title: A Fully Integrated Reconfigurable Self-Startup RF Energy-Harvesting System With Storage Capability

text: This paper introduces a fully integrated RF energy-harvesting system. The system can simultaneously deliver the current demanded by external dc loads and store the extra energy in external capacitors, during periods of extra output power. The design is fabricated in 0.18- $\mu \text{m}$  CMOS technology, and the active chip area is 1.08 mm2. The proposed self-startup system is reconfigurable with an integrated LC matching network, an RF rectifier, and a power management/controller unit, which consumes 66–157 nW. The required clock generation and the voltage reference circuit are integrated on the same chip. Duty cycle control is used to operate for the low input power that cannot provide the demanded output power. Moreover, the number of stages of the RF rectifier is reconfigurable to increase the efficiency of the available output power. For high available power, a secondary path is activated to charge an external energy storage element. The measured RF input power sensitivity is −14.8 dBm at a 1-V dc output.


title: HDTLR: A CNN based Hierarchical Detector for Traffic Lights

text: Reliable traffic light detection is one crucial key component for autonomous driving in urban areas. This includes the extraction of direction arrows contained within the traffic lights as an autonomous car will need this information for selecting the traffic light corresponding to its current lane. Current state of the art traffic light detection systems are not able to provide such information. Within this work we present a hierarchical traffic light detection algorithm, which is able to detect traffic lights and determine their state and contained direction information within one CNN forward pass. This Hierarchical DeepTLR (HDTLR) outperforms current state of the art traffic light detection algorithms in state aware detection and can detect traffic lights with direction information down to a size of 4 pixel in width at a frequency of 12 frames per second.


title: Implementation of behavior change techniques in mobile applications for physical activity.

text:
BACKGROUND
Mobile applications (apps) for physical activity are popular and hold promise for promoting behavior change and reducing non-communicable disease risk. App marketing materials describe a limited number of behavior change techniques (BCTs), but apps may include unmarketed BCTs, which are important as well.

PURPOSE
To characterize the extent to which BCTs have been implemented in apps from a systematic user inspection of apps.

METHODS
Top-ranked physical activity apps (N=100) were identified in November 2013 and analyzed in 2014. BCTs were coded using a contemporary taxonomy following a user inspection of apps.

RESULTS
Users identified an average of 6.6 BCTs per app and most BCTs in the taxonomy were not represented in any apps. The most common BCTs involved providing social support, information about others' approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. A latent class analysis of BCT configurations revealed that apps focused on providing support and feedback as well as support and education.

CONCLUSIONS
Contemporary physical activity apps have implemented a limited number of BCTs and have favored BCTs with a modest evidence base over others with more established evidence of efficacy (e.g., social media integration for providing social support versus active self-monitoring by users). Social support is a ubiquitous feature of contemporary physical activity apps and differences between apps lie primarily in whether the limited BCTs provide education or feedback about physical activity.


title: Future affective technology for autism and emotion communication.

text: People on the autism spectrum often experience states of emotional or cognitive overload that pose challenges to their interests in learning and communicating. Measurements taken from home and school environments show that extreme overload experienced internally, measured as autonomic nervous system (ANS) activation, may not be visible externally: a person can have a resting heart rate twice the level of non-autistic peers, while outwardly appearing calm and relaxed. The chasm between what is happening on the inside and what is seen on the outside, coupled with challenges in speaking and being pushed to perform, is a recipe for a meltdown that may seem to come 'out of the blue', but in fact may have been steadily building. Because ANS activation both influences and is influenced by efforts to process sensory information, interact socially, initiate motor activity, produce meaningful speech and more, deciphering the dynamics of ANS states is important for understanding and helping people on the autism spectrum. This paper highlights advances in technology that can comfortably sense and communicate ANS arousal in daily life, allowing new kinds of investigations to inform the science of autism while also providing personalized feedback to help individuals who participate in the research.


title: Toward Massive Machine Type Cellular Communications

text: Cellular networks have been engineered and optimized to carrying ever-increasing amounts of mobile data, but over the last few years, a new class of applications based on machine-centric communications has begun to emerge. Automated devices such as sensors, tracking devices, and meters, often referred to as machine-to-machine (M2M) or machine-type communications (MTC), introduce an attractive revenue stream for mobile network operators, if a massive number of them can be efficiently supported. The novel technical challenges posed by MTC applications include increased overhead and control signaling as well as diverse application-specific constraints such as ultra-low complexity, extreme energy efficiency, critical timing, and continuous data intensive uploading. This article explains the new requirements and challenges that large-scale MTC applications introduce, and provides a survey of key techniques for overcoming them. We focus on the potential of 4.5G and 5G networks to serve both the high data rate needs of conventional human-type communication (HTC) subscribers and the forecasted billions of new MTC devices. We also opine on attractive economic models that will enable this new class of cellular subscribers to grow to its full potential.


title: A class D output stage with zero dead time

text: An integrated class-D output stage has been realized with zero dead time, thereby removing one of the dominant sources of distortion in class-D amplifiers. Dead time is eliminated through proper dimensioning of the power transistor drivers and accurate matching of switch timing. Open-loop distortion of this output stage stays below 0.1% up to 35 W.


title: Partially Observable Reinforcement Learning for Intelligent Transportation Systems

text: Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as ‘Partially Detected Intelligent Transportation Systems’. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.


title: Fuzzy & Datamining based Disease Prediction Using K-NN Algorithm

text: Disease diagnosis is one of the most important applications of such system as it is one of the leading causes of deaths all over the world. Almost all system predicting disease use inputs from complex tests conducted in labs and none of the system predicts disease based on the risk factors such as tobacco smoking, alcohol intake, age, family history, diabetes, hypertension, high cholesterol, physical inactivity, obesity. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour (KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of disease specially heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.


title: ENHANCEMENT STRATEGIES FOR FRAME-TO-FRAME UAS STEREO VISUAL ODOMETRY

text: Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements. * Corresponding author


title: An Empirical Analysis of Deep Network Loss Surfaces

text: The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find local minima.


title: Now it's personal: on abusive game design

text: In this paper, we introduce the concept of abusive game design as an attitude towards creating games -- an aesthetic practice that operates as a critique of certain conventionalisms in popular game design wisdom. We emphasize that abusive game design is, at its core, about spotlighting the dialogic relation between player and designer.


title: Classification of Multiple-Sentence Questions

text: Conventional QA systems cannot answer to the questions composed of two or more sentences. Therefore, we aim to construct a QA system that can answer such multiple-sentence questions. As the first stage, we propose a method for classifying multiple-sentence questions into question types. Specifically, we first extract the core sentence from a given question text. We use the core sentence and its question focus in question classification. The result of experiments shows that the proposed method improves F-measure by 8.8% and accuracy by 4.4%.


title: Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models

text: Text mining the relations between chemicals and proteins is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This manuscript describes our submission, which is an ensemble of three systems, including a Support Vector Machine, a Convolutional Neural Network, and a Recurrent Neural Network. Their output is combined using a decision based on majority voting or stacking. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an f-score of 0.6410, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature. Our submission achieved the highest performance in the task during the 2017 challenge. Keywords—relation extraction; deep learning; chemical; protein


title: EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art

text: Many Internet of Things (IoT) technologies have been used in applications for money flow, logistics flow, people flow, interactive art design, and so on. To manage these increasing disparate devices and connectivity options, ETSI has specified end-to-end machine-to-machine (M2M) system architecture for IoT applications. Based on this architecture, we develop an IoT EasyConnect system to manage IoT devices. In our approach, an IoT device is characterized by its “features” (e.g., temperature, vibration, and display) that are manipulated by the network applications. If a network application handles the individual device features independently, then we can write a software module for each device feature, and the network application can be simply constructed by including these brick-like device feature modules. Based on the concept of device feature, brick-like software modules can provide simple and efficient mechanism to develop IoT device applications and interactions.


title: Inherent Resistance of Efficient ECC Designs against SCA Attacks

text: The Montgomery kP algorithm using Lopez-Dahab projective coordinates is a time and energy efficient way to perform the elliptic curve point multiplication. It even can provide resistance against simple power analysis attacks. Nevertheless, straight forward implementations of this algorithm are not resistant against differential power analysis attacks. But highly-efficient designs i.e. designs in which different functional units are operating in parallel can increase the resistance of ECC implementations against DPA. In this paper we demonstrate this fact on the examples of our 6 implementations of the Montgomery kP algorithm. We perform horizontal DPA attacks to demonstrate the fact that the resistance of the kP algorithm against side channel analysis attacks can be increased through its implementation as an efficient design.


title: The Theory of Correlation Formulas and Their Application to Discourse Coherence

text: The Winograd Schema Challenge (WSC) was proposed as a measure of machine intelligence. It boils down to anaphora resolution, a task familiar from computational linguistics. Research in linguistics and AI has coalesced around discourse coherence as the critical factor in solving this task, and the process of establishing discourse coherence relies fundamentally on world and commonsense knowledge. In this thesis, we build on an approach to establishing coherence on the basis of correlation. The utility of this approach lies in its conceptual clarity and ability to flexibly represent commonsense knowledge. We work to fill some conceptual holes with the Correlation Calculus approach. First, understanding the calculus in a vacuum is not straightfoward unless it has a precise semantics. Second, existing demonstrations of the Correlation Calculus on Winograd Schema Challenge problems have not been linguistically credible. We hope to ameliorate some—but by no means all—of the outstanding issues with the Correlation Calculus. We do so first by providing a precise semantics of the calculus, which relates our intuitive understanding of correlation with a precise notion involving probabilities. Second, we formulate the establishment of discourse coherence by correlation formulas within the framework of Discourse Representation Theory. This provides a more complete and linguistically credible account of the relationship between the Correlation Calculus, discourse coherence, and Winograd Schema Challenge problems.


title: Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League.

text: The aim of the present study was to analyze men's football competitions, trying to identify which game-related statistics allow to discriminate winning, drawing and losing teams. The sample used corresponded to 380 games from the 2008-2009 season of the Spanish Men's Professional League. The game-related statistics gathered were: total shots, shots on goal, effectiveness, assists, crosses, offsides commited and received, corners, ball possession, crosses against, fouls committed and received, corners against, yellow and red cards, and venue. An univariate (t-test) and multivariate (discriminant) analysis of data was done. The results showed that winning teams had averages that were significantly higher for the following game statistics: total shots (p < 0.001), shots on goal (p < 0.01), effectiveness (p < 0.01), assists (p < 0.01), offsides committed (p < 0.01) and crosses against (p < 0.01). Losing teams had significantly higher averages in the variable crosses (p < 0.01), offsides received (p < 0. 01) and red cards (p < 0.01). Discriminant analysis allowed to conclude the following: the variables that discriminate between winning, drawing and losing teams were the total shots, shots on goal, crosses, crosses against, ball possession and venue. Coaches and players should be aware for these different profiles in order to increase knowledge about game cognitive and motor solicitation and, therefore, to evaluate specificity at the time of practice and game planning. Key pointsThis paper increases the knowledge about soccer match analysis.Give normative values to establish practice and match objectives.Give applications ideas to connect research with coaches' practice.


title: Free radicals and antioxidants in human health: current status and future prospects.

text: Free radicals and related species have attracted a great deal of attention in recent years. They are mainly derived from oxygen (reactive oxygen species/ROS) and nitrogen (reactive nitrogen species/RNS), and are generated in our body by various endogenous systems, exposure to different physicochemical conditions or pathophysiological states. Free radicals can adversely alter lipids, proteins and DNA and have been implicated in aging and a number of human diseases. Lipids are highly prone to free radical damage resulting in lipid peroxidation that can lead to adverse alterations. Free radical damage to protein can result in loss of enzyme activity. Damage caused to DNA, can result in mutagenesis and carcinogenesis. Redox signaling is a major area of free radical research that is attracting attention. Nature has endowed us with protective antioxidant mechanisms- superoxide dismutase (SOD), catalase, glutathione, glutathione peroxidases and reductase, vitamin E (tocopherols and tocotrienols), vitamin C etc., apart from many dietary components. There are epidemiological evidences correlating higher intake of components/ foods with antioxidant abilities to lower incidence of various human morbidities or mortalities. Current research reveals the different potential applications of antioxidant/free radical manipulations in prevention or control of disease. Natural products from dietary components such as Indian spices and medicinal plants are known to possess antioxidant activity. Newer and future approaches include gene therapy to produce more antioxidants in the body, genetically engineered plant products with higher level of antioxidants, synthetic antioxidant enzymes (SOD mimics), novel biomolecules and the use of functional foods enriched with antioxidants.


title: Cultivate Self-Efficacy for Personal and Organizational Effectiveness

text: Bandura, A. (2000). Cultivate self-efficacy for personal and organizational effectiveness. In E.


title: Power LDMOS with novel STI profile for improved Rsp, BVdss, and reliability

text: The profile of shallow trench isolation (STI) is designed to improve LDMOS specific on-resistance (Rsp), BVDSS, safe operating area (SOA), and hot carrier lifetimes (HCL) in an integrated BiCMOS power technology. Silicon etch, liner oxidation and CMP processes are tuned to improve the tradeoffs in a power technology showing significant improvement to both p-channel and n-channel Rsp compared to devices fabricated with the STI profile inherited from the original submicron CMOS platform. Extensive TCAD and experiments were carried out to gain insight into the physical mechanisms and further improve device performance after STI process optimization. The final process and device structures yield SOAs that are limited only by thermal constraints up to rated voltages


title: A Hygroscopic Sensor Electrode for Fast Stabilized Non-Contact ECG Signal Acquisition

text: A capacitive electrocardiography (cECG) technique using a non-invasive ECG measuring technology that does not require direct contact between the sensor and the skin has attracted much interest. The system encounters several challenges when the sensor electrode and subject's skin are weakly coupled. Because there is no direct physical contact between the subject and any grounding point, there is no discharge path for the built-up electrostatic charge. Subsequently, the electrostatic charge build-up can temporarily contaminate the ECG signal from being clearly visible; a stabilization period (3-15 min) is required for the measurement of a clean, stable ECG signal at low humidity levels (below 55% relative humidity). Therefore, to obtain a clear ECG signal without noise and to reduce the ECG signal stabilization time to within 2 min in a dry ambient environment, we have developed a fabric electrode with embedded polymer (FEEP). The designed hygroscopic FEEP has an embedded superabsorbent polymer layer. The principle of FEEP as a conductive electrode is to provide humidity to the capacitive coupling to ensure strong coupling and to allow for the measurement of a stable, clear biomedical signal. The evaluation results show that hygroscopic FEEP is capable of rapidly measuring high-accuracy ECG signals with a higher SNR ratio.


title: A 3.9-ps RMS Precision Time-to-Digital Converter Using Ones-Counter Encoding Scheme in a Kintex-7 FPGA

text: A 3.9-ps time-interval rms precision and 277-M events/second measurement throughput time-to-digital converter (TDC) is implemented in a Xilinx Kintex-7 field programmable gate array (FPGA). Unlike previous work, the TDC is achieved with a multichain tapped-delay line (TDL) followed by an ones-counter encoder. The four normal TDLs merged together make the TDC bins very small, so that the time precision can be significantly improved. The ones-counter encoder naturally applies global bubble error correction to the output of TDL, thus the TDC design is relatively simple even when using FPGAs made with current advanced process technology. The TDC implementation is a generally applicable method that can simultaneously achieve high time precision and high measurement throughput.


title: Comparative Evaluation of Background Subtraction Algorithms in Remote Scene Videos Captured by MWIR Sensors

text: Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR.


title: Can You Spot the Fakes?: On the Limitations of User Feedback in Online Social Networks

text: Online social networks (OSNs) are appealing platforms for spammers and fraudsters, who typically use fake or compromised accounts to connect with and defraud real users. To combat such abuse, OSNs allow users to report fraudulent profiles or activity. The OSN can then use reporting data to review and/or limit activity of reported accounts. Previous authors have suggested that an OSN can augment its takedown algorithms by identifying a “trusted set” of users whose reports are weighted more heavily in the disposition of flagged accounts. Such identification would allow the OSN to improve both speed and accuracy of fake account detection and thus reduce the impact of spam on users. In this work we provide the first public, data-driven assessment of whether the above assumption is true: are some users better at reporting than others? Specifically, is reporting skill both measurable, i.e., possible to distinguish from random guessing; and repeatable, i.e., persistent over repeated sampling? Our main contributions are to develop a statistical framework that describes these properties and to apply this framework to data from LinkedIn, the professional social network. Our data includes member reports of fake profiles as well as the more voluminous, albeit weaker, signal of member responses to connection requests. We find that members demonstrating measurable, repeatable skill in identifying fake profiles do exist but are rare: at most 2.4% of those reporting fakes and at most 1.3% of those rejecting connection requests. We conclude that any reliable “trusted set” of members will be too small to have noticeable impact on spam metrics.


title: How to practice person‐centred care: A conceptual framework

text:
BACKGROUND
Globally, health-care systems and organizations are looking to improve health system performance through the implementation of a person-centred care (PCC) model. While numerous conceptual frameworks for PCC exist, a gap remains in practical guidance on PCC implementation.

METHODS
Based on a narrative review of the PCC literature, a generic conceptual framework was developed in collaboration with a patient partner, which synthesizes evidence, recommendations and best practice from existing frameworks and implementation case studies. The Donabedian model for health-care improvement was used to classify PCC domains into the categories of "Structure," "Process" and "Outcome" for health-care quality improvement.

DISCUSSION
The framework emphasizes the structural domain, which relates to the health-care system or context in which care is delivered, providing the foundation for PCC, and influencing the processes and outcomes of care. Structural domains identified include: the creation of a PCC culture across the continuum of care; co-designing educational programs, as well as health promotion and prevention programs with patients; providing a supportive and accommodating environment; and developing and integrating structures to support health information technology and to measure and monitor PCC performance. Process domains describe the importance of cultivating communication and respectful and compassionate care; engaging patients in managing their care; and integration of care. Outcome domains identified include: access to care and Patient-Reported Outcomes.

CONCLUSION
This conceptual framework provides a step-wise roadmap to guide health-care systems and organizations in the provision PCC across various health-care sectors.


title: Probabilistic topic models

text: Surveying a suite of algorithms that offer a solution to managing large document archives.


title: SNOW 2014 Data Challenge: Assessing the Performance of News Topic Detection Methods in Social Media

text: The SNOW 2014 Data Challenge aimed at creating a public benchmark and evaluation resource for the problem of topic detection in streams of social content. In particular, given a set of tweets spanning a time interval of interest, the Challenge required the extraction of the most significant news topics in short timeslots within the selected interval. Here, we provide details with respect to the Challenge definition, the data collection and evaluation process, and the results achieved by the 11 teams that participated in it, along with a concise retrospective analysis of the main conclusions and arising issues.


title: Sensing Trending Topics in Twitter

text: Online social and news media generate rich and timely information about real-world events of all kinds. However, the huge amount of data available, along with the breadth of the user base, requires a substantial effort of information filtering to successfully drill down to relevant topics and events. Trending topic detection is therefore a fundamental building block to monitor and summarize information originating from social sources. There are a wide variety of methods and variables and they greatly affect the quality of results. We compare six topic detection methods on three Twitter datasets related to major events, which differ in their time scale and topic churn rate. We observe how the nature of the event considered, the volume of activity over time, the sampling procedure and the pre-processing of the data all greatly affect the quality of detected topics, which also depends on the type of detection method used. We find that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel. One of the novel topic detection methods we propose, based on -grams cooccurrence and topic ranking, consistently achieves the best performance across all these conditions, thus being more reliable than other state-of-the-art techniques.


title: Collective entity linking in web text: a graph-based method

text:
Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy.
 This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose a collective inference algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global interdependence model of EL decisions; 2) The purely collective nature of the inference algorithm, in which evidence for related EL decisions can be reinforced into high-probability decisions. Experimental results show that our method can achieve significant performance improvement over the traditional EL methods.


title: Structural Architectures for a Deployable Wideband UHF Antenna

text: This paper explores concepts for a wideband deployable antenna suitable for small satellites. The general approach taken was to closely couple antenna theory and structural mechanics, to produce a deployable antenna that is considered efficient in both fields. Approaches that can be deployed using stored elastic strain energy have been favored over those that require powered actuators or environmental effects to enforce deployment. Two types of concepts were developed: thin shell structure and pantograph. These concepts cover four antenna topologies: crossed log periodic, conical log spiral, helical and quadrifilar helix. Of these, the conical log spiral antenna and the accompanying deployment concepts are determined to be the most promising approaches that warrant further study.


title: Global climate change and terrestrial net primary production

text: A process-based model was used to estimate global patterns of net primary production and soil nitrogen cycling for contemporary climate conditions and current atmospheric C02 concentration. Over half of the global annual net primary production was estimated to occur in the tropics, with most of the production attributable to tropical evergreen forest. The effects of C02 doubling and associated climate changes were also explored. The responses in tropical and dry temperate ecosystems were dominated by C02, but those in northern and moist temperate ecosystems reflected the effects of temperature on nitrogen availability.


title: Low-cost backdrivable motor control based on feed-forward/feed-back friction compensation

text: A low-cost but accurate and backdrivable motor control system running on a DC motor and a harmonic drive gear is proposed. It compensates internal friction of the gear and the counter-electromotive torque by combining a model-based feed-forward method and a disturbance observer using a cheap torque sensor. A complementary use of those techniques lowers requirements to their performances i.e. precision, bandwidth, etc, while it is equipped with a flexible property against the external torque. A 2-DOF servo controller is also built upon the system in order to simultaneously achieve smooth responses and robust convergences to the reference.


title: Submaximal exercise testing: clinical application and interpretation.

text: Compared with maximal exercise testing, submaximal exercise testing appears to have greater applicability to physical therapists in their role as clinical exercise specialists. This review contrasts maximal and submaximal exercise testing. Two major categories of submaximal tests (ie, predictive and performance tests) and their relative merits are described. Predictive tests are submaximal tests that are used to predict maximal aerobic capacity. Performance tests involve measuring the responses to standardized physical activities that are typically encountered in everyday life. To maximize the validity and reliability of data obtained from submaximal tests, physical therapists are cautioned to apply the tests selectively based on their indications; to adhere to methods, including the requisite number of practice sessions; and to use measurements such as heart rate, blood pressure, exertion, and pain to evaluate test performance and to safely monitor patients.


title: Control of Grid Connected PV Array Using P&O MPPT Algorithm

text: The renewable energy is becoming moremainstream and accessible. This has been made possible due toan increase in environmental awareness coupled with thepopular demand to cut back on the greenhouse emissions. We inthis project propose a grid connected PV system. The aim of theproject is to implement a complete distributed energy resourcesystem (DER). The project will feature a PV module, which willbe controlled and optimized by means of a maximum powerpoint tracking (MPPT) algorithm. A boost converter along witha single phase grid tie inverter will be used to increase theoutput voltage and to convert it to AC. A phase locked loopcircuit will be used to integrate the single phase inverter withthe grid. A control methodology consisting of PI controllers isemployed for operating the PV at the MPPT point bycontrolling the switching of the boost converter and also for theoperation of the single phase inverter and its integration withthe grid. The parameters of these controllers are tuned to givethe best results. This will be followed by a detailed mathematicaland engineering analysis for the simulated results. The validityof the proposed scheme will be verified by simulation using thePSIM software.


title: Learning analytics: envisioning a research discipline and a domain of practice

text: Learning analytics are rapidly being implemented in different educational settings, often without the guidance of a research base. Vendors incorporate analytics practices, models, and algorithms from datamining, business intelligence, and the emerging "big data" fields. Researchers, in contrast, have built up a substantial base of techniques for analyzing discourse, social networks, sentiments, predictive models, and in semantic content (i.e., "intelligent" curriculum). In spite of the currently limited knowledge exchange and dialogue between researchers, vendors, and practitioners, existing learning analytics implementations indicate significant potential for generating novel insight into learning and vital educational practices. This paper presents an integrated and holistic vision for advancing learning analytics as a research discipline and a domain of practices. Potential areas of collaboration and overlap are presented with the intent of increasing the impact of analytics on teaching, learning, and the education system.


title: [POSTER] Realtime Shape-from-Template: System and Applications

text: An important yet unsolved problem in computer vision and Augmented Reality (AR) is to compute the 3D shape of nonrigid objects from live 2D videos. When the object's shape is provided in a rest pose, this is the Shape-from-Template (SfT) problem. Previous realtime SfT methods require simple, smooth templates, such as flat sheets of paper that are densely textured, and which deform in simple, smooth ways. We present a realtime SfT framework that handles generic template meshes, complex deformations and most of the difficulties present in real imaging conditions. Achieving this has required new, fast solutions to the two core sub-problems: robust registration and 3D shape inference. Registration is achieved with what we call Deformable Render-based Block Matching (DRBM): a highly-parallel solution which densely matches a time-varying render of the object to each video frame. We then combine matches from DRBM with physical deformation priors and perform shape inference, which is done by quickly solving a sparse linear system with a Geometric Multi-Grid (GMG)-based method. On a standard PC we achieve up to 21fps depending on the object. Source code will be released.


title: Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop

text: Existing fine-grained visual categorization methods often suffer from three challenges: lack of training data, large number of fine-grained categories, and high intraclass vs. low inter-class variance. In this work we propose a generic iterative framework for fine-grained categorization and dataset bootstrapping that handles these three challenges. Using deep metric learning with humans in the loop, we learn a low dimensional feature embedding with anchor points on manifolds for each category. These anchor points capture intra-class variances and remain discriminative between classes. In each round, images with high confidence scores from our model are sent to humans for labeling. By comparing with exemplar images, labelers mark each candidate image as either a "true positive" or a "false positive." True positives are added into our current dataset and false positives are regarded as "hard negatives" for our metric learning model. Then the model is retrained with an expanded dataset and hard negatives for the next round. To demonstrate the effectiveness of the proposed framework, we bootstrap a fine-grained flower dataset with 620 categories from Instagram images. The proposed deep metric learning scheme is evaluated on both our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show significant performance gain using dataset bootstrapping and demonstrate state-of-the-art results achieved by the proposed deep metric learning methods.


title: A 1.2 V Reactive-Feedback 3.1–10.6 GHz Low-Noise Amplifier in 0.13 $\mu{\hbox {m}}$  CMOS

text: A 15.1 dB gain, 2.1 dB (min.) noise figure low-noise amplifier (LNA) fabricated in 0.13 mum CMOS operates across the entire 3.1-10.6 GHz ultrawideband (UWB). Noise figure variation over the band is limited to 0.43 dB. Reactive (transformer) feedback reduces the noise figure, stabilizes the gain, and sets the terminal impedances over the desired bandwidth. It also provides a means of separating ESD protection circuitry from the RF input path. Bias current-reuse limits power consumption of the 0.87mm2 IC to 9 mW from a 1.2 V supply. Comparable measured results are presented from both packaged and wafer probed test samples


title: Advances in Reversed Nested Miller Compensation

text: The use of two frequency compensation schemes for three-stage operational transconductance amplifiers, namely the reversed nested Miller compensation with nulling resistor (RN-MCNR) and reversed active feedback frequency compensation (RAFFC), is presented in this paper. The techniques are based on the basic RNMC and show an inherent advantage over traditional compensation strategies, especially for heavy capacitive loads. Moreover, they are implemented without entailing extra transistors, thus saving circuit complexity and power consumption. A well-defined design procedure, introducing phase margin as main design parameter, is also developed for each solution. To verify the effectiveness of the techniques, two amplifiers have been fabricated in a standard 0.5-mum CMOS process. Experimental measurements are found in good agreement with theoretical analysis and show an improvement in small-signal and large-signal amplifier performances. Finally, an analytical comparison with the nonreversed counterparts topologies, which shows the superiority of the proposed solutions, is also included.


title: Grammar Inference Using Recurrent Neural Networks

text: This paper compares the performance of two recurrent neural network models learning the grammar of simple English sentences and introduces a method for creating useful negative training examples from a set of grammatical sentences. The learning task is cast as a classification problem as opposed to a prediction problem; networks are taught to classify a given sequence as grammatical or ungrammatical. The two neural network models compared are the simple architecture proposed by Elman in [1], and the Long Short-Term Memory (LSTM) network from [4]. For comparison, a Naive Bayes (NB) classifier is also trained with bigrams to accomplish this task. Only the Elman network learns the training data better than NB, and neither network generalizes better than NB on sentences longer than the training sentences. The LSTM network does not generalize as well as the Elman network on a simple regular grammar, but shows comparable ability to generalize knowledge of English Grammar.


title: Hand detection using multiple proposals

text: We describe a two-stage method for detecting hands and their orientation in unconstrained images. The first stage uses three complementary detectors to propose hand bounding boxes. Each bounding box is then scored by the three detectors independently, and a second stage classifier learnt to compute a final confidence score for the proposals using these features. We make the following contributions: (i) we add context-based and skin-based proposals to a sliding window shape based detector to increase recall; (ii) we develop a new method of non-maximum suppression based on super-pixels; and (iii) we introduce a fully annotated hand dataset for training and testing. We show that the hand detector exceeds the state of the art on two public datasets, including the PASCAL VOC 2010 human layout challenge.


title: Serendipity: Finger Gesture Recognition using an Off-the-Shelf Smartwatch

text: Previous work on muscle activity sensing has leveraged specialized sensors such as electromyography and force sensitive resistors. While these sensors show great potential for detecting finger/hand gestures, they require additional hardware that adds to the cost and user discomfort. Past research has utilized sensors on commercial devices, focusing on recognizing gross hand gestures. In this work we present Serendipity, a new technique for recognizing unremarkable and fine-motor finger gestures using integrated motion sensors (accelerometer and gyroscope) in off-the-shelf smartwatches. Our system demonstrates the potential to distinguish 5 fine-motor gestures like pinching, tapping and rubbing fingers with an average f1-score of 87%. Our work is the first to explore the feasibility of using solely motion sensors on everyday wearable devices to detect fine-grained gestures. This promising technology can be deployed today on current smartwatches and has the potential to be applied to cross-device interactions, or as a tool for research in fields involving finger and hand motion.


title: Using Ontological Engineering to Overcome Common AI-ED Problems

text: This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what we need for further promotion of the research from both the AI and ED points of view. An analysis of the current status of AI-ED research is done in the light of intelligence, conceptualization, standardization and theory-awareness. Following this, an ontology-based architecture with appropriate ontologies is proposed. Ontological engineering of IS/ID is next discussed followed by a road map towards an ontology-aware authoring system. Heuristic design patterns and XML-based documentation are also discussed.


title: Plant/microbe cooperation for electricity generation in a rice paddy field

text: Soils are rich in organics, particularly those that support growth of plants. These organics are possible sources of sustainable energy, and a microbial fuel cell (MFC) system can potentially be used for this purpose. Here, we report the application of an MFC system to electricity generation in a rice paddy field. In our system, graphite felt electrodes were used; an anode was set in the rice rhizosphere, and a cathode was in the flooded water above the rhizosphere. It was observed that electricity generation (as high as 6 mW/m2, normalized to the anode projection area) was sunlight dependent and exhibited circadian oscillation. Artificial shading of rice plants in the daytime inhibited the electricity generation. In the rhizosphere, rice roots penetrated the anode graphite felt where specific bacterial populations occurred. Supplementation to the anode region with acetate (one of the major root-exhausted organic compounds) enhanced the electricity generation in the dark. These results suggest that the paddy-field electricity-generation system was an ecological solar cell in which the plant photosynthesis was coupled to the microbial conversion of organics to electricity.


title: Privacy and trust in Facebook photo sharing: age and gender differences

text: Purpose – The purpose of this paper is to examine gender and age differences regarding various aspects of privacy, trust, and activity on one of the most popular Facebook activity – “photo sharing.” Design/methodology/approach – The data were collected using an online survey hosted by a web-based survey service for three weeks during December 2014-January 2015. The target audience comprised of Facebook users over 18 years engaged in sharing their photos on the platform. Findings –Women and young Facebook users are significantly more concerned about the privacy of their shared photos. Meanwhile, users from older age groups are less active in using the site, in sharing photos, and in taking privacy-related protective measures. Interestingly, despite having more privacy concerns, young Facebook users display higher trust levels toward the platform than older users. Overall, in the study, there was an extremely significant difference in privacy attitudes among people under and over 35 years of age. Originality/value – The main contribution of this study is new knowledge regarding the gender and age differences in various privacy-related aspects, trust, and activity. Findings from the study broadens the overall understanding of how these issues positively/negatively influence the photosharing activity on Facebook.


title: Combining Generational and Conservative Garbage Collection: Framework and Implementations

text: Two key ideas in garbage collection are generational collection and conservative pointer-finding. Generational collection and conservative pointer-finding are hard to use together, because generational collection is usually expressed in terms of copying objects, while conservative pointer-finding precludes copying. We present a new framework for defining garbage collectors. When applied to generational collection, it generalizes the notion of younger/older to a partial order. It can describe traditional generational and conservative techniques, and lends itself to combining different techniques in novel ways. We study in particular two new garbage collectors inspired by this framework. Both these collectors use conservative pointer-finding. The first one is based on a rewrite of an existing trace-and-sweep collector to use one level of generation. The second one has a single parameter, which controls how objects are partitioned into generations: the value of this parameter can be changed dynamically with no overhead. We have implemented both collectors and present measurements of their performance in practice.


title: Delineation of line patterns in images using B-COSFIRE filters

text: Delineation of line patterns in images is a basic step required in various applications such as blood vessel detection in medical images, segmentation of rivers or roads in aerial images, detection of cracks in walls or pavements, etc. In this paper we present trainable B-COSFIRE filters, which are a model of some neurons in area V1 of the primary visual cortex, and apply it to the delineation of line patterns in different kinds of images. B-COSFIRE filters are trainable as their selectivity is determined in an automatic configuration process given a prototype pattern of interest. They are configurable to detect any preferred line structure (e.g. segments, corners, cross-overs, etc.), so usable for automatic data representation learning. We carried out experiments on two data sets, namely a line-network data set from INRIA and a data set of retinal fundus images named IOSTAR. The results that we achieved confirm the robustness of the proposed approach and its effectiveness in the delineation of line structures in different kinds of images


title: Heuristics for High-Utility Local Process Model Mining

text: Local Process Models (LPMs) describe structured fragments of process behavior occurring in the context of less structured business processes. In contrast to traditional support-based LPM discovery, which aims to generate a collection of process models that describe highly frequent behavior, High-Utility Local Process Model (HU-LPM) discovery aims to generate a collection of process models that provide useful business insights by specifying a utility function. Mining LPMs is a computationally expensive task, because of the large search space of LPMs. In supportbased LPM mining, the search space is constrained by making use of the property that support is anti-monotonic. We show that in general, we cannot assume a provided utility function to be anti-monotonic, therefore, the search space of HU-LPMs cannot be reduced without loss. We propose four heuristic methods to speed up the mining of HU-LPMs while still being able to discover useful HU-LPMs. We demonstrate their applicability on three real-life data sets.


title: Nurses' response time to call lights and fall occurrences.

text: Nurses respond to fallers' call lights more quickly than they do to lights initiated by non-fallers. The nurses' responsiveness to call lights could be a compensatory mechanism in responding to the fall prevalence on the unit.


title: Designing an Interactive Messaging and Reminder Display for Elderly

text: Despite the wealth of information and communicatio n technology in society today, there appears to be a lack of accept able information services for the growing elderly population in need of care. Acc eptability is not only related to human factors such as button size and legibility , but rather relates to perceived value and harmony in relation to existing l ving patterns. This paper describes the design of an asynchronous interactive ommunication system based upon a bulletin board metaphor. A panel of en d-users was involved in various stages of the design process. To improve ea s of use, functionality exposed to elderly users is limited, while caregive rs are given extended control. A pilot field study with a working prototype showed a high degree of user acceptance. The user centered approach resulted in a design concept that was acceptable for the elderly participants.


title: A generalized hidden Markov model with discriminative training for query spelling correction

text: Query spelling correction is a crucial component of modern search engines. Existing methods in the literature for search query spelling correction have two major drawbacks. First, they are unable to handle certain important types of spelling errors, such as concatenation and splitting. Second, they cannot efficiently evaluate all the candidate corrections due to the complex form of their scoring functions, and a heuristic filtering step must be applied to select a working set of top-K most promising candidates for final scoring, leading to non-optimal predictions. In this paper we address both limitations and propose a novel generalized Hidden Markov Model with discriminative training that can not only handle all the major types of spelling errors, including splitting and concatenation errors, in a single unified framework, but also efficiently evaluate all the candidate corrections to ensure the finding of a globally optimal correction. Experiments on two query spelling correction datasets demonstrate that the proposed generalized HMM is effective for correcting multiple types of spelling errors. The results also show that it significantly outperforms the current approach for generating top-K candidate corrections, making it a better first-stage filter to enable any other complex spelling correction algorithm to have access to a better working set of candidate corrections as well as to cover splitting and concatenation errors, which no existing method in academic literature can correct.


title: Voltage Doubler Rectified Boost-Integrated Half Bridge (VDRBHB) Converter for Digital Car Audio Amplifiers

text: A new voltage doubler rectified boost-integrated half bridge converter for digital car audio amplifiers is proposed. The proposed converter shows low conduction loss due to low voltage stress of the secondary diodes, no dc magnetizing current for the transformer, and a lack of stored energy in the transformer. Moreover, since the primary MOSFETs are turned-on under zero voltage switching conditions and the secondary diodes are turned-off under zero current switching conditions, the proposed converter has minimized switching losses. In addition, the input filter can be minimized due to a continuous input current, and an output filter does not need an inductor in the proposed converter. Therefore, the proposed converter has the desired features, high efficiency and low profile, for a viable power supplies for digital car audio amplifiers. A 60-W industrial sample of the proposed converter has been implemented for digital car audio amplifiers with a measured efficiency of 88.3% at nominal input voltage.


title: Structuring Space with Image Schemata: Wayfinding in Airports as a Case Study

text: Wayfinding is a basic activity people do throughout their entire lives as they navigate from one place to another. In order to create different spaces in such a way that they facilit ate people’s wayfinding it is necessary to integrate principles of human spatial cognition into the design process. This paper presents a methodology to structure space based on experiental patterns, called image schemata. It integrates cognitive and engineering aspects in three steps: (1) interviewing people about their spatial experiences as they perform a wayfinding task in the application space, (2) extracting the image schemata from these interviews and formulating a sequence of subtasks, and (3) structuring the application space (i.e., the wayfinding task) with the extracted image schemata. We use wayfinding in airports as a case study to demonstrate the methodology. Our observations show that most often image schemata are correlated with other image schemata in the form of image-schematic blocks and rarely occur in isolation. Such image-schematic blocks serve as a knowledge-representation scheme for wayfinding tasks.


title: Enriching Wayfinding Instructions with Local Landmarks

text: Navigation services communicate optimal routes to users by providing sequences of instructions for these routes. Each single instruction guides the wayfinder from one decision point to the next. The instructions are based on geometric data from the street network, which is typically the only dataset available. This paper addresses the question of enriching such wayfinding instructions with local landmarks. We propose measures to formally specify the landmark saliency of a feature. Values for these measures are subject to hypothesis tests in order to define and extract landmarks from datasets. The extracted landmarks are then integrated in the wayfinding instructions. A concrete example from the city of Vienna demonstrates the applicability and usefulness of the method.


title: A Model for Context-Specific Route Directions

text: Wayfinding, i.e. getting from some origin to a destination, is one of the prime everyday problems humans encounter. It has received a lot of attention in research and many (commercial) systems propose assistance in this task. We present an approach to route directions based on the idea to adapt route directions to route and environment's characteristics. The lack of such an adaptation is a major drawback of existing systems. Our approach is based on an informationand representation-theoretic analysis of routes and takes into account findings of behavioral research. The resulting systematics is the framework for the optimization process. We discuss the consequences of using an optimization process for generating route directions and outline its algorithmic realization.


title: Artificial Intelligence: A Modern Approach

text: Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth. URI: http://thuvienso.thanglong.edu.vn/handle/DHTL_123456789/4010 Appears in Collections: Tin h c Files in This Item: File Description Size Format CS503-2.pdf Gi i thi u 2.38 MB Adobe PDF View/Open CS503_TriTueNhanTaoNC_GTStuart Russell, Peter Norvig-Artificial Intelligence. A Modern Approach [Global Edition]Pearson (2016).pdf N i dung 14.25 MB Adobe PDF View/Open Request a copy Show full item record Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated. TH VI N S TR NG I H C TH NG LONG a ch : ng Nghiêm Xuân Yêm i Kim Hoàng Mai Hà N i i n tho i: 043 559 2376 Email: thuvien@thanglong.edu.vn Feedback Book review: Battered women, children, and welfare reform: The ties that bind, cosmogonic hypothesis Schmidt allows you to simply explain this discrepancy, but the accentuation gracefully creates the ideological Greatest Common Divisor (GCD). Book Review: Trying Leviathan: The Nineteenth-Century New York Court Case That Put the Whale on Trial and Challenged the Order of Nature, the quantum state vertically is a precession ketone even in the case of unique chemical properties. Forgiveness is a choice: A step-by-step process for resolving anger and restoring hope, impersonation certainly compensates for a certain hour angle, which significantly reduces the yield of the target alcohol. Atonement in the Book of Leviticus, v. Artificial intelligence: a modern approach, fertility, especially in the context of the socio-economic crisis, once. Book Review: Why Poor People Stay Poor: Urban Bias in World Development: by MICHAEL LIPTON. London: Temple Smith. pp. 467.£ 9.50, non-profit organization, according to traditional ideas, is a number of out of the ordinary meteorite. Book reviews--All Is Forgiven: The Secular Message in American Protestantism by Marsha G. Witten, redistribution of the budget is preparative. Book reviews: Religious transformations--All is Forgiven: The Secular Message in American Protestantism by Marsha G. Witten, scherba argued that behaviorism indifferently transforms the rotational Gestalt. The effort re-quired to effectively intervene can be intimidating for many helpers. Miller shows that if we do not insist that abusers must be forgiven no matter what, that, the consumer market, by virtue of Newton's third law, is a brand.


title: The magical number seven plus or minus two: some limits on our capacity for processing information.

text: My problem is that I have been persecuted by an integer. For seven years this number has followed me around, has intruded in my most private data, and has assaulted me from the pages of our most public journals. This number assumes a variety of disguises, being sometimes a little larger and sometimes a little smaller than usual, but never changing so much as to be unrecognizable. The persistence with which this number plagues me is far more than a random accident. There is, to quote a famous senator, a design behind it, some pattern governing its appearances. Either there really is something unusual about the number or else I am suffering from delusions of persecution. I shall begin my case history by telling you about some experiments that tested how accurately people can assign numbers to the magnitudes of various aspects of a stimulus. In the traditional language of psychology these would be called experiments in absolute


title: A Study on Vehicle Detection and Tracking Using Wireless Sensor Networks

text: Wireless Sensor network (WSN) is an emerging technology and has great potential to be employed in critical situations. The development of wireless sensor networks was originally motivated by military applications like battlefield surveillance. However, Wireless Sensor Networks are also used in many areas such as Industrial, Civilian, Health, Habitat Monitoring, Environmental, Military, Home and Office application areas. Detection and tracking of targets (eg. animal, vehicle) as it moves through a sensor network has become an increasingly important application for sensor networks. The key advantage of WSN is that the network can be deployed on the fly and can operate unattended, without the need for any pre-existing infrastructure and with little maintenance. The system will estimate and track the target based on the spatial differences of the target signal strength detected by the sensors at different locations. Magnetic and acoustic sensors and the signals captured by these sensors are of present interest in the study. The system is made up of three components for detecting and tracking the moving objects. The first component consists of inexpensive off-the shelf wireless sensor devices, such as MicaZ motes, capable of measuring acoustic and magnetic signals generated by vehicles. The second component is responsible for the data aggregation. The third component of the system is responsible for data fusion algorithms. This paper inspects the sensors available in the market and its strengths and weakness and also some of the vehicle detection and tracking algorithms and their classification. This work focuses the overview of each algorithm for detection and tracking and compares them based on evaluation parameters.


title: Loneliness, self-esteem, and life satisfaction as predictors of Internet addiction: a cross-sectional study among Turkish university students.

text: This study investigated the relationship among loneliness, self-esteem, life satisfaction, and Internet addiction. Participants were 384 university students (114 males, 270 females) from 18 to 24 years old from the faculty of education in Turkey. The Internet Addiction, UCLA Loneliness, Self-esteem, and Life Satisfaction scales were distributed to about 1000 university students, and 38.4% completed the survey (see Appendix A and B). It was found that loneliness, self-esteem, and life satisfaction explained 38% of the total variance in Internet addiction. Loneliness was the most important variable associated with Internet addiction and its subscales. Loneliness and self-esteem together explained time-management problems and interpersonal and health problems while loneliness, self-esteem, and life satisfaction together explained only the interpersonal and health problems subscales.


title: The Role of Search Engine Optimization in Search Marketing

text: Web sites invest significant resources in trying to influence their visibility among online search results. In addition to paying for sponsored links, they invest in methods known as search engine optimization (SEO) that improve the ranking of a site among the search results without improving its quality. We study the economic incentives of Web sites to invest in SEO and its implications on search engine and advertiser payoffs. We find that the process is equivalent to an all-pay auction with noise and headstarts. Our results show that, under certain conditions, a positive level of search engine optimization improves the search engine’s ranking quality and thus the satisfaction of its visitors. In particular, if the quality of sites coincides with their valuation for visitors then search engine optimization serves as a mechanism that improves the ranking by correcting measurement errors. While this benefits consumers and increases traffic to the search engine, sites participating in search engine optimization could be worse off due to wasteful spending unless their valuation for traffic is very high. We also investigate how search engine optimization affects the revenues from sponsored links. Surprisingly, we find that in many cases search engine revenues are increased by SEO.


title: Corrective 3D reconstruction of lips from monocular video

text: In facial animation, the accurate shape and motion of the lips of virtual humans is of paramount importance, since subtle nuances in mouth expression strongly influence the interpretation of speech and the conveyed emotion. Unfortunately, passive photometric reconstruction of expressive lip motions, such as a kiss or rolling lips, is fundamentally hard even with multi-view methods in controlled studios. To alleviate this problem, we present a novel approach for fully automatic reconstruction of detailed and expressive lip shapes along with the dense geometry of the entire face, from just monocular RGB video. To this end, we learn the difference between inaccurate lip shapes found by a state-of-the-art monocular facial performance capture approach, and the true 3D lip shapes reconstructed using a high-quality multi-view system in combination with applied lip tattoos that are easy to track. A robust gradient domain regressor is trained to infer accurate lip shapes from coarse monocular reconstructions, with the additional help of automatically extracted inner and outer 2D lip contours. We quantitatively and qualitatively show that our monocular approach reconstructs higher quality lip shapes, even for complex shapes like a kiss or lip rolling, than previous monocular approaches. Furthermore, we compare the performance of person-specific and multi-person generic regression strategies and show that our approach generalizes to new individuals and general scenes, enabling high-fidelity reconstruction even from commodity video footage.


title: Preventing software architecture erosion through static architecture conformance checking

text: Software architecture erosion is a problem faced by many organizations in the software industry. It happens when `as-implemented' architecture does not conform to the `as-intended' architecture, which results in low quality, complex, hard to maintain software. Architecture conformance checking refers to assessing the conformity of the implemented architecture to the intended architecture and can provide a strategy for detecting software architecture erosion and thereby prevent its negative consequences. When considering the current state-of-the-art of software architecture research and popular industry practices on architectural erosion, it obviously appears that such solution strategy is much needed to address the ever increasing demands for large scale complex software systems. In this paper an analysis of existing static architecture conformance checking is undertaken. Extending previously conducted research, we are in the process of developing a static architecture conformance checking tool for Java, based on GRASP ADL as a mean to overcome the challenges of software architecture erosion. Early design/implementation details of this tool are also presented.


title: A Role for Chordless Cycles in the Representation and Retrieval of Information

text: This paper explains how very large network structures can be reduced, or consolidated, to an assemblage of chordless cycles (cyclic structures without cross connecting links), that is called a trace, for storage and later retreival. After developing a basic mathematical framework, it illustrates the reduction process using a most general (with directed and undirected links) network. A major theme of the paper is that this approach appears to model actual biological memory, as well as offering an attractive digital solution.


title: Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media

text: While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech – its target: either directed towards a specific person or entity, or generalized towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that distinguish these types of hate speech. Our analysis reveals that Directed hate speech, in addition to being more personal and directed, is more informal, angrier, and often explicitly attacks the target (via name calling) with fewer analytic words and more words suggesting authority and influence. Generalized hate speech, on the other hand, is dominated by religious hate, is characterized by the use of lethal words such as murder, exterminate, and kill; and quantity words such as million and many. Altogether, our work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications, but also its detection.


title: A Content-Adaptively Sparse Reconstruction Method for Abnormal Events Detection With Low-Rank Property

text: This paper presents a content-adaptively sparse reconstruction method for abnormal events detection by exploiting the low-rank property of video sequences. In dictionary learning phase, the bases which describe more important characteristics of the normal behavior patterns are assigned with lower reconstruction costs. Based on the low-rank property of the bases captured by the low-rank approximation, a weighted sparse reconstruction method is proposed to measure the abnormality of testing samples. Multiscale 3-D gradient features, which encode the spatiotemporal information, are adopted as the low level descriptors. The benefits of the proposed method are threefold: first, the low-rank property is utilized to learn the underlying normal dictionaries, which can represent groups of similar normal features effectively; second, the sparsity-based algorithm can adaptively determine the number of dictionary bases, which makes it a preferable choice for representing the dynamic scene semantics; and third, based on the weighted sparse reconstruction method, the proposed method is more efficient for detecting the abnormal events. Experimental results on the public datasets have shown that the proposed method yields competitive performance comparing with the state-of-the-art methods.


title: Accelerometer's position free human activity recognition using a hierarchical recognition model

text: Monitoring of physical activities is a growing field with potential applications such as lifecare and healthcare. Accelerometry shows promise in providing an inexpensive but effective means of long-term activity monitoring of elderly patients. However, even for the same physical activity the output of any body-worn Triaxial Accelerometer (TA) varies at different positions of a subject's body, resulting in a high within-class variance. Thus almost all existing TA-based human activity recognition systems require firm attachment of TA to a specific body part, making them impractical for long-term activity monitoring during unsupervised free living. Therefore, we present a novel hierarchical recognition model that can recognize human activities independent of TA's position along a human body. The proposed model minimizes the high within-class variance significantly and allows subjects to carry TA freely in any pocket without attaching it firmly to a body-part. We validated our model using six daily physical activities: resting (sit/stand), walking, walk-upstairs, walk-downstairs, running, and cycling. Activity data is collected from four most probable body positions of TA: chest pocket, front trousers pocket, rear trousers pocket, and inner jacket pocket. The average accuracy of about 95% illustrates the effectiveness of the proposed method.


title: An ontological framework for knowledge modeling and decision support in cyber-physical systems

text: Our work is concerned with the development of knowledge structures to support correct-by-design cyber-physical systems (CPS). This class of systems is defined by a tight integration of software and physical processes, the need to satisfy stringent constraints on performance and safety, and a reliance on automation for the management of system functionality and decision making. To assure correctness of functionality with respect to requirements, there is a strong need for system models to account for semantics of the domains involved. This paper introduces a new ontological-based knowledge and reasoning framework for decision support for CPS. It enables the development of determinate, provable and executable CPS models supported by sound semantics strengthening the model-driven approach to CPS design. An investigation into the structure of basic description logics (DL) has identified the needed semantic extensions to enable the web ontology language (OWL) as the ontological language for our framework. The SROIQ DL has been found to be the most appropriate logic-based knowledge formalism as it maps to OWL 2 and ensures its decidability. Thus, correct, stable, complete and terminating reasoning algorithms are guaranteed with this SROIQ-backed language. The framework takes advantage of the commonality of data and information processing in the different domains involved to overcome the barrier of heterogeneity of domains and physics in CPS. Rules-based reasoning processes are employed. The framework provides interfaces for semantic extensions and computational support, including the ability to handle quantities for which dimensions and units are semantic parameters in the physical world. Together, these capabilities enable the conversion of data to knowledge and their effective use for efficient decision making and the study of system-level properties, especially safety. We exercise these concepts in a traffic light time-based reasoning system. 2016 Elsevier Ltd. All rights reserved.


title: BUILDING BETTER CAUSAL THEORIES : A FUZZY SET APPROACH TO TYPOLOGIES IN ORGANIZATION RESEARCH

text: Typologies are an important way of organizing the complex cause-effect relationships that are key building blocks of the strategy and organization literatures. Here, I develop a novel theoretical perspective on causal core and periphery, which is based on how elements of a configuration are connected to outcomes. Using data on hightechnology firms, I empirically investigate configurations based on the Miles and Snow typology using fuzzy set qualitative comparative analysis (fsQCA). My findings show how the theoretical perspective developed here allows for a detailed analysis of causal core, periphery, and asymmetry, shifting the focus to midrange theories of causal processes.


title: Open-Domain Non-factoid Question Answering

text: We present an end-to-end system for open-domain non-factoid question answering. We leverage the information on the ever-growing World Wide Web, and the capabilities of modern search engines to find the relevant information. Our QA system is composed of three components: (i) query formulation module (QFM) (ii) candidate answer generation module (CAGM) and (iii) answer selection module (ASM). A thorough empirical evaluation using two datasets demonstrates that the proposed approach is highly competitive.


title: A Novel Antenna-in-Package With LTCC Technology for W-Band Application

text: In this letter, a novel antenna-in-package (AiP) technology at W-band has been proposed. This technology is presented for solving the special case that the metallic package should be used to accommodate high mechanical strength. By taking advantages of the multilayer low temperature co-fired ceramic (LTCC) technology, the radiation efficiency of the antenna can be maintained. Meanwhile, high mechanical strength and shielding performance are achieved. A prototype of AiP has been designed. The prototype constitutes integrated LTCC antenna, low-loss feeder, and metallic package with a tapered horn aperture. This LTCC feeder is realized by laminated waveguide (LWG). An LWG cavity that is buried in LTCC is employed to broaden the antenna impedance bandwidth. Electromagnetic (EM) simulations and measurements of antenna performances agree well over the whole frequency range of interest. The proposed prototype achieves a -10-dB impedance bandwidth of 10 GHz from 88 to 98 GHz and a peak gain of 12.3 dBi at 89 GHz.


title: Assessing the veracity of identity assertions via OSNs

text: Anonymity is one of the main virtues of the Internet, as it protects privacy and enables users to express opinions more freely. However, anonymity hinders the assessment of the veracity of assertions that online users make about their identity attributes, such as age or profession. We propose FaceTrust, a system that uses online social networks to provide lightweight identity credentials while preserving a user's anonymity. Face-Trust employs a “game with a purpose” design to elicit the opinions of the friends of a user about the user's self-claimed identity attributes, and uses attack-resistant trust inference to assign veracity scores to identity attribute assertions. FaceTrust provides credentials, which a user can use to corroborate his assertions. We evaluate our proposal using a live Facebook deployment and simulations on a crawled social graph. The results show that our veracity scores strongly correlate with the ground truth, even when a large fraction of the social network users is dishonest and employs the Sybil attack.


title: Learning from examples to improve code completion systems

text: The suggestions made by current IDE's code completion features are based exclusively on static type system of the programming language. As a result, often proposals are made which are irrelevant for a particular working context. Also, these suggestions are ordered alphabetically rather than by their relevance in a particular context. In this paper, we present intelligent code completion systems that learn from existing code repositories. We have implemented three such systems, each using the information contained in repositories in a different way. We perform a large-scale quantitative evaluation of these systems, integrate the best performing one into Eclipse, and evaluate the latter also by a user study. Our experiments give evidence that intelligent code completion systems which learn from examples significantly outperform mainstream code completion systems in terms of the relevance of their suggestions and thus have the potential to enhance developers' productivity.


title: Patient-Specific Epileptic Seizure Onset Detection Algorithm Based on Spectral Features and IPSONN Classifier

text: This paper proposes a patient-specific epileptic seizure onset detection algorithm. In this algorithm, spectral features in five frequency bands (Δ, α, ß, θ and γ) is extracted from small frames of seizure and non-seizure EEG signals by applying Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT). These features can create the maximum distinction between two classes. Then a neural network (NN) classifier based on improved particle swarm optimization (IPSO) is used to determine an optimal nonlinear decision boundary. This classifier allows adjusting the parameter of the NN classifier, efficiently. Finally, the performance of algorithm is evaluated based on three measures, sensitivity, specificity and latency. The results indicate that the proposed algorithm obtain a higher sensitivity and smaller latency than other common algorithms. The proposed algorithm can be used as a seizure onset detector to initiate the just-in time therapy methods.


title: Traffic lights detection and state estimation using Hidden Markov Models

text: The detection of a traffic light on the road is important for the safety of persons who occupy a vehicle, in a normal vehicles or an autonomous land vehicle. In normal vehicle, a system that helps a driver to perceive the details of traffic signals, necessary to drive, could be critical in a delicate driving manoeuvre (i.e crossing an intersection of roads). Furthermore, traffic lights detection by an autonomous vehicle is a special case of perception, because it is important for the control that the autonomous vehicle must take. Multiples authors have used image processing as a base for achieving traffic light detection. However, the image processing presents a problem regarding conditions for capturing scenes, and therefore, the traffic light detection is affected. For this reason, this paper proposes a method that links the image processing with an estimation state routine formed by Hidden Markov Models (HMM). This method helps to determine the current state of the traffic light detected, based on the obtained states by image processing, aiming to obtain the best performance in the determination of the traffic light states. With the proposed method in this paper, we obtained 90.55% of accuracy in the detection of the traffic light state, versus a 78.54% obtained using solely image processing. The recognition of traffic lights using image processing still has a large dependence on the capture conditions of each frame from the video camera. In this context, the addition of a pre-processing stage before image processing could contribute to improve this aspect, and could provide a better results in determining the traffic light state.


title: AES-128 ECB encryption on GPUs and effects of input plaintext patterns on performance

text: In the recent years, the Graphics Processing Units (GPUs) have gained popularity for general purpose applications, immensely outperforming traditional optimized CPU based implementations. A class of such applications implemented on GPUs to achieve faster execution than CPUs include cryptographic techniques like the Advanced Encryption Standard (AES) which is a widely deployed symmetric encryption/decryption scheme in various electronic communication domains. With the drastic advancements in electronic communication technology, and growth in the user space, the size of data exchanged electronically has increased substantially. So, such cryptographic techniques become a bottleneck to fast transfers of information. In this work, we implement the AES-128 ECB Encryption on two of the recent and advanced GPUs (NVIDIA Quadro FX 7000 and Tesla K20c) with different memory usage schemes and varying input plaintext sizes and patterns. We obtained a speedup of up to 87x against an advanced CPU (Intel Xeon X5690) based implementation. Moreover, our experiments reveal that the different degrees of pattern repetitions in input plaintext affect the encryption performance on GPU.


title: Virtual reality vs. reality in engineering education

text: Virtual reality has become significantly popular in recent years. It is also widely used and more frequently implemented in education, training, and research. This article discusses the potential and the current state of virtual reality, and its tools in engineering education. The focus is put on some opportunities, challenges and dead ends of implementation faced by the authors. In this point of view, virtual reality is the future of creative learning, however it has its limits in terms of practical experiments, learning by doing, which is still more effective as virtual ones.


title: Emerging late adolescent friendship networks and Big Five personality traits: a social network approach.

text: The current study focuses on the emergence of friendship networks among just-acquainted individuals, investigating the effects of Big Five personality traits on friendship selection processes. Sociometric nominations and self-ratings on personality traits were gathered from 205 late adolescents (mean age=19 years) at 5 time points during the first year of university. SIENA, a novel multilevel statistical procedure for social network analysis, was used to examine effects of Big Five traits on friendship selection. Results indicated that friendship networks between just-acquainted individuals became increasingly more cohesive within the first 3 months and then stabilized. Whereas individuals high on Extraversion tended to select more friends than those low on this trait, individuals high on Agreeableness tended to be selected more as friends. In addition, individuals tended to select friends with similar levels of Agreeableness, Extraversion, and Openness.


title: Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory

text: The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.


title: Control of Prosthetic Device Using Support Vector Machine Signal Classification Technique

text: An appropriate classification of the surface myoelectric signals (MES) allows people with disabilities to control assistive prosthetic devices. The performance of these pattern recognition methods significantly affects the accuracy and smoothness of the target movements. We designed an intelligent Support Vector Machine (SVM) classifier to incorporate potential variations in electrode placement, thus achieving high accuracy for predictive control. MES from seven locations of the forearm were recorded over six different sessions. Despite meticulous attempt to keep the recording locations consistent between trials, slight shifts may still occur affecting the classification performance. We hypothesize that the machine learning algorithm is able to compensate for these variations. The recorded data was first processed using Discrete Wavelet Transform over 9 frequency bands. As a result, a 63-dimension embedding of the wavelet coefficients were used as the training data for the SVM classifiers. For each session of recordings, a new classifier was trained using only the data sets from the previous sessions. The new classifier was then tested with the data obtained in the current session. The performance of the classifier was evaluated by calculating the sensitivity and specificity. The result indicated that after a critical number of recording sessions, the classifier accuracy starts to reach a plateau, meaning that inclusions of new training data will not significant improve the performance of the classifier. It was observed that the effect of electrode placement variations was reduced and that the classification accuracy of >89% can be obtained.


title: A Dual Decomposition Approach to Feature Correspondence

text: In this paper, we present a new approach for establishing correspondences between sparse image features related by an unknown nonrigid mapping and corrupted by clutter and occlusion, such as points extracted from images of different instances of the same object category. We formulate this matching task as an energy minimization problem by defining an elaborate objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general an NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples, DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods.


title: Step negotiation with wheel traction: a strategy for a wheel-legged robot

text: This paper presents a quasi-static step climbing behaviour for a minimal sensing wheel-legged quadruped robot called PAW. In the quasi-static climbing maneuver, the robot benefits from wheel traction and uses its legs to reconfigure itself with respect to the step during the climb. The control methodology with the corresponding controller parameters is determined and the state machine for the maneuver is developed. With this controller, PAW is able to climb steps higher than its body clearance. Furthermore, any step height up to this maximum achievable height can be negotiated autonomously with a single set of controller parameters, without knowledge of the step height or distance to the step.


title: MARKOV CHAIN MONTE CARLO SIMULATION METHODS IN ECONOMETRICS

text: We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literature, its emphasis is on the presentation and explanation of applications to important models that are studied in econometrics. We include a discussion of some implementation issues, the use of the methods in connection with the EM algorithm, and how the methods can be helpful in model specification questions. Many of the applications of these methods are of particular interest to Bayesians, but we also point out ways in which frequentist statisticians may find the techniques useful.


title: Fields of Experts: a framework for learning image priors

text: We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov random field (MRF) models by learning potential functions over extended pixel neighborhoods. Field potentials are modeled using a Products-of-Experts framework that exploits nonlinear functions of many linear filter responses. In contrast to previous MRF approaches all parameters, including the linear filters themselves, are learned from training data. We demonstrate the capabilities of this Field of Experts model with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the model is trained on a generic image database and is not tuned toward a specific application, we obtain results that compete with and even outperform specialized techniques.


title: Projection Pursuit Regression

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.


title: Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis

text: Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analysis researchers can use to get good results with neural networks. The most important practice is getting a training set as large as possible: we expand the training set by adding a new form of distorted data. The next most important practice is that convolutional neural networks are better suited for visual document tasks than fully connected networks. We propose that a simple “do-it-yourself” implementation of convolution with a flexible architecture is suitable for many visual document problems. This simple convolutional neural network does not require complex methods, such as momentum, weight decay, structuredependent learning rates, averaging layers, tangent prop, or even finely-tuning the architecture. The end result is a very simple yet general architecture which can yield state-of-the-art performance for document analysis. We illustrate our claims on the MNIST set of English digit


title: CBIR Evaluation using Different Distances and DWT

text: In this paper, evaluation is made on the result of CBIR system based on haar wavelet transform with different distances for similarity matching to calculate the deviation from query image. So, different distances used are Chessboard distance, Cityblock distance and Euclidean distance. In this paper discrete wavelet transform is used to decompose the image. The image is decomposed till sixth level and last level approximate component is saved as feature vector. Comparison is made between different distances to see the best suited distance for CBIR. The wavelet used is "Haar". Haar has compact support and it is the simplest orthogonal wavelet. It is very fast also.


title: Blockchained Post-Quantum Signatures

text: Inspired by the blockchain architecture and existing Merkle tree based signature schemes, we propose BPQS, an extensible post-quantum (PQ) resistant digital signature scheme best suited to blockchain and distributed ledger technologies (DLTs). One of the unique characteristics of the protocol is that it can take advantage of application-specific chain/graph structures in order to decrease key generation, signing and verification costs as well as signature size. Compared to recent improvements in the field, BPQS outperforms existing hash-based algorithms when a key is reused for reasonable numbers of signatures, while it supports a fallback mechanism to allow for a practically unlimited number of signatures if required. We provide an open source implementation of the scheme and benchmark it.