title: Web Spam Taxonomy

abstract: Web spamming refers to actions intended to mislead search engines and give some pages higher ranking than they deserve. Recently, the amount of web spam has increased dramatically, leading to a degradation of search results. This paper presents a comprehensive taxonomy of current spamming techniques, which we believe can help in developing appropriate countermeasures.


title: Video parsing for abnormality detection

abstract: Detecting abnormalities in video is a challenging problem since the class of all irregular objects and behaviors is infinite and thus no (or by far not enough) abnormal training samples are available. Consequently, a standard setting is to find abnormalities without actually knowing what they are because we have not been shown abnormal examples during training. However, although the training data does not define what an abnormality looks like, the main paradigm in this field is to directly search for individual abnormal local patches or image regions independent of another. To address this problem we parse video frames by establishing a set of hypotheses that jointly explain all the foreground while, at same time, trying to find normal training samples that explain the hypotheses. Consequently, we can avoid a direct detection of abnormalities. They are discovered indirectly as those hypotheses which are needed for covering the foreground without finding an explanation by normal samples for themselves. We present a probabilistic model that localizes abnormalities using statistical inference. On the challenging dataset of [15] it outperforms the state-of-the-art by 7% to achieve a frame-based abnormality classification performance of 91% and the localization performance improves by 32% to 76%.


title: Critical path analysis for the execution of parallel and distributed programs

abstract: The authors present the design, implementation, and testing of the critical path analysis technique using the IPS performance measurement tool for parallel and distributed programs. They create a precedence graph of a program's activities (program activity graph) with the data collected during the execution of a program. The critical path, the longest path in the program activity graph, represents the sequence of the program activities that take the longest time to execute. Various algorithms are developed to track the critical path from this graph. The events in this path are associated with the entities in the source program, and the statistical results are displayed on the basis of the hierarchical structure of the IPS. The test results from the measurement of sample programs show that the knowledge of the critical path in a program's execution helps users identify performance problems and better understand the behavior of a program. >


title: Improvement suggestions for DSRM model

abstract: This study addresses the process of Design Science Research Methodology (DSRM). The study’s proposal is that the DSRM process can be revised by relevant elements, such as user experience, co-creativity, and trust building. The research revealed that user-centered aspects and user-computer interactions are important for the success of the design and realization of information systems, and thus are necessary to take into account in the DSRM process model. The main findings of the study are that it is imperative to integrate the users’ experience and design research, trust building, and co-creativity aspects into the DSRM process as early as possible. It is also vital to train users to use the system so that the system’s design-development-dissemination continuums can be as effective as possible. The outcome of this study addresses the DSRM process and its modularity, usefulness, and feasibility in the field of information systems design theory.


Biosynthesis of acaterin: coupling of C(5) unit with octanoate.


title: Multiple resources and performance prediction

abstract: This paper describes the origins and history of multiple resource theory in accounting for differences in dual task interference. One particular application of the theory, the 4-dimensional multiple resources model, is described in detail, positing that there will be greater interference between two tasks to the extent that they share stages (perceptual/cognitive vs response) sensory modalities (auditory vs visual), codes (visual vs spatial) and channels of visual information (focal vs ambient). A computational rendering of this model is then presented. Examples are given of how the model predicts interference differences in operational environments. Finally, three challenges to the model are outlined regarding task demand coding, task allocation and visual resource competition.


title: Geographical patterns of malaria transmission based on serological markers for falciparum and vivax malaria in Ratanakiri, Cambodia

abstract: BACKGROUND ::: Malaria transmission is highly heterogeneous, especially in low endemic countries, such as Cambodia. This results in geographical clusters of residual transmission in the dry, low transmission season, which can fuel the transmission to wider areas or populations during the wet season. A better understanding of spatial clustering of malaria can lead to a more efficient, targeted strategy to reduce malaria transmission. This study aims to evaluate the potential of the use of serological markers to define spatial patterns in malaria exposure. ::: ::: ::: METHODS ::: Blood samples collected in a community-based randomized trial performed in 98 high endemic communities in Ratanakiri province, north-eastern Cambodia, were screened with a multiplex serological assay for five serological markers (three Plasmodium falciparum and two Plasmodium vivax). The antibody half-lives range from approximately six months until more than two years. Geographical heterogeneity in malaria transmission was examined using a spatial scan statistic on serology, PCR prevalence and malaria incidence rate data. Furthermore, to identify behavioural patterns or intrinsic factors associated with malaria exposure (antibody levels), risk factor analyses were performed by using multivariable random effect logistic regression models. The serological outcomes were then compared to PCR prevalence and malaria incidence data. ::: ::: ::: RESULTS ::: A total of 6502 samples from two surveys were screened in an area where the average parasite prevalence estimated by PCR among the selected villages is 3.4 %. High-risk malaria pockets were observed adjacent to the 'Tonle San River' and neighbouring Vietnam for all three sets of data (serology, PCR prevalence and malaria incidence rates). The main risk factors for all P. falciparum antigens and P. vivax MSP1.19 are age, ethnicity and staying overnight at the plot hut. ::: ::: ::: CONCLUSION ::: It is possible to identify similar malaria pockets of higher malaria transmission together with the potential risk factors by using serology instead of PCR prevalence or malaria incidence data. In north-eastern Cambodia, the serological markers show that malaria transmission occurs mainly in adults staying overnight in plot huts in the field. Pf.GLURP.R2 showed a shrinking pocket of malaria transmission over time, and Pf.MSP1.19, CSP, PvAMA1 were also informative for current infection to a lesser extent. Therefore, serology could contribute in future research. However, further in-depth research in selecting the best combination of antigens is required.


title: Data Mining in Education- An Experimental Study

abstract: Data mining first proved to be beneficial to business related fields such as marketing and consumer related service enhancements. Slowly it has made its way toward other fields such as medicine, science, engineering, and education. The focus of this paper is to review several applications of data mining in education and their benefits, present some classification techniques, test some sample data, and then evaluate them against some selected criteria. General Terms Data mining


title: Building trust in m‐commerce: contributions from quality and satisfaction

abstract: Purpose – Mobile commerce (m‐commerce) represents a new area of business opportunity. Past research has often focused on customer acceptance and its antecedents, rather than factors that build trust in m‐commerce. The purpose of this paper is to provide an explanation of factors influencing customer trust towards vendors on the mobile internet.Design/methodology/approach – M‐commerce relies on mobile technology and well‐maintained service quality. This paper has applied the service quality model (SERVQUAL) and technology acceptance model (TAM), coupled with proposed quality factors in relation to m‐commerce that, according to the literature, influence customer trust, to empirically test the formation of trust. The proposed model was empirically evaluated using online survey data from 212 experienced m‐commerce customers.Findings – The results showed that despite customisation, brand image and satisfaction all directly affecting customer trust towards the vendor in m‐commerce, customisation and brand image...


title: Algorithmic improvements for fast concurrent Cuckoo hashing

abstract: Fast concurrent hash tables are an increasingly important building block as we scale systems to greater numbers of cores and threads. This paper presents the design, implementation, and evaluation of a high-throughput and memory-efficient concurrent hash table that supports multiple readers and writers. The design arises from careful attention to systems-level optimizations such as minimizing critical section length and reducing interprocessor coherence traffic through algorithm re-engineering. As part of the architectural basis for this engineering, we include a discussion of our experience and results adopting Intel's recent hardware transactional memory (HTM) support to this critical building block. We find that naively allowing concurrent access using a coarse-grained lock on existing data structures reduces overall performance with more threads. While HTM mitigates this slowdown somewhat, it does not eliminate it. Algorithmic optimizations that benefit both HTM and designs for fine-grained locking are needed to achieve high performance. Our performance results demonstrate that our new hash table design---based around optimistic cuckoo hashing---outperforms other optimized concurrent hash tables by up to 2.5x for write-heavy workloads, even while using substantially less memory for small key-value items. On a 16-core machine, our hash table executes almost 40 million insert and more than 70 million lookup operations per second.


title: News contextualization with geographic and visual information

abstract: In this paper, we investigate the contextualization of news documents with geographic and visual information. We propose a matrix factorization approach to analyze the location relevance for each news document. We also propose a method to enrich the document with a set of web images. For location relevance analysis, we first perform toponym extraction and expansion to obtain a toponym list from news documents. We then propose a matrix factorization method to estimate the location-document relevance scores while simultaneously capturing the correlation of locations and documents. For image enrichment, we propose a method to generate multiple queries from each news document for image search and then employ an intelligent fusion approach to collect a set of images from the search results. Based on the location relevance analysis and image enrichment, we introduce a news browsing system named NewsMap which can support users in reading news via browsing a map and retrieving news with location queries. The news documents with the corresponding enriched images are presented to help users quickly get information. Extensive experiments demonstrate the effectiveness of our approaches.


Intra-platform Envelopment: The Coopetitive Dynamics between the Platform Owner and Platform Complementors ABSTRACT Platform owners can exert a substantial level of influence on their complementors, and this influence can have negative consequences for the firms and entrepreneurs offering complement


title: REVIEW ARTICLE Mechanisms of Mindfulness

abstract: Recently, the psychological construct mindfulness has received a great deal of attention. The majority of research has focused on clinical studies to evaluate the efficacy of mindfulness-based interventions. This line of research has led to promising data suggesting mindfulness-based interventions are effective for treatment of both psychological and physical symptoms. However, an equally important direction for future research is to investigate questions concerning mechanisms of action underlying mindfulness-based interventions. This theoretical paper proposes a model of mindfulness, in an effort to elucidate potential mechanisms to explain how mindfulness affects positive change. Potential implications and future directions for the empirical study of mechanisms involved in mindfulness are addressed. © 2005 Wiley Periodicals, Inc. J Clin Psychol 62: 373–386, 2006.


title: Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage

abstract: While applications for mobile devices have become extremely important in the last few years, little public information exists on mobile application usage behavior. We describe a large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices. We present two types of results from analyzing this data: basic descriptive statistics and contextual descriptive statistics. In the case of the former, we find that the average session with an application lasts less than a minute, even though users spend almost an hour a day using their phones. Our contextual findings include those related to time of day and location. For instance, we show that news applications are most popular in the morning and games are at night, but communication applications dominate through most of the day. We also find that despite the variety of apps available, communication applications are almost always the first used upon a device's waking from sleep. In addition, we discuss the notion of a virtual application sensor, which we used to collect the data.


Lung biopsy in patients with usual interstitial pneumonia


title: Linear AC LED driver with the multi-level structure and variable current regulator

abstract: This paper proposes a linear AC LED driver for LED lighting applications. The proposed circuit is small in size because the circuit structure consists of only semiconductors and resistors without any reactors and electrolytic capacitors. The current bypass circuit which is connected in parallel to the LED string consists of single MOSFET, single zener diode and two resistors. The MOSFET is operated in an active state by a self-bias circuit. Thus, an external controller and high voltage gate drivers are not required. The proposed circuit is experimentally validated by using a 7 W prototype. From the experimental results, the THD of input current is 2.1% and the power factor is 0.999. In addition, the simulation loss analysis demonstrates an efficiency of 87% for a 7 W prototype.


title: Face Demorphing

abstract: The morphing attack proved to be a serious threat for modern automated border control systems where face recognition is used to link the identity of a passenger to his/her e-document. In this paper, we show that by exploiting the live face image acquired at the gate, the morphed face image stored in the document can be reverted (or demorphed) enough to reveal the identity of the legitimate document owner, thus allowing the system to issue a warning. A number of practical experiments on two data sets proves the efficacy of our approach.


title: Pumping of dewatered sludge: Slipping or flowing behavior?

abstract: Abstract The current design of dewatered sludge pumping devices is based on material flowing properties assuming it is a non-Newtonian highly viscous fluid. From rheological analysis, we first clearly established that current rheological models are no longer valid for this purpose. By using parallel-plates geometry, it was shown that the apparent behavior is dependent on the gap between the plates: results are less representative of sludge intrinsic properties that of the interface interactions between sludge and rotating surfaces. By reproducing dewatered sludge pipe flow at lab-scale, it was highlighted that sludge does not flow but slips along the wall of the pipe. The existence of a thin lubrication layer is suspected and the behavior law to be considered is a slippery law, similar to a Norton–Hoff model.


title: CS circles: an in-browser python course for beginners

abstract: Computer Science Circles is a free programming website for beginners that is designed to be fun, easy to use, and accessible to the broadest possible audience. We teach Python since it is simple yet powerful, and the course content is well-structured but written in plain language. The website has over one hundred exercises in thirty lesson pages, plus special features to help teachers support their students. It is available in both English and French. We discuss the philosophy behind the course and its design, we describe how it was implemented, and we give statistics on its use.


title: Blockchain Voting and its effects on Election Transparency and Voter Confidence

abstract: Public elections are one of the basis upon which representative democracy is built. Thus, it is of the utmost importance that governments and organizations are able to successfully hold non-fraudulent representative elections. Several methods are employed in order to allow citizens to cast their votes, such as Ballot-based voting, purely electronic methods, and Electronic Voting Machines, among others. However, we argue that current methods, specially those based on electronic platforms, provide unsatisfactory levels of transparency to voters, thus harming the trust voters have that the vote they cast was the same one counted by election officials, a problem known as voter confidence. Instead of stepping back to traditional and inefficient offline strategies, we suggest the modernization of State structures by the use of emerging technologies. In this research, we explore the possibility of using Blockchain technology to help in solving those transparency and confidence problems. First, we give an overview of Blockchain itself and other uses focused on societal problems and their respective analysis. We then analyze how the adoption of Blockchain into a digital government repertoire can contribute to common e-voting issues and also promote elections transparency, increase auditability, enhance voter confidence and strengthen democracy. By attending to this poster presentation, visitors will have a clear understanding of what is Blockchain and its basic concepts, why are market and researchers so excited about it, how it can help to solve common voting systems issues, who is already using it and the benefits and potential risks of its adoption.


title: QRS width in right bundle branch block. Accuracy and reproducibility of manual measurement.

abstract: BACKGROUND ::: The QRS prolongation and its relation to malignant ventricular arrhythmias are topics of interest. Controversies exist about the methodology of measuring the QRS. The aim of this study was to assess the accuracy and reproducibility of manual measurement of the QRS in standard electrocardiograms in patients with right bundle branch block and compare results with computer reading. ::: ::: ::: METHODS AND RESULTS ::: Five experienced cardiologists at different levels of training were required to measure QRS duration in 30 electrocardiograms with different degrees of right bundle branch block collected from 24 randomly selected patients who had had radical repairs of tetralogy of Fallot. In each set of electrocardiograms there were six records which had been duplicated. The observers were neither told the purpose of the study nor how the electrocardiograms had been obtained, nor informed that some of the electrocardiograms were duplicates. Photocopies were identified by number, covering the patient's name and computerised measurement. Significant differences were found in the measurement of QRS in the same ECG calculated twice by the same observer (with an absolute variation up to 50 ms), within different observers (P=0.037) and measured manually or by computer (P=0.019). The width of the QRS did not influence the measurements as the biggest intra-observer variation (50 ms) was observed for relatively wide complex (median value between the two measurements 155 ms) and the biggest inter-observer (60 ms) for narrow complex (median value between the five measurements 110 ms). The QRS morphology appeared to influence the measurements, as the intra- and inter-observer variations were more consistent in the presence of obvious notching, slurrings and terminal slow vectors. ::: ::: ::: CONCLUSIONS ::: Measurement of QRS is difficult, can be operator dependent and influenced by the presence of conduction abnormalities which reduce its accuracy and reproducibility.


title: IBM-ACS: reminiscences and lessons learned from a 1960's supercomputer project

abstract: This paper contains reminiscences of my work as a young engineer at IBM-Advanced Computing Systems. I met my colleague Brian Randell during a particularly exciting time there --- a time that shaped our later careers in very interesting ways. This paper reflects on those long-ago experiences and the many lessons learned back then. I'm hoping that other ACS veterans will share their memories with us too, and that together we can build ever-clearer images of those heady days.


title: Still Beheading Hydras: Botnet Takedowns Then and Now

abstract: Devices infected with malicious software typically form botnet armies under the influence of one or more command and control (C&C) servers. The botnet problem reached such levels where federal law enforcement agencies have to step in and take actions against botnets by disrupting (or “taking down”) their C&Cs, and thus their illicit operations. Lately, more and more private companies have started to independently take action against botnet armies, primarily focusing on their DNS-based C&Cs. While well-intentioned, their C&C takedown methodology is in most cases ad-hoc, and limited by the breadth of knowledge available around the malware that facilitates the botnet. With this paper, we aim to bring order, measure, and reason to the botnet takedown problem. We improve an existing takedown analysis system called rza . Specifically, we examine additional botnet takedowns, enhance the risk calculation to use botnet population counts, and include a detailed discussion of policy improvements that can be made to improve takedowns. As part of our system evaluation, we perform a postmortem analysis of the recent 3322.org, Citadel, and No-IP takedowns.


title: More Accurate Entity Ranking Using Knowledge Graph and Web Corpus

abstract: Recent years have witnessed some convergence in the architecture of entity search systems driven by a knowledge graph (KG) and a corpus with annotated entity mentions. However, each specific system has some limitations. We present AQQUCN, an entity search system that combines the best design principles into a public reference implementation. AQQUCN does not depend on well formed question syntax, but works equally well with syntax poor keyword queries. It uses several convolutional networks (convnets) to extract subtle, overlapping roles of query words. Instead of ranking structured query interpretations, which are then executed on the KG to return unranked sets, AQQUCN directly ranks response entities, by closely integrating coarse grained predicates from the KG with fine grained scoring from the corpus, into a single ranking model. Over and above competitive F1 score, AQQUCN gets the best entity ranking accuracy on two syntax rich and two syntax poor public query workloads amounting to over 8,000 queries, with 16 to 18 percent absolute improvement in mean average precision (MAP), compared to recent systems.


title: An Upper-Level Ontology for the Biomedical Domain

abstract: At the US National Library of Medicine we have developed the Unified Medical Language System (UMLS), whose goal it is to provide integrated access to a large number of biomedical resources by unifying the vocabularies that are used to access those resources. The UMLS currently interrelates some 60 controlled vocabularies in the biomedical domain. The UMLS coverage is quite extensive, including not only many concepts in clinical medicine, but also a large number of concepts applicable to the broad domain of the life sciences. In order to provide an overarching conceptual framework for all UMLS concepts, we developed an upper-level ontology, called the UMLS semantic network. The semantic network, through its 134 semantic types, provides a consistent categorization of all concepts represented in the UMLS. The 54 links between the semantic types provide the structure for the network and represent important relationships in the biomedical domain. Because of the growing number of information resources that contain genetic information, the UMLS coverage in this area is being expanded. We recently integrated the taxonomy of organisms developed by the NLM's National Center for Biotechnology Information, and we are currently working together with the developers of the Gene Ontology to integrate this resource, as well. As additional, standard, ontologies become publicly available, we expect to integrate these into the UMLS construct.


title: A conceptual framework for assessing electronic banking continued use

abstract: The development and advancement of electronic banking (e-banking) had raised the level competitiveness in the market for financial products and services. More and more banks and other financial institutions adopted such technology to improve service quality and enhance customer satisfaction and loyalty. Despite the increasing number e-banking users globally, little is known on how this technology can help banks to retain customers. Furthermore, it is only recently that researchers have started to understand the importance of consumers' continued use. Therefore, it is the aim of this paper to propose a framework based on an extensive review of current literature in related areas of e-banking domain and continued use. The main benefits of the proposed framework are towards understanding factors that may influence the e-banking continuance usage.


title: Towards the construction of a field: The developments and implications of mobile assisted language learning (MALL)

abstract: There has been little review research with regard to the latest developments of mobile assisted language learning (MALL) research and its implications for professionals in this emerging field of information technology applied to language teaching and research. Hence, this work aimed to review a number of studies that investigated the effectiveness of using such mobile devices to facilitate language learning, teaching, and research. As well as providing an overview of previous research related to MALL, this review article also presents some practical implications for both educators and researchers who intend to use mobile devices in their classrooms and studies. The investigators of this review study searched the available literature published in the ISI Web of Science using keywords and reviews of titles and abstracts, and obtained a total of 24 mobile learning (m-learning) articles. After the data collection process, the researchers used text-mining techniques and adopted the m-learning task model (Taylor et al. , 2006) to categorize and analyze these 24 journal articles in order to examine developments and trends in MALL. Results show that the majority of researchers aim to identify the effectiveness and efficiency of MALL by using newly developed mobile devices, such as smart phones and iPads, and while these approaches are generally considered beneficial, they also present some challenges to users, academics, and teachers. To conclude, more research is needed to further investigate the communication that occurs among learners with the use of various mobile devices, and their developments of various cognitive learning domains.


title: Online Learning of Weighted Relational Rules for Complex Event Recognition

abstract: Systems for symbolic complex event recognition detect occurrences of events in time using a set of event definitions in the form of logical rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, connections to techniques for learning such rules from data. We advance the state-of-the-art by combining an existing online algorithm for learning crisp relational structure with an online method for weight learning in Markov Logic Networks (MLN). The result is an algorithm that learns complex event patterns in the form of Event Calculus theories in the MLN semantics. We evaluate our approach on a challenging real-world application for activity recognition and show that it outperforms both its crisp predecessor and competing online MLN learners in terms of predictive performance, at the price of a small increase in training time. Code related to this paper is available at: https://github.com/nkatzz/OLED.


Automated Recommendation Systems Collaborative Filtering Through Reinforcement Learning


title: Fabric defect detection using morphological filters

abstract: In this paper, a novel defect detection scheme based on morphological filters is proposed to tackle the problem of automated defect detection for woven fabrics. In the proposed scheme, important texture features of the textile fabric are extracted using a pre-trained Gabor wavelet network. These texture features are then used to facilitate the construction of structuring elements in subsequent morphological processing to remove the fabric background and isolate the defects. Since the proposed defect detection scheme requires a few morphological filters only, the amount of computational load involved is not significant. The performance of the proposed scheme is evaluated by using a wide variety of homogeneous textile images with different types of common fabric defects. The test results obtained exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed detection scheme. In addition, the proposed detection scheme is further evaluated in real time by using a prototyped automated inspection system.


title: A New Biometric Technology Based on Mouse Dynamics

abstract: In this paper, we introduce a new form of behavioral biometrics based on mouse dynamics, which can be used in different security applications. We develop a technique that can be used to model the behavioral characteristics from the captured data using artificial neural networks. In addition, we present an architecture and implementation for the detector, which cover all the phases of the biometric data flow including the detection process. Experimental data illustrating the experiments conducted to evaluate the accuracy of the proposed detection technique are presented and analyzed. Specifically, three series of experiments are conducted. The main experiment, in which 22 participants are involved, reproduces real operating conditions in computing systems by giving participants an individual choice of operating environments and applications; 284 hours of raw mouse data are collected over 998 sessions, with an average of 45 sessions per user. The two other experiments, involving seven participants, provided a basis for studying the confounding factors arising from the main experiment by fixing the environment variables. In the main experiment, the performance results presented using receiver operating characteristic (ROC) curves and a confusion matrix yield at the crossover point (that is, the threshold set for an equal error rate) a false acceptance rate (FAR) of 2.4649 percent and a false rejection rate (FRR) of 2.4614 percent.


title: Neural Correlates of Verb Argument Structure Processing

abstract: Neuroimaging and lesion studies suggest that processing of word classes, such as verbs and nouns, is associated with distinct neural mechanisms. Such studies also suggest that subcategories within these broad word class categories are differentially processed in the brain. Within the class of verbs, argument structure provides one linguistic dimension that distinguishes among verb exemplars, with some requiring more complex argument structure entries than others. This study examined the neural instantiation of verbs by argument structure complexity: one-, two-, and three-argument verbs. Stimuli of each type, along with nouns and pseudowords, were presented for lexical decision using an event-related functional magnetic resonance imaging design. Results for 14 young normal participants indicated largely overlapping activation maps for verbs and nouns, with no areas of significant activation for verbs compared to nouns, or vice versa. Pseudowords also engaged neural tissue overlapping with that for both word classes, with more widespread activation noted in visual, motor, and peri-sylvian regions. Examination of verbs by argument structure revealed activation of the supramarginal and angular gyri, limited to the left hemisphere only when verbs with two obligatory arguments were compared to verbs with a single argument. However, bilateral activation was noted when both two-and three-argument verbs were compared to one-argument verbs. These findings suggest that posterior peri-sylvian regions are engaged for processing argument structure information associated with verbs, with increasing neural tissue in the inferior parietal region associated with increasing argument structure complexity. These findings are consistent with processing accounts, which suggest that these regions are crucial for semantic integration.


title: Scalable Hybrid Unstructured and Structured Grid Raycasting

abstract: This paper presents a scalable framework for real-time raycasting of large unstructured volumes that employs a hybrid bricking approach. It adaptively combines original unstructured bricks in important (focus) regions, with structured bricks that are resampled on demand in less important (context) regions. The basis of this focus+context approach is interactive specification of a scalar degree of interest (DOI) function. Thus, rendering always considers two volumes simultaneously: a scalar data volume, and the current DOI volume. The crucial problem of visibility sorting is solved by raycasting individual bricks and compositing in visibility order from front to back. In order to minimize visual errors at the grid boundary, it is always rendered accurately, even for resampled bricks. A variety of different rendering modes can be combined, including contour enhancement. A very important property of our approach is that it supports a variety of cell types natively, i.e., it is not constrained to tetrahedral grids, even when interpolation within cells is used. Moreover, our framework can handle multi-variate data, e.g., multiple scalar channels such as temperature or pressure, as well as time-dependent data. The combination of unstructured and structured bricks with different quality characteristics such as the type of interpolation or resampling resolution in conjunction with custom texture memory management yields a very scalable system.


title: Mobile Edge Computing Resources Optimization: A Geo-Clustering Approach

abstract: Mobile edge computing (MEC) is an emerging technology that aims at pushing applications and content close to the users (e.g., at base stations, access points, and aggregation networks) to reduce latency, improve quality of experience, and ensure highly efficient network operation and service delivery. It principally relies on virtualization-enabled MEC servers with limited capacity at the edge of the network. One key issue is to dimension such systems in terms of server size, server number, and server operation area to meet MEC goals. In this paper, we formulate this problem as a mixed integer linear program. We then propose a graph-based algorithm that, taking into account a maximum MEC server capacity, provides a partition of MEC clusters, which consolidates as many communications as possible at the edge. We use a dataset of mobile communications to extensively evaluate them with real world spatio-temporal human dynamics. In addition to quantifying macroscopic MEC benefits, the evaluation shows that our algorithm provides MEC area partitions that largely offload the core, thus pushing the load at the edge (e.g., with 10 small MEC servers between 55% and 64% of the traffic stay at the edge), and that are well balanced through time.


title: Learning Deep Representations of Fine-Grained Visual Descriptions

abstract: State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manuallyencoded vectors describing shared characteristics among categories. Despite good performance, attributes have limitations: (1) finer-grained recognition requires commensurately more attributes, and (2) attributes do not provide a natural language interface. We propose to overcome these limitations by training neural language models from scratch, i.e. without pre-training and only consuming words and characters. Our proposed models train end-to-end to align with the fine-grained and category-specific content of images. Natural language provides a flexible and compact way of encoding only the salient visual aspects for distinguishing categories. By training on raw text, our model can do inference on raw text as well, providing humans a familiar mode both for annotation and retrieval. Our model achieves strong performance on zero-shot text-based image retrieval and significantly outperforms the attribute-based state-of-the-art for zero-shot classification on the Caltech-UCSD Birds 200-2011 dataset.


title: Automatic projector calibration with embedded light sensors

abstract: Projection technology typically places several constraints on the geometric relationship between the projector and the projection surface to obtain an undistorted, properly sized image. In this paper we describe a simple, robust, fast, and low-cost method for automatic projector calibration that eliminates many of these constraints. We embed light sensors in the target surface, project Gray-coded binary patterns to discover the sensor locations, and then prewarp the image to accurately fit the physical features of the projection surface. This technique can be expanded to automatically stitch multiple projectors, calibrate onto non-planar surfaces for object decoration, and provide a method for simple geometry acquisition.


title: Characterization of blood cells and hematological parameters in Cichlasoma dimerus (Teleostei, Perciformes).

abstract: The aim of the present study was to obtain a basic knowledge of the hematology of Cichlasoma dimerus. The morphological features of blood cells were described according to the observations made by light and electron microscopy. Erythrocytes, thrombocytes and four types of leucocytes: lymphocytes, monocytes, heterophils and eosinophils, were distinguished and characterized. Thrombocytes are the most abundant blood cells after erythrocytes and are recognized easily from lymphocytes by morphological features and size. Heterophils and eosinophils are PAS positive. Hematological indices (RBC, WBC, PCV, Hb, MCV, MCH, MCHC and leucocyte differential count) were measured in one blood sample from 30 adult fish captured in Esteros del Riachuelo, Corrientes, Argentina (27 degrees 25'S, 58 degrees 15'W). The reference interval and the mean were determined for each hematological parameter evaluated. Contrary to other species, the percent of heterophils was found to be high in relation to the percent of lymphocytes. Low lymphocyte counts occurred in C. dimerus, as compared to these found in other fishes. Compared to most teleosts, this species has similar mean values for PCV and Hb and slightly higher for RBC. Statistical analysis revealed that differences in hematological parameters between male and female fish were not significant.


title: Anonymizing transaction data by integrating suppression and generalization

abstract: Privacy protection in publishing transaction data is an important problem. A key feature of transaction data is the extreme sparsity, which renders any single technique ineffective in anonymizing such data. Among recent works, some incur high information loss, some result in data hard to interpret, and some suffer from performance drawbacks. This paper proposes to integrate generalization and suppression to reduce information loss. However, the integration is non-trivial. We propose novel techniques to address the efficiency and scalability challenges. Extensive experiments on real world databases show that this approach outperforms the state-of-the-art methods, including global generalization, local generalization, and total suppression. In addition, transaction data anonymized by this approach can be analyzed by standard data mining tools, a property that local generalization fails to provide.


Constructing the Meaning of Teacher Educator: The Struggle to Learn the Roles.


title: Semi-Supervised Transfer Learning for Convolutional Neural Network Based Chinese Character Recognition

abstract: Although transfer learning has aroused researchers' great interest, how to utilize the unlabeled data is still an open and important problem in this area. We propose a novel semi-supervised transfer learning (STL) method by incorporating Multi-Kernel Maximum Mean Discrepancy (MK-MMD) loss into the traditional fine-tuned Convolutional Neural Network (CNN) transfer learning framework for Chinese character recognition. The proposed method includes three steps. First, a CNN model is trained by massive labeled samples in the source domain. Then the CNN model is fine-tuned by a few labeled samples in the target domain. Finally, the CNN model is trained with both a large number of unlabeled samples and the limited labeled samples in the target domain to minimize the MK-MMD loss. Experiments investigate detailed configurations and parameters of the proposed STL method with several frequently used CNN structures including AlexNet, GoogLeNet, and ResNet. Experimental results on practical Chinese character transfer learning tasks, such as Dunhuang historical Chinese character recognition, indicate that the proposed method can significantly improve recognition accuracy in the target domain.


A Survey of Convolutional Neural Networks : Motivation , Modern Architectures , and Current Applications in the Earth and Ocean Sciences


title: Jet-Images -- Deep Learning Edition

abstract: Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.


title: Evidence updating for stream-processing in big-data: Robust conditioning in soft and hard data fusion environments

abstract: Robust belief revision methods are crucial in streaming data situations for updating existing knowledge (or beliefs) with new incoming evidence. Bayes conditioning is the primary mechanism in use for belief revision in data fusion systems that use probabilistic inference. However, traditional conditioning methods face several challenges due to inherent data/source imperfections in big-data environments that harness soft (i.e., human or human-based) sources in addition to hard (i.e., physics-based) sensors. The objective of this paper is to investigate the most natural extension of Bayes conditioning that is suitable for evidence updating in the presence of such uncertainties. By viewing the evidence updating process as a thought experiment, an elegant strategy is derived for robust evidence updating in the presence of extreme uncertainties that are characteristic of big-data environments. In particular, utilizing the Fagin-Halpern conditional notions, a natural extension to Bayes conditioning is derived for evidence that takes the form of a general belief function. The presented work differs fundamentally from the Conditional Update Equation (CUE) and authors own extensions of it. An overview of this development is provided via illustrative examples. Furthermore, insights into parameter selection under various fusion contexts are also provided.


title: SMV-HUNTER: Large Scale, Automated Detection of SSL/TLS Man-in-the-Middle Vulnerabilities in Android Apps

abstract: Many Android apps use SSL/TLS to transmit sensitive information securely. However, developers often provide their own implementation of the standard SSL/TLS certificate validation process. Unfortunately, many such custom implementations have subtle bugs, have built-in exceptions for self-signed certificates, or blindly assert all certificates are valid, leaving many Android apps vulnerable to SSL/TLS Man-in-the-Middle attacks. In this paper, we present SMV-HUNTER, a system for the automatic, large-scale identification of such vulnerabilities that combines both static and dynamic analysis. The static component detects when a custom validation procedure has been given, thereby identifying potentially vulnerable apps, and extracts information used to guide the dynamic analysis, which then uses user interface enumeration and automation techniques to trigger the potentially vulnerable code under an active Man-in-the-Middle attack. We have implemented SMV-HUNTER and evaluated it on 23,418 apps downloaded from the Google Play market, of which 1,453 apps were identified as being potentially vulnerable by static analysis, with an average overhead of approximately 4 seconds per app, running on 16 threads in parallel. Among these potentially vulnerable apps, 726 were confirmed vulnerable using our dynamic analysis, with an average overhead of about 44 seconds per app, running on 8 emulators in parallel.


title: Korean Mothers' Parenting Self-Efficacy

abstract: This study investigated the effects of child, mother, and family characteristics on parenting self-efficacy of 416 Korean mothers living in Seoul Korea. The results showed that mother's parenting self-efficacy was positively related to mother's perceived social support, child's temperament, and family income, but negatively related to mother' stress. Mother's educational parenting self-efficacy among parenting self-efficacy was positively related to mother's perceived social support, child's temperament, and mother's educational level, but negatively related to mother' stress and family income. Multiple regression indicated that mother characteristics were a strong predicator of mother's parenting self-efficacy. Especially, husband's support among mother characteristics was the strongest predicator of mother's parenting self-efficacy.


title: K-means clustering: A half-century synthesis

abstract: This paper synthesizes the results, methodology, and research conducted concerning the K-means clustering method over the last fifty years. The K-means method is first introduced, various formulations of the minimum variance loss function and alternative loss functions within the same class are outlined, and different methods of choosing the number of clusters and initialization, variable preprocessing, and data reduction schemes are discussed. Theoretic statistical results are provided and various extensions of K-means using different metrics or modifications of the original algorithm are given, leading to a unifying treatment of K-means and some of its extensions. Finally, several future studies are outlined that could enhance the understanding of numerous subtleties affecting the performance of the K-means method.


title: A Distributed Algorithm for Solving a Linear Algebraic Equation

abstract: A distributed algorithm is described for solving a linear algebraic equation of the form $Ax=b$ assuming the equation has at least one solution. The equation is simultaneously solved by $m$ agents assuming each agent knows only a subset of the rows of the partitioned matrix $(A,b)$, the current estimates of the equation's solution generated by its neighbors, and nothing more. Each agent recursively updates its estimate by utilizing the current estimates generated by each of its neighbors. Neighbor relations are characterized by a time-dependent directed graph $\mathbb{N}(t)$ whose vertices correspond to agents and whose arcs depict neighbor relations. It is shown that for any matrix $A$ for which the equation has a solution and any sequence of "repeatedly jointly strongly connected graphs" $\mathbb{N}(t)$, $t=1,2,\ldots$, the algorithm causes all agents' estimates to converge exponentially fast to the same solution to $Ax=b$. It is also shown that the neighbor graph sequence must actually be repeatedly jointly strongly connected if exponential convergence is to be assured. A worst case convergence rate bound is derived for the case when $Ax=b$ has a unique solution. It is demonstrated that with minor modification, the algorithm can track the solution to $Ax = b$, even if $A$ and $b$ are changing with time, provided the rates of change of $A$ and $b$ are sufficiently small. It is also shown that in the absence of communication delays, exponential convergence to a solution occurs even if the times at which each agent updates its estimates are not synchronized with the update times of its neighbors. A modification of the algorithm is outlined which enables it to obtain a least squares solution to $Ax=b$ in a distributed manner, even if $Ax=b$ does not have a solution.


title: Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis

abstract: We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout. Instead of learning a direct mapping from text to image, our algorithm decomposes the generation process into multiple steps, in which it first constructs a semantic layout from the text by the layout generator and converts the layout to an image by the image generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layout, which provides a useful semantic structure of an image matching with the text description. Our model not only generates semantically more meaningful images, but also allows automatic annotation of generated images and user-controlled generation process by modifying the generated scene layout. We demonstrate the capability of the proposed model on challenging MS-COCO dataset and show that the model can substantially improve the image quality, interpretability of output and semantic alignment to input text over existing approaches.


Predicting On-Orbit Static Single Event Upset Rates in Xilinx Virtex FPGAs


title: What is ‘neuromarketing’? A discussion and agenda for future research

abstract: Recent years have seen advances in neuroimaging to such an extent that neuroscientists are able to directly study the frequency, location, and timing of neuronal activity to an unprecedented degree. However, marketing science has remained largely unaware of such advances and their huge potential. In fact, the application of neuroimaging to market research – what has come to be called ‘neuromarketing’ – has caused considerable controversy within neuroscience circles in recent times. This paper is an attempt to widen the scope of neuromarketing beyond commercial brand and consumer behaviour applications, to include a wider conceptualisation of marketing science. Drawing from general neuroscience and neuroeconomics, neuromarketing as a field of study is defined, and some future research directions are suggested.


title: Analysis and evaluation of layout density of FinFET logic gates

abstract: In this paper, the layout density of FinFET logic gates is analyzed and compared to that of bulk CMOS logic. Analysis starts from basic structures, including single- and multi-finger transistors, as well as stacked transistors. As opposite to previous work, four-terminal (4T) FinFETs are also explicitly taken into account. The analysis is extended to the physical design of a standard cell library in 65-nm technology. Comparison with bulk technology confirms that 3T FinFETs suffer from significant layout density degradation, as was previously observed in [1]. Moreover, it is shown that 4T FinFETs have a considerably worse layout density, compared to 3T FinFETs and bulk transistors. The sources of the 3T-4T layout density degradation are also discussed. Finally, the mixed 3T-4T approach, which was recently proposed to reduce the leakage power, is investigated as a compromise between 3T and 4T FinFET circuits in terms of area.


title: Managing the New Product Development Project Portfolio: A Review of the Literature and Empirical Evidence

abstract: Literature on project portfolio management (PPM) has been escalating as interest has intensified. The surge of interest has been attributed to the increased importance of technological innovation and the recognition that successful innovation depends upon effective selection and management of the new product development (NPD) project portfolio. PPM processes are responsible for the alignment of projects with the innovation strategy, maintaining a balance of project types, and ensuring that the project portfolio fits with resource capability so that the organization can gain the maximum value from the investment in NPD. This is the first comprehensive review of the literature on NPD PPM to be published and reveals a wide range of considerations from a variety of sources across several disciplines. The growing importance of NPD PPM is highlighted, and interest in PPM is shown to have stimulated a field of research that is beginning to offer empirical findings to help clarify the relationships between PPM methods and NPD outcomes. Findings reported in the empirical literature are compared with the common beliefs and assertions presented in other published sources. The empirical findings show support for some assertions, and challenge others, while some proposed relationships remain untested.


title: Combining Learning and Word Sense Disambiguation for Intelligent User Profiling

abstract: Understanding user interests from text documents can provide support to personalized information recommendation services. Typically, these services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. Traditional keyword-based approaches are unable to capture the semantics of the user interests. This work proposes the integration of linguistic knowledge in the process of learning semantic user profiles that capture concepts concerning user interests. The proposed strategy consists of two steps. The first one is based on a word sense disambiguation technique that exploits the lexical database WordNet to select, among all the possible meanings (senses) of a polysemous word, the correct one. In the second step, a naive Bayes approach learns semantic sense-based user profiles as binary text classifiers (user-likes and user-dislikes) from disambiguated documents. Experiments have been conducted to compare the performance obtained by keyword-based profiles to that obtained by sense-based profiles. Both the classification accuracy and the effectiveness of the ranking imposed by the two different kinds of profile on the documents to be recommended have been considered. The main outcome is that the classification accuracy is increased with no improvement on the ranking. The conclusion is that the integration of linguistic knowledge in the learning process improves the classification of those documents whose classification score is close to the likes/dislikes threshold (the items for which the classification is highly uncertain).


title: Chemical laboratory safety management service using IoT sensors and open APIs

abstract: In this paper, we propose a safety management service using Internet of Things (IoT) for accidents prevention and management. The reason for applying IoT technology in the service is that it is can be used to semi-automate the service through the IoT sensors, and it is useful to the experimenter with chemicals. We describe the requirements and architecture of our proposed service.


title: Development and Implementation of a Chat Bot in a Social Network

abstract: This document describes how to implement a Chat Bot on the Twitter social network for entertainment and viral advertising using a database and a simple algorithm. Having as a main theme a successfully implementation of a Chat Bot preventing people classify it as SPAM, as a result of this a Twitter account (@DonPlaticador) that works without the intervention of a person and every day earns more followers was obtained.


title: Engineering Privacy

abstract: In this paper we integrate insights from diverse islands of research on electronic privacy to offer a holistic view of privacy engineering and a systematic structure for the discipline's topics. First we discuss privacy requirements grounded in both historic and contemporary perspectives on privacy. We use a three-layer model of user privacy concerns to relate them to system operations (data transfer, storage and processing) and examine their effects on user behavior. In the second part of the paper we develop guidelines for building privacy-friendly systems. We distinguish two approaches: "privacy-by-policy" and "privacy-by-architecture." The privacy-by-policy approach focuses on the implementation of the notice and choice principles of fair information practices (FIPs), while the privacy-by-architecture approach minimizes the collection of identifiable personal data and emphasizes anonymization and client-side data storage and processing. We discuss both approaches with a view to their technical overlaps and boundaries as well as to economic feasibility. The paper aims to introduce engineers and computer scientists to the privacy research domain and provide concrete guidance on how to design privacy-friendly systems.


title: Concise Review With Video Illustrations Partial-Thickness Tears of the Gluteus Medius: Rationale and Technique for Trans-Tendinous Endoscopic Repair

abstract: Tears in the gluteus medius and minimus tendons, often misdiagnosed as trochanteric bursitis, have recently emerged as an important cause of recalcitrant greater trochanter pain syndrome. Advances in endoscopic surgery of the hip have created opportunities to better evaluate and treat pathology in the peritrochanteric compartment. We reviewed the literature on trochanteric pain syndrome and gluteus medius tendon injuries. Existing techniques for endo- scopic and open gluteus tendon repair and potential challenges in restoration of abductor function were analyzed. Partial-thickness undersurface tears of the gluteus medius were iden- tified as a common pathologic entity. Although these tears are otherwise analogous to partial- thickness tears of the rotator cuff, the lack of arthroscopic access to the deep side of the gluteus medius tendon represents a unique technical challenge. To address the difficulty in visualizing and thus repairing undersurface tears of the gluteus medius, a novel endoscopic trans-tendinous repair technique was developed. The purposes of this article are to review the anatomy, pathology, and existing repair techniques of gluteus medius tendon tears and to describe the rationale and surgical steps for endoscopic trans-tendinous repair.


title: Revisiting the uncanny valley theory: Developing and validating an alternative to the Godspeed indices

abstract: Mori (1970) proposed a hypothetical graph describing a nonlinear relation between a character's degree of human likeness and the emotional response of the human perceiver. However, the index construction of these variables could result in their strong correlation, thus preventing rated characters from being plotted accurately. Phase 1 of this study tested the indices of the Godspeed questionnaire as measures of humanlike characters. The results indicate significant and strong correlations among the relevant indices (Bartneck, Kulic, Croft, & Zoghbi, 2009). Phase 2 of this study developed alternative indices with nonsignificant correlations (p>.05) between the proposed y-axis eeriness and x-axis perceived humanness (r=.02). The new humanness and eeriness indices facilitate plotting relations among rated characters of varying human likeness.


EXPLORING FACTORS INFLUENCING ELECTRONIC MONEY ADOPTION (Qualitative Study of T-Cash)


title: A correlation-based approach to robust point set registration

abstract: Correlation is a very effective way to align intensity images. We extend the correlation technique to point set registration using a method we call kernel correlation. Kernel correlation is an affinity measure, and it is also a function of the point set entropy. We define the point set registration problem as finding the maximum kernel correlation configuration of the the two point sets to be registered. The new registration method has intuitive interpretations, simple to implement algorithm and easy to prove convergence property. Our method shows favorable performance when compared with the iterative closest point (ICP) and EM-ICP methods.


title: The automatic summarization of text documents in the Cognitive Integrated Management Information System

abstract: This paper presents issues related to a process of the automatic summarization of the text documents connected with economic knowledge performed by the cognitive agents in an integrated management information system. In contemporary companies, the unstructured knowledge is essential, mainly due to the possibility of obtaining better flexibility and competitiveness of the organization. Therefore more often the decision are taken in the enterprises on the basis of the summaries. The first part of the paper shortly presents the state-of-the-art in the considered field; next, the summarization process in the Cognitive Integrated Management Information System is characterized; the case study related with the summaries generating agent is presented in the last part of this paper.


title: High-performance information extraction with AliBaba

abstract: A wealth of information is available only in web pages, patents, publications etc. Extracting information from such sources is challenging, both due to the typically complex language processing steps required and to the potentially large number of texts that need to be analyzed. Furthermore, integrating extracted data with other sources of knowledge often is mandatory for subsequent analysis. In this demo, we present the AliBaba system for scalable information extraction from biomedical documents. Unlike many other systems, AliBaba performs both entity extraction and relationship extraction and graphically visualizes the resulting network of inter-connected objects. It leverages the PubMed search engine for selection of relevant documents. The technical novelty of AliBaba is twofold: (a) its ability to automatically learn language patterns for relationship extraction without an annotated corpus, and (b) its high performance pattern matching algorithm. We show that a simple yet effective pattern filtering technique improves the runtime of the system drastically without harming its extraction effectiveness. Although AliBaba has been implemented for biomedical texts, its underlying principles should also be applicable in any other domain.


title: Mining MOOC Clickstreams: On the Relationship Between Learner Video-Watching Behavior and Performance

abstract: We study student behavior and performance in two Massive Open Online Courses (MOOCs). In doing so, we present two frameworks by which video-watching clickstreams can be represented: one based on the sequence of events created, and another on the sequence of positions visited. With the event-based framework, we extract recurring subsequences of student behavior, which contain fundamental characteris- tics such as reflecting (i.e., repeatedly playing and pausing) and revising (i.e., plays and skip backs). We find that some of these behaviors are significantly associated with whether a user will be Correct on First Attempt (CFA) or not in answering quiz questions. With the position-based framework, we then devise models for performance. In evaluating these through CFA prediction, we find that three of them can substantially improve prediction quality in terms of accuracy and F1, which underlines the ability to relate behavior to performance. Since our prediction considers videos individually, these benefits also suggest that our models are useful in situations where there is limited training data, e.g., for early detection or in short courses.


title: The Good, the Bad, and the Irrelevant: Neural Mechanisms of Learning Real and Hypothetical Rewards and Effort.

abstract: Natural environments are complex, and a single choice can lead to multiple outcomes. Agents should learn which outcomes are due to their choices and therefore relevant for future decisions and which are stochastic in ways common to all choices and therefore irrelevant for future decisions between options. We designed an experiment in which human participants learned the varying reward and effort magnitudes of two options and repeatedly chose between them. The reward associated with a choice was randomly real or hypothetical (i.e., participants only sometimes received the reward magnitude associated with the chosen option). The real/hypothetical nature of the reward on any one trial was, however, irrelevant for learning the longer-term values of the choices, and participants ought to have only focused on the informational content of the outcome and disregarded whether it was a real or hypothetical reward. However, we found that participants showed an irrational choice bias, preferring choices that had previously led, by chance, to a real reward in the last trial. Amygdala and ventromedial prefrontal activity was related to the way in which participants' choices were biased by real reward receipt. By contrast, activity in dorsal anterior cingulate cortex, frontal operculum/anterior insula, and especially lateral anterior prefrontal cortex was related to the degree to which participants resisted this bias and chose effectively in a manner guided by aspects of outcomes that had real and more sustained relationships with particular choices, suppressing irrelevant reward information for more optimal learning and decision making. ::: ::: SIGNIFICANCE STATEMENT In complex natural environments, a single choice can lead to multiple outcomes. Human agents should only learn from outcomes that are due to their choices, not from outcomes without such a relationship. We designed an experiment to measure learning about reward and effort magnitudes in an environment in which other features of the outcome were random and had no relationship with choice. We found that, although people could learn about reward magnitudes, they nevertheless were irrationally biased toward repeating certain choices as a function of the presence or absence of random reward features. Activity in different brain regions in the prefrontal cortex either reflected the bias or reflected resistance to the bias.


Multi-Agent System for Market Based Microgrid Operation in Smart Grid Environment


title: TrustStore: Making Amazon S3 Trustworthy with Services Composition

abstract: The enormous amount of data generated in daily operations and the increasing demands for data accessibility across organizations are pushing individuals and organizations to outsource their data storage to cloud storage services. However, the security and the privacy of the outsourced data goes beyond the data owners' control. We propose a service composition approach to preserve privacy for data stored in untrusted storage service. A virtual file system, called Trust Store, is prototyped to demonstrate this concept. It allows users utilize untrusted storage service provider with confidentiality and integrity of the data preserved. We deployed the prototype with Amazon S3 and evaluate its performance.


title: Introducing ‘theomorphic robots’

abstract: As robots are going to spread in human society, the study of their appearance becomes a critical matter when assessing robots performance and appropriateness for an application and for the employment in different countries, with different background cultures and religions. Robot appearance categories are commonly divided in anthropomorphic, zoomorphic and functional. In this paper, we offer a theoretical contribution by introducing a new category, called ‘theomorphic robots’, in which robots carry the shape and the identity of a supernatural creature or object within a religion. Discussing the theory of dehumanisation and the different categories of supernatural among different religions, we hypothesise the possible advantages of the theomorphic design for different applications.


title: Does Recent Research on Adolescent Brain Development Inform the Mature Minor Doctrine

abstract: US Supreme Court rulings concerning sanctions for juvenile offenders have drawn on the science of brain development and concluded that adolescents are inherently less mature than adults in ways that render them less culpable. This conclusion departs from arguments made in cases involving the mature minor doctrine, in which teenagers have been portrayed as comparable to adults in their capacity to make medical decisions. I attempt to reconcile these apparently incompatible views of adolescents' decision-making competence. Adolescents are indeed less mature than adults when making decisions under conditions that are characterized by emotional arousal and peer pressure, but adolescents aged 15 and older are just as mature as adults when emotional arousal is minimized and when they are not under the influence of peers, conditions that typically characterize medical decision-making. The mature minor doctrine, as applied to individuals 15 and older, is thus consistent with recent research on adolescent development.


title: On the multi-stage influence maximization problem

abstract: The influence maximization problem turns up in many online social networks (OSN) in which each participant can potentially influence the decisions made by others in the network. Relationships can be friendships, family relationships or even professional relationships. Such influences can be used to achieve some objective and the influence maximization problem attempts to make decisions so as to maximize the effect of these influences. Past work focused on a static problem whereby one tries to identify the participants who are the most influential. Recently, a multi-stage version of the problem was proposed in which outcomes of influence attempts are observed before additional participants are chosen. For example, in online advertising, one can provide an impression to a particular subject and if that subject clicks on the impression, then their friends are informed in the hope that this information will increase the chances that they also click on the impression and eventually purchase the product. This problem is computationally intensive; in this paper we investigate various optimization methods for finding its solution that yield close to optimal results while taking less computation time. These include greedy and particle swarm optimization algorithms.


title: HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization

abstract: Although video summarization has achieved great success in recent years, few approaches have realized the influence of video structure on the summarization results. As we know, the video data follow a hierarchical structure, i.e., a video is composed of shots, and a shot is composed of several frames. Generally, shots provide the activity-level information for people to understand the video content. While few existing summarization approaches pay attention to the shot segmentation procedure. They generate shots by some trivial strategies, such as fixed length segmentation, which may destroy the underlying hierarchical structure of video data and further reduce the quality of generated summaries. To address this problem, we propose a structure-adaptive video summarization approach that integrates shot segmentation and video summarization into a Hierarchical Structure-Adaptive RNN, denoted as HSA-RNN. We evaluate the proposed approach on four popular datasets, i.e., SumMe, TVsum, CoSum and VTW. The experimental results have demonstrated the effectiveness of HSA-RNN in the video summarization task.


title: Model Transformation: The Heart and Soul of Model-Driven Software Development

abstract: The model-driven approach can increase development productivity and quality by describing important aspects of a solution with human-friendly abstractions and by generating common application fragments with templates. This article examines different approaches to model transformations and recommends desirable language characteristics for describing them.


title: Failure-Atomic Slotted Paging for Persistent Memory

abstract: The slotted-page structure is a database page format commonly used for managing variable-length records. In this work, we develop a novel "failure-atomic slotted page structure" for persistent memory that leverages byte addressability and durability of persistent memory to minimize redundant write operations used to maintain consistency in traditional database systems. Failure-atomic slotted paging consists of two key elements: (i) in-place commit per page using hardware transactional memory and (ii) slot header logging that logs the commit mark of each page. The proposed scheme is implemented in SQLite and compared against NVWAL, the current state-of-the-art scheme. Our performance study shows that our failure-atomic slotted paging shows optimal performance for database transactions that insert a single record. For transactions that touch more than one database page, our proposed slot-header logging scheme minimizes the logging overhead by avoiding duplicating pages and logging only the metadata of the dirty pages. Overall, we find that our failure-atomic slotted-page management scheme reduces database logging overhead to 1/6 and improves query response time by up to 33% compared to NVWAL.


title: An Extended Model of IT Governance: A Conceptual Proposal

abstract: Although a significant amount of work has be done on the subject of IT governance there still appears to be some disjoint and confusion about what IT governance really is and how it may be realized in practice. This research-in-progress paper draws on existing research related to IT governance to provide a more in-depth understanding of the concept. It describes the current understanding of IT governance and argues for the extension and further development of the various dimensions of governance. An extended model of IT governance is proposed. A research agenda is outlined.


title: Cloud-enabled privacy-preserving collaborative learning for mobile sensing

abstract: In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.


title: Learning spatially localized, parts-based representation

abstract: In this paper, we propose a novel method, called local non-negative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual patterns. An objective function is defined to impose a localization constraint, in addition to the non-negativity constraint in the standard NMF. This gives a set of bases which not only allows a non-subtractive (part-based) representation of images but also manifests localized features. An algorithm is presented for the learning of such basic components. Experimental results are presented to compare LNMF with the NMF and PCA methods for face representation and recognition, which demonstrates advantages of LNMF.


title: Android security framework: extensible multi-layered access control on Android

abstract: We introduce the Android Security Framework (ASF), a generic, extensible security framework for Android that enables the development and integration of a wide spectrum of security models in form of code-based security modules. The design of ASF reflects lessons learned from the literature on established security frameworks (such as Linux Security Modules or the BSD MAC Framework) and intertwines them with the particular requirements and challenges from the design of Android's software stack. ASF provides a novel security API that supports authors of Android security extensions in developing their modules. This overcomes the current unsatisfactory situation to provide security solutions as separate patches to the Android software stack or to embed them into Android's mainline codebase. This system security extensibility is of particular benefit for enterprise or government solutions that require deployment of advanced security models, not supported by vanilla Android. We present a prototypical implementation of ASF and demonstrate its effectiveness and efficiency by modularizing different security models from related work, such as dynamic permissions, inlined reference monitoring, and type enforcement.


title: Typing of Y chromosome single nucleotide polymorphisms in a Japanese population by a multiplexed single nucleotide primer extension reaction

abstract: Abstract We have developed a new method for typing single nucleotide polymorphisms (SNPs) on the human Y chromosome based on a multiplexed single nucleotide primer extension. This method has the advantage that several SNPs are typed rapidly and simultaneously. We examined 15 different SNP loci on Y chromosome, M9, M105, M122, M125, M128, M130, SRY465, IMS-JST006241, IMS-JST006841, IMS-JST002611, IMS-JST003305, IMS-JST008425, IMS-JST021354, IMS-JST021355 and IMS-JST055457, in 159 Japanese males. From the typing results of these 15 loci, we found 13 haplotypes. Gene diversity for each locus ranged from 0.025 to 0.486 and the haplotype diversity was estimated to be 0.838. This method could be readily applied for personal identification and paternity testing.


Collective action and the evolution of social norms


title: Design of Energy- and Cost-Efficient Massive MIMO Arrays

abstract: Large arrays of radios have been exploited for beamforming and null steering in both radar and communication applications, but cost and form factor limitations have precluded their use in commercial systems. This paper discusses how to build arrays that enable multiuser massive multiple-input–multiple-output (MIMO) and aggressive spatial multiplexing with many users sharing the same spectrum. The focus of the paper is the energy- and cost-efficient realization of these arrays in order to enable new applications. Distributed algorithms for beamforming are proposed, and the optimum array size is considered as a function of the performance of the receiver, transmitter, frequency synthesizer, and signal distribution within the array. The effects of errors such as phase noise and synchronization skew across the array are analyzed. The paper discusses both RF frequencies below 10 GHz, where fully digital techniques are preferred, and operation at millimeter (mm)-wave bands where a combination of digital and analog techniques are needed to keep cost and power low.


title: An E-Band Partially Corporate Feed Uniform Slot Array With Laminated Quasi Double-Layer Waveguide and Virtual PMC Terminations

abstract: A partially corporate feeding waveguide located below the radiating waveguide is introduced to a waveguide slot array to enhance the bandwidth of gain. A PMC termination associated with the symmetry of the feeding waveguide as well as uniform excitation is newly proposed for realizing dense and uniform slot arrangement free of high sidelobes. To exploit the bandwidth of the feeding circuit, the 4 × 4-element subarray is also developed for wider bandwidth by using standing-wave excitation. A 16 × 16-element array with uniform excitation is fabricated in the E-band by diffusion bonding of laminated thin copper plates which has the advantages of high precision and high mass-productivity. The antenna gain of 32.4 dBi and the antenna efficiency of 83.0% are measured at the center frequency. The 1 dB-down gain bandwidth is no less than 9.0% and a wideband characteristic is achieved.


title: Knowledge-Based Distant Regularization in Learning Probabilistic Models

abstract: Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning. In practice, however, the domain knowledge of interest often provides information on the relationship of data attributes only distantly, which hinders direct utilization of such domain knowledge in popular regularization methods. In this paper, we propose the knowledge-based distant regularization framework, in which we utilize the distant information encoded in a knowledge graph for regularization of probabilistic model estimation. In particular, we propose to impose prior distributions on model parameters specified by knowledge graph embeddings. As an instance of the proposed framework, we present the factor analysis model with the knowledge-based distant regularization. We show the results of preliminary experiments on the improvement of the generalization capability of such model.


title: Social selection and peer influence in an online social network

abstract: Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends—except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes.


title: Security and Privacy Issues with Health Care Information Technology

abstract: The face of health care is changing as new technologies are being incorporated into the existing infras- tructure. Electronic Patient Records and sensor networks for in-home patient monitoring are at the current forefront of new technologies. Paper-based patient records are being put in electronic format enabling patients to access their records via the Internet. Remote patient monitoring is becoming more feasible as specialized sensors can be placed inside homes. The combination of these technologies will improve the quality of health care by making it more personalized and reducing costs and medical errors. While there are benefits to technologies, associated privacy and security issues need to be analyzed to make these systems socially acceptable. In this paper we explore the privacy and security implications of these next-generation health care technologies. We describe existing methods for handling issues as well as discussing which issues need further consideration. I. INTRODUCTION The health care system has long been plagued by problems such as diagnoses being written illegibly on paper, doctors not being able to easily access patient information, and limitations on time, space, and personnel for monitoring patients. With advancements in technology, opportunities exist to improve the current state of health care to minimize some of these problems and provide more personalized service. For example, the embracing of the Internet by health care organizations in the last decade has provided a medium for publishing general health information allowing patients to gain more knowledge about medical conditions. Currently, more than 90% of the approximately 5000 member institu- tions of the American Hospital Association reported having web sites, with most having descriptive information about their facilities and services. Many technologies are currently being adopted by the medical field. In this paper we look at the move towards Elec- tronic Patient Records(EPR) and the use of sensor networks for remote patient monitoring. With health care organizations transitioning to EPRs, information that was once stored in paper format will now be stored electronically allowing for easy accessibility and use. Aiding this transition, IEEE has joined forces with the American Medical Association and eight other major nonprofit medical and engineering societies to form an umbrella consortium, the Biotechnology Council


title: Learning Discriminative Latent Attributes for Zero-Shot Classification

abstract: Zero-shot learning (ZSL) aims to transfer knowledge from observed classes to the unseen classes, based on the assumption that both the seen and unseen classes share a common semantic space, among which attributes enjoy a great popularity. However, few works study whether the human-designed semantic attributes are discriminative enough to recognize different classes. Moreover, attributes are often correlated with each other, which makes it less desirable to learn each attribute independently. In this paper, we propose to learn a latent attribute space, which is not only discriminative but also semantic-preserving, to perform the ZSL task. Specifically, a dictionary learning framework is exploited to connect the latent attribute space with attribute space and similarity space. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach.


title: A Novel Robotic Platform for Aerial Manipulation Using Quadrotors as Rotating Thrust Generators

abstract: We propose a novel robotic platform for aerial operation and manipulation, a spherically connected multiquadrotor (SmQ) platform, which consists of a rigid frame and multiple quadrotors that are connected to the frame via passive spherical joints and act as distributed rotating thrust generators to collectively propel the frame by adjusting their attitude and thrust force. Depending on the number of quadrotors and their configuration, this SmQ platform can fully (or partially) overcome the issues of underactuation of the standard multirotor drones for aerial operation/manipulation (e.g., body-tilting with sideway gust/force, dynamic interaction hard to attain, complicated arm–drone integration, etc.). We present the dynamics modeling of this SmQ platform system and establish the condition for its full actuation in SE(3). We also show how to address limited range of spherical joints and rotor saturations as a constrained optimization problem by noticing the similarity with the multifingered grasping problem under the friction-cone constraint. We then design and analyze feedback control laws for the S3Q and S2Q systems as a combination of high-level Lyapunov control design and low-level constrained optimization and show that the (fully actuated) S3Q system can assume any trajectory in SE(3), whereas the S2Q system in $\Re^3\, \times$ S $^2$ with its unactuated dynamics is still internally stable. Experiments are also performed to show the efficacy of the theory.


title: Research note: Connected component labeling on a 2D grid using CUDA

abstract: Connected component labeling is an important but computationally expensive operation required in many fields of research. The goal in the present work is to label connected components on a 2D binary map. Two different iterative algorithms for doing this task are presented. The first algorithm (Row-Col Unify) is based upon the directional propagation labeling, whereas the second algorithm uses the Label Equivalence technique. The Row-Col Unify algorithm uses a local array of references and the reduction technique intrinsically. The usage of shared memory extensively makes the code efficient. The Label Equivalence algorithm is an extended version of the one presented by Hawick et al. (2010) [3]. At the end the comparison depending on the performances of both of the algorithms is presented.


title: Implementation of RFID on Computer Based Test (RF-CBT) system

abstract: The paper presents an improvement of assessment model using RFID (Radio Frequency Identification) technology which implemented on Computer Based Test (CBT). The RFID worked at frequency 13.56 MHz. The RF-CBT system is constructed with User-PC and Server-PC. A RFID reader module (Mifare RC522, Microcontroller ATMEGA328) and LabVIEW program are installed on User-PC. An RFID tag information from User-PC was sent into Server-PC which is installed with MySQL database and PHP. The registered RFID tag on Server-PC could log in the CBT automatically. Our system is proposed not only for auto login to CBT but also for developing a random question and scoring test result on CBT. The system could reduce the opportunity to cheat in test as anyone cannot login to the test except have the authentic RFID tag. This system is feasible to measure students' cognitive abilities at once because it uses multiple choices test type.


title: Evolution of a UAV Autonomy Classification Taxonomy

abstract: At PerMIS 2002, the Air Force Research Lab presented a paper entitled "Metrics Schmetrics", which presented the results of a research study on how to determine the autonomy level of a unmanned aerial vehicle (UAV). The striking point made by the author, given the obvious importance of the need to effectively evaluate and classify autonomous algorithms, was the general dearth of existing effective metrics and/or taxonomies to determine a UAVs autonomy level. To fill the void, the author presented a framework, referred to as the AFRL Autonomy Framework, which identified 10 autonomy control levels (ACLs) and presented the characteristics that differentiate the various levels. The purpose of this paper is to examine how the metrics presented have been applied and how they have evolved and expanded, in theory and practice, as a result of lessons learned during those applications. Specifically, the evolution in metrics development has culminated with the advent of an evaluation technique that blends emerging simulation technologies to create new unmanned aerial vehicle (UAV) autonomy assessment methods which take full advantage of visual virtual environments and statistical constructive simulations to examine the autonomy algorithm along the four dimensions of the Observe, Orient, Decide, Act (OODA) loop commonly applied by military aviators during the decision making process. This approach has been used over the last three years to evaluate emerging autonomy technologies from the Army's Unmanned Autonomous Collaborative Operations (UACO) Science and Technology program. This unique technique for testing UAV autonomy effectiveness is a significant advance in the UAV community. As autonomy algorithms proliferate to the point where multiple candidate algorithms are available for each platform, the ability to characterize the effectiveness of each autonomy algorithm will be critical to further for successful implementation of autonomous capability.


title: A 90 nm CMOS 16 Gb/s Transceiver for Optical Interconnects

abstract: Interconnect architectures which leverage high-bandwidth optical channels offer a promising solution to address the increasing chip-to-chip I/O bandwidth demands. This paper describes a dense, high-speed, and low-power CMOS optical interconnect transceiver architecture. Vertical-cavity surface-emitting laser (VCSEL) data rate is extended for a given average current and corresponding reliability level with a four-tap current summing FIR transmitter. A low-voltage integrating and double-sampling optical receiver front-end provides adequate sensitivity in a power efficient manner by avoiding linear high-gain elements common in conventional transimpedance-amplifier (TIA) receivers. Clock recovery is performed with a dual-loop architecture which employs baud-rate phase detection and feedback interpolation to achieve reduced power consumption, while high-precision phase spacing is ensured at both the transmitter and receiver through adjustable delay clock buffers. A prototype chip fabricated in 1 V 90 nm CMOS achieves 16 Gb/s operation while consuming 129 mW and occupying 0.105 mm2.


title: Expansion of Anopheles maculipennis s.s. (Diptera: Culicidae) to northeastern Europe and northwestern Asia: Causes and Consequences

abstract: BACKGROUND ::: The burden of malaria infection in the modern world remains significant. Specific changes in the relative proportions of malaria vector mosquitoes, Maculipennis Complex species, in the south of Western Siberia over the past 25 years of the 20th century have attracted wide attention as an indicator of their dynamic geographical distribution. In Eurasia, studies of fluctuations in the borders of areas occupied by sibling species of this complex, as well as their relative proportions in the areas where they are sympatric are epidemiologically important. ::: ::: ::: METHODS ::: Species identity and chromosomal polymorphisms within each population were defined by cytogenetic analysis of polytene chromosomes of third- and fourth-instar larvae and adult females of Anopheles mosquitoes collected over the period from 1973 to 2012. A total of 37 Anopheles samples (3,757 specimens) from the Ukraine, European Russia and the Urals were studied. To identify An. messeae s.l. cryptic species A and B, polymerase chain reaction and restriction fragment length polymorphisms of the second internal transcribed spacer rRNA genes sequences (ITS2 PCR-RFLP) were used. ::: ::: ::: RESULTS ::: An. maculipennis s.s. is expanding to the northeast at a speed of approximately 30 km per year. In 2008 or 2009, this species appeared in the Southern Urals. The emergence of An. maculipennis in this region was accompanied by a decrease in the proportions of An. messeae A and An. beklemishevi and by an increase in the proportion of An. messeae B within An. messeae s.l. It is highly likely that the southwestern border of An. beklemishevi distribution area could shift in the same direction as expanding area of An. maculipennis. ::: ::: ::: CONCLUSIONS ::: The geographical distribution of the Palaearctic mosquito species of the Maculipennis Complex is undergoing a gradual shift. Changes detected in the species distribution can be considered as a component of the biocenotic process triggered by global warming. Both the warming itself and consequent expansion of An. maculipennis s.s. to the northeast, followed by changes in the species composition of Anopheles as well as their relative proportions and fluctuations in the species areas, exacerbate the epidemiology of malaria infection in Eurasia.


title: Supervised sentiment analysis in Czech social media

abstract: This article describes in-depth research on machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in the case of the Czech language no systematic research has yet been conducted. We tackle this issue and establish a common ground for further research by providing a large human-annotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. We also experiment with five different feature selection algorithms and investigate the influence of named entity recognition and preprocessing on sentiment classification performance. Moreover, in addition to our newly created social media dataset, we also report results for other popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition, potentially leading to the production of high-end commercial solutions.