title: Design and Analysis of 6-DOF Triple Scissor Extender Robots With Applications in Aircraft Assembly

abstract: A new type of parallel robot mechanism with an extendable structure is presented, and its kinematic properties and design parameters are analyzed. The triple scissor extender (TSE) is a six-degree-of-freedom robotic mechanism for reaching high ceilings and positioning an end effector. Three scissor mechanisms are arranged in parallel, with the bottom ends coupled to linear slides, and the top vertex attached to an end effector plate. Arbitrary positions and orientations of the end effector can be achieved through the coordinated motion of the six linear actuators located at the base. By changing key geometric parameters, the TSE's design can yield a specific desired workspace volume and differential motion behavior. A general kinematic model for diverse TSEs is derived, and the kinematic properties, including workspace, singularity, and the Jacobian singular values, are evaluated. From these expressions, four key design parameters are identified, and their sensitivity upon the workspace volume and the Jacobian singular values is analyzed.A case study in autonomous aircraft assembly is presented using the insights gained from the design parameter studies.


title: Safe robot arm with safe joint mechanism using nonlinear spring system for collision safety

abstract: Collision safety between humans and robots has drawn much attention since service robots are increasingly being used in human environments. A safe robot arm based on passive compliance can usually provide faster and more reliable responses for dynamic collision than an active one involving sensors and actuators. Since both positioning accuracy and collision safety of the robot arm are equally important, a robot arm should have very low stiffness when subjected to a collision force greater than the injury tolerance, but should otherwise maintain very high stiffness. To implement these requirements, a novel safe joint mechanism (SJM-II) which has much smaller size and lighter weight than the previous model, is proposed in this research. The SJM-II has the advantage of nonlinear spring which is achieved using only passive mechanical elements such as linear springs and a double-slider mechanism. Various analyses and experiments on static and dynamic collisions show that stiffness of the SJM-II is kept very high against an external torque less than the predetermined threshold torque, but abruptly drops when the input torque exceeds this threshold, thereby guaranteeing positioning accuracy and collision safety. Furthermore, a robot arm with two SJM-IIs is verified to achieve collision safety in 2D space.


title: Exposure to violence reduces empathetic responses to other’s pain

abstract: Past researches showed that empathy for pain not only triggers a resonance mechanism between other and self, but also is modulated by contextual factors. Using functional magnetic resonance imaging, the present study demonstrated that short-term media violence exposure reduced both pain ratings and also the activation of anterior insula and anterior mid-cingulate cortex to other’s pain. Thus, violence exposure modulated empathic responses to other’s pain based on a physiological desensitization.


title: A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles

abstract: Hybrid electric vehicles employ a hybrid propulsion system to combine the energy efficiency of electric motor and a long driving range of internal combustion engine, thereby achieving a higher fuel economy as well as convenience compared with conventional ICE vehicles. However, the relatively complicated powertrain structures of HEVs necessitate an effective power management policy to determine the power split between ICE and EM. In this work, we propose a deep reinforcement learning framework of the HEV power management with the aim of improving fuel economy. The DRL technique is comprised of an offline deep neural network construction phase and an online deep Q-learning phase. Unlike traditional reinforcement learning, DRL presents the capability of handling the high dimensional state and action space in the actual decision-making process, making it suitable for the HEV power management problem. Enabled by the DRL technique, the derived HEV power management policy is close to optimal, fully model-free, and independent of a prior knowledge of driving cycles. Simulation results based on actual vehicle setup over real-world and testing driving cycles demonstrate the effectiveness of the proposed framework on optimizing HEV fuel economy.


title: Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

abstract: In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu/~junhua.mao/m-RNN.html .


Cryptographically imposed model for Efficient Multiple Keyword-based Search over Encrypted Data in Cloud by Secure Index using Bloom Filter and False Random Bit Generator


title: Lighting design for portraits with a virtual light stage

abstract: We propose a software solution which allows the user to design a realistic illumination for a given 2D image of a face. The user paints a few strokes on the image to give clues of desired novel lighting effects. The algorithm produces an image of the face under the best possible realistic illumination, accordingly. It takes advantage of a 3D Morphable Model framework and a state of the art inverse lighting algorithm for faces. As a result, illumination effects such as cast shadows, specular highlights, multidirectional and colored lighting can be applied to the portrait. The algorithm is versatile for uncontrolled imaging conditions; it is invariant to face pose and image saturation, and produces realistic results from a single input image of an unknown face.


title: Creating Causal Embeddings for Question Answering with Minimal Supervision

abstract: A common model for question answering (QA) is that a good answer is one that is closely related to the question, where relatedness is often determined using general-purpose lexical models such as word embeddings. We argue that a better approach is to look for answers that are related to the question in a relevant way, according to the information need of the question, which may be determined through task-specific embeddings. With causality as a use case, we implement this insight in three steps. First, we generate causal embeddings cost-effectively by bootstrapping cause-effect pairs extracted from free text using a small set of seed patterns. Second, we train dedicated embeddings over this data, by using task-specific contexts, i.e., the context of a cause is its effect. Finally, we extend a state-of-the-art reranking approach for QA to incorporate these causal embeddings. We evaluate the causal embedding models both directly with a casual implication task, and indirectly, in a downstream causal QA task using data from Yahoo! Answers. We show that explicitly modeling causality improves performance in both tasks. In the QA task our best model achieves 37.3% P@1, significantly outperforming a strong baseline by 7.7% (relative).


title: Resting state fMRI: A personal history

abstract: Abstract The goal of this review is to describe, from a personal perspective, the development and emergence of the resting state fMRI. In particular, various concepts derived from the resting state data are discussed in detail, including connectivity, amplitude of the fluctuations, analysis techniques, and use in clinical populations. We also briefly summarize our efforts in creating an open data sharing platform as well as both a journal and a conference dedicated to brain connectivity. All three projects are aimed at significantly increasing the impact of resting state fMRI developments and enabling large, collaborative science projects.


title: Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration

abstract: We present a practical, stratified autocalibration algorithm with theoretical guarantees of global optimality. Given a projective reconstruction, the first stage of the algorithm upgrades it to affine by estimating the position of the plane at infinity. The plane at infinity is computed by globally minimizing a least squares formulation of the modulus constraints. In the second stage, the algorithm upgrades this affine reconstruction to a metric one by globally minimizing the infinite homography relation to compute the dual image of the absolute conic (DIAC). The positive semidefiniteness of the DIAC is explicitly enforced as part of the optimization process, rather than as a post-processing step. For each stage, we construct and minimize tight convex relaxations of the highly non-convex objective functions in a branch and bound optimization framework. We exploit the problem structure to restrict the search space for the DIAC and the plane at infinity to a small, fixed number of branching dimensions, independent of the number of views. Experimental evidence of the accuracy, speed and scalability of our algorithm is presented on synthetic and real data. MATLAB code for the implementation is made available to the community.


title: Neural Networks Method for Fast Web Page Allocation in Mesh Generation

abstract: This paper presents a novel competitive neural network learning approach to schedule requests to a cluster of Web servers. In our new method one of the two main purposes was to try the advantage and effectiveness of the artificial neural networks to produce a triangular grid, where first an artificial neural network is used to order the data and form a grid of control vertices with triangle topology, hence after this step the original, a general scheme for mesh simplification and optimization that allows to control the geometric approximation as well as the element shape and size quality.


title: Scattered Data Interpolation Methods for Electronic Imaging Systems: A Survey

abstract: Numerous problems in electronic imaging systems in- volve the need to interpolate from irregularly spaced data. One ex- ample is the calibration of color input/output devices with respect to a common intermediate objective color space, such as XYZ or L*a*b*. In the present report we survey some of the most impor- tant methods of scattered data interpolation in two-dimensional and in three-dimensional spaces. We review both single-valued cases, where the underlying function has the form f:R 2 !R or f:R 3 !R, and multivalued cases, where the underlying function is f:R 2 !R 2 or f:R 3 !R 3 . The main methods we review include linear triangular (or tetrahedral) interpolation, cubic triangular (Clough-Tocher) interpo- lation, triangle based blending interpolation, inverse distance weighted methods, radial basis function methods, and natural neigh- bor interpolation methods. We also review one method of scattered data fitting, as an illustration to the basic differences between scat- tered data interpolation and scattered data fitting. © 2002 SPIE and


title: The possibility of an epidemic meme analogy for web community population analysis

abstract: The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter's information alone being observable.


title: EEMC: Enabling Energy-Efficient Mobile Crowdsensing with Anonymous Participants

abstract: Mobile Crowdsensing (MCS) requires users to be motivated to participate. However, concerns regarding energy consumption and privacy—among other things—may compromise their willingness to join such a crowd. Our preliminary observations and analysis of common MCS applications have shown that the data transfer in MCS applications may incur significant energy consumption due to the 3G connection setup. However, if data are transferred in parallel with a traditional phone call, then such transfer can be done almost “for free”: with only an insignificant additional amount of energy required to piggy-back the data—usually incoming task assignments and outgoing sensor results—on top of the call. Here, we present an Energy-Efficient Mobile Crowdsensing (EEMC) framework where task assignments and sensing results are transferred in parallel with phone calls. The main objective, and the principal contribution of this article, is an MCS task assignment scheme that guarantees that a minimum number of anonymous participants return sensor results within a specified time frame, while also minimizing the waste of energy due to redundant task assignments and considering privacy concerns of participants. Evaluations with a large-scale real-world phone call dataset show that our proposed EEMC framework outperforms the baseline approaches, and it can reduce overall energy consumption in data transfer by 54--66p when compared to the 3G-based solution.


title: Learning to Cluster for Proposal-Free Instance Segmentation

abstract: This work proposed a novel learning objective to train a deep neural network to perform end-to-end image pixel clustering. We applied the approach to instance segmentation, which is at the intersection of image semantic segmentation and object detection. We utilize the most fundamental property of instance labeling- the pairwise relationship between pixels-as the supervision to formulate the learning objective, then apply it to train a fully convolutional network (FCN) for learning to perform pixel-wise clustering. The resulting clusters can be used as the instance labeling directly. To support labeling of an unlimited number of instance, we further formulate ideas from graph coloring theory into the proposed learning objective. The evaluation on the Cityscapes dataset demonstrates strong performance and therefore proof of the concept. Moreover, our approach won the second place in the lane detection competition of 2017 CVPR Autonomous Driving Challenge, and was the top performer without using external data.


Traffic Flow Prediction from Loop Counter Sensor Data using Machine Learning Methods


Towards Battery-Free HD Video Streaming


title: Linking speaking and looking behavior patterns with group composition, perception, and performance

abstract: This paper addresses the task of mining typical behavioral patterns from small group face-to-face interactions and linking them to social-psychological group variables. Towards this goal, we define group speaking and looking cues by aggregating automatically extracted cues at the individual and dyadic levels. Then, we define a bag of nonverbal patterns (Bag-of-NVPs) to discretize the group cues. The topics learnt using the Latent Dirichlet Allocation (LDA) topic model are then interpreted by studying the correlations with group variables such as group composition, group interpersonal perception, and group performance. Our results show that both group behavior cues and topics have significant correlations with (and predictive information for) all the above variables. For our study, we use interactions with unacquainted members i.e. newly formed groups.


title: Gender, Communication, and Self-Presentation in Teen Chatrooms Revisited: Have Patterns Changed?

abstract: This study evaluates empirically the proposition implicit in much recent gender and CMC research that expressions of gender distinctness among teens in online environments are becoming less frequent and less traditional. Gender preferences were analyzed in linguistic features and communication styles in synchronous text messages, along with self-presentation in user profile pictures, drawing on data from popular English-language teen chat sites collected in 2010. Significant differences were found in speech acts, message tone, and in physical stance, dress, and social distance in profile pictures that generally conform to traditional gender stereotypes. These findings are interpreted in light of previous gender and teen CMC research, adolescent development and socialization patterns, mass media representations, and trends towards media convergence in chat platforms.


Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders


title: Device and Architecture Outlook for Beyond CMOS Switches

abstract: Sooner or later, fundamental limitations destine complementary metal-oxide-semiconductor (CMOS) scaling to a conclusion. A number of unique switches have been proposed as replacements, many of which do not even use electron charge as the state variable. Instead, these nanoscale structures pass tokens in the spin, excitonic, photonic, magnetic, quantum, or even heat domains. Emergent physical behaviors and idiosyncrasies of these novel switches can complement the execution of specific algorithms or workloads by enabling quite unique architectures. Ultimately, exploiting these unusual responses will extend throughput in high-performance computing. Alternative tokens also require new transport mechanisms to replace the conventional chip wire interconnect schemes of charge-based computing. New intrinsic limits to scaling in post-CMOS technologies are likely to be bounded ultimately by thermodynamic entropy and Shannon noise.


title: Motion planning for a reversing general 2-trailer configuration using Closed-Loop RRT

abstract: Reversing with a dolly steered trailer configuration is a hard task for any driver without extensive training. In this work we present a motion planning and control framework that can be used to automatically plan and execute complicated manoeuvres. The unstable dynamics of the reversing general 2-trailer configuration with off-axle hitching is first stabilised by an LQ-controller and then a pure pursuit path tracker is used on a higher level giving a cascaded controller that can track piecewise linear reference paths. This controller together with a kinematic model of the trailer configuration is then used for forward simulations within a Closed-Loop Rapidly Exploring Random Tree framework to generate motion plans that are not only kinematically feasible but also include the limitations of the controller's tracking performance when reversing. The approach is evaluated over a series of Monte Carlo simulations on three different scenarios and impressive success rates are achieved. Finally the approach is successfully tested on a small scale test platform where the motion plan is calculated and then sent to the platform for execution.


title: Contagion of Cheating Behaviors in Online Social Networks

abstract: Human behaviors are known to spread through social contact. The diffusion process on social networks has also been leveraged to understand the spread of undesirable contagion. The contagion of malicious or even criminal behaviors in online social networks is just beginning to attract attention. Here, we study the social contagion problem of cheating behavior found in the massively multiplayer online role-playing game (MMORPG) that provides a lifelike environment with rich and realistic user interactions. Because cheating users boast an abnormal thus conspicuous degree of success, it has a strong chance of being noticed by their friends and leading them to cheat themselves. To detect and prevent cheating, it is beneficial to understand this dynamic as a contagion problem. In this paper, we show the existence of the contagion of cheating. We then explore various possible social reinforcement mechanisms after introducing several factors to quantify the effect of social reinforcement on the contagion and analyze the dynamics of bot diffusion in an extensive user interaction log from a major MMORPG.


title: Effect of Biofield Treatment on Physical, Thermal, and Spectral Properties of SFRE 199-1 Mammalian Cell Culture Medium

abstract: SFRE 199-1 medium (SFRE-M) is important mammalian cell culture medium, used for the culture of primary cells of mammals such as baboon kidney cells. The present study was attempted to evaluate the impact of biofield energy treatment on the physical, thermal and spectral properties of SFRE-M. The study was accomplished in two groups; one was set as control while another was subjected to Mr. Trivedi’s biofield energy treatment and coded as treated group. Subsequently, the control and treated samples were analyzed using various analytical techniques. The CHNO analysis showed about 2.16, 4.87, and 5.89% decrease in percent contents of carbon, hydrogen, and oxygen, respectively; while 9.49% increase in nitrogen contents of treated sample as compared to the control. X-ray diffraction (XRD) analysis showed 7.23% decrease in crystallite size of treated sample as compared to the control. The thermogravimetric analysis (TGA) analysis showed the increase in onset temperature of thermal degradation by 19.61% in treated sample with respect to the control. The control sample showed the 48.63% weight loss during the thermal degradation temperature (Tmax) while the treated sample showed only 13.62% weight loss during the Tmax. The differential scanning calorimetry (DSC) analysis showed the 62.58% increase in the latent heat of fusion of treated sample with respect to the control sample. The Fourier transform infrared spectroscopy (FT-IR) spectrum of treated SFRE-M showed the alteration in the wavenumber of C-O, C-N and C-H vibrations in the treated sample as compared to the control. Altogether, the XRD, TGA-DTG, DSC, and FT-IR analysis suggest that Mr. Trivedi’s biofield energy treatment has the impact on physical, thermal and spectral properties of SFRE-M. The treated SFRE-M was more thermal stable than the control SFRE-M and can be used as the better culture media for mammalian cell culture.


title: A brief review on upper extremity robotic exoskeleton systems

abstract: Robotic exoskeleton systems are one of the highly active areas in recent robotic research. These systems have been developed significantly to be used for the human power augmentation, robotic rehabilitation, human power assist, and haptic interaction in virtual reality. Unlike the robots used in industry, the robotic exoskeleton systems should be designed with special consideration since they directly interact with human user. In the mechanical design of these systems, movable ranges, safety, comfort wearing, low inertia, and adaptability should be especially considered. Controllability, responsiveness, flexible and smooth motion generation, and safety should especially be considered in the controllers of exoskeleton systems. Furthermore, the controller should generate the motions in accordance with the human motion intention. This paper briefly reviews the upper extremity robotic exoskeleton systems. In the short review, it is focused to identify the brief history, basic concept, challenges, and future development of the robotic exoskeleton systems. Furthermore, key technologies of upper extremity exoskeleton systems are reviewed by taking state-of-the-art robot as examples.


title: Practical NFC peer-to-peer relay attack using mobile phones

abstract: NFC is a standardised technology providing short-range RFID communication channels for mobile devices. Peer-to-peer applications for mobile devices are receiving increased interest and in some cases these services are relying on NFC communication. It has been suggested that NFC systems are particularly vulnerable to relay attacks, and that the attacker's proxy devices could even be implemented using off-the-shelf NFC-enabled devices. This paper describes how a relay attack can be implemented against systems using legitimate peer-to-peer NFC communication by developing and installing suitable MIDlets on the attacker's own NFC-enabled mobile phones. The attack does not need to access secure program memory nor use any code signing, and can use publicly available APIs. We go on to discuss how relay attack countermeasures using device location could be used in the mobile environment. These countermeasures could also be applied to prevent relay attacks on contactless applications using 'passive' NFC on mobile phones.


title: A Customer Loyalty Model for E-Service Context

abstract: While the importance of customer loyalty has been recognized in the marketing literature for at least three decades, the conceptualization and empirical validation of a customer loyalty model for e-service context has not been addressed. This paper describes a theoretical model for investigating the three main antecedent influences on loyalty (attitudinal commitment and behavioral loyalty) for e-service context: trust, customer satisfaction, and perceived value. Based on the theoretical model, a comprehensive set of hypotheses were formulated and a methodology for testing them was outlined. These hypotheses were tested empirically to demonstrate the applicability of the theoretical model. The results indicate that trust, customer satisfaction, perceived value, and commitment are separate constructs that combine to determine the loyalty, with commitment exerting a stronger influence than trust, customer satisfaction, and perceived value. Customer satisfaction and perceived value were also indirectly related to loyalty through commitment. Finally, the authors discuss the managerial and theoretical implications of these results.


Discourse Parsing with Attention-based Hierarchical Neural Networks


title: Developing a model of e-governance for urban and rural areas of Bangladesh

abstract: Several developing countries including Bangladesh are realizing the role ICT (information & communication technology) can play in the governance sector, and are putting into practice innovative e-governance models that may be technologically simple but are drastically changing the way information is distributed in the society. This paper depicts and implements a model of e-governance for urban and rural areas in Bangladesh. The project deeply considered existing ICT infrastructure in Bangladesh and put effort to implement the model in a cost effective and flexible manner. A clear, comprehensive layout and network architecture for rural and city areas is given separately. We also described complementary tasks such as database implementation, system development, web related task etc. for e-governance. The main objective of the project is to implement e-governance in an easy and cost effective way to suit with existing ICT infrastructure in Bangladesh to provide better information and service delivery of government to the people.


title: Access Control Models for Cloud-Enabled Internet of Things: A Proposed Architecture and Research Agenda

abstract: The concept and deployment of Internet of Things (IoT) has continued to develop momentum over recent years. Several different layered architectures for IoT have been proposed, although there is no consensus yet on a widely accepted architecture. In general, the proposed IoT architectures comprise three main components: an object layer, one or more middle layers, and an application layer. The main difference in detail is in the middle layers. Some include a cloud services layer for managing IoT things. Some propose virtual objects as digital counterparts for physical IoT objects. Sometimes both cloud services and virtual objects are included.In this paper, we take a first step toward our eventual goal of developing an authoritative family of access control models for a cloud-enabled Internet of Things. Our proposed access-control oriented architecture comprises four layers: an object layer, a virtual object layer, a cloud services layer, and an application layer. This 4-layer architecture serves as a framework to build access control models for a cloud-enabled IoT. Within this architecture, we present illustrative examples that highlight some IoT access control issues leading to a discussion of needed access control research. We identify the need for communication control within each layer and across adjacent layers (particularly in the lower layers), coupled with the need for data access control (particularly in the cloud services and application layers).


title: The obstacle-restriction method for robot obstacle avoidance in difficult environments

abstract: This paper addresses the obstacle avoidance problem in difficult scenarios that usually are dense, complex and cluttered. The proposal is a method called the obstacle-restriction. At each iteration of the control cycle, this method addresses the obstacle avoidance in two steps. First there is procedure to compute instantaneous subgoals in the obstacle structure (obtained by the sensors). The second step associates a motion restriction to each obstacle, which are managed next to compute the most promising motion direction. The advantage of this technique is that it avoids common limitations of previous obstacle avoidance methods, improving their navigation performance in difficult scenarios. Furthermore, we obtain similar results to the recent methods that achieve navigation in troublesome scenarios. However, the new method improves their behavior in open spaces. The performance of this method is illustrated with experimental results obtained with a robotic wheelchair vehicle.


title: Urban Stormwater Runoff: A New Class of Environmental Flow Problem

abstract: Environmental flow assessment frameworks have begun to consider changes to flow regimes resulting from land-use change. Urban stormwater runoff, which degrades streams through altered volume, pattern and quality of flow, presents a problem that challenges dominant approaches to stormwater and water resource management, and to environmental flow assessment. We used evidence of ecological response to different stormwater drainage systems to develop methods for input to environmental flow assessment. We identified the nature of hydrologic change resulting from conventional urban stormwater runoff, and the mechanisms by which such hydrologic change is prevented in streams where ecological condition has been protected. We also quantified the increase in total volume resulting from urban stormwater runoff, by comparing annual streamflow volumes from undeveloped catchments with the volumes that would run off impervious surfaces under the same rainfall regimes. In catchments with as little as 5-10% total imperviousness, conventional stormwater drainage, associated with poor in-stream ecological condition, reduces contributions to baseflows and increases the frequency and magnitude of storm flows, but in similarly impervious catchments in which streams retain good ecological condition, informal drainage to forested hillslopes, without a direct piped discharge to the stream, results in little such hydrologic change. In urbanized catchments, dispersed urban stormwater retention measures can potentially protect urban stream ecosystems by mimicking the hydrologic effects of informal drainage, if sufficient water is harvested and kept out of the stream, and if discharged water is treated to a suitable quality. Urban stormwater is a new class of environmental flow problem: one that requires reduction of a large excess volume of water to maintain riverine ecological integrity. It is the best type of problem, because solving it provides an opportunity to solve other problems such as the provision of water for human use.


title: Detecting Forgery From Static-Scene Video Based on Inconsistency in Noise Level Functions

abstract: Recently developed video editing techniques have enabled us to create realistic synthesized videos. Therefore, using video data as evidence in places such as courts of law requires a method to detect forged videos. In this study, we developed an approach to detect suspicious regions in a video of a static scene on the basis of the noise characteristics. The image signal contains irradiance-dependent noise the variance of which is described by a noise level function (NLF) as a function of irradiance. We introduce a probabilistic model providing the inference of an NLF that controls the characteristics of the noise at each pixel. Forged pixels in the regions clipped from another video camera can be differentiated by using maximum a posteriori estimation for the noise model when the NLFs of the regions are inconsistent with the rest of the video. We demonstrate the effectiveness of our proposed method by adapting it to videos recorded indoors and outdoors. The proposed method enables us to highly accurately evaluate the per-pixel authenticity of the given video, which achieves denser estimation than prior work based on block-level validation. In addition, the proposed method can be applied to various kinds of videos such as those contaminated by large noise and recorded with any scan formats, which limits the applicability of the existing methods.


title: The impact of quality practices on customer satisfaction and business results: product versus service organizations

abstract: The Impact of Quality Practices on Customer Satisfaction and Business Results: Product versus Service Organizations


title: Enabling public verifiability and data dynamics for storage security in cloud computing

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


title: Active Learning for Matching Problems

abstract: Effective learning of user preferences is critical to easing user burden in various types of matching problems. Equally important is active query selection to further reduce the amount of preference information users must provide. We address the problem of active learning of user preferences for matching problems, introducing a novel method for determining probabilistic matchings, and developing several new active learning strategies that are sensitive to the specific matching objective. Experiments with real-world data sets spanning diverse domains demonstrate that matching-sensitive active learning outperforms standard techniques.


title: Systemic metastasis in glioblastoma may represent the emergence of neoplastic subclones.

abstract: Glioblastomas only rarely metastasize to sites outside the central nervous system, for reasons that are poorly understood. We report the clinicopathological and molecular genetic findings in 6 patients with metastatic glioblastoma. Four patients were under the age of 32 and all but 1 patient died within 2 yr of diagnosis. The number of metastases ranged from 1 to 3. At the time of death, 3 patients had apparent tumor control at their primary site. We evaluated DNA from both primary and metastatic glioblastomas for genetic alterations commonly found in glioblastomas: TP53 mutations, CDKN2A/p16 deletions, EGFR amplification, and allelic loss of chromosomes 1p, 10q and 19q. Four of 6 cases had TP53 mutations and only single cases had EGFR amplification, CDKN2A/p16 deletions, or allelic loss of 1p, 10q and 19q; 2 cases had no detectable genetic alterations. In 2 cases, the primary and metastatic tumors had identical genotypes. Remarkably, however, 2 cases had different TP53 alterations in the primary and metastatic lesions, or among the metastatic tumors, which suggests that some metastatic deposits may represent emergence of subclones that were not necessarily dominant in the primary tumor. The present observations and a review of the recent literature demonstrate that metastatic glioblastomas tend to occur in younger adults who do not follow long clinical courses, and may be characterized by TP53 mutations and differential clonal selection.


title: Amino acid residues that modulate the properties of tyrosine YZ and the manganese cluster in the water oxidizing complex of photosystem II

abstract: The catalytic site for photosynthetic water oxidation is embedded in a protein matrix consisting of nearly 30 different polypeptides. Residues from several of these polypeptides modulate the properties of the tetrameric Mn cluster and the redox-active tyrosine residue, YZ, that are located at the catalytic site. However, most or all of the residues that interact directly with YZ and the Mn cluster appear to be contributed by the D1 polypeptide. This review summarizes our knowledge of the environments of YZ and the Mn cluster as obtained from the introduction of site-directed, deletion, and other mutations into the photosystem II polypeptides of the cyanobacterium Synechocystis sp. PCC 6803 and the green alga Chlamydomonas reinhardtii.


title: Chronic granulocytic leukemia in a horse.

abstract: Abstract ::: A nine year old quarter horse exhibited progressive weight loss and inappetance over a 47 day period. There was clinical evidence of pleuritis and pneumonia substantiated by leukocytosis and elevated protein in pleural fluid. Over the entire period the horse was neutropenic and had circulating abnormal immature granulocytes and low numbers of blast cells. Anemia and thrombocytopenia progressively worsened. Bone marrow examination revealed very few mature granulocytes but large numbers of immature cells of the granulocytic series and marked megaloblastic transformation of erythroid cells. These findings were consistent with chronic granulocytic leukemia.


Cooperative Multi-agent Reinforcement Learning for Flappy Bird *


Representing Attitude: Euler Angles, Unit Quaternions, and Rotation Vectors


title: Direction matters: Depth estimation with a surface normal classifier

abstract: In this work we make use of recent advances in data driven classification to improve standard approaches for binocular stereo matching and single view depth estimation. Surface normal direction estimation has become feasible and shown to work reliably on state of the art benchmark datasets. Information about the surface orientation contributes crucial information about the scene geometry in cases where standard approaches struggle. We describe, how the responses of such a classifier can be included in global stereo matching approaches. One of the strengths of our approach is, that we can use the classifier responses for a whole set of directions and let the final optimization decide about the surface orientation. This is important in cases where based on the classifier, multiple different surface orientations seem likely. We evaluate our method on two challenging real-world datasets for the two proposed applications. For the binocular stereo matching we use road scene imagery taken from a car and for the single view depth estimation we use images taken in indoor environments.


Belief as lived relations


title: The FinTech phenomenon: antecedents of financial innovation perceived by the popular press

abstract: The financial industry has been strongly influenced by digitalization in the past few years reflected by the emergence of “FinTech,” which represents the marriage of “finance” and “information technology.” FinTech provides opportunities for the creation of new services and business models and poses challenges to traditional financial service providers. Therefore, FinTech has become a subject of debate among practitioners, investors, and researchers and is highly visible in the popular media. In this study, we unveil the drivers motivating the FinTech phenomenon perceived by the English and German popular press including the subjects discussed in the context of FinTech. This study is the first one to reflect the media perspective on the FinTech phenomenon in the research. In doing so, we extend the growing knowledge on FinTech and contribute to a common understanding in the financial and digital innovation literature. These study contributes to research in the areas of information systems, finance and interdisciplinary social sciences. Moreover, it brings value to practitioners (entrepreneurs, investors, regulators, etc.), who explore the field of FinTech.


On the Near Impossibility of Measuring the Returns to Advertising


title: Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

abstract: No uniform access to neuroimaging analysis software and usage information For current multi-modal datasets, researchers typically resort to using different software packages for different components of the analysis. However, these different software packages are accessed, and interfaced with, in different ways, such as: shell scripting (FSL, AFNI, Camino), MATLAB (SPM), and Python (Nipy). This has resulted in a heterogeneous set of software with no uniform way to use these tools or execute them. With the primary focus on algorithmic improvement, academic software development often lacks a rigorous software engineering framework that involves extensive testing and documentation and ensures compatibility with other IntroduCtIon Over the past 20 years, advances in non-invasive in vivo neuroimaging have resulted in an explosion of studies investigating human cognition in health and disease. Current imaging studies acquire multi-modal image data (e.g., structural, diffusion, functional) and combine these with non-imaging behavioral data, patient and/or treatment history, and demographic and genetic information. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze such extensive data. In a typical analysis, algorithms from these packages, each with its own set of parameters, process the raw data. However, data collected for a single study can be diverse (highly multi-dimensional) and large, and algorithms suited for one dataset may not be optimal for another. This complicates analysis methods and makes data exploration and inference challenging, and comparative analysis of new algorithms difficult. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python


title: Ockham's Razor in Memetic Computing: Three Stage Optimal Memetic Exploration

abstract: Memetic computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligent structures capable of problem-solving. This paper focuses on memetic computing optimization algorithms and proposes a counter-tendency approach for algorithmic design. Research in the field tends to go in the direction of improving existing algorithms by combining different methods or through the formulation of more complicated structures. Contrary to this trend, we instead focus on simplicity, proposing a structurally simple algorithm with emphasis on processing only one solution at a time. The proposed algorithm, namely three stage optimal memetic exploration, is composed of three memes; the first stochastic and with a long search radius, the second stochastic and with a moderate search radius and the third deterministic and with a short search radius. The bottom-up combination of the three operators by means of a natural trial and error logic, generates a robust and efficient optimizer, capable of competing with modern complex and computationally expensive algorithms. This is suggestive of the fact that complexity in algorithmic structures can be unnecessary, if not detrimental, and that simple bottom-up approaches are likely to be competitive is here invoked as an extension to memetic computing basing on the philosophical concept of Ockham's Razor. An extensive experimental setup on various test problems and one digital signal processing application is presented. Numerical results show that the proposed approach, despite its simplicity and low computational cost displays a very good performance on several problems, and is competitive with sophisticated algorithms representing the-state-of-the-art in computational intelligence optimization.


title: Music Ontology for Mood and Situation Reasoning to Support Music Retrieval and Recommendation

abstract: In this paper, we discuss the use of knowledge for analyzing and retrieving music contents semantically. First, we present Context-based Music Recommendation (COMUS) ontology to reason desired user emotion state from context and user preference information in the ontology. COMUS is a music dedicated ontology in OWL constructed by incorporating domain specific classes for music recommendation into the Music Ontology, which include situation, mood and musical features. More specifically, we describe the ontologies of mood and situation in music using low-level features like pitch or duration and musical factors like tempo or rhythm. Our proposed ontology defines generic as well as domain-specific concepts whose detection is important for the analysis and description of music in a specific domain. As a novelty, our ontology can express detailed and complicated relations among the music, moods and situations, enabling users to find appropriate music for the music retrieval and recommendation application. We present some of the experiments we performed as a case-study for music recommendation.


title: Learning Communication for Multi-agent Systems

abstract: We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to learn multi-agent languages for the predator agents in a version of the predator-prey problem. The resulting evolved behavior of the communicating multi-agent system is equivalent to that of a Mealy machine whose states are determined by the evolved language. We also constructed non-learning predators whose capture behavior was designed to take advantage of prey behavior known a priori. Simulations show that introducing noise to the decision process of the hard-coded predators allow them to significantly ourperform all previously published work on similar preys. Furthermore, the evolved communicating predators were able to perform significantly better than the hard-coded predators, which indicates that the system was able to learn superior communicating strategies not readily available to the human designer.


utonomous robotic weed control systems : A review .


title: gem5-gpu: A Heterogeneous CPU-GPU Simulator

abstract: gem5-gpu is a new simulator that models tightly integrated CPU-GPU systems. It builds on gem5, a modular full-system CPU simulator, and GPGPU-Sim, a detailed GPGPU simulator. gem5-gpu routes most memory accesses through Ruby, which is a highly configurable memory system in gem5. By doing this, it is able to simulate many system configurations, ranging from a system with coherent caches and a single virtual address space across the CPU and GPU to a system that maintains separate GPU and CPU physical address spaces. gem5-gpu can run most unmodified CUDA 3.2 source code. Applications can launch non-blocking kernels, allowing the CPU and GPU to execute simultaneously. We present gem5-gpu ’s software architecture and a brief performance validation. We also discuss possible extensions to the simulator. gem5-gpu is open source and available at gem5-gpu.cs.wisc.edu.


title: Analysis of various NoSql database

abstract: In the age of internet, when data production has gone off-bounds, organizations are facing a tough challenge in terms of processing, analyzing and storing big data. The major drawback with this data is that it is not only being created at a lightning fast pace but it is also unstructured i.e. does not have a fixed schema. Moreover it is arising from disparate and discrete sources such as the social media. NoSql or Not Only Sql databases offer a highly flexible and horizontally scalable solution to store structured, semi-structured and unstructured data. These databases store data in the form of key-value pairs which offers better availability and high throughput performance in terms of processing queries. They are designed to be highly customizable according to the user's requirements, and well suited for the needs of the overlying application as well as the underlying data being stored. This paper firstly provides a general overview of the NoSql storage technology. Later a thorough analysis will highlight the features, strengths and limitations of six most popular NoSql databases and thus would help the organizations to choose a NoSql database which is well-suited to their needs.


title: Estimation of Maximum Angular Operation Range for Permanent-Magnet Slotted Limited-Angle Torque Motor

abstract: This paper presents an analytical method for estimating the maximum angular operation range (MAOR) in a permanent-magnet slotted limited-angle torque motor (LATM). The operating principle of three topologies is analyzed and discussed, including a toroidal LATM, a slotted LATM with common pole/slot combination, and a slotted LATM with special pole/slot combination. The analysis indicates that no effective methods have been proposed to estimate the MAOR for a slotted LATM with special pole/slot combination. Hence, this paper is focused on solving this problem. The results obtained from the analytical method are validated by a finite-element (FE) method. The average time of the proposed method takes far less than that of an FE method, so that the design cycle of LATMs can be shortened. Finally, two prototype machines are manufactured, and their experimental results are presented and discussed. The FE and experimental results validate the accuracy of an analytical method.


title: Structure-Sensitive Saliency Detection via Multilevel Rank Analysis in Intrinsic Feature Space

abstract: This paper advocates a novel multiscale, structure-sensitive saliency detection method, which can distinguish multilevel, reliable saliency from various natural pictures in a robust and versatile way. One key challenge for saliency detection is to guarantee the entire salient object being characterized differently from nonsalient background. To tackle this, our strategy is to design a structure-aware descriptor based on the intrinsic biharmonic distance metric. One benefit of introducing this descriptor is its ability to simultaneously integrate local and global structure information, which is extremely valuable for separating the salient object from nonsalient background in a multiscale sense. Upon devising such powerful shape descriptor, the remaining challenge is to capture the saliency to make sure that salient subparts actually stand out among all possible candidates. Toward this goal, we conduct multilevel low-rank and sparse analysis in the intrinsic feature space spanned by the shape descriptors defined on over-segmented super-pixels. Since the low-rank property emphasizes much more on stronger similarities among super-pixels, we naturally obtain a scale space along the rank dimension in this way. Multiscale saliency can be obtained by simply computing differences among the low-rank components across the rank scale. We conduct extensive experiments on some public benchmarks, and make comprehensive, quantitative evaluation between our method and existing state-of-the-art techniques. All the results demonstrate the superiority of our method in accuracy, reliability, robustness, and versatility.


title: Hermes: An Optimization of HyperLogLog Counting in real-time data processing

abstract: HyperLogLog Counting is widely used in cardinality estimation. It is the foundation of many algorithms in data analysis, commodity recommendation and database optimization. Facing the large scale internet business like electronic commerce, internet companies have an urgent requirement of distributed real-time cardinality estimation with high accuracy and low time cost. In this paper, we propose a distributed real-time cardinality estimation algorithm named Hermes. Hermes adjusts the estimated cardinality dynamically according to the result of HyperLogLog Counting and also optimizes the data distribution strategy of existing distributed cardinality estimation algorithms. Experiments have been carried out and the results show that Hermes has lower estimation error and time cost compared with existing algorithms.


title: Word Embeddings via Tensor Factorization

abstract: Most popular word embedding techniques involve implicit or explicit factorization of a word co-occurrence based matrix into low rank factors. In this paper, we aim to generalize this trend by using numerical methods to factor higher-order word co-occurrence based arrays, or \textit{tensors}. We present four word embeddings using tensor factorization and analyze their advantages and disadvantages. One of our main contributions is a novel joint symmetric tensor factorization technique related to the idea of coupled tensor factorization. We show that embeddings based on tensor factorization can be used to discern the various meanings of polysemous words without being explicitly trained to do so, and motivate the intuition behind why this works in a way that doesn't with existing methods. We also modify an existing word embedding evaluation metric known as Outlier Detection [Camacho-Collados and Navigli, 2016] to evaluate the quality of the order-$N$ relations that a word embedding captures, and show that tensor-based methods outperform existing matrix-based methods at this task. Experimentally, we show that all of our word embeddings either outperform or are competitive with state-of-the-art baselines commonly used today on a variety of recent datasets. Suggested applications of tensor factorization-based word embeddings are given, and all source code and pre-trained vectors are publicly available online.


title: Beyond von Neumann: Brain-computer structural metaphor

abstract: Brain-inspired computer architectures have gained a lot of attention from computer scientists in the past few years, in an era where more improvements in performance, power, and scalability are increasingly becoming limited. In this paper, we develop a brain-computer structural metaphor where we raise some research questions to inspire computer architects to design efficient computer architectures in terms of the traditional performance metrics, or maybe in terms of new metrics that reflect the true nature of the future, inspired computing.


title: Stolen Children, Invisible Mothers and Unspeakable Stories: The Experiences of Non-Aboriginal Adoptive and Foster Mothers of Aboriginal Children

abstract: One of the measures of the cultural, if not political, success of sustained Aboriginal activism on the issue of the forced removal of children from Aboriginal and Torres Strait Islander communities, leading up to the instigation of the Human Rights and Equal Opportunity Commission's inquiry into the issue and the widely disseminated publication of its findings in 1997, is that it now appears nearly impossible to tell the story of indigenous child removal in terms other than those provided by the powerful Aboriginalised tropes and narrative modes that have come to shape both Aboriginal and non-Aboriginal understandings of issue. I do not wish to take issue with the long-overdue emergence of Aboriginal voices and an Aboriginal discourse on this issue. However, as the older ways of understanding the meaning of removing indigenous children from their communities ‘for their own good’ (Link-Up & Wilson 1997) have lost their provenance and are replaced by Aboriginal stories with the critically revised meanings o...


title: Knowledge distillation using unlabeled mismatched images

abstract: Current approaches for Knowledge Distillation (KD) either directly use training data or sample from the training data distribution. In this paper, we demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for image classification networks. For illustration, we consider scenarios where this is a complete absence of training data, or mismatched stimulus has to be used for augmenting a small amount of training data. We demonstrate that stimulus complexity is a key factor for distillation's good performance. Our examples include use of various datasets for stimulating MNIST and CIFAR teachers.


title: Bregman Divergence-Based Regularization for Transfer Subspace Learning

abstract: The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples to testing samples. In particular, the new regularization minimizes the Bregman divergence between the distribution of training samples and that of testing samples in the selected subspace, so it boosts the performance when training and testing samples are not independent and identically distributed. To test the effectiveness of the proposed regularization, we introduce it to popular subspace learning algorithms, e.g., principal components analysis (PCA) for cross-domain face modeling; and Fisher's linear discriminant analysis (FLDA), locality preserving projections (LPP), marginal Fisher's analysis (MFA), and discriminative locality alignment (DLA) for cross-domain face recognition and text categorization. Finally, we present experimental evidence on both face image data sets and text data sets, suggesting that the proposed Bregman divergence-based regularization is effective to deal with cross-domain learning problems.


title: Relevance Factor VAE: Learning and Identifying Disentangled Factors

abstract: We propose a novel VAE-based deep auto-encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all meaningful sources of variation and their cardinality. Our model, dubbed Relevance-Factor-VAE, leverages the total correlation (TC) in the latent space to achieve the disentanglement goal, but also addresses the key issue of existing approaches which cannot distinguish between meaningful and nuisance factors of latent variation, often the source of considerable degradation in disentanglement performance. We tackle this issue by introducing the so-called relevance indicator variables that can be automatically learned from data, together with the VAE parameters. Our model effectively focuses the TC loss onto the relevant factors only by tolerating large prior KL divergences, a desideratum justified by our semi-parametric theoretical analysis. Using a suite of disentanglement metrics, including a newly proposed one, as well as qualitative evidence, we demonstrate that our model outperforms existing methods across several challenging benchmark datasets.


title: The Architectural Implications of Cloud Microservices

abstract: Cloud services have recently undergone a shift from monolithic applications to microservices, with hundreds or thousands of loosely-coupled microservices comprising the end-to-end application. Microservices present both opportunities and challenges when optimizing for quality of service (QoS) and cloud utilization. In this paper we explore the implications cloud microservices have on system bottlenecks, and datacenter server design. We first present and characterize an end-to-end application built using tens of popular open-source microservices that implements a movie renting and streaming service, and is modular and extensible. We then use the end-to-end service to study the scalability and performance bottlenecks of microservices, and highlight implications they have on the design of datacenter hardware. Specifically, we revisit the long-standing debate of brawny versus wimpy cores in the context of microservices, we quantify the I-cache pressure they introduce, and measure the time spent in computation versus communication between microservices over RPCs. As more cloud applications switch to this new programming model, it is increasingly important to revisit the assumptions we have previously used to build and manage cloud systems.


title: Multi-object Tracking with Neural Gating Using Bilinear LSTM

abstract: In recent deep online and near-online multi-object tracking approaches, a difficulty has been to incorporate long-term appearance models to efficiently score object tracks under severe occlusion and multiple missing detections. In this paper, we propose a novel recurrent network model, the Bilinear LSTM, in order to improve the learning of long-term appearance models via a recurrent network. Based on intuitions drawn from recursive least squares, Bilinear LSTM stores building blocks of a linear predictor in its memory, which is then coupled with the input in a multiplicative manner, instead of the additive coupling in conventional LSTM approaches. Such coupling resembles an online learned classifier/regressor at each time step, which we have found to improve performances in using LSTM for appearance modeling. We also propose novel data augmentation approaches to efficiently train recurrent models that score object tracks on both appearance and motion. We train an LSTM that can score object tracks based on both appearance and motion and utilize it in a multiple hypothesis tracking framework. In experiments, we show that with our novel LSTM model, we achieved state-of-the-art performance on near-online multiple object tracking on the MOT 2016 and MOT 2017 benchmarks.


title: Autobank: a semi-automatic annotation tool for developing deep Minimalist Grammar treebanks

abstract: This paper presents Autobank, a prototype tool for constructing a wide-coverage Minimalist Grammar (MG) (Sta- ::: bler, 1997), and semi-automatically converting the Penn Treebank (PTB) into a deep Minimalist treebank. The front end of the tool is a graphical user interface which facilitates the rapid development of ::: a seed set of MG trees via manual reannotation of PTB preterminals with MG lexical categories. The system then extracts various dependency mappings between the source and target trees, and uses these in concert with a non-statistical MG parser to automatically reannotate the rest of the corpus. Autobank thus enables deep tree- ::: bank conversions (and subsequent modifications) without the need for complex transduction algorithms accompanied by ::: cascades of ad hoc rules; instead, the locus of human effort falls directly on the task of grammar construction itself.


title: 100kHz IGBT inverter use of LCL topology for high power induction heating

abstract: A power electronic inverter is developed for a high frequency induction heating application. This equipment is available for the implementation of ZVS LCL resonance method was used. Using IGBT 20MVA, 100kHz LCL resonant induction heating equipment during the development process, 100kHz IGBT module development process and understanding gained from the design of LCL resonant Zero Crossing through the PLL control over operation of the IGBT Soft Switching, and focused on minimizing losses. In addition, through the actual design and implementation of devices using the area to test drive the development of large inverter technology based on the test


title: 400 V/1000 kVA Hybrid Automatic Transfer Switch

abstract: A lot of large-scale industrial critical loads suffer from voltage interruptions and sags which can cause a significant financial loss. The thyristor-based solid-state transfer switch (STS) and mechanical-based automatic transfer switch (ATS) are used to protect loads against these power quality problems. However, the STS has a great deal of loss consumption because of the forward voltage drop of the thyristors, and ATS can hardly satisfy the voltage-sensitive load due to the long transfer time. This paper proposes a 400 V/1000 kVA hybrid ATS (thyristor switch in parallel with mechanical switch) (HATS) to realize low power consumption under normal situation and transfer fast when required. The proposed HATS has four transfer strategies which can be chosen according to different applications, it realizes the overlapping transfer of the neutral wire, and it can make a decision to transfer or not according to the different fault locations (transfer on the source side and not transfer on the load side). The design details of the prototype are presented, and laboratory tests and field experiments show the prototype good performance.


title: Examining Trust and Risk in Mobile Money Acceptance in Uganda

abstract: Mobile money adoption can contribute to achieving Sustainable Development Goals in Uganda but the factors affecting its sustainable adoption remain largely unknown. This paper explores the extent to which mobile money users’ trust and risk perceptions affect mobile money services adoption of in Uganda. A survey was conducted with 438 mobile money users from Uganda and data was analyzed using Partial Least Squares (PLS) Structural Equation Modelling (SEM). From the results, we obtained new empirical evidence for applying trust and risk perceptions for analyzing mobile money acceptance. We found that mobile money users rely on the structural soundness of mobile money services providers and their ability to provide mobile money services with low perceived risk. Performance expectancy, perceived risk and structural assurance significantly influenced behavioral intention to adopt mobile money. Trust belief did not significantly influence behavioral intention. These results help us to understand and promote mobile banking services in underdeveloped countries, which is of practical and scientific interests. We finally provide practical implications for mobile money services providers, commercial banks and central banks in Africa.


title: An agile process model for product derivation in software product line engineering

abstract: Software Product Lines (SPL) and Agile practices have emerged as new paradigms for developing software. Both approaches share common goals; such as improving productivity, reducing time to market, decreasing development costs and increasing customer satisfaction. These common goals provide the motivation for this research. We believe that integrating Agile practices into SPL can bring a balance between agility and formalism. However, there has been little research on such integration. We have been researching the potential of integrating Agile approaches in one of the key SPL process areas, product derivation. In this paper we present an outline of our Agile process model for product derivation that was developed through industry based case study research.


title: The effects of a 2-year physical education program (SPARK) on physical activity and fitness in elementary school students. Sports, Play and Active Recreation for Kids.

abstract: OBJECTIVES: This study evaluated a health-related physical education program for fourth- and fifth-grade students designed to increase physical activity during physical education classes and outside of school. METHODS: Seven schools were assigned to three conditions in a quasi-experimental design. Health-related physical education was taught by physical education specialists or trained classroom teachers. Students from these classes were compared with those in control classes. Analyses were conducted on 955 students with complete data. RESULTS: Students spent more minutes per week being physically active in specialist-led (40 min) and teacher-led (33 min) physical education classes than in control classes (18 min; P < .001). After 2 years, girls in the specialist-led condition were superior to girls in the control condition on abdominal strength and endurance (P < .001) and cardiorespiratory endurance (P < .001). There were no effects on physical activity outside of school. CONCLUSIONS: A health-related p...


title: The Coping Strategy Questionnaire

abstract: General description: The coping strategy questionnaire (CSQ), (Rosenstiel & Keefe 1983) in its original version consists of 50 items assessing patient self rated use of cognitive and behavioural strategies to cope with pain. It comprises six subscales for cognitive strategies (ignoring pain, reinterpretation of pain, diverting attention, coping self statements, catastrophising, praying/hoping) and two subscales for behavioural strategies (increasing activity levels and increasing pain behaviours). Each coping strategy subscale consists of six items measured with a numerical rating scale ranging from 0 (never do that) to 6 (always do that) indicating how frequently the strategy is used to cope with pain. Each subscale has a maximum score of 36 and a minimum score of 0. An additional two single item questions each with a scoring range of 0–6 are used as effectiveness ratings of control over pain and ability to decrease pain. The CSQ takes approximately 5 minutes to complete. Reliability and validity: In a sample of 61 patients with chronic low back pain (CLBP), Rosenstiel and Keefe (1983) reported the internal consistency for the subscales with Cronbach’s alphas ranging from 0.71 to 0.85, except for the increasing pain behaviour subscale which had an internal consistency of 0.28. However, in a sample of 282 CLBP patients, Jensen and Linton (1993) showed that all 8 subscales of the CSQ Swedish version have an internal consistency ranging from 0.69 to 0.84. Similarly, in patients with lung cancer, the CSQ subscales have shown good internal consistency with Cronbach’s alphas ranging from 0.60 to 0.90 (Wilkie & Keefe 1991). Test-retest reliability for a 1 day interval has been reported to range between 0.68 and 0.91 (Main & Waddell 1991), 0.48–0.71 for a 1 week interval and 0.58–0.84 for a 5 week interval (Jensen & Linton 1993).


title: Ovicidal and Oviposition Deterrent Activities of Medicinal Plant Extracts Against Aedes aegypti L. and Culex quinquefasciatus Say Mosquitoes (Diptera: Culicidae)

abstract: Objectives ::: To evaluate the ovicidal and oviposition deterrent activities of five medicinal plant extracts namely Aegle marmelos (Linn.), Limonia acidissima (Linn.), Sphaeranthus indicus (Linn.), Sphaeranthus amaranthoides (burm.f), and Chromolaena odorata (Linn.) against Culex quinquefasciatus and Aedes aegypti mosquitoes. Three solvents, namely hexane, ethyl acetate, and methanol, were used for the preparation of extracts from each plant.


title: Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval

abstract: A major difficulty in applying word vector embeddings in IR is in devising an effective and efficient strategy for obtaining representations of compound units of text, such as whole documents, (in comparison to the atomic words), for the purpose of indexing and scoring documents. Instead of striving for a suitable method for obtaining a single vector representation of a large document of text, we rather aim for developing a similarity metric that makes use of the similarities between the individual embedded word vectors in a document and a query. More specifically, we represent a document and a query as sets of word vectors, and use a standard notion of similarity measure between these sets, computed as a function of the similarities between each constituent word pair from these sets. We then make use of this similarity measure in combination with standard IR based similarities for document ranking. The results of our initial experimental investigations shows that our proposed method improves MAP by up to $5.77\%$, in comparison to standard text-based language model similarity, on the TREC ad-hoc dataset.


title: Multiple Kernel Learning for stock price direction prediction

abstract: Unstable and assumptive aspects of the securities makes it hard to predict the next day stock prices. There is no absolute indicator for financial forecasting but there are many technical indicators like simple moving average, exponential moving average, stochastic fast and slow, on balance volume for better accomplishment. It is important to have a significant and well-constructed set of features to elaborate stock trends. In this paper, we have proposed a Multiple Kernel Learning Model which predicts the daily trend of stock prices such as up or down, it comprises of 2-tier framework. In first tier, we extracted some technical indicators based on five raw elements- opening price, daily high price, daily low price, closing price and trading volume. In second tier, we built different base kernels on the extracted feature set and then combined these base kernels through Multiple Kernel learning, we have trained the model through walk forward method and predicted the movement of daily stock trend such as up or down, and then evaluated its performance. Experiment results shows that our proposed solution performs well consistently than baseline methods (Support Vector Machine) in terms of prediction accuracy for two commodities in stock market.


title: Concept annotation in the CRAFT corpus

abstract: BackgroundManually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text.ResultsThis paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement.ConclusionsAs the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.


title: A usability-centred perspective on intention to use mobile payment

abstract: Mobile payment is a new and rapidly adopting alternative payment method especially in Asia. By analysing mobile payment interface usability and expanding the Microsoft Usability Guideline, this paper presented a model for studying intention to use mobile payment. A survey method was used by collecting data from 202 potential mobile payment users. The findings showed that interaction behaviour was a significant antecedent of intention to use mobile payment. Furthermore, five mobile payment interface usability factors (content, ease of use, promotion, made-for-the-medium and emotion) have positive influences on interaction behaviour and intention to use mobile payment. The findings will be beneficial for managers and developers when they make decisions on the development of mobile payment systems.


title: Effects of field of view on presence, enjoyment, memory, and simulator sickness in a virtual environment

abstract: The effects of field-of-view (FOV) in a virtual environment (VE) on presence, enjoyment, memory and simulator sickness (SS) were studied. A refined scale, designed to assess subjects' engagement, enjoyment and immersion (E/sup 2/I), was developed. Items to examine subjects' memory of the VE were included. SS was examined using the Simulator Sickness Questionnaire (SSQ). Using a within-subjects design, data were collected from 10 subjects at four FOVs (60/spl deg/, 100/spl deg/, 140/spl deg/ and 180/spl deg/). The VE, called "Crayolaland", was presented in a driving simulator. The results indicated that presence, enjoyment and SS varied as a function of the display FOV. Subjects exhibited higher SSQ and presence subscale scores with increasing FOV. SSQ and presence values approached asymptotes for FOVs beyond 140/spl deg/. Presence and SS were positively correlated, while enjoyment and SS were negatively correlated.


title: SAGE: system for algebra and geometry experimentation

abstract: SAGE is a framework for number theory, algebra, and geometry computation that is initially being designed for computing with elliptic curves and modular forms. The current implementation is primarily due to William Stein. It is open source and freely available under the terms of the GNU General Public License (GPL).


A Metamodel for GDPR-based Privacy Level Agreements


title: A review of routing protocols for mobile ad hoc networks

abstract: Abstract The 1990s have seen a rapid growth of research interests in mobile ad hoc networking. The infrastructureless and the dynamic nature of these networks demands new set of networking strategies to be implemented in order to provide efficient end-to-end communication. This, along with the diverse application of these networks in many different scenarios such as battlefield and disaster recovery, have seen MANETs being researched by many different organisations and institutes. MANETs employ the traditional TCP/IP structure to provide end-to-end communication between nodes. However, due to their mobility and the limited resource in wireless networks, each layer in the TCP/IP model require redefinition or modifications to function efficiently in MANETs. One interesting research area in MANET is routing. Routing in the MANETs is a challenging task and has received a tremendous amount of attention from researches. This has led to development of many different routing protocols for MANETs, and each author of each proposed protocol argues that the strategy proposed provides an improvement over a number of different strategies considered in the literature for a given network scenario. Therefore, it is quite difficult to determine which protocols may perform best under a number of different network scenarios, such as increasing node density and traffic. In this paper, we provide an overview of a wide range of routing protocols proposed in the literature. We also provide a performance comparison of all routing protocols and suggest which protocols may perform best in large networks.


Job Stressors and Coping Mechanisms among Emergency Department Nurses in the Armed Force Hospitals of Taiwan


title: Arabic sign language recognition using leap motion sensor

abstract: Researchers in sign language recognition customized different sensors to capture hand signs. Gloves, digital cameras, depth cameras and Kinect were used alternatively in most systems. Due to signs closeness, input accuracy is a very essential constraint to reach a high recognition accuracy. Although previous systems accomplished high recognition accuracy, they suffer from stability in realistic environment due to variance in signing speed, lighting, etc… In this paper, a recognition system for ArSL has been developed based on a new digital sensor called “Leap Motion”. This sensor tackles the major issues in vision-based systems such as skin color, lighting etc… Leap motion captures hands and fingers movements in 3D digital format. The sensor throws 3D digital information in each frame of movement. These temporal and spatial features are fed into a Multi-layer perceptron Neural Network (MLP). The system was tested on 50 different dynamic signs (distinguishable without non-manual features) and the recognition accuracy reached 88% for two different persons. Although Leap motion tracks both hands accurately, unfortunately Leap motion does not track non-manual features. This system can be enhanced by adding other sensors to track other non-manual features such as facial expressions and body poses. The proposed sensor can work simultaneously with leap motion to capture all sign's features.


Abstract Asian Face Image Database PF01 Intelligent Multimedia Lab. †


title: Learning to Recognise Unseen Classes by A Few Similes

abstract: Existing image classification systems often suffer from re-training models for novel unseen classes. Zero-shot learning (ZSL) aims to recognise these unseen classes directly using trained models with a further inference procedure. However, existing approaches highly rely on human-defined class-attribute associations to achieve the inference, which significantly increases the annotation cost. This paper aims to address ZSL on non-attribute tasks, i.e. only training images with labels are used as most of the supervised settings. Our main contributions are: 1) to circumvent expensive attributes, we propose to use semantic similes that directly indicate the unseen-to-seen associations; 2) a novel similarity-based representation is proposed to represent both visual images and semantic similes in a unified embedding space; 3) in order to reduce the annotation cost, we use only a few similes to infer a class-level prototype for each unseen class. On two popular benchmarks, AwA and aPY, extensive experiments manifest that our method can significantly improve the state-of-the-art results using only two similes for each unseen class. Furthermore, we revisit the Caltech 101 dataset without attributes. Our ZSL results can exceed that of previous supervised methods.


title: The Advantages of Wearable Green Reflected Photoplethysmography

abstract: This report evaluates the efficacy of reflected-type green light photoplethysmography (green light PPG). Transmitted infrared light was used for PPG and the arterial pulse was monitored transcutaneously. The reflected PPG signal contains AC components based on the heartbeat-related signal from the arterial blood flow and DC components, which include reflectance and scattering from tissue. Generally, changes in AC components are monitored, but the DC components play an important role during heat stress. In this study, we compared the signal of green light PPG to infrared PPG and ECG during heat stress. The wavelengths of the green and infrared light were 525 nm and 880 nm, respectively. Experiments were performed on young healthy subjects in cold (10°C), hot (45°C), and normal environments. The pulse rates were compared among three measurement devices and the AC and DC components of the PPG signal were evaluated during heat stress. The pulse rates obtained from green light PPG were strongly correlated with the R---R interval of an electrocardiogram in all environments, but those obtained from infrared light PPG displayed a weaker correlation with cold exposure. The AC components were of similar signal output for both wavelengths during heat stress. Also, the DC components for green light PPG were similar during heat stress, but showed less signal output for infrared light PPG during hot exposure. The main reason for the reduced DC components was speculated to be the increased blood flow at the vascular bed. Therefore, reflected green light PPG can be useful for pulse rate monitoring because it is less influenced by the tissue and vein region.


title: Design and Construction of Arduino-Hacked Variable Gating Distortion Pedal

abstract: This paper describes the distortion effects often used in an electric guitar. Distortion is an added effect in an electric guitar, which compresses the peaks of the sound waves produced by the musical instrument, to produce a large number of added overtones, which here is done by rigging up a circuit in collaboration with the Arduino UNO circuit board. The digital potentiometer controlled by the Arduino (microcontroller) was an improvement and was able to produce satisfactory results, as compared with the analog potentiometer without the Arduino control. The complex circuitry of a three-stage distortion circuit with the analog potentiometer was replaced by a digital potentiometer controlled by a microcontroller, with better results. This variable-gating distortion pedal has an added advantage of being compact, light, and inexpensive.


title: A Brief Actuarial Assessment for the Prediction of Wife Assault Recidivism: The Ontario Domestic Assault Risk Assessment.

abstract: An actuarial assessment to predict male-to-female marital violence was constructed from a pool of potential predictors in a sample of 589 offenders identified in police records and followed up for an average of almost 5 years. Archival information in several domains (offender characteristics, domestic violence history, nondomestic criminal history, relationship characteristics, victim characteristics, index offense) and recidivism were subjected to setwise and stepwise logistic regression. The resulting 13-item scale, the Ontario Domestic Assault Risk Assessment (ODARA), showed a large effect size in predicting new assaults against legal or common-law wives or ex-wives (Cohen’s d 1.1, relative operating characteristic area .77) and was associated with number and severity of new assaults and time until recidivism. Cross-validation and comparisons with other instruments are also reported. When a man who is already known to authorities as a wife assaulter kills his partner, the public demands to know why the


title: A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis

abstract: An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC) structure via neural techniques. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks and adaptive network-based fuzzy inference system techniques to manage the fuzzy demand with incomplete information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated using real-world data from a company which is active in durable consumer goods industry in Istanbul, Turkey.


title: PID Control of Heat Exchanger System

abstract: exchanger system is widely used in chemical plants because it can sustain wide range of temperature and pressure. The main purpose of a heat exchanger system is to transfer heat from a hot fluid to a cooler fluid, so temperature control of outlet fluid is of prime importance. To control the temperature of outlet fluid of the heat exchanger system a conventional PID controller can be used. Due to inherent disadvantages of conventional control techniques, model based control technique is employed and an internal model based PID controller is developed to control the temperature of outlet fluid of the heat exchanger system. The designed controller regulates the temperature of the outgoing fluid to a desired set point in the shortest possible time irrespective of load and process disturbances, equipment saturation and nonlinearity. The developed internal model based PID controller has demonstrated 84% improvement in the overshoot and 44.6% improvement in settling time as compared to the classical controller.


title: Sound-model-based acoustic source localization using distributed microphone arrays

abstract: Acoustic source localization and sound recognition are common acoustic scene analysis tasks that are usually considered separately. In this paper, a new source localization technique is proposed that works jointly with an acoustic event detection system. Given the identities and the end-points of simultaneous sounds, the proposed technique uses the statistical models of those sounds to compute a likelihood score for each model and for each signal at the output of a set of null-steering beamformers per microphone array. Those scores are subsequently combined to find the MAP-optimal event source positions in the room. Experimental work is reported for a scenario consisting of meeting-room acoustic events, either isolated or overlapped with speech. From the localization results, which are compared with those from the SRP-PHAT technique, it seems that the proposed model-based approach can be an alternative to current techniques for event-based localization.


title: Procedural generation of complex stable structures for angry birds levels

abstract: This paper presents a procedural content generation algorithm for the physics-based puzzle game Angry Birds. The proposed algorithm creates complex stable structures using a variety of 2D objects. These are generated without the aid of pre-defined substructures or composite elements. The structures created are evaluated based on a fitness function which considers several important structural aspects. The results of this analysis in turn affects the likelihood of particular objects being chosen in future generations. Experiments were conducted on the generated structures in order to evaluate the algorithm's expressivity. The results show that the proposed method can generate a wide variety of 2D structures with different attributes and sizes.


title: A survey of water distribution system and new approach to intelligent water distribution system

abstract: This paper reviews the challenges in water distribution systems (WDS), development and deployment of intelligent systems to manage WDS efficiently. Recognizing that intelligent systems has the potential to revolutionize management of precious natural resource by utilities, this paper provides a summary of the research work and practices for researchers and industry practitioners to ensure that the technology cultivates sustainable drinking water management. At present a lot technological developments ensures reduction of labor cost for various tasks in WDS. The technical sophistications of intelligent systems to ensure optimal use of precious resources have increased noticeably in recent decades. This paper addresses all concerned issues with WDS such as real time data collection, forecast of future water consumption, labor cost, recovery cost of water treatment and distribution, power supply requirement, operational time, water leakage, remote capturing of meter reading etc. We proposed a new intelligent water distribution system, which overcomes most of the problems in WDS.


title: Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF

abstract: We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.


title: Optimal PI-Control and Verification of the SIMC Tuning Rule

abstract: Abstract Optimal PI-settings are derived for first-order with delay processes for specified levels of robustness ( M s -value) and compared with the simple SIMC-rule. Optimality (performance) is defined in terms of the integrated absolute error (IAE) of the output for combined step changes in setpoints and input disturbances. With SIMC, the robustness level is adjusted by changing the tuning parameter τ c , and the SIMC-rule was found to give surprisingly good setting with almost Pareto-optimal performance. The exception is a pure time delay processes where the SIMC-rule gives a pure integral controller with somewhat sluggish response. A simple modification to improve on this, is to increase the time constant in the rule by one third of the time delay.


title: GPU-based simulation of side-looking sonar images

abstract: This paper describes an implementation of a sonar image simulator optimized for running on a computer's Graphics Processing Unit (GPU). GPUs are hardware-optimized to obtain maximum performance on computer graphics applications. Because these applications generally simulate focal plane images (i.e. optical systems, video, etc), some specific adaptations are required to render range images such as those generated by a sonar sensor. Considerations for the simulation of side-scan and synthetic aperture images are discussed, including a thorough explanation of the imaging geometry. The use of the GPU to implement the sonar simulation process is described in detail, including the different render stages required to construct the final image and the addition of more refined effects such as noise, multi-path and the sensor's point spread function (PSF). Other advanced uses of the GPU simulator core are discussed, such as Fast Fourier Transform (FFT) computation and fast image correlation. Finally, results and performance figures are presented.


title: Micro CT study of soft/elastic structures of beetle toward insect-inspired robotics

abstract: We propose a new framework for making insect-inspired robots using micro-focus X-ray computed tomography (micro CT). Micro CT system used in this study is originally for non-invasive inspection for industrial and medical applications, not for robotics. The CT data provides three-dimensional information of the complex structure of animal body in micrometer resolution. We shoot CT images of rhinoceros beetles (Trypoxy-lus dichotomus) to reveal its musculoskeletal system, flapping mechanism, wing veins, and multi-segment limbs. The cross sectional images enables to identify the flight muscles and joint mechanism. Moreover, three-dimensional reconstruction of CT data allows replicating insect body with additive manufacturing technique. We used multi-material 3D printer for printing a wing vein and a shell from the volumetric data. The results shows that the 3D printed artificial shell can reproduce the linkage mechanism of flapping motion. Micro CT studies of insects enhance a further understanding of biological systems and development of bio-inspired robots.