title: Design and performance comparison of variable parameter nonlinear PID controller and genetic algorithm based PID controller

abstract: In this study, design and performance comparison of variable parameter nonlinear PID (NL-PID) and Genetic Algorithm (GA) based PID controller are achieved. To begin with the proposed method, an error function depending on the system input and output are defined to determine variable coefficients of the nonlinear PID controller. A new type non linear PID controller is designed by using defined error function. By this way, the nonlinear PID controller changes its own parameters over time according to the output response. Secondly, genetic algorithm based PID controller are defined to performance comparison of the proposed NL-PID controller and Ziegler-Nichols PID controller. Simulation results show that the effects of the PID controllers; nonlinear, GA based and Ziegler-Nichols.


title: Study of Multibeam Synthetic Aperture Sonar line-by-line imaging algorithm

abstract: This paper introduces the Multibeam Synthetic Aperture Sonar line-by-Iine imaging algorithm which based on the traditional Multibeam sonar cross array structure. The Multibeam Synthetic Aperture Sonar is a combination of traditional Mulitbeam sonar and traditional Synthetic Aperture Sonar. It can have the better track direction resolution and get simpler than the traditional Synthetic Aperture Sonar. Comparing with the new InSAS it can detect the bathymetric information of the carrying ship underside. In this paper, the Multibeam Synthetic Aperture Sonar line-by-Iine imaging algorithm is proposed and studied, and get verified by the pool experiment.


title: Research Article Multicore Software-Defined Radio Architecture for GNSS Receiver Signal Processing

abstract: We describe a multicore Software-Defined Radio (SDR) architecture for Global Navigation Satellite System (GNSS) receiver implementation. A GNSS receiver picks up very low power signals from multiple satellites and then uses dedicated processing to demodulate and measure the exact timing of these signals from which the user's position, velocity, and time (PVT) can be estimated. Three GNSS SDR architectures are discussed. (1) A hardware-based SDR that is feasible for embedded devices but relatively expensive, (2) a pure SDR approach that has high level of flexibility and low bill of material, but is not yet suited for handheld applications, and (3) a novel architecture that uses a programmable array of multiple processing cores that exhibits both flexibility and potential for mobile devices. We present the CRISP project where the multicore architecture will be realized along with numerical analysis of application requirements of the platform's processing cores and network payload.


title: Super resolution using edge prior and single image detail synthesis

abstract: Edge-directed image super resolution (SR) focuses on ways to remove edge artifacts in upsampled images. Under large magnification, however, textured regions become blurred and appear homogenous, resulting in a super-resolution image that looks unnatural. Alternatively, learning-based SR approaches use a large database of exemplar images for “hallucinating” detail. The quality of the upsampled image, especially about edges, is dependent on the suitability of the training images. This paper aims to combine the benefits of edge-directed SR with those of learning-based SR. In particular, we propose an approach to extend edge-directed super-resolution to include detail from an image/texture example provided by the user (e.g., from the Internet). A significant benefit of our approach is that only a single exemplar image is required to supply the missing detail – strong edges are obtained in the SR image even if they are not present in the example image due to the combination of the edge-directed approach. In addition, we can achieve quality results at very large magnification, which is often problematic for both edge-directed and learning-based approaches.


title: Haggle: Seamless Networking for Mobile Applications

abstract: This paper presents Haggle, an architecture for mobile devices that enables seamless network connectivity and application functionality in dynamic mobile environments. Current applications must contain significant network binding and protocol logic, which makes them inflexible to the dynamic networking environments facing mobile devices. Haggle allows separating application logic from transport bindings so that applications can be communication agnostic. Internally, the Haggle framework provides a mechanism for late-binding interfaces, names, protocols, and resources for network communication. This separation allows applications to easily utilize multiple communication modes and methods across infrastructure and infrastructure-less environments. We provide a prototype implementation of the Haggle framework and evaluate it by demonstrating support for two existing legacy applications, email and web browsing. Haggle makes it possible for these applications to seamlessly utilize mobile networking opportunities both with and without infrastructure.


title: Trust in electronic commerce: definition and theoretical considerations

abstract: Successful electronic commerce sites allow businesses to create low-cost or more efficient channels for product sales or to create new business opportunities. The success and acceptance of most online businesses depend on several factors, both technological and social. In this paper, we examine the role of trust in the successful use and adoption of electronic commerce applications. We define trust as a belief in the system characteristics, specifically belief in the competence, dependability and security of the system, under conditions of risk. Based on this definition, we develop a theoretical model that identifies the factors that effect users' trust in electronic commerce. The theoretical model presented in this paper serves as the basis for future empirical studies that will aim to measure the impact of these factors on the development of trust in electronic commerce.


title: A Proposed System for Automatic Vehicle Monitoring and Accident Detection in Bangladesh

abstract: Now a days, the alarming rise of road accidents has become one of the major concerns in Bangladesh. Unawareness and disrespect towards traffic rules, reckless driving, rapid growth of vehicles etc can be identified as factors leading to this issue. The distress of the accident victims can be reduced if the detection of accidents can be done in no time. Often emergency supports can not be provided immediately due to unawareness of the accident to the concerned persons. On the other hand, vehicle theft has also become a common issue which everyone faces in insecure parking places. In this paper, an automated system has been proposed to deal with these two interrelated affairs. Our proposed system will help the wretched victims by notifying the nearest necessary emergency supports. The system also have two levels of security, password protection for the vehicle, speed control mechanism and remote ignition cut-off mechanism to make vehicle theft almost impossible. Therefore, our cost effective proposed system will be efficacious for a developing country like Bangladesh to diminish the distress of the people.


title: Profiling mobile malware behaviour through hybrid malware analysis approach

abstract: Nowadays, the usage of mobile device among the community worldwide has been tremendously increased. With this proliferation of mobile devices, more users are able to access the internet for variety of online application and services. As the use of mobile devices and applications grows, the rate of vulnerabilities exploitation and sophistication of attack towards the mobile user are increasing as well. To date, Google's Android Operating System (OS) are among the widely used OS for the mobile devices, the openness design and ease of use have made them popular among developer and user. Despite the advantages the android-based mobile devices have, it also invited the malware author to exploit the mobile application on the market. Prior to this matter, this research focused on investigating the behaviour of mobile malware through hybrid approach. The hybrid approach correlates and reconstructs the result from the static and dynamic malware analysis in producing a trace of malicious event. Based on the finding, this research proposed a general mobile malware behaviour model that can contribute in identifying the key features in detecting mobile malware on an Android Platform device.


title: Design and simulation of DRFM system based on digital channelized receiver and transmitter

abstract: A new model of digital radio frequency memory (DRFM) system based on digital channelized receiver and transmitter is proposed. It uses channelized receiver to achieve broad instantaneous frequency coverage and slow down the data rate to realize complicated amplitude and frequency modulation, and employs channelized transmitter to fulfill simultaneous transmitting of multichannel signals. The theories of digital channelized receiver and transmitter are stated and the realization structures are derived respectively, then a new DRFM architecture is introduced. The related simulation results verify the correctness and feasibility of the theories and methods involved in this paper. The requirement of calculation capacity for DRFM system is reduced significantly due to optimization of this system, so the proposed DRFM system can be applied in radar countermeasure system.


title: 4-2 Compressor Design with New XOR-XNOR Module

abstract: In this paper, a low-power high speed 4-2 compressor circuit is proposed for fast digital arithmetic integrated circuits. The 4-2 compressor has been widely employed for multiplier realizations. Based on a new exclusive OR (XOR) and exclusive NOR (XNOR) module, a 4-2 compressor circuit has been designed. Proposed circuit shows power consumption variation in the range of 718.72 pW to 3357.40 pW. Maximum output delay of the circuit presents variation in the range of 43.83 ps to 27.74 ps. Further, power-delay product (PDP) of circuit is varying from 315.01×10-22(J) to 931.34×10-22(J) with change in supply voltage from 1.8V to 3.3V. Power consumption, delay and PDP of proposed 4-2 compressor circuit have been compared with earlier reported circuits and proposed circuit is proven to have the minimum power consumption and the lowest delay. Simulations have been performed by using SPICE based on TSMC 0.18μm CMOS technology.


title: Epileptic seizure auto-detection using deep learning method

abstract: Traditional method of epileptic seizure detection could not avoid the process of manually selecting the features. Recently, the development of deep learning technology has provided a new direction. This paper introduces a new method of the seizure detection based on EEG signal using the short time Fourier transform(STFT) and convolution neural network(CNN). And the paper verifies the feasibility of this method through the actual research data and parameter setting. Afterwards, the method of single threshold is adopted to combine the multi-channel results. Then, the comparison with the classical method using the support vector machine(SVM) has been done, which shows that the approach presented in this paper is better. And the experimental result on single channel is that the average accuracy is 86%. In addition, the method of the multi-channel could increase the average accuracy to 90% and the average true positive rate(TPR) to 96.5% while decrease the average false positive rate(FPR) to 7%. All of those indexes reveal the high performance and stability of the approach for the epileptic seizure detection.


title: SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases

abstract: We present a shared task on automatically determining sentiment intensity of a word or a phrase. The words and phrases are taken from three domains: general English, English Twitter, and Arabic Twitter. The phrases include those composed of negators, modals, and degree adverbs as well as phrases formed by words with opposing polarities. For each of the three domains, we assembled the datasets that include multi-word phrases and their constituent words, both manually annotated for real-valued sentiment intensity scores. The three datasets were presented as the test sets for three separate tasks (each focusing on a specific domain). Five teams submitted nine system outputs for the three tasks. All datasets created for this shared task are freely available to the research community.


title: Haar features for FACS AU recognition

abstract: We examined the effectiveness of using Haar features and the Adaboost boosting algorithm for FACS action unit (AU) recognition. We evaluated both recognition accuracy and processing time of this new approach compared to the state-of-the-art method of classifying Gabor responses with support vector machines. Empirical results on the Cohn-Kanade facial expression database showed that the Haar+Adaboost method yields AU recognition rates comparable to those of the Gabor+SVM method but operates at least two orders of magnitude more quickly.


title: Algorithms for trie compaction

abstract: The trie data structure has many properties which make it especially attractive for representing large files of data. These properties include fast retrieval time, quick unsuccessful search determination, and finding the longest match to a given identifier. The main drawback is the space requirement. In this paper the concept of trie compaction is formalized. An exact algorithm for optimal trie compaction and three algorithms for approximate trie compaction are given, and an analysis of the three algorithms is done. The analysis indicate that for actual tries, reductions of around 70 percent in the space required by the uncompacted trie can be expected. The quality of the compaction is shown to be insensitive to the number of nodes, while a more relevant parameter is the alphabet size of the key.


title: Generating Steganographic Text with LSTMs

abstract: Motivated by concerns for user privacy, we design a steganographic system ("stegosystem") that enables two users to exchange encrypted messages without an adversary detecting that such an exchange is taking place. We propose a new linguistic stegosystem based on a Long Short-Term Memory (LSTM) neural network. We demonstrate our approach on the Twitter and Enron email datasets and show that it yields high-quality steganographic text while significantly improving capacity (encrypted bits per word) relative to the state-of-the-art.


title: Enabling UAV Navigation with Sensor and Environmental Uncertainty in Cluttered and GPS-Denied Environments

abstract: Unmanned Aerial Vehicles (UAV) can navigate with low risk in obstacle-free environments using ground control stations that plan a series of GPS waypoints as a path to follow. This GPS waypoint navigation does however become dangerous in environments where the GPS signal is faulty or is only present in some places and when the airspace is filled with obstacles. UAV navigation then becomes challenging because the UAV uses other sensors, which in turn generate uncertainty about its localisation and motion systems, especially if the UAV is a low cost platform. Additional uncertainty affects the mission when the UAV goal location is only partially known and can only be discovered by exploring and detecting a target. This navigation problem is established in this research as a Partially-Observable Markov Decision Process (POMDP), so as to produce a policy that maps a set of motion commands to belief states and observations. The policy is calculated and updated on-line while flying with a newly-developed system for UAV Uncertainty-Based Navigation (UBNAV), to navigate in cluttered and GPS-denied environments using observations and executing motion commands instead of waypoints. Experimental results in both simulation and real flight tests show that the UAV finds a path on-line to a region where it can explore and detect a target without colliding with obstacles. UBNAV provides a new method and an enabling technology for scientists to implement and test UAV navigation missions with uncertainty where targets must be detected using on-line POMDP in real flight scenarios.


title: A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

abstract: Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.


title: Interactions between the default network and dorsal attention network vary across default subsystems, time, and cognitive states

abstract: Abstract Anticorrelation between the default network (DN) and dorsal attention network (DAN) is thought to be an intrinsic aspect of functional brain organization reflecting competing functions. However, the effect size of functional connectivity (FC) between the DN and DAN has yet to be established. Furthermore, the stability of anticorrelations across distinct DN subsystems, different contexts, and time, remains unexplored. In study 1 we summarize effect sizes of DN-DAN FC from 20 studies, and in study 2 we probe the variability of DN-DAN interactions across six different cognitive states in a new data set. We show that: (i) the DN and DAN have an independent rather than anticorrelated relationship when global signal regression is not used (median effect size across studies: r =−.06; 95% CI: −.15 to .08); (ii) the DAN exhibits weak negative FC with the DN Core subsystem but is uncorrelated with the dorsomedial prefrontal and medial temporal lobe subsystems; (iii) DN-DAN interactions vary significantly across different cognitive states; (iv) DN-DAN FC fluctuates across time between periods of anticorrelation and periods of positive correlation; and (v) changes across time in the strength of DN-DAN coupling are coordinated with interactions involving the frontoparietal control network (FPCN). Overall, the observed weak effect sizes related to DN-DAN anticorrelation suggest the need to re-conceptualize the nature of interactions between these networks. Furthermore, our findings demonstrate that DN-DAN interactions are not stable, but rather, exhibit substantial variability across time and context, and are coordinated with broader network dynamics involving the FPCN.


title: Convolutional feature pyramid fusion via attention network

abstract: We present a novel fusion scheme between multiple intermediate convolutional features within convolutional neurual network (CNN) for dense correspondence estimation. In contrast to existing CNN-based descriptors that utilize a single convolutional activation, our approach jointly uses multiple intermediate features of CNN through the attention weight that balances the contribution of each features. We formulate the overall network as two sub-networks, correspondence network and attention network. The correspondence network is designed to provide multiple intermediate matching costs while the attention network is to learn the optimal weight between them. These two networks are learned in a joint manner to boost the correspondence estimation performance. Experiments demonstrate that our proposed method outperforms the state-of-the-art methods on various correspondence estimation tasks including depth estimation, optical flow, and semantic correspondence.


title: QR code and augmented reality-supported mobile english learning system

abstract: Mobile learning highly prioritizes the successful acquisition of context-aware contents from a learning server. A variant of 2D barcodes, the quick response (QR) code, which can be rapidly read using a PDA equipped with a camera and QR code reading software, is considered promising for context-aware applications. This work presents a novel QR code and handheld augmented reality (AR) supported mobile learning (m-learning) system: the handheld English language learning organization (HELLO). In the proposed English learning system, the linked information between context-aware materials and learning zones is defined in the QR codes. Each student follows the guide map displayed on the phone screen to visit learning zones and decrypt QR codes. The detected information is then sent to the learning server to request and receive context-aware learning material wirelessly. Additionally, a 3D animated virtual learning partner is embedded in the learning device based on AR technology, enabling students to complete their context-aware immersive learning. A case study and a survey conducted in a university demonstrate the effectiveness of the proposed m-learning system.


title: A unified approach to the definition of random sequences

abstract: Using the concept of test functions, we develop a general framework within which many recent approaches to the definition of random sequences can be described. Using this concept we give some definitions of random sequences that are narrower than those proposed in the literature. We formulate an objection to some of these concepts of randomness. Using the notion of effective test function, we formulate a thesis on the “true” concept of randomness.


title: Detecting Context Dependent Messages in a Conversational Environment

abstract: While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process. The task is challenging, as messages in a conversational environment are short and informal, and evidence that can indicate a message is context dependent is scarce. After a study of social conversation data crawled from the web, we observed that some characteristics estimated from the responses of messages are discriminative for identifying context dependent messages. With the characteristics as weak supervision, we propose using a Long Short Term Memory (LSTM) network to learn a classifier. Our method carries out text representation and classifier learning in a unified framework. Experimental results show that the proposed method can significantly outperform baseline methods on accuracy of classification.


Techniques for Machine Learning based Spatial Data Analysis: Research Directions


title: CYBEX: the cybersecurity information exchange framework (x.1500)

abstract: The cybersecurity information exchange framework, known as CYBEX, is currently undergoing its first iteration of standardization efforts within ITU-T. The framework describes how cybersecurity information is exchanged between cybersecurity entities on a global scale and how the exchange is assured. The worldwide implementation of the framework will eventually minimize the disparate availability of cybersecurity information. This paper provides a specification overview, use cases, and the current status of CYBEX.


title: Interactive SQL query suggestion: Making databases user-friendly

abstract: SQL is a classical and powerful tool for querying relational databases. However, it is rather hard for inexperienced users to pose SQL queries, as they are required to be proficient in SQL syntax and have a thorough understanding of the underlying schema. To give users gratification, we propose SQLSUGG, an effective and user-friendly keyword-based method to help various users formulate SQL queries. SQLSUGG suggests SQL queries as users type in keywords, and can save users' typing efforts and help users avoid tedious SQL debugging. To achieve high suggestion effectiveness, we propose queryable templates to model the structures of SQL queries. We propose a template ranking model to suggest templates relevant to query keywords. We generate SQL queries from each suggested template based on the degree of matchings between keywords and attributes. For efficiency, we propose a progressive algorithm to compute top-k templates, and devise an efficient method to generate SQL queries from templates. We have implemented our methods on two real data sets, and the experimental results show that our method achieves high effectiveness and efficiency.


title: What are Higher Psychological Functions

abstract: The concept of Higher Psychological Functions (HPFs) may seem to be well know in psychology today. Yet closer analysis reveals that HPFs are either not defined at all or if defined, then by a set of characteristics not justified theoretically. It is not possible to determine whether HPFs exist or not, unless they are defined. Most commonly the idea of HPFs is related to Vygotsky’s theory. According to him, HPFs are: (1) psychological systems, (2) developing from natural processes, (3) mediated by symbols, (4) forms of psychological cooperation, which are (5) internalized in the course of development, (6) products of historical development, (7) conscious and (8) voluntary (9) active forms of adaptation to the environment, (10) dynamically changing in development, and (11) ontogeny of HPFs recapitulates cultural history. In this article these characteristics are discussed together with the relations among them. It is concluded that HPFs are real psychological phenomena.


Treating Oriental Patients with Western Psychiatry: A 12-Year Experience with Vietnamese Refugee Psychiatric Patients


title: Self-reconfigurable modular robot (M-TRAN) and its motion design

abstract: We have developed a modular robotic system (M-TRAN), which can change its configuration by itself. By using many DOFs of mechanism and self-reconfiguration capability, it can realize several types of motion, and can make various configurations. We have made two models of the system (M-TRAN I & II). In M-TRAN II, various improvements are integrated such as onboard multi-computers, reliable inter-module communication system, low power consumption, precise motor control, etc. Its hardware design, basic experiments and examples of motion are presented in this paper.


title: Tilting-Type Balancing Mobile Robot Platform for Enhancing Lateral Stability

abstract: In this paper, a tilting-type balancing mobile robot platform is investigated for enhancing lateral stability. In addition to pitch, yaw, and straight motion by the conventional two-wheeled inverted pendulum mechanism, it can generate roll and vertical motion by an additional tilting mechanism. The static force analysis shows that body separation tilting is more advantageous in power consumption than single body tilting, specifically when the payload to body weight is relatively small. Some design considerations are given for the determination of body structure and actuator powers. For the dynamic modeling, the titling balancing platform is assumed as a three-dimensional inverted pendulum with moving base and the nonlinear equation of motion is derived in terms of Kane's method. Then, a velocity/posture control loop is constructed, where the tilt angle reference is naturally generated according to the centrifugal force variation in following a circular path. Experimental results are given to validate the proposed mobile platform with the tilting control strategy.


title: Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

abstract: Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents. We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input. We used a two-tier optimization process in which a population of independent RL agents are trained concurrently from thousands of parallel matches on randomly generated environments. Each agent learns its own internal reward signal and rich representation of the world. These results indicate the great potential of multiagent reinforcement learning for artificial intelligence research.


title: Duloxetine, pregabalin, and duloxetine plus gabapentin for diabetic peripheral neuropathic pain management in patients with inadequate pain response to gabapentin: an open-label, randomized, noninferiority comparison.

abstract: OBJECTIVE ::: To determine whether duloxetine is noninferior to (as good as) pregabalin in the treatment of pain associated with diabetic peripheral neuropathy. ::: ::: ::: PATIENTS AND METHODS ::: We performed a 12-week, open-label study of patients with diabetic peripheral neuropathic pain who had been treated with gabapentin (≥ 900 mg/d) and had an inadequate response (defined as a daily pain score of ≥ 4 on a numerical rating scale [0-10 points]). The first patient was enrolled on September 28, 2006, and the last patient visit occurred on August 26, 2009. Patients were randomized to duloxetine monotherapy (n=138), pregabalin monotherapy (n=134), or a combination of duloxetine and gabapentin (n=135). The primary objective was a noninferiority comparison between duloxetine and pregabalin on improvement in the weekly mean of the diary-based daily pain score (0- to 10-point scale) at end point. Noninferiority would be declared if the mean improvement for duloxetine was no worse than the mean improvement for pregabalin, within statistical variability, by a margin of -0.8 unit. ::: ::: ::: RESULTS ::: The mean change in the pain rating at end point was -2.6 for duloxetine and -2.1 for pregabalin. The 97.5% lower confidence limit was a -0.05 difference in means, establishing noninferiority. As to adverse effects, nausea, insomnia, hyperhidrosis, and decreased appetite were more frequent with duloxetine than pregabalin; insomnia, more frequent with duloxetine than duloxetine plus gabapentin; peripheral edema, more frequent with pregabalin than with duloxetine; and nausea, hyperhidrosis, decreased appetite, and vomiting, more frequent with duloxetine plus gabapentin than with pregabalin. ::: ::: ::: CONCLUSION ::: Duloxetine was noninferior to pregabalin for the treatment of pain in patients with diabetic peripheral neuropathy who had an inadequate pain response to gabapentin. ::: ::: ::: TRIAL REGISTRATION ::: clinicaltrials.gov Identifier: NCT00385671.


title: Why you should care about quantile regression

abstract: Research has shown that correctly conducting and analysing computer performance experiments is difficult. This paper investigates what is necessary to conduct successful computer performance evaluation by attempting to repeat a prior experiment: the comparison between two Linux schedulers. In our efforts, we found that exploring an experimental space through a series of incremental experiments can be inconclusive, and there may be no indication of how much experimentation will be enough. Analysis of variance (ANOVA), a traditional analysis method, is able to partly solve the problems with the previous approach, but we demonstrate that ANOVA can be insufficient for proper analysis due to the requirements it imposes on the data. Finally, we demonstrate the successful application of quantile regression, a recent development in statistics, to computer performance experiments. Quantile regression can provide more insight into the experiment than ANOVA, with the additional benefit of being applicable to data from any distribution. This property makes it especially useful in our field, since non-normally distributed data is common in computer experiments.


title: Design and use of the Simple Event Model (SEM)

abstract: Events have become central elements in the representation of data from domains such as history, cultural heritage, multimedia and geography. The Simple Event Model (SEM) is created to model events in these various domains, without making assumptions about the domain-specific vocabularies used. SEM is designed with a minimum of semantic commitment to guarantee maximal interoperability. In this paper, we discuss the general requirements of an event model for web data and give examples from two use cases: historic events and events in the maritime safety and security domain. The advantages and disadvantages of several existing event models are discussed in the context of the historic example. We discuss the design decisions underlying SEM. SEM is coupled with a Prolog API that enables users to create instances of events without going into the details of the implementation of the model. By a tight coupling to existing Prolog packages, the API facilitates easy integration of event instances to Linked Open Data. We illustrate use of the API with examples from the maritime domain.


title: Automatic Multi Document Summarization Approaches

abstract: Problem statement: Text summarization can be of different nature ranging from indicative summary that identifies the topics of the document to informative summary which is meant to represent the concise description of the original document, providing an idea of what the whole content of document is all about. Approach: Single document summary seems to capture both the information well but it has not been the case for multi document summary where the overall comprehensive quality in presenting informative summary often lacks. It is found that most of the existing methods tend to focus on sentence scoring and less consideration is given to the contextual information content in multiple documents. Results: In this study, some survey on multi document summarization approaches has been presented. We will direct our focus notably on four well known approaches to multi document summarization namely the feature based method, cluster based method, graph based method and knowledge based method. The general ideas behind these methods have been described. Conclusion: Besides the general idea and concept, we discuss the benefits and limitations concerning these methods. With the aim of enhancing multi document summarization, specifically news documents, a novel type of approach is outlined to be developed in the future, taking into account the generic components of a news story in order to generate a better summary.


title: Real-time visual static hand gesture recognition system and its FPGA-based hardware implementation

abstract: Gesture recognition has been paid more and more attention as a new generation of human-computer interaction and visual input mode. In motion sensing games and other applications, gesture recognition is used as the input interface. However, because of its inherent features such as diversity, ambiguity, space-time difference and large computational burden, it is difficult to achieve the real-time application with software, especially in embedded system. Therefore, in this paper we propose a real-time hardware-based gesture recognition system as well as innovative algorithm architecture, which is easy to implement by hardware. Moreover, the operating speed is improved significantly. This paper optimizes the existing fingertip detection algorithms with the proposed essential constraints, which improves the recognition rate to 90.3%. Using parallel and pipeline structure, the gesture recognition system based on FPGA achieves 60 frames per second. The good real-time performance of this gesture recognition system is also attributed to the overall use of synchronous clock design.


title: Iris recognition and the challenge of homeland and border control security in UAE

abstract: This article discusses the implementation of iris recognition in improving the security of border control systems in the United Arab Emirates. The article explains the significance of the implemented solution and the advantages the government has gained to-date. The UAE deployment of iris recognition technology is currently the largest in the world, both in terms of number of Iris records enrolled (more than 840,751) and number of iris comparisons performed daily 6,225,761,155 (6.2 billion) in 'all-against-all' search mode.


A Secure and Efficient Mutual Authentication and Key Agreement Protocol with Smart Cards for Wireless Communications


title: Concertina Effect and Facial Aging: Nonlinear Aspects of Youthfulness and Skeletal Remodeling, and Why, Perhaps, Infants Have Jowls

abstract: The effect of aging on the orbitomaxillary region is evaluated in the present study. The observation was made that infants look like aged individuals in terms of the midface soft-tissue contours of the midface. Because preliminary work has shown that the facial skeleton remodels throughout life, this observation led to the hypothesis that infants and older individuals appear similar because they have comparable skeletal dimensions, specifically in the orbitomaxillary region. The design is a retrospective analysis of three-dimensional computed tomographic scan data. Three groups of male subjects were studied: infant, ages 1 to 12 months (n = 5); youthful, ages 15 to 24 years (n = 13); and old, ages 53 to 76 years (n = 12). Orbital and zygomaticomaxillary vertical dimensions were measured in both medial and lateral planes between fixed anatomical landmarks. Results were compared by using analysis of variance, Student-Newman-Keuls, and Student's t tests. The findings show that skeletal remodeling is such that the ratio of the maxillary height to orbital height is greatest during youth; during infancy and old age, there is a short maxilla relative to a larger orbit. This finding is significant in the medial plane from orbital rim to pyriform aperture (p < 0.05). Furthermore, there is an actual decrease in vertical maxillary height in this medial plane (p < 0.01) from youth until old age, which occurs secondary to normal skeletal remodeling in the dentate individual. The combined effect of downward expansion of the orbital shelf and the upward migration of the pyriform effectively decreases the space available to support the overlying soft tissues of the midface. An accordion-like or "concertina" effect may lead to compression or restriction of the facial soft tissues over a relatively deficient bony platform. These results highlight the importance of skeletal remodeling in determining the soft-tissue contours of the aging face. The process of skeletal remodeling may also allow for a tentative definition of facial youthfulness. Infants are born with a short maxilla relative to a large orbit, and the maxillary wall is angled posteriorly. This ratio and angle change from infancy until youth, when there is a balance between the bony skeletal support and the overlying soft-tissue envelope, i.e., the skin, facial muscles, and adipose tissue. It is when skeletal remodeling continues past this point that a disharmony occurs. Because the ratio of maxilla/orbit, and the angle of the maxillary wall, in the older person reverts toward that of an infant, the attainment of youth occurs partly in a nonlinear or multimodal manner. This work is part of an emerging concept of facial aging, which we would term an integrated model of facial aging. This model allows facial aging to be viewed as a biological "system," in which there are primary and secondary factors that interact in the process of facial aging. Additional research such as this continues to suggest the importance of bony remodeling in facial aging.


title: Developing supply chains in disaster relief operations through cross-sector socially oriented collaborations : a theoretical model

abstract: This study seeks to provide insights into corporate achievements in supply chain management (SCM) and logistics management and to detail how they might help disaster agencies. The authors aim to highlight and identify current practices, particularities, and challenges in disaster relief supply chains. Both SCM and logistics management literature and examples drawn from real-life cases inform the development of the theoretical model. The theoretical, dual-cycle model that focuses on the key missions of disaster relief agencies: first, prevention and planning and, second, response and recovery. Three major contributions are offered: a concise representation of current practices and particularities of disaster relief supply chains compared with commercial SCM; challenges and barriers to the development of more efficient SCM practices, classified into learning, strategising, and coordinating and measurement issues; and a simple, functional model for understanding how collaborations between corporations and disaster relief agencies might help relief agencies meet SCM challenges. The study does not address culture-clash related considerations. Rather than representing the entire scope of real-life situations and practices, the analysis relies on key assumptions to help conceptualise collaborative paths. The study provides specific insights into how corporations might help improve the SCM practices by disaster relief agencies that continue to function without SCM professional expertise, tools, or staff. The paper shows that sharing supply chain and logistics expertise, technology, and infrastructure with relief agencies could be a way for corporations to demonstrate their good corporate citizenship. Collaborations between corporations and disaster agencies offer significant potential benefits.


title: Endophytic Herbaspirillum seropedicae expresses nif genes in gramineous plants.

abstract: Abstract The interactions between maize, sorghum, wheat and rice plants and Herbaspirillum seropedicae were examined microscopically following inoculation with the H. seropedicae LR15 strain, a Nif(+) (Pnif::gusA) mutant obtained by the insertion of a gusA-kanamycin cassette into the nifH gene of the H. seropedicae wild-type strain. The expression of the Pnif::gusA fusion was followed during the association of the diazotroph with the gramineous species. Histochemical analysis of seedlings of maize, sorghum, wheat and rice grown in vermiculite showed that strain LR15 colonized root surfaces and inner tissues. In early steps of the endophytic association, H. seropedicae colonized root exudation sites, such as axils of secondary roots and intercellular spaces of the root cortex; it then occupied the vascular tissue and there expressed nif genes. The expression of nif genes occurred in roots, stems and leaves as detected by the GUS reporter system. The expression of nif genes was also observed in bacterial colonies located in the external mucilaginous root material, 8 days after inoculation. Moreover, the colonization of plant tissue by H. seropedicae did not depend on the nitrogen-fixing ability, since similar numbers of cells were isolated from roots or shoots of the plants inoculated with Nif(+) or Nif(-) strains.


title: A vision system for detecting and tracking of stop-lines

abstract: This paper presents a computer vision algorithm that detects, by analyzing lane-marking detection results, stop-lines and tracks, using an unscented Kalman filter, the detected stop-line over time. To detect lateral and longitudinal lane-markings, this method applies a spatial filter emphasizing the intensity contrast between lane-marking pixels and their neighboring pixels. The authors then examine the detected lane-markings to identify perpendicular, geometry layouts between longitudinal and lateral lane-markings for stop-line detection. To provide reliable stop-line recognition, the authors developed an unscented Kalman filter to track the detected stop-line over frames. Through the tests with real-world, busy urban street videos, this method demonstrated promising results, in terms of the accuracy of the initial detection accuracy and the reliability of the tracking.


title: Research Article Flicker Compensation for Archived Film Sequences Using a Segmentation-Based Nonlinear Model

abstract: A new approach for the compensation of temporal brightness variations (commonly referred to as flicker) in archived film sequences is presented. The proposed method uses fundamental principles of photographic image registration to provide adaptation to temporal and spatial variations of picture brightness. The main novelty of this work is the use of spatial segmentation to identify regions of homogeneous brightness for which reliable estimation of flicker parameters can be obtained. Additionally our scheme incorporates an efficient mechanism for the compensation of long duration film sequences while it addresses problems arising from varying scene motion and illumination using a novel motion-compensated grey-level tracing approach. We present experimental evidence which suggests that our method offers high levels of performance and compares favourably with competing state-of-the-art techniques.


title: Agile and resilient approaches to supply chain management: influence on performance and competitiveness

abstract: Supply chain management must adopt different and more innovative strategies that support a better response to customer needs in an uncertain environment. Supply chains must be more agile and be more capable of coping with disturbances, meaning that supply chains must be more resilient. The simultaneous deployment of agile and resilient approaches will enhance supply chain performance and competitiveness. Accordingly, the main objective of this paper is to propose a conceptual framework for the analysis of relationships between agile and resilient approaches, supply chain competitiveness and performance. Operational and economic performance measures are proposed to facilitate the monitoring of the influence of these practices on supply chain performance. The influence of the proposed agile and resilient practices on supply chain competitiveness is also examined in terms of time to market, product quality and customer service.


title: Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction

abstract: Previous ensemble streamflow prediction (ESP) studies in Korea reported that modelling error significantly affects the accuracy of the ESP probabilistic winter and spring (i.e. dry season) forecasts, and thus suggested that improving the existing rainfall-runoff model, TANK, would be critical to obtaining more accurate probabilistic forecasts with ESP. This study used two types of artificial neural network (ANN), namely the single neural network (SNN) and the ensemble neural network (ENN), to provide better rainfall-runoff simulation capability than TANK, which has been used with the ESP system for forecasting monthly inflows to the Daecheong multipurpose dam in Korea. Using the bagging method, the ENN combines the outputs of member networks so that it can control the generalization error better than an SNN. This study compares the two ANN models with TANK with respect to the relative bias and the root-mean-square error. The overall results showed that the ENN performed the best among the three rainfall-runoff models. The ENN also considerably improved the probabilistic forecasting accuracy, measured in terms of average hit score, half-Brier score and hit rate, of the present ESP system that used TANK. Therefore, this study concludes that the ENN would be more effective for ESP rainfall-runoff modelling than TANK or an SNN. Copyright © 2005 John Wiley & Sons, Ltd.


title: Cooperative adaptive cruise control: Tradeoffs between control and network specifications

abstract: In this study, we consider a Cooperative Adaptive Cruise Control (CACC) system which regulates inter-vehicle distances in a vehicle string. Improved performance can be achieved by utilizing information exchange between vehicles through wireless communication besides local sensor measurements. However, wireless communication introduces network-induced effects that may compromise the performance of the CACC system. Therefore, we approach the design of a CACC system from a Networked Control System (NCS) perspective. Network-induced imperfections in a NCS are mainly due to limited bandwidth of the network, multiple nodes sharing the same medium, and other limitations such as transmission delays and losses. Tradeoffs between CACC performance and network specifications need to be made for achieving desired performance under these network-induced constraints. In this paper, we present a NCS modelling framework that incorporates the effect of sample-and-hold and network delays that occur due to wireless communication. Moreover, we employ this model to study the so-called string stability performance of the string in which vehicles are interconnected by a vehicle following control law and a constant time headway spacing policy. Specifically, we study how string stability is affected by network-induced effects such as delays.


title: Interactive 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

abstract: In this work, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: The system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient-assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The resulting system is able to recognize objects in a scene in less than 7 seconds, offering an interactive frame rate and thus allowing its deployment on a mobile robotic platform. Because of that, the system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human–computer interaction systems based on visual information. A video showing the proposed system while performing online object recognition in various scenes is available on our project website ( ::: http://www.dtic.ua.es/~agarcia/3dobjrecog-jetsontk1/ ::: ).


title: Is It Culture or Is It Language? Examination of Language Effects in Cross-Cultural Research on Categorization.

abstract: Differences in reasoning styles between Chinese and European Americans held even when controlling for the language of testing. Bilingual Chinese organized objects in a more relational and less categorical way than European Americans, whether tested in English or in Chinese. Thus, culture affects categorization independent of the testing language. Nevertheless, language affected some Chinese bilinguals’ categorization. The responses of Chinese from the Mainland and Taiwan were more relational when tested in Chinese than when tested in English. Responses of Chinese from Hong Kong and Singapore were equally relational when tested in Chinese and in English. Age and context of learning English are discussed to explain the differential language effects among different Chinese groups. Theoretical and methodological implications are discussed.


title: A high-speed data transfer protocol for geostationary orbit satellites

abstract: In communication systems using geostationary orbit satellites, throughput of transmission control protocol (TCP) is limited due to the impact of latency on network and packet loss caused by signal attenuation in severe weather conditions like heavy rain. It is high time to develop network techniques and applications in broadband communications over the gigabit satellite and the high throughput satellite (HTS). In this paper, we introduce a high-speed data transfer protocol, named highperformance and flexible protocol (HpFP), to achieve high throughput over a geostationary satellite link even in severe weather conditions. The HpFP is firstly evaluated on a laboratory experiment simulating a geostationary orbit satellite link. It is clarified that the HpFP shows high throughputs even when the packet loss ratio (PLR) is 0.01%. We next carry out a field experiment using the Wideband InterNetworking engineering test and Demonstration Satellite (WINDS). The performance of the HpFP over single, dual, and multiple connections are evaluated. The result shows that the aggregate throughput of dual connections of HpFP almost reaches to the maximum bandwidth, and the time to the maximum bandwidth is within 3 sec which is over 20 times faster than that by the TCP. For multiple connections, the HpFP shares the bandwidth equally among all 50 connections.


title: Increasing Muscle Extensibility: A Matter of Increasing Length or Modifying Sensation?

abstract: Various theories have been proposed to explain increases in muscle extensibility observed after intermittent stretching. Most of these theories advocate a mechanical increase in length of the stretched muscle. More recently, a sensory theory has been proposed suggesting instead that increases in muscle extensibility are due to a modification of sensation only. Studies that evaluated the biomechanical effect of stretching showed that muscle length does increase during stretch application due to the viscoelastic properties of muscle. However, this length increase is transient, its magnitude and duration being dependent upon the duration and type of stretching applied. Most of these studies suggest that increases in muscle extensibility observed after a single stretching session and after short-term (3- to 8-week) stretching programs are due to modified sensation. The biomechanical effects of long-term (8 weeks) and chronic stretching programs have not yet been evaluated. The purposes of this article are to review each of these proposed theories and to discuss the implications for research and clinical practice. C.H. Weppler, PT, MPT, is Independent Researcher, Am Honigbaum 20, 65817 Niederjosbach, Germany. Address all correspondence to Ms Weppler at:


title: in-network aggregation trade-offs for data collection in wireless sensor networks

abstract: This paper explores in-network aggregation as a power-efficient mechanism for collecting data in wireless sensor networks. In particular, we focus on sensor network scenarios where a large number of nodes produce data periodically. Such communication model is typical of monitoring applications, an important application domain sensor networks target. The main idea behind in-network aggregation is that, rather than sending individual data items from sensors to sinks, multiple data items are aggregated as they are forwarded by the sensor network. Through simulations, we evaluate the performance of different in-network aggregation algorithms, including our own cascading timers, in terms of the trade-offs between energy efficiency, data accuracy and freshness. Our results show that timing, that is, how long a node waits to receive data from its children (downstream nodes in respect to the information sink) before forwarding data onto the next hop (toward the sink) plays a crucial role in the performance of aggregation algorithms for applications that generate data periodically. By carefully selecting when to aggregate and forward data, cascading timers achieves considerable energy savings while maintaining data freshness and accuracy. We also study in-network aggregation's cost-efficiency using simple mathematical models. Since wireless sensor networks are prone to transmission errors and losses can have considerable impact when data aggregation is used, we also propose and evaluate a number of techniques for handling packet loss. Simulations show that, when used in conjunction with aggregation protocols, the proposed techniques can effectively mitigate the effects of random transmission losses in a power-efficient way.


title: Reusability and maintainability metrics for object-oriented software

abstract: In this paper, a quality model for object-oriented software and an automated metric tool, Reconfigurable Automated Metrics for Object-Oriented Software (RAMOOS) are proposed. The quality model is targeted at the maintainability and reusability aspects of software which can be effectively predicted from the source code. RAMOOS assists users in applying customized quality model during the development of software. In the beginning of adopting RAMOOS, a user may need to use his intuition to select or modify a system-recommended metric model to fit his specific software project needs. If the initial metrics do not meet the expectation, the user can retrive the saved intermediate results and perform further modification to the metric model. The verified model can then be applied to future similar projects.


title: Dynamic user profile-based job recommender system

abstract: In this paper, we propose a dynamic user profile-based job recommender system. To address the challenge that the job applicants do not update the user profile in a timely manner, we update and extend the user profile dynamically based on the historical applied jobs and behaviors of job applicants. In particular, the statistical results of basic features in the applied jobs are used to update the job applicants'. In addition, feature selection is employed in the text information of jobs that applied by the job applicant for extending the feature. Then a hybrid recommendation algorithm is employed according to the characteristics of user profiles for achieving the dynamic recommendation.


title: Accumulation of γ-aminobutyric acid in apple, strawberry and tomato fruit in response to postharvest treatments.

abstract: γ-Aminobutyric acid (GABA) concentrations have been investigated in apple and strawberry fruit exposed to elevated CO 2 levels, and in tomato fruit of S. pennellii introgression lines in cultivated tomato (S. lycopersicum) stored at 3°C. In apple fruit, external CO 2 injury was much greater in fruit exposed to 2.5 and 5% CO 2 than at 1% CO 2 , and injury was enhanced by 1-methylcyclopropene (1-MCP) and inhibited by diphenylamine (DPA) treatment before exposure to 5% CO 2 . GABA accumulated in fruit skin to a greater extent when exposed to 2.5 and 5% CO 2 than to 1% CO 2 , while 1-MCP treated fruit had enhanced GABA accumulation and DPA treated fruit less GABA accumulation compared with 5% CO 2 alone. Strawberries contained undetectable amounts of GABA at harvest, but its concentration increased markedly during storage in 20% CO 2 . Tomato fruits of L. pennellii introgression lines stored at 3°C for 3 weeks showed elevated GABA concentrations, and the % increase over GABA concentrations at harvest was greater in fruit of introgression lines with higher susceptibility to chilling injury. Overall, the results suggest that GABA accumulations may be related to postharvest stresses in fruit, but further research is required to understand the relationships.


title: State-Space Adaptive Nonlinear Self-Interference Cancellation for Full-Duplex Communication

abstract: Full-duplex transmission comprises the ability to transmit and receive at the same time on the same frequency band. It allows for more efficient utilization of spectral resources, but raises the challenge of strong self-interference (SI). Cancellation of SI is generally implemented as a multi-stage approach. This paper proposes a novel adaptive SI cancellation algorithm in the digital domain based on Kalman filter theory that creates the following advances: first, the number of unknowns of the nonlinear SI model in cascade structure is significantly reduced compared to the conventional Hammerstein parallel model since it decouples the identification of linear and nonlinear elements; second, the remote signal-of-interest (SoI) is explicitly considered in the algorithm since the Kalman filter approach tunes its adaptation by the SoI power or performs successive cancellation; third, temporal variations of the SI channel are covered by a composite state-space model. In our simulation results, we analyze the performance by evaluating residual interference, system identification accuracy, and communication rate. We show that our Kalman filter solution in cascade structure delivers good performance with low computational complexity. In this configuration, the performance lines up with that of the monolithic (parallel) Kalman filter or the recursive-least squares (RLS) algorithms with parallel Hammerstein models. We implement our algorithm as a component of a practical prototype that demonstrates the benefits of a slender algorithmic design like the proposed cascade structure.


title: HDTQ: Managing RDF Datasets in Compressed Space

abstract: HDT (Header-Dictionary-Triples) is a compressed representation of RDF data that supports retrieval features without prior decompression. Yet, RDF datasets often contain additional graph information, such as the origin, version or validity time of a triple. Traditional HDT is not capable of handling this additional parameter(s). This work introduces HDTQ (HDT Quads), an extension of HDT that is able to represent quadruples (or quads) while still being highly compact and queryable. Two HDTQ-based approaches are introduced: Annotated Triples and Annotated Graphs, and their performance is compared to the leading open-source RDF stores on the market. Results show that HDTQ achieves the best compression rates and is a competitive alternative to well-established systems.


title: Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

abstract: Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain with-out requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add a regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.


title: Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting

abstract: User profile is a summary of a consumer's interests and preferences revealed through the consumer's online activity. It is a fundamental component of numerous applications in digital marketing. McKinsey & Company view online user profiling as one of the promising opportunities companies should take advantage of to unlock "big data's" potential. This paper proposes a modeling approach that uncovers individual user profiles from online surfing data and allows online businesses to make profile predictions when limited information is available. The approach is easily parallelized and scales well for processing massive records of user online activity. We demonstrate application of our approach to customer-base analysis and display advertising. Our empirical analysis uncovers easy-to-interpret behavior profiles and describes the distribution of such profiles. Furthermore, it reveals that even for information-rich online firms profile inference that is based solely on their internal data may produce biased results. We find that although search engines cover smaller portions of consumer Web visits than major advertising networks, their data is of higher quality. Thus, even with the smaller information set, search engines can effectively recover consumer behavioral profiles. We also show that temporal limitations imposed on individual-level tracking abilities are likely to have a differential impact across major online businesses, and that our approach is particularly effective for temporally limited data. Using economic simulation we demonstrate potential gains the proposed model may offer a firm if used in individual-level targeting of display ads. ::: ::: Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0956 .


title: Switched by input: power efficient structure for RRAM-based convolutional neural network

abstract: Convolutional Neural Network (CNN) is a powerful technique widely used in computer vision area, which also demands much more computations and memory resources than traditional solutions. The emerging metal-oxide resistive random-access memory (RRAM) and RRAM crossbar have shown great potential on neuromorphic applications with high energy efficiency. However, the interfaces between analog RRAM crossbars and digital peripheral functions, namely Analog-to-Digital Converters (AD-Cs) and Digital-to-Analog Converters (DACs), consume most of the area and energy of RRAM-based CNN design due to the large amount of intermediate data in CNN. In this paper, we propose an energy efficient structure for RRAM-based CNN. Based on the analysis of data distribution, a quantization method is proposed to transfer the intermediate data into 1 bit and eliminate DACs. An energy efficient structure using input data as selection signals is proposed to reduce the ADC cost for merging results of multiple crossbars. The experimental results show that the proposed method and structure can save 80% area and more than 95% energy while maintaining the same or comparable classification accuracy of CNN on MNIST.


title: Opportunities and Challenges of Tunnel FETs

abstract: Sustaining of Moore’s Law over the next decade will require not only continued scaling of the physical dimensions of transistors but also performance improvement and aggressive reduction in power consumption. Heterojunction Tunnel FET (TFET) has emerged as promising transistor candidate for supply voltage scaling down to sub-0.5V due to the possibility of sub- $kT$ /q switching without compromising on-current (ION). Recently, n-type III-V HTFET with reasonable on-current and sub-kT/q switching at supply voltage of 0.5V have been experimentally demonstrated. However, steep switching performance of III-V HTFET till date has been limited to range of drain current ( $\text {I}_{\mathrm {DS}}$ ) spanning over less than a decade. In this work, we will present progress on complimentary Tunnel FETs and analyze primary roadblocks in the path towards achieving steep switching performance in III-V HTFET.


title: Glove Based User Interaction Techniques for Augmented Reality in an Outdoor Environment

abstract: This paper presents a set of pinch glove-based user interface tools for an outdoor wearable augmented reality computer system. The main form of user interaction is the use of hand and head gestures. We have developed a set of augmented reality information presentation techniques. To support direct manipulation, the following three selection techniques have been implemented: two-handed framing, line of sight and laser beam. A new glove-based text entry mechanism has been developed to support symbolic manipulation. A scenario for a military logistics task is described to illustrate the functionality of this form of interaction.


title: Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation

abstract: Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.


A Motivation-Aware Approach for Point of Interest Recommendations


title: Is Caffeine a Cognitive Enhancer?

abstract: The effects of caffeine on cognition were reviewed based on the large body of literature available on the topic. Caffeine does not usually affect performance in learning and memory tasks, although caffeine may occasionally have facilitatory or inhibitory effects on memory and learning. Caffeine facilitates learning in tasks in which information is presented passively; in tasks in which material is learned intentionally, caffeine has no effect. Caffeine facilitates performance in tasks involving working memory to a limited extent, but hinders performance in tasks that heavily depend on working memory, and caffeine appears to rather improve memory performance under suboptimal alertness conditions. Most studies, however, found improvements in reaction time. The ingestion of caffeine does not seem to affect long-term memory. At low doses, caffeine improves hedonic tone and reduces anxiety, while at high doses, there is an increase in tense arousal, including anxiety, nervousness, jitteriness. The larger improvement of performance in fatigued subjects confirms that caffeine is a mild stimulant. Caffeine has also been reported to prevent cognitive decline in healthy subjects but the results of the studies are heterogeneous, some finding no age-related effect while others reported effects only in one sex and mainly in the oldest population. In conclusion, it appears that caffeine cannot be considered a 'pure' cognitive enhancer. The indirect action of caffeine on arousal, mood and concentration contributes in large part to its cognitive enhancing properties.


title: Improved Control of DFIG Systems During Network Unbalance Using PI–R Current Regulators

abstract: This paper presents a new control strategy for a doubly fed induction generator (DFIG) under unbalanced network voltage conditions. Coordinated control of the grid- and rotor-side converters (GSC and RSC, respectively) during voltage unbalance is proposed. Under an unbalanced supply voltage, the RSC is controlled to eliminate the torque pulsation at double supply frequency. The oscillation of the stator output active power is then compensated by the active power output from the GSC, to ensure constant active power output from the overall DFIG generation system. In order to provide precise control of the positive- and negative-sequence currents of the GSC and RSC, a current control scheme consisting of a proportional integral (PI) controller and a resonant (R) compensator is presented. The PI plus R current regulator is implemented in the positive synchronous reference frame without the need to decompose the positive- and negative-sequence components. Simulations on a 1.5-MW DFIG system and experimental tests on a 1.5-kW prototype validate the proposed strategy. Precise control of both positive- and negative-sequence currents and simultaneous elimination of torque and total active power oscillations have been achieved.


title: Capital Structure Effect on Firms Performance: Focusing on Consumers and Industrials Sectors on Malaysian Firms

abstract: This study seeks to investigate the impact of capital structure on firm performance by analyzing the relationship between operating performance of Malaysian firms, measured by return on asset (ROA) and return on equity (ROE) with short-term debt (STD), long-term debt (LTD) and total debt (TD). Four variables found by most literature to have an influence on firm operating performance, namely, size, asset grow, sales grow and efficiency, are used as control variables. This study covers two major sectors in Malaysian equity market which are the consumers and industrials sectors. 58 firms were identified as the sample firms and financial data from the year 2005 through 2010 are used as observations for this study, resulting in a total numbers of observations of 358. A series of regression analysis were executed for each model. Lag values for the proxies were also used to replace the non lag values in order to ensure that any extended effect of capital structure on firm performance is also examined. The study finds that only STD and TD have significant relationship with ROA while ROE has significant on each of debt level. However, the analysis with lagged values shows that non of lagged values for STD, TD and LTD has significant relationship with performance.


openHPI: Evolution of a MOOC Platform from LMS to SOA.


title: The neural correlates of subjective value during intertemporal choice

abstract: Neuroimaging studies of decision-making have generally related neural activity to objective measures (such as reward magnitude, probability or delay), despite choice preferences being subjective. However, economic theories posit that decision-makers behave as though different options have different subjective values. Here we use functional magnetic resonance imaging to show that neural activity in several brain regions—particularly the ventral striatum, medial prefrontal cortex and posterior cingulate cortex—tracks the revealed subjective value of delayed monetary rewards. This similarity provides unambiguous evidence that the subjective value of potential rewards is explicitly represented in the human brain.


title: Residual Autoencoder-LSTM for City Region Vehicle Emission Pollution Prediction

abstract: Previous time series prediction methods, such as autoregressive integrated moving average (ARIMA), which is limited in non-linear models and cannot capture nonlinear patterns, while recurrent neural network (RNN) and deep belief network (DBN) models cannot learn long term dependencies. Based on the assumption that most real-world time series data can be divided into a linear dependency component and a non-linear component which can capture long term dependencies. In this work, we investigate time series forecasting by combining long short-term memory network (LSTM) with AutoEncoder (AE) and Residual component. Our study is based on the data obtained from the remote sensing stations deployed in Hefei. The experiments show that this model can improve the performance of benchmarks on vehicle emission time series prediction, and performs better than those using traditional methods such as ARIMA, ANN and RNN models.


title: Generalized Latent Multi-View Subspace Clustering

abstract: Subspace clustering is an effective method that has been successfully applied to many applications. Here, we propose a novel subspace clustering model for multi-view data using a latent representation termed Latent Multi-View Subspace Clustering (LMSC). Unlike most existing single-view subspace clustering methods, which directly reconstruct data points using original features, our method explores underlying complementary information from multiple views and simultaneously seeks the underlying latent representation. Using the complementarity of multiple views, the latent representation depicts data more comprehensively than each individual view, accordingly making subspace representation more accurate and robust. We proposed two LMSC formulations: linear LMSC (lLMSC), based on linear correlations between latent representation and each view, and generalized LMSC (gLMSC), based on neural networks to handle general relationships. The proposed method can be efficiently optimized under the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) framework. Extensive experiments on diverse datasets demonstrate the effectiveness of the proposed method.


title: A Comparison of Information Seeking Using Search Engines and Social Networks

abstract: The Web has become an important information repository; often it is the first source a person turns to with an informa-tion need. One common way to search the Web is with a search engine. However, it is not always easy for people to find what they are looking for with keyword search, and at times the desired information may not be readily available online. An alternative, facilitated by the rise of social media, is to pose a question to one‟s online social network. In this paper, we explore the pros and cons of using a social net-working tool to fill an information need, as compared with a search engine. We describe a study in which 12 participants searched the Web while simultaneously posing a question on the same topic to their social network, and we compare the results they found by each method.


Transversality criteria in any characteristic


title: Human Activity Recognition and Pattern Discovery

abstract: In principle, activity recognition can be exploited to great societal benefits, especially in real-life, human centric applications such as elder care and healthcare. This article focused on recognizing simple human activities. Recognizing complex activities remains a challenging and active area of research and the nature of human activities poses different challenges. Human activity understanding encompasses activity recognition and activity pattern discovery. The first focuses on accurate detection of human activities based on a predefined activity model. An activity pattern discovery researcher builds a pervasive system first and then analyzes the sensor data to discover activity patterns.


title: Leveraging Complementary Contributions of Different Workers for Efficient Crowdsourcing of Video Captions

abstract: Hearing-impaired people and non-native speakers rely on captions for access to video content, yet most videos remain uncaptioned or have machine-generated captions with high error rates. In this paper, we present the design, implementation and evaluation of BandCaption, a system that combines automatic speech recognition with input from crowd workers to provide a cost-efficient captioning solution for accessible online videos. We consider four stakeholder groups as our source of crowd workers: (i) individuals with hearing impairments, (ii) second-language speakers with low proficiency, (iii) second-language speakers with high proficiency, and (iv) native speakers. Each group has different abilities and incentives, which our workflow leverages. Our findings show that BandCaption enables crowd workers who have different needs and strengths to accomplish micro-tasks and make complementary contributions. Based on our results, we outline opportunities for future research and provide design suggestions to deliver cost-efficient captioning solutions.


title: IgE-dependent and ionophore-induced generation of leukotrienes by dog mastocytoma cells.

abstract: Isolated dog mastocytoma cells sensitized with dog anti-ragweed IgE and challenged with ragweed antigen generate the C-6 peptide leukotriene (LT) constituents of the slow-reacting substance of anaphylaxis (SRS-A), LTC4 and LTD4, and the potent leukocyte chemotactic factor, LTB4, as well as 15-hydroxyeicosatetraenoic acid (15-HETE), 12-HETE, and 5-HETE. Antigen challenge evoked a mean maximum production of LTC4, LTD4, and LTB4, respectively, of 16.3 ng, 4.6 ng, and 6.7 ng per 10(6) mastocytoma cells within 5 min at 37 degrees C, which was maintained for up to 45 min. The maximum production of leukotrienes by mastocytoma cells activated with optimum concentrations of 0.2 to 1 microM calcium ionophore A23187 was 35% to 50% greater than that achieved by antigen challenge, without alterations in the relative quantities of leukotrienes, but was realized only after 30 min at 37 degrees C. The leukotriene products of mastocytoma cells were identified by high-performance liquid chromatography, spectral properties, and immunoreactivity with mono-specific antisera, and exhibited the same concentration-dependence of biologic activity as authentic synthetic standards. The ratio of quantities of C-6 peptide leukotrienes generated was attributable in part to the rate of peptidolytic conversion of LTC4 to LTD4 in the mastocytoma cells, which was 33% per 30 min at 37 degrees C. The rapid maximum response to IgE-dependent stimulation and the unique spectrum of products distinguishes the secretion of leukotrienes by dog mastocytoma cells from that by basophils and some other types of mast cells and suggests diverse contributions of mast cell leukotrienes to immediate and late hypersensitivity reactions.


title: Adverse Childhood Experiences and Smoking During Adolescence and Adulthood

abstract: CONTEXT ::: In recent years, smoking among adolescents has increased and the decline of adult smoking has slowed to nearly a halt; new insights into tobacco dependency are needed to correct this situation. Long-term use of nicotine has been linked with self-medicating efforts to cope with negative emotional, neurobiological, and social effects of adverse childhood experiences. ::: ::: ::: OBJECTIVE ::: To assess the relationship between adverse childhood experiences and 5 smoking behaviors. ::: ::: ::: DESIGN ::: The ACE Study, a retrospective cohort survey including smoking and exposure to 8 categories of adverse childhood experiences (emotional, physical, and sexual abuse; a battered mother; parental separation or divorce; and growing up with a substance-abusing, mentally ill, or incarcerated household member), conducted from August to November 1995 and January to March 1996. ::: ::: ::: SETTING ::: A primary care clinic for adult members of a large health maintenance organization in San Diego, Calif. ::: ::: ::: PARTICIPANTS ::: A total of 9215 adults (4958 women and 4257 men with mean [SD] ages of 55.3 [15.7] and 58.1 [14.5] years, respectively) who responded to a survey questionnaire, which was mailed to all patients 1 week after a clinic visit. ::: ::: ::: MAIN OUTCOME MEASURES ::: Smoking initiation by age 14 years or after age 18 years, and status as ever, current, or heavy smoker. ::: ::: ::: RESULTS ::: At least 1 of 8 categories of adverse childhood experiences was reported by 63% of respondents. After adjusting for age, sex, race, and education, each category showed an increased risk for each smoking behavior, and these risks were comparable for each category of adverse childhood experiences. Compared with those reporting no adverse childhood experiences, persons reporting 5 or more categories had substantially higher risks of early smoking initiation (odds ratio [OR], 5.4; 95% confidence interval [CI], 4.1-7.1), ever smoking (OR, 3.1; 95% CI, 2.6-3.8), current smoking (OR, 2.1; 95% CI, 1.6-2.7), and heavy smoking (OR, 2.8; 95% CI, 1.9-4.2). Each relationship between smoking behavior and the number of adverse childhood experiences was strong and graded (P<.001). For any given number of adverse childhood experiences, recent problems with depressed affect were more common among smokers than among nonsmokers. ::: ::: ::: CONCLUSIONS ::: Smoking was strongly associated with adverse childhood experiences. Primary prevention of adverse childhood experiences and improved treatment of exposed children could reduce smoking among both adolescents and adults.


title: A Low-Cost Lane-Determination System Using GNSS/IMU Fusion and HMM-Based Multistage Map Matching

abstract: This paper presents a low-cost real-time lane-determination system that fuses micro-electromechanical systems inertial sensors (accelerometers and gyroscopes), global navigation satellite system (GNSS), and commercially available road network maps. The system can be used for intelligent transportation systems, telematics applications, and autonomous driving. The system does not depend on visual markings or highly precise GNSS technology, such as DGPS or RTK, and it does not need explicit lane-level resolution maps. High-resolution estimation of the vehicle’s position, velocity, and orientation is implemented by fusing inertial sensors with GNSS in a loosely coupled mode using extended Kalman filter. A curve-to-curve road-level map-matching is implemented using a hidden Markov model followed by a least-square regression step that estimates the vehicle’s lane. The system includes a lane-change detector based on inertial sensors and the filtered vehicle’s state. The system has been realized in real time and tested extensively on real-road data. Experiments showed robust map-matching in challenging road intersections and a 97.14% lane-determination success rate.


title: Some Simple Economics of the Blockchain

abstract: We build on economic theory to discuss how blockchain technology can shape innovation and competition in digital platforms. We identify two key costs affected by the technology: the cost of verification and the cost of networking. The cost of verification relates to the ability to cheaply verify state, including information about past transactions and their attributes, and current ownership in a native digital asset. The cost of networking, instead, relates to the ability to bootstrap and operate a marketplace without assigning control to a centralized intermediary. This is achieved by combining the ability to cheaply verify state with economic incentives targeted at rewarding state transitions that are particularly valuable from a network perspective, such as the contribution of the resources needed to operate, scale, and secure a decentralized network. The resulting digital marketplaces allow participants to make joint investments in shared infrastructure and digital public utilities without assigning market power to a platform operator, and are characterized by increased competition, lower barriers to entry, and a lower privacy risk. Because of their decentralized nature, they also introduce new types of inefficiencies and governance challenges.


title: A new core loss model for rectangular AC voltages

abstract: For most power converters, the voltage waveforms on the inductors or transformers are rectangular or composed of rectangular shapes. The waveform will have an impact on the value of the core loss. An accurate model is required in order to predict the core loss under such excitations and assist the design trade-offs. The most popular core loss models now are the modified Steinmetz equation (MSE), and the improved generalized Steinmetz equation (iGSE). However, the measurement data shows these models don't give accurate prediction under certain waveforms. A new model named rectangular extension of Steinmetz equation (RESE) is proposed to improve the accuracy for the core loss under rectangular AC voltages by employing a parameter γ, which is strongly related to material and frequency. Several ferrite materials are characterized and modeled with this new parameter for rectangular voltage excitation.


title: Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

abstract: In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume.


title: Home advantage and referee bias in European football

abstract: Abstract Home advantage is well documented in a wide range of team sports including association football (soccer). Home team crowd support has been shown to be a likely causal factor and its influence on referee decision-making appears to play a significant role. Match data from the 2009/2010 and 2010/2011 seasons of the Union of European Football Associations (UEFA) Champions League and Europa League were used to investigate referee bias in terms of the association between match location (home vs. away) and disciplinary sanctions used by football referees. The adjusted mean number of yellow cards received by home and away teams and the ratios of these means were estimated from Poisson regression models. After controlling for within-match measures of attacking dominance referees in the Champions League and Europa League issued 25% (p<0.001) and 10% (p=0.002) more yellow cards, respectively, to away teams than to home teams. The higher level of home team bias in the Champions League appeared to be mainly d...


High Dimensional Bayesian Optimization with Elastic Gaussian Process


title: Creative problem solving: A CLARION theory

abstract: Psychological theories of problem solving have largely focused on explicit processes that gradually bring the solver closer to the solution step-by-step in a mostly explicit and deliberative way. This approach to problem solving is typically inefficient or ineffective when the problem is too complex, ill-understood, or ambiguous. In such a case, a ‘creative’ approach to problem solving might be more appropriate. We propose a computational psychological model implementing the Explicit-Implicit Interaction theory of creative problem solving (i.e., the CLARION theory of creative problem solving) that centers on the interaction of implicit and explicit processing. The model based on the CLARION theory has been used to simulate a variety of empirical psychological data sets.


title: Event Linking with Sentential Features from Convolutional Neural Networks

abstract: Coreference resolution for event mentions enables extraction systems to process document-level information. Current systems in this area base their decisions on rich semantic features from various knowledge bases, thus restricting them to domains where such external sources are available. We propose a model for this task which does not rely on such features but instead utilizes sentential features coming from convolutional neural networks. Two such networks first process coreference candidates and their respective context, thereby generating latent-feature representations which are tuned towards event aspects relevant for a linking decision. These representations are augmented with lexicallevel and pairwise features, and serve as input to a trainable similarity function producing a coreference score. Our model achieves state-of-the-art performance on two datasets, one of which is publicly available. An error analysis points out directions for further research.


title: A multi-level approach to understanding the impact of cyber crime on the financial sector

abstract: This paper puts forward a multi-level model, based on system dynamics methodology, to understand the impact of cyber crime on the financial sector. Consistent with recent findings, our results show that strong dynamic relationships, amongst tangible and intangible factors, affect cyber crime cost and occur at different levels of society and value network. Specifically, shifts in financial companies' strategic priorities, having the protection of customer trust and loyalty as a key objective, together with considerations related to market positioning vis-a-vis competitors are important factors in determining the cost of cyber crime. Most of these costs are not driven by the number of cyber crime incidents experienced by financial companies but rather by the way financial companies choose to go about in protecting their business interests and market positioning in the presence of cyber crime. Financial companies' strategic behaviour as response to cyber crime, especially in regard to over-spending on defence measures and chronic under-reporting, has also an important consequence at overall sector and society levels, potentially driving the cost of cyber crime even further upwards. Unwanted consequences, such as weak policing, weak international frameworks for tackling cyber attacks and increases in the jurisdictional arbitrage opportunities for cyber criminals can all increase the cost of cyber crime, while inhibiting integrated and effective measures to address the problem.


title: A PT-SOA Model for CPS/IoT Services

abstract: Service computing technologies have been widely applied to many application domains to facilitate rapid system integration for desired goals. However, existing service models need to be enhanced for cyber-physical systems (CPS) and internet-of-things (IoT). In this paper, we develop an ontology model for the specification of services in CPS/IoT. First, we discuss the major differences in modeling software services and services in CPS/IoT. Then, we propose a novel PT-SOA (PT stands for physical things) model, which is mainly extended from OWL-S, to enhance existing service models for CPS/IoT systems. Finally, we show a case study system to illustrate how our model can facilitate proper service selection and composition.


title: Estimating dynamic skin tension lines in vivo using 3D scans

abstract: Skin tension lines, defined as the lines of maximal tension, often provide guidelines for surgical incisions and pattern design of tight-fit, functional clothing. In this work, we are interested in developing methods for finding personalized, dynamic skin tension lines (DSTL) in a non-invasive manner. We base our study on the kinematic analysis of point markers that are colored on the skin. By tracking the motion-induced displacement of point markers, we locally analyze the skin deformation and numerically compute the maximum tension directions. Then, finding DSTL is transformed to the problem of finding continuous, interpolating locally computed lines of the maximum tension directions. Compared to existing methods, our method involves less invasive measurement on the skin, and is equipped with computational methods for identifying dynamic skin tension automatically. Consequently, our method is convenient to carry out, less prone to erroneous measurement, and repeatable. Our experiments have been successfully carried out on, though not limited to, lower body skins of male subjects.


title: A Noise-Resilient Super-Resolution Framework to Boost OCR Performance

abstract: Recognizing text from noisy low-resolution (LR) images is extremely challenging and is an open problem for the computer vision community. Super-resolving a noisy LR text image results in noisy High Resolution (HR) text image, as super-resolution (SR) leads to spatial correlation in the noise, and further cannot be de-noised successfully. Traditional noise-resilient text image super-resolution methods utilize a denoising algorithm prior to text SR but denoising process leads to loss of some high frequency details, and the output HR image has missing information (texture details and edges). This paper proposes a noise-resilient SR framework for text images and recognizes the text using a deep BLSTM network trained on high resolution images. The proposed end-to-end deep learning based framework for noise-resilient text image SR simultaneously perform image denoising and super-resolution as well as preserves missing details. Stacked sparse denoising auto-encoder (SSDA) is learned for LR text image denoising, and our proposed coupled deep convolutional auto-encoder (CDCA) is learned for text image super-resolution. The pretrained weights for both these networks serve as initial weights to the end-to-end framework during finetuning, and the network is jointly optimized for both the tasks. We tested on several Indian Language datasets and the OCR performance of the noise-resilient super-resolved images is at par with the original HR images.