title: Attitudes Towards Socially Assistive Robots in Intelligent Homes : Results From Laboratory Studies and Field Trials

abstract: The near future will see an increasing demand of elder care and a shortage of professional and informal caregivers. In this context, ageing societies would benefit from the design of intelligent homes that provide assistance. The choice of interfaces between the assistive environment and the user is of great importance and determines the degree of user acceptance of this technology. Socially assistive robots are one of the most promising interfaces. Their embodiment and multimodal communication channels could potentially provide a large number of services that otherwise would have to be carried out by a variety of dedicated systems. Furthermore, evidence suggests that people perceive robots more as companions and social actors than tools and this is likely to steer user acceptance positively. This paper presents the authors' work related to the EU-FP7 project KSERA, a project that aims at introducing a socially assistive robot that acts as a proactive communication interface in smart home environments. In particular, it gives an overview of (1) human--robot interaction studies conducted in Eindhoven (The Netherlands) whose general aim was to preliminary assess the added value of socially assistive robots in intelligent homes and (2) the KSERA project field trials in Schwechat (Vienna) and Tel Aviv (Israel) that tested an integrated smart-home/robot system with real end users (N=16) in three real-world scenarios. Overall, results show that socially assistive robots positively affect user experience and motivation compared to standard smart environment interfaces such as touch screens. However, people still tend to prefer conventional interfaces for receiving information.


title: Zone-Based Routing Protocol for Wireless Sensor Networks

abstract: Extensive research happening across the globe witnessed the importance of Wireless Sensor Network in the present day application world. In the recent past, various routing algorithms have been proposed to elevate WSN network lifetime. Clustering mechanism is highly successful in conserving energy resources for network activities and has become promising field for researches. However, the problem of unbalanced energy consumption is still open because the cluster head activities are tightly coupled with role and location of a particular node in the network. Several unequal clustering algorithms are proposed to solve this wireless sensor network multihop hot spot problem. Current unequal clustering mechanisms consider only intra- and intercluster communication cost. Proper organization of wireless sensor network into clusters enables efficient utilization of limited resources and enhances lifetime of deployed sensor nodes. This paper considers a novel network organization scheme, energy-efficient edge-based network partitioning scheme, to organize sensor nodes into clusters of equal size. Also, it proposes a cluster-based routing algorithm, called zone-based routing protocol (ZBRP), for elevating sensor network lifetime. Experimental results show that ZBRP out-performs interims of network lifetime and energy conservation with its uniform energy consumption among the cluster heads.


title: An Autocalibrated All-Digital Temperature Sensor for On-Chip Thermal Monitoring

abstract: This brief presents an autocalibrated all-digital temperature sensor circuit for use with on-chip thermal sensing applications. The proposed temperature sensor eliminates the need for two-temperature-point calibration in prior temperature sensors. Therefore, temperature sensor calibration efforts in high-volume production can be significantly reduced. The proposed design uses reference clock period information to perform self-calibration, and thus, effects of process variation can be removed. Subsequently, the accuracy of the proposed temperature sensor can be improved with very small area cost and low power consumption. The temperature sensor is implemented with a standard performance 65-nm complementary metal-oxide-semiconductor technology. The core area is 0.01 mm2, and the power consumption of the proposed circuit is 150 μW with a 1-V supply. Since the proposed temperature sensor can be easily calibrated with a reference clock, the proposed design is very suitable for dynamic thermal management applications in a system-on-a-chip era.


title: Behavioral, neurophysiological and evolutionary perspectives on unihemispheric sleep

abstract: Several animals mitigate the fundamental conflict between sleep and wakefulness by engaging in unihemispheric sleep, a unique state during which one cerebral hemisphere sleeps while the other remains awake. Among mammals, unihemispheric sleep is restricted to aquatic species (Cetaceans, eared seals and manatees). In contrast to mammals, unihemispheric sleep is widespread in birds, and may even occur in reptiles. Unihemispheric sleep allows surfacing to breathe in aquatic mammals and predator detection in birds. Despite the apparent utility in being able to sleep unihemispherically, very few mammals sleep in this manner. This is particularly interesting since the reptilian ancestors to mammals may have slept unihemispherically. The relative absence of unihemispheric sleep in mammals suggests that a trade off exists between unihemispheric sleep and other adaptive brain functions occurring during sleep or wakefulness. Presumably, the benefits of sleeping unihemispherically only outweigh the costs under extreme circumstances such as sleeping at sea. Ultimately, a greater understanding of the reasons for little unihemispheric sleep in mammals promises to provide insight into the functions of sleep, in general.


title: Multi-domain Dialog State Tracking using Recurrent Neural Networks

abstract: Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind. This paper shows that dialog data drawn from different dialog domains can be used to train a general belief tracking model which can operate across all of these domains, exhibiting superior performance to each of the domain-specific models. We propose a training procedure which uses out-of-domain data to initialise belief tracking models for entirely new domains. This procedure leads to improvements in belief tracking performance regardless of the amount of in-domain data available for training the model.


title: Music cognition and the cognitive sciences.

abstract: Why should music be of interest to cognitive scientists, and what role does it play in human cognition? We review three factors that make music an important topic for cognitive scientific research. First, music is a universal human trait fulfilling crucial roles in everyday life. Second, music has an important part to play in ontogenetic development and human evolution. Third, appreciating and producing music simultaneously engage many complex perceptual, cognitive, and emotional processes, rendering music an ideal object for studying the mind. We propose an integrated status for music cognition in the Cognitive Sciences and conclude by reviewing challenges and big questions in the field and the way in which these reflect recent developments.


Adoption of Electronic Health Record Systems among U . S . Non-Federal Acute Care Hospitals : 2008-2014


title: Curvature-aware Manifold Learning

abstract: Abstract One of the fundamental assumptions of traditional manifold learning algorithms is that the embedded manifold is globally or locally isometric to Euclidean space. Under this assumption, these algorithms divided manifold into a set of overlapping local patches which are locally isometric to linear subsets of Euclidean space. Then the learnt manifold would be a flat manifold with zero Riemannian curvature. But in the general cases, manifolds may not have this property. To be more specific, the traditional manifold learning does not consider the curvature information of the embedded manifold. In order to improve the existing algorithms, we propose a curvature-aware manifold learning algorithm called CAML. Without considering the local and global assumptions, we will add the curvature information to the process of manifold learning, and try to find a way to reduce the redundant dimensions of the general manifolds which are not isometric to Euclidean space. The experiments have shown that CAML has its own advantage comparing to other traditional manifold learning algorithms in the sense of the neighborhood preserving ratios (NPR) on synthetic databases and classification accuracies on image set classification.


title: Assessment of healthcare claims rejection risk using machine learning

abstract: Modern healthcare service records, called Claims, record the medical treatments by a Provider (Doctor/Clinic), medication advised etc., along with the charges, and payments to be made by the patient and the Payer (insurance provider). Denial and rejection of healthcare claims is a significant administrative burden and source of loss to various healthcare providers and payers as well. Automating the identification of Claims prone to denial by reason, source, cause and other deciding factors is critical to lowering this burden of rework. We present classification methods based on Machine Learning (ML) to fully automate identification of such claims prone to rejection or denial with high accuracy, investigate the reasons for claims denial and recommend methods to engineer features using Claim Adjustment Reason Codes (CARC) as features with high Information Gain. The ML engine reported is first of its kind in Claims risk identification and represents a novel, significant enhancement to the state of practice of using ML for automating and containing claims denial risks.


title: Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014

abstract: This paper discusses the thematic backdrop for Spanish library and information science output. It draws from Web of Science records on papers authored by researchers at Spanish institutions and published under the category ‘Information Science & Library Science’ between 1985 and 2014. Two analytical techniques were used, one based on co-keyword and the other on document co-citation networks. Burst detection was applied to noun phrases and references of the intellectual base. Co-citation analysis identified nine research fronts: ‘digital rights management’, ‘citation analysis’, ‘translation services’, ‘bibliometric analysis’, ‘co-authorship’, ‘electronic books’, ‘webometrics’, ‘information systems’ and ‘world wide web’. The most recent trends in the subject areas addressed in Spain were found to lie in metrics-related sub-specialities: the h-index, scientific collaboration, journal bibliometric indicators, rankings, universities and webometrics.


title: Heart Disease and Filtrable Viruses

abstract: A number of filtrable viruses can infect the heart and there produce myocarditis, enadocarditis, and pericarditis. However, this action depends on a coexisting deprivation of oxygen to the heart.


title: From Gender Identity Disorder to Gender Identity Creativity: True Gender Self Child Therapy

abstract: True gender self child therapy is based on the premise of gender as a web that weaves together nature, nurture, and culture and allows for a myriad of healthy gender outcomes. This article presents concepts of true gender self, false gender self, and gender creativity as they operationalize in clinical work with children who need therapeutic supports to establish an authentic gender self while developing strategies for negotiating an environment resistant to that self. Categories of gender nonconforming children are outlined and excerpts of a treatment of a young transgender child are presented to illustrate true gender self child therapy.


title: Analysis and simulation of a multiple input interleaved boost converter for renewable energy applications

abstract: With the increasing penetration of wind, solar and other renewable energy sources the demand for modular, reliable power electronic devices are necessary to accommodate the intermittent nature of these sources. Many low voltage communication systems supplied by batteries, super capacitors, and fuel cells utilizes a dc-dc boost converter. For high power applications the boost converter is not as efficient because of the high input current ripple that is added to the current flowing out of a fuel cell and the input current spans over a wide range contributing to a reduction in battery life. With the variety of photovoltaic products available commercially, each device has its own electrical characteristics and the challenge therefore is providing for an aggregate amount of devices for hybrid applications or grid integration. A multiple input converter provides improved operation because it accounts for mismatch in photovoltaic devices and provides for independent and simultaneous power flow from these sources. The interleaved boost converter provides for higher efficiency, reduced input and output ripple while maintaining high power density, thereby making it well suited for high power applications. The interleaved dc-dc boost converter consists of two parallel connected boost converters and employs an interleaving method that provides for an 180o phase shifted switching scheme. The multiple input interleaved boost converter (MIIBC) is analyzed using the synthesis approach and is constituted by a cascade of two interleaved boost converters sharing a common output stage. The dc inputs to the multiple input interleaved boost converter is a 45V/6.5kW fuel cell device and a 50V photovoltaic module, which are modelled using the MATLAB/Simulink package. Simulation results confirm the improvements of using this new converter for renewable energy applications.


title: AprilTag array-aided extrinsic calibration of camera–laser multi-sensor system

abstract: This paper presents a new algorithm for extrinsically calibrating a multi-sensor system including multiple cameras and a 2D laser scanner. On the basis of the camera pose estimation using AprilTag, we design an AprilTag array as the calibration target and employ a nonlinear optimization to calculate the single-camera extrinsic parameters when multiple tags are in the field of view of the camera. The extrinsic parameters of camera–camera and laser–camera are then calibrated, respectively. A global optimization is finally used to refine all the extrinsic parameters by minimizing a re-projection error. This algorithm is adapted to the extrinsic calibration of multiple cameras even if there is non-overlapping field of view. For algorithm validation, we have built a micro-aerial vehicle platform with multi-sensor system to collect real data, and the experiment results confirmed that the proposed algorithm yields great performance.


title: Control Knowledge to Improve Plan Quality

abstract: Generating production-quality plans is an essential element in transforming planners from research tools into real-world applications. However most of the work to date on learning planning control knowledge has been aimed at improving the efficiency of planning; this work has been termed "speed-up teaming". This paper focuses on teaming control knowledge to guide a planner towards better solutions, i.e. to improve the quality of the plans produced by the planner, as its problem solving experience increases. We motivate the use. of quality-enhancing search control knowledge and its automated acquisition from problem solving experience. We introduce an implemented mechanism for learning such control knowledge and some of our preliminary results in a process planning domain.


title: Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

abstract: Suction-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact. This ability simplifies planning, and hand-coded heuristics such as targeting planar surfaces are often used to select suction grasps based on point cloud data. In this paper, we propose a compliant suction contact model that computes the quality of the seal between the suction cup and target object and determines whether or not the suction grasp can resist an external wrench (e.g. gravity) on the object. To evaluate a grasp, we measure robustness to perturbations in end-effector and object pose, material properties, and external wrenches. We use this model to generate Dex-Net 3.0, a dataset of 2.8 million point clouds, suction grasps, and grasp robustness labels computed with 1,500 3D object models and we train a Grasp Quality Convolutional Neural Network (GQ-CNN) on this dataset to classify suction grasp robustness from point clouds. We evaluate the resulting system in 375 physical trials on an ABB YuMi fitted with a pneumatic suction gripper. When the object shape, pose, and mass properties are known, the model achieves 99$\%$ precision on a dataset of objects with Adversarial geometry such as sharply curved surfaces. Furthermore, a GQ-CNN-based policy trained on Dex-Net 3.0 achieves 99$\%$ and 97$\%$ precision respectively on a dataset of Basic and Typical objects. Code, datasets, and supplemental material can be found at http://berkeleyautomation.github.io/dex-net .


title: DeepMovRS: A unified framework for deep learning-based movie recommender systems

abstract: A novel unified framework for deep learning-based movie recommender systems, DeepMovRS, is proposed. The proposed framework accepts various heterogeneous inputs from user and item communities knowledge to explicit and implicit feedbacks. To unify deep architecture of the framework for retrieving and ranking items, it uses suitable machine learning tools to improve the quality of recommendations. The proposed framework is flexible and modular, and it can be generalized and distributed easily, so it can be a rational choice for commercial movie recommender systems.


Context-Sensitive Generation of Open-Domain Conversational Responses


title: Lexicon-based Sentiment Analysis for Persian Text

abstract: The vast information related to products and services available online, of both objective and subjective nature, can be used to provide contextualized suggestions and guidance to possible new customers. User feedback and comments left on different shopping websites, portals and social media have become a valuable resource, and text analysis methods have become an invaluable tool to process this kind of data. A lot of business use-cases have applied sentiment analysis in order to gauge people’s response to a service or product, or to support customers with reaching a decision when choosing such a product. Although methods and techniques in this area abound, the majority only address a handful of natural languages at best. In this paper, we describe a lexiconbased sentiment analysis method designed around the Persian language. An evaluation of the developed GATE pipeline shows an encouraging overall accuracy of up to 69%.


title: The effect of critical success factors on IT governance performance

abstract: Purpose - The purpose of this paper is to analyse the effect of critical success factors (CSFs) on information technology (IT) governance performance in public sector organizations in a developing ...


SEQUENTIAL CLUSTERING-BASED EVENT DETECTION FOR NON-


title: Sensation seeking and internet dependence of Taiwanese high school adolescents

abstract: The present study examined excessive Internet use of Taiwanese adolescents and a psychological aspect of users, sensation seeking, thus to differentiate motivation of Internet dependents and non-dependents. Seven hundred and fifty three Taiwanese high school students were selected using cluster sampling and 88 of them were categorized as Internet dependent users. Results indicated that Internet dependents spent more time on-line than non-dependents. While Internet dependents perceived significantly more negative Internet influences on daily routines, school performance, and parental relation than non-dependents, both Internet dependents and non-dependents viewed Internet use as enhancing peer relations. Making friends through the Internet has become a popular activity among adolescents, potentially leading to its excessive use. Internet dependents scored significantly higher on overall sensation seeking and disinhibition than Internet non-dependents. However, both groups did not differ in the life experience seeking subscale and thrill and adventure seeking subscale. This finding contradicts that of Lavin, Marvin, McLarney, Nola, and Scott [CyberPsychol. Behav. 2 (2000) 425]. Possible reasons for this discrepancy and for the relation between Internet dependence and disinhibition in Taiwanese adolescents are also discussed.


title: Design and Implementation of Autonomous Car using Raspberry Pi

abstract: The project aims to build a monocular vision autonomous car prototype using Raspberry Pi as a processing chip. An HD camera along with an ultrasonic sensor is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of human errors. Many existing algorithms like lane detection, obstacle detection are combined together to provide the necessary control to the car.


title: Trend prediction in temporal bipartite networks: the case of Movielens, Netflix, and Digg

abstract: Online systems, where users purchase or collect items of some kind, can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We use this representation to predict which items might become popular in the near future. Various prediction methods are evaluated on three distinct datasets originating from popular online services (Movielens, Netflix, and Digg). We show that the prediction performance can be further enhanced if the user social network is known and centrality of individual users in this network is used to weight their actions.


title: Conceptual biology: Unearthing the gems

abstract: The conceptual review should take its place as an essential component of scientific research.


title: Internet social network communities: Risk taking, trust, and privacy concerns

abstract: Individuals communicate and form relationships through Internet social networking websites such as Facebook and MySpace. We study risk taking, trust, and privacy concerns with regard to social networking websites among 205 college students using both reliable scales and behavior. Individuals with profiles on social networking websites have greater risk taking attitudes than those who do not; greater risk taking attitudes exist among men than women. Facebook has a greater sense of trust than MySpace. General privacy concerns and identity information disclosure concerns are of greater concern to women than men. Greater percentages of men than women display their phone numbers and home addresses on social networking websites. Social networking websites should inform potential users that risk taking and privacy concerns are potentially relevant and important concerns before individuals sign-up and create social networking websites.


Towards a Probabilistic Roadmap for Multi-robot Coordination


title: Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan

abstract: Previous literature suggests that various factors play crucial roles in the adoption of an information system; however, there is little empirical research about the factors affecting adoption of data warehouse technology, particularly in a single information technology intensive industry. In this study, we used a survey to investigate the factors influencing adoption of data warehouse technology in the banking industry in Taiwan. A total of 50 questionnaires were mailed to CIOs in domestic banks. The response rate was 60%. Discriminant analysis was employed to test hypotheses. The results revealed that factors such as support from the top management, size of the bank, effect of champion, internal needs, and competitive pressure would affect the adoption of data warehouse technology. The results and conclusions from this study may be a good reference for global banks in these aforementioned countries to establish and develop operational strategies, which in turn will facilitate the implementation in overseas branches.


title: Biomedical Named Entity Recognition: A Survey of Machine-Learning Tools

abstract: It is well known that the rapid growth and dissemination of the Internet has resulted in huge amounts of information generated and shared, available in the form of textual data, images, videos or sounds. This overwhelming surge of data is also true for specific areas such as biomedicine, where the number of published documents, such as articles, books and technical reports, is increasing exponentially. For instance, the MEDLINE literature database contains over 20 million references to journal papers, covering a wide range of biomedical fields. In order to organize and manage these data, several manual curation efforts have been set up to identify, in texts, information regarding entities (e.g. genes and proteins) and their relations (e.g. protein-protein interactions). The extracted information is stored in structured knowledge resources, such as Swiss-Prot [1] and GenBank [2]. However, the ef‐ fort required to continually update these databases makes this a very demanding and ex‐ pensive task, naturally leading to increasing interest in the application of Text Mining (TM) systems to help perform those tasks.


title: What is beautiful is usable

abstract: Abstract An experiment was conducted to test the relationships between users' perceptions of a computerized system's beauty and usability. The experiment used a computerized application as a surrogate for an Automated Teller Machine (ATM). Perceptions were elicited before and after the participants used the system. Pre-experimental measures indicate strong correlations between system's perceived aesthetics and perceived usability. Post-experimental measures indicated that the strong correlation remained intact. A multivariate analysis of covariance revealed that the degree of system's aesthetics affected the post-use perceptions of both aesthetics and usability, whereas the degree of actual usability had no such effect. The results resemble those found by social psychologists regarding the effect of physical attractiveness on the valuation of other personality attributes. The findings stress the importance of studying the aesthetic aspect of human–computer interaction (HCI) design and its relationships to other design dimensions.


title: Meta-analytic Reviews in the Organizational Sciences: Two Meta-analytic Schools on the Way to MARS (the Meta-analytic Reporting Standards)

abstract: Purpose ::: The purpose of this study was to review the Meta-Analysis Reporting Standards (MARS) of the American Psychological Association (APA) and highlight opportunities for improvement of meta-analytic reviews in the organizational sciences.


title: Behavioral and endocrinological evaluation of music therapy for elderly patients with dementia

abstract: The present study investigated the effectiveness of music therapy for dementia patients using endocrinological and behavioral evaluations. The study comprised 10 patients with senile dementia who received music therapy; six had Alzheimer's dementia and four had vascular dementia. Music therapy was performed twice a week for 8 consecutive weeks (16 sessions). As a result, total scores on the Mini-Mental State Examination (MMSE) did not significantly change, but the scores of a subscale, ‘language’, improved significantly. According to the Multidimensional Observation Scale For Elderly Subjects (MOSES), scores for ‘irritability’ decreased significantly. Regarding changes in salivary chromogranin A (CgA) levels, the average was significantly decreased before session 16 compared to after this. These results suggest that the combination of endocrinological measurements, behavioral evaluations and functional assessment methods are useful in evaluating the effects of music therapy in persons with senile dementia.


title: Routing Protocols for Vehicular Adhoc Networks (VANETs): A Review

abstract: Routing in Vehicular Ad hoc Networks is a challenging task due to the unique characteristics of the network such as high mobility of nodes, dynamically changing topology and highly partitioned network. It is a challenge to ensure reliable, continuous and seamless communication in the presence of speeding vehicles. The performance of routing protocols depends on various internal factors such as mobility of nodes and external factors such as road topology and obstacles that block the signal. This demands a highly adaptive approach to deal with the dynamic scenarios by selecting the best routing and forwarding strategies and by using appropriate mobility and propagation models. In this paper we review the existing routing protocols for VANETs and categorise them into a taxonomy based on key attributes such as network architecture, applications supported, routing strategies, forwarding strategies, mobility models and quality of service metrics. Protocols belonging to unicast, multicast, geocast and broadcast categories are discussed. Strengths and weaknesses of various protocols using topology based, position based and cluster based approaches are analysed. Emphasis is given on the adaptive and context-aware routing protocols. Simulation of broadcast and unicast protocols is carried out and the results are presented.


title: Sensor-Based Abnormal Human-Activity Detection

abstract: With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors attached to a human body. Detecting abnormal activities is a particular important task in security monitoring and healthcare applications of sensor networks, among many others. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. Therefore, there is a lack of training data for many traditional data mining methods to be applied. To solve this problem, our approach first employs a one-class support vector machine (SVM) that is trained on commonly available normal activities, which filters out the activities that have a very high probability of being normal. We then derive abnormal activity models from a general normal model via a kernel nonlinear regression (KNLR) to reduce false positive rate in an unsupervised manner. We show that our approach provides a good tradeoff between abnormality detection rate and false alarm rate, and allows abnormal activity models to be automatically derived without the need to explicitly label the abnormal training data, which are scarce. We demonstrate the effectiveness of our approach using real data collected from a sensor network that is deployed in a realistic setting.


title: vsInk – Integrating Digital Ink with Program Code in Visual Studio

abstract: We present vsInk, a plug-in that affords digital ink annotation in the Visual Studio code editor. Annotations can be added in the same window as the editor and automatically reflow when the underlying code changes. The plug-in uses recognisers built using machine learning to improve the accuracy of the annotation's anchor. The user evaluation shows that the core functionality is sound.


title: Harnessing encrypted data in cloud for secure and efficient image sharing from mobile devices

abstract: In storage outsourcing, highly correlated datasets can occur commonly, where the rich information buried in correlated data can be useful for many cloud data generation/dissemination services. In light of this, we propose to enable a secure and efficient cloud-assisted image sharing architecture for mobile devices, by leveraging outsourced encrypted image datasets with privacy assurance. Different from traditional image sharing, the proposed design aims to save the transmission cost from mobile clients, by directly utilizing outsourced correlated images to reproduce the image of interest inside the cloud for immediate dissemination. While the benefits are obvious, how to leverage the encrypted image datasets makes the problem particular challenging. To tackle the problem, we first propose a secure and efficient index design that allows the mobile client to securely find from the encrypted image datasets the candidate selection pertaining to the image of interest for sharing. We then design two specialized encryption mechanisms that support the secure image reproduction inside the cloud directly from the encrypted candidate selection. We formally analyze the security strength of the design. Our experiments show that up to 90% of the transmission cost at the mobile client can be saved, while achieving all service requirements and security guarantees.


title: Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm

abstract: Stock prices prediction is interesting and challenging research topic. Developed countries' economies are measured according to their power economy. Currently, stock markets are considered to be an illustrious trading field because in many cases it gives easy profits with low risk rate of return. Stock market with its huge and dynamic information sources is considered as a suitable environment for data mining and business researchers. In this paper, we applied k-nearest neighbor algorithm and non-linear regression approach in order to predict stock prices for a sample of six major companies listed on the Jordanian stock exchange to assist investors, management, decision makers, and users in making correct and informed investments decisions. According to the results, the kNN algorithm is robust with small error ratio; consequently the results were rational and also reasonable. In addition, depending on the actual stock prices data; the prediction results were close and almost parallel to actual stock prices.


Improving Distributed Representations of Tweets - Present and Future


title: Information Security Economics - and Beyond

abstract: The economics of information security has recently become a thriving and fastmoving discipline. As distributed systems are assembled from machines belonging to principals with divergent interests, incentives are becoming as important to dependability as technical design. The new field provides valuable insights not just into security topics such as privacy, bugs, spam, and phishing, but into more general areas such as system dependability (the design of peer-to-peer systems and the optimal balance of effort by programmers and testers), and policy (particularly digital rights management). This research program has been starting to spill over into more general security questions (such as law-enforcement strategy), and into the interface between security and the social sciences. Most recently it has started to interact with psychology, both through the psychologyand- economics tradition and in response to phishing. The promise of this research program is a novel framework for analyzing information security problems - one that is both principled and effective.


title: How to make cognitive illusions disappear: Beyond “heuristics and biases

abstract: Most so-called "errors" in probabilistic reasoning are in fact not violations of probability theory. Examples of such "errors" include overconfi dence bias, conjunction fallacy, and base-rate neglect. Research- ers have relied on a very narrow normative view, and have ignored conceptual distinctions—for example, single case versus relative frequency—fundamental to probability theory. By recognizing and using these distinctions, however, we can make apparently stable "errors" disappear, reappear, or even invert. I suggest what a reformed understanding of judgments under uncertainty might look like.


title: Development of Fluorinated Analogues of Perhexiline with Improved Pharmacokinetic Properties and Retained Efficacy

abstract: We designed and synthesized perhexiline analogues that have the same therapeutic profile as the parent cardiovascular drug but lacking its metabolic liability associated with CYP2D6 metabolism. Cycloalkyl perhexiline analogues 6a-j were found to be unsuitable for further development, as they retained a pharmacokinetic profile very similar to that shown by the parent compound. Multistep synthesis of perhexiline analogues incorporating fluorine atoms onto the cyclohexyl ring(s) provided a range of different fluoroperhexiline analogues. Of these, analogues 50 (4,4-gem-difluoro) and 62 (4,4,4',4'-tetrafluoro) were highly stable and showed greatly reduced susceptibility to CYP2D6-mediated metabolism. In vitro efficacy studies demonstrated that a number of derivatives retained acceptable potency against CPT-1. Having the best balance of properties, 50 was selected for further evaluation. Like perhexiline, it was shown to be selectively concentrated in the myocardium and, using the Langendorff model, to be effective in improving both cardiac contractility and relaxation when challenged with high fat buffer.


title: The attraction of online games: An important factor for Internet Addiction

abstract: This study examined the attraction of online games and their contribution to Internet Addiction (IA) among college students in China in a series of cross-sectional studies. Study 1 tested the theoretical framework of the online games attraction and developed an assessment instrument: the Online Game Attraction Inventory (OAI). Study 2 surveyed 635 students using a battery of questionnaires that included the Chen Internet Addiction Scale (CIAS) to assess IA levels and determine which predictor contributed the most to IA. The results indicated the OAI to be a psychometrically sound tool with acceptable levels of reliability and validity. The OAI score was positively related to the CIAS score (r=.397, p<.01), and the structural equation modeling results revealed that online game attraction mediates the relationships among personality, family function, life events, social support and IA. Thus, the attraction of online games may affect the IA of college students; therefore, more attention should be paid to this issue.


title: Discernment of Broken Magnet and Static Eccentricity Faults in Permanent Magnet Synchronous Motors

abstract: This paper deals with the discernment of broken magnet and static eccentricity faults in permanent magnet synchronous motors through the stator phase current. Broken magnet and static eccentricity faults exhibit very similar fault patterns in back-electromotive force (emf) and flux spectrums. Therefore, it is essential to separate these faults from each other for a true diagnosis. In this study, stator emf and phase current waveforms are analyzed in detail to identify the discerning components and characterize their dynamic behaviors. Two-dimensional time-stepping finite-element simulations and experimental results show that the fault classification process can be implemented by using fault-dependent in-phase current fault signatures.


title: Improved solutions for the traveling purchaser problem

abstract: Abstract The traveling purchaser problem (TPP) is a generalization of the well-known traveling salesman problem (TSP), which has many real-world applications. Examples include the purchase of the required raw materials for the manufacturing factories in which the total cost has to be minimized, and the scheduling of a set of jobs over some machines with different set-up and job processing costs in which the total cost for completing the jobs has to be minimized. The TPP has been shown to be computationally intractable. Therefore, many heuristic solution procedures have been proposed to solve the TPP approximately. The purpose of this paper is to consider some variations of the existing solution procedures to improve the solutions. Computational experiments and performance comparisons among the existing algorithms as well as the proposed variations are provided, and the results are analyzed. The traveling purchaser problem (TPP) is an interesting generalization of the well-known traveling salesman problem (TSP), in which a list of commodity items have to be purchased at some markets selling various commodities with different prices, and the total travel and purchase costs must be minimized. Applications include the purchase of raw materials for the manufacturing factories in which the total cost has to be minimized, and the scheduling of jobs over some machines with different set-up and job processing costs in which the total costs for completing the jobs has to be minimized. The TPP has been shown to be computationally intractable. Therefore, many heuristic solution procedures, including the Search algorithm, the Generalized-Savings algorithm, the Tour-Reduction algorithm, and the Commodity-Adding algorithm have been proposed to solve the TPP approximately. In this paper, we consider some variations of these algorithms to improve the solutions. The proposed variations are compared with the existing solution procedures. The results indicate that the proposed variations significantly improve the existing solutions.


title: Human-comfortable navigation for an autonomous robotic wheelchair

abstract: Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.


title: Prevalence of common mental disorders in widowhood: A systematic review and meta-analysis

abstract: Abstract Background Widowed people have a high risk of common mental disorders, however no summary estimates of the prevalences exist. The aim of this study was to conduct a systematic review and meta-analysis of the prevalence of common mental disorders in widowed people in the community. Methods MEDLINE, Embase and PsycInfo were searched for papers reporting on prevalence of common mental disorders and widowhood. Eligible studies were included in random effects meta-analyses of the prevalence of depression and anxiety disorders. Subgroup analyses were performed on method of assessment of depression and age and sex. Results The literature search identified 13,781 titles of which 42 were eligible for meta-analysis. The pooled prevalence of depression in studies using a screening scale was 40.6% (33.6%–47.6%) (n = 30). For studies using full diagnostic criteria the pooled prevalence of depression was 19.2% (13.4%–25.0%) (n = 12). Subgroup analyses of age (≥ or Limitations The prevalence estimates in this study are summary estimates of prevalences from existing literature. Although the reporting bias assessment showed no evidence, there could be some reporting bias, as studies might only present results if there is a high prevalence. Conclusions Widowed people have a high prevalence of depression and anxiety disorders. The high prevalence of depression was independent of age and sex. The study identifies a population group at high risk needing special attention in clinical practice.


title: Power Efficient Level Shifter for 16 nm FinFET Near Threshold Circuits

abstract: Since the minimum feature size has shrunk beyond the sub-30-nm node, power density has become the major factor in modern microprocessors. Techniques such as dynamic voltage scaling operating down to near threshold voltage levels and supporting multiple voltage domains have become necessary to reduce dynamic as well as static power. A key component of these techniques is a level shifter that serves different voltage domains. This level shifter must be high speed and power efficient. The proposed level shifter translates voltages ranging from 250 to 790 mV, and exhibits 42% shorter delay, 45% lower energy consumption, and 48% lower static power dissipation. In addition, the proposed level shifter exhibits symmetric rise and fall transition times with up to 12% skew at the extreme conditions over the maximum range of voltages.


title: Learning about Spanish dialects through Twitter

abstract: This paper maps the large-scale variation of the Spanish language by employing a corpus based on geographically tagged Twitter messages. Lexical dialects are extracted from an analysis of variants of tens of concepts. The resulting maps show linguistic variation on an unprecedented scale across the globe. We discuss the properties of the main dialects within a machine learning approach and find that varieties spoken in urban areas have an international character in contrast to country areas where dialects show a more regional uniformity.


Molecular Mechanisms of Bipolar Disorder: Progress Made and Future Challenges


Ride and attitude control of active suspensions


title: State of the art and challenges facing consensus protocols on blockchain

abstract: Nowadays, the blockchain is a favorite platform, for instance, for use with cryptocurrency, smart contracts, IoT, and so forth. Blockchains are distributed ledgers that enable parties who do not trust each other to maintain states. The parties agree on the existence, values, and histories of the states. The blockchain applies the consensus protocol to verify the block which is distributing the network node. Consensus has many practices such as, for example, Byzantine general problem, Proof of work, and Proof of stake. However, there have not been numerous papers that have undertaken analysis with regard to various aspects, and which have incorporated the adoption summary as appropriate to the application. In this paper, we have provided a technical verification review with regard to the consensus algorithm taken from previous research. The audience will receive a consensus protocol, and the algorithm typically analyzes the application to match which platform is appropriate by viewing the node identity, energy saving and the tolerated power of the adversary, the data model, language, execution, application, and examples. Finally, we finish by presenting several research directions with regard to the consensus protocol.


title: Dot-Diffused Halftoning with Improved Homogeneity

abstract: Compared with the error diffusion, dot diffusion provides an additional pixel-level parallelism for digital halftoning. However, even though its periodic and blocking artifacts had been eased by the previous works, it was still far from satisfactory in terms of the blue noise spectrum perspective. In this paper, we strengthen the relation among the pixel locations of the same processing order by an iterative halftoning method, and the results demonstrate a significant improvement. Moreover, a new approach of deriving the averaged power spectrum density is proposed to avoid the regular sampling of the well-known Bartlett’s procedure which inaccurately presents the halftone periodicity of certain halftoning techniques with parallelism. As a result, the proposed dot diffusion is substantially superior to the state-of-the-art parallel halftoning methods in terms of visual quality and artifact-free property, and competitive runtime to the theoretical fastest ordered dithering is offered simultaneously.


Fractal learning neural networks


title: Billboard clouds for extreme model simplification

abstract: We introduce billboard clouds -- a new approach for extreme simplification in the context of real-time rendering. 3D models are simplified onto a set of planes with texture and transparency maps. We present an optimization approach to build a billboard cloud given a geometric error threshold. After computing an appropriate density function in plane space, a greedy approach is used to select suitable representative planes. A good surface approximation is ensured by favoring planes that are "nearly tangent" to the model. This method does not require connectivity information, but instead avoids cracks by projecting primitives onto multiple planes when needed. For extreme simplification, our approach combines the strengths of mesh decimation and image-based impostors. We demonstrate our technique on a large class of models, including smooth manifolds and composite objects.


title: Genetic differentiation between populations of Plantago lanceolata. 2. Phenotypic selection in a transplant experiment in three contrasting habitats.

abstract: (1) Populations of Plantago lanceolata were grown in a reciprocal-transplant experiment in an early-mown hayfield, a wet, late-mown hayfield and a pasture. At all three sites, populations and clones differed significantly in components of seed yield such as survival, incidence of flowering and seed set, and in annual seed yield. (2) Components of seed yield of transplants were correlated with the height of the surrounding vegetation. In the pasture, plants produced far more seeds in tall vegetation, which was probably due to a low local grazing intensity. In contrast, in the early-mown hayfield tall vegetation was associated with a lower lifetime seed yield. At all three sites, tall vegetation coincided with a low survival of transplants. (3) In the early-mown hayfield selection on timing of flowering and growth habit was found, which agreed with the expectations from the observed differences between populations. At all sites seed production varied above all because of differences in plant size, and no further direct effects of phenotypic traits on seed yield could be detected. (4) Specialization of clones to certain sites was tested by calculating correlations of seed-yield components across sites. No significant correlations were found between components of seed yield per clone across sites. This was probably due to the positive correlation of the mean plant size per clone at different sites. A trade-off between vegetative growth and reproduction is suggested as a possible cause.


title: Tensor completion and low-n-rank tensor recovery via . . .

abstract: In this paper we consider sparsity on a tensor level, as given by the n-rank of a tensor. In an important sparse-vector approximation problem (compressed sensing) and the low-rank matrix recovery problem, using a convex relaxation technique proved to be a valuable solution strategy. Here, we will adapt these techniques to the tensor setting. We use the n-rank of a tensor as a sparsity measure and consider the low-n-rank tensor recovery problem, i.e. the problem of finding the tensor of the lowest n-rank that fulfills some linear constraints. We introduce a tractable convex relaxation of the n-rank and propose efficient algorithms to solve the low-n-rank tensor recovery problem numerically. The algorithms are based on the Douglas–Rachford splitting technique and its dual variant, the alternating direction method of multipliers.


title: Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

abstract: Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.


title: Techniques for sentiment analysis of Twitter data: A comprehensive survey

abstract: The World Wide Web has intensely evolved a novel way for people to express their views and opinions about different topics, trends and issues. The user-generated content present on different mediums such as internet forums, discussion groups, and blogs serves a concrete and substantial base for decision making in various fields such as advertising, political polls, scientific surveys, market prediction and business intelligence. Sentiment analysis relates to the problem of mining the sentiments from online available data and categorizing the opinion expressed by an author towards a particular entity into at most three preset categories: positive, negative and neutral. In this paper, firstly we present the sentiment analysis process to classify highly unstructured data on Twitter. Secondly, we discuss various techniques to carryout sentiment analysis on Twitter data in detail. Moreover, we present the parametric comparison of the discussed techniques based on our identified parameters.


title: Visual odometry

abstract: We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates what we call visual odometry, i.e. motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual odometry can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results with an autonomous ground vehicle. We give examples of camera trajectories estimated purely from images over previously unseen distances and periods of time.


title: Optimization of the Surface Structure on Black Silicon for Surface Passivation

abstract: Black silicon shows excellent anti-reflection and thus is extremely useful for photovoltaic applications. However, its high surface recombination velocity limits the efficiency of solar cells. In this paper, the effective minority carrier lifetime of black silicon is improved by optimizing metal-catalyzed chemical etching (MCCE) method, using an Al2O3 thin film deposited by atomic layer deposition (ALD) as a passivation layer. Using the spray method to eliminate the impact on the rear side, single-side black silicon was obtained on n-type solar grade silicon wafers. Post-etch treatment with NH4OH/H2O2/H2O mixed solution not only smoothes the surface but also increases the effective minority lifetime from 161 μs of as-prepared wafer to 333 μs after cleaning. Moreover, adding illumination during the etching process results in an improvement in both the numerical value and the uniformity of the effective minority carrier lifetime.


Thermal Effect in Underfill Encapsulation of Ball Grid Array


title: Integrating Fuzzy Delphi with Fuzzy Analytic Hierarchy Process for Multiple Criteria Inventory Classification

abstract: A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. In order to efficiently control the inventory items and to determine the suitable ordering policies for them, multiple criteria inventory classification is used. In this paper, a systematic and logical approach is structured for multiple criteria inventory classification through integrating Fuzzy Delphi Method (FDM) with Fuzzy Analytic Hierarchy Process (FAHP). Fuzzy Delphi method used to identify the most important and significant criteria and, Fuzzy AHP is used to determine the relative weights of the attributes or criteria, and to classify inventories into different categories. To accredit the proposed model, it is implemented for the 351 raw materials of switch gear section of Energypac Engineering Limited (EEL), a large power engineering company of Bangladesh. Implementation results show that the proposed method can be used in inventory classification.


title: How neuroinflammation contributes to neurodegeneration

abstract: Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and frontotemporal lobar dementia are among the most pressing problems of developed societies with aging populations. Neurons carry out essential functions such as signal transmission and network integration in the central nervous system and are the main targets of neurodegenerative disease. In this Review, I address how the neuron's environment also contributes to neurodegeneration. Maintaining an optimal milieu for neuronal function rests with supportive cells termed glia and the blood-brain barrier. Accumulating evidence suggests that neurodegeneration occurs in part because the environment is affected during disease in a cascade of processes collectively termed neuroinflammation. These observations indicate that therapies targeting glial cells might provide benefit for those afflicted by neurodegenerative disorders.


title: Compensation Topologies of High-Power Wireless Power Transfer Systems

abstract: Wireless power transfer (WPT) is an emerging technology that can realize electric power transmission over certain distances without physical contact, offering significant benefits to modern automation systems, medical applications, consumer electronics, etc. This paper provides a comprehensive review of existing compensation topologies for the loosely coupled transformer. Compensation topologies are reviewed and evaluated based on their basic and advanced functions. Individual passive resonant networks used to achieve constant (load-independent) voltage or current output are analyzed and summarized. Popular WPT compensation topologies are given as application examples, which can be regarded as the combination of multiple blocks of resonant networks. Analyses of the input zero phase angle and soft switching are conducted as well. This paper also discusses the compensation requirements for achieving the maximum efficiency according to different WPT application areas.


title: Trading Tests of Long-Term Market Forecast by Text Mining

abstract: We propose a new approach for analyzing the Japanese government bond (JGB) market using text-mining technology. First, we extracted the feature vectors of the monthly reports from the Bank of Japan (BOJ). Then, the trends in the JGB market were estimated by a regression analysis using the feature vectors. As a result of comparison with support vector regression and other methods, the proposal method could forecast in higher accuracy about both the level and direction of long-term market trends. Moreover, our method showed high returns with annual rate averages as a result of the implementation test.


title: Game-based forensics course for first year students

abstract: This paper focuses on the design and development of a game-based forensics course. This course uses the game-based learning (GBL) approach that builds the game in a real computing environment that has direct access to actual forensics tools from a forensics machine and the evidence from a suspect machine. Interactive visualizations will be used to help students to understand the intangible and inaccessible abstract concepts such as deleted/hidden/encrypted/over-written digital evidence.


title: Pragmatic functions of emoji in internet-based communication---a corpus-based study

abstract: Emojis have been indispensable and efficient tools in online interaction with the booming Internet and dramatically fast emergence of electronic communication channels. The present research is to study the pragmatic functions of emoji in internet-based communication within the revised framework of Linguagem em (Dis) curso (special issue on relevance theory) 14:511--529, 2014 8-function taxonomy. A total of 10 volunteers (20–40 years) were invited to collect a 3000-word latest corpus from their most active Wechat group respectively. Forty-six types of emoji in a corpus of 34,047 words were classified into 7 functions: attitude/emotion signal, attitude/emotion intensity enhancer, illocutionary force modifier, humor, irony, turn taking/giving, and backchannel device. We found emoji use was of high frequency, 1908 times in the corpus of 34,047 words, mainly as emotion signifier (attitude/emotion signal and attitude/emotion intensity enhancer, 50.8%) and interaction device (turn taking/giving, backchannel device and illocutionary force modifier, 47.8%). The top 3 most frequent emojis were Open image in new window (thump-up)(477 times) Open image in new window (rose) (222 times) and Open image in new window (traditional Chinese way of greeting and thanks) (108 times), which were used in an exaggerative and generalized way, slightly different from people’s preference of other countries. Additionally, positive emojis were used more often than negative ones. These findings suggested that emojis are of high frequency, functionality and efficiency in internet-based communication. Interestingly, online preference to emoji use does not perfectly mirror non-verbal signs in face-to-face communication; and in socio-emotional setting, people tend to frequently use positive emojis to create positive atmosphere to boost group rapport.


title: Protein precipitation by polyethylene glycol: a generalized model based on hydrodynamic radius.

abstract: PEGs for protein precipitation are usually classified by molecular weight. The higher molecular weight precipitants are more efficient but result in higher viscosity. Following empirical evidence that the precipitation efficiency is more comprehensively characterized by PEG hydrodynamic radius (rh,PEG) than molecular weight, this paper proposes a model to explicate the significance of rh,PEG. A general expression was formulated to characterize the PEG effect exclusively by rh,PEG. The coefficients of a linearized form were then fitted using empirical solubility data. The result is a simple numerical relation that models the efficiency of general-shaped PEG precipitants as a function of rh,PEG and protein hydrodynamic radius (rh,prot). This equation also explains the effects of environmental conditions and PEG branching. While predictions by the proposed correlation agree reasonably well with independent solubility data, its simplicity gives rise to potential quantitative deviations when involving small proteins, large proteins and protein mixtures. Nonetheless, the model offers a new insight into the precipitation mechanism by clarifying the significance of rh,PEG. This in turn helps to refine the selection criterion for PEG precipitants.


title: Efficient tracing of failed nodes in sensor networks

abstract: In sensor networks, nodes commonly rely on each other to route messages to a base station. Although this practice conserves power it can obscure the cause of a measurement outage in a portion of the network. For example, when a base station ceases to receive measurements from a region of nodes it can't immediately determine whether this is because of the destruction of all the nodes in that region (due to an enemy attack, for example) or merely the result of the failure of a few nodes bearing much of the routing load. Previous solutions to this problem typically consist of re-running the route-discovery protocol, a process that can be quite expensive in terms of the number of messages that must be exchanged. We demonstrate that the topology of the network can be efficiently conveyed to the base station allowing for the quick tracing of the identities of the failed nodes with moderate communication overhead. Our algorithms work in conjunction with the existing functions of the network, requiring the nodes to send no additional messages.


title: Processing nested complex sequence pattern queries over event streams

abstract: Complex event processing (CEP) has become increasingly important for tracking and monitoring applications ranging from health care, supply chain management to surveillance. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence queries arises, while the state-of-the-art CEP systems mostly focus on the execution of flat sequence queries only. In this paper, we now introduce an iterative execution strategy for nested CEP queries composed of sequence, negation, AND and OR operators. Lastly we have introduced the promising direction of applying selective caching of intermediate results to optimize the execution. Our experimental study using real-world stock trades evaluates the performance of our proposed iterative execution strategy for different query types.


title: Bivariate Shrinkage Functions for Wavelet-Based Denoising Exploiting Interscale Dependency

abstract: Most simple nonlinear thresholding rules for wavelet-based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. We only consider the dependencies between the coefficients and their parents in detail. For this purpose, new non-Gaussian bivariate distributions are proposed, and corresponding nonlinear threshold functions (shrinkage functions) are derived from the models using Bayesian estimation theory. The new shrinkage functions do not assume the independence of wavelet coefficients. We show three image denoising examples in order to show the performance of these new bivariate shrinkage rules. In the second example, a simple subband-dependent data-driven image denoising system is described and compared with effective data-driven techniques in the literature, namely VisuShrink, SureShrink, BayesShrink, and hidden Markov models. In the third example, the same idea is applied to the dual-tree complex wavelet coefficients.


title: EMIT: An efficient MAC paradigm for the Internet of Things

abstract: The future Internet of Things (IoT) networks are expected to be composed of a large population of low-cost devices communicating dynamically with access points or neighboring devices to communicate small bundles of delay-sensitive data. To support the high-intensity and short-lived demands of these emerging networks, we propose an Efficient MAC paradigm for IoT (EMIT). Our paradigm bypasses the high overhead and coordination costs of existing MAC solutions by employing an interference-averaging strategy that allow users to share their resources simultaneously. In contrast to the predominant interference-suppressing approaches, EMIT exploits the dense and dynamic nature of IoT networks to reduce the spatio-temporal variability of interference to achieve low-delay and high-reliability in service. This paper introduces foundational ideas of EMIT by characterizing the global interference statistics in terms of single-device operation and develops power-rate allocation strategies to guarantee low-delay high-reliability performance. A significant portion of our work is aimed at validating these theoretical principles in experimental testbeds, where we compare the performance of EMIT to a CSMA-based MAC protocol. Our comparisons confirm the beneficial characteristics of EMIT, and reveal significant gains over CSMA strategies in the case of IoT traffic.


title: Toward a Cooperative Natural Language Query Interface for Biological Databases

abstract: One of the many unique features of biological databases is that the mere existence of a ground data item is not always a precondition for a query response. From a biologist's standpoint, queries are not always best posed using a structured language. By this we mean that approximate and flexible responses to natural language like queries are well suited for this domain. This is partly due to biologists' tendency to seek simpler interfaces and partly due to the fact that questions in biology involve numerous high level concepts that are open to interpretations computed using sophisticated tools. In such highly interpretive environments, rigidly structured databases do not always perform well. In this paper, our goal is to propose a semantic correspondence plug-in to aid natural language query processing over arbitrary biological database schema with an aim to providing cooperative responses to queries tailored to users' interpretations. We demonstrate the feasibility of our system with a practical example.


title: A Soft-Robotic Gripper With Enhanced Object Adaptation and Grasping Reliability

abstract: A novel soft-robotic gripper design is presented, with three soft bending fingers and one passively adaptive palm. Each soft finger comprises two ellipse-profiled pneumatic chambers. Combined with the adaptive palm and the surface patterned feature, the soft gripper could achieve 40-N grasping force in practice, 10 times the self-weight, at a very low actuation pressure below 100 kPa. With novel soft finger design, the gripper could pick up small objects, as well as conform to large convex-shape objects with reliable contact. The fabrication process was presented in detail, involving commercial-grade three-dimensional printing and molding of silicone rubber. The fabricated actuators and gripper were tested on a dedicated platform, showing the gripper could reliably grasp objects of various shapes and sizes, even with external disturbances.


title: Spotlight SAR data focusing based on a two-step processing approach

abstract: The authors present a new spotlight SAR data-focusing algorithm based on a two-step processing strategy that combines the advantages of two commonly adopted processing approaches: the efficiency of SPECAN algorithms and the precision of stripmap focusing techniques. The first step of the proposed algorithm implements a linear and space-invariant azimuth filtering that is carried out via a deramping-based technique representing a simplified version of the SPECAN approach. This operation allows the authors to perform a bulk azimuth raw data compression and to achieve a pixel spacing smaller than (or equal to) the expected azimuth resolution of the fully focused image. Thus, the azimuth spectral folding phenomenon, typically affecting the spotlight data, is overcome, and the space-variant characteristics of the stripmap system transfer function are preserved. Accordingly, the residual and precise focusing of the SAR data is achieved by applying a conventional stripmap processing procedure requiring a minor modification and implemented in the frequency domain. The extension of the proposed technique to the case of high bandwidth transmitted chirp signals is also discussed. Experiments carried out on real and simulated data confirm the validity of the presented approach, which is mainly focused on spaceborne systems.


title: Implementation of lane detection algorithm for self-driving car on toll road cipularang using Python language

abstract: Self-Driving Car research problem requires several sub-topics that need to be discussed more deeply. Such as Deep Learning, Computer Vision, Fusion Sensor, Localization, Control, until Path Planning. All of them are fusion of several fields of study. This paper discusses the results of implementation of lane detection algorithm on toll road Cipularang as parts of self-driving car system. Video image taken using action camera mounted on top of the vehicle, with 1280×720 resolution. Average speed of the vehicle is 100 km per hour. Programming language of image processing using Python 3. Image processing method are a combination of methods of colour region, line selection, canny edge detection, and Hough transform. The result shows this algorithm needed to be add some method that can changing the parameters during day and night adaptively. Because constant parameters can only be used in the same lighting conditions. Overall the implementation method in Python Language can successfully detect the road lane with an accuracy above 90 percent.


A Real-Time Object Distance Measurement using a Monocular Camera


title: Dual Printed Antenna for Wi-Fi Applications

abstract: A dual-frequency compact printed antenna for Wi-Fi (IEEE 802.11x at 2.45 and 5.5 GHz) applications is presented. The design is successfully optimized using a finite-difference time-domain (FDTD)-algorithm-based procedure. Some prototypes have been fabricated and measured, displaying a very good performance.


title: A 25mA CMOS LDO with −85dB PSRR at 2.5MHz

abstract: A CMOS low-dropout regulator (LDO) with high power-supply rejection ratio (PSRR) achieved by the proposed supply ripple feed-forward path is presented in this paper. The LDO is simple with two additional low-pass filters included. No extra power is consumed when comparing to the traditional design. The proposed LDO is implemented in 0.18-μ m CMOS technology. It occupies active area of 0.042 mm2. With the proposed embedded supply ripple feed-forward path, in the maximum loading of 25 mA, it achieves PSRR of -85 dB at 2.5 MHz and PSRR better than -55 dB when frequency is below 5 MHz with a 4.7-μF output capacitor. The measured quiescent current is 15 μA only. The overshoot and undershoot voltages are less than 40 mV when loading changes between 1 mA and 25 mA within 40 ns. The LDO achieves line and load regulations of 3 mV/V and 50 μV/mA, respectively.


title: Some Properties of Joint Probability Distributions

abstract: Several Artificial Intelligence schemes for reasoning under uncertainty explore either explicitly or implicitly asymmetries among probabilities of various states of their uncertain domain models. Even though the correct working of these schemes is practically contingent upon the existence of a small number of probable states, no formal justification has been proposed of why this should be the case. This paper attempts to fill this apparent gap by studying asymmetries among probabilities of various states of uncertain models. By rewriting the joint probability distribution over a model's variables into a product of individual variables' prior and conditional probability distributions, and applying central limit theorem to this product, we can demonstrate that the probabilities of individual states of the model can be expected to be drawn from highly skewed, log-normal distributions. With sufficient asymmetry in individual prior and conditional probability distributions, a small fraction of states can be expected to cover a large portion of the total probability space with the remaining states having practically negligible probability. Theoretical discussion is supplemented by simulation results and an illustrative real-world example.


title: Promise and Pitfalls of Extending Google's PageRank Algorithm to Citation Networks

abstract: The number of citations is the most commonly used metric for quantifying the importance of scientific publications. However, we all have anecdotal experiences that citations alone do not characterize the importance of a publication. Some of the shortcomings of using citations as a universal measure


title: A game theoretic approach to video streaming over peer-to-peer networks

abstract: We consider the problem of foresighted multimedia resource reciprocation in peer-to-peer (P2P) networks, which consist of rational peers aiming at maximizing their individual utilities. We introduce an artificial currency (credit) to take into account the characteristics of different parts of the video signal. The resource reciprocation with the proposed credit metric can be formulated as a stochastic game, in which the peers determine their optimal strategies using Markov Decision Process (MDP) framework. The introduced framework can be applied to the general video coding, and in particular, is suitable for the scalable video where various parts of the encoded bit stream have significantly different importance for the video quality.


title: Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image

abstract: Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications. While deep learning methods have made significant progress in visual object detection and segmentation, the object pose estimation task is still challenging. In this paper, we introduce an end-toend deep learning framework, named Deep-6DPose, that jointly detects, segments, and most importantly recovers 6D poses of object instances from a single RGB image. In particular, we extend the recent state-of-the-art instance segmentation network Mask R-CNN with a novel pose estimation branch to directly regress 6D object poses without any post-refinements. Our key technical contribution is the decoupling of pose parameters into translation and rotation so that the rotation can be regressed via a Lie algebra representation. The resulting pose regression loss is differential and unconstrained, making the training tractable. The experiments on two standard pose benchmarking datasets show that our proposed approach compares favorably with the state-of-the-art RGB-based multi-stage pose estimation methods. Importantly, due to the end-to-end architecture, Deep-6DPose is considerably faster than competing multi-stage methods, offers an inference speed of 10 fps that is well suited for robotic applications.


title: Wearable Assistive Devices for the Blind

abstract: Assistive devices are a key aspect in wearable systems for biomedical applications, as they represent potential aids for people with physical and sensory disabilities that might lead to improvements in the quality of life. This chapter focuses on wearable assistive devices for the blind. It intends to review the most significant work done in this area, to present the latest approaches for assisting this population and to understand universal design concepts for the development of wearable assistive devices and systems for the blind.


title: EMG-Based Neuro-Fuzzy Control of a 4DOF Upper-Limb Power-Assist Exoskeleton

abstract: We have been developing a 4DOF exoskeleton robot system in order to assist shoulder vertical motion, shoulder horizontal motion, elbow motion, and forearm motion of physically weak persons such as elderly, injured, or disabled persons. The robot is directly attached to a user's body and activated based on EMG (electromyogram) signals of the user's muscles, since the EMG signals directly reflect the user's motion intention. A neuro-fuzzy controller has been applied to control the exoskeleton robot system. In this paper, controller adaptation method to user's EMG signals is proposed. A motion indicator is introduced to indicate the motion intention of the user for the controller adaptation. The experimental results show the effectiveness of the proposed method.


A pr 2 01 8 Enhanced Word Representations for Bridging Anaphora Resolution


Convolutional Neural Network for Brain MR Imaging Extraction Using Silver-Standards Masks


title: Evaluation of the Outcome of Complete Sinus Excision with Reconstruction of the Umbilicus in Patients with Umbilical Pilonidal Sinus

abstract: Umbilical pilonidal sinus is a cause of umbilical discharge. In this study, the outcome of complete excision of the umbilical sinus with umbilical reconstruction is considered. Adult patients with umbilical pilonidal sinus who had not undergone any previous surgeries were operated on using a technique that involves complete excision of the sinus after eversion of the umbilicus followed by reconstruction of the umbilicus. Patients were then followed; and wound complications, recurrence, and patient satisfaction were evaluated at postoperative visits. A total of 45 patients underwent the operation; 39 (86.5%) were male, and 6 (13.5%) were female. The mean age was 22.6 years (18–27 years). Six male patients had synchronous sacrococcygeal pilonidal disease. The mean follow-up period was 34 months (3–62 months). Only four patients had wound drainage after operation, and all required drainage of the wound. No recurrence was observed during the follow-up period, and all patients were satisfied with the appearance of their umbilicus. The technique of complete sinus excision and umbilical reconstruction is an effective and acceptable method for treating umbilical pilonidal sinus and may be recommended for primary treatment of this disease.


title: Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk: A Predictive Model For Credit Card Scoring

abstract: Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, mean ...


title: The six determinants of gait and the inverted pendulum analogy: A dynamic walking perspective

abstract: We examine two prevailing, yet surprisingly contradictory, theories of human walking. The six determinants of gait are kinematic features of gait proposed to minimize the energetic cost of locomotion by reducing the vertical displacement of the body center of mass (COM). The inverted pendulum analogy proposes that it is beneficial for the stance leg to behave like a pendulum, prescribing a more circular arc, rather than a horizontal path, for the COM. Recent literature presents evidence against the six determinants theory, and a simple mathematical analysis shows that a flattened COM trajectory in fact increases muscle work and force requirements. A similar analysis shows that the inverted pendulum fares better, but paradoxically predicts no work or force requirements. The paradox may be resolved through the dynamic walking approach, which refers to periodic gaits produced almost entirely by the dynamics of the limbs alone. Demonstrations include passive dynamic walking machines that descend a gentle slope, and active dynamic walking robots that walk on level ground. Dynamic walking takes advantage of the inverted pendulum mechanism, but requires mechanical work to transition from one pendular stance leg to the next. We show how the step-to-step transition is an unavoidable energetic consequence of the inverted pendulum gait, and gives rise to predictions that are experimentally testable on humans and machines. The dynamic walking approach provides a new perspective, focusing on mechanical work rather than the kinematics or forces of gait. It is helpful for explaining human gait features in a constructive rather than interpretive manner.


Fast evaluation of elementary mathematical functions with correctly rounded last bit


title: Transformation Optics and the Geometry of Light

abstract: Publisher Summary This chapter describes the geometry of light and the concepts of transformation optics. Transformation optics is beginning to transform optics. The chapter introduces connections between geometry and electromagnetism in media that is as consistent and elementary as possible. This chapter analyzes four examples of transformation media—cloaking devices, perfect lenses, vortices, and horizons; these four cases illustrate characteristic non-trivial topologies, each one with different physics, and they have been experimentally verified. This chapter focuses on the main ideas and some connections between optics, in particular, transformation optics, and other areas of physics and mathematics. It also addresses the classical optics of transformation media.


title: Software project risk analysis using Bayesian networks with causality constraints

abstract: Many risks are involved in software development and risk management has become one of the key activities in software development. Bayesian networks (BNs) have been explored as a tool for various risk management practices, including the risk management of software development projects. However, much of the present research on software risk analysis focuses on finding the correlation between risk factors and project outcome. Software project failures are often a result of insufficient and ineffective risk management. To obtain proper and effective risk control, risk planning should be performed based on risk causality which can provide more risk information for decision making. In this study, we propose a model using BNs with causality constraints (BNCC) for risk analysis of software development projects. Through unrestricted automatic causality learning from 302 collected software project data, we demonstrated that the proposed model can not only discover causalities in accordance with the expert knowledge but also perform better in prediction than other algorithms, such as logistic regression, C4.5, Naive Bayes, and general BNs. This research presents the first causal discovery framework for risk causality analysis of software projects and develops a model using BNCC for application in software project risk management.


title: DualRadviz: Preserving Context between Classification Evaluation and Data Exploration with RadViz

abstract: With today's flood of data coming from many types of sources, Machine Learning becomes increasingly important. Though, many times the use of Machine Learning is not enough to make sense of all this data. This makes visualization a very useful tool for Machine Learning practitioners and data analysts alike. Interactive visualization techniques can be very helpful by giving insight on the meaning of the output from classification tasks. This also applies to the data itself, as visualization can make some characteristics of the data become clear. Several bi-dimensional projection methods have been used to visualize data instances based on their attribute values. This visualization is more difficult when the instances have a large number of attributes. One of the visualization techniques that can deal with high dimensional data is Radial Coordinates Visualization (RadViz). RadViz can also be employed to visualize the performance of a probabilistic classifier, helping a user to find problematic instances that might have been misclassified. In this study, a new approach to use RadViz is proposed and investigated. The proposed approach combines the two aforementioned uses of RadViz (attribute-based data exploration and result exploration based on the output of probabilistic classification). For such, it approach provides an easy transition between the two types of visualization. This allows the context to be preserved, since the user can visually track the same data instance from one type of visualization to the other. In order to evaluate the proposed approach, a prototype, named DualRadviz, was implemented. On this prototype, in addition to RadViz, visualization by Parallel Coordinates is also provided, so that precise instance inspection can be performed, since, different from RadViz, Parallel coordinates visualization does not suffer from ambiguity. To illustrate the usefulness of the proposed method, a case study is presented.


title: Cervical spine loads and intervertebral motions during whiplash.

abstract: OBJECTIVE ::: To quantify the dynamic loads and intervertebral motions throughout the cervical spine during simulated rear impacts. ::: ::: ::: METHODS ::: Using a biofidelic whole cervical spine model with muscle force replication and surrogate head and bench-top mini-sled, impacts were simulated at 3.5, 5, 6.5, and 8 g horizontal accelerations of the T1 vertebra. Inverse dynamics was used to calculate the dynamic cervical spine loads at the centers of mass of the head and vertebrae (C1-T1). The average peak loads and intervertebral motions were statistically compared (P < 0.05) throughout the cervical spine. ::: ::: ::: RESULTS ::: Load and motion peaks generally increased with increasing impact acceleration. The average extension moment peaks at the lower cervical spine, reaching 40.7 Nm at C7-T1, significantly exceeded the moment peaks at the upper and middle cervical spine. The highest average axial tension peak of 276.9 N was observed at the head, significantly greater than at C4 through T1. The average axial compression peaks, reaching 223.2 N at C5, were significantly greater at C4 through T1, as compared to head-C1. The highest average posterior shear force peak of 269.5 N was observed at T1. ::: ::: ::: CONCLUSION ::: During whiplash, the cervical spine is subjected to not only bending moments, but also axial and shear forces. These combined loads caused both intervertebral rotations and translations.


title: International Journal of Semantic Computing © World Scientific Publishing Company EVENT AND TEMPORAL EXPRESSION EXTRACTION FROM RAW TEXT: FIRST STEP TOWARDS A TEMPORALLY AWARE SYSTEM

abstract: Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. Our long-term goal is to build complete temporal structure of documents and use the temporal structure in other applications like textual entailment, question answering, visualization, or others. In this paper, we present a first step, a system for extracting events, event features, main events, temporal expressions and their normalized values from raw text. Our system is a combination of deep semantic parsing with extraction rules, Markov Logic Network classifiers and Conditional Random Field classifiers. To compare with existing systems, we evaluated our system on the TempEval-1 and TempEval-2 corpus. Our system outperforms or performs competitively with existing systems that evaluate on the TimeBank, TempEval-1 and TempEval-2 corpus and our performance is very close to inter-annotator agreement of the TimeBank annotators.


title: Constructing Thai Opinion Mining Resource: A Case Study on Hotel Reviews

abstract: Opinion mining and sentiment analysis has recently gained increasing attention among the NLP community. Opinion mining is considered a domaindependent task. Constructing lexicons for different domains is labor intensive. In this paper, we propose a framework for constructing Thai language resource for feature-based opinion mining. The feature-based opinion mining essentially relies on the use of two main lexicons, features and polar words. Our approach for extracting features and polar words from opinionated texts is based on syntactic pattern analysis. The evaluation is performed with a case study on hotel reviews. The proposed method has shown to be very effective in most cases. However, in some cases, the extraction is not quite straightforward. The reasons are due to, firstly, the use of conversational language in written opinionated texts and, secondly, the language semantic. We provide discussion with possible solutions on pattern extraction for some of the challenging cases.


title: A low cost Functional Electrical Stimulation (FES) system for essential tremor suppression using off the shelf components

abstract: Essential tremor (ET) is one of the most common movement disorders that causes a rhythmic trembling of the hands, head, voice, legs or trunk. Currently, no medication is specifically designed to treat ET, However, some solutions exist to temporarily suppress tremor. This paper presents a novel low cost alternative to the existing solutions, known as Functional Electrical Stimulation (FES) and its comparison with existing solutions. The paper focuses on the development of a low cost FES circuit with Inertial Measurement Unit (IMU) feedback using off the shelf components which sends out-of-phase electrical pulses to suppress tremor.


title: A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

abstract: Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.


title: Failure to replicate the internal structure of Greek-specific thalassemia quality of life instrument in adult thalassemia patients in Sabah

abstract: To study the validity and reliability of the Malay version of the Specific Thalassemia Quality of Life Instrument (STQOLI) in Sabah's adult thalassemia patients.This cross-sectional study was done at Thalassemia Treatment Centre, Queen Elizabeth Hospital in Sabah, Malaysia. Eighty-two adult thalassemia patients who fulfilled the inclusion and exclusion criteria were conveniently selected for participation in the study. The English version of STQOLI was translated into Malay by using forward and back translations. The content of the questionnaire was validated by the chief hematologist of the hospital. The construct validity of the 40-item questionnaire was assessed by principal component analysis with varimax rotation and the scale reliability was assessed by Cronbach's alpha.The study failed to replicate the internal structure of the Greek STQOLI. Instead, 12 factors have been identified from the exploratory factor analysis, which accounted for 72.2% of the variance. However, only eight factors were interpretable. The factors were iron chelation pump impact, transfusion impact, time spent on treatment and its impact on work and social life, sex life, side effects of treatment, cardiovascular problems, psychology, and iron chelation pill impact. The overall scale reliability was 0.913.This study was unable to replicate the internal structure of the Greek STQOLI in Sabah's adult thalassemia patients. Instead, a new structure has emerged that can be used as a guide to develop a questionnaire specific for adult thalassemia patients in Sabah. Future research should focus on the eight factors identified from this study.


title: A Human Mixed Strategy Approach to Deep Reinforcement Learning

abstract: In 2015, Google's DeepMind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of"exploration"during the training process, especially when working with high-dimensional problems. In this paper, we propose a mixed strategy approach that mimics behaviors of human when interacting with environment, and create a"thinking"agent that allows for more efficient exploration in the DRL training process. The simulation results based on the Breakout game show that our scheme achieves a higher probability of obtaining a maximum score than does the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic method. The proposed scheme therefore can be applied effectively to solving a complicated task in a real-world application.


title: Internal Fingerprint Identification With Optical Coherence Tomography

abstract: Existing biometric fingerprint devices show numerous reliability problems such as wet or fake fingers. In this letter, a secured method using the internal structures of the finger (papillary layer) for fingerprint identification is presented. With a frequency-domain optical coherence tomography (FD-OCT) system, a 3-D image of a finger is acquired and the information of the internal fingerprint extracted. The right index fingers of 51 individuals were recorded three times. Using a commercial fingerprint identification program, 95% of internal fingerprint images were successfully recognized. These results demonstrate that OCT imaging of internal fingerprints can be used for accurate and reliable fingerprint recognition.


title: Describing Images using Inferred Visual Dependency Representations

abstract: textabstractThe Visual Dependency Representation (VDR) is an explicit model of the spatial relationships between objects in an image. In this paper we present an approach to training a VDR Parsing Model without the extensive human supervision used in previous work. Our approach is to find the objects mentioned in a given description using a state-of-the-art object detector, and to use successful detections to produce training data. The description of an unseen image is produced by first predicting its VDR over automatically detected objects, and then generating the text with a template-based generation model using the predicted VDR. The performance of our approach is comparable to a state-of-the-art multimodal deep neural network in images depicting actions.