implicit rules of society. We will refer to this ability as normative agency. Both of these functions are necessary underpinnings of trustable autonomous systems, and two capabilities are closely intertwined. Consider a scenario in which a person drives a friend with a ruptured appendix to the hospital. The driver exceeds the speed limit, weaves in and out of traffic, runs through red lights, and even drives on a sidewalk, although he is still careful to avoid hitting other cars or losing control on turns. Afterwards, the driver explains that he took such drastic actions because he thought the passenger’s life was in danger, Copyright c © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. so reaching the hospital in short order had higher priority than being polite to others along the way or obeying traffic laws. Humans can defend their behavior in this manner even when they violate serious societal norms, and we want intelligent agents of the future to exhibit the same ability to justify their decisions and actions on demand. We discuss this challenge in the sections that follow, arguing that such justified agency is an important topic that deserves substantial attention from AI researchers. We first examine the two related topics of explainable agency and normative agency. In each case, we describe the desired abilities, offer illustrative examples, and touch on relevant research. We also consider the space of agent designs, including some open issues that require additional work. After this, we turn to justified agency, arguing that it combines the first two abilities and proposing a hypothesis about what else is required to support it. We close by considering some testbeds and experiments that would let the research community evaluate progress in this critical arena. 2 Explainable Agency People can usually explain their reasons for making decisions and taking actions. When someone purchases a microwave oven, rearranges furniture in a room, or plans a vacation, he can state the choices considered, why he selected one alternative over the others, and even how his decision might have differed in other circumstances. Retrospective reports are seldom perfect, but they often offer important windows into the decision process. As intelligent agents become both more autonomous and more prevalent, it is essential that they support similar abilities. We will say that: • An intelligent system exhibits explainable agency if it can provide, on request, the reasons for its activities. Consider some examples from the realm of autonomous vehicles. If we ask a self-driving car why it followed a given route, it should be able to state that the path had few lights and stop signs while still being reasonably short. More importantly, when we ask the vehicle why it swerved into another car, it should explain that it was the only way to avoid hitting an unexpected jaywalker. Like humans, such agents should have reasons for their actions and they should be able to communicate them to others when asked. Tackling this problem requires that we make design decisions about representations and processes needed to support explainable agency, some of which we will borrow from Langley et al.’s (2017) analysis. One issue concerns what will count as legitimate explanations. Should plausible post hoc rationalizations be acceptable if an agent’s decisionmaking procedures are not interpretable or should we require genuine insights into why the agent took its actions? We argue that only the latter should be viewed as reasonable accounts, which implies that the agents should make decisions in ways that are transparent and easily communicated. There are many well-established methods in the AI arsenal that meet this criterion, but opaque policies induced from large training sets will not suffice. Another design choice involves whether to explain activities in terms of individual actions or higher-level structures. Research in machine learning has emphasized reactive control (e.g., Erwig et al., 2018), which supports the first alternative, whereas AI planning systems make choices about entire sequences of actions. We predict that humans will find plan-oriented explanations more familiar and easier to understand, and thus offer a natural approach to adopt. Even when a plan goes awry during execution, an agent can still give the reasons it decided to change course and how its new plan responded to the surprise. Some frameworks, like hierarchical task networks, also specify plans at multiple levels of abstraction, which would let a system offer more or less detailed explanations, down to individual actions if desired. Any intelligent agent must use information to make decisions about which activities to pursue, and a third issue concerns how to encode this content. One response, widely adopted in AI planning research, relies on symbolic goals, often stated as logical expressions. Another option, popular in game-playing systems, instead uses numeric evaluation functions that comprise sets of weighted features. Each approach has advantages for explainable agency: goals provide explicit end points for chains of actions, while functions show how such plans handle trade offs. Although these typically appear in isolation, they can also be combined. For instance, Langley et al. (2016) describe an agent architecture that associates functions with goals, using their weighted sum to guide planning. Such hybrid frameworks offer one promising approach to building explainable agents. Within this design space, we still need research on a number of open issues about explainable agency: These include extending intelligent systems to: • Generate explanatory content. When deciding on courses of action, an agent must consider different alternatives, evaluate them, and select one of the options to pursue. This should take place during generation of plans and during their execution, producing traces that can be used in later explanations of the agent’s activities. • Store generated content. As it makes these decisions, an agent must cache information about the choices that it considered and the reasons that it favored one over the others, in an episodic memory or similar repository. This requires not just retaining the content about decisions, but also indexing it in ways that support later access. • Retrieve stored content. After it has completed an activity, an agent must be able to retrieve decision traces that are relevant to different types of questions. This requires transforming the queries into cues and using them to access content in episodic memory about alternatives considered, their evaluations, and the final choices made. • Communicate retrieved content. Once it has retrieved episodic traces in response to a question, an agent must identify those aspects most relevant to the query, translate them into an understandable form, and share the answer. This should include no more detail than needed to convey the reasons for making the decision under examination. Research on analogical planning (e.g., Jones and Langley, 2005; Veloso et al., 1995) has addressed issues of storage, indexing, and retrieval, but not for the purpose of self report. Leake (1992) presented a computational theory of explanation, but it focused on accounts of other agents’ behaviors. The AI literature also includes other research relevant to this topic. Early explanation systems recorded inference chains and recounted them when asked to justify their conclusions (Swartout, Paris, and Moore, 1991), with some systems supporting higher-level accounts with meta-level rules (Clancey, 1983), but these did not deal with physical activities. More relevant work comes from Johnson (1994) and van Lent et al. (2004), who developed agents for military mssions that recorded their decisions, offered reasons on request, and anwered counterfactual queries. However, they dealt with knowledge-guided execution rather than agentgenerated plans and, despite linking actions to objectives, did not state why some activities were preferable to others. In more recent work, Briggs and Scheutz (2015) have reported an interactive robot that gives reasons why it cannot carry out some task, drawing on five explanation types, including lack of knowledge and physical ability. Th


Return-Oriented Flush-Reload Side Channels on ARM and Their Implications for Android Devices

Cache side-channel attacks have been extensively studied on x86 architectures, but much less so on ARM processors. The technical challenges to conduct side-channel attacks on ARM, presumably, stem from the poorly documented ARM cache implementations, such as cache coherence protocols and cache flush operations, and also the lack of understanding of how different cache implementations will affect side-channel attacks. This paper presents a systematic exploration of vectors for flush-reload attacks on ARM processors. flush-reload attacks are among the most well-known cache side-channel attacks on x86. It has been shown in previous work that they are capable of exfiltrating sensitive information with high fidelity. We demonstrate in this work a novel construction of flush-reload side channels on last-level caches of ARM processors, which, particularly, exploits return-oriented programming techniques to reload instructions. We also demonstrate several attacks on Android OS (e.g., detecting hardware events and tracing software execution paths) to highlight the implications of such attacks for Android devices.