title: Modularity based community detection with deep learning

abstract: Identification of module or community structures is important for characterizing and understanding complex systems. While designed with different objectives, i.e., stochastic models for regeneration and modularity maximization models for discrimination, both these two types of model look for low-rank embedding to best represent and reconstruct network topology. However, the mapping through such embedding is linear, whereas real networks have various nonlinear features, making these models less effective in practice. Inspired by the strong representation power of deep neural networks, we propose a novel nonlinear reconstruction method by adopting deep neural networks for representation. We then extend the method to a semi-supervised community detection algorithm by incorporating pairwise constraints among graph nodes. Extensive experimental results on synthetic and real networks show that the new methods are effective, outperforming most state-of-the-art methods for community detection.


title: Association between daily activities, process skills, and motor skills in community-dwelling patients after left hemiparetic stroke

abstract: [Purpose] The purpose of this study was to evaluate the relationships between daily activities, information processing, and motor skills in individuals with hemineglect after having a left hemiparetic stroke. [Subjects and Methods] The instrumental activities of daily living of 35 patients (22 male and 13 female; age: 57.1 ± 16.9 years) with hemineglect after having a left hemiparetic stroke were assessed by using three clinical measurement tools, including activity card sorting, assessment of motor and process skills, and the modified Barthel Index. [Results] The results of the regression analysis indicated that the patients’ processing skills in instrumental activities of daily living after having a left hemiparetic stroke were reduced. Participation in leisure and social activities was also affected as assessed by using the modified Barthel Index. [Conclusion] This study supports the clinical need for rehabilitation intervention after a left hemiparetic stroke to improve patients’ processing skills and independence in performing activities of daily living.


title: Implications of the Health Reforms for Occupational Therapy Fieldwork Education

abstract: Health sector reforms have resulted in fundamental changes in workplace environments that impact on health professionals' focus and role expectations. In keeping with these changes it is important to ensure that students are well equipped to work within the current health care system. This article summarises key issues in the New Zealand health reforms, the implications for occupational therapists and proposes that there are new ways of conceptualising the rationale for students' fieldwork experiences. It suggests that what is considered to be basic fieldwork experiences should be reconfigured to fit with current health care practice.


title: Cost savings from reduced catheter-related bloodstream infection after simulation-based education for residents in a medical intensive care unit.

abstract: Interventions to reduce preventable complications such as catheter-related bloodstream infections (CRBSI) can also decrease hospital costs. However, little is known about the cost-effectiveness of simulation-based education. The aim of this study was to estimate hospital cost savings related to a reduction in CRBSI after simulation training for residents. ::: This was an intervention evaluation study estimating cost savings related to a simulation-based intervention in central venous catheter (CVC) insertion in the Medical Intensive Care Unit (MICU) at an urban teaching hospital. After residents completed a simulation-based mastery learning program in CVC insertion, CRBSI rates declined sharply. Case-control and regression analysis methods were used to estimate savings by comparing CRBSI rates in the year before and after the intervention. Annual savings from reduced CRBSIs were compared with the annual cost of simulation training. ::: Approximately 9.95 CRBSIs were prevented among MICU patients with CVCs in the year after the intervention. Incremental costs attributed to each CRBSI were approximately $82,000 in 2008 dollars and 14 additional hospital days (including 12 MICU days). The annual cost of the simulation-based education was approximately $112,000. Net annual savings were thus greater than $700,000, a 7 to 1 rate of return on the simulation training intervention. ::: A simulation-based educational intervention in CVC insertion was highly cost-effective. These results suggest that investment in simulation training can produce significant medical care cost savings.


title: Synthesis, characterizations, and optical properties of copper selenide quantum dots

abstract: We demonstrate the synthesis of copper selenide quantum dots (QDs) by element directed, inexpensive, straight forward wet chemical method which is free from any surfactant or template. Copper selenide QDs have been synthesized by elemental copper and selenium in the presence of ethylene glycol, hydrazine hydrate, and a defined amount of water at 70 °C within 8 h. The product is in strong quantum confinement regime, phase analysis, purity and morphology of the product has been well studied by X-ray diffraction (XRD), UV–Visible spectroscopy (UV–Vis), Photo-luminescent spectroscopy (PL), Fourier transform infrared spectroscopy (FTIR), Transmission electron microscopy (TEM), High resolution transmission electron microscopy (HRTEM), and by Atomic force microscopy (AFM) techniques. The absorption and photoluminescence studies display large “blue shift”. TEM and HRTEM analyses revealed that the QDs diameters are in the range 2–5 nm. Due to the quantum confinement effect copper selenide QDs could be potential building blocks to construct functional devices and solar cell. The possible mechanism is also discussed.


title: Unusual defects, generated by wafer sawing: An update, including pick&place processing

abstract: Abstract At ESREF 2008, the paper “Unusual defects, generated by wafer sawing: Diagnosis, mechanisms and how to distinguish from related failures” had won the Best Paper Award. In the meantime, new experiences were collected, related to new methods as laser sawing and its specific ESD risks and additional failure mechanisms as backside damage, charging of foils, pad corrosion and sawing residue damages . This paper explains in detail these failure sources, including detailed explanations on root causes and physical mechanisms as well as important hints for failure analysts how to distinguish related failure signatures from those, which look similar but are of other origin.


title: An Examination of Individual Factors and Employees' Creativity: The Case of Spain

abstract: The objective of this investigation was to identify and analyze the individual attributes responsible for creative performance among employees of a Spanish firm. Drawing from the existing literature on intrinsic motivation, expertise, cognitive style, and individual creativity, research was conducted to test a creativity model of employee characteristics in Spain. Results confirmed that innovative style and intrinsic motivation were related to employee creativity, as measured by self-ratings. Moreover, results suggested that the accumulation of individual attributes that were hypothesized to have a positive relationship with creativity had a greater effect in creativity than when considering separately. A cluster analysis was carried out in order to procure a taxonomy of employees depending on their individual characteristics. Three groups were defined. Their behavior differed significantly in terms of their creativity performance. Recommendations for enhancing creativity in work settings are proposed.


title: A cost-effective, high-bandwidth storage architecture

abstract: This paper describes the Network-Attached Secure Disk (NASD) storage architecture, prototype implementations oj NASD drives, array management for our architecture, and three, filesystems built on our prototype. NASD provides scalable storage bandwidth without the cost of servers used primarily, for transferring data from peripheral networks (e.g. SCSI) to client networks (e.g. ethernet). Increasing datuset sizes, new attachment technologies, the convergence of peripheral and interprocessor switched networks, and the increased availability of on-drive transistors motivate and enable this new architecture. NASD is based on four main principles: direct transfer to clients, secure interfaces via cryptographic support, asynchronous non-critical-path oversight, and variably-sized data objects. Measurements of our prototype system show that these services can be cost-effectively integrated into a next generation disk drive ASK. End-to-end measurements of our prototype drive andfilesysterns suggest that NASD cun support conventional distributed filesystems without performance degradation. More importantly, we show scaluble bandwidth for NASD-specialized filesystems. Using a parallel data mining application, NASD drives deliver u linear scaling of 6.2 MB/s per clientdrive pair, tested with up to eight pairs in our lab.


title: Fundamental limits of location privacy using anonymization

abstract: In [1]–[3], the concept of perfect location privacy is defined and sufficient conditions for achieving it were obtained when anonymization is used. In this paper, necessary conditions for perfect privacy are obtained. Specifically, we prove that the previous sufficient bounds are tight, and thus we obtain the threshold for achieving perfect location privacy using anonymization. First, we assume that a user's current location is independent from her past locations. Using this i.i.d model, we show that if the adversary collects more than equation anonymous observations, then the adversary can successfully recover the users' locations with high probability. Here, n is the number of users in the network and r is the number of all possible locations that users can go to. Next, we model users' movements using Markov chains to better model real-world movement patterns. We show similar results if the adversary collects more than equation observations, where |E| is the number of edges in the user's Markov chain model.


title: Image Annotation by Propagating Labels from Semantic Neighbourhoods

abstract: Automatic image annotation aims at predicting a set of semantic labels for an image. Because of large annotation vocabulary, there exist large variations in the number of images corresponding to different labels ("class-imbalance"). Additionally, due to the limitations of human annotation, several images are not annotated with all the relevant labels ("incomplete-labelling"). These two issues affect the performance of most of the existing image annotation models. In this work, we propose 2-pass k-nearest neighbour (2PKNN) algorithm. It is a two-step variant of the classical k-nearest neighbour algorithm, that tries to address these issues in the image annotation task. The first step of 2PKNN uses "image-to-label" similarities, while the second step uses "image-to-image" similarities, thus combining the benefits of both. We also propose a metric learning framework over 2PKNN. This is done in a large margin set-up by generalizing a well-known (single-label) classification metric learning algorithm for multi-label data. In addition to the features provided by Guillaumin et al. (2009) that are used by almost all the recent image annotation methods, we benchmark using new features that include features extracted from a generic convolutional neural network model and those computed using modern encoding techniques. We also learn linear and kernelized cross-modal embeddings over different feature combinations to reduce semantic gap between visual features and textual labels. Extensive evaluations on four image annotation datasets (Corel-5K, ESP-Game, IAPR-TC12 and MIRFlickr-25K) demonstrate that our method achieves promising results, and establishes a new state-of-the-art on the prevailing image annotation datasets.


title: Dermoscopy pathology correlation in melanoma

abstract: Dermoscopy is a widely used technique whose role in the clinical (and preoperative) diagnosis of melanocytic and non-melanocytic skin lesions has been well established in recent years. The aim of this paper is to clarify the correlations between the "local" dermoscopic findings in melanoma and the underlying histology, in order to help clinicians in routine practice.


An Automata-Theoretic Approach to Automatic Program Verication


title: Isolation and characterization of an anticoagulant from the salivary glands of the tick, Ornithodoros savignyi (Acari: Argasidae)

abstract: An inhibitor of activated coagulation factor X (fXa) was isolated from salivary gland extracts prepared from Ornithodoros savignyi using a two-step procedure, involving reversed-phase high-performance liquid chromatography (RP-HPLC) and diethylaminoethyl (DEAE) ion-exchange chromatography. From its behaviour during DEAE chromatography it could be deduced that it possesses an acidic pI (approximately 4.6). Capillary zone electrophoresis (CZE) of the purified inhibitor showed it to be homogeneous. The molecular mass was determined as 12 kDa using capillary gel electrophoresis (CGE) and as 7183.4 using laser desorption mass spectrometry (LDMS). The N-terminal amino acid sequence (residues 1-12) was determined and found to share a 66% identity with tick anticoagulant peptide (TAP). The O. savignyi peptide is a slow, tight-binding inhibitor of fXa (Ki = 0.83 +/- 0.10 nM). The interaction of the fXa--inhibitor was found to be competitive and dependent on ionic strength. Preliminary investigations show that the inhibitor may be specific for fXa.


title: Hypercolumns for object segmentation and fine-grained localization

abstract: Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as a feature representation. However, the information in this layer may be too coarse spatially to allow precise localization. On the contrary, earlier layers may be precise in localization but will not capture semantics. To get the best of both worlds, we define the hypercolumn at a pixel as the vector of activations of all CNN units above that pixel. Using hypercolumns as pixel descriptors, we show results on three fine-grained localization tasks: simultaneous detection and segmentation [22], where we improve state-of-the-art from 49.7 mean APr [22] to 60.0, keypoint localization, where we get a 3.3 point boost over [20], and part labeling, where we show a 6.6 point gain over a strong baseline.


Reuse Approaches in Software Engineering and Knowledge Engineering: A Comparison


title: Teachers’ forum: Spreadsheet modeling and simulation improves understanding of queues

abstract: Process-driven spreadsheet queuing simulation is a better vehicle for understanding queue behavior than queuing theory or dedicated simulation software. Spreadsheet queuing simulation has many pedagogical benefits in a business school end-user modeling course, including developing students intuition, giving them experience with active modeling skills, and providing access to tools. Spreadsheet queuing simulations are surprisingly easy to program, even for queues with balking and reneging. The ease of prototyping in spreadsheets invites thoughtless design, so careful spreadsheet programming practice is important. Spreadsheet queuing simulation is inferior to dedicated simulation software for analyzing queues but is more likely to be available to managers and students.


Efficient Induction Of Logic Programs


title: An EMG-based force control system for prosthtic arms

abstract: This paper proposes a novel methodology for the control of prosthetic arms. The proposed controller predicts the intended forces at the tip of the prosthetic arm using an artificial neural network fed with electromyogram signals from the residual arm muscles and kinematic data from the prosthetic arm joint. The desired predicted force is then implemented at the prosthetic arm tip using a classic robot force control strategy. The proposed controller is evaluated on healthy subjects using a master-slave telerobotic test-bed.


SOCIAL MOVEMENT LEARNING IN GHANA : COMMUNAL DEFENCE OF RESOURCES IN NEOLIBERAL TIMES


Differentially Private Chi-squared Test by Unit Circle Mechanism.


title: A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare

abstract: Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware fall detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects fall events and starts an indoor positioning engine as soon as a fall happens. In the fall detection scheme, this study proposes a three-phase threshold-based fall detection algorithm to detect critical and normal falls. The fall alarm can be canceled by pressing and holding the emergency fall button only when a normal fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI) collection phase and location calculation phase. Finally, the location of the faller will be calculated by a k-nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the fall detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a fallen person.


title: Modeling of Services and Service Collaboration in UML 2.0

abstract: One of many definitions of Service-Oriented Architecture (SOA) says that SOA is an architectural style for building next-generation distributed information systems. If wc want to get a reliable arrd good working system, it must be well designed first. This paper deals with Scrvice-Oriented Architecture Design (SOAD), especially with modeling of scrviccs. F\rrthermore, abstraction layers of SOA are introduced and possiblc using of object oriented approach on each layer is discussed in this paper. Besides, three types of service collaboration are prcscntcd. The rnain objective of the paper is to dernonstrate how thesc typcs of collaboration can bc dcscribed in UML 2.0.


title: 2T-1R STT-MRAM memory cells for enhanced on/off current ratio

abstract: Novel spin torque transfer magnetic tunnel junction (STT-MTJ) based memory cell topologies are introduced to improve both the sense margin and the current ratio observed by the sense circuitry. These circuits utilize an additional transistor per cell in either a diode connected or gate connected manner and maintain leakage current immunity within the data array. An order of magnitude increase in the current ratio over a traditional 1T-1R structure is observed. This improvement comes at a cost of 61% and 117% increase in area, respectively, for the diode and gate connected cells.


title: A reinforcement learning extension to the Almgren-Chriss model for optimal trade execution

abstract: Reinforcement learning is explored as a candidate machine learning technique to enhance existing analytical solutions for optimal trade execution with elements from the market microstructure. Given a volume-to-trade, fixed time horizon and discrete trading periods, the aim is to adapt a given volume trajectory such that it is dynamic with respect to favourable/unfavourable conditions during realtime execution, thereby improving overall cost of trading. We consider the standard Almgren-Chriss model with linear price impact as a candidate base model. This model is popular amongst sell-side institutions as a basis for arrival price benchmark execution algorithms. By training a learning agent to modify a volume trajectory based on the market's prevailing spread and volume dynamics, we are able to improve post-trade implementation shortfall by up to 10.3% on average compared to the base model, based on a sample of stocks and trade sizes in the South African equity market.


title: Process-Aware Information Systems: Lessons to be Learned from Process Mining

abstract: A Process-Aware Information System (PAIS) is a software system that manages and executes operational processes involving people, applications, and/or information sources on the basis of process models. Example PAISs are workflow management systems, case-handling systems, enterprise information systems, etc. This paper provides a brief introduction to these systems and discusses the role of process models in the PAIS life-cycle. Moreover, it provides a critical reflection on the state-of-the-art based on experiences with process mining . Process mining techniques attempt to extract non-trivial and useful information from event logs. One aspect of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities. The insights provided by process mining are very valuable for the development of the next generation PAISs because they clearly show a mismatch between the models proposed for driving these systems and reality. On the one hand, models tend to oversimplify things resulting in systems that are too restrictive. On the other hand, models fail to capture important aspects of business processes.


title: Turning Real-World Software Development into a Game

abstract: Software development is a challenging, but seldom amusing activity. At the same time, gamification, a recent trend that brings game mechanics to websites and interactive media, together with many past works that propose the use of serious games to teach software engineering in a fun way, show evidence that this type of real-world activity can also incorporate game design elements. In this work, we propose a novel approach: incorporating game mechanics directly into a software development process, effectively turning it into a live game. We show interesting results from a case study with a production team of a software house, and firmly believe it is important that the game academic community spreads this type of knowledge to influence other research areas.


title: Mining Bodily Patterns of Affective Experience during Learning

abstract: We investigated 28 learners’ postural patterns associated with naturally occurring episodes of boredom, flow/engagement, confusion, frustration, and delight during a tutoring session with AutoTutor, a dialoguebased intelligent tutoring system. Training and validation data were collected in a learning session with AutoTutor, after which the learners’ affective states (i.e., emotions) were rated by the learner, a peer, and two trained judges. An automated body pressure measurement system was used to capture the pressure exerted by the learner on the seat and back of a chair during the tutoring session. We extracted 16 posture-related features that focused on the pressure exerted along with the magnitude and direction of changes in pressure during emotional experiences. Binary logistic regression models yielded medium sized effects in discriminating the affective states from neutral. An analysis of the parameters of the models indicated that the affective states were manifested by three unique postural configurations and a general increase in movement (when compared to neutral).


title: A Progressively-Trained Scale-Invariant and Boundary-Aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions

abstract: Volumetric segmentation of lesions on CT scans is important for many types of analysis, including lesion growth kinetic modeling in clinical trials and machine learning of radiomic features. Manual segmentation is laborious, and impractical for large-scale use. For routine clinical use, and in clinical trials that apply the Response Evaluation Criteria In Solid Tumors (RECIST), clinicians typically outline the boundaries of a lesion on a single slice to extract diameter measurements. In this work, we have collected a large-scale database, named LesionVis, with pixel-wise manual 2D lesion delineations on the RECIST-slices. To extend the 2D segmentations to 3D, we propose a volumetric progressive lesion segmentation (PLS) algorithm to automatically segment the 3D lesion volume from 2D delineations using a scale-invariant and boundary-aware deep convolutional network (SIBA-Net). The SIBA-Net copes with the size transition of a lesion when the PLS progresses from the RECIST-slice to the edge-slices, as well as when performing longitudinal assessment of lesions whose size change over multiple time points. The proposed PLS-SiBA-Net (P-SiBA) approach is assessed on the lung lesion cases from LesionVis. Our experimental results demonstrate that the P-SiBA approach achieves mean Dice similarity coefficients (DSC) of 0.81, which significantly improves 3D segmentation accuracy compared with the approaches proposed previously (highest mean DSC at 0.78 on LesionVis). In summary, by leveraging the limited 2D delineations on the RECIST-slices, P-SiBA is an effective semi-supervised approach to produce accurate lesion segmentations in 3D.


title: Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task

abstract: This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS ::: tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).


title: Implant migration into maxillary sinus: description of two asymptomatic cases

abstract: BACKGROUND ::: Rehabilitation of edentulous jaws with dental implants has become a usual practice around the world. A very low incidence of complications is associated with this therapy. One of them is migration of dental implants into maxillary sinus. Foreign bodies inside maxillary sinus should be removed because they can cause sinusitis, but if any symptoms are present, preservation alone can be indicated. Implant removal and bone reconstruction on the same procedure reduce treatment time and allow rehabilitation with implant-supported prosthesis. ::: ::: ::: METHODS ::: In the present study, we report two cases of dental implants displaced into maxillary sinus that receive different treatment. ::: ::: ::: RESULTS ::: In one of them, implant removal and sinus lift were carried out on same procedure while on the other preservation alone was advocated. ::: ::: ::: CONCLUSION ::: Both treatment modalities treatments allowed installation of implant-supported prosthesis.


title: A continuous-time approach to online optimization

abstract: We consider a family of mirror descent strategies for online optimization in continuous-time and we show that they lead to no regret. From a more traditional, discrete-time viewpoint, this continuous-time approach allows us to derive the no-regret properties of a large class of discrete-time algorithms including as special cases the exponential weights algorithm, online mirror descent, smooth fictitious play and vanishingly smooth fictitious play. In so doing, we obtain a unified view of many classical regret bounds, and we show that they can be decomposed into a term stemming from continuous-time considerations and a term which measures the disparity between discrete and continuous time. This generalizes the continuous-time based analysis of the exponential weights algorithm from [ 29 ]. As a result, we obtain a general class of infinite horizon learning strategies that guarantee an \begin{document}$\mathcal{O}(n^{-1/2})$ \end{document} regret bound without having to resort to a doubling trick.


Emotion recognition in human computer interaction using multiple classifier systems


title: Impact of TQM Practices on Firm’s Performance of Pakistan’s Manufacturing Organizations

abstract: This empirical study examines the association between total quality management (TQM) practices and performance, i.e. quality, business, and organizational performance. The quantitative data were obtained through a survey from 171 quality managers of Pakistan’s manufacturing industry. T his study supports the hypothesis that TQM practices positively impact the performance. TQM tools and techniques (Incentive and Recognition System, Process, Monitoring and Control and Continuous Improvement) and Behavioral factors (Fact based - management, t op management’s commitment to quality, employee involvement and customer focus) contribute to the successful implementation of TQM. The study reports that successful adoption and implementation of TQM practices results in improving the performance of organ ization. The main implication of the findings for managers is that with TQM practices, manufacturing organizations are more likely to achieve better performance in customer satisfaction, employee relations, quality and business performance than without TQM


title: A Leakage-Based Precoding Scheme for Downlink Multi-User MIMO Channels

abstract: In multiuser MIMO downlink communications, it is necessary to design precoding schemes that are able to suppress co-channel interference. This paper proposes designing precoders by maximizing the so-called signal-to-leakage-and-noise ratio (SLNR) for all users simultaneously. The presentation considers communications with both single- and multi-stream cases, as well as MIMO systems that employ Alamouti coding. The effect of channel estimation errors on system performance is also studied. Compared with zero-forcing solutions, the proposed method does not impose a condition on the relation between the number of transmit and receive antennas, and it also avoids noise enhancement. Simulations illustrate the performance of the scheme


title: Efficiency analysis of MANETs routing based on a new double metric with mobility and density models

abstract: Scaling to large networks and dealing with high mobility define the major challenges in designing routing protocols for Mobile Ad hoc NETworks (MANETs). These protocols must be efficient in order to ensure quality of service (QoS) support and energy efficiency. In this paper, we investigate the effect of density and mobility on routing operation on the basis of a high level of QoS jointly accounting for the efficiency of energy utilization. For this purpose, we introduce a new double metric “QoS-Energy-Efficiency metric” and we show simulation results of multiple well-known protocols in various mobility and density scenarios.


title: Distributed Power Control for the Downlink of Multi-Cell NOMA Systems

abstract: This paper investigates the power control problem for the downlink of a multi-cell non-orthogonal multiple access system. The problem, called P-OPT, aims to minimize the total transmit power of all the base stations subject to the data rate requirements of the users. The feasibility and optimality properties of P-OPT are characterized through a related optimization problem, called Q-OPT, which is constituted by some relevant power control subproblems. First, we characterize the feasibility of Q-OPT and prove the uniqueness of its optimal solution. Next, we prove that the feasibility of P-OPT can be characterized by the Perron-Frobenius eigenvalues of the matrices arising from the power control subproblems. Subsequently, the relationship between the optimal solutions to P-OPT and that to Q-OPT is presented, which motivates us to obtain the optimal solution to P-OPT through solving the corresponding Q-OPT. Furthermore, a distributed algorithm to solve Q-OPT is designed, and the underlying iteration is shown to be a standard interference function. According to Yates’s power control framework, the algorithm always converges to the optimal solution if exists. Numerical results validate the convergence of the distributed algorithm and quantify the improvement of our proposed method over fractional transmit power control and orthogonal multiple access schemes in terms of power consumption and outage probability.


A Mouse Model of Diet-Induced Obesity Resembling Most Features of Human Metabolic Syndrome


title: Design and Optimization of S-Type Thermal Cutoffs

abstract: A design and optimization procedure for S-type thermal cutoffs is presented in this paper. This type of cutoff is widely used for thermal protection of windings in motors and transformers and in boiling systems. Cutoff temperature is determined by the melting point of the alloy which is used for breaking the electrical circuit. A method for obtaining phase diagrams for ternary alloys by using 3-D surface modeling was developed. Composition of the ternary alloys for the desired melting point could be obtained by using this method. Construction of the thermal cutoffs for 95 degC and 138 degC temperatures in terms of thermal, electrical, and mechanical properties is described. Design and optimization were carried out by using finite-element analysis (FEA) method for 3-D simulation of the devices' steady-state and transient behavior in coupled thermal and electrical domains. Technology for the production of experimental samples is also described. Characterization of the devices was done by using the thermovision technique and reasonable agreement with the simulation results was found. Further optimization steps based on the experimental data are suggested.


title: S-HOT: Scalable High-Order Tucker Decomposition

abstract: Multi-aspect data appear frequently in many web-related applications. For example, product reviews are quadruplets of (user, product, keyword, timestamp). How can we analyze such web-scale multi-aspect data? Can we analyze them on an off-the-shelf workstation with limited amount of memory? Tucker decomposition has been widely used for discovering patterns in relationships among entities in multi-aspect data, naturally expressed as high-order tensors. However, existing algorithms for Tucker decomposition have limited scalability, and especially, fail to decompose high-order tensors since they explicitly materialize intermediate data, whose size rapidly grows as the order increases (≥ 4). We call this problem M-Bottleneck ("Materialization Bottleneck"). To avoid M-Bottleneck, we propose S-HOT, a scalable high-order tucker decomposition method that employs the on-the-fly computation to minimize the materialized intermediate data. Moreover, S-HOT is designed for handling disk-resident tensors, too large to fit in memory, without loading them all in memory at once. We provide theoretical analysis on the amount of memory space and the number of scans of data required by S-HOT. In our experiments, S-HOT showed better scalability not only with the order but also with the dimensionality and the rank than baseline methods. In particular, S-HOT decomposed tensors 1000× larger than baseline methods in terms dimensionality. S- HOT also successfully analyzed real-world tensors that are both large-scale and high-order on an off-the-shelf workstation with limited amount of memory, while baseline methods failed. The source code of S-HOT is publicly available at http://dm.postech.ac.kr/shot to encourage reproducibility.


title: Human-Intent Detection and Physically Interactive Control of a Robot Without Force Sensors

abstract: In this paper, a physically interactive control scheme is developed for a manipulator robot arm. The human touches the robot and applies force in order to make it behave as he/she likes. The communication between the robot and the human is maintained by a physical contact with no sensors. The intent of the human is estimated by observing the change in control effort. The robot receives the estimated human intent and updates its position reference accordingly. The developed method uses the principle of conservation of zero momentum for position-controlled systems. A switching scheme is developed that goes between the modes of pure impedance control with a fixed-position reference and interactive control under human intent. The switching mechanism uses neither a physical switch nor a sensor; it observes the human intent and puts the robot into interactive mode, if there is any. When the human intent disappears, the robot goes into the pure-impedance-control mode, thus stabilizing in the left position.


A Solution to Forecast Demand Using Long Short-Term Memory Recurrent Neural Networks for Time Series Forecasting


title: A review of Internet of Things for smart home: Challenges and solutions

abstract: Abstract Although Internet of Things (IoT) brings significant advantages over traditional communication technologies for smart grid and smart home applications, these implementations are still very rare. Relying on a comprehensive literature review, this paper aims to contribute towards narrowing the gap between the existing state-of-the-art smart home applications and the prospect of their integration into an IoT enabled environment. We propose a holistic framework which incorporates different components from IoT architectures/frameworks proposed in the literature, in order to efficiently integrate smart home objects in a cloud-centric IoT based solution. We identify a smart home management model for the proposed framework and the main tasks that should be performed at each level. We additionally discuss practical design challenges with emphasis on data processing, as well as smart home communication protocols and their interoperability. We believe that the holistic framework ascertained in this paper can be used as a solid base for the future developers of Internet of Things based smart home solutions.


title: Effect of fast power control on CDMA forward link performance

abstract: The third generation (3G) CDMA cellular standards include fast power control (FPC) on the forward link (base-to-mobile). The performance of FPC relative to traditional slow power control (SPC) is analyzed, and it is shown that FPC provides on the order of two times enhancement in capacity. Also, a novel transmit power management scheme is introduced that operates in conjunction with FPC to provide a significant capacity enhancement (as much as ten times).


title: Sapienz: multi-objective automated testing for Android applications

abstract: We introduce Sapienz, an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. Sapienz combines random fuzzing, systematic and search-based exploration, exploiting seeding and multi-level instrumentation. Sapienz significantly outperforms (with large effect size) both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, in 7/10 experiments for coverage, 7/10 for fault detection and 10/10 for fault-revealing sequence length. When applied to the top 1,000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. So far we have managed to make contact with the developers of 27 crashing apps. Of these, 14 have confirmed that the crashes are caused by real faults. Of those 14, six already have developer-confirmed fixes.


title: Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression

abstract: A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D Gabor representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations, which provide a complete image description in terms of locally windowed 2-D spectral coordinates embedded within global 2-D spatial coordinates. In the present neural network approach, based on interlaminar interactions involving two layers with fixed weights and one layer with adjustable weights, the network finds coefficients for complete conjoint 2-D Gabor transforms without restrictive conditions. In wavelet expansions based on a biologically inspired log-polar ensemble of dilations, rotations, and translations of a single underlying 2-D Gabor wavelet template, image compression is illustrated with ratios up to 20:1. Also demonstrated is image segmentation based on the clustering of coefficients in the complete 2-D Gabor transform. >


A Case of Persistent Asthma Resistant to Available Treatment Options: Management Dilemma


title: BCYCLIC: A parallel block tridiagonal matrix cyclic solver

abstract: A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduction algorithm that is easily parallelized. Storage of the factored blocks allows the application of the inverse to multiple right-hand sides which may not be known at factorization time. Scalability with the number of block rows is achieved with cyclic reduction, while scalability with the block size is achieved using multithreaded routines (OpenMP, GotoBLAS) for block matrix manipulation. This dual scalability is a noteworthy feature of this new solver, as well as its ability to efficiently handle arbitrary (non-powers-of-2) block row and processor numbers. Comparison with a state-of-the art parallel sparse solver is presented. It is expected that this new solver will allow many physical applications to optimally use the parallel resources on current supercomputers. Example usage of the solver in magneto-hydrodynamic (MHD), three-dimensional equilibrium solvers for high-temperature fusion plasmas is cited.


title: Autonomous decentralized control for soft-bodied caterpillar-like modular robot exploiting large and continuum deformation

abstract: Building robots from soft materials provides opportunities to create more robust and adaptive designs: soft bodies can conform to complex shapes in the environment and they are able to cushion shocks and store elastic energy. The challenge, however, remains to control highly deformable moveable structures effectively. We have proposed that one useful approach is through an autonomous decentralized control using physically coupled mechano-sensory oscillators. We have developed a highly deformable 3-D printed soft robot (PS robot) as a platform to explore the validity of the control strategy. Based on this platform we introduce a caterpillar-like soft-bodied modular-robot that is controlled in a fully decentralized manner. This paper focuses on one of the advantageous characteristics of autonomous decentralized control, i.e., the extensibility/contractibility of the modularity structures. The numerical and experimental results demonstrate that simple oscillators (controllers) can interact with each other by deforming the soft-bodied structure leading to a phase gradient that propagates crawling locomotion. The PS robots provide a powerful platform for exploring this strategy and they can be generalized for use with different robot shapes and material properties.


Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection


title: Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks

abstract: Deep learning-based style transfer between images has recently become a popular area of research. A common way of encoding"style"is through a feature representation based on the Gram matrix of features extracted by some pre-trained neural network or some other form of feature statistics. Such a definition is based on an arbitrary human decision and may not best capture what a style really is. In trying to gain a better understanding of"style", we propose a metric learning-based method to explicitly encode the style of an artwork. In particular, our definition of style captures the differences between artists, as shown by classification performances, and such that the style representation can be interpreted, manipulated and visualized through style-conditioned image generation through a Generative Adversarial Network. We employ this method to explore the style space of anime portrait illustrations.


title: Lean Software Development: A Tutorial

abstract: To put the concept of lean software development in context, it's useful to point out similarities and differences with agile software development. Agile development methods have generally expected system architecture and interaction design to occur outside the development team, or to occur in very small increments within the team. Because of this, agile practices often prove to be insufficient in addressing issues of solution design, user interaction design, and high-level system architecture. Increasingly, agile development practices are being thought of as good ways to organize software development, but insufficient ways to address design. Because design is fundamentally iterative and development is fundamentally iterative, the two disciplines suffer if they are not carefully integrated with each other. Because lean development lays out a set of principles that demand a whole-product, complete life-cycle, cross-functional approach, it's the more likely candidate to guide the combination of design, development, deployment, and validation into a single feedback loop focused on the discovery and delivery of value.


title: Automatically refining the wikipedia infobox ontology

abstract: The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia's infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia's infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.


title: Managing Schema Evolution in NoSQL Data Stores

abstract: NoSQL data stores are commonly schema-less, providing no means for globally defining or managing the schema. While this offers great flexibility in early stages of application development, developers soon can experience the heavy burden of dealing with increasingly heterogeneous data. This paper targets schema evolution for NoSQL data stores, the complex task of adapting and changing the implicit structure of the data stored. We discuss the recommendations of the developer community on handling schema changes, and introduce a simple, declarative schema evolution language. With our language, software developers and architects can systematically manage the evolution of their production data and perform typical schema maintenance tasks. We further provide a holistic NoSQL database programming language to define the semantics of our schema evolution language. Our solution does not require any modifications to the NoSQL data store, treating the data store as a black box. Thus, we want to address application developers that use NoSQL systems


title: Detection of Brain Tumors from MRI using Gaussian RBF kernel based Support Vector Machine

abstract: The Support Vector Machine (SVM) is a powerful classification technique that has been used extensively in the field of medical imaging. A model based on SVM with Gaussian RBF kernel is proposed here for the automatic detection of brain tumor from MRI images. Various textural characteristics of the MRI images of human brain are extracted to construct a feature set. These features sets are then used to train the classifier. The results obtained are compared with another powerful efficient classifier AdaBoost. AdaBoost classifies data according to the law of majority vote by base classifiers (ensemble learning). The comparative results show that though the difference between the performance measures is marginal, SVM gives higher precision and low error rates.


Experimenting with the Path Ranking Algorithm


title: A Novel Depth-Based Virtual View Synthesis Method for Free Viewpoint Video

abstract: Free-viewpoint rendering (FVR) has become a popular topic in 3-D research. A promising technology in FVR is to generate virtual views using a single texture image and the corresponding depth image. A critical problem that occurs when generating virtual views is that the regions covered by the foreground objects in the original view may be disoccluded in the synthesized views. In this paper, a depth based disocclusion filling algorithm using patch-based texture synthesis is proposed. In contrast to the existing patch-based virtual view synthesis methods, the filling priority is driven by the robust structure tensor that efficiently reflects the overall structure of an image part and a new confidence term that produces fine synthesis results even near the foreground boundaries. Moreover, the best-matched patch is searched in the background regions and finally it is chosen through a new patch distance measure. Significant superiority of the proposed method over the state-of-the-art methods is presented by comparing the experimental results.


title: Wide Pulse Combined With Narrow-Pulse Generator for Food Sterilization

abstract: This paper proposes a wide pulse combined with a narrow-pulse generator for solid-food sterilization. The proposed generator is composed of a full-bridge converter in phase-shift control to generate a high dc-link voltage and a full-bridge inverter associated with an L-C network and a transformer to generate wide pulses combined with narrow pulses. These combined pulses can prevent undesired strong air arcing in free space, reduce power consumption, and save power components, while sterilizing food effectively. The converter and inverter can be operated at high frequencies and with pulse width-modulation control; thus, its weight and size can be reduced significantly, and its efficiency can correspondingly be improved. Experimental results obtained from a prototype with plusmn10-kV wide pulses combined with plusmn10-kV narrow pulses and with 10- to 50-kW peak output power, depending on pulsewidth of the output pulses, have demonstrated its feasibility.


title: Linear Time Complexity Time Series Classification with Bag-of-Pattern-Features

abstract: Time series classification has attracted much attention due to the ubiquity of time series. With the advance of technologies, the volume of available time series data becomes huge and the content is changing rapidly. This requires time series data mining methods to have low computational complexities. In this paper, we propose a parameter-free time series classification method that has a linear time complexity. The approach is evaluated on all the 85 datasets in the well-known UCR time series classification archive. The results show that the new method achieves better overall classification accuracy performance than the widely used benchmark, i.e. 1-nearest neighbor with dynamic time warping, while consuming orders of magnitude less running time. The proposed method is also applied on a large real-world bird sounds dataset to verify its effectiveness.


title: A simple proof of Kostant's theorem that U(g) is free over its center

abstract: In this paper we present a simple proof of the fundamental result by B. Kostant which claims that the universal enveloping algebra of a reductive Lie algebra g is free over its center. We also indicate how this result allows to simplify the proof of another important result of B. Kostant—the description of the algebra of functions on the nilpotent cone. We use this technique to prove some generalizations of Kostant's theorem. We also deduce from it a way to check which subalgebras of g are "centrally free."


Multitasking Trends and Impact on Education: A Literature Review


title: Analysis of the Accuracy and Robustness of the Leap Motion Controller

abstract: The Leap Motion Controller is a new device for hand gesture controlled user interfaces with declared sub-millimeter accuracy. However, up to this point its capabilities in real environments have not been analyzed. Therefore, this paper presents a first study of a Leap Motion Controller. The main focus of attention is on the evaluation of the accuracy and repeatability. For an appropriate evaluation, a novel experimental setup was developed making use of an industrial robot with a reference pen allowing a position accuracy of 0.2 mm. Thereby, a deviation between a desired 3D position and the average measured positions below 0.2 mm has been obtained for static setups and of 1.2 mm for dynamic setups. Using the conclusion of this analysis can improve the development of applications for the Leap Motion controller in the field of Human-Computer Interaction.


abstract: Systems of linear and nonlinear equations with fuzzy parameters are relevant to many practical problems arising in structure mechanics, electrical engineering, finance, economics and physics. In this paper three methods for solving such equations are discussed: method for outer interval solution of systems of linear equations depending linearly on interval parameters, fuzzy finite element method proposed by Rama Rao and sensitivity analysis method. The performance and advantages of presented methods are described with illustrative examples. Extended version of the present paper can be downloaded from the web page of the UTEP [I. Skalna, M.V. Rama Rao, A. Pownuk, Systems of fuzzy equations in structural mechanics, The University of Texas at El Paso, Department of Mathematical Sciences Research Reports Series, , Texas Research Report No. 2007-01, 2007].


title: Development of a pneumatic soft actuator as a hand finger for a collaborative robot

abstract: Pneumatic soft actuators produce flexion and meet the new needs of collaborative robotics, which is rapidly emerging in the industry landscape 4.0. The soft actuators are not only aimed at industrial progress, but their application ranges in the field of medicine and rehabilitation. Safety and reliability are the main requirements for coexistence and human-robot interaction; such requirements, together with the versatility and lightness, are the precious advantages that is offered by this new category of actuators. The objective is to develop an actuator with high compliance, low cost, high versatility and easy to produce, aimed at the realization of the fingers of a robotic hand that can faithfully reproduce the motion of a real hand. The proposed actuator is equipped with an intrinsic compliance thanks to the hyper-elastic silicone rubber used for its realization; the bending is allowed by the high compliance of the silicone and by a square-meshed gauze which contains the expansion and guides the movement through appropriate cuts in correspondence of the joints. A numerical model of the actuator is developed and an optimal configuration of the five fingers of the hand is achieved; finally, the index finger is built, on which the experimental validation tests are carried out.


title: Crystal Structure Prediction via Deep Learning.

abstract: We demonstrate the application of deep neural networks as a machine-learning tool for the analysis of a large collection of crystallographic data contained in the crystal structure repositories. Using input data in the form of multiperspective atomic fingerprints, which describe coordination topology around unique crystallographic sites, we show that the neural-network model can be trained to effectively distinguish chemical elements based on the topology of their crystallographic environment. The model also identifies structurally similar atomic sites in the entire data set of ∼50000 crystal structures, essentially uncovering trends that reflect the periodic table of elements. The trained model was used to analyze templates derived from the known crystal structures in order to predict the likelihood of forming new compounds that could be generated by placing elements into these structural templates in a combinatorial fashion. Statistical analysis of predictive performance of the neural-network model, which was applied to a test set of structures never seen by the model during training, indicates its ability to predict known elemental compositions with a high likelihood of success. In ∼30% of cases, the known compositions were found among the top 10 most likely candidates proposed by the model. These results suggest that the approach developed in this work can be used to effectively guide the synthetic efforts in the discovery of new materials, especially in the case of systems composed of three or more chemical elements.


title: Personalized diversification of search results

abstract: Search personalization and diversification are often seen as opposing alternatives to cope with query uncertainty, where, given an ambiguous query, it is either preferable to adapt the search result to a specific aspect that may interest the user (personalization) or to regard multiple aspects in order to maximize the probability that some query aspect is relevant to the user (diversification). In this work, we question this antagonistic view, and hypothesize that these two directions may in fact be effectively combined and enhance each other. We research the introduction of the user as an explicit random variable in state of the art diversification methods, thus developing a generalized framework for personalized diversification. In order to evaluate our hypothesis, we conduct an evaluation with real users using crowdsourcing services. The obtained results suggest that the combination of personalization and diversification achieves competitive performance, improving the base-line, plain personalization, and plain diversification approaches in terms of both diversity and accuracy measures.


title: Vilnius urban sustainability assessment with an emphasis

abstract: Successful strategies for sustainable urban development should be more or less compatible with political, economic, social, cultural, institutional, technological, environmental, legal / regulatory and educational situations in the country. The list of urban sustainability indicators is not finite. Each year hundreds of social, economic and environmental indicators are being established in the world. Therefore, decision-making methods should be used in order to evaluate successful strategies for sustainable urban development. The above questions have been analysed in the paper. In order to shed more light on the analysis of Vilnius urban sustainability with an emphasis on pollution, particular recommendations for environmental factors such as transport and pollution, noise, indoor microclimate and allergens, their impact on real estate price, market value and mass appraisal value are presented.


title: Relationship between altmetric and bibliometric indicators across academic social sites: The case of CSIC's members

abstract: This study explores the connections between social and usage metrics (altmetrics) and bibliometric indicators at the author level. It studies to what extent these indicators, gained from academic sites, can provide a proxy for research impact. Close to 10,000 author profiles belonging to the Spanish National Research Council were extracted from the principal scholarly social sites: ResearchGate, Academia.edu and Mendeley and academic search engines: Microsoft Academic Search and Google Scholar Citations. Results describe little overlapping between sites because most of the researchers only manage one profile (72%). Correlations point out that there is scant relationship between altmetric and bibliometric indicators at author level. This is due to the almetric ones are site-dependent, while the bibliometric ones are more stable across web sites. It is concluded that altmetrics could reflect an alternative dimension of the research performance, close, perhaps, to science popularization and networking abilities, but far from citation impact.


title: Automatic Detection of Fake News on Social Media Platforms

abstract: This study investigates how fake news shared on social media platforms can be automatically identified. Drawing on the Elaboration Likelihood Model and previous studies on information quality, we develop and test an explorative research model on Facebook news posts during the U.S. presidential election 2016. The study examines how cognitive, visual, affective and behavioral cues of the news posts as well as of the addressed user community can be used by machine learning classifiers to identify fake news fully automatically. The best performing configurations achieve a stratified 10-fold cross validated predictive accuracy of more than 80%, and a recall rate (share of correctly identified fake news) of nearly 90% on a balanced data sample solely based on data directly available on Facebook. Platform operators and users can draw on the results to identify fake news on social media platforms - either automatically or heuristically.


title: Handbook of Data Quality

abstract: The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches.


title: Magic-wall: Visualizing Room Decoration

abstract: This work focuses on Magic-wall, an automatic system for visualizing the effect of room decoration. Given an image of the indoor scene and a preferred color, the Magic-wall can automatically locate the wall regions in the image and smoothly replace the existing color with the required one. The key idea of the proposed Magic-wall is to leverage visual semantics to guide the entire process of color substitution including wall segmentation and color replacement. We propose an edge-aware fully convolutional neural network (FCN) for indoor semantic scene parsing, in which a novel edge-prior branch is introduced to better identify the boundary of different semantic regions. To accurately localize the wall regions, we adapt a semantic-dependent optimized strategy, which pays more attention to those pixels belonging to the wall by adapting larger optimization weights compared with those from other semantic regions. Finally, to naturally replace the color of original walls, a simple yet effective color space conversion method is proposed for replacement with brightness reservation. We build a new indoor scene dataset upon ADE20K for training and testing, which includes 6 semantic labels. Extensive experimental evaluations and visualizations well demonstrate that the proposed Magic-wall is effective and can automatically generate a set of visually pleasing results.


title: An empirical investigation of student adoption model toward mobile e-textbook: UTAUT2 and TTF model

abstract: Faculty of Economics in Universitas Atma Jaya Yogyakarta (UAJY) has replaced printed textbooks with e-textbooks for its academic activities since 2015. These e-textbooks can be accessed via iPad which is given to each student. Hence, the objective of this study is to test the proposed research model based on integrated UTAUT2 and TTF. Questionnaires were distributed to 326 students in 10 classes. Only junior and sophomore students were eligible to fulfill the questionnaires. The result of the study shows that performance expectancy, effort expectancy, social influence, facilitating condition, and habit have a direct significant relationship on behavioral intention to use mobile e-textbook. The result also shows that both Task technology and technology characteristic positively affect task technology fit. There is also a direct significant relationship between Task technology fit and performance expectancy. We suggest that further studies include experimentation to investigate performance expectancy of mobile e-book. 2) More evaluation should be held in the future in more mature environment. The finding of this study can help policymakers in the Faculty of Economics UAJY to evaluate the policy and formulate better strategy. In addition, these findings can be used by others faculties and universities as mobile e-textbook adoption case reference.


title: Action annotated trajectory generation for autonomous maneuvers on structured road networks

abstract: This paper presents a simple path-planning method with low computational cost for autonomous vehicles driving on structured road networks. Characteristics of the road structure are exploited for efficient planning and evaluation. Instead of using a global state machine, necessary actions for maneuvers are generated online and are annotated into the trajectory itself. The method has been tested with our vehicle “MadeInGermany” over several thousand kilometers driving autonomously in urban city scenarios and highway tracks.


title: Fast camera calibration for the analysis of sport sequences

abstract: Semantic analysis of sport sequences requires camera calibration to obtain player and ball positions in real-world coordinates. For court sports like tennis, the marker lines on the field can be used to determine the calibration parameters. We propose a real-time calibration algorithm that can be applied to all court sports simply by exchanging the court model. The algorithm is based on (1) a specialized court-line detector, (2) a RANSAC-based line parameter estimation, (3) a combinatorial optimization step to localize the court within the set of detected line segments, and (4) an iterative court-model tracking step. Our results show real-time calibration of, e.g., tennis and soccer sequences with a computation time of only about 6 ms per frame.


title: Ludus Ex Machina: Building A 3D Game Designer That Competes Alongside Humans

abstract: We describe ANGELINA-5, software capable of creating simple three-dimensional games autonomously. To the best of our knowledge, this is the first system which creates complete games in 3D. We summarise the history of the ANGELINA project so far, describe the architecture of the latest version, and give details of its participation in Ludum Dare, a game design competition. This is the first time that a piece of software has entered a videogame design contest for human designers, and represents a step forward for automated videogame design and computational creativity.


Procedural Map Generation for a Rts Game


title: Trajectory Tracking of an Omni-Directional Wheeled Mobile Robot Using a Model Predictive Control Strategy

abstract: This paper addresses trajectory tracking of an omni-directional mobile robot (OMR) with three mecanum wheels and a fully symmetrical configuration. The omni-directional wheeled robot outperforms the non-holonomic wheeled robot due to its ability to rotate and translate independently and simultaneously. A kinematics model of the OMR is established and a model predictive control (MPC) algorithm with control and system constraints is designed to achieve point stabilization and trajectory tracking. Simulation results validate the accuracy of the established kinematics model and the effectiveness of the proposed MPC controller.


title: Consistent feature attribution for tree ensembles

abstract: Note that a newer expanded version of this paper is now available at: arXiv:1802.03888 ::: It is critical in many applications to understand what features are important for a model, and why individual predictions were made. For tree ensemble methods these questions are usually answered by attributing importance values to input features, either globally or for a single prediction. Here we show that current feature attribution methods are inconsistent, which means changing the model to rely more on a given feature can actually decrease the importance assigned to that feature. To address this problem we develop fast exact solutions for SHAP (SHapley Additive exPlanation) values, which were recently shown to be the unique additive feature attribution method based on conditional expectations that is both consistent and locally accurate. We integrate these improvements into the latest version of XGBoost, demonstrate the inconsistencies of current methods, and show how using SHAP values results in significantly improved supervised clustering performance. Feature importance values are a key part of understanding widely used models such as gradient boosting trees and random forests, so improvements to them have broad practical implications.


title: A new prismatic series elastic actuator with compact size and high performance

abstract: This paper discusses design and control of a prismatic series elastic actuator with high mechanical power output in a small and lightweight form factor. We introduce a design that pushes the performance boundary of electric series elastic actuators by using high motor voltage coupled with an efficient drivetrain to enable large continuous actuator force while retaining speed. Compact size is achieved through the use of a novel piston-style ball screw support mechanism and a concentrically placed compliant element. We develop controllers for force and position tracking based on combinations of PID, model-based, and disturbance observer control structures. Finally, we demonstrate our actuator's performance with a series of experiments designed to operate the actuator at the limits of its mechanical and control capability.


title: Substitutional Reality: Using the Physical Environment to Design Virtual Reality Experiences

abstract: Experiencing Virtual Reality in domestic and other uncontrolled settings is challenging due to the presence of physical objects and furniture that are not usually defined in the Virtual Environment. To address this challenge, we explore the concept of Substitutional Reality in the context of Virtual Reality: a class of Virtual Environments where every physical object surrounding a user is paired, with some degree of discrepancy, to a virtual counterpart. We present a model of potential substitutions and validate it in two user studies. In the first study we investigated factors that affect participants' suspension of disbelief and ease of use. We systematically altered the virtual representation of a physical object and recorded responses from 20 participants. The second study investigated users' levels of engagement as the physical proxy for a virtual object varied. From the results, we derive a set of guidelines for the design of future Substitutional Reality experiences.


title: Clickstream Prediction Using Sequential Stream Mining Techniques with Markov Chains

abstract: As one of data mining tasks, sequential pattern mining provides valuable information about frequent patterns of users over time. For instance, frequent sequential patterns can be applicable to analyze user clickstreams for determination of web navigation patterns, genome sequences, and customer purchasing patterns. In many real-life situations, data to be mined are continuously changing. Moreover, these data are streaming at a high velocity, which leads to impracticality of storing all these data in memory. Hence, to handle these situations, we propose three stream mining algorithms to first find frequent sequential patterns. The algorithms then form statistical models, which are stored as Markov chains or transition matrices capturing frequent sequential patterns mined so far, to predict future user clickstream (e.g., the web page the user will visit next). Experimental results show the efficiency and prediction accuracy of our proposed Markov chain-based sequential stream mining algorithms in clickstream prediction.


title: Evolving sub-grid turbulence for smoke animation

abstract: We introduce a simple turbulence model for smoke animation, qualitatively capturing the transport, diffusion, and spectral cascade of turbulent energy unresolved on a typical simulation grid. We track the mean kinetic energy per octave of turbulence in each grid cell, and a novel "net rotation" variable for modeling the self-advection of turbulent eddies. These additions to a standard fluid solver drive a procedural post-process, layering plausible dynamically evolving turbulent details on top of the large-scale simulated motion. Finally, to make the most of the simulation grid before jumping to procedural sub-grid models, we propose a new multistep predictor to alleviate the nonphysical dissipation of angular momentum in standard graphics fluid solvers.


title: Sexual Strategies Theory: An Evolutionary Perspective on Human Mating

abstract: This article proposes a contextual-evolutionary theory of human mating strategies. Both men and women are hypothesized to have evolved distinct psychological mechanisms that underlie shortterm and long-term strategies. Men and women confront different adaptive problems in short-term as opposed to long-term mating contexts. Consequently, different mate preferences become activated from their strategic repertoires. Nine key hypotheses and 22 predictions from Sexual Strategies Theory are outlined and tested empirically. Adaptive problems sensitive to context include sexual accessibility, fertility assessment, commitment seeking and avoidance, immediate and enduring resource procurement, paternity certainty, assessment of mate value, and parental investment. Discussion summarizes 6 additional sources of behavioral data, outlines adaptive problems common to both sexes, and suggests additional contexts likely to cause shifts in mating strategy. Mating is a human universal. All known societies have formal marriage alliances between men and women. More than 90% of all people in all societies marry at some point in their lives (Buss, 1985; Epstein & Guttman, 1984; Vandenberg, 1972). In a cross-cultural perspective, marriages are usually regarded as formal reproductive alliances that contain the features of (a) mutual obligation between husband and wife, (b) rights of sexual access, (c) an expectation that the marriage will persist through pregnancy, lactation, and child rearing, and (d) recognition of the legitimate status of the couple's children (Daly & Wilson, 1988, p. 187).


title: Generalised spatial modulation with multiple active transmit antennas

abstract: We propose a new generalised spatial modulation (GSM) technique, which can be considered as a generalisation of the recently proposed spatial modulation (SM) technique. SM can be seen as a special case of GSM with only one active transmit antenna. In contrast to SM, GSM uses the indices of multiple transmit antennas to map information bits, and is thus able to achieve substantially increased spectral efficiency. Furthermore, selecting multiple active transmit antennas enables GSM to harvest significant transmit diversity gains in comparison to SM, because all the active antennas transmit the same information. On the other hand, inter-channel interference (ICI) is completely avoided by transmitting the same symbols through these active antennas. We present theoretical analysis using order statistics for the symbol error rate (SER) performance of GSM. The analytical results are in close agreement with our simulation results. The bit error rate performance of GSM and SM is simulated and compared, which demonstrates the superiority of GSM. Moreover, GSM systems with configurations of different transmit and receive antennas are studied. Our results suggest that using a less number of transmit antennas with a higher modulation order will lead to better BER performance.


Space and selective attention


title: Youth Mental Health Interventions via Mobile Phones: A Scoping Review

abstract: Abstract Mobile phone technologies have been hailed as a promising means for delivering mental health interventions to youth and adolescents, the age group with high cell phone penetration and with the onset of 75% of all lifetime mental disorders. Despite the growing evidence in physical health and adult mental health, however, little information is available about how mobile phones are implemented to deliver mental health services to the younger population. The purpose of this scoping study was to map the current state of knowledge regarding mobile mental health (mMental Health) for young people (age 13–24 years), identify gaps, and consider implications for future research. Seventeen articles that met the inclusion criteria provided evidence for mobile phones as a way to engage youth in therapeutic activities. The flexibility, interactivity, and spontaneous nature of mobile communications were also considered advantageous in encouraging persistent and continual access to care outside clinical settings....


title: Deriving operational software specifications from system goals

abstract: Goal orientation is an increasingly recognized paradigm for eliciting, modeling, specifying and analyzing software requirements. Goals are statements of intent organized in AND/OR refinement structures; they range from high-level, strategic concerns to low-level, technical requirements on the software-to-be and assumptions on its environment. The operationalization of system goals into specifications of software services is a core aspect of the requirements elaboration process for which little systematic and constructive support is available. In particular, most formal methods assume such operational specifications to be given and focus on their a posteriori analysis.The paper considers a formal, constructive approach in which operational software specifications are built incrementally from higher-level goal formulations in a way that guarantees their correctness by construction. The operationalization process is based on formal derivation rules that map goal specifications to specifications of software operations; more specifically, these rules map real-time temporal logic specifications to sets of pre-, post- and trigger conditions. The rules define operationalization patterns that may be used for guiding and documenting the operationalization process while hiding all formal reasoning details; the patterns are formally proved correct once and for all. The catalog of operationalization patterns is structured according to a rich taxonomy of goal specification patterns.Our constructive approach to requirements elaboration requires a multiparadigm specification language that supports incremental reasoning about partial models. The paper also provides a formal semantics for goal operationalization and discusses several semantic features of our language that allow for such incremental reasoning.


title: A bayesian market maker

abstract: Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Variants of the logarithmic market scoring rule (LMSR) have emerged as the standard. LMSR market makers are loss-making in general and need to be subsidized. Proposed variants, including liquidity sensitive market makers, suffer from an inability to react rapidly to jumps in population beliefs. In this paper we propose a Bayesian Market Maker for binary outcome (or continuous 0-1) markets that learns from the informational content of trades. By sacrificing the guarantee of bounded loss, the Bayesian Market Maker can simultaneously offer: (1) significantly lower expected loss at the same level of liquidity, and, (2) rapid convergence when there is a jump in the underlying true value of the security. We present extensive evaluations of the algorithm in experiments with intelligent trading agents and in human subject experiments. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.


title: Real-time obstacle avoidance for fast mobile robots in cluttered environments

abstract: The method described, named the vector field histogram (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. A VFH-controlled mobile robot maneuvers quickly and without stopping among densely cluttered obstacles. The VFH method uses a two-dimensional Cartesian histogram grid as a world model. This world model is updated continuously and in real time with range data sampled by the onboard ultrasonic range sensors. Based on the accumulated environmental data, the VFH method then computes a one-dimensional polar histogram that is constructed around the robot's momentary location. Each sector in the polar histogram holds the polar obstacle density in that direction. Finally, the algorithm selects the most suitable sector from among all polar histogram sectors with low obstacle density, and the steering of the robot is aligned with that direction. Experimental results from a mobile robot traversing a densely cluttered obstacle course at an average speed of 0.7 m/s demonstrate the power of the VFH method. >


title: Wearable sensor badge and sensor jacket for context awareness

abstract: The addition of sensors to wearable computers allows them to adapt their functions to more suit the activities and situation of their wearers. A wearable sensor badge is described constructed from (hard) electronic components, which can sense perambulatory activities for context-awareness. A wearable sensor jacket is described that uses advanced knitting techniques to form (soft) fabric stretch sensors positioned to measure upper limb and body movement. Worn on-the-hip, or worn as clothing, these unobtrusive sensors supply abstract information about your current activity to your other wearable computers.


title: Measuring behavioral and endocrine responses to novelty stress in adult zebrafish

abstract: Several behavioral assays are currently used for high-throughput neurophenotyping and screening of genetic mutations and psychotropic drugs in zebrafish (Danio rerio). In this protocol, we describe a battery of two assays to characterize anxiety-related behavioral and endocrine phenotypes in adult zebrafish. Here, we detail how to use the 'novel tank' test to assess behavioral indices of anxiety (including reduced exploration, increased freezing behavior and erratic movement), which are quantifiable using manual registration and computer-aided video-tracking analyses. In addition, we describe how to analyze whole-body zebrafish cortisol concentrations that correspond to their behavior in the novel tank test. This protocol is an easy, inexpensive and effective alternative to other methods of measuring stress responses in zebrafish, thus enabling the rapid acquisition and analysis of large amounts of data. As will be shown here, fish anxiety-like behavior can be either attenuated or exaggerated depending on stress or drug exposure, with cortisol levels generally expected to parallel anxiety behaviors. This protocol can be completed over the course of 2 d, with a variable testing duration depending on the number of fish used.


title: Support vector machines for speech recognition

abstract: Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic modeling which can often be prone to overfitting and does not translate to improved discrimination. We propose a new paradigm centered on principles of structural risk minimization using a discriminative framework for speech recognition based on support vector machines (SVMs). SVMs have the ability to simultaneously optimize the representational and discriminative ability of the acoustic classifiers. We have developed the first SVM-based large vocabulary speech recognition system that improves performance over traditional HMM-based systems. This hybrid system achieves a state-of-the-art word error rate of 10.6% on a continuous alphadigit task—a 10% improvement relative to an HMM system. On SWITCHBOARD, a large vocabulary task, the system improves performance over a traditional HMM system from 41.6% word error rate to 40.6%. This dissertation discusses several practical issues that arise when SVMs are incorporated into the hybrid system.


title: A synthesis of automated planning and reinforcement learning for efficient, robust decision-making

abstract: Automated planning and reinforcement learning are characterized by complementary views on decision making: the former relies on previous knowledge and computation, while the latter on interaction with the world, and experience. Planning allows robots to carry out different tasks in the same domain, without the need to acquire knowledge about each one of them, but relies strongly on the accuracy of the model. Reinforcement learning, on the other hand, does not require previous knowledge, and allows robots to robustly adapt to the environment, but often necessitates an infeasible amount of experience. We present Domain Approximation for Reinforcement LearnING (DARLING), a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process. We demonstrate the effectiveness of the proposed method on a service robot, carrying out a variety of tasks in an office building. We find that when the robot makes decisions by planning alone on a given model it often fails, and when it makes decisions by reinforcement learning alone it often cannot complete its tasks in a reasonable amount of time. When employing DARLING, even when seeded with the same model that was used for planning alone, however, the robot can quickly learn a behavior to carry out all the tasks, improves over time, and adapts to the environment as it changes.


title: Email Classification Research Trends: Review and Open Issues

abstract: Personal and business users prefer to use e-mail as one of the crucial sources of communication. The usage and importance of e-mails continuously grow despite the prevalence of alternative means, such as electronic messages, mobile applications, and social networks. As the volume of business-critical e-mails continues to grow, the need to automate the management of e-mails increases for several reasons, such as spam e-mail classification, phishing e-mail classification, and multi-folder categorization, among others. This paper comprehensively reviews articles on e-mail classification published in 2006–2016 by exploiting the methodological decision analysis in five aspects, namely, e-mail classification application areas, data sets used in each application area, feature space utilized in each application area, e-mail classification techniques, and the use of performance measures. A total of 98 articles (56 articles from Web of Science core collection databases and 42 articles from Scopus database) are selected. To achieve the objective of the study, a comprehensive review and analysis is conducted to explore the various areas where e-mail classification was applied. Moreover, various public data sets, features sets, classification techniques, and performance measures are examined and used in each identified application area. This review identifies five application areas of e-mail classification. The most widely used data sets, features sets, classification techniques, and performance measures are found in the identified application areas. The extensive use of these popular data sets, features sets, classification techniques, and performance measures is discussed and justified. The research directions, research challenges, and open issues in the field of e-mail classification are also presented for future researchers.


title: Temperature and strain-independent curvature sensor based on a singlemode/multimode fiber optic structure

abstract: This work describes a fiber optic sensing structure that is sensitive to curvature, and features a low temperature- and strain cross-sensitivity. It is based on multimode interference, and relies on a singlemode–step index multimode–singlemode fiber structure. It was observed that the transmitted optical power in such a layout is highly sensitive to the wavelength of operation, and to the length of the multimode fiber. The optical spectrum exhibits two dominant loss bands, at wavelengths that have similar responses both to temperature and strain, but different responses to curvature. Based on this result, an interrogation approach is proposed that permits substantial sensitivity to curvature (8.7 ± 0.1 nm m) and residual sensitivities to temperature and strain (0.3 ± 0.1 pm °C−1 and (−0.06 ± 0.01) × 10−6 m m−1, respectively). The beam-propagation method was employed for modeling the propagation of light along the optical fiber sensing device proposed.


Beyond query by example


title: RESURF stepped oxide (RSO) MOSFET for 85V having a record-low specific on-resistance

abstract: A RESURF stepped oxide (RSO) transistor is presented and electrically characterised. The processed RSO MOSFET includes a trench field-plate network in the drift region that is isolated with a thick oxide layer. This trench network has a hexagonal layout that induces an improved RESURF effect at breakdown compared with the more common stripe (2D) layout. Consequently, the effective doping can be two times higher for the hexagonal layout. We have obtained a record value for the specific on-resistance (R/sub ds,on/) of 58 m/spl Omega/.mm/sup 2/ at V/sub gs/=10 V for a breakdown voltage (BV/sub ds/,) of 85 V. These values have been obtained for devices having a 4.0 /spl mu/m cell pitch and a 5 /spl mu/m long drift region with a doping level of 2.10/sup 16/ cm/sup -3/. Measurements of the gate-drain charge density (Q/sub gd/) for these devices show that Q/sub gd/ is fully dominated by the oxide capacitance of the field-plate along the drift region.


title: Device analyzer: large-scale mobile data collection

abstract: We collected usage information from 12,500 Android devices in the wild over the course of nearly 2 years. Our dataset contains 53 billion data points from 894 models of devices running 687 versions of Android. Processing the collected data presents a number of challenges ranging from scalability to consistency and privacy considerations. We present our system architecture for collection and analysis of this highly-distributed dataset, discuss how our system can reliably collect time-series data in the presence of unreliable timing information, and discuss issues and lessons learned that we believe apply to many other big data collection projects.


Mobile Marketing in a Digital Age: Application, Challenges & Opportunities


title: Analyzing Ancient Maya Glyph Collections with Contextual Shape Descriptors

abstract: This paper presents an original approach for shape-based analysis of ancient Maya hieroglyphs based on an interdisciplinary collaboration between computer vision and archeology. Our work is guided by realistic needs of archaeologists and scholars who critically need support for search and retrieval tasks in large Maya imagery collections. Our paper has three main contributions. First, we introduce an overview of our interdisciplinary approach towards the improvement of the documentation, analysis, and preservation of Maya pictographic data. Second, we present an objective evaluation of the performance of two state-of-the-art shape-based contextual descriptors (Shape Context and Generalized Shape Context) in retrieval tasks, using two datasets of syllabic Maya glyphs. Based on the identification of their limitations, we propose a new shape descriptor named Histogram of Orientation Shape Context (HOOSC), which is more robust and suitable for description of Maya hieroglyphs. Third, we present what to our knowledge constitutes the first automatic analysis of visual variability of syllabic glyphs along historical periods and across geographic regions of the ancient Maya world via the HOOSC descriptor. Overall, our approach is promising, as it improves performance on the retrieval task, has been successfully validated under an epigraphic viewpoint, and has the potential of offering both novel insights in archeology and practical solutions for real daily scholar needs.


A Review on Vision-Based Hand Gesture Recognition and Applications


title: Fault investigations on die-cast copper rotors

abstract: It has been known for a long time that replacement of aluminum by copper, which has an advantage in high conductivity, can be an economic solution for various applications including electric vehicle in the development of energy efficient motors. In comparison to aluminum, copper die-casting is difficult to perform due to its high density, viscosity, and melting point. Owing to these reasons, the copper die-cast rotors evince several manufacturing problems. The objective of this work is to identify the various defects in the manufacturing of copper die-cast rotor, and identify the lamination coating condition that sustains copper die-cast pressure and temperature. It evaluates the effectiveness of lamination coating on limiting the eddy current loss and the recoating process to improve in case the eddy current loss is in excess due to copper die-casting.


title: Efficient variants of the ICP algorithm

abstract: The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached. In order to improve convergence for nearly-flat meshes with small features, such as inscribed surfaces, we introduce a new variant based on uniform sampling of the space of normals. We conclude by proposing a combination of ICP variants optimized for high speed. We demonstrate an implementation that is able to align two range images in a few tens of milliseconds, assuming a good initial guess. This capability has potential application to real-time 3D model acquisition and model-based tracking.


title: Direct-Drive Position Control of a Spiral Motor as a Monoarticular Actuator

abstract: This paper presents the direct-drive position control verification of a spiral motor in monoarticular configuration. The spiral motor is a newly developed high-thrust high-backdrivable direct-drive three-phase permanent-magnet motor with a unique 3-D structure. One of the possible uses of the spiral motor is to actuate musculoskeletal-like structure which is shown in animals and humans. This is achieved by indirectly actuating the elbow/shoulder joints via pulling/pushing the links by using a linear actuator. We describe the control methods for the direct drive of the spiral motor which are the decoupling control and independent control. Next, simulations were performed to assess the control parameter variation effects. Then, the experimental results confirm the validity of the control method in monoarticular position. The key element of the linear motion control of the spiral motor is the magnetic levitation (air gap between the stator and rotor) control and simultaneous angular motion control via vector d- and q-axis currents.