title: GraphSum: Discovering correlations among multiple terms for graph-based summarization

abstract: Abstract Graph-based summarization entails extracting a worthwhile subset of sentences from a collection of textual documents by using a graph-based model to represent the correlations between pairs of document terms. However, since the high-order correlations among multiple terms are disregarded during graph evaluation, the summarization performance could be limited unless integrating ad hoc language-dependent or semantics-based analysis. This paper presents a novel and general-purpose graph-based summarizer, namely G raph S um (Graph-based Summarizer). It discovers and exploits association rules to represent the correlations among multiple terms that have been neglected by previous approaches. The graph nodes, which represent combinations of two or more terms, are first ranked by means of a PageRank strategy that discriminates between positive and negative term correlations. Then, the produced node ranking is used to drive the sentence selection process. The experiments performed on benchmark and real-life documents demonstrate the effectiveness of the proposed approach compared to many state-of-the-art summarizers.


Knowledge Management Enabler Factors and Firm Performance: An Empirical Research of the Greek Medium and Large Firms


title: Learning Non-Lambertian Object Intrinsics Across ShapeNet Categories

abstract: We focus on the non-Lambertian object-level intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. Based on existing 3D models in the ShapeNet database, a large-scale object intrinsics database is rendered with HDR environment maps. Millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN, which can decompose an image into the product of albedo and shading components along with an additive specular component. Our CNN delivers accurate and sharp results in this classical inverse problem of computer vision. Evaluated on our realistically synthetic dataset, our method consistently outperforms the state-of-the-art by a large margin. We train and test our CNN across different object categories. Perhaps surprising especially from the CNN classification perspective, our intrinsics CNN generalizes very well across categories. Our analysis shows that feature learning at the encoder stage is more crucial for developing a universal representation across categories. We apply our model to real images and videos from Internet, and observe robust and realistic intrinsics results. Quality non-Lambertian intrinsics could open up many interesting applications such as realistic product search based on material properties and image-based albedo/specular editing.


title: Critical health literacy: A review and critical analysis

abstract: Though there has been a considerable expansion of interest in the health literacy concept worldwide, there has also been criticism that this concept has been poorly defined, that it stretches the idea of "literacy" to an indefensible extent and more specifically, that it adds little to the existing concerns and intervention approaches of the better established discipline of health promotion. This paper takes as a starting point the expanded model of health literacy advanced by Nutbeam (2000) and addresses these concerns by interrogating the concept of "critical health literacy" in order to draw conclusions about its utility for advancing the health of individuals and communities. The constituent domains of critical health literacy are identified; namely information appraisal, understanding the social determinants of health, and collective action, and as far as possible each are clearly delineated, with links to related concepts made explicit. The paper concludes that an appreciation of work undertaken in a range of different disciplines, such as media studies, medical sociology, and evidence-based medicine can enhance our understanding of the critical health literacy construct and help us understand its usefulness as a social asset which helps individuals towards a critical engagement with health information. There is some evidence that aspects of critical health literacy have indeed been found to be a resource for better health outcomes, but more research is needed in this area, both to develop quantitative and qualitative approaches to evaluating health literacy skills, and to offer convincing evidence that investment in programmes designed to enhance critical health literacy are worthwhile.


title: A cascade of deep learning fuzzy rule-based image classifier and SVM

abstract: In this paper, a fast, transparent, self-evolving, deep learning fuzzy rule-based (DLFRB) image classifier is proposed. This new classifier is a cascade of the recently introduced DLFRB classifier called MICE and an auxiliary SVM. The DLFRB classifier serves as the main engine and can identify a number of human interpretable fuzzy rules through a very short, transparent, highly parallelizable training process. The SVM based auxiliary plays the role of a conflict resolver when the DLFRB classifier produces two highly confident labels for a single image. Only the fundamental image transformation techniques (rotation, scaling and segmentation) and feature descriptors (GIST and HOG) are used for pre-processing and feature extraction, but the proposed approach significantly outperforms the state-of-art methods in terms of both time and precision. Numerical experiments based on a handwritten digits recognition problem are used to demonstrate the highly accurate and repeatable performance of the proposed approach.


Literature Review of Mobile Robots for Manufacturing


title: Shadow detection in colour high-resolution satellite images

abstract: Image shadow segmentation has become a major issue in satellite remote sensing because of the recent commercial availability of high-resolution images. Detecting shadows is important for successfully carrying out applications such as change detection, land monitoring, object recognition, scene reconstruction, colour correction, etc. This paper presents a simple and effective procedure to segment shadow regions on high-resolution colour satellite images. The method applies a region growing process on a specific band (namely, the c 3 component of the c 1 c 2 c 3 colour space). To gain in robustness and precision, the region expansion also imposes a restriction on the saturation and intensity values of the shadow pixels, as well as on their edge gradients. The proposed method has been successfully tested on QuickBird images acquired under different lighting conditions and covering both urban and rural areas.


What Influences Consumers ’ Intention to Use Mobile Payments?


title: Predicting the types of file fragments

abstract: A problem that arises in computer forensics is to determine the type of a file fragment. An extension to the file name indicating the type is stored in the disk directory, but when a file is deleted, the entry for the file in the directory may be overwritten. This problem is easily solved when the fragment includes the initial header, which contains explicit type-identifying information, but it is more difficult to determine the type of a fragment from the middle of a file. We investigate two algorithms for predicting the type of a fragment: one based on Fisher's linear discriminant and the other based on longest common subsequences of the fragment with various sets of test files. We test the ability of the algorithms to predict a variety of common file types. Algorithms of this kind may be useful in designing the next generation of file-carvers - programs that reconstruct files when directory information is lost or deleted. These methods may also be useful in designing virus scanners, firewalls and search engines to find files that are similar to a given file.


title: Adaptive neuro-fuzzy modeling for crop yield prediction

abstract: Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to explore the dynamics of neural networks in forecasting crop (tomato) yield using environmental variables; here we aim at giving accurate yield amount. We use the Adaptive Neuro-Fuzzy Inference System (ANFIS). The input to ANFIS is several parameters derived from the crop growth model (temperature, Co2, vapor pressure deficit (VPD), yield, and radiation). ANFIS has only one output node, the yield. One of the difficult issues in predicting yield is that remote sensing data do not go long back in time. Therefore any predicting effort is forced to use a very restricted number of past years in order to construct a model to forecast future values. The system is trained by leaving one year out and using all the other data. We then evaluate the deviation of our estimate compared to the yield of the year that is left out. The procedure is applied to all the years and the average forecasting accuracy is given.


title: A meta-analysis of state-of-the-art electoral prediction from Twitter data

abstract: Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this article aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) propose some considerations to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been somewhat exaggerated: Social media may provide a glimpse on electoral outcomes but, up to now, research has not provided strong evidence to support it can currently replace traditional polls. Nevertheless, there are some reasons for optimism and, hence, further work on this topic is required, along with tighter integration with traditional electoral forecasting research.


title: Smartphone-based portable ultrasound imaging system: Prototype implementation and evaluation

abstract: In this paper, we present a smart US imaging system (SMUS) based on an android-OS smartphone, which can provide maximally optimized efficacy in terms of weight and size in point-of-care diagnostic applications. The proposed SMUS consists of the smartphone (Galaxy S5 LTE-A, Samsung., Korea) and a 16-channel probe system. The probe system contains analog and digital front-ends, which conducts beamforming and mid-processing procedures. Otherwise, the smartphone performs the back-end processing including envelope detection, log compression, 2D image filtering, digital scan conversion, and image display with custom-made graphical user interface (GUI). Note that the probe system and smartphone are interconnected by the USB 3.0 protocol. As a result, the developed SMUS can provide real-time B-mode image with the sufficient frame rate (i.e., 58 fps), battery run-time for point-of-care diagnosis (i.e., 54 min), and 35.0°C of transducer surface temperature during B-mode imaging, which satisfies the temperature standards for the safety and effectiveness of medical electrical equipment, IEC 60601-1 (i.e., 43°C).


title: A Survey of Opinion Mining and Sentiment Analysis

abstract: A huge amount of online information, rich web resources are highly unstructured and such natural language are not solvable by machine directly. The increased demand to capture opinions of general public about social events, campaigns and sales of the product has led to study of the field opinion mining and sentiment analysis. Opinion refers to extraction of lines in raw data which expresses an opinion. Sentiment analysis identifies polarity of extracted opinions. The major challenge lies in analyzing the sentiments and identifying emotions expressed in texts. This paper presents a survey which covers a problem of sentiment analysis, techniques and methods used for the same.


title: Programming Paradigms for Dummies: What Every Programmer Should Know

abstract: This chapter gives an introduction to all the main programming paradigms, their underlying concepts, and the relationships between them. We give a broad view to help programmers choose the right concepts they need to solve the problems at hand. We give a taxonomy of almost 30 useful programming paradigms and how they are related. Most of them differ only in one or a few concepts, but this can make a world of difference in programming. We explain briefly how programming paradigms influence language design, and we show two sweet spots: dual-paradigm languages and a definitive language. We introduce the main concepts of programming languages: records, closures, independence (concurrency), and named state. We explain the main principles of data abstraction and how it lets us organize large programs. Finally, we conclude by focusing on concurrency, which is widely considered the hardest concept to program with. We present four little-known but important paradigms that greatly simplify concurrent programming with respect to mainstream languages: declarative concurrency (both eager and lazy), functional reactive programming, discrete synchronous programming, and constraint programming. These paradigms have no race conditions and can be used in cases where no other paradigm works. We explain why for multi-core processors and we give several examples from computer music, which often uses these paradigms.


title: Credibility Microscope: Relating Web Page Credibility Evaluations to Their Textual Content

abstract: The popularization of the Internet made it a primary information source for many people. Unfortunately quality of information available on the Internet varies. Therefore, evaluation of credibility of web page content, especially while making important decisions like those concerning health care, medical information, and large purchases, is crucial, but users often lack a necessary knowledge. The main goal of the paper is to study to what extent the textual content of a webpage determines its credibility evaluations. This goal is achieved by an experiment in which we ask respondents to rate webpage credibility as well as credibility and importance of each statement from this site. We formulate a number of hypotheses about the nature of the dependence between webpage and statements credibility and we test these hypotheses on the data obtained from experiment. The evaluation of those hypotheses is essential for design of classifier that will aggregate statement credibility into webpage credibility score.


title: Gender differences in autoimmune disease

abstract: Autoimmune diseases are a range of diseases in which the immune response to self-antigens results in damage or dysfunction of tissues. Autoimmune diseases can be systemic or can affect specific organs or body systems. For most autoimmune diseases there is a clear sex difference in prevalence, whereby females are generally more frequently affected than males. In this review, we consider gender differences in systemic and organ-specific autoimmune diseases, and we summarize human data that outlines the prevalence of common autoimmune diseases specific to adult males and females in countries commonly surveyed. We discuss possible mechanisms for sex specific differences including gender differences in immune response and organ vulnerability, reproductive capacity including pregnancy, sex hormones, genetic predisposition, parental inheritance, and epigenetics. Evidence demonstrates that gender has a significant influence on the development of autoimmune disease. Thus, considerations of gender should be at the forefront of all studies that attempt to define mechanisms that underpin autoimmune disease.


title: SPARQLGX in Action: Efficient Distributed Evaluation of SPARQL with Apache Spark

abstract: We demonstrate SPARQLGX: our implementation of a distributed sparql evaluator. We show that sparqlgx makes it possible to evaluate SPARQL queries on billions of triples distributed across multiple nodes, while providing attractive performance figures.


title: Planting misinformation in the human mind: A 30-year investigation of the malleability of memory

abstract: The misinformation effect refers to the impairment in memory for the past that arises after exposure to misleading information. The phenomenon has been investigated for at least 30 years, as investigators have addressed a number of issues. These include the conditions under which people are especially susceptible to the negative impact of misinformation, and conversely when are they resistant. Warnings about the potential for misinformation sometimes work to inhibit its damaging effects, but only under limited circumstances. The misinformation effect has been observed in a variety of human and nonhuman species. And some groups of individuals are more susceptible than others. At a more theoretical level, investigators have explored the fate of the original memory traces after exposure to misinformation appears to have made them inaccessible. This review of the field ends with a brief discussion of the newer work involving misinformation that has explored the processes by which people come to believe falsely that they experienced rich complex events that never, in fact, occurred. In 2005 the journal Learning & Memory published the first experimental work using neuroimaging to reveal the underlying mechanisms of the “misinformation effect,” a phenomenon that had captured the interest of memory researchers for over a quarter century (Okado and Stark 2005). These new investigators used a variation of the standard three-stage procedure typical in studies of misinformation. Their subjects first saw several complex events, for example one involving a man stealing a girl’s wallet. Next some of the subjects got misinformation about the event, such as the fact that the girl’s arm was hurt in the process (rather than her neck). Finally the subjects were asked to remember what they saw in the original event. Many claimed that they saw the misinformation details in the original event. For example, they remembered seeing the arm being hurt, not the neck. Overall, the misinformation was remembered as being part of the original event about 47% of the time. So, expectedly, a robust impairment of memory was produced by exposure to misinformation— the misinformation effect. But the researchers’ new work had a twist: They went on to show that the neural activity that occurred while the subjects processed the events and later the misinformation predicted whether a misinformation effect would occur. In an essay that accompanied the Okado and Stark findings, I placed their results within the context of 30 years of research on


title: Social Networking Sites and Language Learning

abstract: This article examines a study of seven learners who logged their experiences on the language leaning social networking site Livemocha over a period of three months. The features of the site are described and the likelihood of their future success is considered. The learners were introduced to the Social Networking Site SNS and asked to learn a language on the site. They were positive about two aspects of the site: the immediate peer-feedback available and the ability to converse synchronously and asynchronously with native speakers of their target language. However, there was universal criticism of the "word-list" based language learning materials and several participants complained about the regular cyber-flirting they encountered. Other aspects of the site including accessibility, ease of use, syllabus, activities, and relationships with other members are also considered. The potential for integrating some of the features of SNSs for language learning into the Higher Education HE curriculum and the implications of this for educators are also discussed.


title: A hybrid wireless charging system with DDQ pads for dynamic charging of EVs

abstract: Wireless power transfer (WPT) systems are suitable for applications such as integration of Electric Vehicles (EV) with the utility grids. However, physical misalignments between the magnetic couplers are unavoidable and can introduce variations in self-, leakage- and mutual-inductances, which invariably detune the compensation networks employed by the transmitter and receiver of a wireless power transfer system. A hybrid BD-IPT system, which combines a parallel tuned LCL and a series tuned CL networks to provide a constant power transfer over a wide range of spatial displacements is proposed. A pair of DDQ pads with hybrid tuning is utilized to improve the power transfer to EVs' in a dynamic charging application. Mathematical and simulation models are developed to investigate the behavior of the proposed system under practical operating conditions. Both mathematical and simulation results are presented to illustrate that the proposed system can maintain a relatively constant power to an EV in a dynamic charging system.


title: The biology of facial fillers.

abstract: The biologic behavior of a facial filler determines its advantages and disadvantages. The purpose of this article is to look at the relevant biology as part of a logical basis for making treatment decisions. Historical perspectives and biologic characteristics such as local tissue reaction (including phagocytosis and granulomatous inflammation) cross-linking, particle concentration, immunogenicity, biofilm formation, gel hardness, and collagen neogenesis are considered. Bovine collagen is the most immunogenic facial filler. Porcine and bioengineered human collagen implants have very low immunogenicity, but allergic reactions and elevations of IgG are possible. Cross-linking and concentration affect the longevity of collagen and hyaluronic acid fillers. Gel hardness affects how a hyaluronic acid filler flows through the syringe and needle. Calcium hydroxylapatite, poly-L-lactic acid, and polymethylmethacrylate fillers have been shown to stimulate collagen neogenesis. It appears that any facial filler can form a granuloma. Bacterial biofilms may play a role in the activation of quiescent granulomas. Various authors interpret the definition and significance of a granuloma differently.


title: Long-term administration of gonadotropin-releasing hormone agonist and dexamethasone: Assessment of the adrenal role in ovarian dysfunction

abstract: Objective To examine the possible impact of abnormal adrenal steroidogenesis on the ovarian dysfunction seen in polycystic ovarian disease (PCOD). Design Prospective analysis of blood sampling monthly for 6 months, then three times weekly for 90 days. Setting Tertiary institutional outpatient care. Participants Six anovulatory women with a diagnosis of PCOD. Intervention Six-month suppression with gonadotropin-releasing hormone agonist (GnRH-a) followed by suppression with dexamethasone (DEX) for 90 days. Main Outcome Measures Serum levels of testosterone (T), androstenedione (A), dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEAS), Cortisol, estradiol (E 2 ), progesterone (P), follicle-stimulating hormone (FSH), luteinizing hormone (LH) and bioactive LH. Results Gonadotropin-releasing hormone agonist administration suppressed >60% of the circulating levels of T and A, suggesting an ovarian origin. Minimal changes of DHEA, DHEAS, and Cortisol were seen. With the addition of DEX, there was >90% suppression of the total circulating A, T, DHEA, DHEAS, and Cortisol, supporting the adrenal origin of the non-GnRH-a suppressible androgens. Excessive ovarian T and A secretion returned during the 90-day recovery study period in spite of rises of FSH concentrations that changed the ratio of FSH to LH in all subjects. Four of the six women failed to ovulate. In comparison of the women who did and did not ovulate during recovery, no differences in absolute levels or changes in concentrations of steroids or gonadotropins could be detected. Conclusions Using sequential and simultaneous administration of GnRH-a and DEX, we were able to delineate the contributions of the ovaries and adrenals to the abnormal steroidogenesis seen in PCOD. Despite prolonged suppression of ovarian and then adrenal steroidogenesis, ovarian dysfunction, evidenced by abnormal androgen production, returned with cessation of agonist administration.


title: FCM-type co-clustering with exclusive partition of selected items and application to collaborative filtering

abstract: Collaborative Filtering achieves personalized recommendation considering similarities among user preferences and the task has close connections with co-clustering of user-item relational data. FCM-type co-clustering extracts user-item co-clusters, in which users are assigned to clusters in an exclusive manner while item partitions are not necessarily exclusive and each item can be shared (rejected) by multiple (all) clusters. In this paper, an exclusive constraint is introduced into FCM-type co-clustering and the clustering model is demonstrated to be useful in collaborative filtering task.


title: Approximate storage in solid-state memories

abstract: Memories today expose an all-or-nothing correctness model that incurs significant costs in performance, energy, area, and design complexity. But not all applications need high-precision storage for all of their data structures all of the time. This paper proposes mechanisms that enable applications to store data approximately and shows that doing so can improve the performance, lifetime, or density of solid-state memories. We propose two mechanisms. The first allows errors in multi-level cells by reducing the number of programming pulses used to write them. The second mechanism mitigates wear-out failures and extends memory endurance by mapping approximate data onto blocks that have exhausted their hardware error correction resources. Simulations show that reduced-precision writes in multi-level phase-change memory cells can be 1.7× faster on average and using failed blocks can improve array lifetime by 23% on average with quality loss under 10%.


title: Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

abstract: Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an $F_1$-score of 83.7\%, higher than competing methods in the literature.


title: LogMine: Fast Pattern Recognition for Log Analytics

abstract: Modern engineering incorporates smart technologies in all aspects of our lives. Smart technologies are generating terabytes of log messages every day to report their status. It is crucial to analyze these log messages and present usable information (e.g. patterns) to administrators, so that they can manage and monitor these technologies. Patterns minimally represent large groups of log messages and enable the administrators to do further analysis, such as anomaly detection and event prediction. Although patterns exist commonly in automated log messages, recognizing them in massive set of log messages from heterogeneous sources without any prior information is a significant undertaking. We propose a method, named LogMine, that extracts high quality patterns for a given set of log messages. Our method is fast, memory efficient, accurate, and scalable. LogMine is implemented in map-reduce framework for distributed platforms to process millions of log messages in seconds. LogMine is a robust method that works for heterogeneous log messages generated in a wide variety of systems. Our method exploits algorithmic techniques to minimize the computational overhead based on the fact that log messages are always automatically generated. We evaluate the performance of LogMine on massive sets of log messages generated in industrial applications. LogMine has successfully generated patterns which are as good as the patterns generated by exact and unscalable method, while achieving a 500× speedup. Finally, we describe three applications of the patterns generated by LogMine in monitoring large scale industrial systems.


title: Ground-Truth Adversarial Examples

abstract: The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for training networks that are robust to such examples; and each time stronger attacks have been devised, demonstrating the shortcomings of existing defenses. This highlights a key difficulty in designing an effective defense: the inability to assess a network's robustness against future attacks. We propose to address this difficulty through formal verification techniques. We construct ground truths: adversarial examples with a provably-minimal distance from a given input point. We demonstrate how ground truths can serve to assess the effectiveness of attack techniques, by comparing the adversarial examples produced by those attacks to the ground truths; and also of defense techniques, by computing the distance to the ground truths before and after the defense is applied, and measuring the improvement. We use this technique to assess recently suggested attack and defense techniques.


title: ON THE TEMPORAL ANALYSIS OF ACOUSTIC WAVES USING SCHLIEREN IMAGING

abstract: The primary objective of the present study was to develop a quantitative schlieren-imaging technique that can be used to study the dynamics of instability waves. The technique was initially validated by optically capturing a controlled acoustic wave generated by a compression driver and excellent agreement was obtained with microphone measurements. An underexpanded jet was considered as an ideal test case due to the complexity and multitude of instability mechanism. Further analysis of the underexpanded jet demonstrated that this technique can be used to capture the very high frequency mode related to the phenomenon of screech.


title: Learning shape placements by example

abstract: We present a method to learn and propagate shape placements in 2D polygonal scenes from a few examples provided by a user. The placement of a shape is modeled as an oriented bounding box. Simple geometric relationships between this bounding box and nearby scene polygons define a feature set for the placement. The feature sets of all example placements are then used to learn a probabilistic model over all possible placements and scenes. With this model, we can generate a new set of placements with similar geometric relationships in any given scene. We introduce extensions that enable propagation and generation of shapes in 3D scenes, as well as the application of a learned modeling session to large scenes without additional user interaction. These concepts allow us to generate complex scenes with thousands of objects with relatively little user interaction.


title: Short-circuit ruggedness of high-voltage IGBTs

abstract: The IGBT can run into different short-circuit types (SC I, SC II, SC III). Especially in SC II and III, an interaction between the gate drive unit and the IGBT takes place. A self-turn-off mechanism after short-circuit turn on can occur. Parasitic elements in the connection between the IGBT and the gate unit as well as asymmetrical wiring of devices connected in parallel are of effect to the short-circuit capability. In high-voltage IGBTs, filament formation can occur at short-circuit condition. Destructive measurements with its failure patterns and short-circuit protection methods are shown.


title: DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design

abstract: We present DreamSketch, a novel 3D design interface that combines the free-form and expressive qualities of sketching with the computational power of generative design algorithms. In DreamSketch, a user coarsely defines the problem by sketching the design context. Then, a generative design algorithm produces multiple solutions that are augmented as 3D objects in the sketched context. The user can interact with the scene to navigate through the generated solutions. The combination of sketching and generative algorithms enables designers to explore multiple ideas and make better informed design decisions during the early stages of design. Design study sessions with designers and mechanical engineers demonstrate the expressive nature and creative possibilities of DreamSketch.


title: Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix

abstract: Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect the model performance. In this paper, we take a deep look at the application of distant supervision in relation extraction. We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision. The transition matrix can be effectively trained using a novel curriculum learning based method without any direct supervision about the noise. We thoroughly evaluate our approach under a wide range of extraction scenarios. Experimental results show that our approach consistently improves the extraction results and outperforms the state-of-the-art in various evaluation scenarios.


title: Opinion Dynamics and Learning in Social Networks

abstract: We provide an overview of recent research on belief and opinion dynamics in social networks. We discuss both Bayesian and non-Bayesian models of social learning and focus on the implications of the form of learning (e.g., Bayesian vs. non-Bayesian), the sources of information (e.g., observation vs. communication), and the structure of social networks in which individuals are situated on three key questions: (1) whether social learning will lead to consensus, i.e., to agreement among individuals starting with different views; (2) whether social learning will effectively aggregate dispersed information and thus weed out incorrect beliefs; (3) whether media sources, prominent agents, politicians and the state will be able to manipulate beliefs and spread misinformation in a society.


title: k-Stacks: High-density valet parking for automated vehicles

abstract: Automated valet parking not only improves driving comfort, but can have a considerable impact on the urban landscape by reducing the required parking space. We present the first study of parking space optimization for automated valet parking with an in-depth theoretical analysis of the parking lot properties under various aspects, inluding the worst-case extraction time, total shunting distance, and the number of shunting operations (each per car). Most importantly, the proposed model bounds all these values. We verify the theoretical properties of our model in four simulated scenarios, one of which is based on real-world data from a downtown parking garage. We show that very good pick-up times of about 1 min are possible with very little overhead in terms of shunting distance and time, while providing a significantly improved parking density as compared to conventional parking lots.


title: A multi-agent system that facilitates scientific publications search

abstract: It is very difficult for beginners to define and find the most relevant literature in a research field. They can search on the web or look at the most important journals and conference proceedings, but it would be much better to receive suggestions directly from experts of the field. Unfortunately, this is not always possible and systems like CiteSeer and GoogleScholar become extremely useful for beginners (and not only). In this paper, we present an agent-based system that facilitates scientific publications search. Users interacting with their personal agents produce a transfer of knowledge about relevant publications from experts to beginners. Each personal agent observes how publications are used and induces behavioral patterns that are used to create more effective recommendations. Feedback exchange allows agents to share their knowledge and virtual communities of cloned experts can be created to support novice users. We present a set of experimental results, obtained using CiteSeer as a source of information, that show the effectiveness of our approach.


title: Backstepping control with speed estimation of PMSM based on MRAS

abstract: This paper presents a backstepping control method with speed estimation of permanent magnet synchronous motor (PMSM) based on model reference adaptive system (MRAS). First, a comprehensive dynamical model of PMSM in d–q axis and its space state equations are established. Next, using Lyapunov stability theorem, based on the backstepping control theory, the PMSM rotor speed and current backstepping controllers are designed. Furthermore, using Popov stability theory, based on MRAS, the PMSM rotor speed observer is designed. Finally, Matlab/Simulink simulation results show that the backstepping control and speed observer are effective and feasible.


title: Distributed deep learning on edge-devices: Feasibility via adaptive compression

abstract: A large portion of data mining and analytic services use modern machine learning techniques, such as deep learning. The state-of-the-art results by deep learning come at the price of an intensive use of computing resources. The leading frameworks (e.g., TensorFlow) are executed on GPUs or on high-end servers in datacenters. On the other end, there is a proliferation of personal devices with possibly free CPU cycles; this can enable services to run in users' homes, embedding machine learning operations. In this paper, we ask the following question: Is distributed deep learning computation on WAN connected devices feasible, in spite of the traffic caused by learning tasks? We show that such a setup rises some important challenges, most notably the ingress traffic that the servers hosting the up-to-date model have to sustain. ::: In order to reduce this stress, we propose adaComp, a novel algorithm for compressing worker updates to the model on the server. Applicable to stochastic gradient descent based approaches, it combines efficient gradient selection and learning rate modulation. We then experiment and measure the impact of compression, device heterogeneity and reliability on the accuracy of learned models, with an emulator platform that embeds TensorFlow into Linux containers. We report a reduction of the total amount of data sent by workers to the server by two order of magnitude (e.g., 191-fold reduction for a convolutional network on the MNIST dataset), when compared to a standard asynchronous stochastic gradient descent, while preserving model accuracy.


A Comparison Study on Space Syntax as a Computer Model of Space


title: 6LB: Scalable and Application-Aware Load Balancing with Segment Routing

abstract: Network load-balancers generally either do not take the application state into account, or do so at the cost of a centralized monitoring system. This paper introduces a load-balancer running exclusively within the IP forwarding plane, i.e., in an application protocol agnostic fashion – yet which still provides application-awareness and makes real-time, decentralized decisions. To that end, IPv6 Segment Routing is used to direct data packets from a new flow through a chain of candidate servers, until one decides to accept the connection, based solely on its local state. This way, applications themselves naturally decide on how to fairly share incoming connections, while incurring minimal network overhead, and no out-of-band signaling. A consistent hashing algorithm, as well as an in-band stickiness protocol, allow for the proposed solution to be able to be reliably distributed across a large number of instances. Performance evaluation by means of an analytical model and actual tests on different workloads (including a Wikipedia replay as a realistic workload) show significant performance benefits in terms of shorter response times, when compared with the traditional random load-balancer. In addition, this paper introduces and compares kernel bypass high-performance implementations of both 6LB and the state-of-the-art load-balancer, showing that the significant system-level benefits of 6LB are achievable with a negligible data-path CPU overhead.


Framework for Searchable Encryption with SQL Databases


title: Feature vector selection and projection using kernels

abstract: This paper provides new insight into kernel methods by using data selection. The kernel trick is used to select from the data a relevant subset forming a basis in a feature space F. Thus the selected vectors de,ne a subspace in F. Then, the data is projected onto this subspace where classical algorithms are applied. We show that kernel methods like generalized discriminant analysis (Neural Comput. 12 (2000) 2385) or kernel principal component analysis (Neural Comput. 10 (1998) 1299) can be expressed more easily. Moreover, it will turn out that the size of the basis is related to the complexity of the model. Therefore, the data selection leads to a complexity control and thus to a better generalization. The approach covers a wide range of algorithms. We investigate the function approximation on real classi,cation problems and on a regression problem. c


title: Usability Evaluation Practices within Agile Development

abstract: Despite having different underlying concepts, agile methods and User-Centered Design aim ultimately at producing high quality software. Aiming at providing better understanding on how usability evaluation is addressed within agile environments, we carried out multiple-case studies in two large IT companies. On the one hand, our studies corroborate the literature with regards to the execution of usability inspection evaluations on lightweight prototypes, taking advantage of the iterative nature of Agile. On the other hand, we observed contrary to the literature that this iterative nature of Agile has not facilitated the execution of user testing sessions. Furthermore, we noticed that it is extremely difficult to perform traditional user testing sessions due to the tight schedules inherent to Agile. Finally, we describe a set of practices that can help improve specific artifacts in different stages of an agile process in order to successfully evaluate software product usability.


title: Social capital, self-esteem, and use of online social network sites: A . . .

abstract: Alongitudinalanalysisofpaneldatafromusersofapopularonlinesocialnetworksite,Facebook, investigated the relationship between intensity of Facebook use, measures of psychological well-being, and bridging social capital. Two surveys conducted a year apart at a large U.S. university, complemented with in-depth interviews with 18 Facebook users, provide the study data. Intensityof Facebook useinyearone strongly predicted bridging social capitaloutcomes in year two, even after controlling for measures of self-esteem and satisfaction with life. These latter psychological variables were also strongly associated with social capital outcomes. Selfesteem served to moderate the relationship between Facebook usage intensity and bridging social capital: those with lower self-esteem gained more from their use of Facebook in terms of bridging social capital than higher self-esteem participants. We suggest that Facebook affordances help reduce barriers that lower self-esteem students might experience in forming the kinds of large, heterogeneous networks that are sources of bridging social capital.


title: A survey and extension of high efficiency grid connected transformerless solar inverters with focus on leakage current characteristics

abstract: In the utility grid interconnection of photovoltaic (PV) energy sources, inverters determine the overall system performance, which result in the demand to route the grid connected transformerless PV inverters (GCTIs) for residential and commercial applications, especially due to their high efficiency, light weight, and low cost benefits. In spite of these benefits of GCTIs, leakage currents due to distributed PV module parasitic capacitances are a major issue in the interconnection, as they are undesired because of safety, reliability, protective coordination, electromagnetic compatibility, and PV module lifetime issues. This paper classifies the kW and above range power rating GCTI topologies based on their leakage current attributes and investigates and/illustrates their leakage current characteristics by making use of detailed microscopic waveforms of a representative topology of each class. The cause and quantity of leakage current for each class are identified, not only providing a good understanding, but also aiding the performance comparison and inverter design. With the leakage current characteristic investigation, the study places most topologies under small number of classes with similar leakage current attributes facilitating understanding, evaluating, and the design of GCTIs. Establishing a clear relation between the topology type and leakage current characteristic, the topology families are extended with new members, providing the design engineers a variety of GCTI topology configurations with different characteristics.


title: Value locality and load value prediction

abstract: Since the introduction of virtual memory demand-paging and cache memories, computer systems have been exploiting spatial and temporal locality to reduce the average latency of a memory reference. In this paper, we introduce the notion of value locality, a third facet of locality that is frequently present in real-world programs, and describe how to effectively capture and exploit it in order to perform load value prediction. Temporal and spatial locality are attributes of storage locations, and describe the future likelihood of references to those locations or their close neighbors. In a similar vein, value locality describes the likelihood of the recurrence of a previously-seen value within a storage location. Modern processors already exploit value locality in a very restricted sense through the use of control speculation (i.e. branch prediction), which seeks to predict the future value of a single condition bit based on previously-seen values. Our work extends this to predict entire 32- and 64-bit register values based on previously-seen values. We find that, just as condition bits are fairly predictable on a per-static-branch basis, full register values being loaded from memory are frequently predictable as well. Furthermore, we show that simple microarchitectural enhancements to two modern microprocessor implementations (based on the PowerPC 620 and Alpha 21164) that enable load value prediction can effectively exploit value locality to collapse true dependencies, reduce average memory latency and bandwidth requirements, and provide measurable performance gains.


title: A temporal context-aware model for user behavior modeling in social media systems

abstract: Social media provides valuable resources to analyze user behaviors and capture user preferences. This paper focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. To further improve the performance of TCAM, an item-weighting scheme is proposed to enable TCAM to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on TCAM, we design an efficient query processing technique to support fast online recommendation for large social media data. Extensive experiments have been conducted to evaluate the performance of TCAM on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of the TCAM models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.


title: Cognitive Mapping in Literary Analysis

abstract: The way poets think is the way we think. Recent work in cognitive linguistics explores the analogical processes by which the human brain makes sense of its world. The human mind, under this view, thinks analogically. Analogy is the process underlying all the topoi of classical rhetoric (such as definition, classification, comparison and contrast) and figures of speech (such as synedoche, metonymy, metaphor). It also informs the structure of poetry. Its components include cognitive mapping skills that create levels of identification across different domains and projections across multiple mental spaces. In order to understand what literary critics do when analyzing a literary text, we need to identify the kinds of cognitive mappings they use. Cognitive analysis of readings of an Emily Dickinson poem posted to an online discussion group shows that the readers are using metaphorical and metonymical mapping strategies and their own cultural knowledge and backgrounds to understand the poem. By linking literary analysis with the general processes of the human mind, a cognitive approach to literature shows how human creativity can be nourished by reading poetry. Blending, in particular, shows exactly how important metaphorical thinking is to human creativity. There are at least four additional ways in which cognitive linguistics can contribute to literary analysis: 1) it can explicate and possibly evaluate the differences between multiple readings; 2) it can identify what cultural knowledge influences interpretation; 3) it can contribute to a clarification of the writer’s - as opposed to the reader’s - conceptual world view; and 4) it can form the basis for an empirical study of literary interpretations. Finally, cognitive linguistics integrates different literary approaches by showing how they make use of cognitive mapping strategies across ICCMs in exploring the conceptual processes of the writer, reader, and text.


title: RoboCup: The Robot World Cup Initiative

abstract: The Robot World Cup Initiative (R, oboCup) is attempt to foster AI and intelligent rohoties research by providing a standard problem where wide range of technologies especially concerning multi-agent research (:an be integrated and examined. The first RoboCup competition is to be, heht at. IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game. various technologies must I)e incorl)orated including: design principles of autononmus agents, multi-agent collaboration, strategy acquisition, real-time rea.~oning, robotics, and sensor-fllsion. Unlike AAAI robot competition, which is tuned for a single heavy-duty slow-moving robot. RoboCup is a task for a team of multiple f‘ast-moving robots under a dynamic environmen(. Although RoboCnp’s final target is a worhl cup with real robots, RoboCup offers a soft.ware platform for reseaxch on the software aspects of RoboCup. This paper describes teclini(’M challenges involw~d in RoboCup, rules, and simulation environment.


title: The Predictron: End-To-End Learning and Planning

abstract: One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. The predictron consists of a fully abstract model, represented by a Markov reward process, that can be rolled forward multiple "imagined" planning steps. Each forward pass of the predictron accumulates internal rewards and values over multiple planning depths. The predictron is trained end-to-end so as to make these accumulated values accurately approximate the true value function. We applied the predictron to procedurally generated random mazes and a simulator for the game of pool. The predictron yielded significantly more accurate predictions than conventional deep neural network architectures.


title: CrowdMAC: a crowdsourcing system for mobile access

abstract: Staggering growth levels in the number of mobile devices and amount of mobile Internet usage has caused network providers to move away from unlimited data plans to less flexible charging models. As a result, users are being required to pay more for short accesses or under-utilize a longer-term data plan. In this paper, we propose CrowdMAC, a crowdsourcing approach in which mobile users create a marketplace for mobile Internet access. Mobile users with residue capacity in their data plans share their access with other nearby mobile users for a small fee. CrowdMAC is implemented as a middleware framework with incentive-based mechanisms for admission control, service selection, and mobility management. CrowdMAC is implemented and evaluated on a testbed of Android phones and in the well known Qualnet simulator. Our evaluation results show that CrowdMAC: (i) effectively exercises the trade-off between revenue and transfer delay, (ii) adequately satisfies user-specified (delay) quality levels, and (iii) properly adapts to device mobility and achieves performance very close to the ideal case (upper bound).


title: Anti-obesity effects of gut microbiota are associated with lactic acid bacteria

abstract: The prevalence of obesity is rapidly becoming endemic in industrialized countries and continues to increase in developing countries worldwide. Obesity predisposes people to an increased risk of developing metabolic syndrome. Recent studies have described an association between obesity and certain gut microbiota, suggesting that gut microbiota might play a critical role in the development of obesity. Although probiotics have many beneficial health effects in humans and animals, attention has only recently been drawn to manipulating the gut microbiota, such as lactic acid bacteria (LAB), to influence the development of obesity. In this review, we first describe the causes of obesity, including the genetic and environmental factors. We then describe the relationship between the gut microbiota and obesity, and the mechanisms by which the gut microbiota influence energy metabolism and inflammation in obesity. Lastly, we focus on the potential role of LAB in mediating the effects of the gut microbiota in the development of obesity.


A deflationary account of metaphors


title: Evaporation and condensation of SPH-based fluids

abstract: In this paper we present a method to simulate evaporation and condensation of liquids. Therefore, both the air and liquid phases have to be simulated. We use, as a carrier of vapor, a coarse grid for the air phase and mass-preservingly couple it to an SPH-based liquid and rigid body simulation. Since condensation only takes place on rigid surfaces, it is captured using textures that carry water to achieve high surface detail. The textures can exchange water with the air phase and are used to generate new particles due to condensation effects yielding a full two-way coupling of air phase and liquid. In order to allow gradual evaporation and condensation processes, liquid particles can take on variable sizes. Our proposed improved implicit surface definition is able to render dynamic contact angles for moving droplets yielding highly detailed fluid rendering.


title: A 31 pJ/spike hybrid stochastic neuromorphic signal processor

abstract: Neuromorphic signal processing architectures capable of real-time applications are examined as a next generation, post- Moore, ultra-low-power computing solution [1]. Conventional von Neumann-type hardware (such as DSPs, GPUs, and FPGAs) in spiking neural networks require very high bandwidths (in the GHz range), and subsequently, high power dissipation, to efficiently transmit spike signal between the memory and the processor. In contrast, neuromorphic signal processing circuits are implemented on optimized, special purpose hardware, which can provide direct one-to-one mapping and low instruction execution redundancy [2]. Disparity between sequential-processing, conventional computing, and parallel, event-driven, biological neural systems is even more prominent in autonomous, real-time systems, especially in the presence of noisy and uncontrolled sensory input. In neural signal processing systems, the noise offers distinct advantages by inducing neuronal variability [3] and, successively, enhancing the sensitivity of neurons to environmental stimuli [4], inducing synchronization between neurons [5], and facilitating probabilistic inference [6]. Consequently, probabilistic noise models, as a resource for neural computation in the context of neuromorphic systems, are implemented as artificial neural networks and Boltzmann machines [7].


title: What about N? A methodological study of sample-size reporting in focus group studies

abstract: BackgroundFocus group studies are increasingly published in health related journals, but we know little about how researchers use this method, particularly how they determine the number of focus groups to conduct. The methodological literature commonly advises researchers to follow principles of data saturation, although practical advise on how to do this is lacking. Our objectives were firstly, to describe the current status of sample size in focus group studies reported in health journals. Secondly, to assess whether and how researchers explain the number of focus groups they carry out.MethodsWe searched PubMed for studies that had used focus groups and that had been published in open access journals during 2008, and extracted data on the number of focus groups and on any explanation authors gave for this number. We also did a qualitative assessment of the papers with regard to how number of groups was explained and discussed.ResultsWe identified 220 papers published in 117 journals. In these papers insufficient reporting of sample sizes was common. The number of focus groups conducted varied greatly (mean 8.4, median 5, range 1 to 96). Thirty seven (17%) studies attempted to explain the number of groups. Six studies referred to rules of thumb in the literature, three stated that they were unable to organize more groups for practical reasons, while 28 studies stated that they had reached a point of saturation. Among those stating that they had reached a point of saturation, several appeared not to have followed principles from grounded theory where data collection and analysis is an iterative process until saturation is reached. Studies with high numbers of focus groups did not offer explanations for number of groups. Too much data as a study weakness was not an issue discussed in any of the reviewed papers.ConclusionsBased on these findings we suggest that journals adopt more stringent requirements for focus group method reporting. The often poor and inconsistent reporting seen in these studies may also reflect the lack of clear, evidence-based guidance about deciding on sample size. More empirical research is needed to develop focus group methodology.


title: Implementation of autonomous driving of a ground vehicle for narrow high-curvature roads using surround view images

abstract: In this paper, a new approach for the implementation of autonomous driving of a ground vehicle on narrow and high-curvature roads using a surround view system is proposed. The approach called the independent frame consecutive searching technique is an algorithm which is developed to detect the left and right lane markers of a lane stably and to generate virtual lane marker lines especially when the roads are narrow and have sudden corners and windings with some faded lane markers. Then the steering angle of an autonomous vehicle is calculated so that the vehicle drives along the desired path after deriving the smoothed virtual centerline. In the real-time experiment, the suggested approach achieved robust detection of lane markers using surround view images and drove a test vehicle autonomously with the generated desired paths on such narrow and high-curvature roads with any lane departures. Therefore, this study shows that the method can be applied to the development of autonomous vehicles.


title: SOCIAL NETWORK ANALYSIS WITH CONTENT AND GRAPHS Social Network Analysis with Content and Graphs

abstract: Social network analysis has undergone a renaissance with the ubiquity and quantity of content from social media, web pages, and sensors. This content is a rich data source for constructing and analyzing social networks, but its enormity and unstructured nature also present multiple challenges. Work at Lincoln Laboratory is addressing the problems in constructing networks from unstructured data, analyzing the community structure of a network, and inferring information from networks. Graph analytics have proven to be valuable tools in solving these challenges. Through the use of these tools, Laboratory researchers have achieved promising results on real-world data. A sampling of these results are presented in this article.


title: Improvements in the StArt tool to better support the systematic review process

abstract: Context: Systematic Review (SR) is a methodology used to find and aggregate relevant existing evidence about a specific research topic of interest. It can be very time-consuming depending on the number of gathered studies that need to be analyzed by researchers. One of the relevant tools found in the literature and preliminarily evaluated by researchers of SRs is StArt, which supports the whole SR process. It has been downloaded by users from more than twenty countries. Objective: To present new features available in StArt to support SR activities. Method: Based on users' feedback and the literature, new features were implemented and are available in the tool, like the SCAS strategy, snowballing techniques, the frequency of keywords and a word cloud for search string refining, collaboration among reviewers, and the StArt online community. Results: The new features, according to users' positive feedback, make the tool more robust to support the conduct of SRs. Conclusion: StArt is a tool that has been continuously developed such that new features are often available to improve the support for the SR process. The StArt online community can improve the interaction among users, facilitating the identification of improvements and new useful features.


title: General purpose molecular dynamics simulations fully implemented on graphics processing units

abstract: Graphics processing units (GPUs), originally developed for rendering real-time effects in computer games, now provide unprecedented computational power for scientific applications. In this paper, we develop a general purpose molecular dynamics code that runs entirely on a single GPU. It is shown that our GPU implementation provides a performance equivalent to that of fast 30 processor core distributed memory cluster. Our results show that GPUs already provide an inexpensive alternative to such clusters and discuss implications for the future.


title: A 256-Gbps PAM-4 Signal Generator IC in $0.25-\mu \text{m}$ InP DHBT Technology

abstract: This paper presents a 256-Gbps (128-GBaud) four-level pulse amplitude modulation (PAM-4) signal generator (SG) IC fabricated using developed $0.25- \mu\text{m}$ -emitter-width InP double heterojunction bipolar transistors (DHBTs), which have a peak $f_{\text{T}}$ and $f_{\max}$ of 460 and 480 GHz, respectively. The IC is based on a 128-GS/s 2-bit R2R-ladder current-steering digital-to-analog converter (DAC) with integrated two 2:1 multiplexer (MUX) functions. Ultrahigh-speed clear PAM-4 signals of up to 256 Gbps (128 GBaud) were successfully generated using this PAM-4 SG IC. To the best of our knowledge, this is the first demonstration of 256-Gbps (128-GBaud) PAM-4 signal generation without any equalization.


title: Antenna-filter-antenna arrays as a class of bandpass frequency-selective surfaces

abstract: A method is introduced for designing bandpass frequency-selective surfaces (FSSs) using arrays of antenna-filter-antenna (AFA) modules. An AFA module is a filter with radiation ports, which is obtained by integrating two antennas and a nonradiating resonant structure in between. AFA modules are designed based on circuit models and microwave filter design techniques. Three types of these AFA modules are designed using microstrip antennas and coplanar-waveguide resonators, and are used to form FSSs with three- and four-pole shaped bandpass response at 35 GHz. FSS structures are formed by arraying these modules in a periodic grid with an optimal cell size. The proposed concept and the design method are validated using numerical simulation (finite-element method), as well as experimental results.


title: A machine learning architecture for optimizing web search engines

abstract: Indexing systems for the World Wide Web, such as Lycos and Alta Vista, play an essential role in making the Web useful and usable. These systems are based on Information Retrieval methods for indexing plain text documents, but also include heuristics for adjusting their document rankings based on the special HTML structure of Web documents. In this paper, we describe a wide range of such heuristics|including a novel one inspired by reinforcement learning techniques for propagating rewards through a graph|which can be used to a ect a search engine's rankings. We then demonstrate a system which learns to combine these heuristics automatically, based on feedback collected unintrusively from users, resulting in much improved rankings.


title: A frequency compensation scheme for LDO voltage regulators

abstract: A stable low dropout (LDO) voltage regulator topology for low equivalent series resistance (ESR) capacitive loads is presented. The proposed scheme generates a zero internally instead of relying on the zero generated by the load capacitor and its ESR combination for stability. It is demonstrated that this scheme realizes robust frequency compensation, facilitates the use of multilayer ceramic capacitors for the load of LDO regulators, and improves transient response and noise performance. Test results from a prototype fabricated in AMI 0.5-/spl mu/m CMOS technology provide the most important parameters of the regulator viz., ground current, load regulation, line regulation, output noise, and start-up time.


title: Design & analysis of a novel IGBT package with double-sided cooling

abstract: Conventional IGBT modules with bonding wires have been suffered from relatively high thermal resistance from junction to case (Rthjc) due to unavoidable single-sided cooling (SSC), and also suffered from uneven current distribution due to unsymmetrical electrical interconnection. A novel IGBT package with double-sided cooling (DSC) geometry was proposed in this paper to overcome these problems. A comprehensive comparative study between the DSC geometry and the SSC geometry has been conducted by simulations. The analytical results demonstrate that the DSC geometry is well superior to the conventional SSC geometry in both electrical and thermal behavior, which predicts a brighter future in various key applications for this emerging package.


title: Extraction of Endobronchial Tooth by Flexible Bronchoscopy Using Fishnet Basket in Patients of Intensive Care Unit with Artificial Airway

abstract: Foreign body aspiration into the tracheobronchial tree can cause serious problems. Tooth aspiration can occur during emergency endotracheal intubation. However, removal of a tooth from the airway is difficult because of the round shape and smooth surface of the tooth. Here, I report extraction of a tooth from the airway by flexible bronchoscopy using fishnet basket in intensive care patients. The procedure was performed in six patients. All patients were critically ill with an artificial airway (n = 4; endotracheal tube, n = 2; tracheostomy tube). The tooth location was right bronchial tree in three patients and left bronchial tree in three patients. Tooth extraction failed in two patients because the teeth were impacted in the distal bronchus during bronchoscopy. There was no complication such as life threatening arrhythmia or hypoxemia during the procedure. Using flexible bronchoscopy with a fishnet basket, we were able to extract aspirated teeth from patients with an artificial airway.


title: Collective List-Only Entity Linking: A Graph-Based Approach

abstract: List-only entity linking (EL) is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional EL task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, the list-only EL can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in EL, which is yet currently neglected. In this paper, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of the list-only EL, but also opens up the bridge between the list-only EL and conventional EL solutions.


Hyaluronic acid fillers with cohesive polydensified matrix for soft-tissue augmentation and rejuvenation: a literature review


Addressing Female Genital Mutilation/Cutting (FGM/C) in the Era of Clitoral Reconstruction: Plastic Surgery


title: Efficient and wideband envelope amplifiers for envelope tracking and polar transmitters

abstract: Envelope tracking provides the potential for achieving high efficiency in power amplifiers for next generation wireless transmitters with high peak-to-average ratio signals such as OFDMA. Envelope modulators with high fidelity, high efficiency and wide bandwidth are critical enablers for the widespread application of envelope tracking. This presentation reviews the development of various envelope amplifiers for applications in basestation and handset power amplifiers. Requirements of voltage swing, bandwidth, and accuracy are first described. Various technologies for envelope modulators achieving voltages as high as 50V are presented, for operation in basestations with Si-LDMOS, HVHBT, GaN RF power transistors. CMOS-based Si envelope modulator ICs for operation in wireless handsets are also discussed. Envelope tracking microwave power amplifiers that achieve overall efficiency greater than 45% for LTE in handsets and greater than 64 % efficiency in basestations are presented.


title: CudaRF: A CUDA-based implementation of Random Forests

abstract: Machine learning algorithms are frequently applied in data mining applications. Many of the tasks in this domain concern high-dimensional data. Consequently, these tasks are often complex and computationally expensive. This paper presents a GPU-based parallel implementation of the Random Forests algorithm. In contrast to previous work, the proposed algorithm is based on the compute unified device architecture (CUDA). An experimental comparison between the CUDA-based algorithm (CudaRF), and state-of-the-art Random Forests algorithms (Fas-tRF and LibRF) shows that CudaRF outperforms both FastRF and LibRF for the studied classification task.


title: Using Wiki technology to support student engagement: Lessons from the trenches

abstract: This paper reports on a failed experiment to use Wiki technology to support student engagement with the subject matter of a third year undergraduate module. Using qualitative data, the findings reveal that in an educational context, social technologies such as Wiki's, are perceived differently compared with ordinary personal use and this discourages student adoption. A series of insights are then offered which help HE teachers understand the pitfalls of integrating social technologies in educational contexts.


title: Managing the full ERP life-cycle: Considerations of maintenance and support requirements and IT governance practice as integral elements of the formula for successful ERP adoption

abstract: Companies adopting enterprise resource planning (ERP) systems have often focused primarily on implementation-related factors while neglecting those of post-implementation. As a result, the usefulness and operation of the ERP systems, once installed, are compromised. This research adopted a case study approach to demonstrate that ERP adoption efforts that fail to pay attention to post- implementation requirements (especially those relevant to maintenance and support (M&S)) from an early stage in the project lifecycle will face dire consequences. It points out that poor planning and management of M&S services can imperil the normal operations of an ERP system and the daily activities of a business. With the life span of ERP systems getting shorter, sound M&S practices can extend their life and create a stable system platform to support ef?cient and effective business operations. M&S issues deserve to be considered as integral elements among the critical success factors (CSF) of ERP adoption projects. In other words, ERP success requires a full lifecycle perspective to be taken by adopting companies. With lessons having been learned from the mistakes in the ?rst project, the company in this case study revamped its ERP implementation second time out, with due consideration being given to M&S strategies and practices from project initiation onward in order to realize a stable, usable, and maintainable system. The case study explores and identi?es the critical success factors (CSF) of ERP adoption, and shows that M&S must be included as a key element from the outset and throughout the system lifecycle. Our ?ndings capture a great deal of experience for any ERP adopting companies to follow in order to avoid learning costly lessons both in implementation and subsequent M&S throughout the lifespan of the system. A set of propositions is also presented for academic researcher to consider in future ERP research endeavors.


title: S.Sclaroff: Automated camera layout to satisfy task-specific and floorplanspecific coverage requirements

abstract: In many multi-camera vision systems the effect of camera locations on the task-specific quality of service is ignored. Researchers in Computational Geometry have proposed elegant solutions for some sensor location problem classes. Unfortunately, these solutions use unrealistic assumptions about the cameras' capabilities that make these algorithms unsuitable for many real world computer vision applications. In this paper, the general camera placement problem is first defined with assumptions that are more consistent with the capabilities of real world cameras. The region to be observed by cameras may be volumetric, static or dynamic, and may include holes. A subclass of this general problem can be formulated in terms of planar regions that are typical of building floor plans. Given a floor plan to be observed, the problem is then to reliably compute a camera layout such that certain task-specific constraints are met. A solution to this problem is obtained via binary optimization over a discrete problem space. In experiments the performance of the resulting system is demonstrated with different real indoor and outdoor floor plans.


title: Cortical representation of the sensory dimension of pain.

abstract: It is well accepted that pain is a multidimensional experience, but little is known of how the brain represents these dimensions. We used positron emission tomography (PET) to indirectly measure pain-evoked cerebral activity before and after hypnotic suggestions were given to modulate the perceived intensity of a painful stimulus. These techniques were similar to those of a previous study in which we gave suggestions to modulate the perceived unpleasantness of a noxious stimulus. Ten volunteers were scanned while tonic warm and noxious heat stimuli were presented to the hand during four experimental conditions: alert control, hypnosis control, hypnotic suggestions for increased-pain intensity and hypnotic suggestions for decreased-pain intensity. As shown in previous brain imaging studies, noxious thermal stimuli presented during the alert and hypnosis-control conditions reliably activated contralateral structures, including primary somatosensory cortex (S1), secondary somatosensory cortex (S2), anterior cingulate cortex, and insular cortex. Hypnotic modulation of the intensity of the pain sensation led to significant changes in pain-evoked activity within S1 in contrast to our previous study in which specific modulation of pain unpleasantness (affect), independent of pain intensity, produced specific changes within the ACC. This double dissociation of cortical modulation indicates a relative specialization of the sensory and the classical limbic cortical areas in the processing of the sensory and affective dimensions of pain.


title: Automatic detection of cyberbullying on social networks based on bullying features

abstract: With the increasing use of social media, cyberbullying behaviour has received more and more attention. Cyberbullying may cause many serious and negative impacts on a person's life and even lead to teen suicide. To reduce and stop cyberbullying, one effective solution is to automatically detect bullying content based on appropriate machine learning and natural language processing techniques. However, many existing approaches in the literature are just normal text classification models without considering bullying characteristics. In this paper, we propose a representation learning framework specific to cyberbullying detection. Based on word embeddings, we expand a list of pre-defined insulting words and assign different weights to obtain bullying features, which are then concatenated with Bag-of-Words and latent semantic features to form the final representation before feeding them into a linear SVM classifier. Experimental study on a twitter dataset is conducted, and our method is compared with several baseline text representation learning models and cyberbullying detection methods. The superior performance achieved by our method has been observed in this study.


title: A new pulsed power supply topology based on positive buck-boost converters concept

abstract: Improving efficiency and flexibility in pulsed power supply technologies is the most substantial concern of pulsed power systems specifically with regard to plasma generation. Recently, the improvement of pulsed power supply has become of greater concern due to the extension of pulsed power applications to environmental and industrial areas. With this respect, a current source based topology is proposed in this paper as a pulsed power supply which gives the possibility of power flow control during load supplying mode. The main contribution in this configuration is utilization of low-medium voltage semiconductor switches for high voltage generation. A number of switch-diode-capacitor units are designated at the output of topology to exchange the current source energy into voltage form and generate a pulsed power with sufficient voltage magnitude and stress. Simulations carried out in Matlab/SIMULINK platform as well as experimental tests on a prototype setup have verified the capability of this topology in performing desired duties. Being efficient and flexible are the main advantages of this topology.


title: Thermoelectric energy harvesting for mobile phone charging application

abstract: To address the dramatic increase in energy consumption of personal electronic devices, human body heat is harvested using a thermoelectric generator (TEG). This paper presents harvesting the heat energy also known as thermoelectric energy from human body heat which would be wasted otherwise using a TEG to charge a mobile phone battery. Based on the temperature difference between the heat generated from the heat source and the environment, the TEG is able to convert heat energy to thermoelectric energy. Heat energy is a renewable source that is easily accessible. In this paper body heat is being harvested. A step up DC-DC converter is used to step up the harvested voltage. This voltage can be either stored or used to power up low-power electronics. In this work, a typical Universal Serial Bus (USB) hand phone was charged using the harvested heat energy.


title: Olfactory bulb volume and depth of olfactory sulcus in patients with idiopathic olfactory loss

abstract: Olfactory loss is considered as idiopathic (IOL) when no cause can be found, neither on the basis of a standardized history nor a thorough clinical evaluation. Olfactory bulb volume (OB) has been shown to be decreased in patients with olfactory loss due to trauma, infections, or sinonasal disease. However, OB volume has not yet been investigated in IOL patients. Twenty-two patients with IOL were compared with 22 controls in terms of olfactory performance and of OB volume assessed with magnetic resonance imaging (MRI). Psychophysical testing confirmed that patients had lower scores than controls: P < 0.001 for threshold (T), discrimination (D), identification (I) and global TDI score. Mean right, left and total OB volumes were significantly smaller for IOL patients as compared to controls. Both for IOL and controls, there was a significant correlation between odor thresholds and total OB volume, respectively. In conclusion, patients with IOL have decreased olfactory function and decreased OB volume when compared with controls. This was previously demonstrated for patients with postinfectious or posttraumatic olfactory dysfunction, but not for patients with uneventful conditions possibly damaging their olfactory apparatus.


title: A semi-supervised domain adaptation assembling approach for image classification

abstract: Automatic annotation of images is one of the fundamental problems in computer vision applications. With the increasing amount of freely available images, it is quite possible that the training data used to learn a classifier has different distribution from the data which is used for testing. This results in degradation of the classifier performance and highlights the problem known as domain adaptation. Framework for domain adaptation typically requires a classification model which can utilize several classifiers by combining their results to get the desired accuracy. This work proposes depth-based and iterative depth-based fusion methods which are basically rank-based fusion methods and utilize rank of the predicted labels from different classifiers. Two frameworks are also proposed for domain adaptation. The first framework uses traditional machine learning algorithms, while the other works with metric learning as well as transfer learning algorithm. Motivated from ImageCLEF’s 2014 domain adaptation task, these frameworks with the proposed fusion methods are validated and verified by conducting experiments on the images from five domains having varied distributions. Bing, Caltech, ImageNet, and PASCAL are used as source domains and the target domain is SUN. Twelve object categories are chosen from these domains. The experimental results show the performance improvement not only over the baseline system, but also over the winner of the ImageCLEF’s 2014 domain adaptation challenge.


title: Face Super-Resolution Guided by Facial Component Heatmaps

abstract: State-of-the-art face super-resolution methods leverage deep convolutional neural networks to learn a mapping between low-resolution (LR) facial patterns and their corresponding high-resolution (HR) counterparts by exploring local appearance information. However, most of these methods do not account for facial structure and suffer from degradations due to large pose variations and misalignments. In this paper, we propose a method that explicitly incorporates structural information of faces into the face super-resolution process by using a multi-task convolutional neural network (CNN). Our CNN has two branches: one for super-resolving face images and the other branch for predicting salient regions of a face coined facial component heatmaps. These heatmaps encourage the upsampling stream to generate super-resolved faces with higher-quality details. Our method not only uses low-level information (i.e., intensity similarity), but also middle-level information (i.e., face structure) to further explore spatial constraints of facial components from LR inputs images. Therefore, we are able to super-resolve very small unaligned face images \((16\,\times \,16\hbox { pixels})\) with a large upscaling factor of 8\(\times \), while preserving face structure. Extensive experiments demonstrate that our network achieves superior face hallucination results and outperforms the state-of-the-art.


title: Amelia: A Case Report and Literature Review

abstract: Amelia, defined as the complete absence of the skeletal parts of a limb that occurs when the limb formation process is either prevented or interrupted very early in the developing embryo between 24 and 36 days following fertilization (1-4). Amelia is generally thought to be a sporadic anomaly. But more than 50% of the cases it is associated with major malformations in other organ systems (4). The malformations most frequently seen with Amelia include cleft lip and/or palate, body wall defect, malformed head, and defects of the neural tube, kidneys, and diaphragm (5-7). Facial clefts may be accompanied by other facial anomalies such as abnormally small jaw, and missing ears or nose. The body wall defects allow internal organs to protrude through the abdomen (8, 9). Head malformations may be minor to severe with a near absence of the brain. The diaphragm may be herniated or absent and one or both kidneys may be small or absent (8, 10). Other abnormalities associated with Amelia include severe defects of the lungs, vertebrae, heart, internal and external genital system, and anus (10-13). There is usually severe growth deficiency, both before and after birth, and mental retardation may be present in survivors (10). Teratogens such as thalidomide and alcohol, vascular compromise by amniotic bands or other causes, and maternal diabetes have been reported to cause this severe limb deficiency (8-10). The patient was a full-term male neonate, the first child of a 22 year old father and 26 year old mother. There was no consanguinity or any other relevant family history. There was no known teratogenic exposure or any problem during pregnancy. The height, weight, and head circumference were normal. The right arm was completely missing with a cherry sized skin bulging on the lateral end of the right clavicle (Figure 1). In physical examination an imperforate anus was also visible (Figure 2). The ultrasonography showed only one kidney, right kidney was absent, but in VCUG left kidney had reflux grade 5 and right kidney reflux grade 4 (Figure 3). Regarding rising BUN and Cr levels, DMSA was done that showed poor function of the left kidney and the function of the right kidney was moderately to severely decreased. Echocardiography revealed an ASD. X-ray of vertebrae showed hemivertebra of L5 (Figure 4). No other anomalies were found. Prior to clinical observation of absent limbs, certain signs in the pregnant mother may indicate a greater likelihood of amelia. Abnormal vaginal bleeding, diabetes mellitus, and toxemia can all be associated with amelia of the fetus (4, 9). An increased alfa fetoprotein in the maternal blood may indicate neural tube defects that can accompany limb defects (12). Besides seeing missing limbs by ultrasound, signs in the fetus accompanying amelia include breech and other non-cephalic presentations at birth, a single artery in the umbilical cord, low placental weight and extremely low birth weight, not accounted for by the lack of limbs (13). This report presents a case of amelia with imperforate anus, hemivertebra in L5, ASD and renal involvement. VACTERL associated is a disorder that affects many body systems. VACTERL stands for vertebral defects, anal atresia, cardiac defects, trachea-esophageal fistula, renal anomalies, and limb abnormalities. Cases with VACTERL association typically have at least 3 of these characteristic features (14). Limb abnormalities are seen in 40 50% of VACTERL association. These abnormalities most commonly include poorly developed or missing thumbs or underdeveloped forearms and hands but amelia is a rare form of limb abnormality in VACTERL (4, 14, 15). Prenatal diagnosis including detailed ultrasound and amniocentesis examination play a major role in counseling parents with fetal anomalies including amelia. Ar ch ive f S ID


title: VDF: Targeted Evolutionary Fuzz Testing of Virtual Devices

abstract: As cloud computing becomes more and more prevalent, there is increased interest in mitigating attacks that target hypervisors from within the virtualized guest environments that they host. We present VDF, a targeted evolutionary fuzzing framework for discovering bugs within the software-based virtual devices implemented as part of a hypervisor. To achieve this, VDF selectively instruments the code of a given virtual device, and performs record and replay of memory-mapped I/O (MMIO) activity specific to the virtual device. We evaluate VDF by performing cloud-based parallel fuzz testing of eighteen virtual devices implemented within the QEMU hypervisor, executing over two billion test cases and revealing over one thousand unique crashes or hangs in one third of the tested devices. Our custom test case minimization algorithm further reduces the erroneous test cases into only 18.57% of the original sizes on average.


title: Design and Evaluation of a Stance-Control Knee-Ankle-Foot Orthosis Knee Joint

abstract: Conventional knee-ankle-foot orthoses (KAFOs) are prescribed for people with knee-extensor muscle weakness. However, the orthoses lock the knee in full extension and, therefore, do not permit a natural gait pattern. A new electromechanical stance-control knee-ankle-foot orthosis (SCKAFO) knee joint that employs a novel friction-based belt-clamping mechanism was designed to enable a more natural gait. The SCKAFO knee joint allows free knee motion during swing and other non-weight-bearing activities and inhibits knee flexion while allowing knee extension during weight bearing. A prototype SCKAFO knee joint was mechanically tested to determine the moment at failure, loading behavior, and wear resistance. The mean maximum resisting moment of the SCKAFO knee joint over five loading trials was 69 Nm +/- 4.9 Nm. The SCKAFO knee-joint strength and performance were sufficient to allow testing on a 90 kg subject at normal walking cadence. Proper function of the new electromechanical knee joint was verified in walking trials of an able-bodied subject.


title: EmoNets: Multimodal deep learning approaches for emotion recognition in video

abstract: The task of the Emotion Recognition in the Wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies. The videos depict acted-out emotions under realistic conditions with a large degree of variation in attributes such as pose and illumination, making it worthwhile to explore approaches which consider combinations of features from multiple modalities for label assignment. In this paper we present our approach to learning several specialist models using deep learning techniques, each focusing on one modality. Among these are a convolutional neural network, focusing on capturing visual information in detected faces, a deep belief net focusing on the representation of the audio stream, a K-Means based “bag-of-mouths” model, which extracts visual features around the mouth region and a relational autoencoder, which addresses spatio-temporal aspects of videos. We explore multiple methods for the combination of cues from these modalities into one common classifier. This achieves a considerably greater accuracy than predictions from our strongest single-modality classifier. Our method was the winning submission in the 2013 EmotiW challenge and achieved a test set accuracy of 47.67 % on the 2014 dataset.


title: Reverse Backprojection Algorithm for the Accurate Generation of SAR Raw Data of Natural Scenes

abstract: Future synthetic aperture radar (SAR) mission concepts often rely on locally nonlinear (e.g., high orbits and bistatic) surveys or acquisition schemes. The simulation of the raw data of natural scenes as acquired by future systems appears as one powerful tool in order to understand the particularities of these systems and assess the impact of system and propagation errors on their performance. We put forward, in this letter, a new formulation of the reverse backprojection algorithm for the accurate simulation of raw data of natural surfaces. In particular, the algorithm is perfectly suited to accommodate any kind (1-D/2-D) of temporal and spatial variation, e.g., in observation geometry, acquisition strategy, or atmospheric propagation. The algorithm is analyzed with respect to its SAR image formation sibling, and tested under different simulation scenarios. We expect the reverse backprojection algorithm to play a relevant role in the simulation of future geosynchronous and multistatic SAR missions.


title: Methods For Using Textual Entailment In Open-Domain Question Answering

abstract: Work on the semantics of questions has argued that the relation between a question and its answer(s) can be cast in terms of logical entailment. In this paper, we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering (Q/A) systems. In our experiments, we show that when textual entailment information is used to either filter or rank answers returned by a Q/A system, accuracy can be increased by as much as 20% overall.


title: A Linear Permanent-Magnet Motor for Active Vehicle Suspension

abstract: Traditionally, automotive suspension designs with passive components have been a compromise between the three conflicting demands of road holding, load carrying, and passenger comfort. Linear electromagnetic motor-based active suspension has superior controllability and bandwidth, provides shock load isolation between the vehicle chassis and wheel, and, therefore, has great potential. It also has the ability to recover energy that is dissipated in the shock absorber in the passive systems and results in a much more energy-efficient suspension system. This paper describes the issues pertinent to the design of a high force density tubular permanent-magnet (PM) motor for active suspension in terms of performance optimization, the use of a solid stator core for low-cost production and its impact on thrust force, and the assessment of demagnetization risk.


title: Force to Rebalance Control of HRG and Suppression of Its Errors on the Basis of FPGA

abstract: A novel design of force to rebalance control for a hemispherical resonator gyro (HRG) based on FPGA is demonstrated in this paper. The proposed design takes advantage of the automatic gain control loop and phase lock loop configuration in the drive mode while making full use of the quadrature control loop and rebalance control loop in controlling the oscillating dynamics in the sense mode. First, the math model of HRG with inhomogeneous damping and frequency split is theoretically analyzed. In addition, the major drift mechanisms in the HRG are described and the methods that can suppress the gyro drift are mentioned. Based on the math model and drift mechanisms suppression method, four control loops are employed to realize the manipulation of the HRG by using a FPGA circuit. The reference-phase loop and amplitude control loop are used to maintain the vibration of primary mode at its natural frequency with constant amplitude. The frequency split is readily eliminated by the quadrature loop with a DC voltage feedback from the quadrature component of the node. The secondary mode response to the angle rate input is nullified by the rebalance control loop. In order to validate the effect of the digital control of HRG, experiments are carried out with a turntable. The experimental results show that the design is suitable for the control of HRG which has good linearity scale factor and bias stability.