title: From neural PCA to deep unsupervised learning

abstract: Abstract A network supporting deep unsupervised learning is presented. The network is an autoencoder with lateral shortcut connections from the encoder to the decoder at each level of the hierarchy. The lateral shortcut connections allow the higher levels of the hierarchy to focus on abstract invariant features. Whereas autoencoders are analogous to latent variable models with a single layer of stochastic variables, the proposed network is analogous to hierarchical latent variable models. Learning combines denoising autoencoder and denoising sources separation frameworks. Each layer of the network contributes to the cost function a term which measures the distance of the representations produced by the encoder and the decoder. Since training signals originate from all levels of the network, all layers can learn efficiently even in deep networks. The speedup offered by cost terms from higher levels of the hierarchy and the ability to learn invariant features are demonstrated in experiments.


title: Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications

abstract: Fault diagnosis is an important topic both in practice and research. There is intense pressure on industrial systems to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering from potential faults as early as possible. From the historical perspective, this paper divides fault diagnosis into previous research and industrial big data era. According to primary drivers, this paper classifies fault diagnosis into knowledge-driven, data-driven, and value-driven methods. Among them, the former two approaches belong to the previous research on fault diagnosis. They mainly depend on expert experience and shallow models to detect and extract failures from relatively small size data. With the continuous exponential growth of data, it is insufficient to mine valuable fault information from massive multi-source heterogeneous data. The huge diagnostic value embodied in industrial big data has driven the emergence of the third category, which belongs to fault diagnosis based on big data. It consists of big data processing and analysis corresponding to high efficiency, cost effectiveness, and generality, which can deal well with problems that previous methods faced. We introduce the concept of a device electrocardiogram from the perspective of applicability to outline the present status of fault diagnosis for big data, and compare it with traditional diagnostic system. We also discuss issues and challenges that need to be further considered. It would be great valuable to integrate or explore more advanced diagnostic methods to handle collected industrial big data and put them into practice to mine the huge hidden diagnostic value.


title: Gender Incongruence of Adolescence and Adulthood: Acceptability and Clinical Utility of the World Health Organization’s Proposed ICD-11 Criteria

abstract: The World Health Organization (WHO) is currently updating the tenth version of their diagnostic tool, the International Classification of Diseases (ICD, WHO, 1992). Changes have been proposed for the diagnosis of Transsexualism (ICD-10) with regard to terminology, placement and content. The aim of this study was to gather the opinions of transgender individuals (and their relatives/partners) and clinicians in the Netherlands, Flanders (Belgium) and the United Kingdom regarding the proposed changes and the clinical applicability and utility of the ICD-11 criteria of ‘Gender Incongruence of Adolescence and Adulthood’ (GIAA). A total of 628 participants were included in the study: 284 from the Netherlands (45.2%), 8 from Flanders (Belgium) (1.3%), and 336 (53.5%) from the UK. Most participants were transgender people (or their partners/relatives) (n = 522), 89 participants were healthcare providers (HCPs) and 17 were both healthcare providers and (partners/relatives of) transgender people. Participants completed an online survey developed for this study. Most participants were in favor of the proposed diagnostic term of ‘Gender Incongruence’ and thought that this was an improvement on the ICD-10 diagnostic term of ‘Transsexualism’. Placement in a separate chapter dealing with Sexual- and Gender-related Health or as a Z-code was preferred by many and only a small number of participants stated that this diagnosis should be excluded from the ICD-11. In the UK, most transgender participants thought there should be a diagnosis related to being trans. However, if it were to be removed from the chapter on “psychiatric disorders”, many transgender respondents indicated that they would prefer it to be removed from the ICD in its entirety. There were no large differences between the responses of the transgender participants (or their partners and relatives) and HCPs. HCPs were generally positive about the GIAA diagnosis; most thought the diagnosis was clearly defined and easy to use in their practice or work. The duration of gender incongruence (several months) was seen by many as too short and required a clearer definition. If the new diagnostic term of GIAA is retained, it should not be stigmatizing to individuals. Moving this diagnosis away from the mental and behavioral chapter was generally supported. Access to healthcare was one area where retaining a diagnosis seemed to be of benefit.


title: Learning Gaussian Networks

abstract: We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. Previous work has concentrated on metrics for domains containing only discrete variables, under the assumption that data represents a multinomial sample. In this paper, we extend this work, developing scoring metrics for domains containing only continuous variables under the assumption that continuous data is sampled from a multivariate normal distribution. Our work extends traditional statistical approaches for identifying vanishing regression coefficients in that we identify two important assumptions, called event equivalence and parameter modularity, that when combined allow the construction of prior distributions for multivariate normal parameters from a single prior Bayesian network specified by a user.


title: A Novel Soft-Switching Interleaved Coupled-Inductor Boost Converter With Only Single Auxiliary Circuit

abstract: A novel soft-switching interleaved coupled-inductor boost converter is proposed in this paper. Only a single active soft-switching module is needed to simultaneously achieve the soft-switching property of the two switches in the interleaved coupled-inductor boost converter. The better efficiency is achieved with the less components and cost. The two main switches can achieve the ZVT turn-on and smaller-current turn-off simultaneously when the single active soft-switching module is active. Due to the coupling characteristic of the inductors, the voltages across the two inductors are changed at the same time; therefore, the equivalent circuit is equal to the parasitic capacitors of the two main switches in parallel to resonate with the auxiliary inductor. By coupling two input inductors, the volume and cost of the circuit can be reduced. The interleaved coupled-inductor topology can also reduce the input and output current ripples and share the input and output currents. The operating modes, analysis, and design of the proposed circuit have been discussed in this paper. Simulation and experiments are finally conducted to verify the validity of the proposed circuit.


title: Psychological and physiological human responses to simulated and real environments: A comparison between Photographs, 360° Panoramas, and Virtual Reality

abstract: Abstract Psychological research into human factors frequently uses simulations to study the relationship between human behaviour and the environment. Their validity depends on their similarity with the physical environments. This paper aims to validate three environmental-simulation display formats: photographs, 360° panoramas, and virtual reality. To do this we compared the psychological and physiological responses evoked by simulated environments set-ups to those from a physical environment setup; we also assessed the users' sense of presence. Analysis show that 360° panoramas offer the closest to reality results according to the participants' psychological responses, and virtual reality according to the physiological responses. Correlations between the feeling of presence and physiological and other psychological responses were also observed. These results may be of interest to researchers using environmental-simulation technologies currently available in order to replicate the experience of physical environments.


title: Data fusion of radar and image measurements for multi-object tracking via Kalman filtering

abstract: Data fusion is an important issue for object tracking in autonomous systems such as robotics and surveillance. In this paper, we present a multiple-object tracking system whose design is based on multiple Kalman filters dealing with observations from two different kinds of physical sensors. Hardware integration which combines a cheap radar module and a CCD camera has been developed and data fusion method has been proposed to process measurements from those modules for multi-object tracking. Due to the limited resolution of bearing angle measurements of the cheap radar module, CCD measurements are used to compensate for the low angle resolution. Conversely, the radar module provides radial distance information which cannot be measured easily by the CCD camera. The proposed data fusion enables the tracker to efficiently utilize the radial measurements of objects from the cheap radar module and 2D location measurements of objects in image space of the CCD camera. To achieve the multi-object tracking we combine the proposed data fusion method with the integrated probability data association (IPDA) technique underlying the multiple-Kalman filter framework. The proposed complementary system based on the radar and CCD camera is experimentally evaluated through a multi-person tracking scenario. The experimental results demonstrate that the implemented system with fused observations considerably enhances tracking performance over a single sensor system.


title: Biologically Inspired Locomotion Strategies : Novel Ground Mobile Robots at RoMeLa

abstract: A positive displacement machine such as a hydraulic pump or motor comprises a screw with several threads rotatable inside a casing and meshing with at least one pinion, and at least one low pressure port and at least one high pressure port located in the casing on either side of the pinion. The portion of the casing comprised between a low pressure port and a high pressure port is in leaktight contact with the whole length of the top of at least one thread of the screw. When said portion is in contact with the tops of two threads, no pinion tooth is in mesh between them. The threads terminate on each side of the screw on substantially equal diameters and have in their central section a swollen portion with an external diameter larger than at the ends of the threads.


Speech System


Battery Status Not Included: Assessing Privacy in Web Standards


Developmental Psychology as an Ethical Enterprise


title: Classroom Applications of Research on Self-Regulated Learning

abstract: This article describes how self-regulated learning (SRL) has become a popular topic in research in educational psychology and how the research has been translated into classroom practices. Research during the past 30 years on students' learning and achievement has progressively included emphases on cognitive strategies, metacognition, motivation, task engagement, and social supports in classrooms. SRL emerged as a construct that encompassed these various aspects of academic learning and provided more holistic views of the skills, knowledge, and motivation that students acquire. The complexity of SRL has been appealing to educational researchers who seek to provide effective interventions in schools that benefit teachers and students directly. Examples of SRL in classrooms are provided for three areas of research: strategies for reading and writing, cognitive engagement in tasks, and self-assessment. The pedagogical principles and underlying research are discussed for each area. Whether SRL is viewed as a ...


title: MSER-Based Real-Time Text Detection and Tracking

abstract: We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the full-detection approaches in terms of time performance optimization. A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices.


title: Historical Document Layout Analysis Competition

abstract: This paper presents an objective comparative evaluation of layout analysis methods for scanned historical documents. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2011 and the International Workshop on Historical Document Imaging and Processing (HIP2011), presenting the results of the evaluation of four submitted methods. A commercial state-of-the-art system is also evaluated for comparison. Two scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and the other evaluating the whole pipeline of segmentation and region classification (with a text extraction goal). The results indicate that there is a convergence to a certain methodology with some variations in the approach. However, there is still a considerable need to develop robust methods that deal with the idiosyncrasies of historical documents.


title: Quality assessment of photovoltaic cells with p-n junction structure

abstract: In this work research results are given for photovoltaic cells that were explored using differential spectroscopy method for currents. The expressions for semiconductor structure's with p-n junction series and shunt resistances determination were obtained. It is determined specific load resistance value that equal to effective resistance value of electric circuit consists of load and ideal semiconductor structure with p-n junction. It is offered the way to improve load resistance value selection.


title: Benefiting from Social Capital in Online Support Groups: An Empirical Study of Cancer Patients

abstract: With measures specific to the online cancer environment and data from an online survey of cancer patients, the current study finds support for the following model: asynchronous online communication → social interaction → social support → positive health outcomes in terms of stress, depression, and coping. The findings suggest that the Internet can be a positive cyber venue for cancer patients as they confront illness, undergo treatment, and seek out support.


title: Intrinsic Motivation and Organizational Identification Among On-Demand Workers

abstract: On-demand firms provide services for clients through a network of on-demand workers ready to complete specific tasks for a set contractual price. Given such on-demand work is defined by payment on short-term contracts with no obligation for continued employment, there is little reason to believe on-


title: Anomaly Detection on Graph Time Series

abstract: In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the graph convolutional network. In order to incorporate external factors, we use feature extractor to augment the transition of latent variables, which can learn the influence of external factors. With the target function as accumulative ELBO, it is easy to extend this model to on-line method. The experimental study on traffic flow data shows the detection capability of the proposed method.


title: Cyber security of smart grids modeled through epidemic models in cellular automata

abstract: Due to their distributed management, smart grids can be vulnerable to malicious attacks that undermine their cyber security. An adversary can take control of few nodes in the network and spread digital attacks like an infection, whose diffusion is facilitated by the lack of centralized supervision within the smart grid. In this paper, we propose to investigate these phenomena by means of epidemic models applied to cellular automata. We show that the common key parameters of epidemic models, such as the basic reproductive ratio, are also useful in this context to understand the extent of the grid portion that can be compromised. At the same time, the lack of mobility of individuals limits the spreading of the infection. In particular, we evaluate the role of the grid connectivity degree in both containing the epidemics and avoiding its spreading on the entire network, and also increasing the number of nodes that do not get any contact with the cyber attacks.


title: Interpretable Decision Sets: A Joint Framework for Description and Prediction

abstract: One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model's prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then rules. Because each rule can be applied independently, decision sets are simple, concise, and easily interpretable. We formalize decision set learning through an objective function that simultaneously optimizes accuracy and interpretability of the rules. In particular, our approach learns short, accurate, and non-overlapping rules that cover the whole feature space and pay attention to small but important classes. Moreover, we prove that our objective is a non-monotone submodular function, which we efficiently optimize to find a near-optimal set of rules. Experiments show that interpretable decision sets are as accurate at classification as state-of-the-art machine learning techniques. They are also three times smaller on average than rule-based models learned by other methods. Finally, results of a user study show that people are able to answer multiple-choice questions about the decision boundaries of interpretable decision sets and write descriptions of classes based on them faster and more accurately than with other rule-based models that were designed for interpretability. Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency.


title: Omnidirectional Circularly Polarized Dielectric Resonator Antenna With Top-Loaded Alford Loop for Pattern Diversity Design

abstract: A new compact omnidirectional circularly polarized (CP) cylindrical dielectric resonator antenna (DRA) with a top-loaded modified Alford loop is investigated. Fed by an axial probe, the DRA is excited in its TM01δ-mode, which radiates like a vertically polarized electric monopole. The modified Alford loop comprises a central circular patch and four curved branches. It is placed on the top of the DRA and provides an equivalent horizontally polarized magnetic dipole mode. Omnidirectional CP fields can be obtained when the two orthogonally polarized fields are equal in amplitude but different in phase by 90°. This CP DRA is applied to the design of a two-port CP diversity DRA which provides omnidirectional and broadside radiation patterns. The broadside radiation pattern is obtained by making use of the broadside HEM12δ+ 1-mode of the DRA, which is excited by a balanced slot serially fed by a microstrip line. For demonstration, both the omnidirectional CP DRA and the diversity CP DRA were designed at ~ 2.4 GHz for WLAN applications. Their S-parameters, axial ratios, radiation patterns, antenna gains, and antenna efficiencies are studied. The envelope correlation is also found for the diversity design. Reasonable agreement between the simulated and measured results is observed.


Mangrove zonation along an eroding coastline in King Sound, north-western Australia.


title: Design of Low Phase Noise Dielectric Resonator Oscillators with GaInP HBT devices exploiting a Non-Linear Noise Model

abstract: This paper presents the design, implementation and characterization of two dielectric resonator oscillators (DRO) at 7.61 GHz. The circuits consist of a negative resistance monolithic bipole coupled in a series feedback configuration with a ceramic dielectric resonator. The two circuits exploit different sizes for the active device. The adopted monolithic process is a commercially available GalnP HBT with very good low frequency noise performance. The measured phase noise performance of the two circuits are -120 dBc/Hz and -135 dBc/Hz @ 10 kHz from the carrier respectively, which represent to the authors' knowledge state-of-the-art performance for this technology and resonator type. These excellent results have been obtained by means of a proper large signal design technique and exploiting the predictive capability of a non-linear cyclostationary noise model, which proves its excellent predictive capability for these very low phase noise levels.


title: TRACON: Interference-Aware Schedulingfor Data-Intensive Applicationsin Virtualized Environments

abstract: Large-scale data centers leverage virtualization technology to achieve excellent resource utilization, scalability, and high availability. Ideally, the performance of an application running inside a virtual machine (VM) shall be independent of co-located applications and VMs that share the physical machine. However, adverse interference effects exist and are especially severe for data-intensive applications in such vir-tualized environments. In this work, we present TRACON, a novel Task and Resource Allocation CONtrol framework that mitigates the interference effects from concurrent data-intensive applications and greatly improves the overall system performance. TRACON utilizes modeling and control techniques from statistical machine learning and consists of three major components: the interference prediction model that infers application performance from resource consumption observed from different VMs, the interference-aware scheduler that is designed to utilize the model for effective resource management, and the task and resource monitor that collects application characteristics at the runtime for model adaption. We simulate TRACON with a wide variety of data-intensive applications including bioinformatics, data mining, video processing, email and web servers, etc. The evaluation results show that TRACON can achieve up to 50% improvement on application runtime, and up to 80% on I/O throughput for data-intensive applications in virtu-alized data centers.


title: Asynchronous Multi-task Learning

abstract: Many real-world machine learning applications involve several learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a certain disease for many hospitals. The models for each hospital may be different because of the inherent differences in the distributions of the patient populations. However, the models are also closely related because of the nature of the learning tasks modeling the same disease. By simultaneously learning all the tasks, multi-task learning (MTL) paradigm performs inductive knowledge transfer among tasks to improve the generalization performance. When datasets for the learning tasks are stored at different locations, it may not always be feasible to transfer the data to provide a data-centralized computing environment due to various practical issues such as high data volume and privacy. In this paper, we propose a principled MTL framework for distributed and asynchronous optimization to address the aforementioned challenges. In our framework, gradient update does not wait for collecting the gradient information from all the tasks. Therefore, the proposed method is very efficient when the communication delay is too high for some task nodes. We show that many regularized MTL formulations can benefit from this framework, including the low-rank MTL for shared subspace learning. Empirical studies on both synthetic and real-world datasets demonstrate the efficiency and effectiveness of the proposed framework.


title: Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges

abstract: Text is a very important type of data within the biomedical domain. For example, patient records contain large amounts of text which has been entered in a non-standardized format, consequently posing a lot of challenges to processing of such data. For the clinical doctor the written text in the medical findings is still the basis for decision making – neither images nor multimedia data. However, the steadily increasing volumes of unstructured information need machine learning approaches for data mining, i.e. text mining. This paper provides a short, concise overview of some selected text mining methods, focusing on statistical methods, i.e. Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Hierarchical Latent Dirichlet Allocation, Principal Component Analysis, and Support Vector Machines, along with some examples from the biomedical domain. Finally, we provide some open problems and future challenges, particularly from the clinical domain, that we expect to stimulate future research.


title: Ultrastructural integrity of mouse testicular cells separated by velocity sedimentation

abstract: Mouse testicular cells were examined ultrastructurally to determine whether the cells are damaged during the preparation of single-cell suspensions or during cell separation. The testicular cells were dissociated from seminiferous tubules by trypsinization and were fixed immediatley; fixed after being held in suspension for 4 h at 4 degrees C; or fixed after being separated into enriched fractions by sedimentation velocity either at unit gravity or by centrifugal elutriation. In general, the ultrastructural integrity of the cells, compared with that of corresponding testicular cells fixed in situ, was maintained during the dissociation and separation procedures. Ultrastructural abnormalities were most frequently produced in Sertoli cells and were occasionally observed in the acrosomes and nuclei of round spermatids. The cytoplasmic matrix of the midpiece of mature elongated spermatids or spermatozoa and the acrosomes of these cells were often disrupted. It is suggested that the dissociation procedures were responsible for most of the observed alterations of ultrastructural integrity.


title: Evaluating user experience in moodle learning management systems

abstract: Learning management systems are becoming widespread technology adopted at educational systems. Moodle system is used to record the user experience in e-learning environment. In Moodle system, the user experience is recorded and must be studied and analyzed to provide the optimal solution to meet pedagogical requirements of both students and teachers in all areas. In Moodle Learning management systems can offer a large variety of workspaces to smooth the progress of information sharing and communication among participants in e-learning courses. This study evaluates an open source Moodle learning management system to help academicians create effective online learning communities using data mining techniques.


title: Urban growth patterns and growth management boundaries in the Central Puget Sound, Washington, 1986–2007

abstract: Many regions of the globe are experiencing rapid urban growth, the location and intensity of which can have negative effects on ecological and social systems. In some locales, planners and policy makers have used urban growth boundaries to direct the location and intensity of development; however the empirical evidence for the efficacy of such policies is mixed. Monitoring the location of urban growth is an essential first step in understanding how the system has changed over time. In addition, if regulations purporting to direct urban growth to specific locales are present, it is important to evaluate if the desired pattern (or change in pattern) has been observed. In this paper, we document land cover and change across six dates (1986, 1991, 1995, 1999, 2002, and 2007) for six counties in the Central Puget Sound, Washington State, USA. We explore patterns of change by three different spatial partitions (the region, each county, 2000 U.S. Census Tracks), and with respect to urban growth boundaries implemented in the late 1990’s as part of the state’s Growth Management Act. Urban land cover increased from 8 to 19% of the study area between 1986 and 2007, while lowland deciduous and mixed forests decreased from 21 to 13% and grass and agriculture decreased from 11 to 8%. Land in urban classes outside of the urban growth boundaries increased more rapidly (by area and percentage of new urban land cover) than land within the urban growth boundaries, suggesting that the intended effect of the Growth Management Act to direct growth to within the urban growth boundaries may not have been accomplished by 2007. Urban sprawl, as estimated by the area of land per capita, increased overall within the region, with the more rural counties within commuting distance to cities having the highest rate of increase observed. Land cover data is increasingly available and can be used to rapidly evaluate urban development patterns over large areas. Such data are important inputs for policy makers, urban planners, and modelers alike to manage and plan for future population, land use, and land cover changes.


title: Comparison of Skeleton and Non-Skeleton Shape Descriptors for 3D Video

abstract: This paper presents a performance evaluation of shape similarity metrics for 3D video sequences of people with unknown temporal correspondence. Previous evaluation focuses on non-skeletal similarity metrics. Since the human motion is essentially an articulated motion, it is also interesting to investigate skeletal-based similarity metrics. In this paper, we extend previous evaluation of non-skeletal similarity metrics to skeletal-based similarity metrics. A quantitative evaluation based on the Receiver-Operator Characteristic (ROC) curve for the descriptors using a ground-truth dataset for synthetic motion sequences is presented. Reeb Graph, Multi-Dimensional Scaling and Shape Histograms are compared with and without a temporal filter. Reeb Graph and Shape Histograms achieve comparable ROC performance, both outperform MDS in the task of finding similar poses of the same person in 3D video. Finally, temporal Reeb Graph and Shape Histograms are applied to a public database of 3D video of people to identify optimal transitions and synthesise 3D character animation. Results demonstrate the accurate matching of surface shape and motion.


title: You Should Use Regression to Detect Cells

abstract: Automated cell detection in histopathology images is a hard problem due to the large variance of cell shape and appearance. We show that cells can be detected reliably in images by predicting, for each pixel location, a monotonous function of the distance to the center of the closest cell. Cell centers can then be identified by extracting local extremums of the predicted values. This approach results in a very simple method, which is easy to implement. We show on two challenging microscopy image datasets that our approach outperforms state-of-the-art methods in terms of accuracy, reliability, and speed. We also introduce a new dataset that we will make publicly available.


title: High Accuracy Time of Flight Measurement for Ultrasonic Anemometer Applications

abstract: Ultrasonic anemometer system based on new digital signal processing technique is described. The anemometer system consists of two pairs of ultrasonic transducers placed vertically for vector result. In one pair of transducers, short train of waves is transmitted from one transducer to another. The amplitude of the received waveform is an envelope which starts from zero reaches to a peak and then dies out, ultrasonic anemometer measurements are based on the determination of the time of flight (T.o.F), a common method used is threshold crossing technique, which introduces error due to the noise and piezoelectric transducer's diverse mechanical characteristics. In this paper digital signal processing and cluster analysis is used to determine the accurate time delay. Compared to other signal processing technique such as threshold crossing. Result show the new processing technique is stable, accurate and capable in practical measurement.


title: Natural Actor-Critic

abstract: In this paper, we suggest a novel reinforcement learning architecture, the Natural Actor-Critic. The actor updates are achieved using stochastic policy gradients employing Amari's natural gradient approach, while the critic obtains both the natural policy gradient and additional parameters of a value function simultaneously by linear regression. We show that actor improvements with natural policy gradients are particularly appealing as these are independent of coordinate frame of the chosen policy representation, and can be estimated more efficiently than regular policy gradients. The critic makes use of a special basis function parameterization motivated by the policy-gradient compatible function approximation. We show that several well-known reinforcement learning methods such as the original Actor-Critic and Bradtke's Linear Quadratic Q-Learning are in fact Natural Actor-Critic algorithms. Empirical evaluations illustrate the effectiveness of our techniques in comparison to previous methods, and also demonstrate their applicability for learning control on an anthropomorphic robot arm.


title: Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning

abstract: In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 different attack scenarios. We find that our DNN generates fewer false positives than our one-class SVM while our SVM detects slightly more anomalies. Overall, our DNN has a slightly better F measure than our SVM. We discuss the characteristics of the DNN and one-class SVM used in this experiment, and compare the advantages and disadvantages of the two methods.


title: Laryngeal Findings in Superior Laryngeal Nerve Paralysis: A Controversy

abstract: The diagnosis of superior laryngeal nerve paralysis is infrequently made because of disagreement concerning the laryngeal findings in unilateral cricothyroid muscle dysfunction. Results of experimental unilateral superior laryngeal nerve paralysis in dogs and humans are shown with a review of the literature. The findings are documented by electromyographic studies and laryngeal photographs, and serve to clarify aspects of the functional anatomy of the cricothyroid muscle and the cricoid and thyroid cartilages.


title: A prescription fraud detection model

abstract: Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs.


title: Digitally controlled power amplifier for underwater electro acoustic transducers

abstract: A digitally controlled sonar power amplifier for underwater transmitter is explained in this paper. It proposes a method of generating unipolar Sine Pulse Width Modulation (SPWM) signals for echo ranging sonar. This method can be effectively used in all kinds of inverters including high frequency inverters. A low cost digital signal controller was used to generate SPWM signals and used as gating pulses for a Controlled Full Bridge Circuit (CFBC). The power required, frequency, pulse-width and pulse repetition interval are given as the inputs to the digital controller and gating pulses for the CFBC are generated as output from the controller. A power transformer connected across CFBC boosts output voltage followed by power filter which removes unwanted harmonics present in the output. The output after the power filter is used to excite electro-acoustic transducer elements underwater. The voltage and current of the load were given as feedback to the same controller for voltage mode control loop implementation of the system. A prototype of 1kW Switch Mode Power Amplifier (SMPA) for echo ranging sonar has been designed and digital control has been implemented. Salient results are presented.


title: Quasi real-time gait event detection using shank-attached gyroscopes

abstract: This article describes an ambulatory gait event detection method for long-term monitoring of walking. Aminian et al. [2] have developed an automatic gait event detection algorithm based on shank-attached gyroscope signals. However, this algorithm has a drawback in that it is post-processed. We propose a modified algorithm which detects foot initial and end contact timings using the same concept as in [2], but in quasi real-time. The utilization of the knowledge on gait sequence and peak angular acceleration realizes the quasi real-time detection. Furthermore, to be practical, the algorithm has been developed to ensure the robustness of detection (i.e., without missing the gait events in various speed conditions). Validation of the algorithm using footswitches shows that the algorithm detected the end contacts earlier (−8 ms) and the initial contacts later (19 ms) than the footswitch-based method.


title: Guiding InfoGAN with Semi-Supervision

abstract: In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0.22%, max. 10% of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories. The architecture builds on Information Maximizing Generative Adversarial Networks (InfoGAN) and is shown to learn both continuous and categorical codes and achieves higher quality of synthetic samples compared to fully unsupervised settings. Furthermore, we show that using small amounts of labeled data speeds-up training convergence. The architecture maintains the ability to disentangle latent variables for which no labels are available. Finally, we contribute an information-theoretic reasoning on how introducing semi-supervision increases mutual information between synthetic and real data.


Review of Unmanned Aircraft System (UAS)


title: Long-term survival of porcelain laminate veneers using two preparation designs: a retrospective study.

abstract: PURPOSE ::: This study evaluated the long-term survival of anterior porcelain laminate veneers placed with and without incisal porcelain coverage. ::: ::: ::: MATERIALS AND METHODS ::: Two prosthodontists in a private dental practice placed 110 labial feldspathic porcelain veneers in 50 patients; 46 veneers were provided with incisal porcelain coverage, and 64 were not. The veneers were evaluated retrospectively from case records for up to 7 years (mean 4 years). ::: ::: ::: RESULTS ::: At 5, 6, and 7 years, the cumulative survival estimates were 95.8% for veneers with incisal porcelain coverage and 85.5% for those without incisal coverage. The difference was not statistically significant. Six of the nine failures occurred from porcelain fracture in the veneers without incisal coverage. ::: ::: ::: CONCLUSION ::: Although there was a trend for better long-term survival of the veneers with incisal porcelain coverage, this finding was not statistically significant.


title: Transient and Static Hybrid-Triggered Active Clamp Design for Power-Rail ESD Protection

abstract: A transient and static hybrid-triggered active clamp is proposed in this paper. By skillfully incorporating different detection mechanisms, the proposed clamp achieves enhanced static electrical overstress protection capability over the transient one. Furthermore, the proposed clamp achieves improved electrostatic discharge reaction speed in both human body model and charged device model events over the static one. Moreover, the superior transient-noise immunity of the proposed clamp over traditional transient ones is essentially revealed in this paper. The proposed clamp is successfully verified in a 65-nm bulk CMOS process. In addition, the design flexibility of the proposed clamp for other processes is also deeply discussed in this paper.


title: Struggling or exploring?: disambiguating long search sessions

abstract: Web searchers often exhibit directed search behaviors such as navigating to a particular Website. However, in many circumstances they exhibit different behaviors that involve issuing many queries and visiting many results. In such cases, it is not clear whether the user's rationale is to intentionally explore the results or whether they are struggling to find the information they seek. Being able to disambiguate between these types of long search sessions is important for search engines both in performing retrospective analysis to understand search success, and in developing real-time support to assist searchers. The difficulty of this challenge is amplified since many of the characteristics of exploration (e.g., multiple queries, long duration) are also observed in sessions where people are struggling. In this paper, we analyze struggling and exploring behavior in Web search using log data from a commercial search engine. We first compare and contrast search behaviors along a number dimensions, including query dynamics during the session. We then build classifiers that can accurately distinguish between exploring and struggling sessions using behavioral and topical features. Finally, we show that by considering the struggling/exploring prediction we can more accurately predict search satisfaction.


title: An ensemble approach to detect review spam using hybrid machine learning technique

abstract: Online reviews are becoming one of the vital components of e-commerce in recent years as so many people consider having different opinions prior to buying online products or apprehending any online service. Nowadays, in the era of web 2.0, it is completely understandable that people rely on online reviews more than ever while taking a decision. However, guaranteeing the authenticity of these sensitive and valuable information is hardly visible. Due to fulfill some immoral benefits, many people post fake review or fabricated opinion to uphold or devalue a certain product or service which certainly hampers the ingenuousness of the real fact. To detect fake reviews, many methodologies were introduced by harvesting the obvious content features, rating consistency, empirical conditions, helpfulness voting etc. The most of them are supervised models which mostly rely on pseudo fake reviews and the scarcity of good quality large-scale labeled dataset is still a hindrance. In this paper, we introduce an ensemble learning approach which combines two different types of learning methods (active and supervised) by creating a hybrid dataset of both real-life and pseudo reviews. This model holds 3 different filtering phases that is based on KL and JS distance, TF-IDF features and n-gram features of the review content. It achieves phenomenal results while working on almost 3600 reviews from different domains. In the best case, the precision, recall and f-score are above 95% and the accuracy it achieved is slightly above 88%. In the process, about 2000 reviews were manually labeled. After evaluating and comparing the results with other successful methods, it is quite clear that this detecting method is efficient and very promising.


title: Water pollution and human health in China.

abstract: China's extraordinary economic growth, industrialization, and urbanization, coupled with inadequate investment in basic water supply and treatment infrastructure, have resulted in widespread water ...


title: Tracking the activity of participants in a meeting

abstract: A vision system suitable for a smart meeting room able to analyse the activities of its occupants is described. Multiple people were tracked using a particle filter in which samples were iteratively re-weighted using an approximate likelihood in each frame. Trackers were automatically initialised and constrained using simple contextual knowledge of the room layout. Person–person occlusion was handled using multiple cameras. The method was evaluated on video sequences of a six person meeting. The tracker was demonstrated to outperform standard sampling importance re-sampling. All meeting participants were successfully tracked and their actions were recognised throughout the meeting scenarios tested.


title: Formality of Language: definition, measurement and behavioral determinants

abstract: A new concept of formality of linguistic expressions is introduced and argued to be the most important dimension of variation between styles or registers. Formality is subdivided into "deep" formality and "surface" formality. Deep formality is defined as avoidance of ambiguity by minimizing the context-dependence and fuzziness of expressions. This is achieved by explicit and precise description of the elements of the context needed to disambiguate the expression. A formal style is characterized by detachment, accuracy, rigidity and heaviness; an informal style is more flexible, direct, implicit, and involved, but less informative. An empirical measure of formality, the F-score, is proposed, based on the frequencies of different word classes in the corpus. Nouns, adjectives, articles and prepositions are more frequent in formal styles; pronouns, adverbs, verbs and interjections are more frequent in informal styles. It is shown that this measure, though coarse-grained, adequately distinguishes more from less formal genres of language production, for some available corpora in Dutch, French, Italian, and English. A factor similar to the F-score automatically emerges as the most important one from factor analyses applied to extensive data in 7 different languages. Different situational and personality factors are examined which determine the degree of formality in linguistic expression. It is proposed that formality becomes larger when the distance in space, time or background between the interlocutors increases, and when the speaker is male, introverted or academically educated. Some empirical evidence and a preliminary theoretical explanation for these propositions is discussed. Short Abstract: The concept of "deep" formality is proposed as the most important dimension of variation between language registers or styles. It is defined as avoidance of ambiguity by minimizing the context-dependence and fuzziness of expressions. An empirical measure, the F-score, is proposed, based on the frequencies of different word classes. This measure adequately distinguishes different genres of language production using data for Dutch, French, Italian, and English. Factor analyses applied to data in 7 different languages produce a similar factor as the most important one. Both the data and the theoretical model suggest that formality increases when the distance in space,


title: Information technology development creativity: a case study of attempted radical change

abstract: Many calls for reengineering suggest that information technology (IT) can be employed to enable significant organizational change. However, organizational inertia typically inhibits such change, resulting in IT development that reflects the organizational status quo. Of interest, then, are strategies and techniques that can be employed to support IT-enabled reengineering. Key to such reengineering is creativity. Therefore, this paper attempts to better understand creativity during IT requirements and logical design phases, at which time reengineering can be devised. A creativity model is adapted from the organizational literature and used to interpret the requirements gathering and logical design experiences of a firm attempting to reengineer its operations through the use of imaging technology. A comparison of creativity and traditional IT development viewpoints reveals significant differences as well as similarities. Insight resulting from this exercise is offered to help managers and researchers identify important variables and relationships in the IT development creativity management process. The model, and future associated research, can help researchers and managers: (1) determine to what degree creative IT requirements and logical design is feasible in a given organizational context and (2) plan and execute a creative IT requirements and logical design process.


title: Large-Scale Deep Learning For Building Intelligent Computer Systems

abstract: For the past five years, the Google Brain team has focused on conducting research in difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has recently open-sourced the TensorFlow system (tensorflow.org), a system designed to easily express machine ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the design decisions we made in building TensorFlow, discuss research results produced within our group, and describe ways in which these ideas have been applied to a variety of problems in Google's products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.


title: Solar PV-battery based hybrid water pumping system using BLDC motor drive

abstract: This paper deals with a brushless DC (BLDC) motor driven water pump powered by a solar photovoltaic (SPV) array and a battery storage. The SPV-battery based hybrid generation is used as a power source in order to achieve a continuous full volume water delivery regardless of the climate condition. The SPV array is used as a primary source while the battery as a backup. Therefore; the battery is discharged only under bad climate condition or at night when the PV array is insufficient to feed the water pump. Additionally; it is charged by the SPV array when the water delivery is not required. Thus; no external supply is used for the battery charging. A bidirectional charging control enables to switch the mode of operation of the battery automatically through a buck-boost DC-DC converter. The BLDC motor is consistently operated at its rated speed and load. No current sensing is required for the speed control and the power devices of voltage source inverter (VSI) are switched at fundamental frequency. The various performance analysis of the proposed water pumping are carried out in MATLAB/ SIMULINK platform.


title: All-Digital Process-Variation-Calibrated Timing Generator for ATE With 1.95-ps Resolution and Maximum 1.2-GHz Test Rate

abstract: In this paper, an all-digital process-variation-calibrated high-performance timing generator for an automatic test equipment is proposed. The proposed timing generator generates process-variation-tolerant variable delays for high and wide-range testing clock frequency. In order to increase the testing clock frequency, a channel of the proposed timing generator consists of four subtiming generators operating in parallel. In addition, to improve process variation robustness, a precise eight-phase generator consisting of an accurate reference generator and a phase generator with dual-loop calibration (CAL) is proposed, and a phase error of less than 1.21° is achieved. Dynamic and static CAL techniques are also adopted in the edge vernier. A prototype chip having eight channels is fabricated using 0.13- $\mu \text{m}$ CMOS technology. The proposed timing generator has an arbitrary test cycle frequency of up to 1.2 GHz, a timing resolution of 1.95 ps, a power consumption of 90 mW, and an area of 1.5 mm2.


title: Estrogen plus Progesterone Increases Progestin Receptor Immunoreactivity in the Brain of Ovariectomized Guinea Pigs

abstract: The goal of these experiments was to determine the number and distribution of brain cells that contain progestin receptors (PR) and to determine the effect of estrogen and estrogen plus progesterone on PR content of those cells. Ovariectomized adult female guinea pigs were treated with oil (control), or estrogen followed by oil, or estrogen followed by progesterone. As expected, only those animals treated with estrogen plus progesterone became sexually receptive. The cellular content of PR was determined using a monoclonal antibody to the receptor, and standard immunocytochemical techniques. Analysis of the PR-immunoreactive (PR-IR) cells consisted of: (1) mapping the anatomical distribution of PR-IR cells; (2) analyzing the effect of steroid hormones on PR-IR cell number, and (3) determining the effect of steroid hormones on PR immunoreactivity per cell. PR immunoreactivity was located exclusively in the nuclei of cells in the preoptic area and hypothalamus. The most dense collections of PR-IR cells were found in the preoptic area, ventrolateral nucleus of the hypothalamus, and infundibular nucleus. Estrogen caused a dramatic increase in the number of PR-IR cells in these cell groups. Sequential treatment with estrogen plus progesterone further increased PR-IR cell number, in the preoptic area by 65%, in the ventrolateral nucleus by 38%, and in the infundibular nucleus by 49%. A cell-by-cell rating of the PR immunoreactivity was carried out in these three cell groups. We found that the staining intensity across the populations of PR-IR cells was increased by estrogen and further increased by sequential estrogen plus progesterone. Alterations in cellular PR content may contribute importantly to the ability of progesterone target cell groups to perform their specialized roles in steroid-regulated activity.


title: Exploiting Mobile Big Data: Sources, Features, and Applications

abstract: The worldwide rollout of 4G LTE mobile communication networks has accelerated the proliferation of the mobile Internet and spurred a new wave of mobile applications on smartphones. This new wave has provided mobile operators an enormous opportunity to collect a huge amount of data to monitor the technical and transactional aspects of their networks. Recent research on mobile big data mining have shown its great potential for diverse purposes ranging from improving traffic management, enabling personal and contextual services, to monitoring city dynamics and so on. The mobile big data research has a multi-disciplinary nature that demands distinct knowledge from mobile communications, signal processing, and data mining. The research field of mobile big data has emerged quickly in recent years, but is somewhat fragmented. This article aims to provide an integrated picture of this emerging field to bridge multiple disciplines and hopefully to inspire future research.


title: DOI:10.1068/b2725 Exploring isovist fields: space and shape in architectural and urban morphology

abstract: The space that can be seen from any vantage point is called an isovist and the set of such spaces forms a visual field whose extent defines different isovist fields based on different geometric properties. I suggest that our perceptions of moving within such fields might be related to these geometric properties. I begin with a formal representation of isovists and their fields, introducing simple geometric measures based on distance, area, perimeter, compactness, and convexity. I suggest a feasible computational scheme for measuring such fields, and illustrate how we can visualize their spatial and statistical properties by using maps and frequency distributions. I argue that the classification of fields based on these measures must be a prerequisite to the proper analysis of architectural and urban morphologies. To this end, I present two hypothetical examples based on simple geometries and three real examples based on London's Tate Gallery, Regent Street, and the centre of the English town of Wolverhampton. Although such morphologies can often be understood in terms of basic geometrical elements such as corridors, streets, rooms, and squares, isovist analysis suggests that visual fields have their own form which results from the interaction of geometry and movement. To illustrate how such analysis can be used, I outline methods of partitioning space, covering it with a small number of relatively independent isovists, and perceiving space by recording properties of the isovist fields associated with paths through that space.


title: Comparison of Filling Agents for Lip Augmentation

abstract: The authors discuss various filling agents currently available that can be used to augment the lips, correct perioral rhytides, and enhance overall lip appearance. Fillers are compared and information provided about choosing the appropriate agent based on the needs of each patient to achieve the much coveted “pouty” look while avoiding hypercorrection. The authors posit that the goal for the upper lip is to create a form that harmonizes with the patient's unique features, taking into account age and ethnicity; the goal for the lower lip is to create bulk, greater prominence, and projection of the vermillion.


title: Topological Recurrent Neural Network for Diffusion Prediction

abstract: In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an inactive node to be activated next in a cascade. Despite the success of recent deep learning methods for diffusion, we find that they often underexplore the cascade structure. We consider a cascade as not merely a sequence of nodes ordered by their activation time stamps; instead, it has a richer structure indicating the diffusion process over the data graph. As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure. We find it challenging to model diffusion topologies, which are dynamic directed acyclic graphs (DAGs), with the existing neural networks. Therefore, we propose a novel topological recurrent neural network, namely Topo-LSTM, for modeling dynamic DAGs. We customize Topo-LSTM for the diffusion prediction task, and show it improves the state-of-the-art baselines, by 20.1%--56.6% (MAP) relatively, across multiple real-world data sets. Our code and data sets are available online at https://github.com/vwz/topolstm.


title: An Empirical Methodology to Evaluate Vulnerability Discovery Models

abstract: Vulnerability discovery models (VDMs) operate on known vulnerability data to estimate the total number of vulnerabilities that will be reported after a software is released. VDMs have been proposed by industry and academia, but there has been no systematic independent evaluation by researchers who are not model proponents. Moreover, the traditional evaluation methodology has some issues that biased previous studies in the field. In this work we propose an empirical methodology that systematically evaluates the performance of VDMs along two dimensions (quality and predictability) and addresses all identified issues of the traditional methodology. We conduct an experiment to evaluate most existing VDMs on popular web browsers’ vulnerability data. Our comparison shows that the results obtained by the proposed methodology are more informative than those by the traditional methodology. Among evaluated VDMs, the simplest linear model is the most appropriate choice in terms of both quality and predictability for the first 6-12 months since a release date. Otherwise, logistics-based models are better choices.


title: The relationship between personality, approach to learning and academic performance

abstract: This study considers the relationship between students' approaches to learning, as measured by a short-form of Entwistle and Tait's (1995) Revised Approaches to Studying Inventory (RASI), the Big Five personality factors, as measured by Cattell's 16PFi, and the background variables of age, gender and prior educational achievement and academic performance. Subjects were 146 social science undergraduate students at a university in Scotland. Structural equation modelling identifies the Big Five personality factor scores account for between 22.7% and 43.6% of the variance across scores on the three approach to learning dimensions. Four of the Big Five personality factors and the three approach to learning dimensions were found to be poor predictors of academic performance. A linear regression analysis with academic performance as the dependent variable and age, prior educational attainment and conscientiousness as independent variables, accounted for 24.1% of the variance in performance. Our investigation suggests approach to learning is a subset of personality. However, we conclude it makes sense to measure these two groups of variables separately in educational settings.


title: Congestion-aware path selection for Tor

abstract: Tor, an anonymity network formed by volunteer nodes, uses the estimated bandwidth of the nodes as a central feature of its path selection algorithm. The current load on nodes is not considered in this algorithm, however, and we observe that some nodes persist in being under-utilized or congested. This can degrade the network’s performance, discourage Tor adoption, and consequently reduce the size of Tor’s anonymity set. In an effort to reduce congestion and improve load balancing, we propose a congestion-aware path selection algorithm. Using latency as an indicator of congestion, clients use opportunistic and lightweight active measurements to evaluate the congestion state of nodes, and reject nodes that appear congested. Through experiments conducted on the live Tor network, we verify our hypothesis that clients can infer congestion using latency and show that congestion-aware path selection can improve performance.


title: Generalization of backpropagation with application to a recurrent gas market model

abstract: Abstract Backpropagation is often viewed as a method for adapting artificial neural networks to classify patterns. Based on parts of the book by Rumelhart and colleagues, many authors equate backpropagation with the generalized delta rule applied to fully-connected feedforward networks. This paper will summarize a more general formulation of backpropagation, developed in 1974, which does more justice to the roots of the method in numerical analysis and statistics, and also does more justice to creative approaches expressed by neural modelers in the past year or two. It will discuss applications of backpropagation to forecasting over time (where errors have been halved by using methods other than least squares), to optimization, to sensitivity analysis, and to brain research. This paper will go on to derive a generalization of backpropagation to recurrent systems (which input their own output), such as hybrids of perceptron-style networks and Grossberg/Hopfield networks. Unlike the proposal of Rumelhart, Hinton, and Williams, this generalization does not require the storage of intermediate iterations to deal with continuous recurrence. This generalization was applied in 1981 to a model of natural gas markets, where it located sources of forecast uncertainty related to the use of least squares to estimate the model parameters in the first place.


title: Advances in Photopletysmography Signal Analysis for Biomedical Applications

abstract: Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost⁻benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.


title: Image label completion by pursuing contextual decomposability

abstract: This article investigates how to automatically complete the missing labels for the partially annotated images, without image segmentation. The label completion procedure is formulated as a nonnegative data factorization problem, to decompose the global image representations that are used for describing the entire images, for instance, various image feature descriptors, into their corresponding label representations, that are used for describing the local semantic regions within images. The solution provided in this work is motivated by following observations. First, label representations of the regions with the same label often share certain commonness, yet may be essentially different due to the large intraclass variations. Thus, each label or concept should be represented by using a subspace spanned by an ensemble of basis, instead of a single one, to characterize the intralabel diversities. Second, the subspaces for different labels are different from each other. Third, while two images are similar with each other, the corresponding label representations should be similar. We formulate this cross-image context as well as the given partial label annotations in the framework of nonnegative data factorization and then propose an efficient multiplicative nonnegative update rules to alternately optimize the subspaces and the reconstruction coefficients. We also provide the theoretic proof of algorithmic convergence and correctness. Extensive experiments over several challenging image datasets clearly demonstrate the effectiveness of our proposed solution in boosting the quality of image label completion and image annotation accuracy. Based on the same formulation, we further develop a label ranking algorithms, to refine the noised image labels without any manual supervision. We compare the proposed label ranking algorithm with the state-of-the-arts over the popular evaluation databases and achieve encouragingly improvements.


title: Stronger Security of Authenticated Key Exchange

abstract: Recent work by Krawczyk [12] and Menezes [16] has highlighted the importance of understanding well the guarantees and limitations of formal security models when using them to prove the security of protocols. In this paper we focus on security models for authenticated key exchange (AKE) protocols. We observe that there are several classes of attacks on AKE protocols that lie outside the scope of the Canetti-Krawczyk model. Some of these additional attacks have already been considered by Krawczyk [12]. In an attempt to bring these attacks within the scope of the security model we extend the Canetti-Krawczyk model for AKE security by providing significantly greater powers to the adversary. Our contribution is a more compact, integrated, and comprehensive formulation of the security model. We then introduce a new AKE protocol called NAXOS and prove that it is secure against these stronger adversaries.


title: Large scale multi-label classification via metalabeler

abstract: The explosion of online content has made the management of such content non-trivial. Web-related tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multi-label categorization systems on a large scale. Existing multi-label classification methods either do not scale or have unsatisfactory performance. In this work, we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive cross-validation. Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods. Moreover, MetaLabeler scales to millions of multi-labeled instances and can be deployed easily. This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo!, yielding a significant improvement in performance.


title: Arrest of spermatogenesis in mice expressing an active heat shock transcription factor 1

abstract: In mammals, testicular temperature is lower than core body temperature, and the vulnerable nature of spermatogenesis to thermal insult has been known for a century. However, the primary target affected by increases in temperature is not yet clear. We report here that male mice expressing an active form of heat shock transcription factor 1 (HSF1) in the testis are infertile due to a block in spermatogenesis. The germ cells entered meiotic prophase and were arrested at pachytene stage, and there was a significant increase in the number of apoptotic germ cells in these mice. In wild-type mice, a single heat exposure caused the activation of HSF1 and similar histological changes such as a stage-specific apoptosis of pachytene sperm– atocytes. These results suggest that male infertility caused by thermal insult is at least partly due to the activation of HSF1, which induces the primary spermatocytes to undergo apoptosis.


title: Analyzing and Improving Representations with the Soft Nearest Neighbor Loss

abstract: We explore and expand the $\textit{Soft Nearest Neighbor Loss}$ to measure the $\textit{entanglement}$ of class manifolds in representation space: i.e., how close pairs of points from the same class are relative to pairs of points from different classes. We demonstrate several use cases of the loss. As an analytical tool, it provides insights into the evolution of class similarity structures during learning. Surprisingly, we find that $\textit{maximizing}$ the entanglement of representations of different classes in the hidden layers is beneficial for discrimination in the final layer, possibly because it encourages representations to identify class-independent similarity structures. Maximizing the soft nearest neighbor loss in the hidden layers leads not only to improved generalization but also to better-calibrated estimates of uncertainty on outlier data. Data that is not from the training distribution can be recognized by observing that in the hidden layers, it has fewer than the normal number of neighbors from the predicted class.


title: Negative eigenvalues of the Hessian in deep neural networks

abstract: The loss function of deep networks is known to be non-convex but the precise nature of this nonconvexity is still an active area of research. In this work, we study the loss landscape of deep networks through the eigendecompositions of their Hessian matrix. In particular, we examine how important the negative eigenvalues are and the benefits one can observe in handling them appropriately.


title: Global Explanations of Neural Networks: Mapping the Landscape of Predictions

abstract: A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduce neural network decisions to a single set of features. In response, we present an approach for generating global attributions called GAM, which explains the landscape of neural network predictions across subpopulations. GAM augments global explanations with the proportion of samples that each attribution best explains and specifies which samples are described by each attribution. Global explanations also have tunable granularity to detect more or fewer subpopulations. We demonstrate that GAM's global explanations 1) yield the known feature importances of simulated data, 2) match feature weights of interpretable statistical models on real data, and 3) are intuitive to practitioners through user studies. With more transparent predictions, GAM can help ensure neural network decisions are generated for the right reasons.


title: Informed Democracy: Voting-based Novelty Detection for Action Recognition

abstract: Novelty detection is crucial for real-life applications. While it is common in activity recognition to assume a closed-set setting, i.e. test samples are always of training categories, this assumption is impractical in a real-world scenario. Test samples can be of various categories including those never seen before during training. Thus, being able to know what we know and what we do not know is decisive for the model to avoid what can be catastrophic consequences. We present in this work a novel approach for identifying samples of activity classes that are not previously seen by the classifier. Our model employs a voting-based scheme that leverages the estimated uncertainty of the individual classifiers in their predictions to measure the novelty of a new input sample. Furthermore, the voting is privileged to a subset of informed classifiers that can best estimate whether a sample is novel or not when it is classified to a certain known category. In a thorough evaluation on UCF-101 and HMDB-51, we show that our model consistently outperforms state-of-the-art in novelty detection. Additionally, by combining our model with off-the-shelf zero-shot learning (ZSL) approaches, our model leads to a significant improvement in action classification accuracy for the generalized ZSL setting.


title: Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database

abstract: We have investigated the quantitative effects of a number of common elements of QRS detection rules using the MIT/BIH arrhythmia database. A previously developed linear and nonlinear filtering scheme was used to provide input to the QRS detector decision section. We used the filtering to preprocess the database. This yielded a set of event vectors produced from QRS complexes and noise. After this preprocessing, we tested different decision rules on the event vectors. This step was carried out at processing speeds up to 100 times faster than real time. The role of the decision rule section is to discriminate the QRS events from the noise events. We started by optimizing a simple decision rule. Then we developed a progressively more complex decision process for QRS detection by adding new detection rules. We implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process. The resulting QRS detection algorithm has a sensitivity of 99.69 percent and positive predictivity of 99.77 percent when evaluated with the MIT/BIH arrhythmia database.


title: An Optimization Framework for Online Ride-Sharing Markets

abstract: Taxi services and product delivery services are instrumental for our modern society. Thanks to the emergence of sharing economy, ride-sharing services such as Uber, Didi, Lyft and Google's Waze Rider are becoming more ubiquitous and grow into an integral part of our everyday lives. However, the efficiency of these services are severely limited by the sub-optimal and imbalanced matching between the supply and demand. We need a generalized framework and corresponding efficient algorithms to address the efficient matching, and hence optimize the performance of these markets. Existing studies for taxi and delivery services are only applicable in scenarios of the one-sided market. In contrast, this work investigates a highly generalized model for the taxi and delivery services in the market economy (abbreviated as"taxi and delivery market") that can be widely used in two-sided markets. Further, we present efficient online and offline algorithms for different applications. We verify our algorithm with theoretical analysis and trace-driven simulations under realistic settings.


title: Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions

abstract: Time-varying, smooth trajectory estimation is of great interest to the vision community for accurate and well behaving 3D systems. In this paper, we propose a novel principal component local regression filter acting directly on the Riemannian manifold of unit dual quaternions $\mathbb{D} \mathbb{H}_1$. We use a numerically stable Lie algebra of the dual quaternions together with $\exp$ and $\log$ operators to locally linearize the 6D pose space. Unlike state of the art path smoothing methods which either operate on $SO\left(3\right)$ of rotation matrices or the hypersphere $\mathbb{H}_1$ of quaternions, we treat the orientation and translation jointly on the dual quaternion quadric in the 7-dimensional real projective space $\mathbb{R}\mathbb{P}^7$. We provide an outlier-robust IRLS algorithm for generic pose filtering exploiting this manifold structure. Besides our theoretical analysis, our experiments on synthetic and real data show the practical advantages of the manifold aware filtering on pose tracking and smoothing.


title: Multimodal learning for topic sentiment analysis in microblogging

abstract: Microblogging has become a widely-spread platform of human communication. The massive amount of opinion-rich data in microblogging is helpful to analyze and manage public opinion and social emotion. Different from traditional texts, microblogging data are multimodal, containing multifarious data such as emoticons, image, etc. Most existing sentiment and topic detection approaches treat the unique microblogging data as noise. However, this may lead to unsatisfactoriness in sentiment classification and topic identification. In order to address the issue, we propose a multimodal joint sentiment topic model (MJST) for weakly supervised sentiment analysis in microblogging, which applies latent Dirichlet allocation (LDA) to simultaneously analyze sentiment and topic hidden in messages based the introduction of emoticons and microbloggers personality. Extensive experiments show that MJST outperforms state-of-the-art unsupervised approaches JST, SLDA and DPLDA significantly in terms of sentiment classification accuracy and is promising.


title: Detecting Communities under Differential Privacy

abstract: Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks. However, the problem of doing this in a private manner is rarely considered. In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data. We analyze the major challenges behind the problem and propose several schemes to tackle them from two perspectives: input perturbation and algorithm perturbation. We choose Louvain method as the back-end community detection for input perturbation schemes and propose the method LouvainDP which runs Louvain algorithm on a noisy super-graph. For algorithm perturbation, we design ModDivisive using exponential mechanism with the modularity as the score. We have thoroughly evaluated our techniques on real graphs of different sizes and verified that ModDivisive steadily gives the best modularity and avg.F1Score on large graphs while LouvainDP outperforms the remaining input perturbation competitors in certain settings.


title: Design of costas loop for carrier recovery mechanism of BPSK demodulation

abstract: In digital communication system, data hauling from one chunk to another through intermediate channel. Data can be sent over a bearer through changes in its basic characteristics. Retrieving the transmitted data in receiver is very important part. This paper presents the design of Costas loop for carrier recovery mechanism of BPSK demodulation. On the basis of analyzing the mathematical models of Costas loop, digital design of Costas loop is put forward. In order to utilize resources, the digital design of LPF (low-pass filter), LF (loop filter), VCO (voltage-controlled oscillator), PD (Phase detector) and the multiplier are simulate and integrated in Xilinx. Netlist and functional simulation of digital Costas loop is done using VHDL language.


title: Models of Occupational Therapy Service Provision in the School System

abstract: Occupational therapy is a related service when provided within the public schools, which means that services must enhance or support educational goals. Three service provision models have been described for school therapists: direct service, monitoring, and consultation. Direct service addresses individualized needs that require specialized intervention strategies which can safely be performed only by the occupational therapist. Monitoring, which is sometimes referred to as integrated programming, uses therapeutic expertise within functional tasks to maximize opportunities for practice and generalization. Consultation addresses problems by enabling others to work more effectively on the educational goals they have set for the students. Evidence is accumulating to demonstrate that each model is effective when chosen and applied appropriately. This paper reviews each model, provides examples of studies that have been conducted, and suggests directions for future research.


title: Anti-counterfeit System Based on Mobile Phone QR Code and Fingerprint

abstract: With certificates becoming massive nowadays, anti-counterfeit of certificates turns into an urgent task. The emergence of 2D code provides a kind of effective method for anti-counfeit. Encoding biologic feature (fingerprint) and individual information into QR code. Through realtime collection of the certificate holder’s fingerprint and QR code, deciphering the information to authenticate the validity of the holder, the function of dual anti-conterfeit is realized. Using universal equipment like mobile phones to aquire QR code pictures is facile and practical.


title: Single photon avalanche diode (SPAD) VLC system and application to downhole monitoring

abstract: In this paper, it is demonstrated for the first time that the problem of continuous downhole monitoring in the oil and gas industry is effectively addressed by the use of visible light communication (VLC). As a reliable, flexible and low-cost technique, VLC can fulfill a critical need of operators to maintain production efficiency and optimize gas well performance. The proposed VLC system makes use of a light emitting diode (LED) transmitter and a high sensitivity single photon detecting receiver referred to as single-photon avalanche diode (SPAD). The latter is instrumental in achieving long range communications, and the fact that ambient light is not present in a gas pipe is exploited. Specifically, the lack of ambient light enables high signal to noise ratio (SNR) at the receiver which operates in a photon counting mode. In this study, the bit error ratio (BER) performance of the system is simulated for a 4 kilometres long metal pipe. It is shown that the proposed system has superior power efficiency over conventional methods, which is important as it is assumed that the transmitter is battery operated. In addition, the theoretical BER performance is calculated and compared to the simulation results.


title: Soft-start circuit with duty ratio controlled voltage clamping and adaptive sizing technique for integrated DC-DC converters

abstract: A novel soft-start technique with charge-pump based staircase clamping voltage generator to eliminate the inrush-current and over-shoot voltage is presented. Proper duty cycle is introduced as a new freedom to control the soft-start speed which enables a fully on-chip design to reduce the cost of the converter. In addition, adaptive sizing method is presented to control the charging current for boost converters when the output voltage is less than the input voltage. A boost DC-DC converter is implemented with a 0.35µm CMOS process. Simulation results show a smooth start-up with load range from 0–500mA.


title: Humanoid robot avatars: An ‘in the wild’ usability study

abstract: In this paper, we report a breaching study to explore the use of a tele-operated humanoid avatar from both the perspective of the robot operator and the perspective of interlocutors. Humanoid robot avatars provide capabilities that video conferencing and mobile remote presence devices lack, particularly in multi-party conversations. Specifically, we were interested whether new users encountering a tele-operated humanoid avatar in a public setting would spontaneously use head movements and arm gestures to allow better conversational flow and group interaction. Video data shows that both operators and interlocutors initially explore the system's capabilities. Most operators used social gestures such as waving and pointing, as well as gaze which in turn led to related social responses from interlocutors. Interview data additionally show that robot operators and interlocutors rated the experience very positively. This paper discusses the implications of our findings for the design of ‘in the wild’ experiments and the usability of tele-operated humanoid avatars.


title: Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing

abstract: Errors are prevalent in time series data, such as GPS trajectories or sensor readings. Existing methods focus more on anomaly detection but not on repairing the detected anomalies. By simply filtering out the dirty data via anomaly detection, applications could still be unreliable over the incomplete time series. Instead of simply discarding anomalies, we propose to (iteratively) repair them in time series data, by creatively bonding the beauty of temporal nature in anomaly detection with the widely considered minimum change principle in data repairing. Our major contributions include: (1) a novel framework of iterative minimum repairing (IMR) over time series data, (2) explicit analysis on convergence of the proposed iterative minimum repairing, and (3) efficient estimation of parameters in each iteration. Remarkably, with incremental computation, we reduce the complexity of parameter estimation from O(n) to O(1). Experiments on real datasets demonstrate the superiority of our proposal compared to the state-of-the-art approaches. In particular, we show that (the proposed) repairing indeed improves the time series classification application.


title: Node Representation Learning for Multiple Networks: The Case of Graph Alignment

abstract: Recent advances in representation learning produce node embeddings that may be used successfully in many downstream tasks (e.g., link prediction), but do not generally extend beyond a single network. Motivated by the prevalence of multi-network problems, such as graph alignment, similarity, and transfer learning, we introduce an elegant and principled node embedding formulation, Cross-network Matrix Factorization or xNetMF, to effectively and efficiently learn embeddings from similarities based on the nodes' structural and attribute identities (if available), which are comparable across networks. Leveraging the Nystr\"om method for low-rank approximation, we achieve significant reduction in space and time compared to the na\"ive approach of directly factorizing the node similarity matrix, with xNetMF running up to 30x faster than existing representation learning methods. We study its effectiveness on the task of network alignment, and introduce REGAL (REpresentation-based Graph ALignment), the first method to use node representation learning for this task. Extensive experiments on a variety of challenging real-world networks with and without attributes show that REGAL outperforms existing network alignment methods by 20 to 30% in accuracy on average, uses attributes up to 40x faster, and scales to networks with millions of nodes each.


title: On the Resolution Limits of Superimposed Projection

abstract: Multi-projector super-resolution is the dual of multi-camera super-resolution. The goal of projector super-resolution is to produce a high resolution frame via superimposition of multiple low resolution subframes. Prior work claims that it is impossible to improve resolution via superimposed projection except in specialized circumstances. Rigorous analysis has been previously restricted to the special case of uniform display sampling, which reduces the problem to a simple shift-invariant deblurring. To understand the true behavior of superimposed projection as an inverse of classical camera super-resolution, one must consider the effects of non-uniform displacements between component subframes. In this paper, we resolve two fundamental theoretical questions concerning resolution enhancement via superimposed projection. First, we show that it is possible to reproduce frequencies that are well beyond the Nyquist limit of any of the component subframes. Second, we show that nonuniform sampling and pixel reconstruction functions impose fundamental limits on achievable resolution.


title: AN EMPIRICAL ANALYSIS OF THE ASSOCIATION BETWEEN NEIGHBORHOOD INCOME AND UNIT NON-RESPONSE IN THE SURVEY OF HEALTH, AGEING, AND RETIREMENT IN EUROPE

abstract: This paper estimates associations between individual and neighborhood characteristics and unit non-response in a survey of the population aged 50 and over in the Netherlands in 2004. The statistical model includes interviewer fixed effects to control for the non-random distribution of addresses over interviewers. The empirical analysis shows that, relative to individuals living in apartments, there is a lower unit non-response among individuals living in houses and a higher unit non-response among individuals living in old age institutions. Unit non-response is positively associated with the size of a city. No age and gender effects are found. Unit non-response is about 25 percent lower among individuals in the top than among individuals in the bottom of the distribution of neighborhood average income. This latter result implies that the response sample is biased toward individuals living in the more wealthy neighborhoods.


title: Flexible Three‐Axial Force Sensor for Soft and Highly Sensitive Artificial Touch

abstract: A soft tactile sensor able to detect both normal and tangential forces is fabricated with a simple method using conductive textile. Owing to the multi-layered architecture, the capacitive-based tactile sensor is highly sensitive (less than 10 mg and 8 μm, for minimal detectable weight and displacement, respectively) within a wide normal force range (potentially up to 27 N (400 kPa)) and natural touch-like tangential force ranges (from about 0.5 N to 1.8 N). Being flexible, soft, and low cost, this sensor represents an original approach in the emulation of natural touch.


title: Coin-Moving Puzzles

abstract: We introduce a new family of one-player games, involving the movement of coins from one configuration to another. Moves are restricted so that a coin can be placed only in a position that is adjacent to at least two other coins. The goal of this paper is to specify exactly which of these games are solvable. By introducing the notion of a constant number of extra coins, we give tight theorems characterizing solvable puzzles on the square grid and equilateral-triangle grid. These existence results are supplemented by polynomial-time algorithms for finding a solution.


title: Stretch and activation of the human biarticular hamstrings across a range of running speeds

abstract: PURPOSE ::: The human biarticular hamstrings [semimembranosus (SM), semitendinosus (ST) and biceps femoris long head (BF(LH))] have an important role in running. This study determined how hamstrings neuro-mechanical behaviour changed with faster running, and whether differences existed between SM, ST and BF(LH). ::: ::: ::: METHODS ::: Whole-body kinematics and hamstrings electromyographic (EMG) activity were measured from seven participants running at four discrete speeds (range: 3.4 ± 0.1 to 9.0 ± 0.7 m/s). Kinematic data were combined with a three-dimensional musculoskeletal model to calculate muscle-tendon unit (MTU) stretch and velocity. Activation duration and magnitude were determined from EMG data. ::: ::: ::: RESULTS ::: With faster running, MTU stretch and velocity patterns remained similar, but maxima and minima significantly increased. The hamstrings were activated from foot-strike until terminal stance or early swing, and then again from mid-swing until foot-strike. Activation duration was similar with faster running, whereas activation magnitude significantly increased. Hamstrings activation almost always ended before minimum MTU stretch, and it always started before maximum MTU stretch. Comparing the hamstrings, maximum MTU stretch was largest for BF(LH) and smallest for ST irrespective of running speed, while the opposite was true for peak-to-peak MTU stretch. Furthermore, peak MTU shortening velocity was largest for ST and smallest for BF(LH) at all running speeds. Finally, for the two fastest running speeds, the amount of MTU stretch that occurred during terminal swing after activation had started was less for BF(LH) compared to SM and ST. ::: ::: ::: CONCLUSION ::: Differences were evident in biarticular hamstrings neuro-mechanical behaviour during running. Such findings have implications for hamstrings function and injury.