title: Electronic waste management approaches: an overview.

abstract: Electronic waste (e-waste) is one of the fastest-growing pollution problems worldwide given the presence if a variety of toxic substances which can contaminate the environment and threaten human health, if disposal protocols are not meticulously managed. This paper presents an overview of toxic substances present in e-waste, their potential environmental and human health impacts together with management strategies currently being used in certain countries. Several tools including life cycle assessment (LCA), material flow analysis (MFA), multi criteria analysis (MCA) and extended producer responsibility (EPR) have been developed to manage e-wastes especially in developed countries. The key to success in terms of e-waste management is to develop eco-design devices, properly collect e-waste, recover and recycle material by safe methods, dispose of e-waste by suitable techniques, forbid the transfer of used electronic devices to developing countries, and raise awareness of the impact of e-waste. No single tool is adequate but together they can complement each other to solve this issue. A national scheme such as EPR is a good policy in solving the growing e-waste problems.


title: Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis

abstract: In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task. In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction. The proposed model learns high-level discriminative features and double propagate information between aspect and opinion terms, simultaneously. Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance. Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.


title: A Warm Welcome Matters! The Link Between Social Feedback and Weight Loss in /r/loseit

abstract: Social feedback has long been recognized as an important element of successful health-related behavior change. How- ever, most of the existing studies look at the effect that offline social feedback has. This paper fills gaps in the literature by proposing a framework to study the causal effect that receiving social support in the form of comments in an online weight loss community has on (i) the probability of the user to return to the forum, and, more importantly, on (ii) the weight loss reported by the user. Using a matching approach for causal inference we observe a difference of 9 lbs lost between users who do or do not receive comments. Surprisingly, this effect is mediated by neither an increase in lifetime in the community nor by an increased activity level of the user. Our results show the importance that a "warm welcome" has when using online support forums to achieve health outcomes.


title: Design and control of a gas-electric hybrid quad tilt-rotor UAV with morphing wing

abstract: In this work, design and control system development of a gas-electric hybrid quad tilt-rotor UAV with morphing wing are presented. The proposed aircraft has an all carbon-composite body, gas-electric hybrid electric generation system for 3 hours hovering or up to 10 hours of horizontal flight, a novel configuration for VTOL and airplane-like flights with minimized aerodynamic costs and mechanical morphing wings for both low speed and high speed horizontal flights. The mechanical design of the vehicle is performed to achieve a strong and light-weight structure, whereas the aerodynamic and propulsion system designs are aimed for accomplishing both fixed wing and rotary wing aircraft flights with maximized flight endurance. A detailed dynamic model of the aerial vehicle is developed including the effects of tilting rotors, variable fuel weight, and morphing wing lift-drag forces and pitching moments. Control system is designed for both flight regimes and flight simulations are carried out to check the performance of the proposed control system.


title: What’s Worthy of Comment? Content and Comment Volume in Political Blogs ∗

abstract: In this paper we aim to model the relationship be- tween the text of a political blog post and the comment volume—that is, the total amount of response—that a post will receive. We seek to accurately identify which posts will attract a high-volume response, and also to gain insight about the community of readers and their interests. We design and evaluate variations on a latent- variable topic model that links text to comment volume.


title: Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification

abstract: Person Re-identification (ReID) is to identify the same person across different cameras. It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc How to extract powerful features is a fundamental problem in ReID and is still an open problem today. In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn powerful features over full body and body parts, which can well capture the local context knowledge by stacking multi-scale convolutions in each layer. Moreover, instead of using predefined rigid parts, we propose to learn and localize deformable pedestrian parts using Spatial Transformer Networks (STN) with novel spatial constraints. The learned body parts can release some difficulties, eg pose variations and background clutters, in part-based representation. Finally, we integrate the representation learning processes of full body and body parts into a unified framework for person ReID through multi-class person identification tasks. Extensive evaluations on current challenging large-scale person ReID datasets, including the image-based Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed method achieves the state-of-the-art results.


title: Cryptographic Enforcement of Role-Based Access Control

abstract: Many cryptographic schemes have been designed to enforce information flow policies. However, enterprise security requirements are often better encoded, or can only be encoded, using role-based access control policies rather than information flow policies. In this paper, we provide an alternative formulation of role-based access control that enables us to apply existing cryptographic schemes to core and hierarchical role-based access control policies. We then show that special cases of our cryptographic enforcement schemes for role-based access control are equivalent to cryptographic enforcement schemes for temporal access control and to ciphertext-policy and key-policy attribute-based encryption schemes. Finally, we describe how these special cases can be extended to support richer forms of temporal access control and attribute-based encryption.


title: Space-Vector PWM Control Synthesis for an H-Bridge Drive in Electric Vehicles

abstract: This paper deals with a synthesis of space-vector pulsewidth modulation (SVPWM) control methods applied for an H-bridge inverter feeding a three-phase permanent-magnet synchronous machine (PMSM) in electric-vehicle (EV) applications. First, a short survey of existing architectures of power converters, particularly those adapted to degraded operating modes, is presented. Standard SVPWM control methods are compared with three innovative methods using EV drive specifications in the normal operating mode. Then, a rigorous analysis of the margins left in the control strategy is presented for a semiconductor switch failure to fulfill degraded operating modes. Finally, both classic and innovative strategies are implemented in numerical simulation; their results are analyzed and discussed.


title: ALL-IN-1: Short Text Classification with One Model for All Languages

abstract: We present ALL-IN-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.


title: It's all in your head: new insights into craniofacial development and deformation

abstract: ‘Our ignorance of the laws of variation is profound. Not in one case out of a hundred can we pretend to assign any reason why this or that part has varied. But whenever we have the means of instituting a comparison, the same laws appear to have acted in producing the lesser differences between varieties of the same species, and the greater differences between species of the same genus.’ (Darwin, 1859). ::: ::: Charles Darwin did not gild the truth when he candidly pointed out just how imperfect is our knowledge of how morphological diversity is generated. Over the intervening decades we have scrutinized the process of embryonic development and, in doing so, gained a deeper appreciation for the tissue interactions that lie at the basis of morphogenesis. More recently, some of the molecules involved in mediating these tissue interactions have been identified and, in a few rare cases, we even have gleaned insights into how species-specific development is controlled. These instances, however, are few and far between and much remains to be accomplished. ::: ::: In this review we will present some of the most recent advances that lie at the heart of understanding the mechanisms controlling normal craniofacial development, the consequences of when these normal pathways are disrupted and how this information sheds light on the basis for evolutionary diversity among the species. We choose to focus on morphogenesis of the craniofacial complex for two reasons. First, faces show tremendous phenotypic variation, first evident during the later stages of fetal development. Yet despite these differences in the facial appearance of embryos, they all look remarkably similar during earlier stages of embryogenesis. This suggests that whatever factors influence craniofacial diversity primarily act during a discrete period of embryonic development; and knowing when diversity first arises is an important first step towards understanding how diversity is generated. ::: ::: The second reason we use craniofacial development as a model is that variations in the craniofacial complex are tightly associated with adaptive radiations into ecological niches. If the relationship between craniofacial morphology and speciation is causal (and not merely correlative) then perhaps we can understand how modifying the spatial and temporal patterns of gene expression create diversity within a species. ::: ::: One final justification for using craniofacial morphology as a model system is the prodigious amount of documentation that has accrued on head and neck anomalies. As the noted embryologist William Harvey so eloquently wrote, ‘Nature is nowhere more accustomed openly to display her secret mysteries than in cases where she shows traces of her working apart from the beaten path; nor is there any better way to advance the proper practice of medicine than to give our minds to the discovery of the usual law of Nature by careful investigation of cases of rarer forms of disease’ (Harvey, 1657). Thus, thoughtful inspection of the malformed face may offer valuable clues into the mechanisms governing normal craniofacial morphogenesis.


title: Applying cognitive load theory to the design of web-based instruction

abstract: Cognitive load theory can inform the design of web-based instruction. The basic premise of cognitive load theory is that the focus of an instructional module must be the instruction itself. Information that is adjunct to the instruction must be designed to minimize cognitive load and enhance working memory. Because the mental resources of working memory can be overloaded, any information that ignores cognitive load may interfere with the process of acquiring knowledge and skills. Instruction that effectively presents the learning to our working memory has an impact on our ability to store knowledge and skills in our long-term memory. Everything that we "know" is held in our long-term memory. Our team applied the principles of cognitive load theory to the design our instructional web site and discovered that cognitive load theory provides a sound baseline for the design of effective web-based instruction. Further, to effectively enhance web-based instruction, the graphical user interface and multimedia formats must be developed in consideration of cognitive load principles.


title: Voice Activity Detection Based on an Unsupervised Learning Framework

abstract: How to construct models for speech/nonspeech discrimination is a crucial point for voice activity detectors (VADs). Semi-supervised learning is the most popular way for model construction in conventional VADs. In this correspondence, we propose an unsupervised learning framework to construct statistical models for VAD. This framework is realized by a sequential Gaussian mixture model. It comprises an initialization process and an updating process. At each subband, the GMM is firstly initialized using EM algorithm, and then sequentially updated frame by frame. From the GMM, a self-regulatory threshold for discrimination is derived at each subband. Some constraints are introduced to this GMM for the sake of reliability. For the reason of unsupervised learning, the proposed VAD does not rely on an assumption that the first several frames of an utterance are nonspeech, which is widely used in most VADs. Moreover, the speech presence probability in the time-frequency domain is a byproduct of this VAD. We tested it on speech from TIMIT database and noise from NOISEX-92 database. The evaluations effectively showed its promising performance in comparison with VADs such as ITU G.729B, GSM AMR, and a typical semi-supervised VAD.


title: Negative obstacle detection for wearable assistive devices for visually impaired

abstract: The research on developing assistive devices for visually impaired people greatly relies on image processing techniques. Extensive effort is made on detecting the obstacles in front of the user. Beside the normal obstacles that the user can bump into, another category of obstacles, negative obstacles must also be detected. Negative obstacles are usually represented by holes in the ground or regions that lay bellow the ground surface. Such obstacles represent a potential danger to the visually impaired user and could lead to serious injury if not detected. In this paper, we introduce a stereo vision system to identify and track negative obstacles located in front of the user. The identification is performed in the disparity image based on an estimation of the ground surface in the stereo images. The tracking relies on a camera motion estimation approach. The algorithm we introduce in this paper was integrated and tested with a wearable assistive device to prove its efficiency. We evaluate the accuracy of our solution using various real-life scenarios.


title: Electronic Banking Channels and Task-Channel Fit

abstract: The increase in electronically mediated self-service technologies in the banking industry has impacted on the way banks service consumers. Despite a large body of research on electronic banking channels, no study has been undertaken to explore the fit between electronic banking channels and banking tasks. Nor has there been research into how the ‘task-channel fit’ and other factors impact on consumers’ intention to use electronic banking channels. This paper proposes a theoretical model addressing these gaps. An exploratory study was first conducted, investigating industry experts’ perceptions towards the concept of ‘task-channel fit’ and its relationship to other electronic banking channel variables. The findings demonstrated that the concept was perceived as being highly relevant by bank managers. A research model was then developed drawing on the existing literature. To evaluate the research model quantitatively, a survey will be developed and validated, administered to a sample of consumers, and the resulting data used to test both measurement and structural aspects of the research model.


A Study of the Needs of Brain-Based Learning in Physical Education in the Senior High School


title: Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System

abstract: The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.


title: Learning and Tuning Meta-heuristics in Plan Space Planning

abstract: In recent years, the planning community has observed that techniques for learning heuristic functions have yielded improvements in performance. One approach is to use offline learning to learn predictive models from existing heuristics in a domain dependent manner. These learned models are deployed as new heuristic functions. The learned models can in turn be tuned online using a domain independent error correction approach to further enhance their informativeness. The online tuning approach is domain independent but instance specific, and contributes to improved performance for individual instances as planning proceeds. Consequently it is more effective in larger problems. ::: In this paper, we mention two approaches applicable in Partial Order Causal Link (POCL) Planning that is also known as Plan Space Planning. First, we endeavor to enhance the performance of a POCL planner by giving an algorithm for supervised learning. Second, we then discuss an online error minimization approach in POCL framework to minimize the step-error associated with the offline learned models thus enhancing their informativeness. Our evaluation shows that the learning approaches scale up the performance of the planner over standard benchmarks, specially for larger problems.


title: Four quasi-Z-Source inverters

abstract: In this paper, theoretical results are shown for several novel inverters. These inverters are similar to the Z-source inverters presented in previous works, but have several advantages, including in some combination; lower component ratings, reduced source stress, reduced component count and simplified control strategies. Like the Z-source inverter, these inverters are particularly suited for applications which require a large range of gain, such as in motor controllers or renewable energy. Simulation and experimental results are shown for one topology to verify the analysis. Also, a back-to-back inverter system featuring bidirectionality on both inverters, as well as secondary energy storage with only a single additional switch, is shown.


title: Finite element modeling of friction stir welding—thermal and thermomechanical analysis

abstract: Friction stir welding (FSW) is a relatively new welding process that may have significant advantages compared to the fusion processes as follow: joining of conventionally non-fusion weldable alloys, reduced distortion and improved mechanical properties of weldable alloys joints due to the pure solid-state joining of metals. In this paper, a three-dimensional model based on finite element analysis is used to study the thermal history and thermomechanical process in the butt-welding of aluminum alloy 6061-T6. The model incorporates the mechanical reaction of the tool and thermomechanical process of the welded material. The heat source incorporated in the model involves the friction between the material and the probe and the shoulder. In order to provide a quantitative framework for understanding the dynamics of the FSW thermomechanical process, the thermal history and the evolution of longitudinal, lateral, and through-thickness stress in the friction stirred weld are simulated numerically. The X-ray diffraction (XRD) technique is used to measure the residual stress of the welded plate, and the measured results are used to validate the efficiency of the proposed model. The relationship between the calculated residual stresses of the weld and the process parameters such as tool traverse speed is presented. It is anticipated that the model can be extended to optimize the FSW process in order to minimize the residual stress of the weld.


title: An eFace-Template Method for Efficiently Generating Patient-Specific Anatomically-Detailed Facial Soft Tissue FE Models for Craniomaxillofacial Surgery Simulation

abstract: Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft-tissue changes following osteotomy. This can only be accomplished on an anatomically-detailed facial soft tissue model. However, current anatomically-detailed facial soft tissue model generation is not appropriate for clinical applications due to the time intensive nature of manual segmentation and volumetric mesh generation. This paper presents a novel semi-automatic approach, named eFace-template method, for efficiently and accurately generating a patient-specific facial soft tissue model. Our novel approach is based on the volumetric deformation of an anatomically-detailed template to be fitted to the shape of each individual patient. The adaptation of the template is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. This methodology was validated using 4 visible human datasets (regarded as gold standards) and 30 patient models. The results indicated that our approach can accurately preserve the internal anatomical correspondence (i.e., muscles) for finite element modeling. Additionally, our hybrid approach was able to achieve an optimal balance among the patient shape fitting accuracy, anatomical correspondence and mesh quality. Furthermore, the statistical analysis showed that our hybrid approach was superior to two previously published methods: mesh-matching and landmark-based transformation. Ultimately, our eFace-template method can be directly and effectively used clinically to simulate the facial soft tissue changes in the clinical application.


title: Conflict-Oriented Windowed Hierarchical Cooperative A∗

abstract: In the Multi-Agent Path Finding problem (MAPF), we are given a map and a set of agents with distinct source and goal locations. The task is to compute a path for each agent from its initial location to its goal location without conflicting with other agents. MAPF solvers can be divided into classes based on their purpose. One of these classes is the class of online MAPF algorithms, in which the search for paths is interleaved with the actual physical moves of the agents. A prominent algorithm in this class is the Windowed Hierarchical Cooperative A* algorithm (WHCA*) where paths are planned for each agent individually and cooperation is obtained using a reservation table. A number of extensions for WHCA* already exist. In this paper we propose a general approach for the baseline WHCA* algorithm which is orthogonal to all other existing extensions. We improve WHCA* by introducing the Conflict Oriented (CO) principle for focusing the agent coordination around conflicts. In addition, we provide a conflict-oriented prioritization mechanism that intelligently chooses which agent should act next. Experimental results demonstrate the advantage of our approach over WHCA*.


title: Space-Time Signal Optimization for SWIPT: Linear Versus Nonlinear Energy Harvesting Model

abstract: In simultaneous wireless information and power transfer systems, optimization of transmit signals is critical to system performance. Although the optimization problem can be efficiently solved under a linear energy harvesting model, the obtained solution may not work well in practice, since the energy harvester contains nonlinear elements, such as diodes. On the other hand, while a nonlinear model can be used to capture the dynamics of energy harvesting circuits, it introduces additional complexity at the optimization stage. Specifically, under a nonlinear model, traditional convex optimization is not applicable, since the energy harvesting function is fractional. To address this challenge, this letter first derives an optimal solution for static channels by introducing pseudo-inverse of the nonlinear model. Then, an iterative algorithm that converges to a sub-optimal solution is proposed for time varying channels. With the developed methods, the performance-complexity tradeoff between linear and nonlinear models is illustrated.


title: A Dynamic Method to Forecast the Wheel Slip for Antilock Braking System and Its Experimental Evaluation

abstract: The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.


title: Ecotourism: The Evolving Contemporary Definition

abstract: A rise in the popularity of ecotourism has coincided with voluminous definitional discourse. Amongst stakeholders, confusion has resulted from the disparate nature of these definitions. In the absence of a common definition or set of key tenets the challenge has been to ensure operational ecotourism that adheres to the theoretical underpinnings of the concept. Without some semblance of definitional consensus, ecotourism may be on a precarious course whereby the ethics upon which the activity is conceptualised, the natural environment upon which the activity depends, and the legitimacy of the industry are at risk. The ambition of this research is to disentangle a set of themes from the evolving definitional debate in order to provide a framework for the development of ecotourism policy and applications. Recurring themes are identified through the application of content analysis methodology to select contemporary definitions. Those themes that appear most frequently are then introduced as an ecotourism conc...


title: Music emotion recognition using deep Gaussian process

abstract: Music is a powerful force that evokes human emotions. Several investigations of music emotion recognition (MER) have been conducted in recent years. This paper proposes a system for detecting emotion in music that is based on a deep Gaussian process (GP). The system consists of two parts-feature extraction and classification. In the feature extraction part, five types of features that are associated with emotions are selected for representing the music signal; these are rhythm, dynamics, timbre, pitch and tonality. A music clip is decomposed into frames and these features are extracted from each frame. Next, statistical values, such as mean and standard deviation, of frame-based features are calculated to generate a 38-dimensional feature vector. In the classification part, a deep GP is utilized for emotion recognition. We treat classification problem from the perspective of regression. Finally, 9 classes of emotion are categorized by 9 one-versus-all classifiers. The experimental results demonstrate that the proposed system performs well in emotion recognition.


title: A 2kW, 100kHz high speed IGBT based HV-LV DC/DC converter for electric vehicle

abstract: This paper presents a design of IGBT-based zero voltage transition (ZVT) phase shift full bridge (PSFB) synchronous rectification (SR) DC/DC converter. PSFB is frequently used topology for the high voltage to low voltage (HV-LV) DC/DC converter in electric and hybrid electric vehicle (EV/HEV). This paper evaluates the suitability of high speed IGBTs and rapid diodes for application in HV-LV DC/DC converter at 100kHz as an alternative to standard solution such as superjunction MOSFETs. Experimental measurements are obtained using prototype designed for input voltage range of 220V∼400V and output ratings of 14V 145A. The efficiency over 90%is achieved in the wide range of input voltages and load currents.


Classification of Mandibular Condylar Fractures


title: Stan: A Probabilistic Programming Language

abstract: Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.


title: Assessing the Quality of Model Differencing Engines

abstract: In recent years many tools and algorithms for model comparison and differencing were proposed. Typically, the main focus of the research laid on being able to compute the difference in the first place. Only very few papers addressed the quality of the delivered differences sufficiently. Hence, this is a general shortcoming in the state-of-the-art. Currently, there are no established community standards how to assess the quality of differences and it is neither possible to compare the quality of different algorithms, nor can developers decide whether or not an algorithm is able to produce adequate results in a given application scenario. We propose a parallel working session to be held to discuss this general problem and its implications. The goal of the working session is to achieve a common understanding of what the crucial factors in assessing the quality of differences are. Furthermore, it is planed to discuss possible solutions that help the research community as whole, e.g. by drafting the design of an initial benchmark corpus which later could be turned into a standardized, openly available benchmark set.


title: Shape Augmented Regression Method for Face Alignment

abstract: There have been tremendous improvements of the face alignment algorithms, among which the regression framework becomes the most popular one recently. The regression based works start from an initial face shape, and they learn regression models to predict the face shape updates based on the shape-indexed local appearance features. However, most of the regression methods ignore the fact that the regression function should directly rely on the current shape (e.g. regression function for frontal face should be different from that for the left profile face). To utilize this information and improve over the existing regression based methods, we propose the shape augmented regression method for face alignment where the regression function would automatically change for different face shapes. We evaluated the performance of the proposed method on both the general "in-the-wild" database and the 300 Video in the Wild (300-VW) challenge data set. The results show that the proposed method outperforms the state-of-the-art works.


Maintaining Knowledge about Temporal


title: IDENTITY AND AN IDENTITY CRISIS: THE IDENTITY CRISIS OF FIRST-YEAR FEMALE STUDENTS AT LATVIAN UNIVERSITIES AND THEIR SOCIODEMOGRAPHIC INDICATORS

abstract: This paper reflects the results of a study that was aimed at finding links between identity crises and the sociodemographic indicators of first-year female students at Latvian universities. The research cohort consists of 501 (N = 501) students aged 18 to 27 (M = 20.33; SD = 1.937). The author used the Identity Distress Survey (IDS) for the research (Berman, et al., 2004), as well as the sociodemographic data of the students. The Spearman correlation coefficient was used to analyse links between IDS indicators, and detailed analysis of links between IDS factors was also conducted.


title: Closest pair queries in spatial databases

abstract: This paper addresses the problem of finding the K closest pairs between two spatial data sets, where each set is stored in a structure belonging in the R-tree family. Five different algorithms (four recursive and one iterative) are presented for solving this problem. The case of 1 closest pair is treated as a special case. An extensive study, based on experiments performed with synthetic as well as with real point data sets, is presented. A wide range of values for the basic parameters affecting the performance of the algorithms, especially the effect of overlap between the two data sets, is explored. Moreover, an algorithmic as well as an experimental comparison with existing incremental algorithms addressing the same problem is presented. In most settings, the new algorithms proposed clearly outperform the existing ones.


title: Automatic extraction of items from cheque images for payment recognition

abstract: A novel approach is proposed for the extraction of legal and courtesy amounts and date from cheque images based on the structural description of cheques. A method for the representation of cheques is presented. Several image processing techniques and algorithms have been developed in this approach. Experimental results show that the approach is effective and the proposed techniques and algorithms perform well.


title: CPW to microstrip transition using different CPW ground plane structures

abstract: The multilayer circuits are suffers from passive components effect to transit high frequency signal. The passive components are capacitive and inductive reactance of the planar integrated circuit. The part of overall circuit efficiency depending on passivity of the components, interconnections and its geometrical structure, which is helps to build low loss multilayer integrated circuits (MIC). The aim of this paper is to design coplanar wave guide (CPW) to microstrip line transition structure with 1GHz to 75GHz operating frequency band. The linear tapered transition method is used and inspected with four different CPW ground plane structures are conventional, stepped edge, sharp edge and linear curvature edge. The linear curvature edged CPW ground plane structure gives better transition efficiency, operating frequency band and lower insertion loss compare to conventional, stepped edge and sharp edge structures. The designed structures are electromagnetic simulated using NI/AWR tool. The CPW to microstrip line structure is compact in size and widely used for RF/Microwave circuit component development.


title: Trends in biorobotic autonomous undersea vehicles

abstract: The emergence of biorobotic autonomous undersea vehicle (AUV) as a focus for discipline-integrated research in the context of underwater propulsion and maneuvering is considered within the confines of the Biorobotics Program in the Office of Naval Research. The significant advances in three disciplines, namely the biology-inspired high-lift unsteady hydrodynamics, artificial muscle technology and neuroscience-based control, are discussed in an effort to integrate them into viable products. The understanding of the mechanisms of delayed stall, molecular design of artificial muscles and the neural approaches to the actuation of control surfaces is reviewed in the context of devices based on the pectoral fins of fish, while remaining focused on their integrated implementation in biorobotic AUVs. A mechanistic understanding of the balance between cruising and maneuvering in swimming animals and undersea vehicles is given. All aquatic platforms, in both nature and engineering, except during short duration burst speeds that are observed in a few species, appear to lie within the condition where their natural period of oscillation equals the time taken by them to travel the distance of their own lengths. Progress in the development of small underwater experimental biorobotic vehicles is considered where the three aforementioned disciplines are integrated into one novel maneuvering device or propulsor. The potential in maneuvering and silencing is discussed.


title: Joint virtual machine and bandwidth allocation in software defined network (SDN) and cloud computing environments

abstract: Cloud computing provides users with great flexibility when provisioning resources, with cloud providers offering a choice of reservation and on-demand purchasing options. Reservation plans offer cheaper prices, but must be chosen in advance, and therefore must be appropriate to users' requirements. If demand is uncertain, the reservation plan may not be sufficient and on-demand resources have to be provisioned. Previous work focused on optimally placing virtual machines with cloud providers to minimize total cost. However, many applications require large amounts of network bandwidth. Therefore, considering only virtual machines offers an incomplete view of the system. Exploiting recent developments in software defined networking (SDN), we propose a unified approach that integrates virtual machine and network bandwidth provisioning. We solve a stochastic integer programming problem to obtain an optimal provisioning of both virtual machines and network bandwidth, when demand is uncertain. Numerical results clearly show that our proposed solution minimizes users' costs and provides superior performance to alternative methods. We believe that this integrated approach is the way forward for cloud computing to support network intensive applications.


title: A Vision for All-Spin Neural Networks: A Device to System Perspective

abstract: Spin-transfer torque (STT) mechanisms in vertical and lateral spin valves and magnetization reversal/domain wall motion with spin-orbit torque (SOT) have opened up new possibilities of efficiently mimicking “neural” and “synaptic” functionalities with much lower area and energy consumption compared to CMOS implementations. In this paper, we review various STT/SOT devices that can provide a compact and area-efficient implementation of artificial neurons and synapses. We provide a device-circuit-system perspective and envision design of an All-Spin neuromorphic processor (with different degrees of bio-fidelity) that can be potentially appealing for ultra-low power cognitive applications.


A machine learning approach for product matching and categorization


title: Molecular dynamics simulation of structure formation of short chain molecules

abstract: Molecular dynamics simulations are carried out to study the structure formation of 100 short chain molecules, each of which consists of 20 CH2 groups. Our simulations show that the orientationally ordered structure is formed from a random configuration by quenching. The global orientational order starts to increase suddenly after a certain duration and grow in a stepwise fashion afterwards. This behavior is also found in the growth process of the local orientationally-ordered domains. It is found from the microscopic analysis of the structure formation process, that parallel ordering of chain molecules starts to occur after the chain molecules stretch to some extent. From the analysis of the obtained orientationally ordered structure and the molecular mobility, we also find the following characteristic features: (i) The chain molecules are packed hexagonally at 400 K and the transition from the hexagonal phase toward the orthorhombic phase takes place as the temperature decreases. (ii) The gauche bonds in...


title: Systematic model for lean product development implementation in an automotive related company

abstract: Lean product development is a major innovative business strategy that employs sets of practices to achieve an efficient, innovative and a sustainable product development. Despite the many benefits and high hopes in the lean strategy, many companies are still struggling, and unable to either achieve or sustain substantial positive results with their lean implementation efforts. However, as the first step towards addressing this issue, this paper seeks to propose a systematic model that considers the administrative and implementation limitations of lean thinking practices in the product development process. The model which is based on the integration of fuzzy Shannon’s entropy and Modified Technique for Order Preference by Similarity to the Ideal Solution (M-TOPSIS) model for the lean product development practices implementation with respective to different criteria including management and leadership, financial capabilities, skills and expertise and organization culture, provides a guide or roadmap for product development managers on the lean implementation route.


title: 3D shape scanning with a Kinect

abstract: We describe a method for 3D object scanning by aligning depth and color scans which were taken from around an object with a Kinect camera. Our easy-to-use, cost-effective scanning solution could make 3D scanning technology more accessible to everyday users and turn 3D shape models into a much more widely used asset for many new applications, for instance in community web platforms or online shopping.


title: Using Coreference Links to Improve Spanish-to-English Machine Translation

abstract: Reference EPFL-CONF-225953 Related documents: http://publications.idiap.ch/index.php/publications/showcite/Werlen_Idiap-RR-07-2017 Record created on 2017-02-19, modified on 2017-02-19


title: Template-directed covalent conjugation of DNA to native antibodies, transferrin and other metal-binding proteins.

abstract: DNA-protein conjugates are important in bioanalytical chemistry, molecular diagnostics and bionanotechnology, as the DNA provides a unique handle to identify, functionalize or otherwise manipulate proteins. To maintain protein activity, conjugation of a single DNA handle to a specific location on the protein is often needed. However, preparing such high-quality site-specific conjugates often requires genetically engineered proteins, which is a laborious and technically challenging approach. Here we demonstrate a simpler method to create site-selective DNA-protein conjugates. Using a guiding DNA strand modified with a metal-binding functionality, we directed a second DNA strand to the vicinity of a metal-binding site of His6-tagged or wild-type metal-binding proteins, such as serotransferrin, where it subsequently reacted with lysine residues at that site. This method, DNA-templated protein conjugation, facilitates the production of site-selective protein conjugates, and also conjugation to IgG1 antibodies via a histidine cluster in the constant domain.


title: Data privacy for IoT systems: Concepts, approaches, and research directions

abstract: Data are today an asset more critical than ever for all organizations we may think of. Recent advances and trends, such as sensor systems, IoT, cloud computing, and data analytics, are making possible to pervasively, efficiently, and effectively collect data. However such pervasive data collection and the lack of security for IoT devices increase data privacy concerns. In this paper, we discuss relevant concepts and approaches for data privacy in IoT, and identify research challenges that must be addressed by comprehensive solutions to data privacy.


title: SLIP running with an articulated robotic leg

abstract: SLIP models are generally known as one of the best and simplest abstractions describing the spring-like leg behavior found in human and animal running, and have thus been subject to exhaustive investigation. To exploit these findings in real robots, we utilize an operational space controller that projects the behavior of the SLIP model onto the dynamics of an actual segmented robotic leg. Additionally, we introduce a method to compensate for the energetic losses at the impact collisions, which are not accounted for in the simplified SLIP assumptions. This allows the direct application of existing dead-beat control strategies to arbitrary robotic legs, for which we can show that the collision and compensation effects in the actual leg enlarge the regions of stable running and reduce the minimally required locomotion speed. The necessary joint torque profiles can be generated in large part passively, for example by using high compliance series elastic actuators.


title: The Love/Hate Relationship with the C Preprocessor: An Interview Study (Artifact)

abstract: This appendix presents detailed information about the research methods we used in the study, subject characterization, grounded theory process that we followed strictly, and the survey we performed in the study. It provides helpful data for understanding the subtler points of the companion paper and for reproducibility.


Differential Diagnosis of Female-Pattern Hair Loss


title: Measuring Sexual Harassment: Theoretical and Psychometric Advances

abstract: This article describes a program of research designed to yield a conceptually grounded, psychometrically sound instrument for assessing the incidence and prevalence of sexual harassment in the workplace. Following the specification of a theoretical framework that is consistent with both legal guidelines and psychological research, we review the development and evaluation of a three-dimensional model of sexual harassment (gender harassment, unwanted sexual attention, and sexual coercion). Based on this model, we describe the development of a revised version of the Sexual Experiences Questionnaire (SEQ; Fitzgerald et al., 1988). Following extensive pilot work, the instrument was field tested in a large regulated utility. Data from 448 employed women (professional, technical, clerical and blue collar workers) support the reliability of the scales, and confirmatory factor analysis in this new sample confirms the stability and generalizability of the theoretical model. Following a brief review of validity data...


title: Electronic marketplaces: A literature review and a call for supply chain management research

abstract: Abstract These days, Internet-based electronic marketplaces (EMs) are getting more and more popular. They emerge in different industries, supporting the exchange of goods and services of different kinds, with and for different types of actors, and are following different architectural principles. Most observers have assumed that EM would come to dominate the e-business landscape. Once you look beyond the publicity, however, you quickly see that most EMs are struggling. The supply chain dimension of an EM is largely neglected and poorly managed, while basic logistics operation is currently hampering turnover and revenues. The Paper at hand examines, based on a critical literature review, the actual EM discussion and calls for more supply chain management research within this field.


title: A general coefficient of similarity and some of its properties

abstract: A general coefficient measuring the similarity between two sampling units is defined. The matrix of similarities between all pairs of sample units is shown to be positive semidefinite (except possibly when there are missing values). This is important for the multidimensional Euclidean representation of the sample and also establishes some inequalities amongst the similarities relating three individuals. The definition is extended to cope with a hierarchy of characters.


Dynamic Shader Lamps : Painting on Real Objects


title: Towards the Personal Equation of Interaction: The impact of personality factors on visual analytics interface interaction

abstract: These current studies explored the impact of individual differences in personality factors on interface interaction and learning performance behaviors in both an interactive visualization and a menu-driven web table in two studies. Participants were administered 3 psychometric measures designed to assess Locus of Control, Extraversion, and Neuroticism. Participants were then asked to complete multiple procedural learning tasks in each interface. Results demonstrated that all three measures predicted completion times. Additionally, results analyses demonstrated personality factors also predicted the number of insights participants reported while completing the tasks in each interface. We discuss how these findings advance our ongoing research in the Personal Equation of Interaction.


title: High-level synthesis with LegUp: a crash course for users and researchers

abstract: High-level synthesis (HLS) has been gaining traction recently as a design methodology for FPGAs, with the promise of raising the productivity of FPGA hardware designers, and ultimately, opening the door to the use of FPGAs as computing devices targetable by software engineers. In this tutorial, we introduce LegUp, an open-source HLS tool for FPGAs developed at the University of Toronto. With LegUp, a user can compile a C program completely to hardware, or alternately, he/she can choose to compile the program to a hybrid hardware/software system comprising a processor along with one or more accelerators. LegUp supports the synthesis of most of the C language to hardware, including loops, structs, multi-dimensional arrays, pointer arithmetic, and floating point operations. The LegUp distribution includes the CHStone HLS benchmark suite, as well as a test suite and associated infrastructure for measuring quality of results, and for verifying the functionality of LegUp-generated circuits. LegUp is freely downloadable at www.legup.org, providing a powerful platform that can be leveraged for new high-level synthesis research.


title: High Step-Up DC–DC Converter Based on Switched Capacitor and Coupled Inductor

abstract: Due to the relatively low output voltage of photovoltaic (PV) source, a high step-up converter with high efficiency is needed when the PV source is connected to the power grid. A novel high step-up converter based on two switched capacitors and a coupled inductor is proposed in this paper. The operating principle is analyzed and the voltage gain is derived. A 100-W prototype is fabricated in the laboratory to verify the theoretical analysis, and the highest efficiency is 96.4%.


title: Model Predictive Torque Control of Induction Motor Drives With Optimal Duty Cycle Control

abstract: Model predictive torque control (MPTC) is emerging as a powerful control scheme for high performance control of induction motor (IM) drives. Compared to direct torque control, MPTC is more effective and accurate in voltage vector selection by incorporating the system model directly with the finite switching states. However, for two-level inverter-fed IM drives, the sampling frequency of MPTC has to be high to achieve good performance due to the limited number of voltage vectors. Recently, the concept of duty cycle control was introduced in MPTC by inserting a null vector along with the selected active voltage vector to achieve torque ripple reduction. The active vector is first selected from conventional MPTC and then its duration is determined based on a certain principle. The cascaded processing of active vector and its duration leads to poor low-speed performance and the sampling frequency still has to be high. This paper proposes an improved MPTC with duty cycle control by optimizing the vector selection and its duration simultaneously when minimizing both torque and flux errors. As a result, better steady-state performance at both low and high speeds is achieved, even if the sampling frequency is reduced by half. The effectiveness of the proposed MPTC is verified by both simulation and experimental results.


title: Consumers’ purchase of organic food products. A matter of convenience and reflexive practices

abstract: The aim of this study was to gain insight into the purchase of organic food products by consumers and to explore the main factors driving this process. This paper uses evidence from 16 in-depth interviews with consumers in Denmark carried out in 2008–2009. On the basis of the analysis two broad concepts are suggested: convenience behaviours and reflexive practices. Convenience behaviours are characteristic of pragmatic organic consumers. This type of shopping behaviour requires organic foods to be available in the local supermarket, they have to be clearly visible (preferably with an eco-label), and the price differential vis-a-vis conventional products have to be minimal. The analysis also showed that politically/ethically minded consumers have reflexive practices when purchasing organic food products: health considerations, ethical considerations (animal welfare), political considerations (environmentalism) and quality considerations (taste) play an important part for these consumers. Reflexive shopping practices can be sparked by life events (e.g. having children), “shocking” news about conventional food products and similar events, and news capable of creating a “cognitive dissonance” among consumers. The Danish case illustrates that the government needs to actively implement reforms and promote activities which make organic products a convenient choice for the pragmatic oriented consumer if their market share is to increase substantially.


title: Collaborative Learning via the Internet

abstract: Certain researchers suggest that a teaching method which encapsulates collaboration and interaction would likely work well within the framework of Internet based distance education courses. In order to verify how interaction and collaboration work between students in distance education courses, we observed and researched two undergraduate university courses offered via the Internet. All communications were text based and in asynchronous mode relying upon e-mail, group discussions and hypertext navigation to facilitate the collaborative work process. This exploratory study has enabled us to identify some problematic elements which can hamper collaboration between distance education students. For example, even though most of the distance education students enjoyed communicating with one another, with the professor and with the teaching assistant, the collaborative learning assignments were not always a successful endeavour. To begin with, some students complained of technical difficulties which greatly hampered communication, interaction and collaboration with distant partners. Other students, who had a good technical knowledge of the Internet, had trouble developing a working relationship with out of province or overseas partners. Another interesting observation was the fact that some autonomous, highly independent students preferred working alone. They felt that the collaborative tasks placed undue constraints on their personal work schedule. From these results, we developed recommendations for ensuring the success of collaborative assignments for future Internet courses.


title: De Novo Mutations in Moderate or Severe Intellectual Disability

abstract: Genetics is believed to have an important role in intellectual disability (ID). Recent studies have emphasized the involvement of de novo mutations (DNMs) in ID but the extent to which they contribute to its pathogenesis and the identity of the corresponding genes remain largely unknown. Here, we report a screen for DNMs in subjects with moderate or severe ID. We sequenced the exomes of 41 probands and their parents, and confirmed 81 DNMs affecting the coding sequence or consensus splice sites (1.98 DNMs/proband). We observed a significant excess of de novo single nucleotide substitutions and loss-of-function mutations in these cases compared to control subjects, suggesting that at least a subset of these variations are pathogenic. A total of 12 likely pathogenic DNMs were identified in genes previously associated with ID (ARID1B, CHD2, FOXG1, GABRB3, GATAD2B, GRIN2B, MBD5, MED13L, SETBP1, TBR1, TCF4, WDR45), resulting in a diagnostic yield of ∼29%. We also identified 12 possibly pathogenic DNMs in genes (HNRNPU, WAC, RYR2, SET, EGR1, MYH10, EIF2C1, COL4A3BP, CHMP2A, PPP1CB, VPS4A, PPP2R2B) that have not previously been causally linked to ID. Interestingly, no case was explained by inherited mutations. Protein network analysis indicated that the products of many of these known and candidate genes interact with each other or with products of other ID-associated genes further supporting their involvement in ID. We conclude that DNMs represent a major cause of moderate or severe ID.


title: Aspect based sentiment analysis in social media with classifier ensembles

abstract: The analysis of user generated content on social media and the accurate specification of user opinions towards products and events is quite valuable to many applications. With the proliferation of Web 2.0 and the rapid growth of user-generated content on the web, approaches on aspect level sentiment analysis that yield fine grained information are of great interest. In this work, a classifier ensemble approach for aspect based sentiment analysis is presented. The approach is generic and utilizes latent dirichlet allocation to model a topic and to specify the main aspects that users address. Then, each comment is further analyzed and word dependencies that indicate the interactions between words and aspects are extracted. An ensemble classifier formulated by naive bayes, maximum entropy and support vector machines is designed to recognize the polarity of the user's comment towards each aspect. The evaluation results show sound improvement compared to individual classifiers and indicate that the ensemble system is scalable and accurate in analyzing user generated content and in specifying users' opinions and attitudes.


title: An Integrated 802.11p WAVE DSRC and Vehicle Traffic Simulator With Experimentally Validated Urban (LOS and NLOS) Propagation Models

abstract: The IEEE 802.11p, 1609.3, and 1609.4 WAVE standards are designed to facilitate intervehicle communication and ultimately improve traffic safety. Multiple safety applications and control algorithms have been proposed to use 802.11p Dedicated Short-Range Communication (DSRC) radios and message structures. An urban environment provides many challenges for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. These include multiple propagation paths and many occlusions, particularly in areas where V2V messages would be most useful such as blind spots, buildings, and other obstructions. The dense urban environments and high concentration of vehicles make it difficult to predict how reliable this communication will be. The Ohio State University's Vehicle and Traffic Simulator (VaTSim) is designed as a microsimulator of traffic. This paper describes the incorporation of V2V communication into VaTSim using Network Simulator 3 (NS3) and physical layer modeling to determine how different road layouts and building configurations will affect 802.11p communication. This paper explains the theory used to define the simulated line-of-sight (LOS) propagation, non-LOS (NLOS) propagation calculations, channel switching congestion, and the experiments performed to validate the models and the simulation.


title: Low cost real-time system monitoring using Raspberry Pi

abstract: Nowadays, the Closed-Circuit Television (CCTV) surveillance system is being utilized in order to keep peace and provide security to people. There are several defects in the video surveillance system, such as: picture is indistinct, anomalies cannot be identified automatically, a lot of storage spaces are needed to save the surveillance information, and prices remain relatively high. This paper describes the design and implementation of a low-cost system monitoring based on Raspberry Pi, a single board computer which follows Motion Detection algorithm written in Python as a default programming environment. In addition, the system uses the motion detection algorithm to significantly decrease storage usage and save investment costs. The algorithm for motion detection is being implemented on Raspberry Pi, which enables live streaming camera along with detection of motion. The live video camera can be viewed from any web browser, even from mobile in real-time.


Spectral Correlation Mapper ( SCM ) : An Improvement on the Spectral Angle Mapper ( SAM )


title: Detecting Culture-specific Tags for News Videos through Multimodal Embedding

abstract: Many videos on the Web about international events are maintained in different countries, and some come with text descriptions from different cultural points of view. We introduce a new task-detecting culture-specific tags for news videos: given video keyframes and culture information, assign the most relevant tags with cultural preferences. We approach this problem by mapping visual and multilingual textual features into a joint latent space by reliable visual cues, by our proposed two-view pair-pair embedding and three-view embedding, through various canonical correlation analyses variants (Canonical Correlation Analysis, Deep Canonical Correlation Analysis, eneralize Canonical Correlation Analysis). For human-interest international events such as epidemics and transportation disasters, we proof that, for the same news event, tags of videos are significantly different in different cultures.


Using Measures of Risk Perception to Predict Information Security Behavior: Insights from Electroencephalography (EEG)


title: Design and construction of axial flux permanent magnet generator for wind turbine generated DC voltage at rated power 1500 W

abstract: This paper presents a design of permanent magnet machines (PM), such as the permanent magnet axial flux generator for wind turbine generated direct current voltage base on performance requirements. However recent developments in rare earth permanent magnet materials and power electronic devices has awakened interest in alternative generator topologies that can be used to produce direct voltage from wind energy using rectifier circuit convert alternating current to direct current. In preliminary tests the input mechanical energy to drive the rotor of the propose generator. This paper propose a generator which can change mechanical energy into electrical energy with the generator that contains bar magnets move relative generated flux magnetic offset winding coils in stator component. The results show that the direct current output power versus rotor speed of generator in various applications. These benefits present the axial flux permanent magnet generator with generated direct voltage at rated power 1500 W.


title: DeepMove: Predicting Human Mobility with Attentional Recurrent Networks

abstract: Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of three challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; and 3) the heterogeneity and sparsity of the collected trajectory data. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. We perform experiments on three representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10%. Moreover, compared with existing simple neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction.


title: Special Issue on Innovation through Open Data - A Review of the State-of-the-Art and an Emerging Research Agenda: Guest Editors' Introduction

abstract: For decades good governance scholarship has focused attention on the importance of government openness [26], [34]. Since the 1960s, Freedom of Information (FOI) legislation has formed the backbone of institutional support for opening information and documents [76] and participatory processes [4], [79]. However, FOI represents a passive approach to releasing information. Persons or organizations must still request the information they want, referred to casually, as freedom of information requests. Since the 1990s, publishing documents on websites or using communication technologies to engage citizens in participation processes has signaled a more proactive approach to releasing government information and political engagement. Since 2003, governments have re-envisioned their passive and proactive approaches to include an open data agenda, [18], [62], where publishing documents and data in open formats, [35], [63] is the preferred way. Collectively, these developments have forged the basis for what has been commonly referred to as the open government and data movements [30]. Open data practices and policies are praised for their potential to generate public value, particularly through innovation, economic growth, and transparency, [5], [9], [18], [21], [81]. The nature and character of open data has been hailed for its innovative capacity and transformative power [19], [35], [40], [45], [80]. Various studies have confirmed that proactively releasing public and private data in open formats creates considerable benefits for citizens, researchers, companies and other stakeholders, such as business creation or having the ability to understand public or private problems in new ways through advanced data analytics, [5], [9], [18], [21], [81]. Only a handful of articles examine both the unintended consequences and negative side effects of opening data, [33] and the underlying causal mechanisms that actually lead to the desired open data benefits [5]. Open data research is still in its infancy, and as a result, the extant literature uses limited application and development of theory toward understanding the open data phenomenon. While scholars acknowledge diverse perspectives, it is not clear which theories are most relevant, nor whether a single or integrated theory is needed. This special issue is part of a series of two special issues about open data. This issue focuses on the relationship between innovation and open data, while the second special issue emphasizes research on open data related to transparency and open data policies. To realize the practical benefits of this transformative practice and to develop theory, more research needs to focus on understanding how innovation occurs through open data activities. The papers in this special issue begin to address this gap. The introductory article discusses the state-of-the-art with respect to understanding the context of open data innovation, developments, challenges and barriers, presents an overview of open data research and outlines emerging research directions.


title: 8K Extremely-High-Resolution Camera Systems

abstract: Research and development of ultrahigh-definition imaging systems that go beyond high-definition television (HDTV) have converged in the field of “4K” and “8K” imaging systems. The 4K imaging systems are used for digital cinema, while the 8K systems have been developed for use in next-generation TV broadcasting systems. The latter at 60 frames/s represents the highest pixel count and frame rate of any system, which includes their transmission design. In this paper, we will present an overview of two “8K” camera systems. One achieves a full resolution of 8K using three 33-Mpixel image sensors. The other is a more compact and practical camera system that uses four 8-Mpixel image sensors. We will also describe the camera functions, evaluation results, and views for future developments.


title: Startup Techniques for 95 mV Step-Up Converter by Capacitor Pass-On Scheme and ${\rm V}_{\rm TH}$-Tuned Oscillator With Fixed Charge Programming

abstract: This paper presents a 95 mV startup-voltage step-up DC-DC converter for energy harvesting applications. The capacitor pass-on scheme enables operation of the system from an input voltage of 95 mV without using additional off-chip components. To compensate for the die-to-die process variation, post-fabrication threshold voltage (VTH) trimming is applied to reduce the minimum operating voltage (VDDMIN) of the oscillator. Experimental results demonstrate the 34% VDDMIN reduction of the oscillator by post-fabrication VTH trimming. The proposed step-up converter achieves the lowest startup voltage in standard CMOS without using a mechanical switch or large transformer.


title: Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy

abstract: Abstract Vehicle-to-grid power (V2G) uses electric-drive vehicles (battery, fuel cell, or hybrid) to provide power for specific electric markets. This article examines the systems and processes needed to tap energy in vehicles and implement V2G. It quantitatively compares today's light vehicle fleet with the electric power system. The vehicle fleet has 20 times the power capacity, less than one-tenth the utilization, and one-tenth the capital cost per prime mover kW. Conversely, utility generators have 10–50 times longer operating life and lower operating costs per kWh. To tap V2G is to synergistically use these complementary strengths and to reconcile the complementary needs of the driver and grid manager. This article suggests strategies and business models for doing so, and the steps necessary for the implementation of V2G. After the initial high-value, V2G markets saturate and production costs drop, V2G can provide storage for renewable energy generation. Our calculations suggest that V2G could stabilize large-scale (one-half of US electricity) wind power with 3% of the fleet dedicated to regulation for wind, plus 8–38% of the fleet providing operating reserves or storage for wind. Jurisdictions more likely to take the lead in adopting V2G are identified.


Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era


title: An Experiment in Crowdfunding: Assessing the Role and Impact of Transaction-Level Information Controls

abstract: Crowdfunding has received a great deal of attention of late, as a promising avenue to fostering entrepreneurship and innovation. A notable aspect of shifting these financial activities to an online setting is that this brings increased visibility and traceability to a potentially sensitive activity. Most crowdfunding platforms maintain a public record of transactions, though a good number also provide transaction-level information controls, enabling users to conceal information as they see fit. We explore the impact of these information control mechanisms on crowdfunder behavior, acknowledging possible positive (e.g., comfort) and negative (e.g., privacy priming) impacts, employing a randomized experiment at a leading crowdfunding platform. Reducing access to information controls increases conversion rates (by ~5%), yet it also decreases average contribution amounts ($5.81 decline). We offer interpretations for these effects along with some empirical support, and we discuss implications for crowdfunding platform design.


title: Economic incentives for the voluntary disclosure of current cost financial statements

abstract: Abstract This study examines why some New Zealand listed companies voluntarily present current cost financial statements. The results suggest that tax and political cost considerations are influential in the voluntary disclosure of current cost information.


title: Neural Word Segmentation with Rich Pretraining

abstract: Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.


title: Relationship between assistive torque and knee biomechanics during exoskeleton walking in individuals with crouch gait

abstract: Crouch or "flexed knee" gait is a pathological gait pattern affecting many individuals with cerebral palsy. One proposed method to alleviate crouch is to provide robotic assistance to knee extension during walking. The purpose of this study was to evaluate how the magnitude of knee extensor torque affects knee kinematics, kinetics, and muscle activity. Motion capture, ground reaction force and electromyography data were collected while four participants with crouch gait from cerebral palsy walked with assistance from a novel robotic exoskeleton on an instrumented treadmill. Different magnitudes of knee extensor torque were provided during the stance (range: 0.09-0.38 Nm/kg) and swing (range: 0.09-0.29 Nm/kg) phases of the gait cycle. Using a linear regression analysis, we found that greater torque from the exoskeleton was positively associated with increased knee extension (reduction in crouch) at foot contact and mid-stance, negatively associated with the biological knee extensor moment, and positively associated with knee flexor muscle activity. Determining the relationships between exoskeleton assistance and knee kinematics and kinetics will benefit the continued investigation of robotic treatment strategies for treating crouch gait. Our findings indicate the importance of properly tuned robotic control strategies for gait rehabilitation.


title: Cross-National Patterns of Gender Differences in Mathematics: A Meta-Analysis.

abstract: A gender gap in mathematics achievement persists in some nations but not in others. In light of the underrepresentation of women in careers in science, technology, mathematics, and engineering, increasing research attention is being devoted to understanding gender differences in mathematics achievement, attitudes, and affect. The gender stratification hypothesis maintains that such gender differences are closely related to cultural variations in opportunity structures for girls and women. We meta-analyzed 2 major international data sets, the 2003 Trends in International Mathematics and Science Study and the Programme for International Student Assessment, representing 493,495 students 14-16 years of age, to estimate the magnitude of gender differences in mathematics achievement, attitudes, and affect across 69 nations throughout the world. Consistent with the gender similarities hypothesis, all of the mean effect sizes in mathematics achievement were very small (d < 0.15); however, national effect sizes showed considerable variability (ds = -0.42 to 0.40). Despite gender similarities in achievement, boys reported more positive math attitudes and affect (ds = 0.10 to 0.33); national effect sizes ranged from d = -0.61 to 0.89. In contrast to those of previous tests of the gender stratification hypothesis, our results point to specific domains of gender equity responsible for gender gaps in math. Gender equity in school enrollment, women's share of research jobs, and women's parliamentary representation were the most powerful predictors of cross-national variability in gender gaps in math. Results are situated within the context of existing research demonstrating apparently paradoxical effects of societal gender equity and highlight the significance of increasing girls' and women's agency cross-nationally.


title: Towards a Distributional Model of Semantic Complexity

abstract: In this paper, we introduce for the first time a Distributional Model for computing semantic complexity , inspired by the general principles of the Memory, Unification and Control framework (Hagoort, 2013; Hagoort, 2016). We argue that sentence comprehension is an incremental process driven by the goal of constructing a coherent representation of the event represented by the sentence. The composition cost of a sentence depends on the semantic coherence of the event being constructed and on the activation degree of the linguistic constructions. We also report the results of a first evaluation of the model on the Bicknell dataset (Bicknell et al., 2010).


SmartPort: A Platform for Sensor Data Monitoring in a Seaport Based on FIWARE


title: Seismic performance of the existing and newly designed wall buildings

abstract: Buildings with reinforced concrete structural walls are frequently used in Croatia. Wall-to-floor ratio (at least in one direction) is usually high and although these walls were typically lightly reinforced with simple reinforcement details, such structures exhibited well behavior during previous earthquakes. Nevertheless, their analysis is usually quite complicated and time consuming at which end we find out that following minimum requirements were enough in the first place. The basic goal of this research was to simplify design and evaluation of structural wall buildings by introducing “ Performance domain” method as well as by the nonlinear static method N2 (as given in the Annex B of the Eurocode 8: Draft No 6, January 2003). Evaluation is performed on a chosen sample of wall buildings designed following minimum requirements according to the old HRN codes (for existing) and new seismic codes.


title: Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images

abstract: Accurate pose estimation is often a requirement for robust robotic grasping and manipulation of objects placed in cluttered, tight environments, such as a shelf with multiple objects. When deep learning approaches are employed to perform this task, they typically require a large amount of training data. However, obtaining precise 6 degrees of freedom for ground-truth can be prohibitively expensive. This work therefore proposes an architecture and a training process to solve this issue. More precisely, we present a weak object detector that enables localizing objects and estimating their 6D poses in cluttered and occluded scenes. To minimize the human labor required for annotations, the proposed detector is trained with a combination of synthetic and a few weakly annotated real images (as little as 10 images per object), for which a human provides only a list of objects present in each image (no time-consuming annotations, such as bounding boxes, segmentation masks and object poses). To close the gap between real and synthetic images, we use multiple domain classifiers trained adversarially. During the inference phase, the resulting class-specific heatmaps of the weak detector are used to guide the search of 6D poses of objects. Our proposed approach is evaluated on several publicly available datasets for pose estimation. We also evaluated our model on classification and localization in unsupervised and semi-supervised settings. The results clearly indicate that this approach could provide an efficient way toward fully automating the training process of computer vision models used in robotics.


title: Update on Coupled Dark Energy and the $H_0$ tension

abstract: In this work we provide updated constraints on coupled dark energy (CDE) cosmology with Peebles-Ratra (PR) potential and constant coupling strength $\beta$. This modified gravity scenario introduces a fifth force between dark matter particles, mediated by a scalar field that plays the role of dark energy. The mass of the dark matter particles does not remain constant, but changes with time as a function of the scalar field. Here we focus on the phenomenological behavior of the model, and assess its ability to describe updated cosmological data sets that include the Planck 2018 cosmic microwave background (CMB) temperature, polarization and lensing, baryon acoustic oscillations, the Pantheon compilation of supernovae of Type Ia, data on $H(z)$ from cosmic chronometers, and redshift-space distortions. We also study which is the impact of the local measurement of $H_0$ from SH0ES and the strong-lensing time delay data from the H0LICOW collaboration on the parameter that controls the strength of the interaction in the dark sector. We find a peak corresponding to a coupling $\beta>0$ and to a potential parameter $\alpha>0$, more or less evident depending on the data set combination. We show separately the impact of each data set and remark that it is especially CMB lensing the one data set that shifts the peak the most towards $\Lambda$CDM. When a model selection criterion based on the full Bayesian evidence is applied, however, $\Lambda$CDM is still preferred in all cases, due to the additional parameters introduced in the CDE model.


title: SCOPE: parallel databases meet MapReduce

abstract: Companies providing cloud-scale data services have increasing needs to store and analyze massive data sets, such as search logs, click streams, and web graph data. For cost and performance reasons, processing is typically done on large clusters of tens of thousands of commodity machines. Such massive data analysis on large clusters presents new opportunities and challenges for developing a highly scalable and efficient distributed computation system that is easy to program and supports complex system optimization to maximize performance and reliability. In this paper, we describe a distributed computation system, Structured Computations Optimized for Parallel Execution (Scope), targeted for this type of massive data analysis. Scope combines benefits from both traditional parallel databases and MapReduce execution engines to allow easy programmability and deliver massive scalability and high performance through advanced optimization. Similar to parallel databases, the system has a SQL-like declarative scripting language with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. An optimizer is responsible for converting scripts into efficient execution plans for the distributed computation engine. A physical execution plan consists of a directed acyclic graph of vertices. Execution of the plan is orchestrated by a job manager that schedules execution on available machines and provides fault tolerance and recovery, much like MapReduce systems. Scope is being used daily for a variety of data analysis and data mining applications over tens of thousands of machines at Microsoft, powering Bing, and other online services.


title: Tools for Gamification Analytics: A Survey

abstract: Application and gamification data contains valuable information about users and their behavior. This data can be used to measure the success of gamification projects, to analyze user behavior, and to continuously improve gamification designs. Existing software solutions promise support for analyzing game- and gamification-related data. However, research shows that most gamification projects do not monitor and analyze this data in an automated way, even though gamification experts are aware of its potential. In this survey paper we identify relevant software solutions and assesses them with regards to their fulfillment of user requirements in the gamification analytics domain. The survey results can be used by practitioners to make tool decisions. Furthermore, we identify gaps of current solutions that should be addressed by future work in the field of gamification analytics.


title: Phase-Shifted PWM Converter With a Wide ZVS Range and Reduced Circulating Current

abstract: A phase-shifted (PS) pulse-width-modulation (PWM) converter that can solve the drawbacks of existing PS full-bridge converters, such as narrow zero-voltage-switching (ZVS) range, large duty-cycle loss, large circulating current, and serious secondary-voltage overshoot and oscillation, is proposed in this paper. The proposed converter is composed of two symmetric half-bridge converters (TSHBCs) that are placed in parallel on the primary side and are driven in a phase-shifting manner. Their transformers are connected in series on the secondary side. At the output, a simple energy-recovery circuit (ERC) is employed. Due to this structure, the proposed converter has the advantages of wide ZVS range, less duty-cycle loss, reduced circulating current, and no secondary-voltage overshoot and oscillation, which results in the reduction of rectifier diodes' voltage stress and EMI. Therefore, the conversion efficiency is higher and the cost of the secondary rectifier is reduced compared to its PS PWM counterparts. In this paper, the circuit configuration, operation principle, and relevant analysis results of the proposed converters are presented. Experimental results on a prototype converter realized with an 80-in plasma display panel sustain power module (320-385 Vdc input, 205 Vdc/5 A output) validate the theoretical analysis.


title: Real-time reactive motion generation based on variable attractor dynamics and shaped velocities

abstract: This paper describes a novel method for motion generation and reactive collision avoidance. The algorithm performs arbitrary desired velocity profiles in absence of external disturbances and reacts if virtual or physical contact is made in a unified fashion with a clear physically interpretable behavior. The method uses physical analogies for defining attractor dynamics in order to generate smooth paths even in presence of virtual and physical objects. The proposed algorithm can, due to its low complexity, run in the inner most control loop of the robot, which is absolutely crucial for safe Human Robot Interaction. The method is thought as the locally reactive real-time motion generator connecting control, collision detection and reaction, and global path planning.


Program Synthesis Without Full Specifications for Novel Applications


title: EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time

abstract: We present EVO, an event-based visual odometry algorithm. Our algorithm successfully leverages the outstanding properties of event cameras to track fast camera motions while recovering a semidense three-dimensional (3-D) map of the environment. The implementation runs in real time on a standard CPU and outputs up to several hundred pose estimates per second. Due to the nature of event cameras, our algorithm is unaffected by motion blur and operates very well in challenging, high dynamic range conditions with strong illumination changes. To achieve this, we combine a novel, event-based tracking approach based on image-to-model alignment with a recent event-based 3-D reconstruction algorithm in a parallel fashion. Additionally, we show that the output of our pipeline can be used to reconstruct intensity images from the binary event stream, though our algorithm does not require such intensity information. We believe that this work makes significant progress in simultaneous localization and mapping by unlocking the potential of event cameras. This allows us to tackle challenging scenarios that are currently inaccessible to standard cameras.


title: A Solvable High-Dimensional Model of GAN

abstract: We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data. The training dynamics of the proposed model at both microscopic and macroscopic scales can be exactly analyzed in the high-dimensional limit. In particular, we prove that the macroscopic quantities measuring the quality of the training process converge to a deterministic process characterized by an ordinary differential equation (ODE), whereas the microscopic states containing all the detailed weights remain stochastic, whose dynamics can be described by a stochastic differential equation (SDE). This analysis provides a new perspective different from recent analyses in the limit of small learning rate, where the microscopic state is always considered deterministic, and the contribution of noise is ignored. From our analysis, we show that the level of the background noise is essential to the convergence of the training process: setting the noise level too strong leads to failure of feature recovery, whereas setting the noise too weak causes oscillation. Although this work focuses on a simple copy model of GAN, we believe the analysis methods and insights developed here would prove useful in the theoretical understanding of other variants of GANs with more advanced training algorithms.


title: Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

abstract: Data noise is present in many machine learning problems domains, some of these are well studied but others have received less attention. In this paper we propose an algorithm for constructing a kernel Fisher discriminant (KFD) from training examples with noisy labels. The approach allows to associate with each example a probability of the label being flipped. We utilise an expectation maximization (EM) algorithm for updating the probabilities. The E-step uses class conditional probabilities estimated as a by-product of the KFD algorithm. The M-step updates the flip probabilities and determines the parameters of the discriminant. We demonstrate the feasibility of the approach on two real-world data-sets.


Early evidence for syntactic bootstrapping : 15-month-olds use sentence structure in verb learning


title: Exploring the Potential of User Modeling Based on Mind Maps

abstract: Mind maps have not received much attention in the user modeling and recommender system community, although mind maps contain rich information that could be valuable for user-modeling and recommender systems. In this paper, we explored the effectiveness of standard user-modeling approaches applied to mind maps. Additionally, we develop novel user modeling approaches that consider the unique characteristics of mind maps. The approaches are applied and evaluated using our mind mapping and reference-management software Docear. Docear displayed 430,893 research paper recommendations, based on 4,700 user mind maps, from March 2013 to August 2014. The evaluation shows that standard user modeling approaches are reasonably effective when applied to mind maps, with click-through rates (CTR) between 1.16% and 3.92%. However, when adjusting user modeling to the unique characteristics of mind maps, a higher CTR of 7.20% could be achieved. A user study confirmed the high effectiveness of the mind map specific approach with an average rating of 3.23 (out of 5), compared to a rating of 2.53 for the best baseline. Our research shows that mind map-specific user modeling has a high potential, and we hope that our results initiate a discussion that encourages researchers to pursue research in this field and developers to integrate recommender systems into their mind mapping tools.


title: Collaborative Learning for Answer Selection in Question Answering

abstract: Answer selection is an essential step in a question answering (QA) system. Traditional methods for this task mainly focus on developing linguistic features that are limited in practice. With the great success of deep learning method in distributed text representation, deep learning-based answer selection approaches have been well investigated, which mainly employ only one neural network, i.e., convolutional neural network (CNN) or long short term memory (LSTM), leading to failures in extracting some rich sentence features. Thus, in this paper, we propose a collaborative learning-based answer selection model (QA-CL), where we deploy a parallel training architecture to collaboratively learn the initial word vector matrix of the sentence by CNN and bidirectional LSTM (BiLSTM) at the same time. In addition, we extend our model by incorporating the sentence embedding generated by the QA-CL model into a joint distributed sentence representation using a strong unsupervised baseline weight removal (WR), i.e., the QA-CLWR model. We evaluate our proposals on a popular QA dataset, InsuranceQA. The experimental results indicate that our proposed answer selection methods can produce a better performance compared with several strong baselines. Finally, we investigate the models’ performance with respect to different question types and find that question types with a medium number of questions have a better and more stable performance than those types with too large or too small number of questions.


title: Salient Object Detection via Multiple Instance Learning

abstract: Object proposals are a series of candidate segments containing the objects of interest, which are taken as preprocessing and widely applied in various vision tasks. However, most of existing saliency approaches only utilizes the proposals to compute a location prior. In this paper, we naturally take the proposals as the bags of instances of multiple instances learning (MIL), where the instances are the superpixels contained in the proposals, and formulate saliency detection problem as an MIL task (i.e., predict the labels of instances using the classifier in the MIL framework). This method allows some flexibility in finding a decision boundary based on the bag-level representations and can identify salient superpixels from ambiguous proposals. In addition, we introduce the MIL to an optimization mechanism, which iteratively updates training bags from easy to complex ones to learn a strong model. The significant improvement can be consistently achieved when applying the optimization model to existing saliency approaches. Extensive experiments demonstrate that the proposed algorithms perform favorably against the state-of-art saliency detection methods on several benchmark data sets.


title: Potential Health Benefits of Garlic (Allium Sativum): A Narrative Review

abstract: A member of the Liliaceae family, garlic (Allium sativum) is highly regarded throughout the world for both its medicinal and culinary value. Early men of medicine such as Hippocrates, Pliny and Aristotle encouraged a number of therapeutic uses for this botanical. Today, it is commonly used in many cultures as a seasoning or spice. Garlic also stands as the second most utilized supplement. With its sulfur containing compounds, high trace mineral content, and enzymes, garlic has shown anti-viral, anti-bacterial, anti-fungal and antioxidant abilities. Diseases that may be helped or prevented by garlic’s medicinal actions include Alzheimer’s Disease, cancer, cardiovascular disease (including atherosclerosis, strokes, hypertension, thrombosis and hyperlipidemias) children’s conditions, dermatologic applications, stress, and infections. Some research points to possible benefits in diabetes, drug toxicity, and osteoporosis.


Link Prediction via Ranking Metric Dual-Level Attention Network Learning


title: Physically-based glare effects for digital images

abstract: The physical mechanisms and physiological causes of glare in human vision are reviewed. These mechanisms are scattering in the cornea, lens, and retina, and diffraction in the coherent cell structures on the outer radial areas of the lens. This scattering and diffraction are responsible for the “bloom” and “flare lines” seen around very bright objects. The diffraction effects cause the “lenticular halo”. The quantitative models of these glare effects are reviewed, and an algorithm for using these models to add glare effects to digital images is presented. The resulting digital point-spread function is thus psychophysically based and can substantially increase the “perceived” dynamic range of computer simulations containing light sources. Finally, a perceptual test is presented that indicates these added glare effects increase the apparent brightness of light sources in digital images. CR


title: Design and fabrication of posfet devices for tactile sensing

abstract: This work presents the design and fabrication of novel piezoelectric polymer-FET touch sensing devices. With thin piezoelectric polymer (PVDF-TrFE) film directly spun and processed in situ on the gate area of MOS transistor, the device can also be called as POSFET (Piezoelectric Oxide Semiconductor Field Effect Transistor) touch sensing device. The integral POSFET “sensotronics” unit, comprising of transducer and the transistor, senses and conditions the signal at ‘same site’. Challenging issues like in situ poling of piezoelectric polymer film, without damaging or altering the characteristics of underlying MOS devices, have been successfully dealt with.