title: DHA: Lidar and Vision data Fusion-based On Road Object Classifier

abstract: In this paper, we first extract three different kinds of high-level features from LIDAR point cloud, and combine them into the DHA (Depth, Height and Angle) channels. Integrated with the traditional RGB image from camera, we build a rich feature-based road object classifier by training a deep convolutional neural network model with six-channel (RGBDHA) data. Subsequently, this deep convolution neural network is fed by the integration of spacial and RGB information. With additional upsampled LIDAR data, the classifier reaches higher accuracy than single RGB image base methods. Several simulations on the famous autonomous vehicle benchmark of KITTI show that our fusion-based classifier outperforms RGB-based approaches about 15% and reaches average accuracy of 96%.


title: Antecedents of self-regulation: A developmental perspective.

abstract: The emerging ability to comply with caregivers' dictates and to monitor one's own behavior accordingly signifies a major growth of early childhood. However, scant attention has been paid to the developmental course of self-initiated regulation of behavior. This article summarizes the literature devoted to early forms of control and highlights the different philosophical orientations in the literature. Then, focusing on the period from early infancy to the beginning of the preschool years, the author proposes an ontogenetic perspective tracing the kinds of modulation or control the child is capable of along the way. The developmental sequence of monitoring behaviors that is proposed calls attention to contributions made by the growth of cognitive skills. The role of mediators (e.g., caregivers) is also discussed. During the second year of life, children increasingly demonstrate signs of selfhood and autonomy. This growing sense of identity, coupled with the ability to recall the dictates of caregivers, leads to a new dimension in behavior. Children begin to appraise the requirements of social and nonsocial situations and to monitor their own behavior accordingly. Slowly and precariously they move toward self-regulation, an achievement that Flavell (1977) descibes as being "one of the really central and significant cognitive-developmental hallmarks of the early childhood period" (p. 64).


title: Educational System for the Holy Quran and Its Sciences for Blind and Handicapped People Based on Google Speech API

abstract: There is a great need to provide ::: educational environments for blind and handicapped people. There are many ::: Islamic websites and applications dedicated to the educational services for the ::: Holy Quran and Its Sciences (Quran Recitations, the interpretations, etc.) on ::: the Internet. Unfortunately, blind and handicapped people could not use these ::: services. These people cannot use the keyboard and the mouse. In addition, the ::: ability to read and write is essential to benefit from these ::: services. In this paper, we present an educational environment that allows ::: these people to take full advantage of the scientific materials. This is done ::: through the interaction with the system using voice commands by speaking ::: directly without the need to write or to use the mouse. Google Speech API is used for the universal speech ::: recognition after a preprocessing and post processing phases to improve the ::: accuracy. For blind people, responses of these commands will be played back ::: through the audio device instead of displaying the text to the screen. The text ::: will be displayed on the screen to help other people make use of the system.


title: Learning Features from Music Audio with Deep Belief Networks

abstract: Feature extraction is a crucial part of many MIR tasks. In this work, we present a system that can automatically extract relevant features from audio for a given task. The feature extraction system consists of a Deep Belief Network (DBN) on Discrete Fourier Transforms (DFTs) of the audio. We then use the activations of the trained network as inputs for a non-linear Support Vector Machine (SVM) classifier. In particular, we learned the features to solve the task of genre recognition. The learned features perform significantly better than MFCCs. Moreover, we obtain a classification accuracy of 84.3% on the Tzanetakis dataset, which compares favorably against state-of-the-art genre classifiers using frame-based features. We also applied these same features to the task of auto-tagging. The autotaggers trained with our features performed better than those that were trained with timbral and temporal features.


title: Cultural Differences in Collaborative Authoring of Wikipedia

abstract: This article explores the relationship between national culture and computer-mediated communication (CMC) in Wikipedia. The articles on the topic game from the French, German, Japanese, and Dutch Wikipedia websites were studied using content analysis methods. Correlations were investigated between patterns of contributions and the four dimensions of cultural influences proposed by Hofstede (Power Distance, Collectivism versus Individualism, Femininity versus Masculinity, and Uncertainty Avoidance). The analysis revealed cultural differences in the style of contributions across the cultures investigated, some of which are correlated with the dimensions identified by Hofstede. These findings suggest that cultural differences that are observed in the physical world also exist in the virtual world.


title: Deep Neural Network Based Energy Disaggregation

abstract: In smart electricity grids, energy disaggregation significantly contributes to better demand side management, load forecasting and energy savings via estimating appliance level energy consumption from the aggregated smart meter data. This paper proposes a deep neural network based system by combining convolutional neural networks and variational auto-encoders for energy disaggregation. Domestic Appliance-Level Electricity dataset (UK-DALE) is used along with the standard error measures such as Mean Absolute Error (MAE) and Signal Aggregate Error (SAE) in order to evaluate the proposed system performance. Test results show that the proposed system improves the state-of-the-art performance by 44% and 19% based on SAE and MAE respectively.


title: A Multi-Scale CRNN Model for Chinese Papery Medical Document Recognition

abstract: Paper-based medical documents are still widely used in many countries, while the contents within are difficult for patients to store and manage. In contrast, electronic medical documents not only help solve these problems, but also promote the development of telemedicine and medical big data. Thus, how to transform traditional printed medical documents into electronic ones becomes a key issue. It is worth noting that recognizing Chinese medical document in image form is a challenging task, as there are a variety of characters and symbols, including Greek alphabets, mathematical symbols and so on. The structure of Chinese characters is also often intricate. At present, the popular Optical Character Recognition methods are designed for single-scale characters, which tend to have poor performance in those complex scenarios. Based on Convolutional Recurrent Neural Network (CRNN), this paper proposes a multiscale architecture to recognize multi-lingual characters. To verify the effectiveness, the model is trained on a synthetic dataset and evaluated on a real Chinese medical document dataset. The experimental results demonstrate that the proposed method achieves substantial improvement over the recent methods.


title: The Neurophysiology of Memory

abstract: : How do the structures of the medial temporal lobe contribute to memory? To address this question, we examine the neurophysiological correlates of both recognition and associative memory in the medial temporal lobe of humans, monkeys, and rats. These cross-species comparisons show that the patterns of mnemonic activity observed throughout the medial temporal lobe are largely conserved across species. Moreover, these findings show that neurons in each of the medial temporal lobe areas can perform both similar as well as distinctive mnemonic functions. In some cases, similar patterns of mnemonic activity are observed across all structures of the medial temporal lobe. In the majority of cases, however, the hippocampal formation and surrounding cortex signal mnemonic information in distinct, but complementary ways.


title: Scaling Human-Object Interaction Recognition Through Zero-Shot Learning

abstract: Recognizing human object interactions (HOI) is an important part of distinguishing the rich variety of human action in the visual world. While recent progress has been made in improving HOI recognition in the fully supervised setting, the space of possible human-object interactions is large and it is impractical to obtain labeled training data for all interactions of interest. In this work, we tackle the challenge of scaling HOI recognition to the long tail of categories through a zero-shot learning approach. We introduce a factorized model for HOI detection that disentangles reasoning on verbs and objects, and at test-time can therefore produce detections for novel verb-object pairs. We present experiments on the recently introduced large-scale HICODET dataset, and show that our model is able to both perform comparably to state-of-the-art in fully-supervised HOI detection, while simultaneously achieving effective zeroshot detection of new HOI categories.


Optical consequences of long-range order in wurtzite Al x Ga 1 À x N alloys


title: Positioning, Splinting, and Contracture Management

abstract: Abstract : The development of burn scar contracture is a pathologic condition. Burn scar contracture is defined as a loss of motion of a joint or anatomic structure as a result of normal skin being replaced by inextensible scar tissue.1 The presence of a burn scar contracture has been shown to have a negative impact on a burn survivor s quality of life, particularly in regards to physical functioning.2 A focus of burn rehabilitation should be to prevent or minimize contractures of the affected areas. Based on biomechanical considerations (discussed later), appropriate splinting and positioning is a critical component of any comprehensive burn rehabilitation program designed to attain optimal range-of-motion (ROM) outcomes. In 2008, a schema of burn rehabilitation phases (acute, intermediate, and long term) was developed to delineate the components of burn care treatment that are emphasized at different points in the continuum of care (Fig. 1).3 Table 1 describes the primary goals for positioning and splinting of burn patients during each burn rehabilitation phase.


Temporally Grounding Natural Sentence in Video


title: Psychological Perspectives on Motivation through Gamification

abstract: Gamification is an innovative approach to foster motivation. It refers to the use of game elements in non-game contexts. This article adopts a differentiated view on the topic of gamification and investigates, how and why different game elements can address different motivational mechanisms. At first, the concept of gamification and specific game elements characteristic of gamification are described. After that, different motivational perspectives were analyzed and motivational mechanisms in form of effect hypotheses were derived from these perspectives. To investigate the motivational pull of gamification, game elements are matched with motivational mechanisms. Our theoretical inquiry shows that gamification potentially addresses motivational mechanisms and thereby fosters motivation. These theoretical results can be used for the effective design of gamification environments and represent a basis for empirical research. Further research is required to confirm these theoretical findings.


Cytotaxonomic Studies of Three Ranid Species (Amphibia: Anura) from Hong Kong


title: Regression verification: proving the equivalence of similar programs

abstract: SUMMARY ::: ::: Proving the equivalence of successive, closely related versions of a program has the potential of being easier in practice than functional verification, although both problems are undecidable. There are three main reasons for this claim: (i) it circumvents the problem of specifying what the program should do; (ii) the problem can be naturally decomposed and hence is computationally easier; and (iii) there is an automatic invariant that enables to prove equivalence of loops and recursive functions in most practical cases. Theoretical and practical aspects of this problem are considered. Copyright © 2012 John Wiley & Sons, Ltd.


title: Exploring motivations for contributing to open source initiatives: The roles of contribution context and personal values

abstract: We explore contextual and dispositional correlates of the motivation to contribute to open source initiatives. We examine how the context of the open source project, and the personal values of contributors, are related to the types of motivations for contributing. A web-based survey was administered to 300 contributors in two prominent open source contexts: software and content. As hypothesized, software contributors placed a greater emphasis on reputation-gaining and self-development motivations, compared with content contributors, who placed a greater emphasis on altruistic motives. Furthermore, the hypothesized relationships were found between contributors' personal values and their motivations for contributing.


title: Taming information-stealing smartphone applications (on Android)

abstract: Smartphones have been becoming ubiquitous and mobile users are increasingly relying on them to store and handle personal information. However, recent studies also reveal the disturbing fact that users' personal information is put at risk by (rogue) smartphone applications. Existing solutions exhibit limitations in their capabilities in taming these privacy-violating smartphone applications. In this paper, we argue for the need of a new privacy mode in smartphones. The privacy mode can empower users to flexibly control in a fine-grained manner what kinds of personal information will be accessible to an application. Also, the granted access can be dynamically adjusted at runtime in a fine-grained manner to better suit a user's needs in various scenarios (e.g., in a different time or location). We have developed a system called TISSA that implements such a privacy mode on Android. The evaluation with more than a dozen of information-leaking Android applications demonstrates its effectiveness and practicality. Furthermore, our evaluation shows that TISSA introduces negligible performance overhead.


title: Decentralized Longitudinal Tracking Control for Cooperative Adaptive Cruise Control Systems in a Platoon

abstract: This paper presents a longitudinal tracking control law for Cooperative Adaptive Cruise Control (CACC) systems in a platoon that can comprehensively enable tracking capability of various spacing policies, designed expected velocity, and designed expected acceleration. Taking into account heterogeneous traffic, i.e., a platoon of vehicles with possibly different characteristics, the longitudinal control problem is formulated as an output tracking control problem with a quadratic function so that the contradictions among the different tracking requirements are realized, which include inter-vehicle spacing, velocity and acceleration. Then, the decentralized longitudinal tracking control law is proposed by using a limited communication structure and maximum principle (in this case, a wireless communication link with the nearest preceding vehicle and designed platoon leader only), in which the feedback items are composed of the states of host vehicles, and additional information of the nearest preceding vehicle and designed platoon leader are used as feed forward items. In addition, the concepts of "expected velocity" and "expected acceleration" are introduced to design the desired velocity and acceleration, realize additional objectives, and improve the predictive abilities. Numerous simulation results show that the proposed tracking controller provides a reliable tool for a systematic and efficient design of a platoon controller within CACC systems.


title: Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations

abstract: Many e-commerce websites use recommender systems or personalized rankers to personalize search results based on their previous interactions. However, a large fraction of users has no prior inter-actions, making it impossible to use collaborative filtering or rely on user history for personalization. Even the most active users mayvisit only a few times a year and may have volatile needs or different personas, making their personal history a sparse and noisy signal at best. This paper investigates how, when we cannot rely on the user history, the large scale availability of other user interactions still allows us to build meaningful profiles from the contextual data and whether such contextual profiles are useful to customize the ranking, exemplified by data from a major online travel agentBooking.com.Our main findings are threefold: First, we characterize the Continuous Cold Start Problem(CoCoS) from the viewpoint of typical e-commerce applications. Second, as explicit situational con-text is not available in typical real world applications, implicit cues from transaction logs used at scale can capture essential features of situational context. Third, contextual user profiles can be created offline, resulting in a set of smaller models compared to a single huge non-contextual model, making contextual ranking available with negligible CPU and memory footprint. Finally we conclude that, in an online A/B test on live users, our contextual ranker in-creased user engagement substantially over a non-contextual base-line, with click-through-rate (CTR) increased by 20%. This clearly demonstrates the value of contextual user profiles in a real world application.


title: A 6b 600MHz 10mW ADC array in digital 90nm CMOS

abstract: A 6b converter array operates at a 600MHz clock frequency with input signals up to 600MHz and only 10mW power consumption. The array consists of 8 interleaved successive approximation converters implemented in a 90nm digital CMOS technology.


title: Properties of Dry and Non-contact Electrodes for Wearable Physiological Sensors

abstract: Dry and non-contact electrodes are an important part of building user-friendly wearable body sensor networks for physiological (ECG, EEG, EMG) recording. This paper presents the theory and characterization of several inexpensive and non-irritating electrode materials that implement dry and non-contact sensors, including lead-free PCB solder finish, latex, cotton, silver cloth and solder mask. The noise spectra of the electrodes were measured on a live subject to fully mode all the effects of biological and chemical noise processes at the skin- electrode interface. In addition, exemplary ECG physiological measurements are also presented and compared against the signal from reference clinical wet Ag/AgCl electrodes. Results show that PCB finish can function as a general purpose, low-noise dry electrode. Latex and solder mask also work well as insulated electrodes.


title: Computing morphosyntactic differences. A typological perspective on German and Norwegian

abstract: Abstract Complex NP constructions involving nominalisations and preposed or postposed attributive modifiers of various kinds abound in German informational and academic prose. Similar constructions are fairly rare, and their possibilities of formation restricted, in Scandinavian languages as, for instance, Norwegian. The purpose of this paper is to illustrate and discuss some of the interlingual differences in question in a way which may hopefully prove conducive to an understanding of their systematic typological motivation.


title: Social Spammer Detection with Sentiment Information

abstract: Social media is a popular platform for spammers to unfairly overwhelm normal users with unwanted or fake content via social networking. The spammers significantly hinder the use of social media systems for effective information dissemination and sharing. Different from the spammers in traditional platforms such as email and the Web, spammers in social media can easily connect with each other, sometimes without mutual consent. They collude with each other to imitate normal users by quickly accumulating a large number of "human" friends. In addition, content information in social media is noisy and unstructured. It is infeasible to directly apply traditional spammer detection methods in social media. Understanding and detecting deception has been extensively studied in traditional sociology and social sciences. Motivated by psychological findings in physical world, we investigate whether sentiment analysis can help spammer detection in online social media. In particular, we first conduct an exploratory study to analyze the sentiment differences between spammers and normal users, and then present an optimization formulation that incorporates sentiment information into a novel social spammer detection framework. Experimental results on real-world social media datasets show the superior performance of the proposed framework by harnessing sentiment analysis for social spammer detection.


Automated speech-unit delimitation in spoken learner English


title: Long range and low powered RFID tags with tunnel diode

abstract: In this paper, we present a 5.8 GHz RFID tag equipped with a high gain, low power reflection amplifier based on a tunnel diode. Experimental results show that the realized prototype achieves gains above 40 dB and requires only 29 μW of biasing power. The tag detects very low RF signals (< −90 dBm) and provides read ranges up to 2 km. Long communication ranges and Manchester encoding are achieved by biasing on and off the tunnel diode.


title: Continuously adaptive continuous queries over streams

abstract: We present a continuously adaptive, continuous query (CACQ) implementation based on the eddy query processing framework. We show that our design provides significant performance benefits over existing approaches to evaluating continuous queries, not only because of its adaptivity, but also because of the aggressive cross-query sharing of work and space that it enables. By breaking the abstraction of shared relational algebra expressions, our Telegraph CACQ implementation is able to share physical operators --- both selections and join state --- at a very fine grain. We augment these features with a grouped-filter index to simultaneously evaluate multiple selection predicates. We include measurements of the performance of our core system, along with a comparison to existing continuous query approaches.


title: Gaussian processes for multi-sensor environmental monitoring

abstract: Efficiently monitoring environmental conditions across large indoor spaces (such as warehouses, factories or data centers) is an important problem with many applications. Deployment of a sensor network across the space can provide very precise readings at discrete locations. However, construction of a continuous model from this discrete sensor data is a challenge. The challenge is made harder by economic and logistical constraints that may limit the number of sensor motes in the network. The required model, therefore, must be able to interpolate sparse data and give accurate predictions at unsensed locations, as well as provide some notion of the uncertainty on those predictions. We propose a Gaussian process based model to answer both of these issues. We use Gaussian processes to model temperature and humidity distributions across an indoor space as functions of a 3-dimensional point. We study the model selection process and show that good results can be obtained, even with sparse sensor data. Deployment of a sensor network across an indoor lab provides real-world data that we use to construct an environmental model of the lab space. We seek to refine the model obtained from the initial deployment by using the uncertainty estimates provided by the Gaussian process methodology to modify sensor distribution such that each sensor is most advantageously placed. We explore multiple sensor placement techniques and experimentally validate a near-optimal criterion.


title: SIMD Acceleration for Main-Memory Index Structures – A Survey

abstract: Index structures designed for disk-based database systems do not fulfill the requirements for modern database systems. To improve the performance of these index structures, different approaches are presented by several authors, including horizontal vectorization with SIMD and efficient cache-line usage.


Enabled Generalized Vector Space Model to Improve Document Retrieval


title: CloudExp: A comprehensive cloud computing experimental framework

abstract: Abstract Cloud computing is an emerging and fast-growing computing paradigm that has gained great interest from both industry and academia. Consequently, many researchers are actively involved in cloud computing research projects. One major challenge facing cloud computing researchers is the lack of a comprehensive cloud computing experimental tool to use in their studies. This paper introduces CloudExp , a modeling and simulation environment for cloud computing. CloudExp can be used to evaluate a wide spectrum of cloud components such as processing elements, data centers, storage, networking, Service Level Agreement (SLA) constraints, web-based applications, Service Oriented Architecture (SOA), virtualization, management and automation, and Business Process Management (BPM). Moreover, CloudExp introduces the Rain workload generator which emulates real workloads in cloud environments. Also, MapReduce processing model is integrated in CloudExp in order to handle the processing of big data problems.


title: Regression by dependence minimization and its application to causal inference in additive noise models

abstract: Motivated by causal inference problems, we propose a novel method for regression that minimizes the statistical dependence between regressors and residuals. The key advantage of this approach to regression is that it does not assume a particular distribution of the noise, i.e., it is non-parametric with respect to the noise distribution. We argue that the proposed regression method is well suited to the task of causal inference in additive noise models. A practical disadvantage is that the resulting optimization problem is generally non-convex and can be difficult to solve. Nevertheless, we report good results on one of the tasks of the NIPS 2008 Causality Challenge, where the goal is to distinguish causes from effects in pairs of statistically dependent variables. In addition, we propose an algorithm for efficiently inferring causal models from observational data for more than two variables. The required number of regressions and independence tests is quadratic in the number of variables, which is a significant improvement over the simple method that tests all possible DAGs.


Web Based Air Pollution Monitoring System ( Air Pollution Monitoring Using Smart Phone )


title: Structured Volume Decomposition via Generalized Sweeping

abstract: In this paper, we introduce a volumetric partitioning strategy based on a generalized sweeping framework to seamlessly partition the volume of an input triangle mesh into a collection of deformed cuboids. This is achieved by a user-designed volumetric harmonic function that guides the decomposition of the input volume into a sequence of two-manifold level sets. A skeletal structure whose corners correspond to corner vertices of a 2D parameterization is extracted for each level set. Corners are placed so that the skeletal structure aligns with features of the input object. Then, a skeletal surface is constructed by matching the skeletal structures of adjacent level sets. The surface sheets of this skeletal surface partition the input volume into the deformed cuboids. The collection of cuboids does not exhibit T-junctions, significantly simplifying the hexahedral mesh generation process, and in particular, it simplifies fitting trivariate B-splines to the deformed cuboids. Intersections of the surface sheets of the skeletal surface correspond to the singular edges of the generated hex-meshes. We apply our technique to a variety of 3D objects and demonstrate the benefit of the structure decomposition in data fitting.


title: Gigascope: a stream database for network applications

abstract: We have developed Gigascope, a stream database for network applications including traffic analysis, intrusion detection, router configuration analysis, network research, network monitoring, and performance monitoring and debugging. Gigascope is undergoing installation at many sites within the AT&T network, including at OC48 routers, for detailed monitoring. In this paper we describe our motivation for and constraints in developing Gigascope, the Gigascope architecture and query language, and performance issues. We conclude with a discussion of stream database research problems we have found in our application.


title: Keyframe based large-scale indoor localisation using geomagnetic field and motion pattern

abstract: This paper studies indoor localisation problem by using low-cost and pervasive sensors. Most of existing indoor localisation algorithms rely on camera, laser scanner, floor plan or other pre-installed infrastructure to achieve sub-meter or sub-centimetre localisation accuracy. However, in some circumstances these required devices or information may be unavailable or too expensive in terms of cost or deployment. This paper presents a novel keyframe based Pose Graph Simultaneous Localisation and Mapping (SLAM) method, which correlates ambient geomagnetic field with motion pattern and employs low-cost sensors commonly equipped in mobile devices, to provide positioning in both unknown and known environments. Extensive experiments are conducted in large-scale indoor environments to verify that the proposed method can achieve high localisation accuracy similar to state-of-the-arts, such as vision based Google Project Tango.


title: How celebrities can be used in advertising to the

abstract: -The ever increasing product diversity and competition on the market of goods and services has dictated the pace of growth in the number of advertisements. Despite their admittedly diminished effectiveness over the recent years, advertisements remain the favored method of sales promotion. Consequently, the challenge for an advertiser is to explore every possible avenue of making an advertisement more noticeable, attractive and impellent for consumers. One way to achieve this is through invoking celebrity endorsements. On the one hand, the use of a celebrity to endorse a product involves substantial costs, however, on the other hand, it does not immediately guarantee the success of an advertisement. The question of how celebrities can be used in advertising to the best advantage is therefore of utmost importance. Celebrity endorsements have become commonplace: empirical evidence indicates that approximately 20 to 25 per cent of advertisements feature some famous person as a product endorser. The popularity of celebrity endorsements demonstrates the relevance of the topic, especially in the context of the current global economic downturn, when companies are forced to save in order to survive, yet simultaneously to heavily invest in advertising and sales promotion. The issue of the effective use of celebrity endorsements also figures prominently in the academic discourse. The study presented below is thus aimed at exploring what qualities (characteristics) of a celebrity endorser have an impact on the effectiveness of the advertisement in which he/she


title: An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

abstract: In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 “prior” negative screening cases was assembled. In the next sequential (“current”) screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.


Reboot for the AI revolution


title: A low-cost room-level indoor localization system with easy setup for medical applications

abstract: Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.


title: Toxicity of Benzalkonium Chloride on Monoalgal Cultures and Natural Assemblages of Marine Phytoplankton

abstract: The current widespread use of benzalkonium chloride (BAC) in different cleaning and disinfecting products led us to investigate its potential toxic effect on phytoplankton. To this aim, different physiological variables were monitored to detect toxicity of four ecologically relevant concentrations of BAC (15–200 μg l−1) on batch cultures of the microalgae Isochrysis galbana and Chaetoceros gracilis, and on a coastal phytoplankton assemblage. Fluorescence variables measured by fast repetition rate fluorometry, specifically variable fluorescence (Fv), were the most suitable to be used as endpoint among all the variables recorded (biomass, growth rate, size structure, and species composition). Calculated effective concentrations from Fv data provided evidences of higher sensitivity in the natural assemblage at short exposure times (natural assemblage—EC50 [24 h] = 36.4 μg l−1 vs. monoalgae—EC50 [24 h] > 120 μg l−1). Despite these differences, standard monoalgal laboratory toxicity tests provided comparable estimates of safe concentrations for microalgae in the environment. Assessed EC10 fall within the range of BAC concentrations reported in the literature for different effluents.


Biostratigraphic and Paleoceanographic Interpretation of Lower and Middle Miocene Sediments, Rockall Plateau Region, North Atlantic Ocean


title: Human-robot interaction in precision agriculture: Sharing the workspace with service units

abstract: Currently, Chile and Argentina experience serious challenges that affect their agricultural productivity. For example, in Chile, the loss of farmable field due to recent earthquakes, and volcanic eruptions as well as the loss of water reserves due to climate changes are affecting the agriculture. Additionally, both countries are facing a same problem: the loss of human labor force. Field workers are migrating from the farm to other fields in the industry which offer them more stable and more profitable jobs (like the mining industry in Chile, or car assembling lines in Argentina). In this adverse scenario, it becomes necessary to introduce and develop agricultural automation and sensing technologies for both primary (harvesting, seeding, fertilizing, spraying) and secondary tasks (grove supervision, weed detection, hauling, mowing). However, fully robotized farms are not yet a possibility since the transition from human labor force dependent farming to autonomous farming needs to be smooth and requires legal regulation not yet in discussion. In this paper, we summarize the state of the art in human-robot interaction in farmable fields, with emphasis in the current constrains associated with flexible automatization of farms in Argentina and Chile. In particular, we introduce the guidelines for designing a humanrobot interaction strategy for harvesting tasks, that could be used for other agricultural tasks.


title: Monitoring respiration during sleep.

abstract: Monitoring of respiration during sleep allows the assessment of physiologic variables that are required to characterize SRBD events. The patency of the upper airway, the pattern of breathing, oxygenation, and ventilation usually can be inferred from simultaneous measurements of airflow, respiratory effort, thoracic volume, and blood gases. As new techniques of respiratory monitoring emerge, the respiratory therapist and sleep technologist must be familiar with the advantages and shortcomings of each modality.


title: Towards effective feature selection in machine learning-based botnet detection approaches

abstract: Botnets, as one of the most formidable cyber security threats, are becoming more sophisticated and resistant to detection. In spite of specific behaviors each botnet has, there exist adequate similarities inside each botnet that separate its behavior from benign traffic. Several botnet detection systems have been proposed based on these similarities. However, offering a solution for differentiating botnet traffic (even those using same protocol, e.g. IRC) from normal traffic is not trivial. Extraction of features in either host or network level to model a botnet has been one of the most popular methods in botnet detection. A subset of features, usually selected based on some intuitive understanding of botnets, is used by the machine learning algorithms to classify/ cluster botnet traffic. These approaches, tested against two or three botnet traces, have mostly showed satisfactory detection results. Even though, their effectiveness in detection of other botnets or real traffic remains in doubt. Additionally, effectiveness of different combination of features in terms of providing more detection coverage has not been fully studied. In this paper we revisit flow-based features employed in the existing botnet detection studies and evaluate their relative effectiveness. To ensure a proper evaluation we create a dataset containing a diverse set of botnet traces and background traffic.


title: Wireless Cloud Networks for the Factory of Things: Connectivity Modeling and Layout Design

abstract: Large-scale adoption of dense cloud-based wireless network technologies in industrial plants is mandatorily paired with the development of methods and tools for connectivity prediction and deployment validation. Layout design procedures must be able to certify the quality (or reliability) of network information flow in industrial scenarios characterized by harsh propagation environments. In addition, these procedures must account for possibly coexisting heterogeneous radio access technologies as part of the Internet of Things (IoT) paradigm, easily allow post-layout validation steps, and be integrated by industry-standard CAD-based planning systems. The goal of the paper is to set the fundamentals for comprehensive industry-standard methods and procedures supporting plant designer during wireless coverage prediction, virtual network deployment, and post-layout verification. The proposed methods carry out the prediction of radio signal coverage considering typical industrial environments characterized by highly dense building blockage. They also provide a design framework to properly deploy the wireless infrastructure in interference-limited radio access scenarios. In addition, the model can be effectively used to certify the quality of machine-type communication by considering also imperfect descriptions of the network layout. The design procedures are corroborated by experimental measurements in an oil refinery site [modeled by three-dimensional (3-D) CAD] using industry-standard ISA IEC 62734 devices operating at 2.4 GHz. A graph-theoretic approach to node deployment is discussed by focusing on practical case studies, and also by looking at fundamental connectivity properties for random deployments.


title: Panache: A Parallel File System Cache for Global File Access

abstract: Cloud computing promises large-scale and seamless access to vast quantities of data across the globe. Applications will demand the reliability, consistency, and performance of a traditional cluster file system regardless of the physical distance between data centers. ::: ::: Panache is a scalable, high-performance, clustered file system cache for parallel data-intensive applications that require wide area file access. Panache is the first file system cache to exploit parallelism in every aspect of its design--parallel applications can access and update the cache from multiple nodes while data and metadata is pulled into and pushed out of the cache in parallel. Data is cached and updated using pNFS, which performs parallel I/O between clients and servers, eliminating the single-server bottleneck of vanilla client-server file access protocols. Furthermore, Panache shields applications from fluctuating WAN latencies and outages and is easy to deploy as it relies on open standards for high-performance file serving and does not require any proprietary hardware or software to be installed at the remote cluster. ::: ::: In this paper, we present the overall design and implementation of Panache and evaluate its key features with multiple workloads across local and wide area networks.


title: On-demand transparency for improving hardware Trojan detectability

abstract: Malevolent Trojan circuits inserted by layout modifications in an IC at untrustworthy fabrication facilities are difficult to detect by traditional post-manufacturing testing. In this paper, we develop a novel low-overhead design methodology that facilitates the detection of inserted Trojan hardware in an IC through logic testing. As a byproduct, it also increases the security of the design by design obfuscation. Application of the proposed design methodology to an 8-bit RISC processor and a JPEG encoder resulted in improvement in Trojan detection probability significantly. It also obfuscated the design with verification mismatch for 90% of the verification points, while incurring moderate area, power and delay overheads.


title: MRCNN : A Stateful Fast R-CNN Using temporal consistency in R-CNN for video object localization and classification

abstract: Deep convolutional neural networks (DCNNs) perform on par or better than humans for image classification. Hence efforts have now shifted to more challenging tasks such as object detection and classification in images, video or RGBD. Recently developed region CNNs (R-CNN) such as Fast R-CNN [7] address this detection task for images. Instead, this paper is concerned with video and also focuses on resource-limited systems. Newly proposed methods accelerate R-CNN by sharing convolutional layers for proposal generation, location regression and labeling [12][13][19][25]. These approaches when applied to video are stateless: they process each image individually. This suggests an alternate route: to make R-CNN stateful and exploit temporal consistency. We extend Fast R-CNNs by making it employ recursive Bayesian filtering and perform proposal propagation and reuse. We couple multi-target proposal/detection tracking (MTT) with R-CNN and do detection-to-track association. We call this approach MRCNN as short for MTT + R-CNN. In MRCNN, region proposals that are vetted via classification and regression in R-CNNs — are treated as observations in MTT and propagated using assumed kinematics. Actual proposal generation (e.g. via Selective Search) need only be performed sporadically and/or periodically and is replaced at all other times by MTT proposal predictions. Preliminary results show that MRCNNs can economize on both proposal and classification computations, and can yield up to a 10 to 30 factor decrease in number of proposals generated, about one order of magnitude proposal computation time savings and nearly one order magnitude improvement in overall computational time savings, for comparable localization and classification performance. This method can additionally be beneficial for false alarm abatement.


title: Pedestrian tracking using Velodyne data — Stochastic optimization for extended object tracking

abstract: Environment perception is a key enabling technology in autonomous vehicles, and multiple object tracking is an important part of this. High resolution sensors, such as automotive radar and lidar, leads to the so called extended target tracking problem, in which there are multiple detections per tracked object. For computationally feasible multiple extended target tracking, the data association problem must be handled. Previous work has relied on the use of clustering algorithms, together with assignment algorithms, to achieve this. In this paper we present a stochastic optimisation method that directly maximises the desired likelihood function, and solves the problem in a single step, rather than two steps (clustering+assignment). The proposed method is evaluated against previous work in an experiment where Velodyne data is used to track pedestrians, and the results clearly show that the proposed method achieves the best performance, especially in challenging scenarios.


title: NMR analysis of the interaction between protein L and Ig light chains.

abstract: Abstract Protein L is a cell wall protein expressed by some strains of the anaerobic bacterial species Peptostreptococcus magnus . It binds to immunoglobulin (Ig) light chains predominantly of the κ subtype from a wide range of animal species. This binding is mediated by five highly homologous repeats designated as B1-B5, each of which comprises 72 to 76 amino acid residues. The fold of the Ig light chain-binding B1 domain of protein L has previously been shown to comprise an α-helix packed against a four-stranded β-sheet. The Ig-binding region of the protein L domain involves most of the residues in the second β-strand, the C-terminal residues of the α-helix, and residues in the loop connecting the α-helix with the third β-strand. In the present study, we have identified the protein L-binding site of an Ig light chain by use of stable isotope-assisted NMR spectroscopy. The light chain of a murine monoclonal anti-17 α-hydroxyprogesterone Fab fragment (IgG2b, κ) was selectively labeled with 13 C at carbonyl groups of Ala, Arg, Cys, Ile, Lys, Met, Phe, Trp, or Tyr. The residues in which the carbonyl 13 C chemical shift was significantly perturbed upon binding of the protein L B1 domain were preferentially found in the second β-strand of the variable κ domain and parts of its flanking β-strands. None of these residues were affected by the addition of the antigen against which the monoclonal Fab fragment is directed. Therefore, we conclude that protein L binds to the outer surface of the framework region of the V L domain, primarily involving the V L second strand, and that this binding is independent of antigen-binding. The present NMR data, in combination with sequence comparisons between κ light chains with and without protein L affinity, suggest that the amino acid substitutions at positions 9, 20, and/or 74 of the κ light chains could crucially affect the interaction between protein L and the V L domain.


title: Soft decoding of JPEG 2000 compressed images using bit-rate-driven deep convolutional neural networks

abstract: Lossy image compression methods always introduce various unpleasant artifacts into the compressed results, especially at low bit-rates. In recent years, many effective soft decoding methods for JPEG compressed images have been proposed. However, to the best of our knowledge, very few works have been done on soft decoding of JPEG 2000 compressed images. Inspired by the outstanding performance of Convolution Neural Network (CNN) in various computer vision tasks, we presents a soft decoding method for JPEG 2000 by using multiple bit-rate-driven deep CNNs. More specifically, in training stage, we train a series of deep CNNs using lots of high quality training images and the corresponding JPEG 2000 compressed images at different coding bit-rates. In testing stage, for an input compressed image, the CNN trained with the nearest coding bit-rate is selected to perform soft decoding. Extensive experiments demonstrate the effectiveness of the presented soft decoding framework, which greatly improves the visual quality and objective scores of JPEG 2000 compressed images.


The Impact of Computer Self Efficacy and Technology Acceptance Model on Behavioral Intention in Internet Banking System


title: Music type classification by spectral contrast feature

abstract: Automatic music type classification is very helpful for the management of digital music databases. In this paper, the octave-based spectral contrast feature is proposed to represent the spectral characteristics of a music clip. It represented the relative spectral distribution instead of average spectral envelope. Experiments show that the octave-based spectral contrast feature performs well in music type classification. Another comparison experiment demonstrates that the octave-based spectral contrast feature has a better discrimination among different music types than mel-frequency cepstral coefficients (MFCC), which is often used in previous music type classification systems.


title: Aggregating Local Image Descriptors into Compact Codes

abstract: This paper addresses the problem of large-scale image search. Three constraints have to be taken into account: search accuracy, efficiency, and memory usage. We first present and evaluate different ways of aggregating local image descriptors into a vector and show that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension. We then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation. The evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy. Searching a 100 million image data set takes about 250 ms on one processor core.


title: Ku-band Car-borne FMCW stripmap synthetic aperture radar

abstract: This paper introduces a Ku-band car-borne frequency modulated continuous wave synthetic aperture radar (FMCW SAR) system in order to acquire clear images in stripmap mode. The specification and the architecture of the system are provided to demonstrate the outstanding performance. The conventional range doppler algorithm(RDA) is modified to be suitable for the radar system equipped in an automobile. After gathering raw data on the highway and signal processing, the effectiveness of the radar to get SAR images is verified in the practical results.


title: Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors

abstract: Activity classification in smartphones helps us to monitor and analyze the physical activities of the user in daily life and has potential applications in healthcare systems. This paper proposes a descriptor-based approach for activity classification using built-in sensors of smartphones. Accelerometer and gyroscope sensor signals are acquired to identify the activities performed by the user. In addition, time and frequency domain signals are derived using the collected signals. In the proposed approach, two descriptors, namely, histogram of gradient and centroid signature-based Fourier descriptor, are employed to extract feature sets from these signals. Feature and score level fusion are explored for information fusion. For classification, we have studied the performance of multiclass support vector machine and $k$ -nearest neighbor classifiers. The proposed approach is evaluated on two publicly available data sets, namely, UCI HAR data set and physical activity sensor data. Our experimental results show that the feature level fusion provides better performance than the score level fusion. In addition, our approach provides considerable improvement in classifying different activities as compared with the existing works. The average activity classification accuracy achieved using the proposed method is 97.12% as against the existing work, which provided 96.33% on UCI HAR data set. On the second data set, the proposed approach attained 96.83% classification accuracy, whereas the existing work achieved 90.2%.


title: FM based passive bistatic radar target range improvement - Part II

abstract: The bistatic radar has been revitalized over the last few years through the rapid development of the passive bistatic radar technology. Passive bistatic radar systems have unique properties, especially is time on target high compared to traditional radar systems, as well as the freedom in choosing from the available transmitters of opportunity. This paper will focus on how to take advantage of the latter of these two special properties in order to achieve better range resolution.


title: Online Detection of Fire in Video

abstract: This paper describes an online learning based method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. Our fire detection method consists of weak classifiers based on temporal and spatial modeling of flames. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish temporal flame flicker process from motion of flame colored moving objects. Boundary of flames are represented in wavelet domain and high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker spatially. Results from temporal and spatial weak classifiers based on flame flicker and irregularity of the flame region boundaries are updated online to reach a final decision. False alarms due to ordinary and periodic motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.


title: Modeling of the supporting legs for designing biomimetic water strider robots

abstract: Recent studies on the insect water strider showed that the insect heavily relies on surface tension force to stay afloat. Inspired by this insect, water strider robots have been developed using the same locomotive principles as the insect. This paper focuses on numerically modeling the supporting legs of the insect and the robots. The rigid-leg model as well as the compliant-leg model is developed using numerical approaches, under an assumption made on the water surface breaking condition. The effect of different leg material and geometry are discussed. It is shown through simulations that four 7 cm-long Teflonreg coated compliant supporting legs with optimized shapes can lift up to 4.3 grams (0.15 g/cm), while an actual prototype carried 3.7 grams. Another prototype using twelve of these legs successfully lifted 9.3 grams. Experiments show that the analyses capture the important features of the supporting legs. The design rules proposed in this paper are useful in understanding the insect statics and also the robotic water strider supporting leg design. This study allows a heavier robot to be used for education, entertainment or environment monitoring purposes


title: Investigation Into The Effectiveness Of Long Short Term Memory Networks For Stock Price Prediction

abstract: The effectiveness of long short term memory networks trained by backpropagation through time for stock price prediction is explored in this paper. A range of different architecture LSTM networks are constructed trained and tested.


Continuous Measurements of ECG and SpO2 for Cardiology Information System


title: Femto-photography: capturing and visualizing the propagation of light

abstract: We present femto-photography, a novel imaging technique to capture and visualize the propagation of light. With an effective exposure time of 1.85 picoseconds (ps) per frame, we reconstruct movies of ultrafast events at an equivalent resolution of about one half trillion frames per second. Because cameras with this shutter speed do not exist, we re-purpose modern imaging hardware to record an ensemble average of repeatable events that are synchronized to a streak sensor, in which the time of arrival of light from the scene is coded in one of the sensor's spatial dimensions. We introduce reconstruction methods that allow us to visualize the propagation of femtosecond light pulses through macroscopic scenes; at such fast resolution, we must consider the notion of time-unwarping between the camera's and the world's space-time coordinate systems to take into account effects associated with the finite speed of light. We apply our femto-photography technique to visualizations of very different scenes, which allow us to observe the rich dynamics of time-resolved light transport effects, including scattering, specular reflections, diffuse interreflections, diffraction, caustics, and subsurface scattering. Our work has potential applications in artistic, educational, and scientific visualizations; industrial imaging to analyze material properties; and medical imaging to reconstruct subsurface elements. In addition, our time-resolved technique may motivate new forms of computational photography.


title: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning

abstract: Purpose – The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems. Design/methodology/approach – The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted. Findings – The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases. Originality/value – In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is prese...


title: Irregular Interval Valued Fuzzy Graphs

abstract: Department of Mathematics, University of Mazandaran, Babolsar, Iran Email: hrashmanlou@yahoo.com Received 6 December 2012; accepted 22 April 2013 Abstract. In this paper, we define irregular interval-valued fuzzy graphs and their various classifications. Size of regular interval-valued fuzzy graphs is derived. The relation between highly and neighbourly irregular interval-valued fuzzy graphs are established. Some basic theorems related to the stated graphs have also been presented. Keywords: Interval-valued fuzzy graphs, irregular interval-valued fuzzy graphs, totally irregular interval-valued fuzzy graph AMS Mathematics Subject Classification (2010): 05C72 1. Introduction Presently, science and technology is featured with complex processes and phenomena for which complete information is not always available. For such cases, mathematical models are developed to handle various types of systems containing elements of uncertainty. A large number of these models are based on an extension of the ordinary set theory, namely, fuzzy sets. In 1965, Zadeh [31] introduced the notion of fuzzy subset of a set as a method of presenting uncertainty. The fuzzy systems have been used with success in last years, in problems that involve the approximate reasoning. It has become a vast research area in different disciplines including medical and life science, management sciences, social sciences, engineering, statistics, graph theory, artificial intelligence, signal processing, multi agent systems, pattern recognition, robotics, computer networks, expert systems, decision making, etc. In 1975, Rosenfeld [19] introduced the concept of fuzzy graphs. In 1975, Zadeh [30] introduced the notion of interval-valued fuzzy sets as an extension of fuzzy sets [31] in which the values of the membership degrees are intervals of numbers instead of real numbers between 0 and 1. Interval-valued fuzzy sets provide a more adequate description of uncertainty than traditional fuzzy sets. It is therefore important to use interval-valued fuzzy sets in applications, such as fuzzy control. One of the computationally most intensive parts of fuzzy control is defuzzification [10]. Since interval-valued fuzzy sets


Error Detection and Error Correction tn Real-Time Digital Computers *


title: MEMS in RF-filter applications: thin film bulk-acoustic-wave technology

abstract: RF-filters based on MEMS technology have started to substitute conventional filters in mobile phones as they offer clear performance and size advantages. Bulk acoustic wave (BAW) and FBAR technologies today dominate the market for US-PCS duplexers and transmit filters which are needed between the power amplifier and antenna in mobile phone applications. No other RF-MEMS component has yet emerged in mobile phone applications. The working principle and fundamentals of RF-MEMS filters - and in particular thin film bulk-acoustic-wave (BAW) filters - are summarized in a comprehensive form. Requirements and key performance parameters for filters in next generation mobile phone applications are reviewed in detail. The presentation includes an outlook on future RF architectures and consolidates the demand for filters. BAW and FBAR technologies in wafer-level-packaging enable the building of complete "system-in-package" (SiP) RF-modules using cheap substrates and established assembly methods as are reviewed in detail. The state-of-the-art in BAW filters and future technology trends are presented. Supplementary information covers manufacturing and commercialization issues.


title: An Investigation into Annual Faculty Evaluation Process of EFL Teachers in the Middle East: Challenges for Leadership & Management

abstract: This study examined the effectiveness of the Annual Faculty Evaluations Process at an English language centre in the Middle Eastern context. It sought to determine to what extent this evaluation process helped the English language teachers improve their teaching practice. A mixed-method design was used to collect quantitative and qualitative data. The data were collected via teacher questionnaire & teacher interviews and triangulated via cross examination of the documents pertaining to the teacher evaluation policy and procedures used by the language centre. The findings of this case study revealed that the teachers believe that the process of annual faculty evaluation (AFE) has had a very positive impact on their professional development. However, the AFE process is quite rigid and does not take cultural diversity of the teachers into account. The report makes various recommendation in terms of teacher autonomy as well as participation in the evaluation process and using the three-track evaluation system (Danielson and McGreal, 2000) by the administration.


title: Histological effects in the iris after 3 months of latanoprost therapy: the Mainz 1 study.

abstract: OBJECTIVE ::: To determine whether 3 months of topical latanoprost treatment caused proliferative or degenerative effects on the peripheral iris of patients with glaucoma. ::: ::: ::: METHODS ::: Seventeen patients requiring filtering surgery for primary open-angle glaucoma or pseudoexfoliation glaucoma were randomized to receive topical latanoprost for 3 months (n = 8) or alternative medication (n = 9) before surgery. A trabeculectomy and a peripheral iridectomy specimen was obtained from each patient during surgery. The tissue was subjected to histological and immunohistochemical evaluation using 2 cell cycle markers: proliferating cell nuclear antigen and nuclear-associated protein (Ki-67). ::: ::: ::: RESULTS ::: No degenerative or pathological changes were seen in the latanoprost-treated irides, including the one specimen in this series in which there was an eye color change. Proliferating cell nuclear antigen and nuclear-associated protein markers were negative for changes in all the test specimens. ::: ::: ::: CONCLUSION ::: Short-term treatment with latanoprost does not produce morphological changes or cellular proliferation changes in the iris.


title: Selecting targets and markers in drought-resistant breeding of soybean

abstract: This paper analyzes the practical situation of selecting targets and markers in droughtresistant breeding of soybean. It show s that, with the advance of research, various physiological and biochemical targ ets and markers have emerged on the basis of morphological ones. In addition, wi th the development of molecular biology, the molecular markers related to drough tresistance of soybean are under investigation and some have been applied in b reeding activities.


title: A New Mimicking Attack by LSGAN

abstract: Discriminating Distributed Denial of Service Attacks (DDoS) from Flash Crowds (FC) is a tough and challenging problem. If attackers could generate mimicking traffic which have little difference from the traffic produced by legitimate users in FC, are existing methods and defense systems still able to distinguish DDoS from FC? To verify the possibility of the existence of this mimicking attack and prove the existing methods cannot discriminate this attack from FC, this paper proposes an idea employed Least Squares Generative Adversarial Networks (LSGAN) to generate mimicking traffic based on a statistical features achieved from an extensive analysis of user traffic behavior of DDoS and FC. Then to establish an efficient defense system employed Random Forest to prove it can achieve better performance on real network traffic traces, but cannot discriminate this mimicking attack traffic from FC. The experiments results show the proposed idea can generate this mimicking attack traffic and the defense system cannot discriminate it from FC. In addition, a comparison with GAN has been made to show that LSGAN is better than GAN in performance.


title: Automated Detection of Diabetic Retinopathy using Deep Learning

abstract: Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.


title: Foldio: Digital Fabrication of Interactive and Shape-Changing Objects With Foldable Printed Electronics

abstract: Foldios are foldable interactive objects with embedded input sensing and output capabilities. Foldios combine the advantages of folding for thin, lightweight and shape-changing objects with the strengths of thin-film printed electronics for embedded sensing and output. To enable designers and end-users to create highly custom interactive foldable objects, we contribute a new design and fabrication approach. It makes it possible to design the foldable object in a standard 3D environment and to easily add interactive high-level controls, eliminating the need to manually design a fold pattern and low-level circuits for printed electronics. Second, we contribute a set of printable user interface controls for touch input and display output on folded objects. Moreover, we contribute controls for sensing and actuation of shape-changeable objects. We demonstrate the versatility of the approach with a variety of interactive objects that have been fabricated with this framework.


title: Adaptive Duty Cycling for Energy Harvesting Systems

abstract: Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment, (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint, and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used.


title: Question Classification using Machine Learning Approaches

abstract: Question classification is the process by which a system analyzes a question and labels the question based on the category to which it belongs. The automated categorization (or classification) of questions into predefined categories has witnessed a booming interest due to the increased popularity of web technologies. The recent advancement in the form of E-Learning calls for the need of question categorization. In network based learning the questions posted by students need to be categorized on the basis of the concerned concepts. This point to the relevance of question categorization in this area. Many approaches to question classification have been proposed and have achieved reasonable results. The dominant approaches are machine learning and context based classification. There are several Machine Learning methods for question categorization. Here we are extending the previous methods for text categorization to question categorization and making a comparative study of the performance of two approaches, Naive Bayes and Support Vector Machine General Terms Machine Learning Algorithms


A framework for transfer in reinforcement learning


Sentential Representations in Distributional Semantics


title: Extinction patterns, δ18 O trends, and magnetostratigraphy from a southern high-latitude Cretaceous–Paleogene section: Links with Deccan volcanism

abstract: Although abundant evidence now exists for a massive bolide impact coincident with the Cretaceous–Paleogene (K–Pg) mass extinction event (~ 65.5 Ma), the relative importance of this impact as an extinction mechanism is still the subject of debate. On Seymour Island, Antarctic Peninsula, the Lopez de Bertodano Formation yields one of the most expanded K–Pg boundary sections known. Using a new chronology from magnetostratigraphy, and isotopic data from carbonate-secreting macrofauna, we present a high-resolution, high-latitude paleotemperature record spanning this time interval. We find two prominent warming events synchronous with the three main phases of Deccan Traps flood volcanism, and the onset of the second is contemporaneous with a local extinction that pre-dates the bolide impact. What has been termed the K–Pg extinction is potentially the sum of multiple, independent events, at least at high latitudes.


title: Wireless Geophone Network for remote monitoring and detection of landslides

abstract: Recent years have shown an alarmous increase in rain fall induced landslides. This has facilitated the need for having a monitoring system to predict the landslides which could eventually reduce the loss of human life. We have developed and deployed a Wireless Sensor Network to monitor rainfall induced landslide, in Munnar, South India. A successful landslide warning was issued in June 2009 using this system. The system is being enhanced by incorporating a Wireless Geophone Network to locate the initiation of landslide. The paper discusses an algorithm that was developed to analyze the geophone data and automatically detect the landslide signal. A novel method to localize the landslide initiation point is detailed. The algorithm is based on the time delay inherent in the transmission of waves through the surface of the earth. The approach detailed here does not require additional energy since the geophones are self excitatory. The error rate of the approach is much less when compared to the other localization methods like RSSI. The proposed algorithm is being tested and validated, in the landslide laboratory set up at our university.


title: Balancing Awareness and Interruption in Mobile Patrol using Context-Aware Notification

abstract: In mobile computing, a fundamental problem is maintaining awareness of the environment and of information presented as messages on a mobile device. In mobile police patrols, officers need to pay attention to their direct environment and stay informed of incidents elsewhere. To prevent unwanted interruption, a context-aware notification system adapts the timing and appearance of incident messages, based on user activity available, in transit, or busy and message priority high, normal, or low. The authors evaluated the benefits and costs of adaptive notification compared to three uniform notification styles presenting full messages, postponing messages or presenting indicators. Thirty-two trained student participants used a prototype notification system in a controlled mobile patrol task. The results were validated in a follow-up study with twenty-four police officers.


title: Providing database as a service

abstract: We explore a novel paradigm for data management in which a third party service provider hosts "database as a service", providing its customers with seamless mechanisms to create, store, and access their databases at the host site. Such a model alleviates the need for organizations to purchase expensive hardware and software, deal with software upgrades, and hire professionals for administrative and maintenance tasks which are taken over by the service provider. We have developed and deployed a database service on the Internet, called NetDB2, which is in constant use. In a sense, a data management model supported by NetDB2 provides an effective mechanism for organizations to purchase data management as a service, thereby freeing them to concentrate on their core businesses. Among the primary challenges introduced by "database as a service" are the additional overhead of remote access to data, an infrastructure to guarantee data privacy, and user interface design for such a service. These issues are investigated. We identify data privacy as a particularly vital problem and propose alternative solutions based on data encryption. The paper is meant as a challenge for the database community to explore a rich set of research issues that arise in developing such a service.


title: Mind your blocks: On the stealthiness of malicious BGP hijacks

abstract: Some recent research presented evidence of blocks of IP addresses being stolen by BGP hijackers to launch spam campaigns (35). This was the first time BGP hijacks were seen in the wild. Since then, only a very few anecdotal cases have been reported as if hackers were not interested in running these attacks. However, it is a common belief among network operators and ISPs that these attacks could be taking place but, so far, no one has produced evidence to back up that claim. In this paper, we analyse 18 months of data collected by an infrastructure specifically built to answer that question: are intentional stealthy BGP hijacks routinely taking place in the Internet? The identification of what we believe to be more than 2,000 malicious hijacks leads to a positive answer. The lack of ground truth is, of course, a problem but we managed to get confirmation of some of our findings thanks to an ISP unwittingly involved in hijack cases we have spotted. This paper aims at being an eye opener for the community by shedding some light on this undocumented threat. We also hope that it will spur new research to understand why these hijacks are taking place and how they can be mitigated. Depending on how BGP attacks are carried out, they can be very disruptive for the whole Internet and should be looked at very closely. As of today, as much as 20% of the whole IPv4 address space is currently allocated but not publicly announced, which makes it potentially vulnerable to such malicious BGP hijacks.


title: Gradient Descent Learns Linear Dynamical Systems

abstract: We prove that gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant dynamical system from a sequence of noisy observations generated by the system. Even though the objective function is non-convex, we provide polynomial running time and sample complexity bounds under strong but natural assumptions. Linear systems identification has been studied for many decades, yet, to the best of our knowledge, these are the first polynomial guarantees for the problem we consider.


title: Silver Tourism As A Possible Factor For Regional Development. A Case Study In The South-East Region Of Romania

abstract: As a result of the growing ageing, senior tourists will become a powerful group in the near tourism future. According to this statement, our research asses that the silver tourism could represent, in the next years, a real instrument for the regional development. The aim of our study is to highlight the opportunity of capitalizing the aging phenomenon by the administrative institutions and local tour operators that might regenerate economically the southeast Romanian region. Methodologically, we used a survey based on two questionnaires. The first one was applied to a group of seniors enrolled at the University of Third Age Galati city. For now, this type of structure is unique in Romania.Â The second questionnaire containing a single question was applied to four categories of actors possible to be responsible and/or involved in the developing regional tourism process. The resultsÂ reveals the connections existing between the main options of elderly and the forms of tourism developed at regional level, one the one hand, and the very minor attention paid by the local factors to it, on the other hand.


title: Loneliness, social isolation and social relationships: what are we measuring? A novel framework for classifying and comparing tools

abstract: OBJECTIVES ::: We present a novel way of classifying and comparing measures of social relationships to help readers interpret the growing literature on loneliness and social isolation and to provide researchers with a starting point to guide their choice of measuring tool. ::: ::: ::: METHODS ::: Measures of social relationships used in epidemiological studies were identified from two systematic reviews-one review on the association between social relationships and health and social care service use, and a second review on the association between social relationships and health. Questions from each measure were retrieved and tabulated to derive a classification of social relationship measures. ::: ::: ::: RESULTS ::: We present a classification of measures according to two dimensions: (1) whether instruments cover structural or functional aspects of social relationships and (2) the degree of subjectivity asked of respondents. We explain how this classification can be used to clarify the remit of the many questionnaires used in the literature and to compare them. ::: ::: ::: CONCLUSIONS ::: Different dimensions of social relationships are likely to have different implications for health. Our classification of social relationship measures transcends disciplinary and conceptual boundaries, allowing researchers to compare tools that developed from different theoretical perspectives. Careful choice of measures is essential to further our understanding of the links between social relationships and health, to identify people in need of help and to design appropriate prevention and intervention strategies.


Ki-67 IMMUNOREACTIVITY IN HUMAN CENTRAL NERVOUS SYSTEM TUMORS. A RETROSPECTIVE STUDY WITH MIB 1 MONOCLONAL ANTIBODY


title: PRODUCTION AND OPERATIONS MANAGEMENT Customer Behavior Modeling in Revenue Management and Auctions: A Review and New Research Opportunities

abstract: Customer behavior modeling has been gaining increasing attention in the operations management community. In this paper we review current models of customer behavior in the revenue management and auction literatures and suggest several future research directions.


title: Influence of polling technologies on student engagement: An analysis of student motivation, academic performance, and brainwave data

abstract: This study compared clicker technology against mobile polling and the Just-in-Time Teaching (JiTT) strategy to investigate how these methods may differently affect students' anxiety, self-efficacy, engagement, academic performance, and attention and relaxation as indicated by brainwave activity. The study utilized a quasi-experimental research design. To assess the differences between the effects of clickers and mobile polling, the study collected data from two courses at a large research university in Taiwan in which 69 students used either clickers or mobile polling. The results showed that mobile polling along with the JiTT strategy and in-class polls reduce graduate students' anxiety, improve student outcomes in an environment comprising both graduate and undergraduate students, and increase students' attention during polling. However, brainwave data revealed that during the polling activities, students' attention in the clicker and mobile polling groups respectively increased and decreased. Students nowadays do not find smartphones a novelty; however, incorporating them into class is still a potentially effective way to increase student attention and provide a direct way for instructors to observe the learning effects of lectures and improve their teaching approach on that basis. Clicker technology and mobile polling with JiTT strategy were compared.Graduate students' anxiety reduced; attention during polling activities increased.Student outcomes in environment with graduate and undergraduate students improved.During polling, clicker polling group's students' attention increased.During polling, mobile polling group's students' attention decreased.


title: Mixed method approach to identify analytic questions to be visualized for military cyber incident handlers

abstract: Our multi-disciplinary team developed and applied a 6-step mixed method approach to efficiently identify the cognitive work of early stage military cyber incident handlers, extract a subset of that work that could benefit from visualization, and specify the information needs as Analytic Questions (AQs) posed by operators that the visualizations would have to support. The methodology included a survey of subject matter experts to validate that the major findings of prior research on the cognitive work of cyber defenders, conducted over a decade ago, are still valid today. It also utilized a Goal Directed Task Analysis (GDTA) structure to represent the major task, goals, decisions, AQs and data source requirements of early stage cyber incident handlers. This yielded 40 AQs which are reported in this paper. Knowledge Elicitation (KE) interviews of domain practitioners were used to select the AQs with greatest potential for incorporation into a follow-on project to measure the effects of visualization on early stage incident handler performance. The AQs represent measurable units of cognitive work which must be performed using available data in a severely time-constrained work environment. Thus, they can serve as indicators of operator performance to be used in experiments on the effectiveness of visualization for event detection and preliminary analysis. They can also provide requirements for visualization designers and security products.


title: Improved Estimation of Trilateration Distances for Indoor Wireless Intrusion Detection

abstract: Detecting wireless network intruders is challenging since logical addressing information may be spoofed and the attacker may be located anywhere within radio range. Accurate indoor geolocation provides a method by which the physical location of rogue wireless devices may be pinpointed whilst providing an additional option for location-based access control. Existing methods for geolocation using received signal strength (RSS) are imprecise, due to the multipath nature of indoor radio propagation and additional pathloss due to walls, and aim to minimise location estimate error. This paper presents an approach to indoor geolocation that improves measurements of RSS by averaging across multiple frequency channels and determining the occurrence of walls in the signal path. Experimental results demonstrate that the approach provides improved distance estimates for trilateration and thus aids intrusion detection for wireless networks.


title: ELMD: an automatically generated entity linking gold standard dataset in the music domain

abstract: Paper presented at 10th International Conference on Language Resources and Evaluation LREC 2016; 2016 May 23-28; Portoroz, Slovenia.


title: Amplification of TruSeq ancient DNA libraries with AccuPrime Pfx: consequences on nucleotide misincorporation and methylation patterns

abstract: AbstractRecent developments in high-throughput sequencing (HTS) technologies have enabled the reconstruction of complete genome sequence and nucleosome and methylation maps from ancient individuals. A diversity of degradation reactions affect DNA molecules after an organism dies, introducing specific nucleotide misincorporation patterns in HTS data that are typically exploited to distinguish modern contaminants and authentic DNA templates. In this study, we used two different DNA polymerases to amplify Illumina TruSeq DNA libraries built on DNA extracts from ancient equines. The AmpliTaq Gold, widely used in ancient DNA studies, generated amplified libraries showing typical misincorporation patterns. Such patterns were partially lost following AccuPrime Pfx amplification, for which a two-fold reduction of endogenous content was also observed. This is explained by the inability of the AccuPrime Pfx to bypass uracils, which represent the most common post-mortem base modification and derive from the post-mor...


title: The relation between ADHD and ANS-CNS integration

abstract: Background: Attention deficit hyperactivity disorder ::: (ADHD) was long considered to be limited to children and adolescents, but it is ::: now known that ADHD symptoms may persist into adulthood. It is plausible that ::: the etiology of ADHD is not one-dimensional, but instead involves various ::: neuroanatomical and neurochemical systems, with the causes of the main abnormalities ::: believed to be catecholaminergic. Iodine-123-labeled meta-iodobenzylguanidine (123I-MIBG) ::: is a physiological analogue of norepinephrine (NE). To the best of our ::: knowledge, there are no reports about the abnormalities of MIBG scintigraphy in ::: patients with ADHD. The cases presented are male adult patients with different ::: comorbid psychiatric disorders. Case presentation: The cases presented are male ::: adult patients with different psychiatric disorders. Case 1 was a 52-year-old ::: male residential construction foreman, who had been diagnosed with acute schizophrenia-like psychotic disorder. 123I-MIBG planar and SPECTs of the studies were performed at 20 min (early phase) and 3 h ::: (late phase) after intravenous injection of 111 MBq. Planar images were ::: processed to determine the heart (H) to mediastinum (M) ratio (H/M). The early ::: and late H/M ratios were 1.22 and 1.07, respectively. Case 2 was a 52-year-old ::: male civil servant who was diagnosed with adjustment disorder. The early and ::: late H/M ratios were 1.98 and 2.08, respectively. Conclusion: Clinical studies ::: make it clear that symptoms of ADHD are more heterogenous and subtle in adults ::: than children. Adult ADHD is significantly commorbid with wide range of other ::: 12-month disorders. MIBG imaging can be useful to diagnose patients with ::: preexisting psychiatric disorders and ADHD. It might be possible to diagnose ::: objectively though the psychiatric statuses resemble or coexist with other ::: psychiatric disorders. Needless to say, further research is important.


The role of 5-HT receptors in depression


Data-Driven Text Simplification


From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources


title: Efficient introduction of specific homozygous and heterozygous mutations using CRISPR/Cas9

abstract: The bacterial CRISPR/Cas9 system allows sequence-specific gene editing in many organisms and holds promise as a tool to generate models of human diseases, for example, in human pluripotent stem cells. CRISPR/Cas9 introduces targeted double-stranded breaks (DSBs) with high efficiency, which are typically repaired by non-homologous end-joining (NHEJ) resulting in nonspecific insertions, deletions or other mutations (indels). DSBs may also be repaired by homology-directed repair (HDR) using a DNA repair template, such as an introduced single-stranded oligo DNA nucleotide (ssODN), allowing knock-in of specific mutations. Although CRISPR/Cas9 is used extensively to engineer gene knockouts through NHEJ, editing by HDR remains inefficient and can be corrupted by additional indels, preventing its widespread use for modelling genetic disorders through introducing disease-associated mutations. Furthermore, targeted mutational knock-in at single alleles to model diseases caused by heterozygous mutations has not been reported. Here we describe a CRISPR/Cas9-based genome-editing framework that allows selective introduction of mono- and bi-allelic sequence changes with high efficiency and accuracy. We show that HDR accuracy is increased dramatically by incorporating silent CRISPR/Cas-blocking mutations along with pathogenic mutations, and establish a method termed 'CORRECT' for scarless genome editing. By characterizing and exploiting a stereotyped inverse relationship between a mutation's incorporation rate and its distance to the DSB, we achieve predictable control of zygosity. Homozygous introduction requires a guide RNA targeting close to the intended mutation, whereas heterozygous introduction can be accomplished by distance-dependent suboptimal mutation incorporation or by use of mixed repair templates. Using this approach, we generated human induced pluripotent stem cells with heterozygous and homozygous dominant early onset Alzheimer's disease-causing mutations in amyloid precursor protein (APP(Swe)) and presenilin 1 (PSEN1(M146V)) and derived cortical neurons, which displayed genotype-dependent disease-associated phenotypes. Our findings enable efficient introduction of specific sequence changes with CRISPR/Cas9, facilitating study of human disease.


Lattice thermal conductivity of SrTiO 3 / TiO 2 superlattice nanowires


title: Dielectric Properties Characterization From 0.5 to 50 GHz of Breast Cancer Tissues

abstract: Knowledge of the dielectric properties of human tissues is important for several biomedical applications, including imaging and hyperthermia treatment, as well as for determining safety thresholds in policy making. Breast tissues, both normal and tumorous, are of particular interest because of the medical and social impact of breast cancers. While experimental data is available up to 20 GHz, for higher frequencies, this information is missing, or has been extrapolated from models based on lower-frequency data. Emerging technologies and applications in the millimeter-wave region would benefit from experimental data that bridge this gap. This paper presents the characterization of dielectric properties of breast tissues for the frequency range from 0.5 to 50 GHz. Cole–Cole models are derived for normal and tumorous tissues based on experimental measurements on more than 220 tissue samples obtained at surgery ( ex vivo ) from a population exceeding 50 patients, covering a wide span of normal and tumorous tissues, from patients ranging in age from 28 to 85 years, with a time from excision to measurements under 3.5 h. This paper also presents a comprehensive analysis of the differences between normal and tumorous breast tissues at different frequencies in terms of sensitivity and specificity.


title: Age estimation from face images: Human vs. machine performance

abstract: There has been a growing interest in automatic age estimation from facial images due to a variety of potential applications in law enforcement, security control, and human-computer interaction. However, despite advances in automatic age estimation, it remains a challenging problem. This is because the face aging process is determined not only by intrinsic factors, e.g. genetic factors, but also by extrinsic factors, e.g. lifestyle, expression, and environment. As a result, different people with the same age can have quite different appearances due to different rates of facial aging. We propose a hierarchical approach for automatic age estimation, and provide an analysis of how aging influences individual facial components. Experimental results on the FG-NET, MORPH Album2, and PCSO databases show that eyes and nose are more informative than the other facial components in automatic age estimation. We also study the ability of humans to estimate age using data collected via crowdsourcing, and show that the cumulative score (CS) within 5-year mean absolute error (MAE) of our method is better than the age estimates provided by humans.


title: Sensorless control of permanent magnet synchronous motor using extended Kalman filter

abstract: This paper presents the modeling, analysis, design and experimental validation of a robust sensorless control method for permanent magnet synchronous motor (PMSM) based on extended Kalman filter (EKF). A real-time PMSM and its EKF models in the MATLAB/Simulink simulation environment are developed. The position/speed sensorless control scheme along with the power electronic circuitry is modeled. The performance of the proposed control is assessed and verified for different types of dynamic and static torque loads. The robustness of the sensorless method is demonstrated by starting the motor with different rotor initial positions. The proposed EKF speed/position estimation method is also proved insensitive to the PMSM parameter variations. Proper operations of this EKF based sensorless control method for a high-speed permanent magnet synchronous machine are verified experimentally in the research laboratory at Honeywell.