title: Error estimates for the finite element approximation of a semilinear elliptic control problem with state constraints and finite dimensional control space

abstract: The finite element approximation of optimal control problems for semilinear elliptic partial differential equation is considered, where the control belongs to a finite-dimensional set and state constraints are given in finitely many points of the domain. Under the standard linear independency condition on the active gradients and a strong second-order sufficient optimality condition, optimal error estimates are derived for locally optimal controls.


title: PPS — Placement prediction system using logistic regression

abstract: This paper presents the development of placement predictor system (PPS) using logistic regression model. Based on the student scores in matriculation, senior secondary, subjects in various semesters of technical education and demographics, PPS predicts the placement of a student in upcoming recruitment session. The steps involved in designing and building logistic regression model is stated using the past academic and in-house placement data of Guru Nanak Dev Engineering College (GNDEC), Ludhiana. Machine learning parameterized approach is used to support research and analyze the students performance in previous sessions. The results are generated from an open source GNU Octave programming tool. The developed model has been applied to predict the placement of students at training and placement office (TPO). The testing of PPS brings about promising 83.33% accuracy. The learned parameters of the model gave insights into the placement process. Hence, the TPO decided to adopt this system to help them in informed decision making. This application endows the targeted group of students to boost their placement probability.


title: A Survey of Location Prediction on Twitter

abstract: Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we make a conclusion of the survey and list future research directions.


title: ASTLOG: A Language for Examining Abstract Syntax Trees

abstract: We desired a facility for locating/analyzing syntactic artifacts in abstract syntax trees of C/C++ programs, similar to the facility grep or awk provides for locating artifacts at the lexical level. Prolog, with its implicit pattern-matching and backtracking capabilities, is a natural choice for such an application. We have developed a Prolog variant that avoids the overhead of translating the source syntactic structures into the form of a Prolog database; this is crucial to obtaining acceptable performance on large programs. An interpreter for this language has been implemented and used to find various kinds of syntactic bugs and other questionable constructs in real programs like Microsoft SQL server (450Klines) and Microsoft Word (2Mlines) in time comparable to the runtime of the actual compiler. ::: ::: The model in which terms are matched against an implicit current object, rather than simply proven against a database of facts, leads to a distinct "inside-out functional" programming style that is quite unlike typical Prolog, but one that is, in fact, well-suited to the examination of trees. Also, various second-order Prolog set-predicates may be implemented via manipulation of the current object, thus retaining an important feature without entailing that the database be dynamically extensible as the usual implementation does.


title: Defending Against False Data Injection Attacks on Power System State Estimation

abstract: This paper investigates the problem of defending against false data injection (FDI) attacks on power system state estimation. Although many research works have been previously reported on addressing the same problem, most of them made a very strong assumption that some meter measurements can be absolutely protected. To address the problem practically, a reasonable approach is to assume whether or not a meter measurement could be compromised by an adversary does depend on the defense budget deployed by the defender on the meter. From this perspective, our contributions focus on designing the least-budget defense strategy to protect power systems against FDI attacks. In addition, we also extend to investigate choosing which meters to be protected and determining how much defense budget to be deployed on each of these meters. We further formulate the meter selection problem as a mixed integer nonlinear programming problem, which can be efficiently tackled by Benders’ decomposition. Finally, extensive simulations are conducted on IEEE test power systems to demonstrate the advantages of the proposed approach in terms of computing time and solution quality, especially for large-scale power systems.


title: Short Signatures from the Weil Pairing

abstract: We introduce a short signature scheme based on the Computational Diffie–Hellman assumption on certain elliptic and hyperelliptic curves. For standard security parameters, the signature length is about half that of a DSA signature with a similar level of security. Our short signature scheme is designed for systems where signatures are typed in by a human or are sent over a low-bandwidth channel. We survey a number of properties of our signature scheme such as signature aggregation and batch verification.


title: Werner syndrome protein contains three structure-specific DNA binding domains.

abstract: Werner syndrome (WS) is a premature aging syndrome caused by mutations in the WS gene (WRN) and a deficiency in the function of the Werner protein (WRN). WRN is a multifunctional nuclear protein that catalyzes three DNA-dependent reactions: a 3'-5'-exonuclease, an ATPase, and a 3'-5'-helicase. Deficiency in WRN results in a cellular phenotype of genomic instability. The biochemical characteristics of WRN and the cellular phenotype of WRN mutants suggest that WRN plays an important role in DNA metabolic pathways such as recombination, transcription, replication, and repair. The catalytic activities of WRN have been extensively studied and are fairly well understood. However, much less is known about the domain-specific interactions between WRN and its DNA substrates. This study identifies and characterizes three distinct WRN DNA binding domains using recombinant truncated fragments of WRN and five DNA substrates (long forked duplex, blunt-ended duplex, single-stranded DNA, 5'-overhang duplex, and Holliday junction). Substrate-specific DNA binding activity was detected in three domains, one N-terminal and two different C-terminal WRN fragments (RecQ conserved domain and helicase RNase D conserved domain-containing domains). The substrate specificity of each DNA binding domain may indicate that each protein domain has a distinct biological function. The importance of these results is discussed with respect to proposed roles for WRN in distinct DNA metabolic pathways.


title: Conduits of innovation or imitation?assessing the effect of alliances on the persistence of profits in U.S. firms

abstract: Abstract This paper examines if a firm’s alliances affect the persistence of its financial performance. The literature suggests two conflicting views concerning this effect. In particular, access to resources and innovation and the risk of imitation from alliances can have different impacts on performance. In our empirical analysis, based on a panel of 509 firms covering the years 1992 to 2002, return on assets was regressed on the number of alliances and other control variables using hierarchical linear modeling. Results support the positive view of alliances as mechanisms to sustain competitive advantage and escape from competitive disadvantage through access to external, valuable resources held by other firms. Alliances also help firms to constantly innovate and buffer themselves from external shocks that erode existing advantages. Our results, however, may be specific to the period and the institutional context under consideration and we do not distinguish between types, purposes and “strength” of alliances. We contribute to the debate about profit persistence by examining one particular factor that has been neglected in the literature: the extent to which firms engage in alliances with other actors. From a managerial perspective, our study shows that alliances can be used as an effective tool to support superior performance or avoid lock-in into inferior performance.


title: Search-Based Peer Reviewers Recommendation in Modern Code Review

abstract: Code review is of primary importance in modern software development. It is widely recognized that peer review is an efficient and effective practice for improving software quality and reducing defect proneness. For successful review process, peer reviewers should have a deep experience and knowledge with the code being reviewed, and familiar to work and collaborate together. However, one of the main challenging tasks in modern code review is to find the most appropriate reviewers for submitted code changes. So far, reviewers assignment is still a manual, costly and time-consuming task. In this paper, we introduce a search-based approach, namely RevRec, to provide decision-making support for code change submitters and/or reviewers assigners to identify most appropriate peer reviewers for their code changes. RevRec aims at finding reviewers to be assigned for a code change based on their expertise and collaboration in past reviews using genetic algorithm (GA). We evaluated our approach on a benchmark of three open-source software systems, Android, OpenStack, and Qt. Results indicate that RevRec accurately recommends code reviewers with up to 59% of precision and 74% of recall. Our experiments provide evidence that leveraging reviewers expertise from their prior reviews and the socio-technical aspects of the team work and collaboration is relevant in improving the performance of peer reviewers recommendation in modern code review.


title: Exploiting Temporal and Social Factors for B2B Marketing Campaign Recommendations

abstract: Business to Business (B2B) marketing aims at meeting the needs of other businesses instead of individual consumers. In B2B markets, the buying processes usually involve series of different marketing campaigns providing necessary information to multiple decision makers with different interests and motivations. The dynamic and complex nature of these processes imposes significant challenges to analyze the process logs for improving the B2B marketing practice. Indeed, most of the existing studies only focus on the individual consumers in the markets, such as movie/product recommender systems. In this paper, we exploit the temporal behavior patterns in the buying processes of the business customers and develop a B2B marketing campaign recommender system. Specifically, we first propose the temporal graph as the temporal knowledge representation of the buying process of each business customer. The key idea is to extract and integrate the campaign order preferences of the customer using the temporal graph. We then develop the low-rank graph reconstruction framework to identify the common graph patterns and predict the missing edges in the temporal graphs. We show that the prediction of the missing edges is effective to recommend the marketing campaigns to the business customers during their buying processes. Moreover, we also exploit the community relationships of the business customers to improve the performances of the graph edge predictions and the marketing campaign recommendations. Finally, we have performed extensive empirical studies on real-world B2B marketing data sets and the results show that the proposed method can effectively improve the quality of the campaign recommendations for challenging B2B marketing tasks.


title: Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local Versus Global Image Context

abstract: Chronic kidney disease affects one of every ten adults in USA (over 20 million). Computed tomography (CT) is a widely used imaging modality for kidney disease diagnosis and quantification. However, automatic pathological kidney segmentation is still a challenging task due to large variations in contrast phase, scanning range, pathology, and position in the abdomen, etc. Methods based on global image context (e.g., atlas- or regression-based approaches) do not work well. In this work, we propose to combine deep learning and marginal space learning (MSL), both using local context, for robust kidney detection and segmentation. Here, deep learning is exploited to roughly estimate the kidney center. Instead of performing a whole axial slice classification (i.e., whether it contains a kidney), we detect local image patches containing a kidney. The detected patches are aggregated to generate an estimate of the kidney center. Afterwards, we apply MSL to further refine the pose estimate by constraining the position search to a neighborhood around the initial center. The kidney is then segmented using a discriminative active shape model. The proposed method has been trained on 370 CT scans and tested on 78 unseen cases. It achieves a mean segmentation error of 2.6 and 1.7 mm for the left and right kidney, respectively. Furthermore, it eliminates all gross failures (i.e., segmentation is totally off) in a direct application of MSL.


title: Cellular Internalization of Exosomes Occurs Through Phagocytosis

abstract: Exosomes play important roles in many physiological and pathological processes. However, the exosome-cell interaction mode and the intracellular trafficking pathway of exosomes in their recipient cells remain unclear. Here, we report that exosomes derived from K562 or MT4 cells are internalized more efficiently by phagocytes than by non-phagocytic cells. Most exosomes were observed attached to the plasma membrane of non-phagocytic cells, while in phagocytic cells these exosomes were found to enter via phagocytosis. Specifically, they moved to phagosomes together with phagocytic polystyrene carboxylate-modified latex beads (biospheres) and were further sorted into phagolysosomes. Moreover, exosome internalization was dependent on the actin cytoskeleton and phosphatidylinositol 3-kinase, and could be inhibited by the knockdown of dynamin2 or overexpression of a dominant-negative form of dynamin2. Further, antibody pretreatment assays demonstrated that tim4 but not tim1 was involved in exosomes uptake. We also found that exosomes did not enter the internalization pathway involving caveolae, macropinocytosis and clathrin-coated vesicles. Our observation that the cellular uptake of exosomes occurs through phagocytosis has important implications for exosome-cell interactions and the exosome intracellular trafficking pathway.


title: Texting as a distraction to learning in college students

abstract: Participants were randomly assigned to texting or control groups during a lecture.Participants in the texting group scored significantly lower on a quiz.No moderating effects were found for texting's effect on the quiz score.Participants believe text messaging is distracting to other users, but not them. Texting has been shown to be cognitively distracting for students in lecture settings, but few have done empirical work, or looked at moderating effects between texting and academic outcomes. This experimental study compared the proportion of correct answers on a lecture quiz between students who were randomly assigned to text message during a pre-recorded lecture and those who were not, while investigating possible moderators. The participants who text messaged throughout the lecture scored significantly lower in percent of correct responses (t(95)=-4.6, p<.001, d=.93). No moderating effects were found, including: perceived distraction, perceived texting ability, number of text messages sent and received during the lecture, age, and gender.


title: Global Trajectory Optimization on Multilane Roads

abstract: This paper names several risks with iterative optimization of vehicle trajectories modeled through the calculus of variations, as used recently in several approaches to automated driving, and demonstrates how these can be addressed by global optimization, while retaining the original, variational optimization goals. A method for transforming variational methods into Hidden Markov Models is applied to multilane scenarios, such as highways or larger city streets. A particular side-effect of this method is the capability of providing a fail-safe emergency trajectory at almost no further computational effort. The proposed global optimization is demonstrated on several real-world situations and compared to a ground truth of human maneuvers.


title: Focused Page Rank in Scientific Papers Ranking

abstract: We propose Focused Page Rank (FPR) algorithm adaptation for the problem of scientific papers ranking. FPR is based on the Focused Surfer model, where the probability to follow the reference in a paper is proportional to its citation count. Evaluation on Citeseer autonomous digital library content showed that proposed model is a tradeoff between traditional citation count and basic Page Rank (PR). In contrast to basic Page Rank, proposed Focused Surfer model suffers less from the "outbound links" problem. We believe that FPR algorithm is closer to reality because highly cited papers are more visible and tend to attract more citations in future. This is in accordance with the one of the most significant principles of Scientometrics. No need for lexical analysis of the domain corpus and simplicity of implementation are among the strong points of the proposed model and make the proposed ranking technique attractive for academia digital libraries.


title: Bandwidth enhancement using dual-band frequency selective surface with Jerusalem cross elements for 2.4/5.8 GHz WLAN antennas

abstract: A dual-band FSS with Jerusalem cross elements was proposed to improve the bandwidth of U-slot patch antennas. From studies, it is shown that the simulation data in general agree with the measurement results for the U-slot patch antennas implemented with a FSS. The U-slot patch antenna using a FSS with Jerusalem cross elements has been proven to provide dual-band and wide-band operation for WLAN applications.


title: Development of a Cargo Screening Process Simulator: A First Approach

abstract: The efficiency of current cargo screening processes at sea and air ports is largely unknown as few benchmarks exists against which they could be measured. Some manufacturers provide benchmarks for individual sensors but we found no benchmarks that take a holistic view of the overall screening procedures and no benchmarks that take operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role. Our aim is to develop a decision support tool (cargo-screening system simulator) that will map the right technology and manpower to the right ::: commodity-threat combination in order to maximise detection rates. In this paper we present our ideas for developing such a system and highlight the research challenges we have identified. Then we introduce our first case study and report on the progress we have made so far.


title: A novel control strategy to suppress DC current injection of single-phase PV inverter to the grid

abstract: PV inverters without the isolation transformer become more attractive due to higher efficiency and lower weight. However, it may exist DC offset current problem and is critical to power system. In this paper a novel control strategy to suppress DC current injection of PV inverter to the grid is investigated. It is based on idea of accurately sensing the DC offset voltage of PV inverter. Since DC component of the inverter output can be eliminated, DC injection to the grid can be effectively suppressed. Finally the control scheme is verified by the experiment.


title: Intelligent Virtual Assistant knows Your Life

abstract: In the IoT world, intelligent virtual assistant (IVA) is a popular service to interact with users based on voice command. For optimal performance and efficient data management, famous IVAs like Amazon Alexa and Google Assistant usually operate based on the cloud computing architecture. In this process, a large amount of behavioral traces that include user voice activity history with detailed descriptions can be stored in the remote servers within an IVA ecosystem. If those data (as also known as IVA cloud native data) are leaked by attacks, malicious person may be able to not only harvest detailed usage history of IVA services, but also reveals additional user related information through various data analysis techniques. In this paper, we firstly show and categorize types of IVA related data that can be collected from popular IVA, Amazon Alexa. We then analyze an experimental dataset covering three months with Alexa service, and characterize the properties of user lifestyle and life patterns. Our results show that it is possible to uncover new insights on personal information such as user interests, IVA usage patterns and sleeping, wakeup patterns. The results presented in this paper provide important implications for and privacy threats to IVA vendors and users as well.


title: A Fast Learning Algorithm for Deep Belief Nets

abstract: We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.


title: Anusaaraka: Machine Translation in Stages

abstract: Fully-automatic general-purpose high-quality machine translation systems (FGH-MT) are extremely difficult to build. In fact, there is no system in the world for any pair of languages which qualifies to be called FGH-MT. The reasons are not far to seek. Translation is a creative process which involves interpretation of the given text by the translator. Translation would also vary depending on the audience and the purpose for which it is meant. This would explain the difficulty of building a machine translation system. Since, the machine is not capable of interpreting a general text with sufficient accuracy automatically at present - let alone re-expressing it for a given audience, it fails to perform as FGH-MT. FOOTNOTE{The major difficulty that the machine faces in interpreting a given text is the lack of general world knowledge or common sense knowledge.}


title: Review: Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling

abstract: The application of Artificial Neural Networks (ANNs) in the field of environmental and water resources modelling has become increasingly popular since early 1990s. Despite the recognition of the need for a consistent approach to the development of ANN models and the importance of providing adequate details of the model development process, there is no systematic protocol for the development and documentation of ANN models. In order to address this shortcoming, such a protocol is introduced in this paper. In addition, the protocol is used to critically review the quality of the ANN model development and reporting processes employed in 81 journal papers since 2000 in which ANNs have been used for drinking water quality modelling. The results show that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity.


title: Smart Personalized Routing for Smart Cities

abstract: In smart cities, commuters have the opportunities for smart routing that may enable selecting a route with less car accidents, or one that is more scenic, or perhaps a straight and flat route. Such smart personalization requires a data management framework that goes beyond a static road network graph. This paper introduces PreGo, a novel system developed to provide real time personalized routing. The recommended routes by PreGo are smart and personalized in the sense of being (1) adjustable to individual users preferences, (2) subjective to the trip start time, and (3) sensitive to changes of the road conditions. Extensive experimental evaluation using real and synthetic data demonstrates the efficiency of the PreGo system.


title: Haar wavelet transform and principal component analysis for fetal QRS classification from abdominal maternal ECG recordings

abstract: Fetal heart rate monitoring plays an essential role in helping to decrease the perinatal mortality rate associated with abnormalities in the cardiovascular system of the fetus. In this sense, a new approach to detect fetal QRS (fQRS) complexes from abdominal maternal ECG signals is proposed in this paper. First, signals were segmented into contiguous frames of 250 ms duration and then labeled in four classes. Principal component analysis was applied on Haar-Wavelet transform for dimensionality reduction and feature extraction, and interquartile ranges and sampling without replacement were employed to deal with outliers and imbalanced class problems, respectively. K-nearest neighborhood (KNN), support vector machine (SVM) and Bayesian network (BN) were trained and tested on the ECG signals of four electrodes of the PhysioNet/CinC challenge 2013 dataset, using 10-fold stratified cross-validation. Results show that KNN and SVM got better average accuracies over BN, that reach to 89.59% and 89.19%, respectively. Although KNN yielded better results, SVM was less time-consuming in prediction. In addition, the fourth electrode signals are less noisy and contain more representative data that helps SVM reaches an accuracy about 80% for fQRS estimation.


title: Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network

abstract: Language processing mechanism by humans is generally more robust than computers. The Cmabrigde Uinervtisy (Cambridge University) effect from the psycholinguistics literature has demonstrated such a robust word processing mechanism, where jumbled words (e.g. Cmabrigde / Cambridge) are recognized with little cost. On the other hand, computational models for word recognition (e.g. spelling checkers) perform poorly on data with such noise. Inspired by the findings from the Cmabrigde Uinervtisy effect, we propose a word recognition model based on a semi-character level recurrent neural network (scRNN). In our experiments, we demonstrate that scRNN has significantly more robust performance in word spelling correction (i.e. word recognition) compared to existing spelling checkers and character-based convolutional neural network. Furthermore, we demonstrate that the model is cognitively plausible by replicating a psycholinguistics experiment about human reading difficulty using our model.


title: Mindsets and Human Nature: Promoting Change in the Middle East, the Schoolyard, the Racial Divide, and Willpower.

abstract: Debates about human nature often revolve around what is built in. However, the hallmark of human nature is how much of a person's identity is not built in; rather, it is humans' great capacity to adapt, change, and grow. This nature versus nurture debate matters-not only to students of human nature-but to everyone. It matters whether people believe that their core qualities are fixed by nature (an entity theory, or fixed mindset) or whether they believe that their qualities can be developed (an incremental theory, or growth mindset). In this article, I show that an emphasis on growth not only increases intellectual achievement but can also advance conflict resolution between long-standing adversaries, decrease even chronic aggression, foster cross-race relations, and enhance willpower. I close by returning to human nature and considering how it is best conceptualized and studied. (PsycINFO Database Record (c) 2012 APA, all rights reserved). Language: en


title: Comparison of energy consumption of SRM and IPMSM in automotive driving schedules

abstract: Permanent magnets with rare earth materials are widely used in interior permanent magnet synchronous motors (IPMSMs) in Hybrid Electric Vehicles (HEVs). The recent price rise of rare earth materials has become a serious concern. A Switched Reluctance Motor (SRM) is one of the candidates for HEV rare-earth-free-motors. An SRM has been developed with dimensions, maximum torque, operating area, and maximum efficiency that all compete with the IPMSM. The efficiency map of the SRM is different from that of the IPMSM; thus, direct comparison has been rather difficult. In this paper, a comparison of energy consumption between the SRM and the IPMSM using four standard driving schedules is carried out. In HWFET and NEDC driving schedules, the SRM is found to have better efficiency because its efficiency is high at the high-rotational-speed region.


title: Usage and usefulness of technical software documentation: An industrial case study

abstract: Abstract Context Software documentation is an integral part of any software development process. However, software practitioners are often concerned about the value, degree of usage and usefulness of documentation during development and maintenance. Objective Motivated by the needs of NovAtel Inc. (NovAtel), a world-leading company developing software systems in support of global navigation satellite systems, and based on the results of a former systematic mapping study, we aimed at better understanding of the usage and the usefulness of various technical documents during software development and maintenance. Method We utilized the results of a former systematic mapping study and performed an industrial case study at NovAtel. From the joint definition of the analysis goals, the research method incorporates qualitative and quantitative analysis of 55 documents (design, test and process related) and 1630 of their revisions. In addition, we conducted a survey on the usage and usefulness of documents. A total of 25 staff members from the industrial partner, all having a medium to high level of experience, participated in the survey. Results In the context of the case study, a number of findings were derived. They include that (1) technical documentation was consulted least frequently for maintenance purpose and most frequently as an information source for development, (2) source code was considered most frequently as the preferred information source during software maintenance, (3) there is no significant difference between the usage of various documentation types during both development and maintenance, and (4) initial hypotheses stating that up-to-date information, accuracy and preciseness have the highest impact on usefulness of technical documentation. Conclusions It is concluded that the usage of documentation differs for various purposes and it depends on the type of the information needs as well as the tasks to be completed (e.g., development and maintenance). The results have been confirmed to be helpful for the company under study, and the firm is currently implementing some of the recommendations given.


title: Recent Research Advances on Interactive Machine Learning

abstract: Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.


title: Blindfold Baselines for Embodied QA

abstract: We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question by intelligently navigating in a simulated environment, gathering necessary visual information only through first-person vision before finally answering. Consequently, a blindfold baseline which ignores the environment and visual information is a degenerate solution, yet we show through our experiments on the EQAv1 dataset that a simple question-only baseline achieves state-of-the-art results on the EmbodiedQA task in all cases except when the agent is spawned extremely close to the object.


title: Detecting tip spam in location-based social networks

abstract: Location Based Social Networks (LBSNs) are attracting new users in exponential rates. LBSNs like Foursquare and Gowalla allow users to share their geographic location with friends, search for interesting places as well as posting tips about existing locations. By allowing users to comment on locations, LBSNs increasingly have to deal with a new wave of spammers, which aim at advertising unsolicited messages on tips and comments about locations. In this paper, we investigated the task of identifying tip spam on a popular Brazilian LBSN system, namely Apontador. Based on a labeled collection of tips provided by Apontador as well as crawled information about users and locations, we identified a number of attributes able to distinguish spam from non-spam tips. We leveraged our characterization study towards a spam detection mechanism. Using a classification technique, we were able to correctly identify 84% of spam tips and 91.8% of non-spam tips. Our results also highlight the importance that places and related user activity have for detecting tip spam on LBSNs.


title: A Novel Approach for Data-Driven Automatic Site Recommendation and Selection

abstract: This paper presents a novel, generic, and automatic method for data-driven site selection. Site selection is one of the most crucial and important decisions made by any company. Such a decision depends on various factors of sites, including socio-economic, geographical, ecological, as well as specific requirements of companies. The existing approaches for site selection (commonly used by economists) are manual, subjective, and not scalable, especially to Big Data. The presented method for site selection is robust, efficient, scalable, and is capable of handling challenges emerging in Big Data. To assess the effectiveness of the presented method, it is evaluated on real data (collected from Federal Statistical Office of Germany) of around 200 influencing factors which are considered by economists for site selection of Supermarkets in Germany (Lidl, EDEKA, and NP). Evaluation results show that there is a big overlap (86.4 \%) between the sites of existing supermarkets and the sites recommended by the presented method. In addition, the method also recommends many sites (328) for supermarket where a store should be opened.


title: Complex clock gating with integrated clock gating logic cell

abstract: Clock gating is an effective technique for minimizing dynamic power in sequential circuits. Applying clock-gating at gate-level not only saves time compared to implementing clock-gating in the RTL code but also saves power and can easily be automated in the synthesis process. This paper presents simulation results on various types of clock-gating at different hierarchical levels on a serial peripheral interface (SPI) design. In general power savings of about 30% and 36% reduction on toggle rate can be seen with different complex clock- gating methods with respect to no clock-gating in the design.


title: Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential

abstract: Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to make diagnostic and prognostic assessments, mainly based on the cell morphology and architecture distribution. Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection, segmentation, and classification. In cancer research, these approaches have played, and will continue to play a key (often bottleneck) role in minimizing human intervention, consolidating pertinent second opinions, and providing traceable clinical information. Pathological studies have been conducted for numerous cancer detection and grading applications, including brain, breast, cervix, lung, and prostate cancer grading. Our study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. This study also enables us to measure the challenges that remain, in order to reach robust analysis of whole slide images, essential high content imaging with diagnostic biomarkers and prognosis support in digital pathology.


title: Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions

abstract: Diagnostic imaging often requires the simultaneous identification of a multitude of findings of varied size and appearance. Beyond global indication of said findings, the prediction and display of localization information improves trust in and understanding of results when augmenting clinical workflow. Medical training data rarely includes more than global image-level labels as segmentations are time-consuming and expensive to collect. We introduce an approach to managing these practical constraints by applying a novel architecture which learns at multiple resolutions while generating saliency maps with weak supervision. Further, we parameterize the Log-Sum-Exp pooling function with a learnable lower-bounded adaptation (LSE-LBA) to build in a sharpness prior and better handle localizing abnormalities of different sizes using only image-level labels. Applying this approach to interpreting chest x-rays, we set the state of the art on 9 abnormalities in the NIH's CXR14 dataset while generating saliency maps with the highest resolution to date.


Mechanisms of initial alloy formation for Pd on Cu(100) studied by STM.


title: Attention-based Wav2Text with feature transfer learning

abstract: Conventional automatic speech recognition (ASR) typically performs multi-level pattern recognition tasks that map the acoustic speech waveform into a hierarchy of speech units. But, it is widely known that information loss in the earlier stage can propagate through the later stages. After the resurgence of deep learning, interest has emerged in the possibility of developing a purely end-to-end ASR system from the raw waveform to the transcription without any predefined alignments and hand-engineered models. However, the successful attempts in end-to-end architecture still used spectral-based features, while the successful attempts in using raw waveform were still based on the hybrid deep neural network - Hidden Markov model (DNN-HMM) framework. In this paper, we construct the first end-to-end attention-based encoder-decoder model to process directly from raw speech waveform to the text transcription. We called the model as "Attention-based Wav2Text". To assist the training process of the end-to-end model, we propose to utilize a feature transfer learning. Experimental results also reveal that the proposed Attention-based Wav2Text model directly with raw waveform could achieve a better result in comparison with the attentional encoder-decoder model trained on standard front-end filterbank features.


title: The sound transmission loss of a single panel measured with the two-microphone and the conventional method-comparison with the statistical energy analysis model

abstract: Abstract The sound transmission loss of a single metal panel obtained with the sound intensity technique and the conventional method have been compared with the theoretical model of Statistical Energy Analysis (SEA). This method of comparison with a theoretical model is useful in order to explain small systematic deviations between the results of both experimental methods. It happens that the difference between the so-called residual reactivity level and the measured reactivity gives a good estimate of the accuracy of the intensity measurement. Even in very simple cases this quantity can disturb the measuring results due to the complex sound field close to the sound radiating panel. By correcting the sound transmission loss results with the well known Waterhouse correction and by choosing an appropriate probe distance to the sound radiating construction, good agreement is obtained between the results of both experimental methods as well as with the SEA model.


title: Long-term Outcome after Endoscopic Treatment for Early Gastric Cancer in Korea

abstract: The early diagnosis of gastric cancer allows patients and physicians to pursue the option of endoscopic resection, which is significantly less invasive than conventional surgical resection. Newly developed endoscopic technique and devices have helped to overcome the limitations of therapeutic endoscopy in terms of lesion size, location, and presence of fibrotic scarring. As a result, the indications for endoscopic submucosal dissection (ESD) have been expanded from the classical indication for endoscopic mucosal resection (EMR), and there is now support for a further expansion of ESD indications. Long-term follow up data are needed for the clinical application of the expanded criteria of endoscopic treatment. In Korea, the use of endoscopic treatment has been increasing, and many reports on endoscopic treatment such as EMR or ESD have been published. This review was to elucidate the feasibility of the expanded guidelines, based on evidence from long-term outcome. (Korean J Helicobacter Up Gastrointest Res 2012;12:1-7)


title: Near/farfield measurements of a polarisation AGILE phased array at Ku-band

abstract: This article describes the near field measurements of a first prototype realised within the ESA-project NATALIA. This phased array antenna is designed for mobile satellite services and is a dedicated concept for the automotive sector. Key components of the overall buildup are a highly compact Printed Circuit Board (PCB) including antenna aperture, various circuits like RF-feeding network and digital logic, designed for mass production, and a dedicated MMIC-component that is the core element responsible for the beam steering of the array. In order to verify the complete buildup including RF-components, a 4×4 phased array has been realised. Nearfield and farfield measurements have been performed and show that the array performs according to specifications. Also, the nearfield measurement proofs to be a good diagnostic tool to analyse the proper functioning of the key RF-components.


Knot Removal for B-Spline Curves


title: Evaluation of two heuristic approaches to solve the ontology meta-matching problem

abstract: Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.


title: Modeling of Interconnected Critical Infrastructure Systems Using Complex Network Theory

abstract: This paper reviews several complex network theory-based methodologies to model and analyze interconnected networks. To address the deficiencies reported in past studies, an advanced model of coupled electric power system (EPS) and information and communication technology (ICT) is developed. The new model is designed as a 3-D graph with edges and nodes, and it highlights the interaction and interdependency of both EPS and ICT. The criticality of each component within the interconnected system is investigated by analyzing the efficiency-weighted node degree of each node symbolizing the components. The model has been tested on an ICT supported 14-bus distribution network under three different loading conditions. Results show that the coupled infrastructure complies with the typical characteristics of scale-free network. It is also shown the ICT system, although smaller in scale, contains more critical hubs than the power distribution network. The results of the proposed approach can be used as a reference to analyze the criticality in other coupled infrastructure by incorporating the internal characteristics of coupled systems.


title: Using Arduino to Develop a Bluetooth Electronic Scale for Water Intake

abstract: With the increasing popularity of the Internet of Things applications, investors are more willing to invest on small digital electronic products and seek business opportunities. At the same time, along with the improvement of the data processing method, people want to get more accurate data. This paper presents an application that using Arduino and Bluetooth electronic scale to connect to smart phones to obtain weight information of daily water intake. Bluetooth technology is low-power, it can ensure the reliability of data transmission, and the price is relatively cheap. These features are very suitable to our scale. We use Arduino board as our major application development tools. Arduino's hardware are based on open source platform and using like C or Java development environment to make the development process simple and easy to understand. And on the communication tool, mobile App, we are using Google App Inventor for development. App Inventor is a Lego-style stacking method to accomplish App. The system is combining with an algorithm and applied to water intake for users.


title: A Fast Liquid-Metal Droplet Microswitch Using EWOD-Driven Contact-Line Sliding

abstract: Liquid-metal (LM) droplet-based MEMS switches have mostly been restricted to slow applications until now due to the following reasons: (1) a relatively large switching gap (distance) needed to accommodate imprecise volumes and locations of droplets on the device and (2) lack of high-speed actuation to move the droplets quickly across the switching gap. To combat these problems, we explore switching by sliding the solid-LM-gas triple contact line rather than the entire droplet. This new approach allows us to use a microframe, which not only consistently positions the LM droplet but also makes the switching gap less sensitive to the errors in the deposited-droplet volume, allowing us to design microswitches with very small switching gaps (e.g., 10 mum for 600 mum-diameter droplets). Furthermore, a study of electrowetting-on-dielectric identifies a regime of fast contact-line sliding at the onset of droplet spreading. By moving the contact line fast across a small switching distance, we demonstrate a low-latency LM switch with 60 mus switch-on latency ( ~ 20 times better than other LM-switch technologies) and better than 5 mus signal rise/fall time, while boasting no contact bounce, as expected from an LM switch. High power-handling capability and long-term reliability are also discussed.


title: A conservative approach is feasible in unexplained pain after knee replacement: A SELECTED COHORT STUDY

abstract: Pain is the main indication for performing total knee replacement (TKR). In most patients after TKR there is an improvement, but a few continue to have pain. Generally, the cause of the pain can be addressed when it is identified. However, unexplained pain can be more difficult to manage because revision surgery is likely to be unrewarding in this group. In our study of 622 cemented TKRs in 512 patients with a mean age of 69 years (23 to 90) treated between January 1995 and August 1998, we identified 24 patients (knees) with unexplained pain at six months. This group was followed for five years (data was available for 18 knees in the study) [corrected] and ten patients (55.5%) went on to show an improvement without intervention. In the case of unexplained pain, management decisions must be carefully considered, but reassurance can be offered to patients that the pain will improve in more than half with time.


title: A segmentation-free approach for skeletonization of gray-scale images via anisotropic vector diffusion

abstract: In this paper we describe a method for skeletonization of gray-scale images without segmentation. Our method is based on anisotropic vector diffusion. The skeleton strength map, calculated from the diffused vector field, provides us a measure of how possible each pixel could be on the skeletons. The final skeletons are traced from the skeleton strength map, which mimics the behavior of edge detection from the edge strength map of the original image. A couple of real or synthesized images will be shown to demonstrate the performance of our algorithm.


title: The Airport Gate Assignment Problem: A Survey

abstract: The airport gate assignment problem (AGAP) is one of the most important problems operations managers face daily. Many researches have been done to solve this problem and tackle its complexity. The objective of the task is assigning each flight (aircraft) to an available gate while maximizing both conveniences to passengers and the operational efficiency of airport. This objective requires a solution that provides the ability to change and update the gate assignment data on a real time basis. In this paper, we survey the state of the art of these problems and the various methods to obtain the solution. Our survey covers both theoretical and real AGAP with the description of mathematical formulations and resolution methods such as exact algorithms, heuristic algorithms, and metaheuristic algorithms. We also provide a research trend that can inspire researchers about new problems in this area.


title: Searching for Sequential Plans Using Tabled Logic Programming

abstract: Logic programming provides a declarative framework for modeling and solving many combinatorial problems. Until recently, it was not competitive with state of the art planning techniques partly due to search capabilities limited to backtracking. Recent development brought more advanced search techniques to logic programming such as tabling that simplifies implementation and exploitation of more sophisticated search algorithms. Together with rich modeling capabilities this progress brings tabled logic programing on a par with current best planners. The paper brings an initial experimental study comparing various approaches to search for sequential plans in the Picat planning module.


title: Anatomic Reconstruction of the Proximal Tibiofibular Joint.

abstract: Proximal tibiofibular joint (PTFJ) instability can be easily missed or confused for other, more common lateral knee pathologies such as meniscal tears, fibular collateral ligament injury, biceps femoris pathology, or iliotibial band syndrome. Because of this confusion, some authors believe that PTFJ instability is more common than initially appreciated. Patients with PTFJ subluxation may have no history of inciting trauma or injury, and it is not uncommon for these patients to have bilateral symptoms and generalized ligamentous laxity. Currently, the optimal surgical treatment for patients with chronic PTFJ instability is unknown. Historically, a variety of surgical treatments have been reported. Initially, joint arthrodesis and fibular head resection were recommended. More recently, temporary screw fixation, nonanatomic reconstruction with strips of the biceps femoris tendon or iliotibial band, and reconstruction with free hamstring autograft have been described. The purpose of this report is to present our surgical technique for treatment of chronic PTFJ instability using an anatomic reconstruction of the posterior ligamentous structures of the PTFJ with a semitendinosus autograft.


title: Similarity and dissimilarity methods for processing chemical structure databases

abstract: This paper reviews measures of similarity and dissimilarity between pairs of chemical molecules and the use of such measures for processing chemical databases. The applications discussed include similarity searching, database clustering and diversity analysis, focusing upon measures that are based on fragment bit-string occurrence data. The paper then discusses recent work on the calculation of similarity by aligning molecular fields and on the selection of structurally diverse subsets of chemical databases.


title: Multiple emitter location and signal Parameter estimation

abstract: Processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions. The general problem considers sensors with arbitrary locations and arbitrary directional characteristics (gain/phase/polarization) in a noise/interference environment of arbitrary covariance matrix. This report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution. A description is given of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength. Examples and comparisons with methods based on maximum likelihood (ML) and maximum entropy (ME), as well as conventional beamforming are included. An example of its use as a multiple frequency estimator operating on time series is included.


title: VVC in the Smart Grid era

abstract: VVC facilities that maintain acceptable voltage at all points along the distribution feeder and maintain high power factor have been a fundamental requirement for all electric distribution feeders. Recent Smart Grid initiatives have created growing interest in the deployment of more advanced Volt-VAR systems for efficiency improvement, demand reduction, and better overall asset utilization. This paper identifies the ideal characteristics of the VVC system, describes several alternatives for accomplishing the VVC requirements and explains how well each approach provides the characteristics of the ideal system..


Evaluating information: A guide for users of social science research.


title: Software design issues: a very large information systems perspective

abstract: Research in software engineering is concerned with the enhancement and automation of the processes of building computer application systems. While there is a broad consensus on the problems associated with software development, a specific understanding of the software engineering problem and the appropriate solutions are inevitably driven by the target application domain. Much of the current software engineering research is driven by the development of large scale embedded software systems. Our understanding of the problems is based on a different domain: that of very large information systems (VLIS). In this paper, we identify some significant software engineering problems from the context of developing very large information systems.


title: Editing plant genomes with CRISPR/Cas9

abstract: CRISPR/Cas9 is a rapidly developing genome editing technology that has been successfully applied in many organisms, including model and crop plants. Cas9, an RNA-guided DNA endonuclease, can be targeted to specific genomic sequences by engineering a separately encoded guide RNA with which it forms a complex. As only a short RNA sequence must be synthesized to confer recognition of a new target, CRISPR/Cas9 is a relatively cheap and easy to implement technology that has proven to be extremely versatile. Remarkably, in some plant species, homozygous knockout mutants can be produced in a single generation. Together with other sequence-specific nucleases, CRISPR/Cas9 is a game-changing technology that is poised to revolutionise basic research and plant breeding.


title: O.: A study of mobile mood awareness and communication through mobimood

abstract: Recent research shows that there has been increased interest in investigating the role of mood and emotions in the HCI domain. Our moods, however, are complex. They are affected by many dynamic factors and can change multiple times throughout each day. Furthermore, our mood can have significant implications in terms of our experiences, our actions and most importantly on our interactions with other people. We have developed MobiMood, a proof-of-concept social mobile application that enables groups of friends to share their moods with each other. In this paper, we present the results of an exploratory field study of MobiMood, focusing on explicit mood sharing in-situ. Our results highlight that certain contextual factors had an effect on mood and the interpretation of moods. Furthermore, mood sharing and mood awareness appear to be good springboards for conversations and increased communication among users. These and other findings lead to a number of key implications in the design of mobile social awareness applications.


title: Human Motion Analysis with Deep Metric Learning

abstract: Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identi- fication and action retrieval. Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks. This work addresses this limitation by means of a triplet-based deep metric learning specifically tailored to deal with human motion data, in particular with the prob- lem of varying input size and computationally expensive hard negative mining due to motion pair alignment. Specifically, we propose (1) a novel metric learn- ing objective based on a triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep architecture based on attentive recurrent neural networks. One benefit of our objective function is that it enforces a better separation within the learned embedding space of the different motion categories by means of the associated distribution moments. At the same time, our attentive recurrent neural network allows processing varying input sizes to a fixed size of embedding while learning to focus on those motion parts that are semantically distinctive. Our ex- periments on two different datasets demonstrate significant improvements over conventional human motion metrics.


WordNet Embeddings


title: A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation

abstract: We propose a novel GAN-based framework for detecting shadows in images, in which a shadow detection network (D-Net) is trained together with a shadow attenuation network (A-Net) that generates adversarial training examples. The A-Net modifies the original training images constrained by a simplified physical shadow model and is focused on fooling the D-Net's shadow predictions. Hence, it is effectively augmenting the training data for D-Net with hard-to-predict cases. The D-Net is trained to predict shadows in both original images and generated images from the A-Net. Our experimental results show that the additional training data from A-Net significantly improves the shadow detection accuracy of D-Net. Our method outperforms the state-of-the-art methods on the most challenging shadow detection benchmark (SBU) and also obtains state-of-the-art results on a cross-dataset task, testing on UCF. Furthermore, the proposed method achieves accurate real-time shadow detection at 45 frames per second.


title: Vision-Based Calibration of Dual RCM-Based Robot Arms in Human-Robot Collaborative Minimally Invasive Surgery

abstract: This letter reports the development of a vision-based calibration method for dual remote center-of-motion (RCM) based robot arms in a human-robot collaborative minimally invasive surgery (MIS) scenario. The method does not require any external tracking sensors and directly uses images captured by the endoscopic camera and the robot encoder readings as calibration data, which leads to a minimal and practical system in the operating room. By taking advantage of the motion constraints imposed by the RCM-based kinematics of the robotic surgical tools and cameras, we can find unique relationships between the endoscope and the surgical tool using camera perspective projection geometry without the geometric information of the tool. A customized vision-based centerline detection algorithm is also proposed, which provides robust estimation of centerline positions for a variety of settings. We validate the method through simulations and an experimental study in a simulated MIS scenario, in which the first generation da Vinci surgical system controlled by the open source da Vinci research kit electronics and cisst/SAW software environment is used.


title: DeepFix: Fixing Common C Language Errors by Deep Learning.

abstract: The problem of automatically fixing programming errors is a very active research topic in software engineering. This is a challenging problem as fixing even a single error may require analysis of the entire program. In practice, a number of errors arise due to programmer's inexperience with the programming language or lack of attention to detail. We call these common programming errors. These are analogous to grammatical errors in natural languages. Compilers detect such errors, but their error messages are usually inaccurate. In this work, we present an end-to-end solution, called DeepFix, that can fix multiple such errors in a program without relying on any external tool to locate or fix them. At the heart of DeepFix is a multi-layered sequence-to-sequence neural network with attention which is trained to predict erroneous program locations along with the required correct statements. On a set of 6971 erroneous C programs written by students for 93 programming tasks, DeepFix could fix 1881 (27%) programs completely and 1338 (19%) programs partially.


Ubiquitous Information Systems – Understanding privacy concerns


title: Automatic Bridge Bidding Using Deep Reinforcement Learning

abstract: Bridge is among the zero-sum games for which artificial intelligence has not yet outperformed expert human players. The main difficulty lies in the bidding phase of bridge, which requires cooperative decision making under partial information. Existing artificial intelligence systems for bridge bidding rely on and are thus restricted by human-designed bidding systems or features. In this work, we propose a pioneering bridge bidding system without the aid of human domain knowledge. The system is based on a novel deep reinforcement learning model, which extracts sophisticated features and learns to bid automatically based on raw card data. The model includes an upper-confidence-bound algorithm and additional techniques to achieve a balance between exploration and exploitation. Our experiments validate the promising performance of our proposed model. In particular, the model advances from having no knowledge about bidding to achieving superior performance when compared with a champion-winning computer bridge program that implements a human-designed bidding system.


title: Semantic Parsing via Paraphrasing

abstract: A central challenge in semantic parsing is handling the myriad ways in which knowledge base predicates can be expressed. Traditionally, semantic parsers are trained primarily from text paired with knowledge base information. Our goal is to exploit the much larger amounts of raw text not tied to any knowledge base. In this paper, we turn semantic parsing on its head. Given an input utterance, we first use a simple method to deterministically generate a set of candidate logical forms with a canonical realization in natural language for each. Then, we use a paraphrase model to choose the realization that best paraphrases the input, and output the corresponding logical form. We present two simple paraphrase models, an association model and a vector space model, and train them jointly from question-answer pairs. Our system PARASEMPRE improves stateof-the-art accuracies on two recently released question-answering datasets.


title: A role-based delegation framework for healthcare information systems

abstract: As organizations implement information strategies that call for sharing access to resources in the networked environment, mechanisms must be provided to protect the resources from adversaries. The proposed delegation framework addresses the issue of how to advocate selective information sharing in role-based systems while minimizing the risks of unauthorized access. We introduce a systematic approach to specify delegation and revocation policies using a set of rules. We demonstrate the feasibility of our framework through policy specification, enforcement, and a proof-of-concept implementation on specific domains, e.g. the healthcare environment. We believe that our work can be applied to organizations that rely heavily on collaborative tasks.


title: Pedestrian Color Naming via Convolutional Neural Network

abstract: Color serves as an important cue for many computer vision tasks. Nevertheless, obtaining accurate color description from images is non-trivial due to varying illumination conditions, view angles, and surface reflectance. This is especially true for the challenging problem of pedestrian description in public spaces. We made two contributions in this study: (1) We contribute a large-scale pedestrian color naming dataset with 14,213 hand-labeled images. (2) We address the problem of assigning consistent color name to regions of single object’s surface. We propose an end-to-end, pixel-to-pixel convolutional neural network (CNN) for pedestrian color naming. We demonstrate that our Pedestrian Color Naming CNN (PCN-CNN) is superior over existing approaches in providing consistent color names on real-world pedestrian images. In addition, we show the effectiveness of color descriptor extracted from PCN-CNN in complementing existing descriptors for the task of person re-identification. Moreover, we discuss a novel application to retrieve outfit matching and fashion (which could be difficult to be described by keywords) with just a user-provided color sketch.


title: New inspirations in swarm intelligence: a survey

abstract: The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics. We describe the biological behaviours from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such meta-heuristics are reported.


New task allocation methods for robotic swarms


title: Egress of varicella-zoster virus from the melanoma cell: a tropism for the melanocyte.

abstract: The pathway of envelopment and egress of the varicella-zoster virus (VZV) and the primary site of viral production within the epidermal layer of the skin are not fully understood. There are several hypotheses to explain how the virus may receive an envelope as it travels to the surface of the monolayer. In this study, we expand earlier reports and provide a more detailed explanation of the growth of VZV in human melanoma cells. Human melanoma cells were selected because they are a malignant derivative of the melanocyte, the melanin-producing cell which originates in the neural crest. We were able to observe the cytopathic effects of syncytial formation and the pattern of egress of virions at the surfaces of infected monolayers by scanning electron microscopy and laser-scanning confocal microscopy. The egressed virions did not appear uniformly over the syncytial surface, rather they were present in elongated patterns which were designated viral highways. In order to document the pathway by which VZV travels from the host cell nucleus to the outer cell membrane, melanoma cells were infected and then processed for examination by transmission electron microscopy (TEM) at increasing intervals postinfection. At the early time points, within minutes to hours postinfection, it was not possible to localize the input virus by TEM. Thus, viral particles first observed at 24 h postinfection were considered progeny virus. On the basis of the TEM observations, the following sequence of events was considered most likely. Nucleocapsids passed through the inner nuclear membrane and acquired an envelope, after which they were seen in the endoplasmic reticulum. Enveloped virions within vacuoles derived from the endoplasmic reticulum passed into the cytoplasm. Thereafter, vacuoles containing nascent enveloped particles acquired viral glycoproteins by fusion with vesicles derived from the Golgi. The vacuoles containing virions fused with the outer plasma membrane and the particles appeared on the surface of the infected cell. Late in infection, enveloped virions were also present within the nuclei of infected cells; the most likely mechanism was retrograde flow from the perinuclear space back into the nucleus. Thus, this study suggests a role for the melanocyte in the pathogenesis of VZV infection, because all steps in viral egress can be accounted for if VZV subsumes the cellular pathways required for melanogenesis.


title: Language Learning in Educational Virtual Worlds - a TAM Based Assessment

abstract: We are currently experiencing a boom in the presence of Spanish language in the world, which is reflected in its inclusion as a second language in the educational system of countries like Brazil and the emergence of language in the U.S. and China. To confront this situation there is a wide variety of courses for learning Spanish. However, specific initiatives combining proven teaching methods in university classroom experiences with the creation of new multidisciplinary content displayed on 3D virtual sets have not been detected. This study proposes the establishment of a learner's role-play to improve learner's skills. Foreign languages' learning is the focus of the report because can serve as an appropriate context to analyze self-directed learning strategies and the culture of Lifelong Learning. The goal of this research is the creation of an integrated Massively Multiuser Online Learning (MMOL) platform that enables the creation, development and deployment of contents and activities for teaching Spanish in an ad hoc educational virtual world named SLRoute. Such environment promotes an immersive, creative and collaborative experience in the process of learning a foreign language. In order to assess the validity and reliability of this technology we used the Technology Acceptance Model (TAM). The ultimate intention is to measure the acceptability of MMOL platforms for educational issues.


title: Empowering developers to estimate app energy consumption

abstract: Battery life is a critical performance and user experience metric on mobile devices. However, it is difficult for app developers to measure the energy used by their apps, and to explore how energy use might change with conditions that vary outside of the developer's control such as network congestion, choice of mobile operator, and user settings for screen brightness. We present an energy emulation tool that allows developers to estimate the energy use for their mobile apps on their development workstation itself. The proposed techniques scale the emulated resources including the processing speed and network characteristics to match the app behavior to that on a real mobile device. We also enable exploring multiple operating conditions that the developers cannot easily reproduce in their lab. The estimation of energy relies on power models for various components, and we also add new power models for components not modeled in prior works such as AMOLED displays. We also present a prototype implementation of this tool and evaluate it through comparisons with real device energy measurements.


title: Memory's echo: Vivid remembering reactivates sensory-specific cortex

abstract: A fundamental question in human memory is how the brain represents sensory-specific information during the process of retrieval. One hypothesis is that regions of sensory cortex are reactivated during retrieval of sensory-specific information (1). Here we report findings from a study in which subjects learned a set of picture and sound items and were then given a recall test during which they vividly remembered the items while imaged by using event-related functional MRI. Regions of visual and auditory cortex were activated differentially during retrieval of pictures and sounds, respectively. Furthermore, the regions activated during the recall test comprised a subset of those activated during a separate perception task in which subjects actually viewed pictures and heard sounds. Regions activated during the recall test were found to be represented more in late than in early visual and auditory cortex. Therefore, results indicate that retrieval of vivid visual and auditory information can be associated with a reactivation of some of the same sensory regions that were activated during perception of those items.


title: Explicit Analytic Solution for Inverse Kinematics of Bioloid Humanoid Robot

abstract: The aim of this paper is to explain basic kinematic concepts applied to humanoid robots. The structure of a Bioloid with three degrees of freedom (DoFs) for the arms and six DoFs for the legs is considered. The solution of direct and inverse kinematics (IK) is presented for both arms and legs. Our main contribution is to solve in a detailed manner the legs IK because this is the basis for planning walking motions.


title: SDN-based 5G mobile networks: architecture, functions, procedures and backward compatibility

abstract: In this paper, we describe an SDN-based plastic architecture for 5G networks, designed to fulfill functional and performance requirements of new generation services and devices. The 5G logical architecture is presented in detail, and key procedures for dynamic control plane instantiation, device attachment, and service request and mobility management are specified. Key feature of the proposed architecture is flexibility, needed to support efficiently a heterogeneous set of services, including Machine Type Communication, Vehicle to X and Internet of Things traffic. These applications are imposing challenging targets, in terms of end-to-end latency, dependability, reliability and scalability. Additionally, backward compatibility with legacy systems is guaranteed by the proposed solution, and Control Plane and Data Plane are fully decoupled. The three levels of unified signaling unify Access, Non-access and Management strata, and a clean-slate forwarding layer, designed according to the software defined networking principle, replaces tunneling protocols for carrier grade mobility. Copyright © 2014 John Wiley & Sons, Ltd.


title: Statistical binary patterns and post-competitive representation for pattern recognition

abstract: During the last decade, sparse representations have been successfully applied to design high-performing classification algorithms such as the classical sparse representation based classification (SRC) algorithm. More recently, collaborative representation based classification (CRC) has emerged as a very powerful approach, especially for face recognition. CRC takes advantage of SRC through the notion of collaborative representation, relying on the observation that the collaborative property is more crucial for classification than the l1-norm sparsity constraint on coding coefficients used in SRC. This paper follows the same general philosophy of CRC and its main novelty is the application of a virtual collaborative projection (VCP) routine designed to train images of every class against the other classes to improve fidelity before the projection of the query image. We combine this routine with a method of local feature extraction based on high-order statistical moments to further improve the representation. We demonstrate using extensive experiments of face recognition and classification that our approach performs very competitively with respect to state-of-the-art classification methods. For instance, using the AR face dataset, our method reaches 100% of accuracy for dimensionality 300.


title: A New Concept Varistor With Epoxy/Microvaristor Composite

abstract: A new concept composite varistor is proposed in this paper. This composite varistor has the chains of microvaristors, which are formed by applying an electric field during the curing process and work as current paths. This composite varistor shows superior nonlinear voltage-current characteristics despite small microvaristor content and has a good response against a steep voltage surge of several tens of nanoseconds in front time. In comparison with ceramic varistors where shapes are usually formed at their sintering temperatures of around 1000°C, the composite varistor has an advantage in flexible shape because its shape is formed by curing the base polymer at a low temperature.


title: QTID: Quran Text Image Dataset

abstract: Improving the accuracy of Arabic text recognition ::: in imagery requires a big modern dataset as data is the fuel for ::: many modern machine learning models. This paper proposes a ::: new dataset, called QTID, for Quran Text Image Dataset, the ::: first Arabic dataset that includes Arabic marks. It consists of ::: 309,720 different 192x64 annotated Arabic word images that ::: contain 2,494,428 characters in total, which were taken from ::: the Holy Quran. These finely annotated images were randomly ::: divided into 90%, 5%, 5% sets for training, validation, and ::: testing, respectively. In order to analyze QTID, a different ::: dataset statistics were shown. Experimental evaluation shows that ::: current best Arabic text recognition engines like Tesseract and ::: ABBYY FineReader cannot work well with word images from ::: the proposed dataset.


title: Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor

abstract: Presents a study of the noise suppression technique proposed by Ephraim and Malah (1984,1985). This technique has been used previously for the restoration of degraded audio recordings because it is free of the frequently encountered 'musical noise' artifact. It is demonstrated how this artifact is actually eliminated without bringing distortion to the recorded signal even if the noise is only poorly stationary. >


title: Modeling and simulation of the power transformer faults and related protective relay behavior

abstract: The modeling of power transformer faults and its application to performance evaluation of a commercial digital power transformer relay are the objectives of this study. A new method to build an EMTP/ATP power transformer model is proposed in this paper. Detailed modeling of the transformer relay is also discussed. The transient waveforms generated by ATP under different operating conditions are utilized to evaluate the performance of the transformer relay. The computer simulation results presented in this paper are consistent with the laboratory test results obtained using an analog power system model.


title: Establishing darknet connections: an evaluation of usability and security

abstract: In many applications, hosts in a peer to peer network may wish to maintain their anonymity or the privacy of their queries. In some applications, an even stronger guarantee is desirable: hosts would like to prevent others from determining whether they participate in the network at all. Darknets, or friend-to-friend networks, are one approach to preventing the discovery of hosts within a peer to peer network [1]. In such a network, hosts only form Internet connections with and directly communicate with a small set of hosts whose operators are known and trusted a priori. That is, each user only connects to her friends, trusting that her friends will not reveal her identity or existence in the network.


New Antennas Based on Triangular Patch as a Solution for RFID Application


title: Overcoming Limits of Blockchain for IoT Applications

abstract: Blockchain technology allows the implementation of a public ledger securely recording transactions among peers without the need of trusted third parties. For both researchers and industry IoT appears a domain in which there would be extraordinary benefits if the features of Blockchain can be exploited. Indeed, the possibility that IoT devices participate in public shared transactions enables a lot of challenging applications. However, there are some aspects that may limit the use of Blockchain in IoT. These are mainly related to the low computational power and storage capabilities of IoT devices. In this paper, we propose an alternative way to implement a public ledger overcoming the above drawbacks, thus appearing more suitable to IoT applications. The proposed protocol leverages the popular social network Twitter and works by building a meshed chain of tweets to ensure transaction security. Importantly, Twitter does not play neither the role of trusted third party nor the role of ledger provider.


title: Fractal marker fields: No more scale limitations for fiduciary markers

abstract: One limitation of existing fiduciary markers is that the camera motion is tightly limited: the marker (one of the markers) must be visible and it must be observed at a proper scale. This paper introduces a fractal structure of markers similar to matrix codes (such as QR-code or DataMatrix): the FractalMarker Field. The FMF allows for embedding markers of a virtually unlimited number of scales. At the same time, for each of the scales it guarantees a constant density of markers at that scale over the whole marker field's surface. The FractalMarker Field can provide unprecedented freedom of motion to camera-based augmented reality applications.


title: A Design Methodology for Miniaturized Power Dividers Using Periodically Loaded Slow Wave Structure With Dual-Band Applications

abstract: This paper proposes an analytically-based approach for the design of a miniaturized single-band and dual-band two-way Wilkinson power divider. This miniaturization is achieved by realizing the power divider's impedance transformers using slow wave structures. These slow wave structures are designed by periodically loading transmission lines with capacitances, which reduces the phase velocity of the propagating waves and hence engender higher electric lengths using smaller physical lengths. The dispersive analysis of the slow wave structure used is included in the design approach to ensure a smooth nondispersive transmission line operation in the case of dual-band applications. The design methodology is validated with the design of a single-band, reduced size, two-way Wilkinson power divider at 850 and 620 MHz. An approximate length reduction of 25%-35% is achieved with this technique. For dual-band applications, this paper describes the design of a reduced size, two-way Wilkinson power divider for dual-band global system for mobile communications and code division multiple access applications at 850 and 1960 MHz, respectively. An overall reduction factor of 28%, in terms of chip area occupied by the circuit, is achieved. The electromagnetic simulation and experimental results validate the design approach. The circuit is realized with microstrip technology, which can be easily fabricated using conventional printed circuit processes.


title: A GaN MMIC chipset suitable for integration in future X-band spaceborne radar T/R module Frontends

abstract: A set of monolithic microwave integrated circuits (MMICs) has been successfully developed by using a qualified European GaN HEMT technology. In particular a high power amplifier (HPA), a low noise amplifier (LNA) and a single pole double throw (SPDT) switch have been designed, manufactured and tested. The presented chipset is very suitable for integration in future GaN-based T/R module Frontend for spaceborne X-band SAR applications. In particular, the MMIC HPA integrates two stages of gain in 5.5 × 4.0 mm2 of area. Its measured performance is an output power of 27 W, a PAE of 36% with a linear gain greater than 24 dB from 8.8 GHz to 10.2 GHz. The MMIC LNA integrates three stages of gain in 3.0 × 2.02 mm2 of area. Its measured performance is a small signal gain greater than 23 dB with an associated noise figure of 1.6 dB in the frequency range from 7.4 to 11.4 GHz. Besides, its output power at 1 dB of gain compression is greater than 22 dBm. The MMIC SPDT switch exploits a robust asymmetrical absorptive/reflective topology in 3.0 × 1.0 mm2 of area. The chosen topology allows obtaining different functionalities of each switched branch. In the frequency range from 8.4 GHz to 10.8 GHz its measured performance is an insertion loss lower than 1 dB for both tx and rx path, and tx-mode rx-mode isolations better than 20 dB and 28 dB respectively. Besides, the tx path 1 dB insertion loss compression occurs at nearly 20 W of input power.


title: Investigating app store ranking algorithms using a simulation of mobile app ecosystems

abstract: App stores are one of the most popular ways of providing content to mobile device users today. But with thousands of competing apps and thousands new each day, the problem of presenting the developers' apps to users becomes nontrivial. There may be an app for everything, but if the user cannot find the app they desire, then the app store has failed. This paper investigates app store content organisation using AppEco, an Artificial Life model of mobile app ecosystems. In AppEco, developer agents build and upload apps to the app store; user agents browse the store and download the apps. This paper uses AppEco to investigate how best to organise the Top Apps Chart and New Apps Chart in Apple's iOS App Store. We study the effects of different app ranking algorithms for the Top Apps Chart and the frequency of updates of the New Apps Chart on the download-to-browse ratio. Results show that the effectiveness of the shop front is highly dependent on the speed at which content is updated. A slowly updated New Apps Chart will impact the effectiveness of the Top Apps Chart. A Top Apps Chart that measures success by including too much historical data will also detrimentally affect app downloads.


title: Node splitting: A constructive algorithm for feed-forward neural networks

abstract: A constructive algorithm is proposed for feed-forward neural networks, which uses node-splitting in the hidden layers to build large networks from smaller ones. The small network forms an approximate model of a set of training data, and the split creates a larger more powerful network which is initialised with the approximate solution already found. The insufficiency of the smaller network in modelling the system which generated the data leads to oscillation in those hidden nodes whose weight vectors cover regions in the input space where more detail is required in the model. These nodes are identified and split in two using principal component analysis, allowing the new nodes to cover the two main modes of each oscillating vector. Nodes are selected for splitting using principal component analysis on the oscillating weight vectors, or by examining the Hessian matrix of second derivatives of the network error with respect to the weights. The second derivative method can also be applied to the input layer, where it provides a useful indication of the relative importances of parameters for the classification task. Node splitting in a standard Multi Layer Perceptron is equivalent to introducing a hinge in the decision boundary to allow more detail to be learned. Initial results were promising, but further evaluation indicates that the long range effects of decision boundaries cause the new nodes to slip back to the old node position, and nothing is gained. This problem does not occur in networks of localised receptive fields such as radial basis functions or gaussian mixtures, where the technique appears to work well.


title: PREA: Personalized recommendation algorithms toolkit

abstract: Recommendation systems are important business applications with significant economic impact. In recent years, a large number of algorithms have been proposed for recommendation systems. In this paper, we describe an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics. In contrast to other packages, our toolkit implements recent state-of-the-art algorithms as well as most classic algorithms.


title: Addressing big data issues in Scientific Data Infrastructure

abstract: Big Data are becoming a new technology focus both in science and in industry. This paper discusses the challenges that are imposed by Big Data on the modern and future Scientific Data Infrastructure (SDI). The paper discusses a nature and definition of Big Data that include such features as Volume, Velocity, Variety, Value and Veracity. The paper refers to different scientific communities to define requirements on data management, access control and security. The paper introduces the Scientific Data Lifecycle Management (SDLM) model that includes all the major stages and reflects specifics in data management in modern e-Science. The paper proposes the SDI generic architecture model that provides a basis for building interoperable data or project centric SDI using modern technologies and best practices. The paper explains how the proposed models SDLM and SDI can be naturally implemented using modern cloud based infrastructure services provisioning model and suggests the major infrastructure components for Big Data.


title: American Orthodontics Maurice Berman Prize 2005

abstract: This paper describes the orthodontic treatment of two cases, which were awarded the 2005 Maurice Berman Prize.


title: Automatic graphic logo detection via Fast Region-based Convolutional Networks

abstract: Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing. In this paper we present an automatic graphic logo detection system that robustly handles unconstrained imaging conditions. Our approach is based on Fast Region-based Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown state-of-the-art performance in several generic object recognition tasks (PASCAL Visual Object Classes challenges). In particular, we use two CNN models pre-trained with the ILSVRC ImageNet dataset and we look at the selective search of windows `proposals' in the pre-processing stage and data augmentation to enhance the logo recognition rate. The novelty lies in the use of transfer learning to leverage powerful Convolutional Neural Network models trained with large-scale datasets and repurpose them in the context of graphic logo detection. Another benefit of this framework is that it allows for multiple detections of graphic logos using regions that are likely to have an object. Experimental results with the FlickrLogos-32 dataset show not only the promising performance of our developed models with respect to noise and other transformations a graphic logo can be subject to, but also its superiority over state-of-the-art systems with hand-crafted models and features.


title: FMCW radar for life-sign detection

abstract: In this paper we investigate the use of Frequency Modulated Continuous Wave (FMCW) radars for detecting life-sign of people, i.e. breathing and heartbeat. An optimum frequency has been selected to observe life-sign, taking into consideration also design factors, such as bandwidth availability and interference with other systems. A new compact X-band FMCW radar has been built at TNO laboratories and experimental results are presented here, which demonstrate the ability of this new system to detect life-sign.


title: Learning Multiscale Deep Features for High-Resolution Satellite Image Scene Classification

abstract: In this paper, we propose a multiscale deep feature learning method for high-resolution satellite image scene classification. Specifically, we first warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multiscale satellite images are fed into their corresponding SPP-nets, respectively, to extract multiscale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult data sets show that the proposed method achieves favorable performance compared with other state-of-the-art methods.


Laser Vaginal Tightening ( LVT ) – evaluation of a novel noninvasive laser treatment for vaginal relaxation syndrome


title: Distributional semantics in linguistic and cognitive research

abstract: The hypothesis that word co-occurrence statistics extracted from text corpora can provide a basis for semantic representations has been gaining growing attention both in computational linguistics and in cognitive science. The terms distributional, context-theoretic, corpusbased or statistical can all be used (almost interchangeably) to qualify a rich family of approaches to semantics that share a “usage-based” perspective on meaning, and assume that the statistical distribution of words in context plays a key role in characterizing their semantic behavior. Besides this common core, many differences exist depending on the specific mathematical and computational techniques, the type of semantic properties associated with text distributions, the definition of the linguistic context used to determine the combinatorial spaces of lexical items, etc. Yet, at a closer look, we may discover that the commonalities are more than we could expect prima facie, and that a general model of meaning can indeed be discerned behind the differences, a model that formulates specific hypotheses on the format of semantic representations, and on the way they are built and processed by the human mind. Methods for computational analysis of word distributional properties have been developed both in computational linguistics and in psychology. Because of the different aims of each field, these lines of research have typically proceeded totally in a parallel fashion, often ignoring each other. The drawbacks of this situation are clear: many