title: A Language for Modular Answer Set Programming: Application to ACC Tournament Scheduling

abstract: In this paper we develop a declarative language for modular answer set programming (ASP). Our language allows to declaratively state how one ASP module can import processed answer sets from another ASP module. We define the syntax and semantics of our language and illustrate its applicability by modeling the ACC tournament scheduling problem. Besides the elegance of developing declarative programs in a modular manner, our illustration shows that a problem that is not timely solvable when done in a monolithic way, but becomes solvable when done in a modular way.


title: A new adaptive Cuckoo search algorithm

abstract: This paper presents a new adaptive Cuckoo search (ACS) algorithm based on the Cuckoo search (CS) for optimization. The main thrust is to decide the step size adaptively from its fitness value without using the Levy distribution. The other idea is to enhance the performance from the point of time and global minima. The performance of ACS against standard benchmark function show that the proposed algorithm converges to best solution with less time than Cuckoo search.


DEVELOPMENT OF THE NIST ROBOT CRANE TELEOPERATION CONTROLLER


title: Impact of Status and Meme Content on the Spread of Memes in Virtual Communities

abstract: We examined the influence of meme consistency (vs. inconsistency) and intragroup status on the spread of memes in virtual communities. Prior research suggests that information consistent with the theme of the group is remembered better and that ideas threatening to the group identity are rejected. In addition, previous research shows that low-status group members mimic high-status members and communicate with them to seek information and approval. We analyzed social interactions among members of four online forums from January 1, 2010, to February 21, 2014. Contrary to our prediction, our results show that memes initiated by low-status members spread faster than memes started by highor moderate-status members. In line with prior research, memes that were consistent with a forum theme were spread more frequently than inconsistent memes.


title: Development, Reliability, and Validity of the Handwriting Proficiency Screening Questionnaire (HPSQ)

abstract: OBJECTIVE. This study was used to (1) develop an occupational therapy screening questionnaire (the Handwriting Proficiency Screening Questionnaire [HPSQ]) to identify handwriting difficulties among schoolaged children and (2) examine its reliability and validity. METHOD. The questionnaire’s content validity was established. Internal consistency, interrater and test–retest reliability, and concurrent and construct validity were initiated. Participants included 7- to 14-year-old (N = 230) typically developing school-aged children. RESULTS. The tool demonstrated good internal consistency (α = .90). Test–retest reliability for the score revealed an intraclass correlation coefficient (ICC) of .84 and interrater reliability of ICC =.92 for the total questionnaire score. Construct and concurrent validity were also confirmed. CONCLUSION. The HPSQ is suitable for use by occupational therapists for identification of handwriting defi ciency among school-aged children and is appropriate for varied academic and clinical uses. More studies with larger samples of varied age groups are required to further support the questionnaire’s reliability and validity. Rosenblum, S. (2008). Development, reliability, and validity of the Handwriting Proficiency Screening Questionnaire


title: Inconsistency in deception for defense

abstract: The use of deception is one of many defensive techniques being explored today. In the past, defenders of systems have used deception haphazardly, but now researchers are developing systematic methods of deception. The cornerstone of these methods is internal consistency: projecting a "false reality", or "fiction", that the attacker is to accept as reality. We challenge the necessity of this cornerstone, and explore the nature and possible uses of inconsistency in deception as a defense.


title: Identifying and characterising health policy and system-relevant documents in Uganda: a scoping review to develop a framework for the development of a one-stop shop

abstract: Health policymakers in low- and middle-income countries continue to face difficulties in accessing and using research evidence for decision-making. This study aimed to identify and provide a refined categorisation of the policy documents necessary for building the content of a one-stop shop for documents relevant to health policy and systems in Uganda. The on-line resource is to facilitate timely access to well-packaged evidence for decision-making. We conducted a scoping review of Uganda-specific, health policy, and systems-relevant documents produced between 2000 and 2014. Our methods borrowed heavily from the 2005 Arksey and O’Malley approach for scoping reviews and involved five steps, which that include identification of the research question; identification of relevant documents; screening and selection of the documents; charting of the data; and collating, summarising and reporting results. We searched for the documents from websites of relevant government institutions, non-governmental organisations, health professional councils and associations, religious medical bureaus and research networks. We presented the review findings as numerical analyses of the volume and nature of documents and trends over time in the form of tables and charts. We identified a total of 265 documents including policies, strategies, plans, guidelines, rapid response summaries, evidence briefs for policy, and dialogue reports. The top three clusters of national priority areas addressed in the documents were governance, coordination, monitoring and evaluation (28%); disease prevention, mitigation, and control (23%); and health education, promotion, environmental health and nutrition (15%). The least addressed were curative, palliative care, rehabilitative services and health infrastructure, each addressed in three documents (1%), and early childhood development in one document. The volume of documents increased over the past 15 years; however, the distribution of the different document types over time has not been uniform. The review findings are necessary for mobilising and packaging the local policy-relevant documents in Uganda in a one-stop shop; where policymakers could easily access them to address pressing questions about the health system and interventions. The different types of available documents and the national priority areas covered provide a good basis for building and organising the content in a meaningful way for the resource.


title: Design and Calibration of an RF Actuator for Low-Level RF Systems

abstract: X-ray free electron laser (FEL) machines like the Linac Coherent Light Source (LCLS) at SLAC require high-quality electron beams to generate X-ray lasers for various experiments. Digital low-level RF (LLRF) systems are widely used to control the high-power RF klystrons to provide a highly stable RF field in accelerator structures for beam acceleration. Feedback and feedforward controllers are implemented in LLRF systems to stabilize or adjust the phase and amplitude of the RF field. To achieve the RF stability and the accuracy of the phase and amplitude adjustment, low-noise and highly linear RF actuators are required. Aiming for the upgrade of the $S$ -band Linac at SLAC, an RF actuator is designed with an ${\hbox{I/Q}}$ modulator driven by two digital-to-analog converters (DAC) for the digital LLRF systems. A direct upconversion scheme is selected for RF actuation, and an on-line calibration algorithm is developed to compensate the RF reference leakage and the imbalance errors in the ${\hbox{I/Q}}$ modulator, which may cause significant phase and amplitude actuation errors. This paper presents the requirements on the RF actuator, the design of the hardware, the calibration algorithm, and the implementation in firmware and software and the test results at LCLS.


title: A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs)

abstract: Abstract The last decade has seen a growing interest in the use of Unmanned Aerial Vehicles (UAVs) for various applications and services. UAVs, or drones as referred to, have shown to be efficient in completing complex tasks when organized as ad hoc connected groups, thus forming a Flying Ad hoc Network (FANET). Although similar to Mobile Ad hoc Network (MANET) and Vehicular Ad hoc Network (VANET), FANETs have their own characteristics. One of the main difference is the fact that UAVs in general, but particularly when organized are FANETs, are mission-based, and their mobility models are often dictated by the purpose of their mission and the nature of the task they plan to accomplish. Therefore, routing protocols for FANETs should take into consideration the nature of the applications and services that the UAVs are deployed for, and factor in the mobility models. However, designing routing protocols for FANETs is not an easy task given the highly dynamic topology of FANETs and the flying constraints they are subjected to. Compared to topology-based routing, position-based routing demonstrated high efficiency and resilience to handle the high mobility of FANET nodes. To this end, in this paper, we propose a comprehensive survey of position-based routing protocols for FANETs with their various categories. We propose a classification and a taxonomy of these protocols, including a detailed description of the routing schemes used in each category. We propose a comparative study based on various criteria, and discuss the advantages and weaknesses of each protocol. Furthermore, new challenges for future research are presented, which introduce a new kind of coordination between UAVs and existing VANETs on the ground. The originality of this survey is that it complements the existing surveys on the same theme by providing more details on some aspects that have been addressed only ostensibly by other surveys in the literature.


Deep fisher faces


title: Cloning and trapping of magnetostatic spin-wave pulses by parametric pumping

abstract: Magnetostatic backward volume spin-wave SW pulses have been cloned and trapped through the use of multiple, properly timed, parametric pumping PP pulses. The experiment was done with 30-ns-wide pulses at 5 GHz in a 7.1-m-thick yttrium iron garnet film. Well-timed, large-area, high-power, 2-ns-wide microwave pulses at 10 GHz were used to pump the SW pulses. The interaction between the initial SW and PP pulses created a mirror-image, time-reversed idle SW pulse with the same frequency as, but opposite wave vector to, the initial SW pulse. Additional properly timed PP pulses continued the reversal process and yielded exact clones of the original SW pulse. The relative peak output powers of the clone pulses could be adjusted through changes in the pumping power.


title: Stochastic Joint Optimization of Wind Generation and Pumped-Storage Units in an Electricity Market

abstract: One of the main characteristics of wind power is the inherent variability and unpredictability of the generation source, even in the short-term. To cope with this drawback, hydro pumped-storage units have been proposed in the literature as a good complement to wind generation due to their ability to manage positive and negative energy imbalances over time. This paper investigates the combined optimization of a wind farm and a pumped-storage facility from the point of view of a generation company in a market environment. The optimization model is formulated as a two-stage stochastic programming problem with two random parameters: market prices and wind generation. The optimal bids for the day-ahead spot market are the ldquohere and nowrdquo decisions while the optimal operation of the facilities are the recourse variables. A joint configuration is modeled and compared with an uncoordinated operation. A realistic example case is presented where the developed models are tested with satisfactory results.


title: Gain-Enhanced Patch Antenna Without Enlarged Size via Loading of Slot and Shorting Pins

abstract: A patch antenna with loading of slot and shorting pins is proposed in this paper to enhance the radiation gain without increasing the patch size. At first, a straight slot is transversely introduced in the central line of a patch. For the dominant TM01 mode, this central line behaves as a virtual electric wall with zero electric field and maximum surface current underneath and on the patch, respectively. By doing so, the central slot serves as an extra radiator so that this slot-loaded patch produces radiation as an equivalent three-slot array. Meanwhile, a pair of shorting pins is longitudinally placed at the two symmetrical sides of this central slot to maintain the resonant frequency. Due to the out of phase between the electric fields in the central slot and two sided slots, the far-field radiated by the three slots can be canceled with each other in specific directions away from broadside. Thus, this cancellation results in sidelobe of low level in the E-plane due to emergence of radiation nulls, and further makes the main radiation beam more directionally selective. Accordingly, the directivity and gain of this proposed patch antenna are enhanced with no need of enlarging the overall size of patch. Finally, the simulated and measured results are provided to validate the operating principle and show that the directivity is increased by about 1.7 dB increment.


title: Generating Sentiment Lexicons for German Twitter

abstract: Despite a substantial progress made in developing new sentiment lexicon generation (SLG) methods for English, the task of transferring these approaches to other languages and domains in a sound way still remains open. In this paper, we contribute to the solution of this problem by systematically comparing semi-automatic translations of common English polarity lists with the results of the original automatic SLG algorithms, which were applied directly to German data. We evaluate these lexicons on a corpus of 7,992 manually annotated tweets. In addition to that, we also collate the results of dictionary- and corpus-based SLG methods in order to find out which of these paradigms is better suited for the inherently noisy domain of social media. Our experiments show that semi-automatic translations notably outperform automatic systems (reaching a macro-averaged F1-score of 0.589), and that dictionary-based techniques produce much better polarity lists as compared to corpus-based approaches (whose best F1-scores run up to 0.479 and 0.419 respectively) even for the non-standard Twitter genre.


title: Customised OCR correction for historical medical text

abstract: Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, owing to large-scale digitisation efforts. Searchable access is typically provided by applying Optical Character Recognition (OCR) software to scanned page images. Often, however, the automatically recognised text contains a large number of errors, since OCR systems are typically optimised to deal with modern documents, and can struggle with historical document features, including variable print characteristics and archaic vocabulary usage. Low quality OCR text can reduce the efficiency of search systems over historical archives, particularly semantic systems that are based on the application of sophisticated text mining (TM) techniques. We report on a new OCR correction strategy, customised for historical medical documents. The method combines rule-based correction of regular errors with a medically-tuned spell-checking strategy, whose corrections are guided by information about subject-specific language usage from the publication period of the article to be corrected. The performance of our method compares favourably to other OCR post-correction strategies, in improving word-level accuracy of poor-quality documents by up to 16%.


title: A Weakly-Supervised Approach for Discovering New User Intents from Search Query Logs

abstract: State-of-the art spoken language understanding models that automatically capture user intents in human to machine dialogs are trained with manually annotated data, which is cumbersome and time-consuming to prepare. For bootstrapping the learning algorithm that detects relations in natural language queries to a conversational system, one can rely on publicly available knowledge graphs, such as Freebase, and mine corresponding data from the web. In this paper, we present an unsupervised approach to discover new user intents using a novel Bayesian hierarchical graphical model. Our model employs search query click logs to enrich the information extracted from bootstrapped models. We use the clicked URLs as implicit supervision and extend the knowledge graph based on the relational information discovered from this model. The posteriors from the graphical model relate the newly discovered intents with the search queries. These queries are then used as additional training examples to complement the bootstrapped relation detection models. The experimental results demonstrate the effectiveness of this approach, showing extended coverage to new intents without impacting the known intents. Index Terms: spoken language understanding, graphical models, search query click logs, intent discovery.


title: A Novel Design and Modeling Paradigm for Memristor-Based Crossbar Circuits

abstract: Over 30 years ago L. Chua proposed the existence of a new class of passive circuit elements, which he called memristors and memristive devices. The unique electrical characteristics associated with them, along with the advantages of crossbar structures, have the potential to revolutionize computing architectures. A well-defined and effective memristor model for circuit design combined with a design paradigm based on well-understood underlying logic design principles would certainly accelerate research on nanoscale circuits and systems. Toward this goal, we propose a memristor crossbar circuit design paradigm in which memristors are modeled using the quantum mechanical phenomenon of tunneling. We use this circuit model to design and simulate various logic circuit designs capable of universal computation. Finally, we develop and present a new design paradigm for memristor-based crossbar circuits.


title: A dead-reckoning scheme for skid-steered vehicles in outdoor environments

abstract: A dead-reckoning scheme appropriate for skid-steered mobile robots is introduced to serve in a wider scheme for simultaneous localization and map-building. It is based on internal sensors (inertial data and odometry) only. The information from an experimentally derived kinematic model and an onboard inertial navigation system (INS), after a necessary pre-filtering stage, is fused using a simple and fast modified Kalman filter. We verify our approach with large scale experiments in outdoors structured environments and variable terrains, following paths with steep turns and variable velocity.


title: A Simple Power-Analysis (SPA) Attackon Implementations of the AES Key Expansion

abstract: This article presents a simple power-analysis (SPA) attack on implementations of the AES key expansion. The attack reveals the secret key of AES software implementations on smart cards by exploiting the fact that the power consumption of most smart-card processors leaks information during the AES key expansion. The presented attack efficiently utilizes this information leakage to substantially reduce the key space that needs to be considered in a brute-force search for the secret key. The details of the attack are described on the basis of smart cards that leak the Hamming weight of intermediate results occurring during the AES key expansion.


title: A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

abstract: Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie/TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49% enhancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain.


title: Cultural Congruence and Infusion Nursing Practice.

abstract: The importance of cultural competence in every nursing practice setting in today's world cannot be understated. Unconscious bias can have detrimental effects on therapeutic relationships and health outcomes. Nursing models of cultural competence by Purnell, Leininger, and Campinha-Bacote are reviewed. The Kleinman Model and LEARN Model offer questions and guidelines to facilitate assessment of patients' understanding of illness and treatment. The Infusion Nursing Standards of Practice contains elements of diversity and cultural competence throughout. Self-reflection of one's own values, beliefs, biases, and practice as an infusion nurse will promote the development of cultural competence.


title: P..: The Software Station: A System for Version Controlled Development and Web Based Deployment of Software for a Mobile Environment

abstract: As mobile devices become more wide spread, making available software for such devices is crucial for making use of their capabilities. However, due to the inherent constraints of mobile devices, it is highly non trivial to do so. Especially for larger software projects, additional support for the development and deployment phase is required. Therefore, in this paper, we propose a system that provides such support, i.e., the Software Station. The system’s requirements result from the analysis of our project environment. On the one hand, the development of software components is based upon a version control system. On the other hand, a web based interface facilitates the automated yet flexible deployment of software components to the respective mobile devices. We highlight design and implementation issues of the Software Station. In this context, our experiences with the applied technologies and tools are shared.


title: Similarity Measures for Text Document Clustering

abstract: Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets. A wide variety of distance functions and similarity measures have been used for clustering, such as squared Euclidean distance, cosine similarity, and relative entropy. In this paper, we compare and analyze the eectiveness of these measures in partitional clustering for text document datasets. Our experiments utilize the standard Kmeans algorithm and we report results on seven text document datasets and five distance/similarity measures that have been most commonly used in text clustering.


title: Review of Action Recognition and Detection Methods

abstract: In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and/or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and/or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two-step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision-based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state-of-the-art methods are reviewed and some remaining problems are addressed in the final chapter.


title: Hungarian optimum assignment algorithm with Java computer animation

abstract: The classical, popular Hungarian algorithm for solving the “optimum assignment” problems (with its broad engineering/science applications) has been well-documented in the literature. Other (more efficient) variations of the Hungarian algorithm have also been extensively studied by the research communities. In this paper, the basic Hungarian algorithm is revisited, with the ultimate goal of developing a useful, user friendly, attractive Java computer animation for “effectively teaching” this basic/important optimum assignment algorithm. The final product from this work will help both the students and their instructor to not only mastering this technical subject, but also provide valuable tool for obtaining the solutions for homework assignments, class examinations, self-assessment tools, etc. A demo video of the Hungarian Algorithm's animation and result can be viewed online from any web browser using the website provided in reference [9].


title: KEEPING YOUR HEADCOUNT WHEN ALL ABOUT YOU ARE LOSING THEIRS : DOWNSIZING , VOLUNTARY TURNOVER RATES , AND THE MODERATING ROLE OF HR PRACTICES

abstract: Although both downsizing and voluntary turnover have been topics of great interest in the organizational literature, little research addresses the topics' possible relationship. Using organization-level data from multiple industries, we first investigate whether downsizing predicts voluntary turnover rates. Second, to support our causal model, we examine whether aggregated levels of organizational commitment mediate this relationship. Third, we test whether the downsizing-turnover rate relationship is (1) mitigated by HR practices that either embed employees in their organization or convey procedural fairness and (2) strengthened by HR practices that enhance career development. Results support the hypothesized main, mediated, and moderated effects.


title: Super-resolution from image sequences-a review

abstract: Growing interest in super-resolution (SR) restoration of video sequences and the closed related problem of construction of SR still images from image sequences has led to the emergence of several competing methodologies. We review the state of the art of SR techniques using a taxonomy of existing techniques. We critique these methods and identified areas which promise performance improvements.


title: Bi-Level Online Aggregation on Raw Data

abstract: In-situ processing has been proposed as a novel data exploration solution in many domains generating massive amounts of raw data, e.g., astronomy, since it provides immediate SQL querying over raw files. The performance of in-situ processing across a query workload is, however, limited by the speed of full scan, tokenizing, and parsing of the entire data. Online aggregation (OLA) has been introduced as an efficient method for data exploration that identifies uninteresting patterns faster by continuously estimating the result of a computation during the actual processing---the computation can be stopped as early as the estimate is accurate enough to be deemed uninteresting. However, existing OLA solutions have a high upfront cost of randomly shuffling and/or sampling the data. In this paper, we present OLA-RAW, a bi-level sampling scheme for parallel online aggregation over raw data. Sampling in OLA-RAW is query-driven and performed exclusively in-situ during the runtime query execution, without data reorganization. This is realized by a novel resource-aware bi-level sampling algorithm that processes data in random chunks concurrently and determines adaptively the number of sampled tuples inside a chunk. In order to avoid the cost of repetitive conversion from raw data, OLA-RAW builds and maintains a memory-resident bi-level sample synopsis incrementally. We implement OLA-RAW inside a modern in-situ data processing system and evaluate its performance across several real and synthetic datasets and file formats. Our results show that OLA-RAW chooses the sampling plan that minimizes the execution time and guarantees the required accuracy for each query in a given workload. The end result is a focused data exploration process that avoids unnecessary work and discards uninteresting data.


title: Code-Red: a case study on the spread and victims of an internet worm

abstract: On July 19, 2001, more than 359,000 computers connected to the Internet were infected with the Code-Red (CRv2) worm in less than 14 hours. The cost of this epidemic, including subsequent strains of Code-Red, is estimated to be in excess of $2.6 billion. Despite the global damage caused by this attack, there have been few serious attempts to characterize the spread of the worm, partly due to the challenge of collecting global information about worms. Using a technique that enables global detection of worm spread, we collected and analyzed data over a period of 45 days beginning July 2nd, 2001 to determine the characteristics of the spread of Code-Red throughout the Internet.In this paper, we describe the methodology we use to trace the spread of Code-Red, and then describe the results of our trace analyses. We first detail the spread of the Code-Red and CodeRedII worms in terms of infection and deactivation rates. Even without being optimized for spread of infection, Code-Red infection rates peaked at over 2,000 hosts per minute. We then examine the properties of the infected host population, including geographic location, weekly and diurnal time effects, top-level domains, and ISPs. We demonstrate that the worm was an international event, infection activity exhibited time-of-day effects, and found that, although most attention focused on large corporations, the Code-Red worm primarily preyed upon home and small business users. We also qualified the effects of DHCP on measurements of infected hosts and determined that IP addresses are not an accurate measure of the spread of a worm on timescales longer than 24 hours. Finally, the experience of the Code-Red worm demonstrates that wide-spread vulnerabilities in Internet hosts can be exploited quickly and dramatically, and that techniques other than host patching are required to mitigate Internet worms.


title: Bandpass Filter With Tunable Bandwidth Using Quadruple-Mode Stub-Loaded Resonator

abstract: In this letter, a novel quadruple-mode resonator bandpass filter (BPF) with tunable bandwidth is proposed. By using two types of microwave varactors to tune the resonant frequencies and transmission zeros (TZs), the high-side edge of the passband and low-side edge of the passband can be separately varied. Therefore, the bandwidth could be tunable with reasonable adjust of the values of varactors. Theoretical expressions have been derived for the resonant frequencies and the TZs. The measured results of the BPF show that the fractional bandwidth can be tuned from 22% to 34% at a fixed centre frequency 2.0 GHz with a return loss better than dB. The tested results show good agreement with simulated results.


title: A taxonomy of scientific workflow systems for grid computing

abstract: With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.


title: The music notepad

abstract: We present a system for entering common music notation based on 2D gestural input. The key feature of the system is the look-and-feel of the interface which approximates sketching music with paper and pencil. A probability-based interpreter integrates sequences of gestural input to perform the most common notation and editing operations. In this paper, we present the user’s model of the system, the components of the high-level recognition system, and a discussion of the evolution of the system including user feedback.


Emotion Analysis of Twitter Data That Use Emoticons and Emoji Ideograms


Universal Dependencies : A cross-linguistic typology


title: VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

abstract: In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps). The benchmark and the VPGNet model will be publicly available.


title: Phenomenology of subnanosecond gas discharges at pressures below one atmosphere

abstract: Volume breakdown and surface flashover in quasi-homogeneous applied fields in 10-5 to 600 torr argon and dry air are investigated, using voltage pulses with 150 ps risetime, 100 torr, indicating the role of runaway electrons during breakdown. Maximum X-ray emission coincides with shortest breakdown delay times at several 10 torr. Simple modeling using the average force equation and cross sections for momentum transfer and ionization supports the experimental results


title: Traumatic perforation of the hypopharynx--an unusual form of abuse.

abstract: Two infants presented with extensive interstitial emphysema of the neck as a result of non-accidental trauma to the pharynx. The clinical presentation, diagnosis, and management of this unusual form of child abuse is discussed.


title: Epidemic prediction and control in weighted networks

abstract: Contact networks are often used in epidemiological studies to describe the patterns of interactions within a population. Often, such networks merely indicate which individuals interact, without giving any indication of the strength or intensity of interactions. Here, we use weighted networks, in which every connection has an associated weight, to explore the influence of heterogeneous contact strengths on the effectiveness of control measures. We show that, by using contact weights to evaluate an individual's influence on an epidemic, individual infection risk can be estimated and targeted interventions such as preventative vaccination can be applied effectively. We use a diary study of social mixing behaviour to indicate the patterns of contact weights displayed by a real population in a range of different contexts, including physical interactions; we use these data to show that considerations of link weight can in some cases lead to improved interventions in the case of infections that spread through close contact interactions. However, we also see that simpler measures, such as an individual's total number of social contacts or even just their number of contacts during a single day, can lead to great improvements on random vaccination. We therefore conclude that, for many infections, enhanced social contact data can be simply used to improve disease control but that it is not necessary to have full social mixing information in order to enhance interventions.


title: Audio-visual speech activity detection in a two-speaker scenario incorporating depth information from a profile or frontal view

abstract: Motivated by increasing popularity of depth visual sensors, such as the Kinect device, we investigate the utility of depth information in audio-visual speech activity detection. A two-subject scenario is assumed, allowing to also consider speech overlap. Two sensory setups are employed, where depth video captures either a frontal or profile view of the subjects, and is subsequently combined with the corresponding planar video and audio streams. Further, multi-view fusion is regarded, using audio and planar video from a sensor at the complementary view setup. Support vector machines provide temporal speech activity classification for each visually detected subject, fusing the available modality streams. Classification results are further combined to yield speaker diarization. Experiments are reported on a suitable audio-visual corpus recorded by two Kinects. Results demonstrate the benefits of depth information, particularly in the frontal depth view setup, reducing speech activity detection and speaker diarization errors over systems that ignore it.


University of Surrey Participation in TREC8: Weirdness Indexing for Logical Document Extrapolation and Retrieval (WILDER)


title: Cyber Security and Privacy Issues in Smart Grids

abstract: Smart grid is a promising power delivery infrastructure integrated with communication and information technologies. Its bi-directional communication and electricity flow enable both utilities and customers to monitor, predict, and manage energy usage. It also advances energy and environmental sustainability through the integration of vast distributed energy resources. Deploying such a green electric system has enormous and far-reaching economic and social benefits. Nevertheless, increased interconnection and integration also introduce cyber-vulnerabilities into the grid. Failure to address these problems will hinder the modernization of the existing power system. In order to build a reliable smart grid, an overview of relevant cyber security and privacy issues is presented. Based on current literatures, several potential research fields are discussed at the end of this paper.


title: Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

abstract: Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation strategies. We demonstrate that meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime. Our experiments with a population of agents that learn and compete suggest that meta-learners are the fittest.


title: A group mobility model for ad hoc wireless networks

abstract: In this paper, we present a survey of various mobility models in both cellular networks and multi-hop networks. We show that group motion occurs frequently in ad hoc networks, and introduce a novel group mobility model Reference Point Group Mobility (RPGM) to represent the relationship among mobile hosts. RPGM can be readily applied to many existing applications. Moreover, by proper choice of parameters, RPGM can be used to model several mobility models which were previously proposed. One of the main themes of this paper is to investigate the impact of the mobility model on the performance of a specific network protocol or application. To this end, we have applied our RPGM model to two different network protocol scenarios, clustering and routing, and have evaluated network performance under different mobility patterns and for different protocol implementations. As expected, the results indicate that different mobility patterns affect the various protocols in different ways. In particular, the ranking of routing algorithms is influenced by the choice of mobility pattern.


The Influence of Information Overload on the Development of Trust and Purchase Intention Based on Online Product Reviews in a Mobile vs. Web Environment: A Research Proposal


title: Distributed formation control via global orientation estimation

abstract: In this paper, we propose a novel distributed formation control strategy, which is based on the measurements of relative position of neighbors, with global orientation estimation. Since agents do not share a common reference frame, orientations of the local reference frames are not aligned with each other. The proposed strategy includes a combination of global orientation estimation and formation control law. Under the proposed strategy, the orientation of each agent is estimated in the global sense, if interaction graph has a spanning tree. With the estimated orientations of local frames, formation control strategy ensures that the formation globally exponentially converges to the desired formation.


title: Adding Concurrency to Smart Contracts

abstract: Modern cryptocurrency systems, such as Ethereum, permit complex financial transactions through scripts called smart contracts. These smart contracts are executed many, many times, always without real concurrency. First, all smart contracts are serially executed by miners before appending them to the blockchain. Later, those contracts are serially re-executed by validators to verify that the smart contracts were executed correctly by miners. ::: Serial execution limits system throughput and fails to exploit today's concurrent multicore and cluster architectures. Nevertheless, serial execution appears to be required: contracts share state, and contract programming languages have a serial semantics. ::: This paper presents a novel way to permit miners and validators to execute smart contracts in parallel, based on techniques adapted from software transactional memory. Miners execute smart contracts speculatively in parallel, allowing non-conflicting contracts to proceed concurrently, and "discovering" a serializable concurrent schedule for a block's transactions, This schedule is captured and encoded as a deterministic fork-join program used by validators to re-execute the miner's parallel schedule deterministically but concurrently. ::: Smart contract benchmarks run on a JVM with ScalaSTM show that a speedup of of 1.33x can be obtained for miners and 1.69x for validators with just three concurrent threads.


title: Auditory training improves neural timing in the human brainstem

abstract: Abstract The auditory brainstem response reflects neural encoding of the acoustic characteristic of a speech syllable with remarkable precision. Some children with learning impairments demonstrate abnormalities in this preconscious measure of neural encoding especially in background noise. This study investigated whether auditory training targeted to remediate perceptually-based learning problems would alter the neural brainstem encoding of the acoustic sound structure of speech in such children. Nine subjects, clinically diagnosed with a language-based learning problem (e.g., dyslexia), worked with auditory perceptual training software. Prior to beginning and within three months after completing the training program, brainstem responses to the syllable /da/ were recorded in quiet and background noise. Subjects underwent additional auditory neurophysiological, perceptual, and cognitive testing. Ten control subjects, who did not participate in any remediation program, underwent the same battery of tests at time intervals equivalent to the trained subjects. Transient and sustained (frequency-following response) components of the brainstem response were evaluated. The primary pathway afferent volley – neural events occurring earlier than 11 ms after stimulus onset – did not demonstrate plasticity. However, quiet-to-noise inter-response correlations of the sustained response (∼11–50 ms) increased significantly in the trained children, reflecting improved stimulus encoding precision, whereas control subjects did not exhibit this change. Thus, auditory training can alter the preconscious neural encoding of complex sounds by improving neural synchrony in the auditory brainstem. Additionally, several measures of brainstem response timing were related to changes in cortical physiology, as well as perceptual, academic, and cognitive measures from pre- to post-training.


title: Labeling images with a computer game

abstract: We introduce a new interactive system: a game that is fun and can be used to create valuable output. When people play the game they help determine the contents of images by providing meaningful labels for them. If the game is played as much as popular online games, we estimate that most images on the Web can be labeled in a few months. Having proper labels associated with each image on the Web would allow for more accurate image search, improve the accessibility of sites (by providing descriptions of images to visually impaired individuals), and help users block inappropriate images. Our system makes a significant contribution because of its valuable output and because of the way it addresses the image-labeling problem. Rather than using computer vision techniques, which don't work well enough, we encourage people to do the work by taking advantage of their desire to be entertained.


Latent Dirichlet Allocation For Text And Image Topic Modeling


title: Review on thermal energy storage with phase change materials and applications

abstract: The use of a latent heat storage system using phase change materials (PCMs) is an effective way of storing thermal energy and has the advantages of high-energy storage density and the isothermal nature of the storage process. PCMs have been widely used in latent heat thermal-storage systems for heat pumps, solar engineering, and spacecraft thermal control applications. The uses of PCMs for heating and cooling applications for buildings have been investigated within the past decade. There are large numbers of PCMs that melt and solidify at a wide range of temperatures, making them attractive in a number of applications. This paper also summarizes the investigation and analysis of the available thermal energy storage systems incorporating PCMs for use in different applications.


title: Exploring the Application of Multilayer Networks in Enterprise Architecture: A Case Study in the Smart Grid

abstract: We present an exploratory investigation on the use of multilayer network concepts and metrics to quantitatively analyse enterprise architectures. Multilayer networks can be used to visualise the overall architectural design and perform analyses across layers within elaborate architectures. Preliminary results are presented and discussed based on a case study in the Smart Grid, which is modelled using the Smart Grid Architecture Model (SGAM). The SGAM representation is later on mapped onto a multilayer network notation that allows visualising the overall structure of the architecture as well as computing quantitative metrics. We conclude with a summary of insights and open issues to be addressed.


title: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

abstract: Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. ::: ::: In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.


title: Mining Multilabel data

abstract: A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label.


Planar Array Antennas with Travelling-Wave Excitation in Millimeter-Wave Band


title: Narcissism at the crossroads: Phenotypic description of pathological narcissism across clinical theory, social/personality psychology, and psychiatric diagnosis

abstract: Abstract This review documents two themes of emphasis found in phenotypic descriptions of pathological narcissism across clinical theory, social/personality psychology, and psychiatric diagnosis. Clinical theories of narcissism spanning 35 years consistently describe variations in the expression of pathological narcissism that emphasize either grandiosity or vulnerable affects and self-states. Recent research in social/personality psychology examining the structure of narcissistic personality traits consistently finds two broad factors representing Grandiosity–Exhibitionism and Vulnerability–Sensitivity–Depletion respectively. However, the majority of psychiatric criteria for narcissistic personality disorder (NPD) in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 1994) emphasize expressions of grandiosity. By placing most of the diagnostic emphasis on overt grandiosity, DSM NPD has been limited by poor discriminant validity, modest levels of temporal stability, and the lowest prevalence rate on Axis II. Despite converging support for two phenotypic themes associated with pathological narcissism, psychiatric diagnosis and social/personality psychology research often focus only on grandiosity in the assessment of narcissism. In contrast, clinical theory struggles with a proliferation of labels describing these broad phenotypic variations. We conclude that the construct of pathological narcissism is at a crossroads and provide recommendations for diagnostic assessment, clinical conceptualization, and future research that could lead to a more integrated understanding of narcissistic personality and narcissistic personality pathology.


title: Development and Application of a Methodology to Determine Free Ferulic Acid and Ferulic Acid Ester-Linked to Different Types of Carbohydrates in Cereal Products

abstract: ABSTRACT Ferulic acid bioavailability is dependent on its form present in food. This necessitates a methodology to quantify different groups of ferulic acid derivatives in food products, especially cereal-based products. The aim of the proposed methodology is to separate and quantify ferulic acid ester-linked to mono- and/or oligosaccharides (OF), to soluble polysaccharides (SPF), and to insoluble polysaccharides (IPF) as well as in its free form. Development and partial validation of this method, which was widely based on liquid/liquid extraction and precipitation steps, was performed using characterized standard materials isolated from corn bran. As the determination of OF was one of the major goals of this methodology, three different feruloylated mono- and oligosaccharides were used for method development and validation. To determine the accuracy of the method, ferulic acid–containing standard materials added to a starch matrix were extracted and separated according to the developed protocol. The sepa...


title: On the GPU-CPU Performance Portability of OpenCL for 3D Stencil Computations

abstract: Although OpenCL programming provides full code portability between different hardware platforms, performance portability can be far from satisfactory. In this work, we use a set of representative 3D stencil computations to study OpenCL's performance portability between GPUs and CPUs. For each stencil computation, we have devised different implementations of the computational kernel function, all being 100% code-portable between the two architectures. The most straightforward and compact implementation gives satisfactory CPU performance but performs poorly on GPUs, because such an implementation hampers effective use of the GPU hardware. By injecting code complexity into the involved loop nests, we can create kernel functions that still have full code portability but with increased performance portability. It is found that spatial data blocking and register reuse can be beneficial for performance on both GPUs and CPUs, whereas use of OpenCL's local memory (and subsequent temporal blocking) may only have positive effects on GPUs.


title: Running Head: Experiential learning critics Experiential Learning and Its Critics: Preserving the Role of Experience in Management Learning and Education

abstract: In this article, I consider John Dewey's dual reformist-preservationist agenda for education in the context of current debates about the role of experience in management learning. I argue for preserving experience-based approaches to management learning by revising the concept of experience to more clearly account for the relationship between personal and social (i.e., tacit/explicit) knowledge. By reviewing, comparing, and extending critiques of Kolb's experiential learning theory (ELT) and reconceptualizing the learning process based on poststructural analysis of psychoanalyst Jacque Lacan, I define experience within the context of language and social action. This perspective is contrasted to action, cognition, critical reflection and other experience-based approaches to management learning. Implications for management theory, pedagogy, and practice suggest greater emphasis on language and conversation in the learning process. Future directions for research are explored.


title: The Anatomy of a Design Theory

abstract: Design work and design knowledge in Information Systems (IS) is important for both research and practice. Yet there has been comparatively little critical attention paid to the problem of specifying design theory so that it can be communicated, justified, and developed cumulatively. In this essay we focus on the structural components or anatomy of design theories in IS as a special class of theory. In doing so, we aim to extend the work of Walls, Widemeyer and El Sawy (1992) on the specification of information systems design theories (ISDT), drawing on other streams of thought on design research and theory to provide a basis for a more systematic and useable formulation of these theories. We identify eight separate components of design theories: (1) purpose and scope, (2) constructs, (3) principles of form and function, (4) artifact mutability, (5) testable propositions, (6) justificatory knowledge (kernel theories), (7) principles of implementation, and (8) an expository instantiation. This specification includes components missing in the Walls et al. adaptation of Dubin (1978) and Simon (1969) and also addresses explicitly problems associated with the role of instantiations and the specification of design theories for methodologies and interventions as well as for products and applications. The essay is significant as the unambiguous establishment of design knowledge as theory gives a sounder base for arguments for the rigor and legitimacy of IS as an applied discipline and for its continuing progress. A craft can proceed with the copying of one example of a design artifact by one artisan after another. A discipline cannot.


title: Digital Ecosystems

abstract: We view Digital Ecosystems to be the digital counterparts of biological ecosystems, which are considered to be robust, self-organising and scalable architectures that can automatically solve complex, dynamic problems. So, this work is concerned with the creation, investigation, and optimisation of Digital Ecosystems, exploiting the self-organising properties of biological ecosystems. First, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. We then investigated its self-organising aspects, starting with an extension to the definition of Physical Complexity to include evolving agent populations. Next, we established stability of evolving agent populations over time, by extending the Chli-DeWilde definition of agent stability to include evolutionary dynamics. Further, we evaluated the diversity of the software agents within evolving agent populations. To conclude, we considered alternative augmentations to optimise and accelerate our Digital Ecosystem, by studying the accelerating effect of a clustering catalyst on the evolutionary dynamics. We also studied the optimising effect of targeted migration on the ecological dynamics, through the indirect and emergent optimisation of the agent migration patterns. Overall, we have advanced the understanding of creating Digital Ecosystems, the self-organisation that occurs within them, and the optimisation of their Ecosystem-Oriented Architecture.


title: Emotion recognition from speech signals using new harmony features

abstract: In this paper we propose a new set of harmony features for automatic emotion recognition from speech signals. They are based on the psychoacoustic harmony perception known from music theory. Starting from the estimated pitch contour of an utterance, we calculate the circular autocorrelation of the pitch histogram on the logarithmic semitone scale. It measures the occurrence of different two-pitch intervals which cause a consonant or dissonant impression. Experiments of emotion recognition using these harmony parameters in addition to state of the art features show an improved recognition performance.


title: Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition

abstract: Since Chavez proposed the highpass filtering procedure to fuse multispectral and panchromatic images, several fusion methods have been developed based on the same principle: to extract from the panchromatic image spatial detail information to later inject it into the multispectral one. In this paper, we present new fusion alternatives based on the same concept, using the multiresolution wavelet decomposition to execute the detail extraction phase and the intensity-hue-saturation (IHS) and principal component analysis (PCA) procedures to inject the spatial detail of the panchromatic image into the multispectral one. The multiresolution wavelet decomposition has been performed using both decimated and undecimated algorithms and the resulting merged images compared both spectral and spatially. These fusion methods, as well as standard IHS-, PCA-, and wavelet-based methods have been used to merge Systeme Pour l'Observation de la Terre (SPOT) 4 XI and SPOT 4 M images with a ratio 4:1. We have estimated the validity of each fusion method by analyzing, visually and quantitatively, the quality of the resulting fused images. The methodological approaches proposed in this paper result in merged images with improved quality with respect to those obtained by standard IHS, PCA, and standard wavelet-based fusion methods. For both proposed fusion methods, better results are obtained when an undecimated algorithm is used to perform the multiresolution wavelet decomposition.


title: The WSRT HALOGAS Survey

abstract: The WSRT Hydrogen Accretion in LOcal GAlaxieS (HALOGAS) survey, now being conducted with the Westerbork Synthesis Radio Telescope (WSRT), aims at performing the first systematic investigation of cold gas accretion in nearby spiral galaxies. In the scope of this new WSRT survey, we will observe neutral hydrogen gas in 22 nearby spiral galaxies with a wide spread in morphological type, with an integration time of 120 hours per target. This is sufficient to detect faint, neutral gas in the outskirts of the target galaxies. The telescope design enables us to achieve a significantly better picture of the diffuse HI structure and its kinematics in the outskirts of the observed galaxies than has been achieved in most other HI surveys of nearby spiral galaxies. The detected amount of anomalous gas, indicative of either gas accretion or a fountain mechanism, varies significantly from galaxy to galaxy.


title: A unified account of the effects of distinctiveness, inversion, and race in face recognition

abstract: A framework is outlined in which individual faces are assumed to be encoded as a point in a multidimensional space, defined by dimensions that serve to discriminate faces. It is proposed that such a framework can account for the effects of distinctiveness, inversion, and race on recognition of faces. Two specific models within this framework are identified: a norm-based coding model, in which faces are encoded as vectors from a population norm or prototype; and a purely exemplar-based model. Both models make similar predictions, albeit in different ways, concerning the interactions between the effects of distinctiveness, inversion and race. These predictions were supported in five experiments in which photographs of faces served as stimuli. The norm-based coding version and the exemplar-based version of the framework cannot be distinguished on the basis of the experiments reported, but it is argued that a multidimensional space provides a useful heuristic framework to investigate recognition of faces. Fin...


title: Broadcast news story segmentation using latent topics on data manifold

abstract: This paper proposes to use Laplacian Probabilistic Latent Semantic Analysis (LapPLSA) for broadcast news story segmentation. The latent topic distributions estimated by LapPLSA are used to replace term frequency vector as the representation of sentences and measure the cohesive strength between the sentences. Subword n-gram is used as the basic term unit in the computation. Dynamic Programming is used for story boundary detection. LapPLSA projects the data into a low-dimensional semantic topic representation while preserving the intrinsic local geometric structure of the data. The locality preserving property attempts to make the estimated latent topic distributions more robust to the noise from automatic speech recognition errors. Experiments are conducted on the ASR transcripts of TDT2 Mandarin broadcast news corpus. Our proposed approach is compared with other approaches which use dimensionality reduction technique with the locality preserving property, and two different topic modeling techniques. Experiment results show that our proposed approach provides the highest F1-measure of 0.8228, which significantly outperforms the best previous approaches.


title: Oral cavity cancer in developed and in developing countries: Population-based incidence

abstract: Background. ::: ::: The incidence of oral cavity cancer (OCC) is not well documented because it is rarely described in accord with the anatomic definition but is usually grouped with oropharyngeal subsites. We studied the incidence of OCC in developed and in developing countries. ::: ::: ::: ::: Methods. ::: ::: The age-standardized and age-specific incidence rates of OCC were calculated for the period 1998-2002, using the topographic definition used by the Union Internationale Contre le Cancer (UICC), based on data from CI5-IX. ::: ::: ::: ::: Results. ::: ::: The highest rates are observed in Pakistan, Brazil, India, and France and were consistent with country-specific risk factors and their prevalence. ::: ::: ::: ::: Conclusions. ::: ::: In developing countries, people are exposed to a wider range of risk factors, starting at younger ages, and primary prevention measures and policies are needed. Awareness of professionals must be improved to identify people at risk and target them for prevention and to minimize the consequences of OCC. © 2009 Wiley Periodicals, Inc. Head Neck, 2010


Improvements in Tone Pronunciation Scoring for Strongly Accented Mandarin Speech 1


title: A Cost-Sensitive Model for Preemptive Intrusion Response Systems

abstract: The proliferation of complex and fast-spreading intrusions not only requires advances in intrusion detection mechanisms but also demands development of sophisticated and automated intrusion response systems. In this paper we present a novel cost-sensitive model for intrusion response that incorporates preemptive deployment of the response actions. Specifically, our technique relies on comparing the cost of deploying a response against the cost of damage caused by an "'un-attended" intrusion and decides to preemptively deploy a response with maximum benefit. Our technique further allows adaptation of responses to the changing environment through evaluation of success and failure of previously triggered responses. We demonstrate the advantages of the approach and evaluate it using a damage reduction metric.


title: A deep bidirectional long short-term memory based multi-scale approach for music dynamic emotion prediction

abstract: Music Dynamic Emotion Prediction is a challenging and significant task. In this paper, We adopt the dimensional valence-arousal (V-A) emotion model to represent the dynamic emotion in music. Considering the high context correlation among the music feature sequence and the advantage of Bidirectional Long Short-Term Memory (BLSTM) in capturing sequence information, we propose a multi-scale approach, Deep BLSTM (DBLSTM) based multi-scale regression and fusion with Extreme Learning Machine (ELM), to predict the V-A values in music. We achieved the best performance on the database of Emotion in Music task in MediaEval 2015 compared with other submitted results. The experimental results demonstrated the effectiveness of our novel proposed multi-scale DBLSTM-ELM model.


title: Vision-based Real-time Driver Fatigue Detection System for Efficient Vehicle Control

abstract: In modern days, a large no of automobile accidents are caused due to driver fatigue. To address the problem we propose a vision-based real-time driver fatigue detection system based on eye-tracking, which is an active safety system. Eye tracking is one of the key technologies, for, future driver assistance systems since human eyes contain much information about the driver's condition such as gaze, attention level, and fatigue level. Face and eyes of the driver are first localized and then marked in every frame obtained from the video source. The eyes are tracked in real time using correlation function with an automatically generated online template. Additionally, driver's distraction and conversations with passengers during driving can lead to serious results. A real-time vision-based model for monitoring driver's unsafe states, including fatigue state is proposed. A time-based eye glance to mitigate driver distraction is proposed.


title: Arbitrary-Angle Squint-Free Beamforming in Series-Fed Antenna Arrays Using Non-Foster Elements Synthesized by Negative-Group-Delay Networks

abstract: Beamforming in series-fed antenna arrays can inherently suffer from beam-squinting. To overcome the beam-squinting problem, low-dispersion, fast-wave transmission lines can be employed. Such transmission lines can be designed by loading a regular transmission line with non-Foster reactive elements (e.g., negative capacitors and inductors). As a result of a recent development, these non-Foster reactive elements can be implemented using loss-compensated negative-group-delay (NGD) networks, providing a solution to the stability issues associated with conventional non-Foster networks. In this work, transmission lines augmented by loss-compensated NGD networks, representing the non-Foster reactive-element loading, are employed for designing wideband fast-wave, low-dispersion transmission lines. This work consolidates this non-Foster reactive element loading method with earlier efforts where NGD networks were used to implement zero-degree phase shifters for beamforming at the broadside direction, and generalizes these methods for arbitrary-angle beamforming from backfire to endfire including the broadside direction. Experimental results are presented for a wideband linear four-element transmitting array feed network for beamforming at 30° with respect to the broadside direction in the frequency range 1–1.5 GHz. By connecting this feed network to four wideband tapered-slot antennas, the beamforming performance is experimentally verified inside an anechoic chamber. Moreover, the antenna array is experimentally tested for transmission of a narrow pulse, where low distortion is observed at the beamforming angle over the entire operating bandwidth. The physical length of the feed network is realistic and is 0.96 wavelengths long at the center of this frequency range. In addition, switched-line phase shifters are employed for squint-free beamforming in three other angles: 60°, 0°, and $-30^{\circ}$ .


title: The Issues of Solving Staffing and Scheduling Problems in Software Development Projects

abstract: Search-Based Software Engineering (SBSE) applies search-based optimization techniques in order to solve complex Software Engineering problems. In the recent years there has been a dramatic increase in the number of SBSE applications in areas such as Software Test, Requirements Engineering, and Project Planning. Our focus is on the analysis of the literature in Project Planning, specifically the researches conducted in software project scheduling and resource allocation. SBSE project scheduling and resource allocation solutions basically use optimization algorithms. Considering the results of a previous Systematic Literature Review, in this work, we analyze the issues of adopting these optimization algorithms in what is considered typical settings found in software development organizations. We found few evidence signaling that the expectations of software development organizations are being attended.


title: Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance.

abstract: Salinity represents an abiotic stress constraint affecting growth and productivity of plants in many regions of the world. One of the possible solutions is to improve the level of salt resistance using natural genetic variability within crop species. In the context of recent knowledge on salt stress effects and mechanisms of salt tolerance, this review present useful phenomic approach employing different non-invasive imaging systems for detection of quantitative and qualitative changes caused by salt stress at the plant and canopy level. The focus is put on hyperspectral imaging technique, which provides unique opportunities for fast and reliable estimate of numerous characteristics associated both with various structural, biochemical and physiological traits. The method also provides possibilities to combine plant and canopy analyses with a direct determination of salinity in soil. The future perspectives in salt stress applications as well as some limits of the method are also identified.


title: Sensorless Sliding-Mode MTPA Control of an IPM Synchronous Motor Drive Using a Sliding-Mode Observer and HF Signal Injection

abstract: This paper proposes a nonlinear sliding-mode speed-control scheme for interior permanent-magnet synchronous motor (IPMSM) drives incorporating the maximum-torque-per-ampere trajectory. The drive uses an adaptive sliding-mode observer (SMO) for rotor-speed estimation. The global asymptotic stabilities of both the controller and observer are guaranteed by Lyapunov stability analysis. The very low speed and standstill performance of the drive is further enhanced by combining high-frequency signal injection with the SMO. Hence, the sensorless drive is capable of exhibiting high dynamic and steady-state performances over a wide speed range. Experimental results confirm the effectiveness of the proposed method.


title: BDS as a Mediator

abstract: The Boycotts, Divestment and Sanctions (BDS) Movement is a global political campaign led by Palestinian civil society since 2005. Its official aim is to put pressure on Israel to radically change its policies towards Palestinians. Drawing on Gilles Deleuze and Felix Guattari's political philosophy, this paper discusses the BDS movement by looking into its affective powers. In particular, it claims that, above all else, acting as a political mediator BDS weakens the predominance of the current assemblage of public discourses on Israel-Palestine.


title: A supervised approach to electric tower detection and classification for power line inspection

abstract: Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution. Research efforts are focused on automating the power line inspection process by looking for strategies that satisfy the different requirements of the inspection: simultaneously detect transmission towers, check for defects, and analyze security distances. Following this direction, this paper proposes a supervised learning approach for solving the tower detection and classification problem, where HOG (Histograms of Oriented Gradients) features are used to train two MLP (multi-layer perceptron) neural networks. The first classifier is used for background-foreground segmentation, and the second multi-class MLP is used for classifying within 4 different types of electric towers. A thorough evaluation of the tower detection and classification approach has been carried out on image data from real inspections tasks with different types of towers and backgrounds. In the different evaluations, highly encouraging results were obtained. This shows that a learning-based approach is a promising technique for power line inspection.


title: Using Fuzzy AHP to manage Intellectual Capital assets: An application to the ICT service industry

abstract: In today's competitive business environment, Intellectual Capital (IC) management is ever more recognized as a fundamental factor in gaining competitive advantage. Actually, most firms have only a vague idea of how to manage investments in IC and what they should obtain from these investments. As a result, many companies overlook to balance IC investments, overinvesting in some IC components and neglecting other ones. Following this lead, the aim of the paper is to assess the relative importance of IC components, with respect to their contribution to the company value creation, in order to obtain guidelines for IC management and investments. We propose a model for IC evaluation by integrating Fuzzy Logic and Analytic Hierarchy Process (AHP). This Fuzzy AHP approach allows to capture and foster IC dynamics: experts and managers are greatly supported by the use of linguistic variables in the evaluation process of the company intangible assets. Finally, the application of the Fuzzy AHP methodology to a group of ICT service companies is presented.


title: Collaborative cache allocation and computation offloading in mobile edge computing

abstract: Mobile data traffic is increasing astronomically. This enormous extent was not only caused by the increasing in the number of mobile devices, but also the growing number of applications running on clouds. Most of these mobile devices have limited resources for computing, they are relying on cloud computing, which is inadequate to realize millisecond-scale latency in the 5G network. To deal with this issue, Mobile Edge Computing (MEC) has been introduced to supplement cloud computing by pushing Computing, Caching, Communication, and Control (4C) to the edges. However, the proposed MEC server at each base station is not enough to deal with 4C, when it operates independently, and without any collaboration with other MEC servers. This results in increasing the delay and making backhaul to continue suffering from huge data exchange between end-users and remote clouds. To address this challenge, we propose collaborative cache allocation and computation offloading, where the MEC servers collaborate for executing computation tasks and data caching. We formulate an optimization problem that aims at maximizing the resource utilization. The simulation results show that our proposal is easy to be implemented in production network.


title: Using linear programming to Decode Binary linear codes

abstract: A new method is given for performing approximate maximum-likelihood (ML) decoding of an arbitrary binary linear code based on observations received from any discrete memoryless symmetric channel. The decoding algorithm is based on a linear programming (LP) relaxation that is defined by a factor graph or parity-check representation of the code. The resulting "LP decoder" generalizes our previous work on turbo-like codes. A precise combinatorial characterization of when the LP decoder succeeds is provided, based on pseudocodewords associated with the factor graph. Our definition of a pseudocodeword unifies other such notions known for iterative algorithms, including "stopping sets," "irreducible closed walks," "trellis cycles," "deviation sets," and "graph covers." The fractional distance d/sub frac/ of a code is introduced, which is a lower bound on the classical distance. It is shown that the efficient LP decoder will correct up to /spl lceil/d/sub frac//2/spl rceil/-1 errors and that there are codes with d/sub frac/=/spl Omega/(n/sup 1-/spl epsi//). An efficient algorithm to compute the fractional distance is presented. Experimental evidence shows a similar performance on low-density parity-check (LDPC) codes between LP decoding and the min-sum and sum-product algorithms. Methods for tightening the LP relaxation to improve performance are also provided.


title: Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional Networks

abstract: This paper investigates how to rapidly and accurately localize facial landmarks in unconstrained, cluttered environments rather than in the well segmented face images. We present a novel Backbone-Branches Fully-Convolutional Neural Network (BB-FCN), which produces facial landmark response maps directly from raw images without relying on pre-process or sliding window approaches. BB-FCN contains one backbone and a number of network branches with each corresponding to one landmark type, and it operates in a progressive manner. Specifically, the backbone roughly detects the locations of facial landmarks by taking the whole image as input, and the branches further refine the localizations based on a local observation from the backbone's intermediate feature map. Moreover, our backbone-branches architecture does not contain full-connection layers for location regression, leading to efficient learning and inference. Our extensive experiments show that our model achieves superior performances over other state-of-the-arts under both the constrained (i.e. with face regions) and the "in the wild" scenarios.


title: iParker—A New Smart Car-Parking System Based on Dynamic Resource Allocation and Pricing

abstract: Parking in major cities, particularly with dense traffic, directly effects the traffic flow and people's life. In this paper, we introduce a new smart parking system that is based on intelligent resource allocation, reservation, and pricing. The proposed system solves the current parking problems by offering guaranteed parking reservations with the lowest possible cost and searching time for drivers and the highest revenue and resource utilization for parking managers. New fair pricing policies are also proposed that can be implemented in practice. The new system is based on mathematical modeling using mixed-integer linear programming (MILP) with the objective of minimizing the total monetary cost for the drivers and maximizing the utilization of parking resources.


title: Unstructured Multi-View Depth Estimation Using Mask-Based Multiplane Representation

abstract: This paper presents a novel method, MaskMVS, to solve depth estimation for unstructured multi-view image-pose pairs. In the plane-sweep procedure, the depth planes are sampled by histogram matching that ensures covering the depth range of interest. Unlike other plane-sweep methods, we do not rely on a cost metric to explicitly build the cost volume, but instead infer a multiplane mask representation which regularizes the learning. Compared to many previous approaches, we show that our method is lightweight and generalizes well without requiring excessive training. We outperform the current state-of-the-art and show results on the sun3d, scenes11, MVS, and RGBD test data sets.


title: Machine Learning for Data Management: Problems and Solutions

abstract: Machine learning has made great strides in recent years, and its applications are spreading rapidly. Unfortunately, the standard machine learning formulation does not match well with data management problems. For example, most learning algorithms assume that the data is contained in a single table, and consists of i.i.d. (independent and identically distributed) samples. This leads to a proliferation of ad hoc solutions, slow development, and suboptimal results. Fortunately, a body of machine learning theory and practice is being developed that dispenses with such assumptions, and promises to make machine learning for data management much easier and more effective [1]. In particular, representations like Markov logic, which includes many types of deep networks as special cases, allow us to define very rich probability distributions over non-i.i.d., multi-relational data [2]. Despite their generality, learning the parameters of these models is still a convex optimization problem, allowing for efficient solution. Learning structure-in the case of Markov logic, a set of formulas in first-order logic-is intractable, as in more traditional representations, but can be done effectively using inductive logic programming techniques. Inference is performed using probabilistic generalizations of theorem proving, and takes linear time and space in tractable Markov logic, an object-oriented specialization of Markov logic [3]. These techniques have led to state-of-the-art, principled solutions to problems like entity resolution, schema matching, ontology alignment, and information extraction. Using tractable Markov logic, we have extracted from the Web a probabilistic knowledge base with millions of objects and billions of parameters, which can be queried exactly in subsecond times using an RDBMS backend [3]. With these foundations in place, we expect the pace of machine learning applications in data management to continue to accelerate in coming years.


The North-Western Italian experience with anti IL-5 therapy amd comparison with regulatory trials


title: Systematic review of bankruptcy prediction models: towards a framework for tool selection

abstract: The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings.


title: A Cloud-Native Approach to 5G Network Slicing

abstract: 5G networks will support very diverse and challenging requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements using a common network infrastructure. This article presents a cloud-native approach to network slicing that advances a fundamental rethinking of the mobile network to shift its architectural vision from a network of entities to a network of capabilities, and its driving purpose from a network for connectivity to a network for services. The approach covers the entire life cycle of network slices, encompassing their design, creation, deployment, customization, and optimization. We provide an overview of our cloud-native approach to network slicing and describe a proof-of-concept implementation that demonstrates its key principles.


Mining Information from Heterogeneous Sources : A Topic Modeling Approach


title: Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data

abstract: For increased sensitivity, an improved position sensor includes a magnetic circuit in which the stationary portion includes a permanent magnet whose width is optimally 1.5 times the tooth pitch of the exciter portion of the sensor and the magnet face proximate the exciter includes a thin layer of ferromagnetic material over which is centered a narrow magnetic sensing element, such as a magnetoresistor. The sensing element has a width typically less than the tooth width. The sensing element includes a thin film of a monocrystalline semiconductive material, preferably having only a moderate bulk mobility and a larger band gap, such as indium arsenide. Current carriers flow along the length of the thin film in a surface accumulation layer, effective to provide a significant apparent increase in mobility and conductivity of said semiconductive material, and an actual increase in magnetic sensitivity and temperature insensitivity. The flux density is typically applied by appropriate magnet thickness or choice of magnet material without the need of a flux guide.


title: Bayesian Nonparametric Learning of Cloth Models for Real-Time State Estimation

abstract: Robotic solutions to clothing assistance can significantly improve quality of life for the elderly and disabled. Real-time estimation of the human–cloth relationship is crucial for efficient learning of motor skills for robotic clothing assistance. The major challenge involved is cloth-state estimation due to inherent nonrigidity and occlusion. In this study, we present a novel framework for real-time estimation of the cloth state using a low-cost depth sensor, making it suitable for a feasible social implementation. The framework relies on the hypothesis that clothing articles are constrained to a low-dimensional latent manifold during clothing tasks. We propose the use of manifold relevance determination (MRD) to learn an offline cloth model that can be used to perform informed cloth-state estimation in real time. The cloth model is trained using observations from a motion capture system and depth sensor. MRD provides a principled probabilistic framework for inferring the accurate motion-capture state when only the noisy depth sensor feature state is available in real time. The experimental results demonstrate that our framework is capable of learning consistent task-specific latent features using few data samples and has the ability to generalize to unseen environmental settings. We further present several factors that affect the predictive performance of the learned cloth-state model.


title: A Joint Model of Orthography and Morphological Segmentation

abstract: We present a model of morphological segmentation that jointly learns to segment and restore orthographic changes, e.g., funniest7! fun-y-est. We term this form of analysis canonical segmentation and contrast it with the traditional surface segmentation, which segments a surface form into a sequence of substrings, e.g., funniest7! funn-i-est. We derive an importance sampling algorithm for approximate inference in the model and report experimental results on English, German and Indonesian.


title: The Loss Surfaces of Multilayer Networks

abstract: We study the connection between the highly non-convex loss function of a simple model of the fully-connected feed-forward neural network and the Hamiltonian of the spherical spin-glass model under the assumptions of: i) variable independence, ii) redundancy in network parametrization, and iii) uniformity. These assumptions enable us to explain the complexity of the fully decoupled neural network through the prism of the results from random matrix theory. We show that for large-size decoupled networks the lowest critical values of the random loss function form a layered structure and they are located in a well-defined band lower-bounded by the global minimum. The number of local minima outside that band diminishes exponentially with the size of the network. We empirically verify that the mathematical model exhibits similar behavior as the computer simulations, despite the presence of high dependencies in real networks. We conjecture that both simulated annealing and SGD converge to the band of low critical points, and that all critical points found there are local minima of high quality measured by the test error. This emphasizes a major difference between large- and small-size networks where for the latter poor quality local minima have non-zero probability of being recovered. Finally, we prove that recovering the global minimum becomes harder as the network size increases and that it is in practice irrelevant as global minimum often leads to overfitting.


title: Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks

abstract: Speaker intent detection and semantic slot filling are two critical tasks in spoken language understanding (SLU) for dialogue systems. In this paper, we describe a recurrent neural network (RNN) model that jointly performs intent detection, slot filling, and language modeling. The neural network model keeps updating the intent estimation as word in the transcribed utterance arrives and uses it as contextual features in the joint model. Evaluation of the language model and online SLU model is made on the ATIS benchmarking data set. On language modeling task, our joint model achieves 11.8% relative reduction on perplexity comparing to the independent training language model. On SLU tasks, our joint model outperforms the independent task training model by 22.3% on intent detection error rate, with slight degradation on slot filling F1 score. The joint model also shows advantageous performance in the realistic ASR settings with noisy speech input.


title: Automatic Feature Decomposition for Single View Co-training

abstract: One of the most successful semi-supervised learning approaches is co-training for multi-view data. In co-training, one trains two classifiers, one for each view, and uses the most confident predictions of the unlabeled data for the two classifiers to "teach each other". In this paper, we extend co-training to learning scenarios without an explicit multi-view representation. Inspired by a theoretical analysis of Balcan et al. (2004), we introduce a novel algorithm that splits the feature space during learning, explicitly to encourage co-training to be successful. We demonstrate the efficacy of our proposed method in a weakly-supervised setting on the challenging Caltech-256 object recognition task, where we improve significantly over previous results by (Bergamo & Torresani, 2010) in almost all training-set size settings.


title: Duplicate-skins for compatible mesh modelling

abstract: As compatible meshes play important roles in many computer-aided design applications, we present a new approach for modelling compatible meshes. Our compatible mesh modelling method is derived from the skin algorithm [Markosian et al. 1999] which conducts an active particle-based mesh surface to approximate the given models serving as skeletons. To construct compatible meshes, we developed a duplicate-skins algorithm to simultaneously grow two skins with identical connectivity over two skeleton models; therefore, the resultant skin meshes are compatible. Our duplicate-skins algorithm has less topological constraints on the input models: multiple polygonal models, models with ill-topology meshes, or even point clouds could all be employed as skeletons to model compatible meshes. Based on the results of our duplicate-skins algorithm, the modelling method of n-Ary compatible meshes is also developed in this paper.


title: Regression testing of database applications

abstract: Database applications features such as SQL, exception programming, integrity constraints, and table triggers pose some difficulties for maintenance activities, especially for regression testing that follows modifications to database applications. In this work, we address these difficulties and propose a two-phase regression testing methodology. In Phase 1, we explore control flow and data flow analysis issues of database applications. Then, we propose an impact analysis technique that is based on dependencies that exist among the components of database applications. This analysis leads to selecting test cases from the initial test suite for regression testing the modified application. In Phase 2, further reduction in the regression test cases is performed by using reduction algorithms. We present two such algorithms. Finally, a maintenance environment for database applications is described. Our experience with the environment prototype shows promising results.


HPV DNA Testing in Cervical Cancer Screening: Results From Women in a High-Risk Province of Costa Rica


Deep neural networks and fraud detection


title: Elliptic Curves and Their Applications to Cryptography: An Introduction

abstract: List of Tables. List of Figures. Foreword. Preface. 1. Public Key Cryptography. 2. The Group Law on Elliptic Curves. 3. Elliptic Curves Over Finite Fields. 4. The Discrete Logarithm Problem. 5. Counting Points On Elliptic Curves. References. Index