title: ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores

abstract: In this paper, we present ATHENA, an ontology-driven system for natural language querying of complex relational databases. Natural language interfaces to databases enable users easy access to data, without the need to learn a complex query language, such as SQL. ATHENA uses domain specific ontologies, which describe the semantic entities, and their relationships in a domain. We propose a unique two-stage approach, where the input natural language query (NLQ) is first translated into an intermediate query language over the ontology, called OQL, and subsequently translated into SQL. Our two-stage approach allows us to decouple the physical layout of the data in the relational store from the semantics of the query, providing physical independence. Moreover, ontologies provide richer semantic information, such as inheritance and membership relations, that are lost in a relational schema. By reasoning over the ontologies, our NLQ engine is able to accurately capture the user intent. We study the effectiveness of our approach using three different workloads on top of geographical (GEO), academic (MAS) and financial (FIN) data. ATHENA achieves 100% precision on the GEO and MAS workloads, and 99% precision on the FIN workload which operates on a complex financial ontology. Moreover, ATHENA attains 87.2%, 88.3%, and 88.9% recall on the GEO, MAS, and FIN workloads, respectively.


title: A PDF Wrapper for Table Processing

abstract: We propose a PDF document wrapper system that is specifically targeted at table processing applications. We (i) review the PDF specifications and identify particular challenges from the table processing point of view, (ii) specify a table-oriented document model containing the required atomic elements for table extraction and understanding applications. Our evaluation showed that the wrapper was able to detect important features such as page columns, bullets and numbering in all measures, recording over 90% accuracy, leading to better table locating and segmenting.


title: Aerial Video Surveillance and Exploitation

abstract: There is growing interest in performing aerial surveillance using video cameras. Compared to traditional framing cameras, video cameras provide the capability to observe ongoing activity within a scene and to automatically control the camera to track the activity. However, the high data rates and relatively small field of view of video cameras present new technical challenges that must be overcome before such cameras can be widely used. In this paper, we present a framework and details of the key components for real-time, automatic exploitation of aerial video for surveillance applications. The framework involves separating an aerial video into the natural components corresponding to the scene. Three major components of the scene are the static background geometry, moving objects, and appearance of the static and dynamic components of the scene. In order to delineate videos into these scene components, we have developed real time, image-processing techniques for 2-D/3-D frame-to-frame alignment, change detection, camera control, and tracking of independently moving objects in cluttered scenes. The geo-location of video and tracked objects is estimated by registration of the video to controlled reference imagery, elevation maps, and site models. Finally static, dynamic and reprojected mosaics may be constructed for compression, enhanced visualization, and mapping applications.


title: Flexible sliding frame for gait enhancing mechatronic system (GEMS)

abstract: This paper presents a novel flexible sliding thigh frame for a gait enhancing mechatronic system. With its two-layered unique structure, the frame is flexible in certain locations and directions, and stiff at certain other locations, so that it can fit well to the wearer's thigh and transmit the assisting torque without joint loading. The paper describes the basic mechanics of this 3D flexible frame and its stiffness characteristics. We implemented the 3D flexible frame on a gait enhancing mechatronic system and conducted experiments. The performance of the proposed mechanism is verified by simulation and experiments.


title: A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

abstract: Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles’ driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario’s characteristics. Finally, the target vehicle’s future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%.


title: Iliac artery kinking with endovascular therapies: technical considerations.

abstract: Iliac artery tortuosity should be considered when planning endovascular interventions from a femoral approach. Stiff guide wires across tortuous iliac segments can introduce foreshortening and temporary kinking. Recognition of this phenomenon and its implications is important when making anatomic measurements before endovascular device placement, when assessing iliac runoff, and when considering adjunctive procedures after aortoiliac interventions. Two illustrative cases of external iliac artery kinking are presented, one during an abdominal aortic aneurysm endograft procedure and another encountered during stent placement in an external iliac artery dissection. In both cases, the temporary nature of the deformity was recognized, avoiding unnecessary additional intervention.


title: Robust LCC compensation in wireless power transfer with variable coupling factor due to coil misalignment

abstract: The paper provides an investigation of a possible application of wireless power transfer (WPT) technology to recharge the battery of a short-distance electric vehicle. The performance of the WPT system working at 85 kHz is first investigated by simulations. A robust LCC compensation network suitably tuned is then investigated with the goal to analyze the influence of design parameters on the compensation strategy. Particular attention is addressed to evaluate the deterioration of the WPT system performance due to coil misalignment. Finally, the WPT performances are evaluated demonstrating the feasibility of the proposed WPT technology.


title: Virtual Anastylosis of Greek Sculpture as Museum Policy for Public Outreach and Cognitive Accessibility

abstract: This paper deals with a virtual anastylosis of a Greek Archaic statue from ancient Sicily and the development of a public outreach protocol for those with visual impairment or cognitive disabilities through the application of three-dimensional (3-D) printing and haptic technology. The case study consists of the marble head from Leontinoi in southeastern Sicily, acquired in the 18th century and later kept in the collection of the Museum of Castello Ursino in Catania, and a marble torso, retrieved in 1904 and since then displayed in the Archaeological Museum of Siracusa. Due to similar stylistic features, the two pieces can be dated to the end of the sixth century BC. Their association has been an open problem, largely debated by scholars, who have based their hypotheses on comparisons between pictures, but the reassembly of the two artifacts was never attempted. As a result the importance of such an artifact, which could be the only intact Archaic statue of a kouros ever found in Greek Sicily, has not fully been grasped by the public. Consequently, the curatorial dissemination of the knowledge related with such artifacts is purely based on photographic material. As a response to this scenario, the two objects have been 3-D scanned and virtually reassembled. The result has been shared digitally with the public via a web platform and, in order to include increased accessibility for the public with physical or cognitive disabilities, copies of the reassembled statue have been 3-D printed and an interactive test with the 3-D model has been carried out with a haptic device.


title: A Bidirectional Isolated DC–DC Converter as a Core Circuit of the Next-Generation Medium-Voltage Power Conversion System

abstract: This paper describes a bidirectional isolated dc-dc converter considered as a core circuit of 3.3-kV/6.6-kV high-power-density power conversion systems in the next generation. The dc-dc converter is intended to use power switching devices based on silicon carbide (SiC) and/or gallium nitride, which will be available on the market in the near future. A 350-V, 10-kW and 20 kHz dc-dc converter is designed, constructed and tested. It consists of two single-phase full-bridge converters with the latest trench-gate insulated gate bipolar transistors and a 20-kHz transformer with a nano-crystalline soft-magnetic material core and litz wires. The transformer plays an essential role in achieving galvanic isolation between the two full-bridge converters. The overall efficiency from the dc-input to dc-output terminals is accurately measured to be as high as 97%, excluding gate drive and control circuit losses from the whole loss. Moreover, loss analysis is carried out to estimate effectiveness in using SiC-based power switching devices. Loss analysis clarifies that the use of SiC-based power devices may bring a significant reduction in conducting and switching losses to the dc-dc converter. As a result, the overall efficiency may reach 99% or higher


title: Pedestrian detection in infrared images using HOG, LBP, gradient magnitude and intensity feature channels

abstract: Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible. A popular and successful approach for monocular intensity pedestrian detection is based on the approximation (instead of computation) of image features for multiple scales based on the features computed on set of predefined scales. The authors port this idea to the infrared domain. Their contributions reside in the combination of four channel features, namely infrared, histogram of gradient orientations, normalized gradient magnitude and local binary patterns with the objective of detecting pedestrians for night vision applications dealing with far infrared sensors. Multiple scale feature computation is done by feature approximation. Another contribution is the study of different formulations for Local Binary Patterns like uniform patterns and rotation invariant patterns and their effect on detection performance. The detection speed is also boosted by the aid of a fast morphological based region of interest generator. The authors vary the number of approximated scales per octave and study the impact on execution time and accuracy. A reasonable result hits a speed of 18fps with a log average miss rate of 39%.


title: How infants perceive the toy robot Pleo. An exploratory case study on infant-robot-interaction

abstract: In order to develop robot systems that can interact with untrained, naive human users, it is important to understand how people - from different age groups - perceive a given robot system and which features might be relevant for this. While existing studies generally use questionnaires/interviews and/or coding schemes rendering either abstract categories or single features of individual behaviour towards a robot, we suggest to use a different methodological approach: to use the concepts and methodological tools from Ethnomethodological Conversation Analysis (EM/CA). Investigating video data from a study in which users - here: infants 3 to 8 years old - play with the toy robot Pleo, we show that and how (1) a user's perception, categorization and re-interpretation of a robot system emerges step by step during and from the interaction with the system, and (2) how the user's attempts to establish coordinated 'sequences of action' play a central role in this. The results of our initial exploratory case analysis are discussed in the light of studies which suggest that, in infants, robotic pets seem to blur foundational ontological categories, such as animate vs. inanimate. 12


title: Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients

abstract: Abstract In this paper, we present a novel approach to deal with the problem of detecting whether the eyes in a given still face image are closed, which has wide potential applications in human–computer interface design, facial expression recognition, driver fatigue detection, and so on. The approach combines the strength of multiple feature sets to characterize the rich information of eye patches (concerning both local/global shapes and local textures) and to construct the eye state model. To further improve the model׳s robustness against image noise and scale changes, we propose a new feature descriptor named Multi-scale Histograms of Principal Oriented Gradients (MultiHPOG). The resulting eye closeness detector handles a much wider range of eye appearance caused by expression, lighting, individual identity, and image noise than prior ones. We test our method on real-world eye datasets including the ZJU dataset and a new Closed Eyes in the Wild (CEW) dataset with promising results. In addition, several crucial design considerations that may have significant influence on the performance of a practical eye closeness detection system, including geometric normalization, feature extraction, and classification strategies, are also studied experimentally in this work.


title: Computer-aided generation of multiple-choice tests

abstract: Summary form only given. The paper describes a novel automatic procedure for the generation of multiple-choice tests from electronic documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the system makes use of language resources such as corpora and ontologies. The system operates in a fully automatic mode and also a semiautomatic environment where the user is offered the option to post-edit the generated test items. The results from the conducted evaluation suggest that the new procedure is very effective saving time and labour considerably and that the test items produced with the help of the program are not of inferior quality to those produced manually.


title: Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics

abstract: Developing transparent predictive analytics has attracted significant research attention recently. There have been multiple theories on how to model learning transparency but none of them aims to understand the internal and often complicated modeling processes. In this paper we adopt a contemporary philosophical concept called "constructivism", which is a theory regarding how human learns. We hypothesize that a critical aspect of transparent machine learning is to "reveal" model construction with two key process: (1) the assimilation process where we enhance our existing learning models and (2) the accommodation process where we create new learning models. With this intuition we propose a new learning paradigm, constructivism learning, using a Bayesian nonparametric model to dynamically handle the creation of new learning tasks. Our empirical study on both synthetic and real data sets demonstrate that the new learning algorithm is capable of delivering higher quality models (as compared to base lines and state-of-the-art) and at the same time increasing the transparency of the learning process.


Au th or ' s pe rs on al co py Evidence for publication bias in video game violence effects literature : A meta-analytic review


title: Social Media Data Analytics for Tourism - A Preliminary Study

abstract: Social media data are increasingly used as the source of research in a variety of domains. A typical example is urban analytics, which aims at solving urban problems by analyzing data from different sources including social media. The potential value of social media data in tourism studies, which is one of the key topics in urban research, however has been much less investigated. This paper seeks to understand the relationship between social media dynamics and the visiting patterns of visitors to touristic locations in real-world cases. By conducting a comparative study, we demonstrate how social media characterizes touristic locations differently from other data sources. Our study further shows that social media data can provide real-time insights of tourists' visiting patterns in big events, thus contributing to the understanding of social media data utility in tourism studies.


title: Ocean Acidification and Its Potential Effects on Marine Ecosystems

abstract: Ocean acidification is rapidly changing the carbonate system of the world oceans. Past mass extinction events have been linked to ocean acidification, and the current rate of change in seawater chemistry is unprecedented. Evidence suggests that these changes will have significant consequences for marine taxa, particularly those that build skeletons, shells, and tests of biogenic calcium carbonate. Potential changes in species distributions and abundances could propagate through multiple trophic levels of marine food webs, though research into the long-term ecosystem impacts of ocean acidification is in its infancy. This review attempts to provide a general synthesis of known and/or hypothesized biological and ecosystem responses to increasing ocean acidification. Marine taxa covered in this review include tropical reef-building corals, cold-water corals, crustose coralline algae, Halimeda, benthic mollusks, echinoderms, coccolithophores, foraminifera, pteropods, seagrasses, jellyfishes, and fishes. The risk of irreversible ecosystem changes due to ocean acidification should enlighten the ongoing CO(2) emissions debate and make it clear that the human dependence on fossil fuels must end quickly. Political will and significant large-scale investment in clean-energy technologies are essential if we are to avoid the most damaging effects of human-induced climate change, including ocean acidification.


title: Biogenesis of extracellular vesicles (EV): exosomes, microvesicles, retrovirus-like vesicles, and apoptotic bodies

abstract: Recent studies suggest both normal and cancerous cells secrete vesicles into the extracellular space. These extracellular vesicles (EVs) contain materials that mirror the genetic and proteomic content of the secreting cell. The identification of cancer-specific material in EVs isolated from the biofluids (e.g., serum, cerebrospinal fluid, urine) of cancer patients suggests EVs as an attractive platform for biomarker development. It is important to recognize that the EVs derived from clinical samples are likely highly heterogeneous in make-up and arose from diverse sets of biologic processes. This article aims to review the biologic processes that give rise to various types of EVs, including exosomes, microvesicles, retrovirus like particles, and apoptotic bodies. Clinical pertinence of these EVs to neuro-oncology will also be discussed.


title: Combining labeled and unlabeled data with co-training

abstract: We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks t,hat point to that page. We assume that either view of the example would be sufficient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Specifically, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm’s predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to significant improvement of hypotheses in practice. *This research was supported in part by the DARPA HPKB program under contract F30602-97-1-0215 and by NSF National Young investigator grant CCR-9357793. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. TO copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. COLT 98 Madison WI USA Copyright ACM 1998 l-58113-057--0/98/ 7...%5.00 92 Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 mitchell+@cs.cmu.edu


title: A new path planning and obstacle avoidance algorithm in dynamic environment

abstract: By developments of robotics and increments in utilizing this knowledge in different scientific and industrial branches, the issue of path planning, either in intelligent and online state, or by pre-determined algorithms has been arised as one of the discussion fields in control and specially robotics. In the path planning literature, several algorithms have been used repetitively, among which can be mentioned potential field algorithms and intelligent algorithms such as genetic and A-Star, in which the main challenge is to find an obstacle free path, not considering capabilities like computation time, considering obstacles in path finding procedure. In this paper, we developed a new path planning algorithm, not only find an obstacle-free path but also has an optimal computation time in term of obstacle importance and consider the issues like adjustable obstacle avoidance and smoothness of the final path. The intention of this paper is introducing a path planning which is able to be applied in a typical needle penetration surgery. The proposed algorithm, by suitable environment segmentation, as hexagonal segmentation, concludes to a path, that is very smoother than pathes which are resulted in other traditional algorithms.


title: Are all literature citations equally important? Automatic citation strength estimation and its applications

abstract: Literature citation analysis plays a very important role in bibliometrics and scientometrics, such as the Science Citation Index (SCI) impact factor, h-index. Existing citation analysis methods assume that all citations in a paper are equally important, and they simply count the number of citations. Here we argue that the citations in a paper are not equally important and some citations are more important than the others. We use a strength value to assess the importance of each citation and propose to use the regression method with a few useful features for automatically estimating the strength value of each citation. Evaluation results on a manually labeled data set in the computer science field show that the estimated values can achieve good correlation with human-labeled values. We further apply the estimated citation strength values for evaluating paper influence and author influence, and the preliminary evaluation results demonstrate the usefulness of the citation strength values.


title: Control Strategy for Power Flow Management in a PV System Supplying DC Loads

abstract: The growing concern for energy saving has increased the usage of LED-based street lights, electronic chokes, compact fluorescent lamps, and inverter-fed drives. Hence, the load profile seen by the electrical grid is undergoing a notable change as these devices have to operate from a dc source. Photovoltaics (PV) being a major energy source, the aforementioned loads can be connected directly to the dc bus. A grid-connected PV system involves a power source (PV array), a power sink (load), and two power sources/sink (utility and battery), and hence, a power flow management system is required to balance the power flow among these sources. One such system is developed for selecting the operating mode of the bidirectional converter by sensing the battery voltage. The viability of the scheme has been ascertained by performing experimental studies on a laboratory prototype. The control strategy is digitally implemented on an Altera Cyclone II Field Programmable Gate Array (FPGA) board, and the algorithm is verified for different modes of operation by varying the load. Experimental results are presented to bring out the usefulness of the control strategy.


title: Fraud detection in reputation systems in e-markets using logistic regression and stepwise optimization

abstract: Reputation systems are specially important in e-markets, where they help buyers to decide whether or not to purchase a product. This work addresses the task of finding attempts to deceive reputation systems in e-markets. Our goal is to generate a list of users (sellers) ranked by the probability of fraud. First we describe characteristics related to transactions that may indicate frauds evidence and they are expanded to the sellers. We describe results of a simple approach that ranks sellers by counting characteristics of fraud. Then we incorporate characteristics that cannot be used by the counting approach, and we apply logistic regression to both, improved and not improved. We use real data from a large Brazilian e-market to train and evaluate our methods and the improved set with logistic regression performes better. The list with 32.1% of topmost probable fraudsters against the reputation system was selected. We increased by 110% the number of identified fraudsters against the reputation system and no false positives were confirmed.


title: Importance of unpredictability for reward responses in primate dopamine neurons

abstract: 1. We used single neuron recording techniques in two behaving monkeys to investigate the conditions in which dopamine neurons respond to primary rewarding or potentially rewarding stimuli. Animals received drops of liquid either outside behavioral tasks or as rewards during learning or established performance of an auditory reaction time task. 2. Three quarters of dopamine neurons showed a short-latency, phasic response to liquid that was delivered outside the task without being predicted by phasic stimuli. The same neurons responded to liquid reward during learning but not when task performance was established, at which time the neuronal response occurred to the conditioned, reward-predicting, movement-triggering stimulus. 3. These data suggest that the responses of dopamine neurons to rewarding or potentially rewarding liquid are due to the temporally unpredicted stimulus occurrence. A known, reward-predicting, tonic context does not prevent dopamine neurons from responding to the rewarding liquid. The responses during learning apparently occur because reward is not yet reliably predicted by a conditioned phasic stimulus. Because the unpredicted occurrence of reward is of central importance for learning, these responses allow dopamine neurons to play an important role in reward-driven learning.


title: The consequences of unintended births for maternal and child health in India.

abstract: Data from the Indian National Family Health Survey, 2005-06 were used to explore how pregnancy intention at the time of conception influences a variety of maternal and child health and health care outcomes. Results indicate that mistimed children are more likely than wanted children to be delivered without a skilled attendant present (OR = 1.3), to not receive all recommended vaccinations (OR = 1.4), and to die during the neonatal and postneonatal periods (OR = 1.8 and 2.6, respectively). Unwanted children are more likely than wanted children to not receive all recommended vaccinations (OR = 2.2), to be stunted (OR = 1.3), and to die during the neonatal, postneonatal, and early childhood periods (OR = 2.2, 3.6, and 5.9, respectively). Given the high levels of unintended fertility in India (21 per cent of all births), these are striking findings that underscore the importance of investments in family planning.


title: Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers

abstract: Automated detection and segmentation of histologic primitives are critical steps for developing computer-aided diagnosis and prognosis system on histopathological tissue specimens. For a number of cancers, the clinical cancer grading system is highly correlated with the pathomic features of histologic primitives that appreciated from histopathological images. However, automated detection and segmentation of histologic primitives is pretty challenged because of the complicity and high density of histologic data. Therefore, there is a high demand for developing intelligent and computational image analysis tools for digital pathology images. Recently there have been interests in the application of “Deep Learning” strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this chapter, we present deep learning based approaches for two challenged tasks in histological image analysis: (1) Automated nuclear atypia scoring (NAS) on breast histopathology. We present a Multi-Resolution Convolutional Network (MR-CN) with Plurality Voting (MR-CN-PV) model for automated NAS. MR-CN-PV consists of three Single-Resolution Convolutional Network (SR-CN) with Majority Voting (SR-CN-MV) model for getting independent NAS. MR-CN-PV combines three scores via plurality voting for getting final score. (2) Epithelial (EP) and stromal (ST) tissues discrimination. The work utilized a pixel-wise Convolutional Network (CN-PI) based segmentation model for automated EP and ST tissues discrimination. We present experiments on two challenged datasets. For automated NAS, the MR-CN-PV model was evaluated on MITOS-ATYPIA-14 Challenge dataset. MR-CN-PV model got 67 score which was placed the second comparing with the scores of other five teams. The proposed CN-PI model outperformed patch-wise CN (CN-PA) models in discriminating EP and ST tissues on a breast histological images.


title: Life events and suicidality in adolescents with schizophrenia

abstract: OBJECTIVE: Suicide is the leading cause of premature death in patients with schizophrenia. Studies have shown a weaker association between suicidal behavior and stressful life events in schizophrenic than in nonschizophrenic subjects. The aim of the present study was to further investigate the complicated relationship of suicide attempts and life events in adolescent schizophrenic patients. METHODS: Forty adolescents with a diagnosis of schizophrenia, including 20 who had attempted suicide and 20 who had not, were compared with 20 age-matched subjects with no psychiatric history. The instruments used for the assessment were the Life Events Checklist, the Suicidal Risk Scale, the Sexual Abuse in Childhood Questionnaire, and the Beck Depression Inventory. RESULTS: Control subjects reported fewer life events in general, and fewer negative events, events of sexual abuse, and events associated with impaired family functioning than the schizophrenic patients. Within the schizophrenic group, the suicidal patients reported fewer life events than the nonsuicidal patients, but there was no difference between the groups in the number of negative or sexual-abuse events. However, the proportion of negative life events out of total life events was higher in the suicidal group, and their perceived impact was stronger. Levels of depression and suicidality were higher in the suicidal schizophrenic patients than in the nonsuicidal patients. CONCLUSIONS: In adolescent patients with schizophrenia, suicidal behavior is associated less with the number of life events and more with their perception of the events as negative and the impact of these events on the individual.


title: Still out there: Modeling and Identifying Russian Troll Accounts on Twitter

abstract: There is evidence that Russia's Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter - often referred to as "Russian trolls". In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K control accounts; and, 2) demonstrate that it is possible to use this model to find active accounts on Twitter still likely acting on behalf of the Russian state. Using both behavioral and linguistic features, we show that it is possible to distinguish between a troll and a non-troll with a precision of 78.5% and an AUC of 98.9%, under cross-validation. Applying the model to out-of-sample accounts still active today, we find that up to 2.6% of top journalists' mentions are occupied by Russian trolls. These findings imply that the Russian trolls are very likely still active today. Additional analysis shows that they are not merely software-controlled bots, and manage their online identities in various complex ways. Finally, we argue that if it is possible to discover these accounts using externally - accessible data, then the platforms - with access to a variety of private internal signals - should succeed at similar or better rates.


title: MONet: Unsupervised Scene Decomposition and Representation

abstract: The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, such decompositions can simplify reasoning and facilitate imagination of novel scenarios. In particular, representing perceptual observations in terms of entities should improve data efficiency and transfer performance on a wide range of tasks. Thus we need models capable of discovering useful decompositions of scenes by identifying units with such regularities and representing them in a common format. To address this problem, we have developed the Multi-Object Network (MONet). In this model, a VAE is trained end-to-end together with a recurrent attention network -- in a purely unsupervised manner -- to provide attention masks around, and reconstructions of, regions of images. We show that this model is capable of learning to decompose and represent challenging 3D scenes into semantically meaningful components, such as objects and background elements.


title: Mining cross-domain rating datasets from structured data on twitter

abstract: While rating data is essential for all recommender systems research, there are only a few public rating datasets available, most of them years old and limited to the movie domain. With this work, we aim to end the lack of rating data by illustrating how vast amounts of ratings can be unambiguously collected from Twitter. We validate our approach by mining ratings from four major online websites focusing on movies, books, music and video clips. In a short mining period of 2 weeks, close to 3 million ratings were collected. Since some users turned up in more than one dataset, we believe this work to be amongst the first to provide a true cross-domain rating dataset.


title: A simple structure of LLC resonant DC-DC converter for multi-output applications

abstract: Multi-output LLC resonant topologies with asymmetrical structures dedicating to multi-output power supply systems, having limitation of lowest load, are presented in this paper. According to output power level, different configuration of secondary output end can be flexibly adapted. Consequently, the presented topology is a favorable candidate for such application as whose outputs are not symmetrical, i.e. compared with auxiliary output, power level of main output is comparatively higher. In addition, all switches of the very topology operate under the condition of ZVS and therefore it is applicable to such occasion as high frequency, high power density. Asymmetrical operating principle and ZVS operation are analyzed in detail, moreover key parameter design considerations are presented. Finally, the analysis above is verified both by simulation and experiment.


title: Guaranteed stability of haptic systems with nonlinear virtual environments

abstract: Design of haptic systems that guarantee stable interaction is a challenging task. Virtual environments are typically highly nonlinear-resulting in a nonpassive discrete-time model. This paper will investigate how nonlinear mass/spring/damper virtual environments can be designed to guarantee the absence of oscillations and other chaotic behavior in the signal presented to the human operator. In particular, delayed and nondelayed implementation of the mass/spring/damper virtual environment is considered, revealing a nonintuitive result with regard to the allowable local stiffness.


title: Practical Privacy-Preserving Medical Diagnosis Using Homomorphic Encryption

abstract: The use of remote services offered by cloud providers have been popular in the last lustrum. Services allow users to store remote files, or to analyze data for several purposes, like health-care or message analysis. However, when personal data are sent to the Cloud, users may lose privacy on the data-content, and on the other side cloud providers may use those data for their own businesses. In this paper, we present our solution to analyze users health-data directly into the Cloud while preserving users privacy. Our solution makes use of homomorphic encryption to protect users data during the analysis. In particular, we developed a mobile application that offloads users data into the Cloud, and a homomorphic encryption algorithm that processes those data without leaking any information to the Cloud provider. Performed empirical tests show that our HE algorithm is able to evaluate users data in reasonable time proving the feasibility of this emerging way of private-data evaluation.


title: A Review of Heritage Building Information Modeling (H-BIM)

abstract: Many projects concerning the protection, conservation, restoration, and dissemination of cultural heritage are being carried out around the world due to its growing interest as a driving force of socio-economic development. The existence of reliable, digital three-dimensional (3D) models that allow for the planning and management of these projects in a remote and decentralized way is currently a growing necessity. There are many software tools to perform the modeling and complete three-dimensional documentation of the intervened monuments. However, the Architecture, Engineering and Construction (AEC) sector has adopted the Building Information Modeling (BIM) standard over the last few decades due to the progress that has been made in its qualities and capabilities. The complex modeling of cultural heritage through commercial BIM software leads to the consideration of the concept of Heritage BIM (H-BIM), which pursues the modeling of architectural elements, according to artistic, historical, and constructive typologies. In addition, H-BIM is considered to be an emerging technology that enables us to understand, document, advertize, and virtually reconstruct the built heritage. This article is a review of the existing literature on H-BIM and its effective implementation in the cultural heritage sector, exploring the effectiveness and the usefulness of the different methodologies that were developed to model families of elements of interest.


title: Rotation Equivariant CNNs for Digital Pathology

abstract: We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing recent findings on rotation equivariant CNNs, the proposed model leverages these symmetries in a principled manner. We present a visual analysis showing improved stability on predictions, and demonstrate that exploiting rotation equivariance significantly improves tumor detection performance on a challenging lymph node metastases dataset. We further present a novel derived dataset to enable principled comparison of machine learning models, in combination with an initial benchmark. Through this dataset, the task of histopathology diagnosis becomes accessible as a challenging benchmark for fundamental machine learning research.


Mnemonic Reader: Machine Comprehension with Iterative Aligning and Multi-hop Answer Pointing


title: Implementation of Explicit and Implicit Runge-Kutta methods to reduce the computational cost of pollutant transport modeling

abstract: Additional thesis report - Heimovaara et al. (2015) have developed a solute transport model (STM) so as to paint a dire portrait of the leachate generation and transport in the Braambergen landfill (located in the western portion of the Netherlands) with the ultimate goal of quantifying the chemical composition of the landfill's overall emissions. In fact, the model simulates the Chloride (Cl-) concentration in the outflowing from the landfill leachate and the mass of Cl- that remains in the landfill by using an ordinary differential equation (ODE) that describes dualporosity, mobile-immobile solute transport phenomena. This environmental model, developed in MATLAB, simulates these state variables by solving the aforementioned ODE by means of the well-known ode45 solver in MATLAB. In spite of that, the computational expense of the ode45 solver does not seem to be identical (~20-30 seconds of calculations). As is widely known, various ODE solvers usually require different computing times. Namely, the selection of the method for solving such insightful equations most often affects the time efficiency of ODE-based models. In the light of these considerations, this paper exploits the robustness of several explicit and implicit Runge-Kutta methods to solve the STM's ODE in an effort to make this model more cost-effective. By virtue of the results, the fourth order explicit Runge-Kutta method along with the fourth and sixth order implicit Runge-Kutta methods have successfully managed to drastically reduce the computational cost of the STM (~4-9 seconds of calculations).


title: A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using Hue, Saturation and Intensity (HSI) approach

abstract: To increase the quality of palm oil means to accurately grade the oil palm fresh fruit bunches (FFB) for processing. In this paper, HSI color model was used to determine the relationship between FFB ' s color with the underipe and ripe category so that the grading system could be accurately done. From the analysis manipulation, a formula was generated and applied to the data obtained. The by linear regression in the data shows an average success rate at 45% accuracy for oil palm ripeness detection. Artificial Neural Network (ANN) however return a better accuracy result for both underipe and ripe categories which are 60% and 80% respectively. This yield an overall accuracy of 70%. This can be increased more by improving the grading system.


title: Attribute-Based Encryption Supporting Direct/Indirect Revocation Modes

abstract: Attribute-based encryption (ABE) enables an access control mechanism over encrypted data by specifying access policies among private keys and ciphertexts. In this paper, we focus on ABE that supports revocation. Currently, there are two available revocable ABE schemes in the literature. Their revocation mechanisms, however, differ in the sense that they can be considered as direct and indirect methods. Direct revocation enforces revocation directly by the sender who specifies the revocation list while encrypting. Indirect revocation enforces revocation by the key authority who releases a key update material periodically in such a way that only non-revoked users can update their keys (hence, revoked users' keys are implicitly rendered useless). An advantage of the indirect method over the direct one is that it does not require senders to know the revocation list. In contrast, an advantage of the direct method over the other is that it does not involve key update phase for all non-revoked users interacting with the key authority. In this paper, we present the first Hybrid Revocable ABE scheme that allows senders to select on-the-fly when encrypting whether to use either direct or indirect revocation mode; therefore, it combines best advantages from both methods.


title: An FPGA implementation of the SHA-3: The BLAKE hash function

abstract: Following the attacks considerable standard SHA-2, In this paper, a new version of hash was developed known as SHA-3. We discussed the study of the SHA-3 hash exposing the protocol chosen for our is BLAKE-256 application. The optimization of this function and all steps taken to achieve this implementation was done are performed the synthesis of IP hash and optimization. The resulting hardware requirements as well the computation time are presented and compared with previous work. A comparison between our implementation SHA3 and the Blake 256; the proposed design is implemented on the most recent Xilinx Virtex FPGAs. The number of occupied slices, the maximum working frequency (in megahertz), the throughput (in gigabits per second), and the efficiency (in gigabits per second/slice) have been compared. An FPGA architectural for BLAKE-256 was developed using VHDL, and synthesized using Virtex-5, Virtex-6 and Virtex-7 chips. Blake-256 show tremendous throughput increase of 179% when compared with the implementation of the original Blake -256.


Extraction of P wave and T wave in Electrocardiogram using Wavelet Transform


title: A fault-tolerant real-time microcontroller with multiprocessor architecture

abstract: The occurrence of faults within microelectronic systems concern an increasing number of application fields. A possible way to build reliable systems is make them fault tolerant by the adoption of redundancies. In this paper we present the design and implementation of a fault tolerant microcontroller device using hardware redundancy of a multiprocessor. This system is FPGA based and parametrized, and can be configured to operate both as a multiprocessor or a single redundant processor, with three processors working in TMR and some spare processors. The indicated recovery procedure allows to rapidly correct transient faults, but also to replace the damaged processors after a permanent fault. The complete system employs three redundant processors on distinct boards, that are used in conjunction so as to have a further TMR level. In this way we obtain a module that is capable to solve all of the system faults, both at processor or boards levels. The programmable system is capable of masking faults, correcting errors, and also showing the possible gradual system degradation due to permanent faults.


title: Defensive loop tiling for shared cache

abstract: Loop tiling is a compiler transformation that tailors an application's working set to fit in a cache hierarchy. On today's multicore processors, part of the hierarchy especially the last level cache (LLC) is shared. The available cache space in shared cache changes depending on co-run applications. Furthermore on machines with an inclusive cache hierarchy, the interference in the shared cache can cause evictions in the private cache, a problem known as the inclusion victims. This paper presents defensive tiling, a set of compiler techniques to estimate the effect of cache sharing and then choose the tile sizes that can provide robust performance in co-run environments. The goal of the transformation is to optimize the use of the cache while at the same time guarding against interference. It is entirely a static technique and does not require program profiling. The paper shows how it can be integrated into a production-quality compiler and evalutes its effect on a set of tililing benchmarks for both program co-run and solo-run performance, using both simulation and testing on real systems.


title: A Polynomial Time Approximation Scheme for the Multiple Knapsack Problem

abstract: The multiple knapsack problem (MKP) is a natural and well-known generalization of the single knapsack problem and is defined as follows. We are given a set of $n$ items and $m$ bins (knapsacks) such that each item $i$ has a profit $p(i)$ and a size $s(i)$, and each bin $j$ has a capacity $c(j)$. The goal is to find a subset of items of maximum profit such that they have a feasible packing in the bins. MKP is a special case of the generalized assignment problem (GAP) where the profit and the size of an item can vary based on the specific bin that it is assigned to. GAP is APX-hard and a 2-approximation, for it is implicit in the work of Shmoys and Tardos [Math. Program. A, 62 (1993), pp. 461-474], and thus far, this was also the best known approximation for MKP\@. The main result of this paper is a polynomial time approximation scheme (PTAS) for MKP\@. Apart from its inherent theoretical interest as a common generalization of the well-studied knapsack and bin packing problems, it appears to be the strongest special case of GAP that is not APX-hard. We substantiate this by showing that slight generalizations of MKP are APX-hard. Thus our results help demarcate the boundary at which instances of GAP become APX-hard. An interesting aspect of our approach is a PTAS-preserving reduction from an arbitrary instance of MKP to an instance with $O(\log n)$ distinct sizes and profits.


title: Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition

abstract: We analyze stochastic gradient descent for optimizing non-convex functions. In many cases for non-convex functions the goal is to find a reasonable local minimum, and the main concern is that gradient updates are trapped in saddle points. In this paper we identify strict saddle property for non-convex problem that allows for efficient optimization. Using this property we show that stochastic gradient descent converges to a local minimum in a polynomial number of iterations. To the best of our knowledge this is the first work that gives global convergence guarantees for stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points. Our analysis can be applied to orthogonal tensor decomposition, which is widely used in learning a rich class of latent variable models. We propose a new optimization formulation for the tensor decomposition problem that has strict saddle property. As a result we get the first online algorithm for orthogonal tensor decomposition with global convergence guarantee.


title: Time Warping Symbolic Aggregation Approximation with Bag-of-Patterns Representation for Time Series Classification

abstract: Standard Symbolic Aggregation Approximation (SAX) is at the core of many effective time series data mining algorithms. Its combination with Bag-of-Patterns (BoP) has become the standard approach with state-of-the-art performance on standard datasets. However, standard SAX with the BoP representation might neglect internal temporal correlation embedded in the raw data. In this paper, we proposed time warping SAX, which extends the standard SAX with time delay embedding vector approaches to account for temporal correlations. We test time warping SAX with the BoP representation on 12 benchmark datasets from the UCR Time Series Classification/Clustering Collection. On 9 datasets, time warping SAX overtakes the state-of-the-art performance of the standard SAX. To validate our methods in real world applications, a new dataset of vital signs data collected from patients who may require blood transfusion in the next 6 hours was tested. All the results demonstrate that, by considering the temporal internal correlation, time warping SAX combined with BoP improves classification performance.


title: Substrate integrated waveguide (SIW) broadband compensating phase shifter

abstract: In this paper, a broadband compensating phase shifter is presented based on the substrate integrated waveguide (SIW) technology. The SIW is a dispersive guided-wave structure, but the phase shifts generated by two different structures (the delay line and the equal-length unequal-width phase shifter) have just reverse varying tendencies versus frequency. Thus, the complementing combination of them will make phase shift almost constant over a wide band. Detailed design equations are given following a mathematical analysis. The limitation of variation of SIW width and the tolerance issue are investigated as well. As an example, a 90° compensating phase shifter is designed on a single-layer substrate with normal PCB process at the center frequency of 30 GHz. The amplitude imbalance between the two paths is within 0.2 dB while the phase error is less than 2.5° over the frequency band from 25.1125 to 39.75 GHz, or around 49% relative bandwidth. The return loss is found to be better than 12 dB over the whole frequency band. Measured and simulated results of the proposed structure are in a good agreement. This type of SIW phase shifter is superior to all of its counterparts and increases the relative bandwidth of early designs about five times.


title: Large-Scale Adversarial Sports Play Retrieval with Learning to Rank

abstract: As teams of professional leagues are becoming more and more analytically driven, the interest in effective data management and access of sports plays has dramatically increased. In this article, we present a retrieval system that can quickly find the most relevant plays from historical games given an input query. To search through a large number of games at an interactive speed, our system is built upon a distributed framework so that each query-result pair is evaluated in parallel. We also propose a pairwise learning to rank approach to improve search ranking based on users’ clickthrough behavior. The similarity metric in training the rank function is based on automatically learnt features from a convolutional autoencoder. Finally, we showcase the efficacy of our learning to rank approach by demonstrating rank quality in a user study.


title: Automatic Generation of Constrained Furniture Layouts

abstract: Efficient authoring of vast virtual environments hinges on algorithms that are able to automatically generate content while also being controllable. We propose a method to automatically generate furniture layouts for indoor environments. Our method is simple, efficient, human-interpretable and amenable to a wide variety of constraints. We model the composition of rooms into classes of objects and learn joint (co-occurrence) statistics from a database of training layouts. We generate new layouts by performing a sequence of conditional sampling steps, exploiting the statistics learned from the database. The generated layouts are specified as 3D object models, along with their positions and orientations. We show they are of equivalent perceived quality to the training layouts, and compare favorably to a state-of-the-art method. We incorporate constraints using a general mechanism -- rejection sampling -- which provides great flexibility at the cost of extra computation. We demonstrate the versatility of our method by applying a wide variety of constraints relevant to real-world applications.


title: Optimal trajectory planning for autonomous driving integrating logical constraints: An MIQP perspective

abstract: This paper considers the problem of optimal trajectory generation for autonomous driving under both continuous and logical constraints. Classical approaches based on continuous optimization formulate the trajectory generation problem as a nonlinear program, in which vehicle dynamics and obstacle avoidance requirements are enforced as nonlinear equality and inequality constraints. In general, gradient-based optimization methods are then used to find the optimal trajectory. However, these methods are ill-suited for logical constraints such as those raised by traffic rules, presence of obstacles and, more generally, to the existence of multiple maneuver variants. We propose a new formulation of the trajectory planning problem as a Mixed-Integer Quadratic Program. This formulation can be solved efficiently using widely available solvers, and the resulting trajectory is guaranteed to be globally optimal. We apply our framework to several scenarios that are still widely considered as challenging for autonomous driving, such as obstacle avoidance with multiple maneuver choices, overtaking with oncoming traffic or optimal lane-change decision making. Simulation results demonstrate the effectiveness of our approach and its real-time applicability.


title: Over 1.7 kV normally-off GaN hybrid MOS-HFETs with a lower on-resistance on a Si substrate

abstract: In this study, normally-off GaN hybrid MOS-HFET devices on 4-inch Si substrates were fabricated, and the device characteristics were examined. As a result, the breakdown voltage (Vb) was improved using a combination of a high-resistive carbon-doped back barrier layer and a thin channel layer of 50 nm. The specific on-resistance (RonA) was estimated to be less than 7.1 mΩcm2 for Lgd = 12 μm, and Vb was estimated to be over 1.71 kV for Lgd = 18 μm. To our knowledge, these values are the best results ever reported for normally-off GaN-based MOSFETs.


title: Object Detection Networks on Convolutional Feature Maps

abstract: Most object detectors contain two important components: a feature extractor and an object classifier. The feature extractor has rapidly evolved with significant research efforts leading to better deep convolutional architectures. The object classifier, however, has not received much attention and many recent systems (like SPPnet and Fast/Faster R-CNN) use simple multi-layer perceptrons. This paper demonstrates that carefully designing deep networks for object classification is just as important. We experiment with region-wise classifier networks that use shared, region-independent convolutional features. We call them “Networks on Convolutional feature maps” (NoCs). We discover that aside from deep feature maps, a deep and convolutional per-region classifier is of particular importance for object detection, whereas latest superior image classification models (such as ResNets and GoogLeNets) do not directly lead to good detection accuracy without using such a per-region classifier. We show by experiments that despite the effective ResNets and Faster R-CNN systems, the design of NoCs is an essential element for the 1st-place winning entries in ImageNet and MS COCO challenges 2015.


title: Report on International Spaceborne Imaging Spectroscopy Technical Committee calibration and validation workshop, national environment research council field spectroscopy facility, University of Edinburgh

abstract: Calibration and validation are fundamental for obtaining quantitative information from Earth Observation (EO) sensor data. Recognising this and the impending launch of at least five sensors in the next five years, the International Spaceborne Imaging Spectroscopy Technical Committee instigated a calibration and validation initiative. A workshop was conducted recently as part of this initiative with the objective of establishing a good practice framework for radiometric and spectral calibration and validation in support of spaceborne imaging spectroscopy missions. This paper presents the outcomes and recommendations for future work arising from the workshop.


title: Dedekind sums, reciprocity, and non-arithmetic groups

abstract: Dedekind sums, arithmetic correlation sums that arose in Dedekind's study of the modular transformation of the logarithm of the eta-function, are surprisingly ubiquitous. Their arithmetic properties attracted the attention of number theorists, combinatorists, and theoretical computer scientists alike, and they appear more broadly in geometry, topology, and physics. Accordingly, there is a similarly vast literature on variations and generalizations of Dedekind sums. This note surveys recent developments of some aspects of Dedekind sums for (non-uniform) lattices in SL_2(R), otherwise referred to as Dedekind symbols.


title: Appliance Scheduling Optimization in Smart Home Networks

abstract: In this paper, we propose a solution to the problem of scheduling of a smart home appliance operation in a given time range. In addition to power-consuming appliances, we adopt a photovoltaic (PV) panel as a power-producing appliance that acts as a micro-grid. An appliance operation is modeled in terms of uninterruptible sequence phases, given in a load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm, which can provide a schedule for smart home appliance usage, is proposed based on the mixed-integer programming technique. Simulation results demonstrate the utility of our proposed solution for appliance scheduling. We further show that adding a PV system in the home results in the reduction of electricity bills and the export of energy to the national grid in times when solar energy production is more than the demand of the home.


title: Active and Logistical Networking for Grid Computing: The E-toile Architecture

abstract: While active networks provide new solutions for the deployment of dynamic services in the network by exposing network processing resources, logistical networks focus on exposing storage resources inside networks by optimizing the global scheduling of data transport, and data storage. In this paper, we show how active and logistical environments working together can improve Grid middleware and provide new and innovative high-level services for Grid applications. We validate and experiment this approach combining the Internet Backplane Protocol suite with the Tamanoir Active Node environment. Our target architecture is the French e-Toile Grid infrastructure [1] based on high performance backbone (VTHD) [2].


title: An integrated model for customer online repurchase intention

abstract: The explosion of e-commerce activities required industry and academia to understand the key determinants of consumers' online repurchase intention. We developed an integrated model by examining how...


Opinion Mining and Sentiment Analysis: A Survey


title: Modular Ecosystem Modeling

abstract: The Library of Hydro-Ecological Modules (LHEM, http://giee.uvm.edu/LHEM) was designed to create flexible landscape model structures that can be easily modified and extended to suit the requirements of a variety of goals and case studies. The LHEM includes modules that simulate hydrologic processes, nutrient cycling, vegetation growth, decomposition, and other processes, both locally and spatially. Where possible the modules are formulated as STELLA models, which adds to transparency and helps reuse. Spatial transport processes are presented as C++ code. The modular approach takes advantage of the spatial modeling environment (http://giee.uvm.edu/SME3) that allows integration of various STELLA models and C++ user code, and embeds local simulation models into a spatial context. Using the LHEM/SME the Patuxent landscape model (PLM) was built to simulate fundamental ecological processes in the watershed scale driven by temporal (nutrient loadings, climatic conditions) and spatial (land use patterns) forcings. Local ecosystem dynamics were replicated across a grid of cells that compose the rasterized landscape. Different habitats and land use types translate into different modules and parameter sets. Spatial hydrologic modules link the cells together. These are also part of the LHEM and define horizontal fluxes of material and information. This approach provides additional flexibility in scaling up and down over a range of spatial resolutions. Model results show good agreement with data for several components of the model at several scales. Other applications include several subwatersheds of the Patuxent, the Gwynns Falls watershed in Baltimore, and others.  2003 Elsevier Ltd. All rights reserved.


title: HPFBench: a high performance Fortran benchmark suite

abstract: The high performance Fortran (HPF) benchmark suite HPFBench is designed for evaluating the HPF language and compilers on scalable architectures. The functionality of the benchmarks covers scientific software library functions and application kernels that reflect the computational structure and communication patterns in fluid dynamic simulations, fundamental physics, and molecular studies in chemistry and biology. The benchmarks are characterized in terms of FLOP count, memory usage, communication pattern, local memory accesses, array allocation mechanism, as well as operation and communication counts per iteration. The benchmarks output performance evaluation metrics in the form of elapsed times, FLOP rates, and communication time breakdowns. We also provide a benchmark guide to aid the choice of subsets of the benchmarks for evaluating particular aspects of an HPF compiler. Furthermore, we report an evaluation of an industry-leading HPF compiler from the Portland Group Inc. using the HPFBench benchmarks on the distributed-memory IBM SP2


title: Experimental analysis of attacks on next generation air traffic communication

abstract: This work studies the security of next generation air traffic surveillance technology based on Automatic Dependent Surveillance --- Broadcast (ADS-B). ADS-B is already supported by a majority of international aircraft and will become mandatory in 2020 for most airspaces worldwide. While it is known that ADS-B might be susceptible to different spoofing attacks, the complexity and impact of launching these attacks has been debated controversially by the air traffic control community. Yet, the literature remains unclear on the requirements of launching ADS-B attacks in real-world environments, and on the constraints which affect their feasibility. In this paper, we take a scientific approach to systematically evaluate realistic ADS-B attacks. Our objective is to shed light on the practicability of different threats and to quantify the main factors that impact the success of such attacks. Our results reveal some bad news: attacks on ADS-B can be inexpensive and highly successful. Using a controlled experimental design, we offer insights from a real-world feasibility analysis that leads to the conclusion that any safety-critical air traffic decision process should not rely exclusively on the ADS-B system.


title: Performance Analysis of RFID Protocols: CDMA Versus the Standard EPC Gen-2

abstract: Radio frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique identification (ID) number. RFID tags can be categorized into passive and active tags. For passive tags, a standard communication protocol known as EPC-global Generation-2, or briefly EPC Gen-2, is currently in use. RFID systems are prone to transmission collisions due to the shared nature of the wireless channel used by tags. The EPC Gen-2 standard recommends using dynamic framed slotted ALOHA technique to solve the collision issue and to read the tag IDs successfully. Recently, some researchers have suggested to replace the dynamic framed slotted ALOHA technique used in the standard EPC Gen-2 protocol with the code division multiple access (CDMA) technique to reduce the number of collisions and to improve the tag identification procedure. In this paper, the standard EPC Gen-2 protocol and the CDMA-based tag identification schemes are modeled as absorbing Markov chain systems. Using the proposed Markov chain systems, the analytical formulae for the average number of queries and the total number of transmitted bits needed to identify all tags in an RFID system are derived for both the EPC Gen-2 protocol and the CDMA-based tag identification schemes. In the next step, the performance of the EPC Gen-2 protocol is compared with the CDMA-based tag identification schemes and it is shown that the standard EPC Gen-2 protocol outperforms the CDMA-based tag identification schemes in terms of the number of transmitted bits and the average time required to identify all tags in the system.


title: Community structure in social and biological networks

abstract: A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known—a collaboration network and a food web—and find that it detects significant and informative community divisions in both cases.


title: 28 GHz propagation measurements for outdoor cellular communications using steerable beam antennas in New York city

abstract: The millimeter wave frequency spectrum offers unprecedented bandwidths for future broadband cellular networks. This paper presents the world's first empirical measurements for 28 GHz outdoor cellular propagation in New York City. Measurements were made in Manhattan for three different base station locations and 75 receiver locations over distances up to 500 meters. A 400 megachip-per-second channel sounder and directional horn antennas were used to measure propagation characteristics for future mm-wave cellular systems in urban environments. This paper presents measured path loss as a function of the transmitter - receiver separation distance, the angular distribution of received power using directional 24.5 dBi antennas, and power delay profiles observed in New York City. The measured data show that a large number of resolvable multipath components exist in both non line of sight and line of sight environments, with observed multipath excess delay spreads (20 dB) as great as 1388.4 ns and 753.5 ns, respectively. The widely diverse spatial channels observed at any particular location suggest that millimeter wave mobile communication systems with electrically steerable antennas could exploit resolvable multipath components to create viable links for cell sizes on the order of 200 m.


title: Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks

abstract: Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.


title: Efficient visual attention based framework for extracting key frames from videos

abstract: The huge amount of video data on the internet requires efficient video browsing and retrieval strategies. One of the viable solutions is to provide summaries of the videos in the form of key frames. The video summarization using visual attention modeling has been used of late. In such schemes, the visually salient frames are extracted as key frames on the basis of theories of human attention modeling. The visual attention modeling schemes have proved to be effective in video summarization. However, the high computational costs incurred by these techniques limit their applicability in practical scenarios. In this context, this paper proposes an efficient visual attention model based key frame extraction method. The computational cost is reduced by using the temporal gradient based dynamic visual saliency detection instead of the traditional optical flow methods. Moreover, for static visual saliency, an effective method employing discrete cosine transform has been used. The static and dynamic visual attention measures are fused by using a non-linear weighted fusion method. The experimental results indicate that the proposed method is not only efficient, but also yields high quality video summaries.


title: Robust classification using structured sparse representation

abstract: In many problems in computer vision, data in multiple classes lie in multiple low-dimensional subspaces of a high-dimensional ambient space. However, most of the existing classification methods do not explicitly take this structure into account. In this paper, we consider the problem of classification in the multi-sub space setting using sparse representation techniques. We exploit the fact that the dictionary of all the training data has a block structure where the training data in each class form few blocks of the dictionary. We cast the classification as a structured sparse recovery problem where our goal is to find a representation of a test example that uses the minimum number of blocks from the dictionary. We formulate this problem using two different classes of non-convex optimization programs. We propose convex relaxations for these two non-convex programs and study conditions under which the relaxations are equivalent to the original problems. In addition, we show that the proposed optimization programs can be modified properly to also deal with corrupted data. To evaluate the proposed algorithms, we consider the problem of automatic face recognition. We show that casting the face recognition problem as a structured sparse recovery problem can improve the results of the state-of-the-art face recognition algorithms, especially when we have relatively small number of training data for each class. In particular, we show that the new class of convex programs can improve the state-of-the-art face recognition results by 10% with only 25% of the training data. In addition, we show that the algorithms are robust to occlusion, corruption, and disguise.


title: New trends in gender and mathematics performance: A meta-analysis.

abstract: Policy decisions, such as funding for same-sex education, as well as the continuing stereotype that girls and women lack mathematical ability, call for up-to-date information about gender differences in mathematical performance. Such stereotypes can discourage women from entering or persisting in careers in science, technology, engineering, and mathematics (STEM). Today women earn 45% of the undergraduate degrees in mathematics (NSF, 2008a), but women make up only 17% of university faculty in mathematics (NSF, 2008b). We report on a meta-analysis of recent studies of gender and mathematics. We estimate the magnitude of the gender difference and test whether it varies as a function of factors such as age and the difficulty level of the test.


title: Predicting Friends and Foes in Signed Networks Using Inductive Inference and Social Balance Theory

abstract: Besides the notion of friendship, trust or support in social networking sites (SNSs), quite often social interactions also reflect users' antagonistic attitude towards each other. Thus, the hidden knowledge contained in social network data can be considered as an important resource to discover the formation of such positive and negative links. In this work, an inductive learning framework is presented to suggest 'friends' and 'foes' links to individuals which envisage the social balance among users in the corresponding friends and foes networks (FFN). First we learn a model by applying C4.5, the most widely adopted decision tree based classification algorithm, to exploit the feature patterns presented in the users' FFN and utilizing it to further predict friend/foe relationship of unknown links. Secondly, a quantitative measure of social balance, balance index, is used to support our decision on the recommendation of new friends and foes links (FFL) to avoid possible imbalance in the extended FFN with newly suggested links. The proposed scheme ensures that the recommendation of new FFLs either maintains or enhances the balancing factor of the existing FFN of an individual. Experimental results show the effectiveness of our proposed schemes.


title: Information Security Management of Web Portals Based on Joomla CMS

abstract: Information is the key asset of all organizations and can exist in many forms. It can be printed or written on paper, stored electronically, transmitted by mail or by electronic means, shown in films, or spoken in conversation. In today's competitive business environment, such information is constantly under threat from many sources, which can be internal, external, accidental, or malicious. Joomla is a very popular Content Management System (CMS) used for web page maintenance. This highly versatile software has found itself in both large corporate web portals, and simple web pages such as blogs. Such popularity increases its vulnerability to potential attacks and therefore needs an appropriate security management. ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Commission) created the series of standards aimed at providing a model for establishing, implementing, operating, monitoring, reviewing, maintaining and improving an Information Security Management System (ISMS). This paper shows how principles set in ISO/IEC 27000 series of standards can be used to improve security of Joomla based web portals.


title: Time Series Based Link Prediction

abstract: Link prediction is a task in Social Network Analysis that consists of predicting connections that are most likely to appear considering previous observed links in a social network. The majority of works in this area only performs the task by exploring the state of the network at a specific moment to make the prediction of new links, without considering the behavior of links as time goes by. In this light, we investigate if temporal information can bring any performance gain to the link prediction task. A traditional approach for link prediction uses a chosen topological similarity metric on non-connected pairs of nodes of the network at present time to obtain a score that is going to be used by an unsupervised or a supervised method for link prediction. Our approach initially consists of building time series for each pair of non-connected nodes by computing their similarity scores at different past times. Then, we deploy a forecasting model on these time series and use their forecasts as the final scores of the pairs. Our preliminary results using two link prediction methods (unsupervised and supervised) on co-authorship networks revealed satisfactory results when temporal information was considered.


title: A review of security of the cloud computing over business with implementation

abstract: The organization productivity and employee prosperity depends upon the business outcome. Now a day, there is tough competition in the business world for more business outcomes. The key players or decision makers of any organization are always seeking the best path to do to achieve the desired goal of business outcomes. The business firms are also dedicated with the different technology to reduce the time of initiation for business activities. The cloud computing implementation requirements are mandatory with respect to its functions. The security and privacy is also a concern. The advantages of cloud system are very much deliberative. So all the facts suggest the management to properly developed and then consider the adoption of cloud system with relative advantages. Cloud computing interfaces require cost in its primary phase of implementation. In case of public cloud all the systems used for cloud under organization can be rented through the public cloud service provider. The Service License Agreement (SLA) is committed between service provider and organization by considering the applications and performance requirements. As the SLA is self explanatory and also maintains the service and performance conditions.


title: Nudge to nobesity I: Minor changes in accessibility decrease food intake

abstract: Very small but cumulated decreases in food intake may be sufficient to erase obesity over a period of years. We examine the effect of slight changes in the accessibility of different foods in a pay-by-weight-of-food salad bar in a cafeteria serving adults for the lunch period. Making a food slightly more difficult to reach (by varying its proximity by about 10 inches) or changing the serving utensil (spoon or tongs) modestly but reliably reduces intake, in the range of 8-16%. Given this effect, it is possible that making calorie-dense foods less accessible and low-calorie foods more accessible over an extended period of time would result in significant weight loss.


title: Facial Attributes: Accuracy and Adversarial Robustness

abstract: Abstract Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems. While recent methods extract facial attributes using deep neural networks (DNNs) trained on labeled facial attribute data, the robustness of deep attribute representations has not been evaluated. In this paper, we examine the representational stability of several approaches that recently advanced the state of the art on the CelebA benchmark by generating adversarial examples formed by adding small, non-random perturbations to inputs yielding altered classifications. We show that our fast flipping attribute (FFA) technique generates more adversarial examples than traditional algorithms, and that the adversarial robustness of DNNs varies highly between facial attributes. We also test the correlation of facial attributes and find that only for related attributes do the formed adversarial perturbations change the classification of others. Finally, we introduce the concept of natural adversarial samples, i.e., misclassified images where predictions can be corrected via small perturbations. We demonstrate that natural adversarial samples commonly occur and show that many of these images remain misclassified even with additional training epochs, even though their correct classification may require only a small adjustment to network parameters.


title: A Collaborative Model of Low-Level and High-Level Descriptors for Semantics-Based Music Information Retrieval

abstract: Although technologies of both low-level and high-level descriptors for music information retrieval (MIR) are advancing, there are some essential deficiencies while utilizing them separately. In this paper we propose a model where the low-level and high-level descriptors collaborate to support semantics-based MIR. The ontology of ldquomusic scenerdquo domain is constructed as a demonstration, and a set of domain related low-level and high-level descriptor analyses are introduced. Given the domain ontology and the analysis results as input, an abduction process is adopted to compute the semantics-based interpretations. Evaluations show that the collaborative model does not only give a better recall rate of semantics-based retrieval than separated models, but also maintains a promising precision meanwhile.


A voronoi diagram-visibility graph-potential field compound algorithm for robot path planning


title: An expansion of rare lineage intestinal microbes characterizes rheumatoid arthritis

abstract: BackgroundThe adaptive immune response in rheumatoid arthritis (RA) is influenced by an interaction between host genetics and environment, particularly the host microbiome. Association of the gut microbiota with various diseases has been reported, though the specific components of the microbiota that affect the host response leading to disease remain unknown. However, there is limited information on the role of gut microbiota in RA. In this study we aimed to define a microbial and metabolite profile that could predict disease status. In addition, we aimed to generate a humanized model of arthritis to confirm the RA-associated microbe.MethodsTo identify an RA biomarker profile, the 16S ribosomal DNA of fecal samples from RA patients, first-degree relatives (to rule out environment/background as confounding factors), and random healthy non-RA controls were sequenced. Analysis of metabolites and their association with specific taxa was performed to investigate a potential mechanistic link. The role of an RA-associated microbe was confirmed using a human epithelial cell line and a humanized mouse model of arthritis.ResultsPatients with RA exhibited decreased gut microbial diversity compared with controls, which correlated with disease duration and autoantibody levels. A taxon-level analysis suggested an expansion of rare taxa, Actinobacteria, with a decrease in abundant taxa in patients with RA compared with controls. Prediction models based on the random forests algorithm suggested that three genera, Collinsella, Eggerthella, and Faecalibacterium, segregated with RA. The abundance of Collinsella correlated strongly with high levels of alpha-aminoadipic acid and asparagine as well as production of the proinflammatory cytokine IL-17A. A role for Collinsella in altering gut permeability and disease severity was confirmed in experimental arthritis.ConclusionsThese observations suggest dysbiosis in RA patients resulting from the abundance of certain rare bacterial lineages. A correlation between the intestinal microbiota and metabolic signatures could determine a predictive profile for disease causation and progression.


title: Training with Noise is Equivalent to Tikhonov Regularization

abstract: It is well known that the addition of noise to the input data of a neural network during training can, in some circumstances, lead to significant improvements in generalization performance. Previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function. However, the regularization term, which involves second derivatives of the error function, is not bounded below, and so can lead to difficulties if used directly in a learning algorithm based on error minimization. In this paper we show that for the purposes of network training, the regularization term can be reduced to a positive semi-definite form that involves only first derivatives of the network mapping. For a sum-of-squares error function, the regularization term belongs to the class of generalized Tikhonov regularizers. Direct minimization of the regularized error function provides a practical alternative to training with noise.


title: Efficient Virtualization-Based Application Protection Against Untrusted Operating System

abstract: Commodity monolithic operating systems are abundant with vulnerabilities that lead to rootkit attacks. Once an operating system is subverted, the data and execution of user applications are fully exposed to the adversary, regardless whether they are designed and implemented with security considerations. Existing application protection schemes have various drawbacks, such as high performance overhead, large Trusted Computing Base (TCB), or hardware modification. In this paper, we present the design and implementation of AppShield, a hypervisor-based approach that reliably safeguards code, data and execution integrity of a critical application, in a more efficient way than existing systems. The protection overhead is localized to the protected application only, so that unprotected applications and the operating system run without any performance loss. In addition to the performance advantage, AppShield tackles several newly identified threats in this paper which are not systematically addressed previously. We build a prototype of AppShield with a tiny hypervisor, and experiment with AppShield by running several off-the-shelf applications on a Linux platform. The results testify to AppShield's low performance costs in terms of CPU computation, disk I/O and network I/O.


title: Adoption and use of social media among public health departments

abstract: Effective communication is a critical function within any public health system. Social media has enhanced communication between individuals and organizations and has the potential to augment public health communication. However, there is a lack of reported data on social media adoption within public health settings. The purposes of this study were to assess: 1) the extent to which state public health departments (SHDs) are using social media; 2) which social media applications are used most often; and 3) how often social media is used interactively to engage audiences. This was a non-experimental, cross sectional study of SHD social media sites. Screen capture software Snag-It® was used to obtain screenshots of SHD social media sites across five applications. These sites were coded for social media presence, interactivity, reach, and topic. Sixty percent of SHDs reported using at least one social media application. Of these, 86.7% had a Twitter account, 56% a Facebook account, and 43% a YouTube channel. There was a statistically significant difference between average population density and use of social media (p = .01). On average, SHDs made one post per day on social media sites, and this was primarily to distribute information; there was very little interaction with audiences. SHDs have few followers or friends on their social media sites. The most common topics for posts and tweets related to staying healthy and diseases and conditions. Limitations include the absence of a standard by which social media metrics measure presence, reach, or interactivity; SHDs were only included if they had an institutionally maintained account; and the study was cross sectional. Social media use by public health agencies is in the early adoption stage. However, the reach of social media is limited. SHDs are using social media as a channel to distribute information rather than capitalizing on the interactivity available to create conversations and engage with the audience. If public health agencies are to effectively use social media then they must develop a strategic communication plan that incorporates best practices for expanding reach and fostering interactivity and engagement.


title: Implementation of network intrusion detection system using variant of decision tree algorithm

abstract: As the need of internet is increasing day by day, the significance of security is also increasing. The enormous usage of internet has greatly affected the security of the system. Hackers do monitor the system minutely or keenly, therefore the security of the network is under observation. A conventional intrusion detection technology indicates more limitation like low detection rate, high false alarm rate and so on. Performance of the classifier is an essential concern in terms of its effectiveness; also number of feature to be examined by the IDS should be improved. In our work, we have proposed two techniques, C4.5 Decision tree algorithm and C4.5 Decision tree with Pruning, using feature selection. In C4.5 Decision tree with pruning we have considered only discrete value attributes for classification. We have used KDDCup'99 and NSL_KDD dataset to train and test the classifier. The Experimental Result shows that, C4.5 decision tree with pruning approach is giving better results with all most 98% of accuracy.


title: A Review on Efficient Temperature Prediction System Using Back Propagation Neural Network

abstract: This paper presents a review of applications of artificial neural networks in weather forecasting area. Artificial neural networks in general are explained; some limitations and some proven benefits of neural networks are discussed. Accurate weather forecasting has been one of the most challenging problems around the world. The technical milestones, that have been achieved by the researchers in this field has been reviewed and presented in this survey paper. This paper also contains a proposed approach of artificial neural network that uses analysis of data and learn from it for future predictions of temperature, with the combination of wireless technology and statistica software.


title: Slew-rate and gain enhancement in two stage operational amplifiers

abstract: A two stage op-amp with an effective technique to enhance slew-rate and gain is presented. The enhancement is provided by an auxiliary monitor circuit which is activated in slewing conditions, but can contribute to the gain in normal conditions. The amplifier, simulated in a 0.18 µm technology, achieves 74 dB DC gain, 160 MHz bandwidth and 26.8 V/µs slew-rate for a load capacitance of 1.75 pF with 362 µW power consumption, considering a supply voltage of 1.8 V.


title: Emotional convergence between people over time

abstract: The authors propose that people in relationships become emotionally similar over time—as this similarity would help coordinate the thoughts and behaviors of the relationship partners, increase their mutual understanding, and foster their social cohesion. Using laboratory procedures to induce and assess emotional response, the authors found that dating partners (Study 1) and college roommates (Studies 2 and 3) became more similar in their emotional responses over the course of a year. Further, relationship partners with less power made more of the change necessary for convergence to occur. Consistent with the proposed benefits of emotional similarity, relationships whose partners were more emotionally similar were more cohesive and less likely to dissolve. Discussion focuses on implications of emotional convergence and on potential mechanisms. A primary function of emotion is the coordination of social interactions (Frijda & Mesquita, 1994; Keltner & Haidt, 1999, in press). Emotional feelings and displays help facilitate mother– infant attachment (Bowlby, 1969), coordinate courtship and romantic processes (Eibl-Eibesfeldt, 1989), promote interpersonal trust and reconciliation following norm transgressions (Keltner, 1995), and deter cheating behavior (Frank, 1988). One implication of the foregoing analysis is that the emotions of individuals in relationships will become increasingly similar over time—a process we call emotional convergence— as they navigate the terrain of long-term bonds. Emotional similarity, it is believed, promotes coordinated thoughts and actions, mutual understanding, and interpersonal cohesion and attraction (Hatfield, Cacioppo, & Rapson, 1994; Preston & de Waal, in press; Schachter, 1951). In three studies of opposite-sex dating couples and same-sex roommates, we pursued three aims. First, we examined whether emotional convergence occurs, using longitudinal designs and multiple inductions and measures of emotion. Second, we examined whether individuals with less power make more of the change necessary for emotional convergence to occur. Third, we examined whether emotional similarity indeed benefits long-term relationships, as is so widely assumed.