title: Efficient Exploitation of Mobile Edge Computing for Virtualized 5G in EPC Architectures

abstract: To pave the way towards disclosing the full potential of 5G networking, emerging Mobile Edge Computing techniques are gaining momentum in both academic and industrial research as a means to enhance infrastructure scalability and reliability by moving control functions close to the edge of the network. After the promising results under achievement within the EU Mobile Cloud Networking project, we claim the suitability of deploying Evolved Packet Core (EPC) support solutions as a Service (EPCaaS) over a uniform edge cloud infrastructure of Edge Nodes, by following the concepts of Network Function Virtualization (NFV). This paper originally focuses on the support needed for efficient elasticity provisioning of EPCaaS stateful components, by proposing novel solutions for effective subscribers' state management in quality-constrained 5G scenarios. In particular, to favor flexibility and high-availability against network function failures, we have developed a state sharing mechanism across different data centers even in presence of firewall/network encapsulation. In addition, our solution can dynamically select which state portions should be shared and to which Edge Nodes. The reported experimental results, measured over the widely recognized Open5GCore testbed, demonstrate the feasibility and effectiveness of the approach, as well as its capability to satisfy "carrier-grade" quality requirements while ensuring good elasticity and scalability.


title: Caroline: An Autonomously Driving Vehicle for Urban Environments

abstract: The 2007 DARPA Urban Challenge afforded the golden opportunity for the Technische Universit\"at Braunschweig to demonstrate its abilities to develop an autonomously driving vehicle to compete with the world's best competitors. After several stages of qualification, our team CarOLO qualified early for the DARPA Urban Challenge Final Event and was among only eleven teams from initially 89 competitors to compete in the final. We had the ability to work together in a large group of experts, each contributing his expertise in his discipline, and significant organisational, financial and technical support by local sponsors who helped us to become the best non-US team. In this report, we describe the 2007 DARPA Urban Challenge, our contribution"Caroline", the technology and algorithms along with her performance in the DARPA Urban Challenge Final Event on November 3, 2007.


title: Metacognition and the spacing effect: the role of repetition, feedback, and instruction on judgments of learning for massed and spaced rehearsal

abstract: Although memory performance benefits from the spacing of information at encoding, judgments of learning (JOLs) are often not sensitive to the benefits of spacing. The present research examines how practice, feedback, and instruction influence JOLs for spaced and massed items. In Experiment 1, in which JOLs were made after the presentation of each item and participants were given multiple study-test cycles, JOLs were strongly influenced by the repetition of the items, but there was little difference in JOLs for massed versus spaced items. A similar effect was shown in Experiments 2 and 3, in which participants scored their own recall performance and were given feedback, although participants did learn to assign higher JOLs to spaced items with task experience. In Experiment 4, after participants were given direct instruction about the benefits of spacing, they showed a greater difference for JOLs of spaced vs massed items, but their JOLs still underestimated their recall for spaced items. Although spacing effects are very robust and have important implications for memory and education, people often underestimate the benefits of spaced repetition when learning, possibly due to the reliance on processing fluency during study and attending to repetition, and not taking into account the beneficial aspects of study schedule.


title: Abstract LP models for bin packing and cutting stock problems

abstract: Abstract We review several linear programming (LP) formulations for the one-dimensional cutting stock and bin packing problems, namely, the models of Kantorovich, Gilmore–Gomory, onecut models, as in the Dyckhoff–Stadtler approach, position-indexed models, and a model derived from the vehicle routing literature. We analyse some relations between the corresponding LP relaxations, and their relative strengths, and refer how to derive branching schemes that can be used in the exact solution of these problems, using branch-and-price.


title: Beyond pleasure and pain

abstract: People approach pleasure and avoid pain. To discover the true nature of approach-avoidance motivation, psychologists need to move beyond this hedonic principle to the principles that underlie the different ways that it operates. One such principle is regulatory focus, which distinguishes self-regulation with a promotion focus (accomplishments and aspirations) from self-regulation with a prevention focus (safety and responsibilities). This principle is used to reconsider the fundamental nature of approach-avoidance, expectancy-value relations, and emotional and evaluative sensitivities. Both types of regulatory focus are applied to phenomena that have been treated in terms of either promotion (e.g., well-being) or prevention (e.g., cognitive dissonance). Then, regulatory focus is distinguished from regulatory anticipation and regulatory reference, 2 other principles underlying the different ways that people approach pleasure and avoid pain.


title: A Nonlocal Denoising Algorithm for Manifold-Valued Images Using Second Order Statistics

abstract: Nonlocal patch-based methods, in particular the Bayes' approach of Lebrun, Buades and Morel (2013), are considered as state-of-the-art methods for denoising (color) images corrupted by white Gaussian noise of moderate variance. This paper is the first attempt to generalize this technique to manifold-valued images. Such images, for example images with phase or directional entries or with values in the manifold of symmetric positive definite matrices, are frequently encountered in real-world applications. Generalizing the normal law to manifolds is not canonical and different attempts have been considered. Here we focus on a straightforward intrinsic model and discuss the relation to other approaches for specific manifolds. We reinterpret the Bayesian approach of Lebrun et al. (2013) in terms of minimum mean squared error estimation, which motivates our definition of a corresponding estimator on the manifold. With this estimator at hand we present a nonlocal patch-based method for the restoration of manifold-valued images. Various proof of concept examples demonstrate the potential of the proposed algorithm.


title: Enhancing scholarly use of digital libraries: A comparative survey and review of bibliographic metadata ontologies

abstract: The HathiTrust Research Center (HTRC) is engaged in the development of tools that will give scholars the ability to analyze the HathiTrust digital library's 14 million volume corpus. A cornerstone of the HTRC's digital infrastructure is the workset -- a kind of scholar-built research collection intended for use with the HTRC's analytics platform. Because more than 66% of the digital corpus is subject to copyright restrictions, scholarly users remain dependent upon the descriptive accounts provided by traditional metadata records in order to identify and gather together bibliographic resources for analysis. This paper compares the MADSRDF/MODSRDF, Bibframe, schema.org, BIBO, and FaBiO ontologies by assessing their suitability for employment by the HTRC to meet scholars' needs. These include distinguishing among multiple versions of the same work; representing the complex historical and physical relationships among those versions; and identifying and providing access to finer grained bibliographic entities, e.g., poems, chapters, sections, and even smaller segments of content.


title: Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior

abstract: We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55n(p 0.01), which has far-reaching implications for past and future research on social networks.


title: Multi-image Semantic Matching by Mining Consistent Features

abstract: This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method to reconstruct object-class models and discover object-class landmarks from images without any annotation.


title: High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS

abstract: User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.


title: Detection of leukemia in microscopic white blood cell images

abstract: Leukemia means blood cancer which is featured by the uncontrolled and abnormal production of white blood cells (leukocytes) by the bone marrow in the blood. Analyzing microscopic blood cell images, diseases can be identified and diagnosed early. Hematologist are using technique of image processing to analyze, detect and identify leukemia types in patients recently. Detection through images is fast and cheap method as there is no special need of equipment for lab testing. In this paper, we proposed a method of detection of leukemia in patients from microscopic white blood cell images. We have focused on the changes in the geometry of cells and statistical parameters like mean and standard deviation which separates white blood cells from other blood components using processing tools like MATLAB and LabVIEW. Images processing steps like image enhancement, image segmentation and feature extraction are applied on microscopic images.


title: The modernity of classification

abstract: Purpose – The purpose of this paper is to explore the modernity of current classification theory and work, and outline a foundation for moving classification toward a late‐modern conception.Design/methodology/approach – The paper examines the conceptual foundation for current modern classification work, provides critical analysis of that approach, and outlines three conflicts with modernity that shape the path out of the consequences of modernity.Findings – The paper presents an understanding of classification that establishes classification on a late‐modern epistemology, and it lays the contours of how to reclaim the intellectual core of classification theory and work.Originality/value – The paper establishes a foundation for rethinking classification work, outlines consequences of current mainstream work, and provides concept for developing late‐modern classification theory and practice.


title: Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation

abstract: In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages on our finding that entropy minimization is induced by the optimal alignment of second order statistics between source and target domains. We formally demonstrate this hypothesis and, aiming at achieving an optimal alignment in practical cases, we adopt a more principled strategy which, differently from the current Euclidean approaches, deploys alignment along geodesics. Our pipeline can be implemented by adding to the standard classification loss (on the labeled source domain), a source-to-target regularizer that is weighted in an unsupervised and data-driven fashion. We provide extensive experiments to assess the superiority of our framework on standard domain and modality adaptation benchmarks.


title: Optimum Design Rules for CMOS Hall Sensors

abstract: This manuscript analyzes the effects of design parameters, such as aspect ratio, doping concentration and bias, on the performance of a general CMOS Hall sensor, with insight on current-related sensitivity, power consumption, and bandwidth. The article focuses on rectangular-shaped Hall probes since this is the most general geometry leading to shape-independent results. The devices are analyzed by means of 3D-TCAD simulations embedding galvanomagnetic transport model, which takes into account the Lorentz force acting on carriers due to a magnetic field. Simulation results define a set of trade-offs and design rules that can be used by electronic designers to conceive their own Hall probes.


title: Cannabidiol Claims and Misconceptions

abstract: Once a widely ignored phytocannabinoid, cannabidiol now attracts great therapeutic interest, especially in epilepsy and cancer. As with many rising trends, various myths and misconceptions have accompanied this heightened public interest and intrigue. This forum article examines and attempts to clarify some areas of contention.


title: The study of association algorithm BGL based on binary system and oriented graph

abstract: The actuality of current algorithms in mining association rule is analyzed in this paper. A new algorithm, based on binary and graph, is put forward. The main idea and realization project of the algorithm are introduced detailed here. Then, comparing the performance of all kinds of algorithms proves the new algorithm having improved the efficiency of mining rules.


title: A survey of effective heuristics and their application to a variety of knapsack problems

abstract: We present a family of knapsack problems (KPs) while highlighting their particular applications. Though most of the problems are derived from the classical KP, the differences arise in the addition or modification of the constraints or in the way the objective function is defined. Appropriate techniques that were found to be successful in solving these problems are briefly reviewed. Hybrid methods that combine the strengths of different methods such as exact and heuristics are also briefly discussed. Some research avenues that we believe to be useful and challenging are also pointed out.


A new model for Machine Comprehension via multi-perspective context matching and bidrectional attention flow


Outpatient Services for the Mentally Ill Involved in the Criminal Justice System


title: Industrial robot capability models for agile manufacturing

abstract: Purpose ::: ::: ::: ::: ::: This paper aims to represent a capability model for industrial robot as they pertain to assembly tasks. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The architecture of a real kit building application is provided to demonstrate how robot capabilities can be used to fully automate the planning of assembly tasks. Discussion on the planning infrastructure is done with the Planning Domain Definition Language (PDDL) for heterogeneous multi robot systems. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The paper describes PDDL domain and problem files that are used by a planner to generate a plan for kitting. Discussion on the plan shows that the best robot is selected to carry out assembly actions. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The author presents a robot capability model that is intended to be used for helping manufacturers to characterize the different capabilities their robots contribute to help the end user to select the appropriate robots for the appropriate tasks, selecting backup robots during robot’s failures to limit the deterioration of the system’s productivity and the products’ quality and limiting robots’ failures and increasing productivity by providing a tool to manufacturers that outputs a process plan that assigns the best robot to each task needed to accomplish the assembly.


title: Gait and balance in Parkinson’s disease subtypes: objective measures and classification considerations

abstract: Parkinson’s disease (PD) is often divided into tremor dominant (TD) and postural instability gait difficulty (PIGD) subtypes. However, objective measures of gait (e.g., stride length, variability) and balance have not been well studied in these subtypes. To better understand these motor subtypes, we objectively quantified gait and balance and their behavioral correlates. 110 patients with PD underwent a clinical evaluation and were stratified into PIGD and TD subtypes. Participants walked under single and dual task conditions while wearing a single body-fixed sensor, both “OFF” and “ON” medications and at home for 3 days. We also examined performance-based tests of mobility, balance, and fall risk. Stricter criteria were also applied, dividing the subjects into predominant representative subgroups: p-PIGD and p-TD. Both the PIGD (n = 62) and TD (n = 42) groups and the p-PIGD (n = 31) and p-TD (n = 32) subgroups were similar with respect to basic disease characteristics (e.g., disease duration, p > 0.69). Surprisingly gait speed, stride length, and variability did not differ between the PIGD and TD groups (p > 0.05). In contrast, the p-PIGD group had reduced gait speed (under single and dual task conditions), shorter strides, increased stride variability, and decreased stride regularity (regularity: p-PIGD 0.66 ± 0.10; p-TD 0.74 ± 0.08; p = 0.003). The p-PIGD group also scored worse on performance-based tests, compared to the p-TD. Clinical assessments of the disturbances seen in patients with the PIGD subtype are not consistent with objective measures; overlapping between the groups is seen in many objective features of gait and balance. These findings suggest that the proposed alternate classification scheme may be useful.


Preparation, characterization and antimicrobial property of ag+- nano Chitosan/ZSM-5: novel Hybrid Biocomposites


title: Genetic evaluation of crossbred lamb production. 5. Age of puberty and lambing performance of yearling crossbred ewes

abstract: The age and liveweight at puberty were evaluated in 2155 crossbred ewe lambs at 2 sites over 3 years. A separate dataset examined the lambing performance of 1177 crossbred ewe lambs that were joined naturally at ~7 months of age at 2 sites over 3 years and also their lambing performances in the subsequent 2 years. The ewe lambs were the progeny of Merino dams and sires from several breeds including Border Leicester, East Friesian, Finnsheep, Coopworth, White Suffolk, Corriedale, and Booroola Leicester. Overall, 85% of the ewe lambs reached puberty (detected by teasers) in their first autumn at an average age of 248 days and liveweight of 42 kg. There was significant variation in age and weight at puberty (P < 0.01) among the site and year cohorts of ewes. Sire breed was significant for weight (P < 0.01) but not age at puberty. Of the crossbred ewe lambs joined at 7 months of age, 54% lambed with an average litter size of 1.31 and 49% of lambs were weaned from the ewes joined. There was a significant site effect for all reproduction traits (P < 0.01), with the higher performance at one site associated with higher liveweight at joining. Maternal sire breed was significant for all reproduction traits (P < 0.01) and there was considerable variation between sires within sire breeds. Ewes that reared lambs in their first year weaned 12% more lambs per year in their second and third years compared with ewes that failed to lamb or rear any lambs at 1 year of age. The results demonstrate that lamb weaning rates of 90% or more can be achieved from crossbred ewe lambs with optimal genetics and management. The opportunities for improving productivity by joining crossbred ewe lambs and the selection and management considerations required for successful joinings and lambings are reviewed and discussed.


title: Simulating Human Behavior in Fighting Games Using Reinforcement Learning and Artificial Neural Networks

abstract: The study of intelligent agent training is of great interest to the gaming industry due to its wide application in various game genres and its capabilities of simulating a human-like behavior. In this work two machine learning techniques, namely, a reinforcement learning approach and an Artificial Neural Network (ANN), are used in a fighting game in order to allow the agent/fighter to emulate a human player. We propose a special reward function for the reinforcement learning approach that is capable of integrating specific human-like behaviors to the agent. The ANN is trained with several recorded battles of a human player. The proposed methods are compared to other two reinforcement learning methods presented in the literature. Furthermore, we present a detailed discussion of the empirical evaluations performed, regarding the training process and the main characteristics of each method used. The results obtained in the experiments indicated that the proposed methods have a good performance against human players and are also more enjoyable to play against when compared to the other existing methods.


title: Fear , Anger , and Risk

abstract: Drawing on an appraisal-tendency framework (J. S. Lemer & D. Keltner, 2000), the authors predicted and found that fear and anger have opposite effects on risk perception. Whereas fearful people expressed pessimistic risk estimates and risk-averse choices, angry people expressed optimistic risk estimates and risk-seeking choices. These opposing patterns emerged for naturally occurring and experimentally induced fear and anger. Moreover, estimates of angry people more closely resembled those of happy people than those of fearful people. Consistent with predictions, appraisal tendencies accounted for these effects: Appraisals of certainty and control moderated and (in the case of control) mediated the emotion effects. As a complement to studies that link affective valence to judgment outcomes, the present studies highlight multiple benefits of studying specific emotions.


title: Automatic classification of toco-signals: An approach towards comprehensive monitoring of labour progress

abstract: Childbirth is an intricate process which is marked by an increased cervical dilation rate caused due to steady increments in the frequency and strength of uterine contractions. The contractions may be characterized by its strength, duration and frequency (count) — which are monitored through Tocography. However, the procedure is prone to subjectivity and an automated approach for the classification of the contractions is needed. In this paper, we use three different Weighted K-Nearest Neighbor classifiers and Decision Trees to classify the contractions into three types: Mild, Moderate and Strong. Further, we note the fact that our training data consists of fewer samples of Contractions as compared to those of Non-contractions — resulting in “Class Imbalance”. Hence, we use the Synthetic Minority Oversampling Technique (SMOTE) in conjunction with the K-NN classifier and Decision Trees to alleviate the problems of the same. The ground truth for Tocography signals was established by a doctor having an experience of 36 years in Obstetrics and Gynaecology. The annotations are in three categories: Mild (33 samples), Moderate (64 samples) and Strong (96 samples), amounting to a total of 193 contractions whereas the number of Non-contraction samples was 1217. Decision Trees using SMOTE performed the best with accuracies of 95%, 98.25% and 100% for the aforementioned categories, respectively. The sensitivities achieved for the same are 96.67%, 96.52% and 100% whereas the specificities amount to 93.33%, 100% and 100%, respectively. Our method may be used to monitor the labour progress efficiently.


title: Stress-free stretchable electronic device using folding deformation

abstract: We developed a two-dimensionally (2-D) stretchable electronic device with stress-free region by applying origami folding. The key idea is to achieve “a stretching deformation of whole device” by “a local bending deformation”. Because our device has stress-free region, we can use a rigid chip (such as LED chips and MEMS sensors) and a metal wire without mechanical fracture or metal fatigue. In this paper, first, we proposed a 2-D stretchable structure by developing “miura-ori” folding. Next, we fabricated a 2-D stretchable electronic device, and confirmed that wire fatigue or cracks are not generated by repeated deformation. Finally, we demonstrated 2-D stretchability and bendability using the device with inorganic LED chips.


title: High-efficiency solution-processed perovskite solar cells with millimeter-scale grains

abstract: State-of-the-art photovoltaics use high-purity, large-area, wafer-scale single-crystalline semiconductors grown by sophisticated, high-temperature crystal growth processes. We demonstrate a solution-based hot-casting technique to grow continuous, pinhole-free thin films of organometallic perovskites with millimeter-scale crystalline grains. We fabricated planar solar cells with efficiencies approaching 18%, with little cell-to-cell variability. The devices show hysteresis-free photovoltaic response, which had been a fundamental bottleneck for the stable operation of perovskite devices. Characterization and modeling attribute the improved performance to reduced bulk defects and improved charge carrier mobility in large-grain devices. We anticipate that this technique will lead the field toward synthesis of wafer-scale crystalline perovskites, necessary for the fabrication of high-efficiency solar cells, and will be applicable to several other material systems plagued by polydispersity, defects, and grain boundary recombination in solution-processed thin films.


title: DC Voltage Balancing for PWM Cascaded H-Bridge Converter Based STATCOM

abstract: This paper presents a new control method for Cascaded connected H-Bridge converter based STATCOMs. These converters have been classically commutated at fundamental line-frequencies, but the evolution of Power Semiconductors has allowed to increase switching frequencies and the power ratings of these devices, permitting the use of PWM modulation techniques. This paper mainly focuses the DC Bus voltage balancing problems, and proposes a new control technique that solves these balancing problems maintaining the delivered reactive power equally distributed among all the H-Bridges of the converter.


title: Cold-Start Recommendation Using Bi-Clustering and Fusion for Large-Scale Social Recommender Systems

abstract: Social recommender systems leverage collaborative filtering (CF) to serve users with content that is of potential interesting to active users. A wide spectrum of CF schemes has been proposed. However, most of them cannot deal with the cold-start problem that denotes a situation that social media sites fail to draw recommendation for new items, users or both. In addition, they regard that all ratings equally contribute to the social media recommendation. This supposition is against the fact that low-level ratings contribute little to suggesting items that are likely to be of interest of users. To this end, we propose bi-clustering and fusion (BiFu)-a newly-fashioned scheme for the cold-start problem based on the BiFu techniques under a cloud computing setting. To identify the rating sources for recommendation, it introduces the concepts of popular items and frequent raters. To reduce the dimensionality of the rating matrix, BiFu leverages the bi-clustering technique. To overcome the data sparsity and rating diversity, it employs the smoothing and fusion technique. Finally, BiFu recommends social media contents from both item and user clusters. Experimental results show that BiFu significantly alleviates the cold-start problem in terms of accuracy and scalability.


Motivating Employees to Comply with Information Security Policies


title: DOC: Deep Open Classification of Text Documents

abstract: Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.


title: SVO: Fast semi-direct monocular visual odometry

abstract: We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods. The semi-direct approach eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Our algorithm operates directly on pixel intensities, which results in subpixel precision at high frame-rates. A probabilistic mapping method that explicitly models outlier measurements is used to estimate 3D points, which results in fewer outliers and more reliable points. Precise and high frame-rate motion estimation brings increased robustness in scenes of little, repetitive, and high-frequency texture. The algorithm is applied to micro-aerial-vehicle state-estimation in GPS-denied environments and runs at 55 frames per second on the onboard embedded computer and at more than 300 frames per second on a consumer laptop. We call our approach SVO (Semi-direct Visual Odometry) and release our implementation as open-source software.


title: A Visible Light Communication Link Protection Mechanism for Smart Factory

abstract: In recent years, wireless networks and applications have achieved marvelous successes in government, enterprise, home, and personal communication systems. The desired features of wireless communications draw lots of attention to the industrial communication and expected to bring benefits such as reduce deployment and maintenance after employed. However, the industrial communication system required real-time communication, which means the control systems in the factory are required accurate control and rapid communication, such as the industrial motion control system. In this type of application, the communication system performance and efficiency will be evaluated to ensure it applicable to the industrial network. However, there are a few original issues in the wireless communication, such as fading, multipath propagation and interference problems, which will affect the reliability and performance of industrial communication system operation. Therefore, we proposed a connection protection mechanism that cooperates with wireless network and visible light communication to achieve reliability and performance in industrial communication network. We will consider implementing this mechanism by using industrial wireless Ethernet in the near future.


title: RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS)

abstract: We believe that Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex tasks such as computer vision, natural language processing and speech recognition. Despite this, only little work has been published on Deep Learning methods for Recommender Systems. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the workshop is to encourage the application of Deep Learning techniques in Recommender Systems, to promote research in deep learning methods for Recommender Systems, and to bring together researchers from the Recommender Systems and Deep Learning communities.


title: A new electrically tunable frequency for ⵣ-shaped microstrip patch array based on N-LC featuring dual-band dual-beam

abstract: A new electrically tunable frequency for X-shaped microstrip patch array based on the nematic liquid crystal N-LC technology featuring dual-band dual-beam is presented. A new ⵣ-shaped microstrip (Berbers alphabet; pronounced |z|) is proposed to obtain dual-beam farfield radiation pattern. The dual-band is tuned by increasing the applied AC voltage on the inverted microstrip line IMSL, that controls the orientation angle of the liquid crystal's molecules and its permittivity. The dual resonant frequency band is centered at 10.27 GHz and at 12.655 GHz, due to the applied AC voltage from 0–15 V, the variation of dual resonant frequency band is tuned up to 0.935 GHz and 1.135 GHz after applying a continuous AC voltage respectively. At the resonant frequency band 10.27 GHz, the dual-beam radiation pattern is obtained with symmetric beam angles at ±38° in YZ-plane, 47.5° half power beam width (−3 dB) at each beam. At the resonant frequency band 12.655 GHz, the single-beam radiation pattern is achieved with a maximum with 46.8° half power beam width (−3 dB). In addition, the realized gain of the proposed antenna is larger than 8.2 dB. This antenna is suitable for satellite communications and radar systems.


title: From Bayesian Sparsity to Gated Recurrent Nets

abstract: The iterations of many first-order algorithms, when applied to minimizing common regularized regression functions, often resemble neural network layers with pre-specified weights. This observation has prompted the development of learning-based approaches that purport to replace these iterations with enhanced surrogates forged as DNN models from available training data. For example, important NP-hard sparse estimation problems have recently benefitted from this genre of upgrade, with simple feedforward or recurrent networks ousting proximal gradient-based iterations. Analogously, this paper demonstrates that more powerful Bayesian algorithms for promoting sparsity, which rely on complex multi-loop majorization-minimization techniques, mirror the structure of more sophisticated long short-term memory (LSTM) networks, or alternative gated feedback networks previously designed for sequence prediction. As part of this development, we examine the parallels between latent variable trajectories operating across multiple time-scales during optimization, and the activations within deep network structures designed to adaptively model such characteristic sequences. The resulting insights lead to a novel sparse estimation system that, when granted training data, can estimate optimal solutions efficiently in regimes where other algorithms fail, including practical direction-of-arrival (DOA) and 3D geometry recovery problems. The underlying principles we expose are also suggestive of a learning process for a richer class of multi-loop algorithms in other domains.


The Effects of Violent Music on Children and Adolescents


title: A Three-Phase Inductive Power Transfer System for Roadway-Powered Vehicles

abstract: The development of a new three-phase bipolar inductive power transfer system that provides power across the entire width of a roadway surface for automatic guided vehicles and people mover systems is described. A prototype system was constructed to verify the feasibility of the design for a number of moving loads (toy cars). Here, 40 A/phase is supplied at 38.4 kHz to a 13-m-long test track. Flat pickups are used on the underside of each vehicle to couple power from the track to the vehicle. Finite element modeling software was used to design the geometrical position of the track cables and to predict the power output. This design resulted in a considerably wider power delivery zone than possible using a single-phase track layout and has been experimentally verified. Mutual coupling effects between the various track phases require additional compensation to be added to ensure balanced three-phase currents.


title: The flooding time synchronization protocol

abstract: Wireless sensor network applications, similarly to other distributed systems, often require a scalable time synchronization service enabling data consistency and coordination. This paper describes the Flooding Time Synchronization Protocol (FTSP), especially tailored for applications requiring stringent precision on resource limited wireless platforms. The proposed time synchronization protocol uses low communication bandwidth and it is robust against node and link failures. The FTSP achieves its robustness by utilizing periodic flooding of synchronization messages, and implicit dynamic topology update. The unique high precision performance is reached by utilizing MAC-layer time-stamping and comprehensive error compensation including clock skew estimation. The sources of delays and uncertainties in message transmission are analyzed in detail and techniques are presented to mitigate their effects. The FTSP was implemented on the Berkeley Mica2 platform and evaluated in a 60-node, multi-hop setup. The average per-hop synchronization error was in the one microsecond range, which is markedly better than that of the existing RBS and TPSN algorithms.


title: The Curse of Planning : Dissecting Multiple Reinforcement-Learning Systems by Taxing the Central Executive A .

abstract: A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. In these accounts, a flexible but computationally expensive model-based reinforcement-learning system has been contrasted with a less flexible but more efficient model-free reinforcement-learning system. The factors governing which system controls behavior—and under what circumstances—are still unclear. Following the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrated that having human decision makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement-learning strategy. Further, we showed that, across trials, people negotiate the trade-off between the two systems dynamically as a function of concurrent executive-function demands, and people’s choice latencies reflect the computational expenses of the strategy they employ. These results demonstrate that competition between ...


title: Modeling of a standalone Wind-PV Hybrid generation system using MATLAB/SIMULINK and its performance analysis

abstract: This work focuses on the modeling and analysis of a Standalone wind-PV Hybrid generation system under different conditions in MATLAB/SIMULINK environment. The proposed system consists of two renewable sources i.e. wind and solar energy. Modeling of PV array and wind turbine is clearly explained. The wind subsystem is equipped of a direct driven permanent-magnet synchronous generator, a diode rectifier and a buck converter for the tracking of the maximum power point. In photovoltaic system the variable DC output voltage is controlled by another buck converter used for the MPPT. These two systems are combined to operate in parallel and the common DC bus collects the total energy from the wind and photovoltaic subsystems and uses it partly to charge the battery and partly to the DC load. This paper offers a useful wind-PV hybrid model which can be used for performance analysis of such systems. Index Terms—Buck converter , Insolation,MATLAB simulation , PV array, PMSG , temperature, turbine, wind speed. ——————————a——————————


title: Construction the Model on the Breast Cancer Survival Analysis Use Support Vector Machine, Logistic Regression and Decision Tree

abstract: The aim of the paper is to use data mining technology to establish a classification of breast cancer survival patterns, and offers a treatment decision-making reference for the survival ability of women diagnosed with breast cancer in Taiwan. We studied patients with breast cancer in a specific hospital in Central Taiwan to obtain 1,340 data sets. We employed a support vector machine, logistic regression, and a C5.0 decision tree to construct a classification model of breast cancer patients' survival rates, and used a 10-fold cross-validation approach to identify the model. The results show that the establishment of classification tools for the classification of the models yielded an average accuracy rate of more than 90% for both; the SVM provided the best method for constructing the three categories of the classification system for the survival mode. The results of the experiment show that the three methods used to create the classification system, established a high accuracy rate, predicted a more accurate survival ability of women diagnosed with breast cancer, and could be used as a reference when creating a medical decision-making frame.


title: IOT Based Door Access Control Using Face Recognition

abstract: In recent years, it is important to own a reliable security system that can secure our assets as well as to protect our privacy. The traditional security system needs an individual to use a key, identification (ID) card or password to access an area such as home and workplace. However, the present security system has many weaknesses wherever it is simply cast and taken. Most doors are controlled by persons with the employment of keys, security cards, countersign or pattern to open the door. The aim of this paper is to assist users for improvement of the door security of sensitive locations by using face detection and recognition. The proposed system mainly consists of subsystems namely image capture, face detection and recognition, email notification and automatic door access management. Face Recognition supported openCV is brought up because it uses Eigen faces and reduces the scale of face images without losing vital features, facial images for many persons can be stored in the database. The door lock can also be accessed remotely from any part of the world by using Telegram android application. The captured image from pi camera will be sent to the authorized person through email for safety purposes.


title: The Power of Normalization: Faster Evasion of Saddle Points

abstract: A commonly used heuristic in non-convex optimization is Normalized Gradient Descent (NGD) - a variant of gradient descent in which only the direction of the gradient is taken into account and its magnitude ignored. We analyze this heuristic and show that with carefully chosen parameters and noise injection, this method can provably evade saddle points. We establish the convergence of NGD to a local minimum, and demonstrate rates which improve upon the fastest known first order algorithm due to Ge e al. (2015). ::: The effectiveness of our method is demonstrated via an application to the problem of online tensor decomposition; a task for which saddle point evasion is known to result in convergence to global minima.


title: CVC-MUSCIMA: a ground truth of handwritten music score images for writer identification and staff removal

abstract: The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper, we present the CVC-MUSCIMA database and ground truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground truth, partitioning, and evaluation metrics, we also provide some baseline results for easing the comparison between different approaches.


title: 500W Long-Pulse High Efficiency UHF BJT Transistor for Radar Applications

abstract: A 500 W very long-pulse high efficiency BJT power transistor is presented, which operates at 6 mS pulse width, 20% duty cycle and 36 V supply voltage. The output power exceeds 500 Watts; the efficiency is higher than 60%; and power gain is greater than 11dB across 400 MHz to 450 MHz. The die layout, internal impedance matching technique and external impedance matching of the test fixture design is also described. The thermal resistance is 0.25C/W. The junction temperature remains below 150 C at the heat sink temperature of 70 C. This transistor uses state-of-the-art silicon BJT technology in a common emitter configuration and class C operation. This combination of high power, long-pulse, and high efficiency makes these devices ideally suited for transmitter designs in UHF radar systems.


title: An FPGA-based accelerator implementation for deep convolutional neural networks

abstract: Deep convolutional neural networks (CNN) is highly efficient in image recognition tasks such as MNIST digit recognition. Accelerators based on FPGA platform are proposed since general purpose processor is disappointing in terms of performance when dealing with recognition tasks. Recently, an optimized FPGA-based accelerator design (work 1) has been proposed claiming best performance compared with existing implementations. But as the author acknowledged, performance could be better if fixed point presentation and computation elements had been used. Inspired by its methodology in implementing the Alexnet convolutional neural network, we implement a 5-layer accelerator for MNIST digit recognition task using the same Vivado HLS tool but using 11-bits fixed point precision on a Virtex7 FPGA. We compare performance on FPGA platform with the performance of the target CNN on MATLAB/CPU platform; we reach a speedup of 16.42. Our implementation runs at 150MHz and reaches a peak performance of 16.58 GMACS. Since our target CNN is simpler, we use much less resource than work 1 has used.


Transfer Learning for Traffic Speed Prediction with Effective Spatiotemporal Features


title: Semantic Word Clusters Using Signed Normalized Graph Cuts

abstract: Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other. We present a new signed spectral normalized graph cut algorithm, signed clustering, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships between word pairs are represented by negative weights. Our signed clustering algorithm produces clusters of words which simultaneously capture distributional and synonym relations. We evaluate these clusters against the SimLex-999 dataset (Hill et al.,2014) of human judgments of word pair similarities, and also show the benefit of using our clusters to predict the sentiment of a given text.


title: Deuterated cholesterol uptake revealed with stimulated Raman microscopy

abstract: Cholesterol is involved in multiple vital processes, thus in multiple diseases. Synergies between coherent Raman microscopy and deuterated compounds provide the ultimate tool towards live visualization of cholesterol pathways.


title: Transcranial Doppler Ultrasound: A Review of the Physical Principles and Major Applications in Critical Care

abstract: Transcranial Doppler (TCD) is a noninvasive ultrasound (US) study used to measure cerebral blood flow velocity (CBF-V) in the major intracranial arteries. It involves use of low-frequency (≤2 MHz) US waves to insonate the basal cerebral arteries through relatively thin bone windows. TCD allows dynamic monitoring of CBF-V and vessel pulsatility, with a high temporal resolution. It is relatively inexpensive, repeatable, and portable. However, the performance of TCD is highly operator dependent and can be difficult, with approximately 10-20% of patients having inadequate transtemporal acoustic windows. Current applications of TCD include vasospasm in sickle cell disease, subarachnoid haemorrhage (SAH), and intra- and extracranial arterial stenosis and occlusion. TCD is also used in brain stem death, head injury, raised intracranial pressure (ICP), intraoperative monitoring, cerebral microembolism, and autoregulatory testing.


title: Strengthening Authentication with Privacy-Preserving Location Verification of Mobile Phones

abstract: Mobile devices are increasingly used in security-sensitive contexts such as physical access control and authorization of payment transactions. In this paper we contribute a mechanism to verify whether a mobile device currently resides within a geographical area at a given time, thus enabling the use of the location as an additional authentication factor. Trustworthiness, privacy, and practicability are central to our mechanism. In particular, to provide trustworthy location information, our mechanism uses the location of the phone as detected by the Mobile Network Operator instead of relying on the location detected by the phone itself, which can be manipulated. We have followed a privacy-by-design approach to ensure that sensitive information, e.g., location and subscriber data, are only revealed to parties with a need to know. Privacy safeguards are realized using anonymous credentials, an established privacy-enhancing technology. Finally, our mechanism is practical and has little requirements on the mobile phone beyond the ability to run computations on anonymous credentials, as well as Internet and mobile network connectivity. These requirements are fulfilled by most smartphones in the market.


title: Metaphor in idiom comprehension

abstract: Psycholinguistic research has shown that people's tacit knowledge of conceptual metaphors, such as ANGER IS HEATED FLUID IN A CONTAINER, partly motivates how they make sense of idiomatic phrases likeblow your stackandflip your lid.But do people quickly access conceptual metaphors each time an idiom is encountered in discourse? The present studies used a priming method to examine the role of conceptual metaphors in immediate idiom comprehension. Experiment 1 showed that people access conceptual metaphors when understanding idioms, but significantly less so when processing literal paraphrases of idioms. Experiment 2 demonstrated that people access the appropriate conceptual metaphors, such as ANGER IS HEAT, when processing some idioms, such asblow your stack,but not when they read idioms, such asjump down your throat,which have similar figurative meanings that are motivated by different conceptual metaphors (e.g., ANGER IS ANIMAL BEHAVIOR). The findings from these studies provide important evidence on the constraining role that common patterns of metaphoric thought have in figurative language understanding.


title: Tone Recognition Of Continuous Thai Speech Under Tonal Assimilation And Declination Effects Using Half-Tone Model

abstract: This paper presents a method for continuous Thai tone recognition. One of the main problems in tone recognition is that several interacting factors affect F0 realization of tones. In this paper, we focus on the tonal assimilation and declination effects. These effects are compensated by the tone information of neighboring syllables, the F0 downdrift and the context-dependent tone model. However, the context-dependent tone model is too large and its training time is very long. To overcome these problems, we propose a novel model called the half-tone model. The experiments, which compare all tone features and all tone models, were simulated by feedforward neural networks. The results show that the proposed tone features increase the recognition rates and the half-tone model outperforms conventional tone models, i.e. context-independent and context-dependent tone models, in terms of recognition rate and speed. The best results are 94.77% and 93.82% for the inside test and outside test, respectively.


title: BlocHIE: A BLOCkchain-Based Platform for Healthcare Information Exchange

abstract: Nowadays, a great number of healthcare data are generated every day from both medical institutions and individuals. Healthcare information exchange (HIE) has been proved to benefit the medical industry remarkably. To store and share such large amount of healthcare data is important while challenging. In this paper, we propose BlocHIE, a Blockchain-based platform for healthcare information exchange. First, we analyze the different requirements for sharing healthcare data from different sources. Based on the analysis, we employ two loosely-coupled Blockchains to handle different kinds of healthcare data. Second, we combine off-chain storage and on-chain verification to satisfy the requirements of both privacy and authenticability. Third, we propose two fairness-based packing algorithms to improve the system throughput and the fairness among users jointly. To demonstrate the practicability and effectiveness of BlocHIE, we implement BlocHIE in a minimal-viable-product way and evaluate the proposed packing algorithms extensively.


title: HashKV: enabling efficient updates in KV storage via hashing

abstract: Persistent key-value (KV) stores mostly build on the Log-Structured Merge (LSM) tree for high write performance, yet the LSM-tree suffers from the inherently high I/O amplification. KV separation mitigates I/O amplification by storing only keys in the LSM-tree and values in separate storage. However, the current KV separation design remains inefficient under update-intensive workloads due to its high garbage collection (GC) overhead in value storage. We propose HashKV, which aims for high update performance atop KV separation under updateintensive workloads. HashKV uses hash-based data grouping, which deterministically maps values to storage space so as to make both updates and GC efficient. We further relax the restriction of such deterministic mappings via simple but useful design extensions. We compare HashKV with state-of-the-art KV stores via extensive testbed experiments, and show that HashKV achieves 4.6× throughput and 53.4% less write traffic compared to the current KV separation design.


title: Deep Cube-Pair Network for Hyperspectral Imagery Classification

abstract: Advanced classification methods, which can fully utilize the 3D characteristic of hyperspectral image (HSI) and generalize well to the test data given only limited labeled training samples (i.e., small training dataset), have long been the research objective for HSI classification problem. Witnessing the success of deep-learning-based methods, a cube-pair-based convolutional neural networks (CNN) classification architecture is proposed to cope this objective in this study, where cube-pair is used to address the small training dataset problem as well as preserve the 3D local structure of HSI data. Within this architecture, a 3D fully convolutional network is further modeled, which has less parameters compared with traditional CNN. Provided the same amount of training samples, the modeled network can go deeper than traditional CNN and thus has superior generalization ability. Experimental results on several HSI datasets demonstrate that the proposed method has superior classification results compared with other state-of-the-art competing methods.


Reconceptualizing Instructional Supervision for 3rd Millennium School Systems.


title: A computational intelligence scheme for the prediction of the daily peak load

abstract: Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to privatization and deregulation of the power industry, accurate electricity forecasting has become an important research area for efficient electricity production. This paper presents a time series approach for mid-term load forecasting (MTLF) in order to predict the daily peak load for the next month. The proposed method employs a computational intelligence scheme based on the self-organizing map (SOM) and support vector machine (SVM). According to the similarity degree of the time series load data, SOM is used as a clustering tool to cluster the training data into two subsets, using the Kohonen rule. As a novel machine learning technique, the support vector regression (SVR) is used to fit the testing data based on the clustered subsets, for predicting the daily peak load. Our proposed SOM-SVR load forecasting model is evaluated in MATLAB on the electricity load dataset provided by the Eastern Slovakian Electricity Corporation, which was used in the 2001 European Network on Intelligent Technologies (EUNITE) load forecasting competition. Power load data obtained from (i) Tenaga Nasional Berhad (TNB) for peninsular Malaysia and (ii) PJM for the eastern interconnection grid of the United States of America is used to benchmark the performance of our proposed model. Experimental results obtained indicate that our proposed SOM-SVR technique gives significantly good prediction accuracy for MTLF compared to previously researched findings using the EUNITE, Malaysian and PJM electricity load datasets.


title: Electrocatalytic properties of prussian blue nanoparticles supported on poly(m-aminobenzenesulphonic acid)-functionalised single-walled carbon nanotubes towards the detection of dopamine.

abstract: Edged plane pyrolytic graphite electrode (EPPGE) was modified with and without Prussian blue (PB) nanoparticles and polyaminobenzene sulphonated single-walled carbon nanotubes (SWCNTPABS) using the chemical deposition method. The electrodes were characterised using microscopy, spectroscopy and electrochemical techniques. Results showed that edged plane pyrolytic graphite-single-walled carbon nanotubes-prussian blue (EPPGE-SWCNT-PB) electrode gave the best dopamine (DA) current response, which increases with increasing PB layers. The catalytic rate constant of 1.69 × 10(5)mol(-1)cm(3)s(-1), Tafel value of 112 mV dec(-1), and limit of detection of DA (2.8 nM) were obtained. Dopamine could be simultaneously detected with ascorbic acid. The electrode was found to be electrochemically stable, reusable and can be used for the analysis of DA in real drug samples.


title: Synthesis of developmentally regulated proteins in Dictyostelium discoideum which are dependent on continued cell-cell interaction

abstract: Abstract In the previous paper we showed that the major changes in the pattern of protein synthesis during differentiation of Dictyostelium discoideum occur during the 4-hr period when the cells are forming tight, visible aggregates. During this time, synthesis of 10 discrete polypeptides made by preaggregation cells ceases or is reduced considerably, and synthesis of 40 new proteins is induced. Induction or cessation of synthesis of these proteins was parallelled by the appearance or disappearance of the corresponding messenger RNAs. In this paper we show that many of these changes are induced by continued cell-cell contact. None of these occurs in aggregation-competent cells kept in suspension culture, but changes do take place when such cells are allowed to form tight aggregates. Disaggregation of cells causes cessation of synthesis of “aggregation-stage” proteins and reinduction of synthesis of polypeptides characteristic of preaggregation cells.


title: Refine pedestrian detections by referring to features in different ways

abstract: The performance of object detection has been improved as the success of deep architectures. The main algorithm predominantly used for general detection is Faster R-CNN because of their high accuracy and fast inference time. In pedestrian detection, Region Proposal Network (RPN) itself which is used for region proposals in Faster R-CNN can be used as a pedestrian detector. Also, RPN even shows better performance than Faster R-CNN for pedestrian detection. However, RPN generates severe false positives such as high score backgrounds and double detections because it does not have downstream classifier. From this observations, we made a network to refine results generated from the RPN. Our Refinement Network refers to the feature maps of the RPN and trains the network to rescore severe false positives. Also, we found that different type of feature referencing method is crucial for improving performance. Our network showed better accuracy than RPN with almost same speed on Caltech Pedestrian Detection benchmark.


title: Network Intrusion Detection System for Denial of Service Attack based on Misuse Detection

abstract: In a wireless network system the security is a main concern for a user. It is basically suffering from mainly two security attacks i) Virus Attack ii) Intruders. Intruder does not only mean it want to hack the private information over the network, it also includes using a node bandwidth and increasing the Delay of Service for other host over the network. This paper is basically based on such type of attack. This paper reviews the comparison of different Intrusion Detection System. On the behalf of the reviewed work we proposed a new Network Intrusion System that will mainly detects the most prominent attack of Wireless Network i.e. DoS Attack. The proposed system is an intelligent system that will detect the intrusion dynamically on the bases of Misuse Detection which has very less false negative. The system not only detects the intruders by the IP address, it detects the system with its contents also.


title: Scene Grammars, Factor Graphs, and Belief Propagation

abstract: We consider a class of probabilistic grammars for generating scenes with multiple objects. Probabilistic scene grammars capture relationships between objects using compositional rules that provide important contextual cues for inference with ambiguous data. We show how to represent the distribution defined by a probabilistic scene grammar using a factor graph. We also show how to efficiently perform message passing in this factor graph. This leads to an efficient approach for inference with a grammar model using belief propagation as the underlying computational engine. Inference with belief propagation naturally combines bottom-up and top-down contextual information and leads to a robust algorithm for aggregating evidence. We show experiments on two different applications to demonstrate the generality of the framework. The first application involves detecting curves in noisy images, and we address this problem using a grammar that generates a collection of curves using a first-order Markov process. The second application involves localizing faces and parts of faces in images. In this case, we use a grammar that captures spatial relationships between the parts of a face. In both applications the same framework leads to robust inference algorithms that can effectively combine weak local information to reason about a scene.


title: Clinical Case Discussion: Combat PTSD and Substance Use Disorders.

abstract: his clinical case discussion focuses on a veteran of theIraq war with alcohol dependence, comorbid post-trau-matic stress disorder (PTSD) and some depressive symptoms,and traumatic brain injury (TBI). The case illustrates the useof exposure therapy for PTSD via telehealth technology.After the case presentation, experts in the ﬁelds of PTSD andaddiction provide discussion.


title: Big data based retail recommender system of non E-commerce

abstract: Recommender system, as a means of achieving precision marketing, has been widely used and brought about significant benefits in modern ecommerce systems. However, there is a lack of study on the applying of recommender system to traditional non e-commerce retailing mode. This paper presents a retail recommender model based on collaborative filtering, and designs the corresponding distributed computing algorithm on MapReduce, so as to implement a big data based retail recommender system. The big data mechanism helps the system do scalable data processing easily. Experimental results show that the system is effective for the estimation of retail sales for each store and product. As a result, non ecommerce enterprises could benefit from this novel way of precision marketing supports.


title: Developing and assessing college student teamwork skills

abstract: This chapter describes research on team member contributions to overall team effectiveness, and various applications of this research to developing and assessing teamwork by students on team and group projects and assignments.


title: A Novel Rate Control Framework for SIFT/SURF Feature Preservation in H.264/AVC Video Compression

abstract: This paper presents a novel rate control framework for H.264/Advanced Video Coding-based video coding that improves the preservation of gradient-based features like scale-invariant feature transform or speeded up robust feature compared with the default rate control algorithm in the JM reference software. First, a criterion (matching score) for feature preservation on the basis of the bag-of-features concept is proposed. Then, the matching scores are collected as a function of the quantization parameters and analyzed for different feature types. With this analysis, macroblocks are categorized into different groups before encoding. Our rate control algorithm assigns different quantization parameters to each group according to the importance of the group for feature extraction. The experimental results show that our rate control algorithm achieves the desired target bit rate, and more features are preserved compared with videos encoded using the default rate control. The proposed approach not only improves feature preservation, but also leads to a noticeable performance improvement in a real image retrieval system. The rate control framework proposed in this paper is fully standard compatible.


title: The Role of Network Analysis in Industrial and Applied Mathematics: A Physical-Applied-Mathematics Perspective

abstract: Many problems in industry --- and in the social, natural, information, and medical sciences --- involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. The study of networks is concerned explicitly with connectivity between different entities, and it has become very prominent in industrial settings, an importance that has intensified amidst the modern data deluge. In this commentary, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry. We focus, in particular, on discussing a physical-applied-mathematics approach to the study of networks. We also discuss several of our own collaborations with industry on projects in network analysis.


title: Physical Activity and Gynecologic Cancer Prevention

abstract: This chapter reviews the findings from epidemiologic studies of the associations of physical activity with gynecologic cancers, including those of the endometrium, ovaries, and cervix, and the biologic mechanisms mediating the associations. The epidemiologic evidence to date suggests that physical activity probably protects against endometrial cancer, with a risk reduction of about 20–30% for those with the highest levels of physical activity compared to those with the lowest levels, and that light to moderate physical activity including housework, gardening, or walking for transportation may reduce risk. The role of physical activity in ovarian cancer development remains uncertain, as findings from these studies have been inconsistent with about half the studies suggesting physical activity modestly decreases risk and about half the studies suggesting no association. A recent meta-analysis of studies examining recreational physical activity with ovarian cancer risk estimated a 20% reduced risk for the most active versus least active women. There is mounting evidence that sedentary behaviors such as sitting time probably increase risk of endometrial and ovarian cancers. Overall, there is insufficient evidence to draw a conclusion on a possible role of physical activity in the development of cervical cancer, although a modest influence on risk is possible through effects on sex steroid hormones and immune function. The biologic evidence provides strong support for a protective role of physical activity on cancer of the endometrium, and moderate support for cancer of the ovaries, as these cancers have a strong hormonal etiology. The more established biologic mechanisms that are supported by epidemiologic and experimental data involve endogenous sex hormone levels, insulin-mediated pathways, and maintenance of energy balance.


title: A TMDTO Attack Against Lizard

abstract: Lizard is a very recently proposed lightweight stream cipher that claims 60 bit security against distinguishing (related to state recovery) and 80 bit security against key recovery attack. This cipher has 121 bit state size. In this paper, we first note that using $\psi$ key stream bits one can recover $\psi$ unknown bits of the state when $\tau$ state bits are fixed to a specific pattern. This is made possible by guessing the remaining state bits. We present certain values of $\psi, \tau$ based on the state size that helps in mounting a generic conditional TMDTO attack following the BSW sampling. For Lizard, we obtain the preprocessing complexity as $2^{67}$ , and the maximum of Data, Time and Memory complexity during the online phase as $2^{54}$ . The parameters in the online phase are significantly less than $2^{60}$ .


title: Handwritten Chinese text line segmentation by clustering with distance metric learning

abstract: Separating text lines in unconstrained handwritten documents remains a challenge because the handwritten text lines are often un-uniformly skewed and curved, and the space between lines is not obvious. In this paper, we propose a novel text line segmentation algorithm based on minimal spanning tree (MST) clustering with distance metric learning. Given a distance metric, the connected components (CCs) of document image are grouped into a tree structure, from which text lines are extracted by dynamically cutting the edges using a new hypervolume reduction criterion and a straightness measure. By learning the distance metric in supervised learning on a dataset of pairs of CCs, the proposed algorithm is made robust to handle various documents with multi-skewed and curved text lines. In experiments on a database with 803 unconstrained handwritten Chinese document images containing a total of 8,169 lines, the proposed algorithm achieved a correct rate 98.02% of line detection, and compared favorably to other competitive algorithms.


title: Type Synthesis of 3-DOF RPR-Equivalent Parallel Mechanisms

abstract: The moving platform of an RPR-equivalent parallel mechanism (PM) can undergo a 3-degree-of-freedom (DOF) motion that is the product of a rotation followed by a translation and another rotation. A 5-DOF hybrid parallel manipulator can be developed by adding an x-y gantry or an RR serial mechanism to an RPR-equivalent PM, which is suitable for manipulations requiring high rigidity and accuracy with good dexterity along surfaces of the 3-D space. This paper investigates the type synthesis of the RPR-equivalent PM. First, the RPR motion is briefly discussed. Then, the kinematic bonds of limb chains and their mechanical generators are presented. Structural conditions for constructing an RPR-equivalent PM are presented. Furthermore, the RPR-equivalent PMs are classified into several categories, depending on the DOF of its limb chains. Numerous new architectures of the RPR-equivalent PMs are synthesized.


title: Managing Market Thickness in Online B2B Markets

abstract: Platforms can obtain sizable returns by operationally managing their market thickness, i.e., the availability of supply-side inventory. Using data from a natural experiment on a major B2B auction platform specializing in the $424 billion secondary market for liquidating retail merchandise, we find that thickening the platformâ€™s market by consolidating the ending times of auctions to certain weekdays substantially increases its revenue by roughly 6.5%, due primarily to the biddersâ€™ participation frictions. We study two complementary design levers to calibrate and control the platformâ€™s market thickness in the face of complex demand-side decision making: (i) its listing policy, which determines the ending times of auctions, and (ii) a recommendation system. To optimize these design decisions, we first develop a structural model to characterize how bidders form expectations and respond to the imminent availability of auctions in equilibrium, including how frequently they visit the platform, in which auctions they choose to participate, and their bidding strategies. In calibrating its market thickness, the platform trades off increasing bidder participation in each auction by appropriately thickening the market (demand-side competition) against limiting the extent to which auctions for substitutable goods ultimately cannibalize one another under thicker market conditions (supply-side competition). Using our structural estimates, we illustrate how the platform can optimize its listing policy as a function of the incoming liquidation inventory and its bidder pool so as to achieve a supply-demand sweet spot, thereby increasing its revenue significantly relative to having auctions end after a fixed time. Furthermore, we find that real-time recommendations sent on the marketâ€™s thickest days would add 3% revenue on such days (on top of the benefits obtained by optimizing the platformâ€™s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders.


title: Peeking Beneath the Hood of Uber

abstract: Recently, Uber has emerged as a leader in the "sharing economy". Uber is a "ride sharing" service that matches willing drivers with customers looking for rides. However, unlike other open marketplaces (e.g., AirBnB), Uber is a black-box: they do not provide data about supply or demand, and prices are set dynamically by an opaque "surge pricing" algorithm. The lack of transparency has led to concerns about whether Uber artificially manipulate prices, and whether dynamic prices are fair to customers and drivers. In order to understand the impact of surge pricing on passengers and drivers, we present the first in-depth investigation of Uber. We gathered four weeks of data from Uber by emulating 43 copies of the Uber smartphone app and distributing them throughout downtown San Francisco (SF) and midtown Manhattan. Using our dataset, we are able to characterize the dynamics of Uber in SF and Manhattan, as well as identify key implementation details of Uber's surge price algorithm. Our observations about Uber's surge price algorithm raise important questions about the fairness and transparency of this system.


title: Llama: leveraging columnar storage for scalable join processing in the MapReduce framework

abstract: To achieve high reliability and scalability, most large-scale data warehouse systems have adopted the cluster-based architecture. In this paper, we propose the design of a new cluster-based data warehouse system, LLama, a hybrid data management system which combines the features of row-wise and column-wise database systems. In Llama, columns are formed into correlation groups to provide the basis for the vertical partitioning of tables. Llama employs a distributed file system (DFS) to disseminate data among cluster nodes. Above the DFS, a MapReduce-based query engine is supported. We design a new join algorithm to facilitate fast join processing. We present a performance study on TPC-H dataset and compare Llama with Hive, a data warehouse infrastructure built on top of Hadoop. The experiment is conducted on EC2. The results show that Llama has an excellent load performance and its query performance is significantly better than the traditional MapReduce framework based on row-wise storage.


title: Moments and Absolute Moments of the Normal Distribution

abstract: We present formulas for the (raw and central) moments and absolute moments of the normal distribution. We note that these results are not new, yet many textbooks miss out on at least some of them. Hence, we believe that it is worthwhile to collect these formulas and their derivations in these notes.


title: A stochastic coordinate descent primal-dual algorithm and applications

abstract: Based on the idea of randomized coordinate descent of {\alpha}-averaged operators, we provide a randomized primal-dual algorithm. The algorithm builds upon a variant of a recent (deterministic) algorithm proposed by Vu and Condat. Next, we address two applications of our method. (i) In the case of stochastic approximation methods, the algorithm can be used to split a composite objective function into blocks, each of these blocks being processed sequentially by the computer. (ii) In the case of distributed optimization, we consider a set of N agents having private composite objective functions and seeking to find a consensus on the minimum of the aggregate objective. In that case, our method yields a distributed iterative algorithm where each agent use both local computations and message passing in an asynchronous manner. Numerical results demonstrate the attractive performance of the method in the framework of large scale machine learning applications.


Hybrid text mining for finding terms and their abbreviations


title: eHealth in Europe – Status and Challenges

abstract: OBJECTIVES ::: To present European reflections on the concept of eHealth and emerging challenges related to further development of eHealth in Europe. ::: ::: ::: METHODS ::: A survey with 10 questions was distributed to representatives of the national member associations of the European Federation of Medical Informatics (EFMI). ::: ::: ::: RESULTS ::: The results document a shift from a constricting ICT-orientation to development of the entire health system where eHealth strategies, organizational change, and appropriate technological infrastructure are singled out as important aspects. ::: ::: ::: CONCLUSION ::: There are urgent needs to ensure that eHealth strategies and policies for further design and deployment of eHealth applications support sociable services and innovations in health care.


title: Layout-sensitive language extensibility with SugarHaskell

abstract: Programmers need convenient syntax to write elegant and concise programs. Consequently, the Haskell standard provides syntactic sugar for some scenarios (e.g., do notation for monadic code), authors of Haskell compilers provide syntactic sugar for more scenarios (e.g., arrow notation in GHC), and some Haskell programmers implement preprocessors for their individual needs (e.g., idiom brackets in SHE). But manually written preprocessors cannot scale: They are expensive, error-prone, and not composable. Most researchers and programmers therefore refrain from using the syntactic notations they need in actual Haskell programs, but only use them in documentation or papers. We present a syntactically extensible version of Haskell, SugarHaskell, that empowers ordinary programmers to implement and use custom syntactic sugar. ::: Building on our previous work on syntactic extensibility for Java, SugarHaskell integrates syntactic extensions as sugar libraries into Haskell's module system. Syntax extensions in SugarHaskell can declare arbitrary context-free and layout-sensitive syntax. SugarHaskell modules are compiled into Haskell modules and further processed by a Haskell compiler. We provide an Eclipse-based IDE for SugarHaskell that is extensible, too, and automatically provides syntax coloring for all syntax extensions imported into a module. ::: We have validated SugarHaskell with several case studies, including arrow notation (as implemented in GHC) and EBNF as a concise syntax for the declaration of algebraic data types with associated concrete syntax. EBNF declarations also show how to extend the extension mechanism itself: They introduce syntactic sugar for using the declared concrete syntax in other SugarHaskell modules.


title: A Survey on Context-aware systems

abstract: Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behaviour accordingly. Especially in combination with mobile devices, these mechanisms are of high value and are used to increase usability tremendously. In this paper, we present common architecture principles of context-aware systems and derive a layered conceptual design framework to explain the different elements common to most context-aware architectures. Based on these design principles, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyse important aspects in context-aware computing on the basis of the presented systems.


title: Meta-analysis of scientometric research of knowledge management: discovering the identity of the discipline

abstract: Purpose – The purpose of this study is to conduct a meta-analysis of prior scientometric research of the knowledge management (KM) field. Design/methodology/approach – A total of 108 scientometric studies of the KM discipline were subjected to meta-analysis techniques. Findings – The overall volume of scientometric KM works has been growing, reaching up to ten publications per year by 2012, but their key findings are somewhat inconsistent. Most scientometric KM research is published in non-KM-centric journals. The KM discipline has deep historical roots. It suffers from a high degree of over-differentiation and is represented by dissimilar research streams. The top six most productive countries for KM research are the USA, the UK, Canada, Germany, Australia, and Spain. KM exhibits attributes of a healthy academic domain with no apparent anomalies and is progressing towards academic maturity. Practical implications – Scientometric KM researchers should use advanced empirical methods, become aware of prior ...


title: Plantar pressures are elevated in the neuroischemic and the neuropathic diabetic foot.

abstract: OBJECTIVE ::: Clinical observation has noted that diabetic neuropathic ulcers occur frequently on the plantar surface, whereas neuroischemic ulcers seem to occur often on the foot margins. The reason for this difference in the site of ulceration is unknown, but it may be related to differences in pressure loading. The aim of the study was to compare vertical in-shoe foot pressures measured during walking (using the F-SCAN system) in four groups of patients whose degree of neuropathy was measured by vibration perception threshold (VPT). ::: ::: ::: RESEARCH DESIGN AND METHODS ::: Subjects included 14 neuroischemic diabetic patients (VPT 29.3 +/- 13.5 V) with history of ulceration on the margins of the foot, 18 patients with neuropathy alone (VPT 38.7 +/- 12.7 V) and previous history of ulceration on the plantar surface, 10 diabetic control patients (VPT 9.9 +/- 2.7 V), and 15 nondiabetic control subjects (VPT 7.0 +/- 0.5 V). ::: ::: ::: RESULTS ::: When compared with the other three groups, neuroischemic patients had higher foot pressures when measured as mean peak pressures and highest peak pressures under four areas of the foot: medial and lateral forefoot, hallux, and heel. Furthermore, when measuring the maximum pressures developed at any point under the plantar surface, the neuroischemic patients also had the most elevated pressures (757.6 +/- 135.9 kPa), significantly higher than those found in the neuropathic group (482.8 +/- 68.6 kPa, P = 0.04) and in both diabetic control patients (310.2 +/- 34.7 kPa, P = 0.008) and nondiabetic controls subjects (365.1 +/- 49.8 kPa, P = 0.007). ::: ::: ::: CONCLUSIONS ::: Despite having increased plantar pressures and a comparable degree of neuropatny, the neuroischemic patients did not have a history of ulceration on the plantar surface. These observations may have relevance to different mechanisms of ulcer formation in the neuroischemic and neuropathic foot.


title: Rapid Prototyping for Wildlife and Ecological Monitoring

abstract: Wildlife tracking and ecological monitoring are important for scientific monitoring, wildlife rehabilitation, disease control, and sustainable ecological development. Yet technologies for both of them are expensive and not scalable. Also it is important to tune the monitoring system parameters for different species to adapt their behavior and gain the best result of monitoring. In this paper, we propose using wireless sensor networks to build both short term and long term wildlife and ecological monitoring systems. For the short term system, everything used is off-the-shelf and can be easily purchased from the market. We suggest that before establishing a large scale wildlife/ecological monitoring network, it is worthwhile to first spend a short period of time constructing a rapid prototype of the targeted network. Through verifying the correctness of the prototype network, ecologists can find potential problems, avoid total system failure, and use the best-tuned parameters for the long-term monitoring network.


Factorization Machines Factorized Polynomial Regression Models


title: Concrete-Pictorial-Abstract: Surveying its origins and charting its future

abstract: The Concrete-Pictorial-Abstract (CPA) approach, based on Bruner's conception of the enactive, iconic and symbolic modes of representation, is a well-known instructional heuristic advocated by the Singapore Ministry of Education since early 1980's. Despite its ubiquity as a teaching strategy throughout the entire mathematics education community in Singapore, it is somewhat surprising to see a lack of an account of its theoretical roots. This paper is an attempt to contribute to this discussion on the CPA strategy and its potential role in continuing advancement of quality mathematics education.


Phylogenetic diversity and abundance of bacteria from surface microlayer and subsurface water in eutrophic lake


title: Chinese paper-cut generated from human portrait

abstract: This paper presents a software package that renders artistic paper-cut of human portrait. It is an application software package suitable for the mobile devices, e.g., the iPad. The face fidelity is an essential factor in creating a portrait, while in the traditional algorithms, rendering portrait paper-cut is considerd as an inhomogeneous binarization process. Specifically, the binarization process is inhomogeneous for different facial areas, which always cannot correspond strictly to the grayscale, and lead to the edge being unsmoothed. In order to address this issue and make the portrait more similar to the person shown in a photograph, this paper presents an improved paper-cut algorithm. We first use a dynamic thresholding method to binarize the facial components, such as nose, eyes. Then, the principle of temple matching is employed to find a template for every facial component from a pre-prepared template library, which is closest to the real component. In addition, the software package on the iPad is not a simple migration from that on PC. Compared with the software on PC, our package has a friendly interface and is easier for operating. Experimental results show that we can get a more realistic portrait paper-cut using the package on the iPad.


title: Video OCR: indexing digital news libraries by recognition of superimposed captions

abstract: The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multiframe integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.


title: A fully differential CMOS potentiostat

abstract: A CMOS potentiostat for chemical sensing in a noisy environment is presented. The potentiostat measures bidirectional electrochemical redox currents proportional to the concentration of a chemical down to pico-ampere range. The fully differential architecture with differential recording electrodes suppresses the common mode interference. A 200µm×200µm prototype was fabricated in a standard 0.35µm standard CMOS technology and yields a 70dB dynamic range. The in-channel analog-to-digital converter (ADC) performs 16-bit current-to-frequency quantization. The integrated potentiostat functionality is validated in electrical and electrochemical experiments.


title: Microfinance as a pathway out of poverty and viable strategy for livelihood diversification in Ethiopia

abstract: Lack of capital is among the prominent factors which hinder livelihood diversification mainly in developing countries. Microfinance intervention is taken as a strategy to overcome such constraints and it is seen as one of the most efficient instruments to fight poverty in poorer countries. The objective of this paper is to examine the role of access to microfinance as a pathway out of poverty and livelihood diversification. Empirical evidences and secondary data from different articles, proceedings and research works have been critically examined. Microfinance intervention brings livelihood diversification in urban than rural one. In the rural areas, even though there are some promising contributions like asset building, women empowerment and an increase in income, most of the loan is used for consumption smoothening. Using the loan for non-productive purpose; lack of appropriate skill training and lack of follow up of the clients; lack of flexibility in the repayment period and low saving interest; low amount of loan allowed at a time are some of the commonly mentioned problems. Concerned bodies are expected to take in to consideration of all these issues and comprehensive impact assessment has to be done so as to examine the overall impact of microfinance and to identify prominent problems.


title: Quantum dots versus organic dyes as fluorescent labels

abstract: Suitable labels are at the core of luminescence and fluorescence imaging and sensing. One of the most exciting, yet also controversial, advances in label technology is the emerging development of quantum dots (QDs)—inorganic nanocrystals with unique optical and chemical properties but complicated surface chemistry—as in vitro and in vivo fluorophores. Here we compare and evaluate the differences in physicochemical properties of common fluorescent labels, focusing on traditional organic dyes and QDs. Our aim is to provide a better understanding of the advantages and limitations of both classes of chromophores, to facilitate label choice and to address future challenges in the rational design and manipulation of QD labels.


title: A Strategy for an Uncompromising Incremental Learner

abstract: Multi-class supervised learning systems require the knowledge of the entire range of labels they predict. Often when learnt incrementally, they suffer from catastrophic forgetting. To avoid this, generous leeways have to be made to the philosophy of incremental learning that either forces a part of the machine to not learn, or to retrain the machine again with a selection of the historic data. While these hacks work to various degrees, they do not adhere to the spirit of incremental learning. In this article, we redefine incremental learning with stringent conditions that do not allow for any undesirable relaxations and assumptions. We design a strategy involving generative models and the distillation of dark knowledge as a means of hallucinating data along with appropriate targets from past distributions. We call this technique, phantom sampling.We show that phantom sampling helps avoid catastrophic forgetting during incremental learning. Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting. We apply these strategies to competitive multi-class incremental learning of deep neural networks. Using various benchmark datasets and through our strategy, we demonstrate that strict incremental learning could be achieved. We further put our strategy to test on challenging cases, including cross-domain increments and incrementing on a novel label space. We also propose a trivial extension to unbounded-continual learning and identify potential for future development.


title: Modeling of 4-element planar inverted F-antenna array for 5G applications

abstract: A MIMO antenna array design comprising of four PIFA components has been presented. The arrangement of the four components has been selected after trying various permutations to achieve an optimum isolation and gain among the elements in the array, compared to the existing ones. The main objective of the paper is to minimize the isolation among the antenna array elements without compromising the overall gain.


title: 3D Reconstruction of Indoor Scenes Based on Feature and Graph Optimization

abstract: In this paper, we propose an approach of visual SLAM (Simultaneous localization and mapping) system, in which we build 3D scene models and estimate the camera poses at the same time. We detect SURF feature points from RGB images and use their relevant depth data to build 3D correspondences. With these correspondences, we align consecutive frames together using RANSAC, while ICP is performed when RANSAC fails. Keyframes are selected based on visual overlap between current frame and the latest keyframe. Furthermore, we detect loop closures between current keyframe and selected candidates of keyframes. Among these candidates, we perform a radius search to further prune the search space. With both sequential constraints and loop closure constraints, a pose graph is created and optimized using a robust global optimizer based on line processes.


title: Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

abstract: Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.