title: A critical view on transgenerational epigenetic inheritance in humans

abstract: Transgenerational epigenetic inheritance refers to the transmission of epigenetic information through the germline. While it has been observed in plants, nematodes and fruit flies, its occurrence in mammals-and humans in particular-is the matter of controversial debate, mostly because the study of transgenerational epigenetic inheritance is confounded by genetic, ecological and cultural inheritance. In this comment, I discuss the phenomenon of transgenerational epigenetic inheritance and the difficulty of providing conclusive proof for it in experimental and observational studies.


title: A New Soft Pneumatic Elbow Pad for Joint Assistance With Application to Smart Campus

abstract: This paper presents an assistance equipment of human elbow joint, which aims to help patients who have elbow injury issues in smart campus environment. A new type of soft pneumatic elbow pad (SPEP) which adopted soft material manufacturing technology is presented. Compared with the rigid assistant equipment, the SPEP shows more comfort, smaller equipment size, and convenience of wearing. Meanwhile, three different types of sensors (the air pressure sensor, bending sensor, and force sensor) are used in the SPEP to discover the two important characteristics which are applied. In order to verify the effectiveness of the designed SPEP, two analysis methods are presented. The surface electromyogram signal and the root mean square analysis are used in the first method, which prove that the SPEP could reduce the activity of the muscle and assist power during the elbow movement. Compared with the first method, the second method which is the model of calculation after wearing SPEP can obtain specific value of assisting power in the biceps and olecranon. Thus, the aforementioned methods illustrate the feasibility of the SPEP. Finally, in order to make the SPEP used in daily life, a portable backpack with the control system and the energy system is applied to the field of human assistance equipment.


title: Dissecting cellulitis (Perifolliculitis Capitis Abscedens et Suffodiens): a comprehensive review focusing on new treatments and findings of the last decade with commentary comparing the therapies and causes of dissecting cellulitis to hidradenitis suppurativa.

abstract: Dissecting cellulitis (DC) also referred to as to as perifolliculitis capitis abscedens et suffodiens (Hoffman) manifests with perifollicular pustules, nodules, abscesses and sinuses that evolve into scarring alopecia. In the U.S., it predominantly occurs in African American men between 20-40 years of age. DC also occurs in other races and women more rarely. DC has been reported worldwide. Older therapies reported effective include: low dose oral zinc, isotretinoin, minocycline, sulfa drugs, tetracycline, prednisone, intralesional triamcinolone, incision and drainage, dapsone, antiandrogens (in women), topical clindamycin, topical isotretinoin, X-ray epilation and ablation, ablative C02 lasers, hair removal lasers (800nm and 694nm), and surgical excision. Newer treatments reported include tumor necrosis factor blockers (TNFB), quinolones, macrolide antibiotics, rifampin, alitretinoin, metronidazole, and high dose zinc sulphate (135-220 mg TID). Isotretinoin seems to provide the best chance at remission, but the number of reports is small, dosing schedules variable, and the long term follow up beyond a year is negligible; treatment failures have been reported. TNFB can succeed when isotretinoin fails, either as monotherapy, or as a bridge to aggressive surgical treatment, but long term data is lacking. Non-medical therapies noted in the last decade include: the 1064 nm laser, ALA-PDT, and modern external beam radiation therapy. Studies that span more than 1 year are lacking. Newer pathologic hair findings include: pigmented casts, black dots, and "3D" yellow dots. Newer associations include: keratitis-ichthyosis-deafness syndrome, Crohn disease and pyoderma gangrenosum. Older associations include arthritis and keratitis. DC is likely a reaction pattern, as is shown by its varied therapeutic successes and failures. The etiology of DC remains enigmatic and DC is distinct from hidradenitis suppurativa, which is shown by their varied responses to therapies and their histologic differences. Like HS, DC likely involves both follicular dysfunction and an aberrant cutaneous immune response to commensal bacteria, such as coagulase negative staphylococci. The incidence of DC is likely under-reported. The literature suggests that now most cases of DC can be treated effectively. However, the lack of clinical studies regarding DC prevents full understanding of the disease and limits the ability to define a consensus treatment algorithm.


title: HQ replication: a hybrid quorum protocol for byzantine fault tolerance

abstract: There are currently two approaches to providing Byzantine-fault-tolerant state machine replication: a replica-based approach, e.g., BFT, that uses communication between replicas to agree on a proposed ordering of requests, and a quorum-based approach, such as Q/U, in which clients contact replicas directly to optimistically execute operations. Both approaches have shortcomings: the quadratic cost of inter-replica communication is un-necessary when there is no contention, and Q/U requires a large number of replicas and performs poorly under contention. We present HQ, a hybrid Byzantine-fault-tolerant state machine replication protocol that overcomes these problems. HQ employs a lightweight quorum-based protocol when there is no contention, but uses BFT to resolve contention when it arises. Furthermore, HQ uses only 3f + 1 replicas to tolerate f faults, providing optimal resilience to node failures. We implemented a prototype of HQ, and we compare its performance to BFT and Q/U analytically and experimentally. Additionally, in this work we use a new implementation of BFT designed to scale as the number of faults increases. Our results show that both HQ and our new implementation of BFT scale as f increases; additionally our hybrid approach of using BFT to handle contention works well.


Instance segmentation for assessment of plant growth dynamics in artificial soilless conditions


title: 5G Edge Resource Federation: Dynamic and Cross-domain Network Slice Deployment

abstract: By stating that edge resource federation is crucial in future 5G ecosystems, we demonstrate an architecture, where a network operator can achieve the federation of access and edge resources owned by private third-party actors through B2B relationships. Through a Proof-of-Concept, we illustrate on-demand creation and deployment of network slices dynamically over multiple domains for live content services in a Stadium.


title: Globalization of Operations

abstract: In this chapter, we introduce the methods of operational globalization, mainly answering the question “how are operations globalized?” This chapter starts by introducing background knowledge of a firm’s globalization and studies the globalization of operations strategy from three views including competency, resource, and process-based views. Then we discuss the globalization of manufacturing and basic manufacturing globalization modes, and the globalization of service operations and basic services globalization modes. Several case examples including Globalization of Coca-Cola, Operational Globalization of Lenovo, BMW Global Manufacturing, KFC Global Service, and a case Globalization of Huawei illustrate the globalization of operations.


title: The Psychology of Change: Self-Affirmation and Social Psychological Intervention

abstract: People have a basic need to maintain the integrity of the self, a global sense of personal adequacy. Events that threaten self-integrity arouse stress and self-protective defenses that can hamper performance and growth. However, an intervention known as self-affirmation can curb these negative outcomes. Self-affirmation interventions typically have people write about core personal values. The interventions bring about a more expansive view of the self and its resources, weakening the implications of a threat for personal integrity. Timely affirmations have been shown to improve education, health, and relationship outcomes, with benefits that sometimes persist for months and years. Like other interventions and experiences, self-affirmations can have lasting benefits when they touch off a cycle of adaptive potential, a positive feedback loop between the self-system and the social system that propagates adaptive outcomes over time. The present review highlights both connections with other disciplines and lessons for a social psychological understanding of intervention and change.


title: A new linearization method of unbalanced electrical distribution networks

abstract: Abstract--- With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. As DN control and operation strategies are mostly based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages, line currents, power loss) and control variables (e.g., power injections, transformer tap positions), efficient and precise calculation of these sensitivity coefficients, i.e. linearization of DN, is of fundamental importance. In this paper, the derivation of the node voltages and power loss as functions of the nodal power injections and transformers' tap-changers positions is presented, and then solved by a Gauss-Seidel method. Compared to other approaches presented in the literature, the proposed method takes into account different load characteristics (e.g., constant PQ, constant impedance, constant current and any combination of above) of a generic multi-phase unbalanced DN and improves the accuracy of linearization. Numerical simulations on both IEEE 13 and 34 nodes test feeders show the efficiency and accuracy of the proposed method.


title: Computer Anti-forensics Methods and Their Impact on Computer Forensic Investigation

abstract: Electronic crime is very difficult to investigate and prosecute, mainly due to the fact that investigators have to build their cases based on artefacts left on computer systems. Nowadays, computer criminals are aware of computer forensics methods and techniques and try to use countermeasure techniques to efficiently impede the investigation processes. In many cases investigation with such countermeasure techniques in place appears to be too expensive, or too time consuming to carry out. Often a case can end up being abandoned and investigators are left with a sense of personal defeat. The methodologies used against the computer forensics processes are collectively called Anti-Forensics. This paper explores the anti forensics problem in various stages of computer forensic investigation from both a theoretical and practical point of view.


title: Automated Generation of Context-Aware Tests

abstract: The incorporation of context-awareness capabilities into pervasive applications allows them to leverage contextual information to provide additional services while maintaining an acceptable quality of service. These added capabilities, however, introduce a distinct input space that can affect the behavior of these applications at any point during their execution, making their validation quite challenging. In this paper, we introduce an approach to improve the test suite of a context-aware application by identifying context-aware program points where context changes may affect the application's behavior, and by systematically manipulating the context data fed into the application to increase its exposure to potentially valuable context variations. Preliminary results indicate that the approach is more powerful than existing testing approaches used on this type of application.


title: Provable data possession at untrusted stores

abstract: We introduce a model for provable data possession (PDP) that allows a client that has stored data at an untrusted server to verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking supports large data sets in widely-distributed storage system. We present two provably-secure PDP schemes that are more efficient than previous solutions, even when compared with schemes that achieve weaker guarantees. In particular, the overhead at the server is low (or even constant), as opposed to linear in the size of the data. Experiments using our implementation verify the practicality of PDP and reveal that the performance of PDP is bounded by disk I/O and not by cryptographic computation.


title: Free-free absorption coefficient of the negative hydrogen ion

abstract: The free-free absorption coefficient of the negative ion of hydrogen is calculated within the framework of the R-matrix method. The 1s, 2s and 2p states of hydrogen together with three pseudostates have been employed in the eigenfunction expansion. The results have been found to be in good agreement with other calculations and are believed to be currently the most accurate available.


title: A Novel Intrusion Detection Method Based on Principal Component Analysis

abstract: Intrusion detection is an important technique in the defense-in-depth network security framework and a hot topic in computer security in recent years. In this paper, a new intrusion detection method based on Principle Component Analysis (PCA) with low overhead and high efficiency is presented. System call data and command sequences data are used as information sources to validate the proposed method. The frequencies of individual system calls in a trace and individual commands in a data block are computed and then data column vectors which represent the traces and blocks of the data are formed as data input. PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for real-time intrusion detection.


title: Single Shot Text Detector with Regional Attention

abstract: We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN- based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allow- ing the detector to work reliably on multi-scale and multi- orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench- mark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http://sstd.whuang.org/.


title: Economic Analysis of Ransomware

abstract: We present in this work an economic analysis of ransomware, with relevant data from Cryptolocker, CryptoWall, TeslaCrypt and other major strands. We include a detailed study of the impact that different price discrimination strategies can have on the success of a ransomware family, examining uniform pricing, optimal price discrimination and bargaining strategies and analysing their advantages and limitations. In addition, we present results of a preliminary survey that can helps in estimating an optimal ransom value. We discuss at each stage whether the different schemes we analyse have been encountered already in existing malware, and the likelihood of them being implemented and becoming successful. We hope this work will help to gain some useful insights for predicting how ransomware may evolve in the future and be better prepared to counter its current and future threat.


title: Incremental training of support vector machines

abstract: We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.


title: Inner Product Similarity Search using Compositional Codes

abstract: This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of the selected elements. The inner product between a query vector and a database vector is efficiently estimated from the query vector and the short code of the database vector. We show the superior performance of the proposed group $M$-selection algorithm that selects $M$ elements from $M$ source dictionaries for vector approximation in terms of search accuracy and efficiency for compact codes of the same length via theoretical and empirical analysis. Experimental results on large-scale datasets ($1M$ and $1B$ SIFT features, $1M$ linear models and Netflix) demonstrate the superiority of the proposed approach.


title: Experimental studies of balancing control for a disc-typed mobile robot using a neural controller: GYROBO

abstract: In this paper, a disc typed one wheeled mobile robot is designed, implemented and controlled. The disk type robot called GYROBO has three actuators to move forward and backward and to balance itself. Gyro effects generated by two actuators are used to help the GYROBO to maintain balancing. Design of balancing mechanism is raised an important issue since there is a limited space. Thus, we have a successful design after several modifications of design. Since the performance of a simple PD control algorithm is poor, neural network as a nonlinear controller is added to form the reference compensation technique as a neural network control. Experimental studies of a self balancing task are presented to show the better performance by the neural network.


title: Ambient Intelligence: Technologies, Applications, and Opportunities

abstract: Ambient intelligence is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence. Because these contributing fields have experienced tremendous growth in the last few years, AmI research has strengthened and expanded. Because AmI research is maturing, the resulting technologies promise to revolutionarize daily human life by making people's surroundings flexible and adaptive. In this paper, we provide a survey of the technologies that comprise ambient intelligence and of the applications that are dramatically affected by it. In particular, we specifically focus on the research that makes AmI technologies ''intelligent''. We also highlight challenges and opportunities that AmI researchers will face in the coming years.


title: HYDROïD Humanoid Robot Head with Perception and Emotion Capabilities: Modeling, Design, and Experimental Results

abstract: In the framework of the HYDROiD humanoid robot project, this paper describes the modeling and design of an electrically actuated head mechanism. Perception and emotion capabilities are considered in the design process. Since HYDROiD humanoid robot is hydraulically actuated, the choice of electrical actuation for the head mechanism addressed in this paper is justified. Considering perception and emotion capabilities leads to a total number of 15 degrees of freedom for the head mechanism which are split on four main sub-mechanisms: the neck, the mouth, the eyes and the eyebrows. Biological data and kinematics performances of human head are taken as inputs of the design process. A new solution of uncoupled eyes is developed to possibly address the master-slave process that links the human eyes as well as vergence capabilities. Modeling each sub-system is carried out in order to get equations of motion, their frequency responses and their transfer functions. The neck pitch rotation is given as a study example. Then, the head mechanism performances are presented through a comparison between model and experimental results validating the hardware capabilities. Finally, the head mechanism is integrated on the HYDROiD upper-body. An object tracking experiment coupled with emotional expressions is carried out to validate the synchronization of the eye rotations with the body motions.


title: Bidirectional LSTM-CRF for Clinical Concept Extraction

abstract: Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). State-of-the-art concept extraction approaches heavily rely on handcrafted features and domain-specific resources which are hard to collect and define. For this reason, this paper proposes an alternative, streamlined approach: a recurrent neural network (the bidirectional LSTM with CRF decoding) initialized with general-purpose, off-the-shelf word embeddings. The experimental results achieved on the 2010 i2b2/VA reference corpora using the proposed framework outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge.


title: Failure-atomic msync(): a simple and efficient mechanism for preserving the integrity of durable data

abstract: Preserving the integrity of application data across updates is difficult if power outages and system crashes may occur during updates. Existing approaches such as relational databases and transactional key-value stores restrict programming flexibility by mandating narrow data access interfaces. We have designed, implemented, and evaluated an approach that strengthens the semantics of a standard operating system primitive while maintaining conceptual simplicity and supporting highly flexible programming: Failureatomic msync() commits changes to a memory-mapped file atomically, even in the presence of failures. Our Linux implementation of failure-atomic msync() has preserved application data integrity across hundreds of whole-machine power interruptions and exhibits good microbenchmark performance on both spinning disks and solid-state storage. Failure-atomic msync() supports higher layers of fully general programming abstraction, e.g., a persistent heap that easily slips beneath the C++ Standard Template Library. An STL built atop failure-atomic msync() outperforms several local key-value stores that support transactional updates. We integrated failure-atomic msync() into the Kyoto Tycoon key-value server by modifying exactly one line of code; our modified server reduces response times by 26--43% compared to Tycoon's existing transaction support while providing the same data integrity guarantees. Compared to a Tycoon server setup that makes almost no I/O (and therefore provides no support for data durability and integrity over failures), failure-atomic msync() incurs a three-fold response time increase on a fast Flash-based SSD---an acceptable cost of data reliability for many.


title: Adaptive ε-greedy exploration in reinforcement learning based on value differences

abstract: This paper presents "Value-Difference Based Exploration" (VDBE), a method for balancing the exploration/exploitation dilemma inherent to reinforcement learning. The proposed method adapts the exploration parameter of e-greedy in dependence of the temporal-difference error observed from value-function backups, which is considered as a measure of the agent's uncertainty about the environment. VDBE is evaluated on a multi-armed bandit task, which allows for insight into the behavior of the method. Preliminary results indicate that VDBE seems to be more parameter robust than commonly used ad hoc approaches such as e-greedy or softmax.


title: PLEASE STAY WITH ME! AN EMPIRICAL INVESTIGATION ON HEDONIC IS CONTINUANCE MODEL FOR SOCIAL NETWORK GAMES

abstract: It has long been a challenge for online game providers that, after playing a game for a certain period of time, online game players frequently switch to alternative games without much hesitation. Current IS continuance theories, that are mainly developed to interpret user continuance of general utilitarian IS, are ineffective to interpret user continuance of hedonic IS. In this study, based on the uses and gratifications theory, a hedonic IS continuance model is developed by incorporating three sorts of gratifications, namely hedonic gratification (enjoyment, fantasy and escapism), social gratification (social interaction and social presence) and utilitarian gratification (achievement and self-presentation). Age and gender are the moderating factors in the model. Based on 3919 validated respondents, the research model is assessed while a number of interesting findings are reported.


title: Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

abstract: We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection. We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%.


title: VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator

abstract: One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation. In this paper, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. Our approach starts with a robust procedure for estimator initialization. A tightly coupled, nonlinear optimization-based method is used to obtain highly accurate visual-inertial odometry by fusing preintegrated IMU measurements and feature observations. A loop detection module, in combination with our tightly coupled formulation, enables relocalization with minimum computation. We additionally perform 4-DOF pose graph optimization to enforce the global consistency. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. The current and previous maps can be merged together by the global pose graph optimization. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform an onboard closed-loop autonomous flight on the microaerial-vehicle platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy in localization. We open source our implementations for both PCs ( https://github.com/HKUST-Aerial-Robotics/VINS-Mono ) and iOS mobile devices ( https://github.com/HKUST-Aerial-Robotics/VINS-Mobile ).


title: Moral Decision Making in Autonomous Systems: Enforcement, Moral Emotions, Dignity, Trust, and Deception

abstract: As humans are being progressively pushed further downstream in the decision-making process of autonomous systems, the need arises to ensure that moral standards, however defined, are adhered to by these robotic artifacts. While meaningful inroads have been made in this area regarding the use of ethical lethal military robots, including work by our laboratory, these needs transcend the warfighting domain and are pervasive, extending to eldercare, robot nannies, and other forms of service and entertainment robotic platforms. This paper presents an overview of the spectrum and specter of ethical issues raised by the advent of these systems, and various technical results obtained to date by our research group, geared towards managing ethical behavior in autonomous robots in relation to humanity. This includes: 1) the use of an ethical governor capable of restricting robotic behavior to predefined social norms; 2) an ethical adaptor which draws upon the moral emotions to allow a system to constructively and proactively modify its behavior based on the consequences of its actions; 3) the development of models of robotic trust in humans and its dual, deception, drawing on psychological models of interdependence theory; and 4) concluding with an approach towards the maintenance of dignity in human-robot relationships.


title: Social Turing Tests: Crowdsourcing Sybil Detection

abstract: As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effectively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explore the feasibility of a crowdsourced Sybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both "experts" and "turkers" under a variety of conditions, and find that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools.


title: CONSUMER PSYCHOLOGY: Categorization, Inferences, Affect, and Persuasion

abstract: This chapter reviews research on consumer psychology with emphasis on the topics of categorization, inferences, affect, and persuasion. The chapter reviews theory-based empirical research during the period 1994–2004. Research on categorization includes empirical research on brand categories, goals as organizing frameworks and motivational bases for judgments, and self-based processing. Research on inferences includes numerous types of inferences that are cognitively and/or experienced based. Research on affect includes the effects of mood on processing and cognitive and noncognitive bases for attitudes and intentions. Research on persuasion focuses heavily on the moderating role of elaboration and dual-process models, and includes research on attitude strength responses, advertising responses, and negative versus positive evaluative dimensions.


title: Improving ITIL compliance using change management practices: a finance sector case study

abstract: Purpose – Although various practices to facilitate organizational change are proposed in the literature, very little is known about how these practices impact on ITIL adoptions. Therefore, the purpose of this study is to address this gap in the literature by offering insights on how change management practices impact on levels of ITIL compliance.Design/methodology/approach – This exploratory research reports on four case studies of completed implementations of IT service management using the process – based ITILv2 framework. The firms studied are from the Peruvian financial sector. Data were gained primarily through semi‐structured in‐depth interviews with managers involved in the implementation process. Compliance was measured using the itSMF self assessment questionnaire, which assesses the overall process capability.Findings – This study finds that not all of the case firms took full advantage of change management practices while implementing the ITIL framework. The results show that the firms with a h...


title: Implementation and simulation of IIR digital filters in FPGA using MatLab system generator

abstract: This article shows a procedure for IIR digital filters implementation using MatLab System Generator for testing and verification of results. In this particular case, the IIR filters are expressed in a State-Space representation, and the coefficients of the filter matrix are given in a fixed point format. This procedure aims to make implementations on devices with finite accuracy. Thus, the implementation is done using a state equation model with balanced realizations because of its numerical properties. The balanced forms exhibit low sensitivity to parameter variations, and avoid, to some extent, the numeric overflow. A user interface is designed in order to enter the filter transfer function or the filter state equation, and it automatically generates the VHDL language in a standard format compatible with the format used in a Black Box object of the System Generator. In this work, a 4th order low-pass Butterworth filter was implemented as an example of the methodology in a Xilinx FPGA device. The practical results obtained are shown.


title: Tropospheric phase delay in interferometric synthetic aperture radar estimated from meteorological model and multispectral imagery

abstract: accuracy of 0.44 rad or 0.03 g cm � 2 . MERIS water product overestimates W in the clouds shadow due to mismodeling of multiple scattering and underestimates Won pixels with undetected semitransparent clouds. The proposed atmospheric filter models dynamic atmospheric signal which cannot be recovered by previous filtering techniques which are based on a static atmospheric correction. Analysis of filter efficiency with spatial wavelength shows that � 43% of the atmospheric signal is removed at all wavelengths.


title: Towards a functional neuroanatomy of speech perception

abstract: Abstract The functional neuroanatomy of speech perception has been difficult to characterize. Part of the difficulty, we suggest, stems from the fact that the neural systems supporting ‘speech perception' vary as a function of the task. Specifically, the set of cognitive and neural systems involved in performing traditional laboratory speech perception tasks, such as syllable discrimination or identification, only partially overlap those involved in speech perception as it occurs during natural language comprehension. In this review, we argue that cortical fields in the posterior–superior temporal lobe, bilaterally, constitute the primary substrate for constructing sound-based representations of speech, and that these sound-based representations interface with different supramodal systems in a task-dependent manner. Tasks that require access to the mental lexicon (i.e. accessing meaning-based representations) rely on auditory-to-meaning interface systems in the cortex in the vicinity of the left temporal–parietal–occipital junction. Tasks that require explicit access to speech segments rely on auditory–motor interface systems in the left frontal and parietal lobes. This auditory–motor interface system also appears to be recruited in phonological working memory.


title: Social media and tourism promotion: the case of travelmarketers facebook fan pages after Nepal earthquake

abstract: Purpose – the purpose of this research is to systematically understand the behavioral and emotional aspects of potential tourists and investigated the conceptual model in the context of the travel marketers’ (TMs) facebook fan pages. Research methodology – the present study performed the two-step SEM approach suggested. the first step involved confirmatory factor analysis (CFA), which was used to validate scales for the measurement of specific constructs proposed in a research model and SEM followed. Findings – the supportive part of the conceptual framework studied how the cognitive (information source, social interaction ties, design characteristics) and effective (entertainment) factors influenced attitudes. Research limitations – the key limitation of this topic may belong to the propensity of the sample to embody the population. This also has a certain influence on SEM exploration. Practical implications – this study provides important guidelines for fan pages’ designers and marketers in the tourism sector especially during the time of destination image crisis. Originality/Value – this research was the earliest to relate the prototype willingness model on travel and tourism Facebook fan pages. In a sense, this research offers a basis for acclimating the prototype willingness model to the touristry social media setting. DOI: https://doi.org/10.3846/cibmee.2019.089


title: A differential-geometric approach for 2D and 3D object grasping and manipulation

abstract: Abstract This article presents an expository work on a differential-geometric treatment of fundamental problems of 2D and 3D object grasping and manipulation by a pair of robot fingers with multi-joints under holonomic or nonholonomic constraints. First, Lagrange’s equation of motion of a fingers-object system whose motion is confined to a vertical plane is derived under holonomic constraints when rolling contacts between finger-ends and object surfaces are permitted. Then, a class of control signals called “blind grasping” and constructed without knowing the object kinematics or using any external sensing like vision or tactile sensation is shown to realize stable object grasping in a dynamic sense. Stability of motion and its convergence to an equibrium manifold are treated on the basis of differential geometry of solution trajectories of the closed-loop dynamics on the constraint manifolds. Second, a mathematical model of 3D object grasping and manipulation by a pair of multi-joint robot fingers is derived under the assumption that spinning motion of rotation around the opposing axis between contact points does no more arise. It is shown that, differently from the 2D case, the instantaneous axis of rotation of the object is time-varying, which induces a nonholonomic constraint expressed as a linear differential equation of rotational motion of the pinched object. It is shown that there is a class of control signals constructed without knowing the object kinematics or using external sensings that can realize “blind grasping” in a dynamic sense. Finally, it is shown that the proposed differential geometric treatment of stability can naturally cope with redundancy resolution problems of surplus degrees-of-freedom (d.f.) of the overall fingers-object system, which is closely related to Bernstein’s d.f. problem.


title: Shaping electronic news : A case study of genre perspectives on interaction design

abstract: This thesis describes and analyzes implications of going from hypertext news to hypermedia news through a process of design, involving users and producers. As in any product development, it is difficult to conceive design of a novel news format that does not relate to earlier genres, and thus to antecedent designs. The hypothesis is that this problem can be addressed by explicitly taking a genre perspective to guide interaction design. This thesis draws on genre theory, which has previously been used in rhetoric, literature, and information systems. It is also informed by theories from humancomputer interaction. The methodological approach is a case study of the ELIN project, in which new tools for online hypermedia newspaper production were developed and integrated. The study follows the project from concept design to interaction design and implementation of user interfaces, over three years. The thesis makes three contributions. Firstly, a genre perspective on interaction design is described, revealing broadly in what respects genre affects design. Secondly, the online newspaper genre is described. Based on a content analysis of online newspaper front-pages, and interviews with users and producers, genre specific design recommendations regarding hypertext news front-page design are given. A content analysis of Swedish online newspapers provides a basis for a design rationale of the context stream element, which is an important part of the news context on article pages. Regarding hypervideo news, design rationale is given for the presentation of hypervideo links, in the context of a hypermedia news site. The impact on news production in terms of dynamics of convergence is also discussed. Thirdly, the design processes in cooperative scenario building workshops are evaluated, regarding how the users and producers were able to contribute. It provides implications and lessons learned for the workshop phase model. A discourse analysis also reveals important facilitator skills and how participants relied on genre in the design process.


title: 3D GrabCut: interactive foreground extraction for reconstructed 3D scenes

abstract: In the near future, mobile devices will be able to measure the 3D geometry of an environment using integrated depth sensing technology. This technology will enable anyone to reconstruct a 3D model of their surroundings. Similar to natural 2D images, a 3D model of a natural scene will occasionally contain a desired foreground object and an unwanted background region. Inspired by GrabCut for still images, we propose a system to perform interactive foreground/background segmentation on a reconstructed 3D scene using an intuitive user interface. Our system is designed to enable anyone, regardless of skill, to extract a 3D object from a 3D scene with a minimal amount of effort. The only input required by the user is a rectangular box around the desired object. We performed several experiments to demonstrate that our system produces high-quality segmentation on a wide variety of 3D scenes.


title: ATL: A model transformation tool

abstract: In the context of Model Driven Engineering, models are the main development artifacts and model transformations are among the most important operations applied to models. A number of specialized languages have been proposed, aimed at specifying model transformations. Apart from the software engineering properties of transformation languages, the availability of high quality tool support is also of key importance for the industrial adoption and ultimate success of MDE. In this paper we present ATL: a model transformation language and its execution environment based on the Eclipse framework. ATL tools provide support for the major tasks involved in using a language: editing, compiling, executing, and debugging.


title: Development of a superior frontal–intraparietal network for visuo-spatial working memory

abstract: Working memory capacity increases throughout childhood and adolescence, which is important for the development of a wide range of cognitive abilities, including complex reasoning. The spatial-span task, in which subjects retain information about the order and position of a number of objects, is a sensitive task to measure development of spatial working memory. This review considers results from previous neuroimaging studies investigating the neural correlates of this development. Older children and adolescents, with higher capacity, have been found to have higher brain activity in the intraparietal cortex and in the posterior part of the superior frontal sulcus, during the performance of working memory tasks. The structural maturation of white matter has been investigated by diffusion tensor magnetic resonance imaging (DTI). This has revealed several regions in the frontal lobes in which white matter maturation is correlated with the development of working memory. Among these is a superior fronto-parietal white matter region, located close to the grey matter regions that are implicated in the development of working memory. Furthermore, the degree of white matter maturation is positively correlated with the degree of cortical activation in the frontal and parietal regions. This suggests that during childhood and adolescence, there is development of networks related to specific cognitive functions, such as visuo-spatial working memory. These networks not only consist of cortical areas but also the white matter tracts connecting them. For visuo-spatial working memory, this network could consist of the superior frontal and intraparietal cortex.


title: Are there such things as ''Narcissists'' in social psychology? A taxometric analysis of the Narcissistic Personality Inventory

abstract: Narcissism is typically viewed as a dimensional construct in social psychology. Direct evidence supporting this position is lacking, however, and recent research suggests that clinical measures of narcissism exhibit categorical properties. It is therefore unclear whether social psychological researchers should conceptualize narcissism as a category or continuum. To help remedy this, the latent structure of narcissism—measured by the Narcissistic Personality Inventory (NPI)—was examined using 3895 participants and three taxometric procedures. Results suggest that NPI scores are distributed dimensionally. There is no apparent shift from ‘‘normal’’ to ‘‘narcissist’’ observed across the NPI continuum. This is consistent with the prevailing view of narcissism in social psychology and suggests that narcissism is structured similar to other aspects of general personality. This also suggests a difference in how narcissism is structured in clinical versus social psychology (134 words). 2007 Elsevier Ltd. All rights reserved.


An Innovative Approach to Investigate Various Software Testing Techniques and Strategies


title: Analysis of Trust-based Access Control Using Game Theory

abstract: Access control is an important security mechanism that is used to limit user access to information and resources and to prevent malicious users from making unauthorized access. Traditional access control models are well suited for centralized and relatively static environments in which information about subjects and objects are known in priori, but they can hardly meet the needs of open and dynamic network environments. Access control in open network environments must therefore adapt to dynamic addition and deletion of subjects and objects. In this paper, we use game theory to analyze trust-based access control to help compute trust values that involve several factors. By viewing access control as a game played between the requester and the provider entities, we can develop strategies that would motivate subjects to make honest access to objects continuously to get the most payoffs.


Oblivious Dynamic Searchable Encryption via Distributed PIR and ORAM


title: Autonomous indoor robot navigation using a sketch interface for drawing maps and routes

abstract: Hand-Drawn sketches are natural means by which abstract descriptions of environments can be provided. They represent weak prior information about the scene, thereby enabling a robot to perform autonomous navigation and exploration when a full metrical description of the environment is not available beforehand. In this paper, we present an extensive evaluation of our navigation system that uses a sketch interface to allow the operator of a robot to draw a rough map of an indoor environment as well as a desired trajectory for the robot to follow. We employ a theoretical framework for sketch interpretation, in which associations between the sketch and the real world are modeled as local deformations of a suitable metric manifold. We investigate the effectiveness of our system and present empirical results from a set of experiments in real-world scenarios, focusing both on the navigation capabilities and the usability of the interface.


title: A smart building system integrated with an edge computing algorithm and IoT mesh networks: demo abstract

abstract: This poster presents a smart building system integrated with the emerging edge computing technology and IoT Mesh networks. More specifically, first we have established an IoT Mesh network with one IoT host/server and three devices. And the next step is to develop the data analysis algorithm at the network edge and to connect to the GoKit cloud service with a GizWits V3.0 board. The system will show an effective solution to smart home/building with proposing a cloud-edge-IoT system, and fundamentally extending the connection area to cover an entire farm or factory.


title: Perception-based 3D triangle mesh segmentation using fast marching watersheds

abstract: In this paper, we describe an algorithm called fast marching watersheds that segments a triangle mesh into visual parts. This computer vision algorithm leverages a human vision theory known as the minima rule. Our implementation computes the principal curvatures and principal directions at each vertex of a mesh, and then our hill-climbing watershed algorithm identifies regions bounded by contours of negative curvature minima. These regions fit the definition of visual parts according to the minima rule. We present evaluation analysis and experimental results for the proposed algorithm.


title: Error management theory: A new perspective on biases in cross-sex mind reading

abstract: A new theory of cognitive biases, called error management theory (EMT), proposes that psychological mechanisms are designed to be predictably biased when the costs of false-positive and false-negative errors were asymmetrical over evolutionary history. This theory explains known phenomena such as men's overperception of women's sexual intent, and it predicts new biases in social inference such as women's underestimation of men's commitment. In Study 1 (N = 217), the authors documented the commitment underperception effect predicted by EMT. In Study 2 (N = 289), the authors replicated the commitment bias and documented a condition in which men's sexual overperception bias is corrected. Discussion contrasts EMT with the heuristics and biases approach and suggests additional testable hypotheses based on EMT.


title: Distance measures for effective clustering of ARIMA time-series

abstract: Much environmental and socioeconomic time-series data can be adequately modeled using autoregressive integrated moving average (ARIMA) models. We call such time series "ARIMA time series". We propose the use of the linear predictive coding (LPC) cepstrum for clustering ARIMA time series, by using the Euclidean distance between the LPC cepstra of two time series as their dissimilarity measure. We demonstrate that LPC cepstral coefficients have the desired features for accurate clustering and efficient indexing of ARIMA time series. For example, just a few LPC cepstral coefficients are sufficient in order to discriminate between time series that are modeled by different ARIMA models. In fact, this approach requires fewer coefficients than traditional approaches, such as DFT (discrete Fourier transform) and DWT (discrete wavelet transform). The proposed distance measure can be used for measuring the similarity between different ARIMA models as well. We cluster ARIMA time series using the "partition around medoids" method with various similarity measures. We present experimental results demonstrating that, using the proposed measure, we achieve significantly better clusterings of ARIMA time series data as compared to clusterings obtained by using other traditional similarity measures, such as DFT, DWT, PCA (principal component analysis), etc. Experiments were performed both on simulated and real data.


title: Dynamic replication strategies in data grid systems: a survey

abstract: In data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv) objective function based. Dynamic data replication is a form of data replication that is performed with respect to the changing conditions of the grid environment. In this paper, we present a survey of recent dynamic data replication strategies. We study and classify these strategies by taking the target data grid architecture as the sole classifier. We discuss the key points of the studied strategies and provide feature comparison of them according to important metrics. Furthermore, the impact of data grid architecture on dynamic replication performance is investigated in a simulation study. Finally, some important issues and open research problems in the area are pointed out.


title: R2: an efficient MCMC sampler for probabilistic programs

abstract: We present a new Markov Chain Monte Carlo (MCMC) sampling algorithm for probabilistic programs. Our approach and tool, called R2, has the unique feature of employing program analysis in order to improve the efficiency of MCMC sampling. Given an input program P, R2 propagates observations in P backwards to obtain a semantically equivalent program P′ in which every probabilistic assignment is immediately followed by an observe statement. Inference is performed by a suitably modified version of the Metropolis-Hastings algorithm that exploits the structure of the program P′. This has the overall effect of preventing rejections due to program executions that fail to satisfy observations in P. We formalize the semantics of probabilistic programs and rigorously prove the correctness of R2. We also empirically demonstrate the effectiveness of R2--in particular, we show that R2 is able to produce results of similar quality as the CHURCH and STAN probabilistic programming tools with much shorter execution time.


title: Reverse engineering for obsolete single layer printed circuit board (PCB)

abstract: One of the backbones in electronic manufacturing industry is the printed circuit board (PCB). The recent rapid growth in electronics devices, results escalating in the production number of the PCBs. For electronic equipment and appliances which are PCB based, new generations of PCB's are produced to suit the requirements of new products. This development can lead to waste and inefficiency when perfectly serviceable electronic components and appliances have to be scrapped because of the unavailability of spare PCB's from the Original Equipment Manufacturer (OEM) or are already obsolete. This paper proposed a novel framework for reverse engineering of obsolete single layer PCB. Equivalent PCB's which can be used as spares will be reproduced utilizing this new framework. This framework involves several steps, such as data acquisition, image processing, CAD editing, PCB fabrication and circuitry testing and analysis. Each stage of the framework and the functionality evaluation of the reproduced PCB will be discussed in detail in following sections.


title: Investigating predictors of smartphone dependency symptoms and effects on academic performance, improper phone use and perceived sociability

abstract: This study investigates the relationships between predictors (i.e., personal traits and mobile activities) of smartphone dependency and its impacts on improper phone use, academic performance and perceived sociability. Using a stratified sampling method, a web survey obtained data from 438 undergraduate smartphone users in Singapore. PLS results show that improper phone use is a critical factor which mediates the effects of smartphone dependency symptoms on grade point average and perceived sociability. Youths who feel higher leisure boredom tend to have more smartphone dependency. Female users are more likely to have smartphone dependency symptoms than males. The results also show that using mobile phones for videos and mobile gaming are better predictors of smartphone dependency symptoms than using it for social media and traditional phone activities. Theoretical and practical implications are discussed.


title: Direct Raytracing of Particle-based Fluid Surfaces Using Anisotropic Kernels

abstract: Particle-based simulation models have assumed a significant role in the numerical computation of high-fidelity transient flow and continuum mechanical problems. However, direct visualization of surfaces from particle data without intermediary discrete triangulation remains a challenging task. We demonstrate a novel direct raytracing scheme for free surface intersection based on anisotropic smoothing kernels. Our approach efficiently reduces the number of candidate kernels evaluated to converge to the surface threshold, thereby running in image space rather than object space complexity. We conduct comprehensive benchmarks with respect to data set size, scene complexity, visual fidelity and hardware setup. Our versatile system is suitable for both high quality and interactive desktop rendering, scales reasonably well even with trivial parallelization and renders up to 170 million particles on 32 distributed compute nodes at close to interactive frame rates at 4K resolution with ambient occlusion.


title: Compact and efficient encryption/decryption module for FPGA implementation of the AES Rijndael very well suited for small embedded applications

abstract: Hardware implementations of the advanced encryption standard (AES) Rijndael algorithm have recently been the object of an intensive evaluation. Several papers describe efficient architectures for ASICs and FPGAs. In this context, the highest effort was devoted to high throughput (up to 20 Gbps) encryption-only designs, fewer works studied low area encryption-only architectures and only a few papers have investigated low area encryption/decryption structures. However, in practice, only a few applications need throughput up to 20 Gbps while flexible and low cost encryption/decryption solutions are needed to protect sensible data, especially for embedded hardware applications. We purpose an efficient solution to combine Rijndael encryption and decryption in one FPGA design, with a strong focus on low area constraints. The proposed design fits into the smallest Xilinx FPGAs, deals with data streams of 208 Mbps, uses 163 slices and 3 RAM blocks and improves by 68% the best-known similar designs in terms of ratio Throughput/Area. We also propose implementations in other FPGA Families (Xilinx Virtex-II) and comparisons with similar DES, triple-DES and AES implementations.


title: 3D head pose estimation with convolutional neural network trained on synthetic images

abstract: In this paper, we propose a method to estimate head pose with convolutional neural network, which is trained on synthetic head images. We formulate head pose estimation as a regression problem. A convolutional neural network is trained to learn head features and solve the regression problem. To provide annotated head poses in the training process, we generate a realistic head pose dataset by rendering techniques, in which we consider the variation of gender, age, race and expression. Our dataset includes 74000 head poses rendered from 37 head models. For each head pose, RGB image and annotated pose parameters are given. We evaluate our method on both synthetic and real data. The experiments show that our method improves the accuracy of head pose estimation.


title: Tactical language and culture training systems: using artificial intelligence to teach foreign languages and cultures

abstract: The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire communicative skills in foreign languages and cultures. More than 20,000 learners worldwide have used TLCTS courses. TLCTS utilizes artificial intelligence technologies in multiple ways: during the authoring process, and at run time to process learner speech, interpret learner actions, control the response of non-player characters, and evaluate and assess learner performance and proficiency. This paper describes the architecture of TLCTS and the artificial intelligence technologies that it employs, and presents results from multiple evaluation studies that demonstrate the benefits of learning foreign language and culture using this approach.


title: Survey the Effective Factors on Customers' Mind Image from Internet Banking Services (Case study: Mellat Bank)

abstract: With rapid advancement of technology and daily increased competition among companies, the customers would be the basic element of each company and is considered as their main assets. This research has been done to identify factors affecting customers' mind image of the Internet Banking Services of Mellat bank, Mashhad branch. The statistical population includes the bank's 145 customers (88 male & 57 female) in this study that the required information has been collected using the questionnaire. Research hypothesis is tested by using One-Sample T Test. Findings show that following factors affect the mental image of the Mellat bank costumers in Mashhad: reliability of services, speed of services, ease of use, variety of services and the quality of Internet banking services.


Real-Time Temporal Action Localization in Untrimmed Videos by Sub-Action Discovery


title: A Case Study for Grain Quality Assurance Tracking based on a Blockchain Business Network

abstract: One of the key processes in Agriculture is quality measurement throughout the transportation of grains along its complex supply chain. This procedure is suitable for failures, such as delays to final destinations, poor monitoring, and frauds. To address the grain quality measurement challenge through the transportation chain, novel technologies, such as Distributed Ledger and Blockchain, can bring more efficiency and resilience to the process. Particularly, Blockchain is a new type of distributed database in which transactions are securely appended using cryptography and hashed pointers. Those transactions can be generated and ruled by special network-embedded software -- known as smart contracts -- that may be public to all nodes of the network or may be private to a specific set of peer nodes. This paper analyses the implementation of Blockchain technology targeting grain quality assurance tracking in a real scenario. Preliminary results support a potential demand for a Blockchain-based certification that would lead to an added valuation of around 15% for GM-free soy in the scope of a Grain Exporter Business Network in Brazil.


title: The usefulness of metrics in the quantification of speech rhythm

abstract: Abstract The performance of the rhythm metrics ΔC, %V, PVIs and Varcos, said to quantify rhythm class distinctions, was tested using English, German, Greek, Italian, Korean and Spanish. Eight participants per language produced speech using three elicitation methods, spontaneous speech, story reading and reading a set of sentences divided into “uncontrolled” sentences from original works of each language, and sentences devised to maximize or minimize syllable structure complexity (“stress-timed” and “syllable-timed” sets respectively). Rhythm classifications based on pooled data were inconsistent across metrics, while cross-linguistic differences in scores were often statistically non-significant even for comparisons between prototypical languages like English and Spanish. Metrics showed substantial inter-speaker variation and proved very sensitive to elicitation method and syllable complexity, so that the size of both effects was large and often comparable to that of language. These results suggest that any cross-linguistic differences captured by metrics are not robust; metric scores range substantially within a language and are readily affected by a variety of methodological decisions, making cross-linguistic comparisons and rhythmic classifications based on metrics unsafe at best.


title: [Antiadhesive molecule T-cadherin is an atypical low-density lipoprotein receptor in vascular cells].

abstract: Elevated serum LDL level, which results in cholesterol accumulation in vascular wall, is widely accepted as a risk factor in atherosclerosis development. Additionally to metabolic effects, LDL can produce hormone-like effects in a number of cells: activate second messenger systems, regulate gene expression and activate platelets and stimulate cell proliferation. The responses elicited by LDL are rapid, dose-dependent and capable of being saturated, indicating the involvement of specific receptor/binding sites in LDI-stimulated signal transduction. This LDL-binding protein was isolated from human aorta media and identified as T-cadherin. Cadherins are a superfamily of adhesion molecules that mediate Ca2+ -dependent cell-cell adhesion in embryogenesis and in adult organism's solid tissues. Intercellular junctions are formed as a result of interactions between extracellular domains of the neighboring cells' cadherins. Binding of the intercellular domain to the acting cytoskeleton ensures stability of cadherin-mediated adhesive junctions. T-cadherin is a unique member of calcium-dependent adherent proteins; in contrast to classical cadherins T-cadherin is anchored to the cell surface membranes via a glycosyl phosphatidyl inositol (GPI) moiety. Subcellular distribution of T-cadherin is restricted to lipid rafts on the cell membranes where it co-localizes with signal-transducing molecules. The function of T-cadherin has not yet been revealed. It was originally cloned from chicken embryo brain where the spatial-temporally restricted pattern of T-cadherin suggests its role as a negative guidance cue in tegulating the segmental organization of trunk neural crest migration and motor axon projections. Comparative study of the T-cadherin expression in human organs and tissues revealed that T-cadherin content was maximal in cardiovascular system. Its expression in VSMC depends on the cell phenotype and proliferate activity and increases in atherosclerotic lesion and restenosis. T-cadherin seems to play a key role in the regulation of the vascular cell phenotype, migration and growth. We hypothesize that T-cadherin is an anti-adhesive molecule which participates in intercellular interactions informing cells about their environment and regulating migration and proliferation of cells in vascular wall, while LDL interfere with the normal function of T-cadherin.


title: Balancing Exploration and Exploitation in Sampling-Based Motion Planning

abstract: We present the exploring/exploiting tree (EET) algorithm for motion planning. The EET planner deliberately trades probabilistic completeness for computational efficiency. This tradeoff enables the EET planner to outperform state-of-the-art sampling-based planners by up to three orders of magnitude. We show that these considerable speedups apply for a variety of challenging real-world motion planning problems. The performance improvements are achieved by leveraging work space information to continuously adjust the sampling behavior of the planner. When the available information captures the planning problem's inherent structure, the planner's sampler becomes increasingly exploitative. When the available information is less accurate, the planner automatically compensates by increasing local configuration space exploration. We show that active balancing of exploration and exploitation based on workspace information can be a key ingredient to enabling highly efficient motion planning in practical scenarios.


DENSE PIXEL MATCHING BETWEEN UNRECTIFIED AND DISTORTED IMAGES USING DYNAMIC PROGRAMMING


title: Modeling and Optimizing Automotive Electric/Electronic (E/E) Architectures: Towards Making Clafer Accessible to Practitioners

abstract: Modern automotive electric/electronic (E/E) architectures are growing to the point where architects can no longer manually predict the effects of their design decisions. Thus, in addition to applying an architecture reference model to decompose their architectures, they also require tools for synthesizing and evaluating candidate architectures during the design process. Clafer is a modeling language, which has been used to model variable multi-layer, multi-perspective automotive system architectures according to an architecture reference model. Clafer tools allow architects to synthesize optimal candidates and evaluate effects of their design decisions. However, since Clafer is a general-purpose structural modeling language, it does not help the architects in building models conforming to the given architecture reference model. In this paper, we present an E/E architecture domain-specific language (DSL) built on top of Clafer, which embodies the reference model and which guides the architects in correctly applying it. We evaluate the DSL and its implementation by modeling two existing automotive systems, which were originally modeled in plain Clafer. The evaluation showed that by using the DSL, an evaluator obtained correct models by construction because the DSL helped prevent typical errors that are easy to make in plain Clafer. The evaluator was also able to synthesize and evaluate candidate architectures as with plain Clafer.


title: CLOVE: How I learned to stop worrying about the core and love the edge

abstract: Multi-tenant datacenters predominantly use equal-cost multipath (ECMP) routing to distribute traffic over multiple network paths. However, ECMP static hashing causes unequal load-balancing and collisions, leading to low throughput and high latencies. Recently proposed alternatives for load-balancing perform better, but are impractical as they require either changing the tenant VM network stacks (e.g., MPTCP) or replacing all the network switches (e.g., CONGA). In this paper, we argue that the end-host hypervisor provides a sweet spot for implementing a spectrum of load-balancing algorithms that are fine-grained, congestion-aware, and reactive to network dynamics at round-trip timescales. We propose CLOVE, a scalable hypervisor-based load-balancer that requires no changes to guest VMs or to physical network switches. CLOVE uses standard ECMP in the physical network, learns about equal-cost network paths using a traceroute mechanism, and learns about congestion state along these paths using standard switch features such as ECN. It then manipulates packet header fields in the hypervisor virtual switch to route traffic over less congested paths. We introduce different variants of CLOVE that differ in the way they learn about congestion in the physical network. Using extensive simulations, we show that CLOVE captures some 80% of the performance gain of best-of-breed hardware-based load-balancing algorithms without the need for expensive hardware replacement.


title: Detection of Fake Indian Currency

abstract: Fake currency detection is a serious issue worldwide, affecting the economy of almost every country including India. The use of counterfeit currency is one of the major issues faced throughout the world now-a-days. The counterfeiters are becoming harder to track down because of their use of highly advanced technology. One of the most effective methods to stop counterfeiting can be the use of counterfeit detection software that is easily available and is efficient. Our project will recognize Indian currency notes using a real time image obtained from a webcam. The background of our topic is image processing technology and applying it for the purpose of verifying valid currency notes. The software will detects fake currency by extracting features of notes. The success rate of this software can be measured in terms of accuracy and speed. So our aim is to work on those parameters which will be impossible to implement on counterfeit notes so we started working on minutiae parameters which will be enough to differentiate between fake and original notes.


title: Therapeutic Benefits of Laughter in Mental Health: A Theoretical Review

abstract: In modern society, fierce competition and socioeconomic interaction stress the quality of life, causing a negative influence on a person's mental health. Laughter is a positive sensation, and seems to be a useful and healthy way to overcome stress. Laughter therapy is a kind of cognitive-behavioral therapies that could make physical, psychological, and social relationships healthy, ultimately improving the quality of life. Laughter therapy, as a non-pharmacological, alternative treatment, has a positive effect on the mental health and the immune system. In addition, laughter therapy does not require specialized preparations, such as suitable facilities and equipment, and it is easily accessible and acceptable. For these reasons, the medical community has taken notice and attempted to include laughter therapy to more traditional therapies. Decreasing stress-making hormones found in the blood, laughter can mitigate the effects of stress. Laughter decreases serum levels of cortisol, epinephrine, growth hormone, and 3,4-dihydrophenylacetic acid (a major dopamine catabolite), indicating a reversal of the stress response. Depression is a disease, where neurotransmitters in the brain, such as norepinephrine, dopamine, and serotonin, are reduced, and there is something wrong in the mood control circuit of the brain. Laughter can alter dopamine and serotonin activity. Furthermore, endorphins secreted by laughter can help when people are uncomfortable or in a depressed mood. Laughter therapy is a noninvasive and non-pharmacological alternative treatment for stress and depression, representative cases that have a negative influence on mental health. In conclusion, laughter therapy is effective and scientifically supported as a single or adjuvant therapy.


title: Lean Accounting Based on Lean Production

abstract: With the diversification of customer demand and competition of globalization, mass production is being replaced by lean production. In this background, the traditional accounting system is no longer applicable to lean production, and lean accounting supporting lean production comes into being. This general trend affects China's enterprises. This paper introduces the background of lean accounting and the content of lean accounting, gives the measures of implementing lean accounting, finally compares lean accounting with traditional accounting and summarizes obstacles in the process of implementing lean accounting. The research and application of lean accounting will greatly enhance the competitive advantage of China's enterprises.


title: The epidemiology and pathophysiology of lumbar disc herniations

abstract: Abstract Lumbar intervertebral disc herniations are the most common causes for working-age individuals to undergo lumbar spine surgery. Patients with a family history of disc disease or are in physically demanding jobs, or who have certain medical comorbidities such as obesity, are at an increased risk of developing a lumbar disc herniation. Symptomatic herniations present as lumbar radiculopathy from both a mechanical compression and chemical irritation of the nerve root.


title: CatchSync: catching synchronized behavior in large directed graphs

abstract: Given a directed graph of millions of nodes, how can we automatically spot anomalous, suspicious nodes, judging only from their connectivity patterns? Suspicious graph patterns show up in many applications, from Twitter users who buy fake followers, manipulating the social network, to botnet members performing distributed denial of service attacks, disturbing the network traffic graph. We propose a fast and effective method, CatchSync, which exploits two of the tell-tale signs left in graphs by fraudsters: (a) synchronized behavior: suspicious nodes have extremely similar behavior pattern, because they are often required to perform some task together (such as follow the same user); and (b) rare behavior: their connectivity patterns are very different from the majority. We introduce novel measures to quantify both concepts ("synchronicity" and "normality") and we propose a parameter-free algorithm that works on the resulting synchronicity-normality plots. Thanks to careful design, CatchSync has the following desirable properties: (a) it is scalable to large datasets, being linear on the graph size; (b) it is parameter free; and (c) it is side-information-oblivious: it can operate using only the topology, without needing labeled data, nor timing information, etc., while still capable of using side information, if available. We applied CatchSync on two large, real datasets 1-billion-edge Twitter social graph and 3-billion-edge Tencent Weibo social graph, and several synthetic ones; CatchSync consistently outperforms existing competitors, both in detection accuracy by 36% on Twitter and 20% on Tencent Weibo, as well as in speed.


title: Variable step size P&O MPPT algorithm for PV systems

abstract: Maximum power point tracking (MPPT) techniques are employed in photovoltaic (PV) systems to maximize the PV array output power which depends on solar irradiance and temperature. Among all the MPPT strategies, The P&O Maximum Power Point Tracking algorithm is mostly used, due to its ease of implementation. On the other hand, its main drawbacks are the waste of energy in steady state conditions, when the working point moves across the MPP and the poor dynamic performances exhibited when a step change in solar irradiance or in temperature occurs. In this paper, a modified variable step size P&O MPPT algorithm is proposed, the step size is automatically tuned according to the operating point. Compared with the conventional fixed step size method, the proposed approach can effectively improve the MPPT speed and efficiency simultaneously. A theoretical analysis and the design principle of the proposed algorithm are provided and its feasibility is also verified by simulation results.


title: Inducible Expression of the 2-5A Synthetase/RNase L System Results in Inhibition of Vaccinia Virus Replication

abstract: Studies of interferon (IFN)-treated virus-infected animal cells have revealed the 2-5A system (2-5A synthetase/RNase L enzymes) as being responsible for virus inhibition only in the case of picornaviridae. To investigate whether those IFN-induced enzymes could be responsible for inhibition of poxvirus replication, we have generated recombinant vaccinia viruses (VV) containing the corresponding genes (VV-2-5AS and VV-RL, respectively). RNase L produced in cells infected with VV-RL leads to rRNA degradation and inhibition of virus protein synthesis, which correlates with about 92% reduction in virus yields by 48 hr after infection. Combined expression of this enzyme with 2-5A-synthetase further inhibits virus yields. The pattern of rRNA fragments produced by infection with viruses VV-RL and/or VV-2-5AS is the characteristic for activation of the 2-5A pathway by IFN treatment. Combined infection of VV-RL together with vesicular stomatitis virus (VSV) demonstrates this inhibition to be specific for VV and not due to a general effect. Breakdown of rRNA is largely due to the recombinant vector-derived enzyme, since a C-terminal deletion mutant of RNase L is inactive and the extent of rRNA degradation induced by infection with VV-RL is similar in cells treated or not with IFN. Moreover, the anti-VV effects of RNase L is also observed in a cell line lacking the endogenous ds RNA-dependent protein kinase (PKR). Thus, our findings provide direct evidence for antiviral activity of the 2-5A system on poxviruses.


title: Mining Professional's Data from LinkedIn

abstract: Social media has become very popular communication tool among internet users in the recent years. A large unstructured data is available for analysis on the social web. The data available on these sites have redundancies as users are free to enter the data according to their knowledge and interest. This data needs to be normalized before doing any analysis due to the presence of various redundancies in it. In this paper, LinkedIn data is extracted by using LinkedIn API and normalized by removing redundancies. Further, data is also normalized according to locations of LinkedIn connections using geo coordinates provided by Microsoft Bing. Then, clustering of this normalized data set is done according to job title, company names and geographic locations using Greedy, Hierarchical and K-Means clustering algorithms and clusters are visualized to have a better insight into them.


title: Analysis of Winding Configurations and Slot-Pole Combinations in Fractional-Slots Resolvers

abstract: Resolvers, due to their robust structure, are widely used in automation systems. The need for accurate position estimation beside the precise control of present PM motors, persuade machine designers to select a resolver with high pole numbers. By the aid of concentrated winding topologies high pole numbers is accessible. Among different topologies of concentrated windings fractional slot (FS) type has been focused by a lot of researches. FS concentrated winding (FSCW) offers high pole number under limited slots, identical on tooth coils, short end winding, and compact design. However, depending on the topology, there are major drawbacks, such as magnetic noises, high sub-harmonics, high leakage inductances, high core losses, and low fault-tolerance, resulted from inappropriate slot-pole combinations. So, in this paper after developing FSCW principles for resolvers, different configurations, including slot-pole combinations and winding layers are suggested. Then, 3-D time stepping finite element method is used to introduce five indices for evaluating the perfectness of these combinations and arrangements on the performance of resolvers. Finally, a prototype of 10-pole axial flux resolver has been constructed and tested. Good agreement between experimental and simulation results are obtained.


title: CEIVE: Combating Caller ID Spoofing on 4G Mobile Phones Via Callee-Only Inference and Verification

abstract: Caller ID spoofing forges the authentic caller identity, thus making the call appear to originate from another user. This seemingly simple attack technique has been used in the growing telephony frauds and scam calls, resulting in substantial monetary loss and victim complaints. Unfortunately, caller ID spoofing is easy to launch, yet hard to defend; no effective and practical defense solutions are in place to date. In this paper, we propose CEIVE (Callee-only inference and verification), an effective and practical defense against caller ID spoofing. It is a victim callee only solution without requiring additional infrastructure support or changes on telephony systems. We formulate the design as an inference and verification problem. Given an incoming call, CEIVE leverages a callback session and its associated call signaling observed at the phone to infer the call state of the other party. It further compares with the anticipated call state, thus quickly verifying whether the incoming call comes from the originating number. We exploit the standardized call signaling messages to extract useful features, and devise call-specific verification and learning to handle diversity and extensibility. We implement CEIVE on Android phones and test it with all top four US mobile carriers, one landline and two small carriers. It shows 100% accuracy in almost all tested spoofing scenarios except one special, targeted attack case.


title: Language Expansion In Text-Based Games

abstract: Text-based games are suitable test-beds for designing agents that can learn by interaction with the environment in the form of natural language text. Very recently, deep reinforcement learning based agents have been successfully applied for playing text-based games. In this paper, we explore the possibility of designing a single agent to play several text-based games and of expanding the agent's vocabulary using the vocabulary of agents trained for multiple games. To this extent, we explore the application of recently proposed policy distillation method for video games to the text-based game setting. We also use text-based games as a test-bed to analyze and hence understand policy distillation approach in detail.


Formal Concept Analysis


title: Hadoop-HBase for large-scale data

abstract: Today we are inundated with digital data. Yet we are very poor in managing and processing it. It is becoming increasingly difficult to store and analyze data efficiently and economically via conventional database management tools. Not only that, type of data, appearing in the databases, are also changing. Now a day, binary large objects are a standard integral part of any database. Researchers, all over the globe, are baffling with analysis of these ultra large databases. Apache HBase is one such attempt. HBase is a noSQL distributed database developed on top of Hadoop Distributed File System (HDFS). In this paper, we present an evaluation of hybrid architecture where HDFS contains the non-textual data like images and location of such data is stored in HBase. This hybrid architecture enables faster search and retrieval of the data which is a growing need in any organization who are flooded with data. The paper aims at evaluating the performance of random reads and random writes of data storage location information to HBase and retrieving and storing data in HDFS respectively. We also present a comparative study of HBase-HDFS architecture with MySQL-HDFS architecture.


title: Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned Transform

abstract: A major challenge in X-ray computed tomography (CT) is reducing radiation dose while maintaining high quality of reconstructed images. To reduce the radiation dose, one can reduce the number of projection views (sparse-view CT); however, it becomes difficult to achieve high quality image reconstruction as the number of projection views decreases. Researchers have applied the concept of learning sparse representations from (high-quality) CT image dataset to the sparse-view CT reconstruction. We propose a new statistical CT reconstruction model that combines penalized weighted-least squares (PWLS) and $\ell_1$ regularization with learned sparsifying transform (PWLS-ST-$\ell_1$), and an algorithm for PWLS-ST-$\ell_1$. Numerical experiments for sparse-view 2D fan-beam CT and 3D axial cone-beam CT show that the $\ell_1$ regularizer significantly improves the sharpness of edges of reconstructed images compared to the CT reconstruction methods using edge-preserving regularizer and $\ell_2$ regularization with learned ST.


title: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

abstract: Gearboxes are crucial transmission components in mechanical systems. Fault diagnosis is an important tool to maintain gearboxes in healthy conditions. It is challenging to recognize fault existences and, if any, failure patterns in such transmission elements due to their complicated configurations. This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation-fusion based deep learning in order to perform fault diagnosis tasks for gearboxes. Considering that different modalities can be made to describe same object, multimodal homologous features of the gearbox vibration measurements are first separated in time, frequency and wavelet modalities, respectively. A Gaussian-Bernoulli deep Boltzmann machine (GDBM) without final output is subsequently suggested to learn pattern representations for features in each modality. A support vector classifier is finally applied to fuse GDBMs in different modalities towards the construction of the MDSVC model. With the present model, "deep" representations from "wide" modalities improve fault diagnosis capabilities. Fault diagnosis experiments were carried out to evaluate the proposed method on both spur and helical gearboxes. The proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods. Results indicate that the proposed separation-fusion based deep learning strategy is effective for the gearbox fault diagnosis.


Next Generation (Nano) Photonic and Cell Technologies for Solar Energy Conversion II


title: How Faith Heals: A Theoretical Model

abstract: This paper summarizes theoretical perspectives from psychology supportive of a healing effect of faith. First, faith is defined as a congruence of belief, trust, and obedience in relation to God or the divine. Second, evidence for a faith-healing association is presented, empirically and in theory. To exemplify religiously sanctioned affirmation of such a connection, selected passages are cited from the Jewish canon attesting to biblical and rabbinic support for a faith factor in longevity, disease risk, mental health and well-being, disease prevention, and healing. Third, reference to theories of hope, learned optimism, positive illusions, and opening up or disclosure, and to theory and research on psychoneuroimmunology and placebos, demonstrates that contemporary psychology can accommodate a healing power of faith. This is summarized in a typology of five hypothesized mechanisms underlying a faith-healing association, termed behavioral/conative, interpersonal, cognitive, affective, and psychophysiological. Finally, implications are discussed for the rapprochement of religion and medicine.


title: Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

abstract: The availability of new generation multispectral sensors of the Landsat 8 and Sentinel-2 satellite platforms offers unprecedented opportunities for long-term high-frequency monitoring applications. The present letter aims at highlighting some potentials and challenges deriving from the spectral and spatial characteristics of the two instruments. Some comparisons between corresponding bands and band combinations were performed on the basis of different datasets: the first consists of a set of simulated images derived from a hyperspectral Hyperion image, the other five consist instead of pairs of real images (Landsat 8 and Sentinel-2A) acquired on the same date, over five areas. Results point out that in most cases the two sensors can be well combined; however, some issues arise regarding near-infrared bands when Sentinel-2 data are combined with both Landsat 8 and older Landsat images.


title: Hydrogels in Biology and Medicine: From Molecular Principles to Bionanotechnology†

abstract: Hydrophilic polymers are the center of research emphasis in nanotechnology because of their perceived “intelligence”. They can be used as thin films, scaffolds, or nanoparticles in a wide range of biomedical and biological applications. Here we highlight recent developments in engineering uncrosslinked and crosslinked hydrophilic polymers for these applications. Natural, biohybrid, and synthetic hydrophilic polymers and hydrogels are analyzed and their thermodynamic responses are discussed. In addition, examples of the use of hydrogels for various therapeutic applications are given. We show how such systems’ intelligent behavior can be used in sensors, microarrays, and imaging. Finally, we outline challenges for the future in integrating hydrogels into biomedical applications.


title: White-matter pathways for speech and language processing.

abstract: Abstract The language-relevant brain regions, Brodmann's area in the inferior frontal cortex and Wernicke's area in the superior temporal cortex, are connected via long-range fiber bundles, which are located dorsally and ventrally to the sylvian fissure. These dorsal and ventral pathways consist of a number of partly parallel-running fiber tracts, which can be differentiated by their termination regions and by the particular language functions of these termination regions. Dorsally, there are two major fiber tracts connecting the posterior temporal cortex with the frontal cortex: one terminating in the premotor cortex that subserves sensory-to-motor mapping and one terminating in posterior Broca's area, the pars opercularis, that supports the processing of complex syntactic structures. Ventrally, two language-related fiber tracts are discussed: one connects the inferior frontal cortex, i.e., the pars triangularis and orbitalis, with Wernicke's area and supports semantic processes and another one connects the most ventral portions of the inferior frontal cortex, including the frontal operculum, with the anterior temporal cortex. This latter ventral tract is suggested to subserve elementary combinatorial processes in language. Together these fiber tracts guarantee the transmission of information between different brain regions within the neural language network.


title: A Survey of Various Machine Learning Techniques for Text Classification

abstract: Sentiments are expressions of one’s words in a sentence. Hence understanding the meaning of text in the sentence is of utmost importance to people of various fields like customer reviews in companies, movie reviews in movies, etc. It may involve huge text data to analyze and it becomes totally unviable for manually understanding the meaning of sentences. Classifier algorithms should be used to classify the various meaning of the sentences. By using pre-defined data to train our classifier and three different algorithms namely Naive Bayes, Support Vector Machines, Decision Trees, we can simplify the task of text classification. Using relevant results and examples we will prove that SVM is one of the better algorithms in providing higher accuracy over the other two algorithms i.e. Naive Bayes and Decision Tree.


title: A Digital Door Lock System for the Internet of Things with Improved Security and Usability

abstract: Recently, digital door locks have been widely used as part of the IoT (Internet of Things). However, the media has reported digital door locks being opened by invalid users to invade homes and offices. In this study, a digital door lock system that can work with the IoT environment is proposed. It is designed and implemented to enhance security and convenience. The proposed system provides strengthened security functions that can transfer recorded images to a user"s mobile device when an invalid user attempts an illegal operation; it can also deliver alarm information to the mobile device when the door lock is physically damaged. The proposed system enables a user to check the access information and remotely operate the door lock to enhance convenience.


Electrical Characterization of Defects in Gate Dielectrics


title: Privacy protection based access control scheme in cloud-based services

abstract: With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection (PS-ACS). In the PS-ACS scheme, we divide users into private domain (PRD) and public domain (PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption (KAE) and the Improved Attribute-based Signature (IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption (CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users' privacy in cloud-based services.


Action research


title: Recent trends in social aware robot navigation

abstract: With the robots tending to accumulate more and more capabilities beyond the level of acting in a deterministic fashion, the idea of introducing them into our every day lives seems to be closer now. Robotics systems and techniques appeared during the recent years have achieved astonishing potential to perceive and interpret their surrounding not only as low level features but also close to human understandable concepts. Such advances, in conjunction with the aspiration to incorporate robots into domestic or public places, led to the flourishing of fields dealing with their response in human presence. Following this notion, the field of social mapping was recently introduced in order to manage the shared space among robots and individuals in an ordinary fashion. This manuscript aims to systemize the recent literature by describing the required levels of robot perception, focusing on methods related to robots social awareness, the availability of datasets these methods can be compared with, as well as issues that remain open and need to be confronted when robots operate in close proximity with humans.