title: ERUCA: Efficient DRAM Resource Utilization and Resource Conflict Avoidance for Memory System Parallelism

abstract: Memory system performance is measured by access latency and bandwidth, and DRAM access parallelism critically impacts for both. To improve DRAM parallelism, previous research focused on increasing the number of effective banks by sub-dividing one physical bank. We find that without avoiding conflicts on the shared resources among (sub)banks, the benefits are limited. We propose mechanisms for efficient DRAM resource utilization and resource-conflict avoidance (ERUCA). ERUCA reduces conflicts on shared (sub)bank resources utilizing row address locality between sub-banks and improving the DRAM chip-level data bus. Area overhead for ERUCA is kept near zero with a unique implementation that exploits under-utilized resources available in commercial DRAM chips. Overall ERUCA provides 15% speedup while incurring <0.3% DRAM die area overhead.


title: Towards radiologist-level cancer risk assessment in CT lung screening using deep learning

abstract: Importance: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and it has been recently demonstrated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Objective: To compare the performance of a deep learning model to state-of-the-art automated algorithms and radiologists as well as assessing the robustness of the algorithm in heterogeneous datasets. Design, Setting, and Participants: Three low-dose CT lung cancer screening datasets from heterogeneous sources were used, including National Lung Screening Trial (NLST, n=3410), Lahey Hospital and Medical Center (LHMC, n=3174) data, Kaggle competition data (from both stages, n=1595+505) and the University of Chicago data (UCM, a subset of NLST, annotated by radiologists, n=197). Relevant works on automated methods for Lung Cancer malignancy estimation have used significantly less data in size and diversity. At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image area around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk for the entire CT scan. We trained our two-stage algorithm on a part of the NLST dataset, and validated it on the other datasets. Results, Conclusions, and Relevance: The proposed deep learning model: (a) generalizes well across all three data sets, achieving AUC between 86% to 94%; (b) has better performance than the widely accepted PanCan Risk Model, achieving 11% better AUC score; (c) has improved performance compared to the state-of-the-art represented by the winners of the Kaggle Data Science Bowl 2017 competition on lung cancer screening; (d) has comparable performance to radiologists in estimating cancer risk at a patient level.


title: Analysis of length of hospital stay using electronic health records: A statistical and data mining approach

abstract: Background ::: The length of stay (LOS) is an important indicator of the efficiency of hospital management. Reduction in the number of inpatient days results in decreased risk of infection and medication side effects, improvement in the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to determine which factors are associated with length of hospital stay, based on electronic health records, in order to manage hospital stay more efficiently. ::: ::: ::: Materials and methods ::: Research subjects were retrieved from a database of patients admitted to a tertiary general university hospital in South Korea between January and December 2013. Patients were analyzed according to the following three categories: descriptive and exploratory analysis, process pattern analysis using process mining techniques, and statistical analysis and prediction of LOS. ::: ::: ::: Results ::: Overall, 55% (25,228) of inpatients were discharged within 4 days. The department of rehabilitation medicine (RH) had the highest average LOS at 15.9 days. Of all the conditions diagnosed over 250 times, diagnoses of I63.8 (cerebral infarction, middle cerebral artery), I63.9 (infarction of middle cerebral artery territory) and I21.9 (myocardial infarction) were associated with the longest average hospital stay and high standard deviation. Patients with these conditions were also more likely to be transferred to the RH department for rehabilitation. A range of variables, such as transfer, discharge delay time, operation frequency, frequency of diagnosis, severity, bed grade, and insurance type was significantly correlated with the LOS. ::: ::: ::: Conclusions ::: Accurate understanding of the factors associating with the LOS and progressive improvements in processing and monitoring may allow more efficient management of the LOS of inpatients.


title: Stigmatizing Materialism: On Stereotypes and Impressions of Materialistic and Experiential Pursuits

abstract: Five studies examined the stigmatization of materialism. Participants expressed negative stereotypes of materialistic people, considering them to be more selfish and self-centered than experiential people (Study 1). Participants also viewed materialistic pursuits as more extrinsically motivated than experiential pursuits (Study 2). These stereotypes led respondents from varied demographic backgrounds to form less favorable impressions of individuals who were associated with prototypically materialistic versus experiential purchases, a result that was statistically mediated by impressions that materialistic purchases were more extrinsically motivated (Study 3). These differential impressions are primarily attributable to the denigration of materialistic people rather than the admiration of experiential people (Study 4). The stigmatization of materialism led participants to like less and enjoy interacting less with their conversation partners when discussing materialistic rather than experiential purchases ...


title: Capacitor Voltage Regulation in Single-DC-Source Cascaded H-Bridge Multilevel Converters Using Phase-Shift Modulation

abstract: Cascaded H-bridge multilevel power electronic converters generally require several dc sources. An alternative option is to replace all the separate dc sources feeding the H-bridge cells with capacitors, leaving only one H-bridge cell with a real dc voltage source. This will yield a cost-effective converter. However, the required capacitor voltage balancing is challenging. In this paper, using the phase-shift modulation approach, a new control method for cascaded H-bridge multilevel converters fed with only one independent dc source is presented. The proposed method has a wide voltage regulation range for the replacement capacitors in the H-bridge cells. Experimental and simulation results support the proposed control method.


title: Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks

abstract: Recently heterogeneous information network (HIN) analysis has attracted a lot of attention, and many data mining tasks have been exploited on HIN. As an important data mining task, recommender system includes a lot of object types (e.g., users, movies, actors, and interest groups in movie recommendation) and the rich relations among object types, which naturally constitute a HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendations. However, conventional HINs do not consider the attribute values on links, and the widely used meta path in HIN may fail to accurately capture semantic relations among objects, due to the existence of rating scores (usually ranging from 1 to 5) between users and items in recommender system. In this paper, we are the first to propose the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values. Furthermore, we propose a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items. Through setting meta paths, SemRec not only flexibly integrates heterogeneous information but also obtains prioritized and personalized weights representing user preferences on paths. Experiments on two real datasets illustrate that SemRec achieves better recommendation performance through flexibly integrating information with the help of weighted meta paths.


Tobacco Carcinogenesis


title: Generating a linked soccer dataset

abstract: The provision of Linked Data about sporting results enables extensive statistics, while connections to further datasets allow enhanced and sophisticated analyses. Moreover, providing sports data as Linked Open Data may promote new applications, which are currently impossible due to the locked nature of today's proprietary sports databases. Though the sport domain is strongly underrepresented in the Linked Open Data Cloud. In this paper we present a dataset containing information about soccer entities crawled from heterogeneous sources and linked to related entities from the LOD cloud. To enable easy exploration and to illustrate the capabilities of the dataset a web interface is providing a structured overview and extensive statistics.


title: Promising 5.0-16.0 GHz CMOS-Based Oscillators with Tuned LC Tank

abstract: A newly comprehended, CMOS based oscillator circuit design is presented. With tuned inductances and capacitances, LC tank dominantly determines the specific oscillation frequency. Unlike the traditional thinking of avoiding using a resistor, a gate resistor is deliberately implemented to suppress the output voltage, especially within radio frequency range. In addition, the supply voltages, Vdd and v1, indirectly provides the capability of sustaining the oscillator. By adjusting supply voltages to devices fabricated by the standard 0.18 micron CMOS processes, a wide range of frequencies, 5 to 16 GHz, corresponding to different set of L, C, and R is achieved. Low power consumption of a single transistor, such as 3mW, can even be found in this application from 5 GHz to 16 GHz.


title: On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification

abstract: Semi-supervised classification methods have received much attention as suitable tools to tackle training sets with large amounts of unlabeled data and a small quantity of labeled data. Several semi-supervised learning models have been proposed with different assumptions about the characteristics of the input data. Among them, the self-training process has emerged as a simple and effective technique, which does not require any specific hypotheses about the training data. Despite its effectiveness, the self-training algorithm usually make erroneous predictions, mainly at the initial stages, if noisy examples are labeled and incorporated into the training set. Noise filters are commonly used to remove corrupted data in standard classification. In 2005, Li and Zhou proposed the addition of a statistical filter to the self-training process. Nevertheless, in this approach, filtering methods have to deal with a reduced number of labeled instances and the erroneous predictions it may induce. In this work, we analyze the integration of a wide variety of noise filters into the self-training process to distinguish the most relevant features of filters. We will focus on the nearest neighbor rule as a base classifier and ten different noise filters. We provide an extensive analysis of the performance of these filters considering different ratios of labeled data. The results are contrasted with nonparametric statistical tests that allow us to identify relevant filters, and their main characteristics, in the field of semi-supervised learning.


title: Improved Test Pattern Generation for Hardware Trojan Detection using Genetic Algorithm and Boolean Satisfiability

abstract: Test generation for Hardware Trojan Horses (HTH) detection is extremely challenging, as Trojans are designed to be triggered by very rare logic conditions at internal nodes of the circuit. In this paper, we propose a Genetic Algorithm (GA) based Automatic Test Pattern Generation (ATPG) technique, enhanced by automated solution to an associated Boolean Satisfiability problem. The main insight is that given a specific internal trigger condition, it is not possible to attack an arbitrary node (payload) of the circuit, as the effect of the induced logic malfunction by the HTH might not get propagated to the output. Based on this observation, a fault simulation based framework has been proposed, which enumerates the feasible payload nodes for a specific triggering condition. Subsequently, a compact set of test vectors is selected based on their ability to detect the logic malfunction at the feasible payload nodes, thus increasing their effectiveness. Test vectors generated by the proposed scheme were found to achieve higher detection coverage over large population of HTH in ISCAS benchmark circuits, compared to a previously proposed logic testing based Trojan detection technique.


Development of a double-antibody sandwich ELISA for rapid detection of Bacillus Cereus in food


title: Journalism is twerking? How web analytics is changing the process of gatekeeping

abstract: New communication technologies have allowed not only new ways in which the audience interacts with the news but also new ways in which journalists can monitor online audience behavior. Through new audience information systems, such as web analytics, the influence of the audience on the news construction process is increasing. This occurs as the journalistic field tries to survive a shrinking audience for news. In this study, I argue that how journalists conceive of the audience as a form of capital influences the extent to which journalists integrate audience feedback from web analytics in their news work. I developed this theoretical framework through case studies of three online newsrooms that included a total of 150 hours of observations and 30 respondent interviews. The findings showed the extent of influence of web analytics on traditional gatekeeping processes and on a new gatekeeping practice online, which I call the process of de-selection.


title: From diversity to creativity: stimulating group brainstorming with cultural differences and conversationally-retrieved pictures

abstract: Group brainstorming, or collaboratively generating ideas through idea sharing, demands diverse contributions to spark more ideas and improve creativity. One approach to supporting group brainstorming is to introduce conceptual diversity. In this study, we evaluate the effects of two sources of diversity on group brainstorming: cultural differences internal to multicultural groups and pictures related to the conversation retrieved by a computer agent. The pictures generally enhanced performance as measured by both originality and diversity of ideas. The pictures also helped to convert cultural diversity into a creative outcome, the diversity of ideas generated. We argue that with appropriate technology mediation, cultural diversity may be used strategically to enhance task outcomes.


title: Analyzing CyberCrimes Strategies: The Case of Phishing Attack

abstract: The growth in technology usage came along with many risks and increase in cybercrimes incidents. Phishing attacks are a form of cybercrimes by which attackers trick victims in order to obtain personal and sensitive information. Phishers motivations include gaining unauthorized information and access, cause financial loss and many more negative impacts. While cybercriminals keep developing their techniques, investigating different attack styles and methodologies are important steps toward developing effective protection mechanisms. This paper contributes to this direction by presenting and analyzing various phishing attack styles including Nigerian, Ghanaian, Chinese and Russian cybercrime styles. Due to richness of learning resources Russians and Chinese were found to be using more advanced techniques than Ghanaians and Nigerians who has limited resources.


title: The premature infant pain profile: evaluation 13 years after development.

abstract: OBJECTIVE ::: To review the (1) reliability, validation, feasibility, and clinical utility and (2) the use of the Premature Infant Pain Profile (PIPP) from 1996 to 2009 to determine the effectiveness of pain management strategies. ::: ::: ::: METHODS ::: Data sources included MEDLINE, CINAHL, EMBASE, PsycINFO, and the Web of Science. Published studies evaluating the measurement properties of the PIPP and intervention studies using the PIPP as an outcome measure of acute pain were included. One reviewer screened studies for relevance and inclusion. Four reviewers rated intervention studies for methodological quality and extracted data for the evidence tables. ::: ::: ::: RESULTS ::: Of the 62 studies included, 14 focused on the measurement properties of the PIPP. Reliability of the PIPP was supported in 5 studies and construct validation was supported in 13 studies. The feasibility of the PIPP was addressed in 4 studies, whereas clinical utility was discussed in 2 studies. Twenty-seven of the 48 studies that were considered to have high methodological quality used the PIPP as the major outcome to evaluate the effectiveness of pain management interventions in infants. ::: ::: ::: DISCUSSION ::: The PIPP continues to be a reliable and valid measure of acute pain in infants with numerous positive validation studies. There is substantial support for the use of the PIPP as an effective outcome measure in pain intervention studies in infants. Further research with health professionals is required to better support the feasibility and clinical utility of this measure.


title: An integrated charger for plug-in hybrid electric vehicles based on a special interior permanent magnet motor

abstract: For a plug-in hybrid electric vehicle (PHEV), the battery needs to be charged from the grid while the vehicle is parked. The traction system components are normally not engaged during the charging time so there is a possibility to use them in the charger system to develop an integrated charger. An innovative high power isolated three-phase bi-directional integrated charger with unit power factor operation is introduced for PHEVs based on a special configuration of the ac motor. The winding of the machine is re-arranged in charging mode to have a three-phase boost based high power battery charger. The system configuration, the device model (machine with multiple windings), traction and charging system functionality and charger control are presented in this paper.


title: Online Cross-Modal Adaptation for Audio–Visual Person Identification With Wearable Cameras

abstract: We propose an audio–visual target identification approach for egocentric data with cross-modal model adaptation. The proposed approach blindly and iteratively adapts the time-dependent models of each modality to varying target appearance and environmental conditions using the posterior of the other modality. The adaptation is unsupervised and performed online; thus, models can be improved as new unlabeled data become available. In particular, accurate models do not deteriorate when a modality is underperforming thanks to an appropriate selection of the parameters in the adaptation. Importantly, unlike traditional audio–visual integration methods, the proposed approach is also useful for temporal intervals during which only one modality is available or when different modalities are used for different tasks. We evaluate the proposed method in an end-to-end multimodal person identification application with two challenging real-world datasets and show that the proposed approach successfully adapts models in presence of mild mismatch. We also show that the proposed approach is beneficial to other multimodal score fusion algorithms.


title: EMOD: an efficient on-device mobile visual search system

abstract: Recently, researchers have proposed solutions to build on-device mobile visual search (ODMVS) systems. Different from traditional client-server mobile visual search systems, an ODMVS supports image searching directly within a mobile device. An ODMVS needs to be designed with constrained hardware in mind e.g. limited memory, less powerful CPU. In this paper, we present, EMOD, an efficient on-device mobile visual search system based on the Bag-of-Visual-Word (BOVW) framework but uses a small visual dictionary. An Object Word Ranking (OWR) algorithm is proposed to efficiently identify the most useful visual words of an image so as to construct a compact image signature for fast retrieval and greatly improved retrieval performance. Due to having a small visual dictionary, we propose the Top Inverted Index Ranking scheme to reduce the number of candidate images for similarity calculation. In addition, EMOD adopts a more efficient version of the recently proposed Ranking Consistency re-ranking algorithm for further performance enhancement. Via extensive experimental evaluations, we demonstrate that our prototype EMOD system yields good retrieval accuracy and query response times for a database with over 10K images.


title: Volume Ranking and Sequential Selection in Programmatic Display Advertising

abstract: Programmatic display advertising, which enables advertisers to make real-time decisions on individual ad display opportunities so as to achieve a precise audience marketing, has become a key technique for online advertising. However, the constrained budget setting still restricts unlimited ad impressions. As a result, a smart strategy for ad impression selection is necessary for the advertisers to maximize positive user responses such as clicks or conversions, under the constraints of both ad volume and campaign budget. In this paper, we borrow in the idea of top-N ranking and filtering techniques from information retrieval and propose an effective ad impression volume ranking method for each ad campaign, followed by a sequential selection strategy considering the remaining ad volume and budget, to smoothly deliver the volume filtering while maximizing campaign efficiency. The extensive experiments on two benchmarking datasets and a commercial ad platform demonstrate large performance superiority of our proposed solution over traditional methods, especially under tight budgets.


title: Increased Range Bistatic Scatter Radio

abstract: Scatter radio achieves communication by reflection and requires low-cost and low-power RF front-ends. However, its use in wireless sensor networks (WSNs) is limited, since commercial scatter radio (e.g. RFID) offers short ranges of a few tens of meters. This work redesigns scatter radio systems and maximizes range through non-classic bistatic architectures: the carrier emitter is detached from the reader. It is shown that conventional radio receivers may show a potential 3dB performance loss, since they do not exploit the correct signal model for scatter radio links. Receivers for on-off-keying (OOK) and frequency-shift keying (FSK) that overcome the frequency offset between the carrier emitter and the reader are presented. Additionally, non-coherent designs are also offered. This work emphasizes that sensor tag design should accompany receiver design. Impact of important parameters such as the antenna structural mode are presented through bit error rate (BER) results. Experimental measurements corroborate the long-range ability of bistatic radio; ranges of up to 130 meters with 20 milliwatts of carrier power are experimentally demonstrated, with commodity software radio and no directional antennas. Therefore, bistatic scatter radio may be viewed as a key enabling technology for large-scale, low-cost and low-power WSNs.


title: Appearance invariance in convolutional networks with neighborhood similarity

abstract: We present a neighborhood similarity layer (NSL) which induces appearance invariance in a network when used in conjunction with convolutional layers. We are motivated by the observation that, even though convolutional networks have low generalization error, their generalization capability does not extend to samples which are not represented by the training data. For instance, while novel appearances of learned concepts pose no problem for the human visual system, feedforward convolutional networks are generally not successful in such situations. Motivated by the Gestalt principle of grouping with respect to similarity, the proposed NSL transforms its input feature map using the feature vectors at each pixel as a frame of reference, i.e. center of attention, for its surrounding neighborhood. This transformation is spatially varying, hence not a convolution. It is differentiable; therefore, networks including the proposed layer can be trained in an end-to-end manner. We analyze the invariance of NSL to significant changes in appearance that are not represented in the training data. We also demonstrate its advantages for digit recognition, semantic labeling and cell detection problems.


title: Exploring Content-based Artwork Recommendation with Metadata and Visual Features

abstract: Compared to other areas, artwork recommendation has received little attention, despite the continuous growth of the artwork market. Previous research has relied on ratings and metadata to make artwork recommendations, as well as visual features extracted with deep neural networks (DNN). However, these features have no direct interpretation to explicit visual features (e.g. brightness, texture) which might hinder explainability and user-acceptance. In this work, we study the impact of artwork metadata as well as visual features (DNN-based and attractiveness-based) for physical artwork recommendation, using images and transaction data from the UGallery online artwork store. ::: Our results indicate that: (i) visual features perform better than manually curated data, (ii) DNN-based visual features perform better than attractiveness-based ones, and (iii) a hybrid approach improves the performance further. Our research can inform the development of new artwork recommenders relying on diverse content data.


title: A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets

abstract: In this study, we provide a direct comparison of the Stochastic Maximum Likelihood algorithm and Contrastive Divergence for training Restricted Boltzmann Machines using the MNIST data set. We demonstrate that Stochastic Maximum Likelihood is superior when using the Restricted Boltzmann Machine as a classifier, and that the algorithm can be greatly improved using the technique of iterate averaging from the field of stochastic approximation. We further show that training with optimal parameters for classification does not necessarily lead to optimal results when Restricted Boltzmann Machines are stacked to form a Deep Belief Network. In our experiments we observe that fine tuning a Deep Belief Network significantly changes the distribution of the latent data, even though the parameter changes are negligible.


title: Interactive Bayesian Hierarchical Clustering

abstract: Clustering is a powerful tool in data analysis, but it is often difficult to find a grouping that aligns with a user's needs. To address this, several methods incorporate constraints obtained from users into clustering algorithms, but unfortunately do not apply to hierarchical clustering. We design an interactive Bayesian algorithm that incorporates user interaction into hierarchical clustering while still utilizing the geometry of the data by sampling a constrained posterior distribution over hierarchies. We also suggest several ways to intelligently query a user. The algorithm, along with the querying schemes, shows promising results on real data.


title: Truth, Justice, and Cake Cutting

abstract: Cake cutting is a common metaphor for the division of a heterogeneous divisible good. There are numerous papers that study the problem of fairly dividing a cake; a small number of them also take into account self-interested agents and consequent strategic issues, but these papers focus on fairness and consider a strikingly weak notion of truthfulness. In this paper we investigate the problem of cutting a cake in a way that is truthful and fair, where for the first time our notion of dominant strategy truthfulness is the ubiquitous one in social choice and computer science. We design both deterministic and randomized cake cutting algorithms that are truthful and fair under different assumptions with respect to the valuation functions of the agents.


title: Design of a Low-Profile Dual-Polarized Stepped Slot Antenna Array for Base Station

abstract: In this letter, a microstrip-fed dual-polarized stepped-impedance (SI) slot antenna element with a low profile is first proposed. The antenna is composed of two pairs of SI slots excited by two orthogonal stepped microstrip feedlines. The broadband characteristic is achieved by combining the fundamental and spurious resonances of the SI slot resonators, while the good cross polarization is mainly due to the introduction of the shorting pins. Secondly, based on the proposed antenna, a four-element antenna array is designed, constructed, and measured for base-station applications. Measured results demonstrate the bandwidths (return loss ) of the antenna array are 46.9% (1.55-2.5 GHz) and 38.7% (1.69-2.5 GHz) for Port 1 and Port 2, respectively. The isolation between the two ports is greater than 35 dB, whereas the cross-polarization level maintains lower than -27 dB across the entire operating band. In addition, the antenna array prototype achieves average gains of 13.5 and 13.9 dBi for horizontal polarization and vertical polarization, respectively.


title: Neurobiological Correlates in Internet Gaming Disorder: A Systematic Literature Review

abstract: Internet Gaming Disorder (IGD) is a potential mental disorder currently included in the third section of the latest (fifth) edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as a condition that requires additional research to be included in the main manual. Although research efforts in the area have increased, there is a continuing debate about the respective criteria to use as well as the status of the condition as mental health concern. Rather than using diagnostic criteria which are based on subjective symptom experience, the National Institute of Mental Health advocates the use of Research Domain Criteria (RDoC) which may support classifying mental disorders based on dimensions of observable behavior and neurobiological measures because mental disorders are viewed as biological disorders that involve brain circuits that implicate specific domains of cognition, emotion, and behavior. Consequently, IGD should be classified on its underlying neurobiology, as well as its subjective symptom experience. Therefore, the aim of this paper is to review the neurobiological correlates involved in IGD based on the current literature base. Altogether, 853 studies on the neurobiological correlates were identified on ProQuest (in the following scholarly databases: ProQuest Psychology Journals, PsycARTICLES, PsycINFO, Applied Social Sciences Index and Abstracts, and ERIC) and on MEDLINE, with the application of the exclusion criteria resulting in reviewing a total of 27 studies, using fMRI, rsfMRI, VBM, PET and EEG methods. The results indicate there are significant neurobiological differences between healthy controls and individuals with IGD. The included studies suggest that compared to healthy controls, gaming addicts have poorer response-inhibition and emotion regulation, impaired prefrontal cortex functioning and cognitive control, poorer working memory and decision-making capabilities, decreased visual and auditory functioning, and a deficiency in their neuronal reward system, similar to those found in individuals with substance-related addictions. This suggests both substance-related addictions and behavioral addictions share common predisposing factors and may be part of an addiction syndrome. Future research should focus on replicating the reported findings in different cultural contexts, in support of a neurobiological basis of classifying IGD and related disorders.


title: Doped zinc oxide films grown by hot-wire chemical vapour deposition

abstract: Abstract Hot-wire chemical vapour deposition (CVD) was applied to grow zinc oxide (ZnO)-based transparent conducting oxide films. Indium (In)-, gallium (Ga)-, and aluminium (Al)-doped ZnO films were deposited at 400 °C on sapphire-R, Si (100) and glass substrates using a cold wall pulsed liquid injection CVD system containing nichrome wires installed in front of the substrate holder. Zn, In, Al 2,2,6,6-tetramethyl-3,5-heptanedionates, and Ga 3,5-pentanedionate dissolved in 1,2-dimethoxyethane were used as precursors. Hall measurements were performed to evaluate the resistivity, carrier concentration, and carrier mobility in doped ZnO films grown on sapphire substrates at wire currents of 6 A and 9 A. The influence of the dopant type, doping level, substrate, and wire heating current on crystallinity and the electrical and optical properties of the films was investigated and discussed. The best electrical properties were obtained for Al- and Ga-doped films grown at 9 A wire current (resistivity ≈ 1 × 10 − 3 Ωcm, carrier mobility ≈ 50 cm 2 V − 1 s − 1 and carrier concentration ≈ 1 × 10 20 cm − 3 ). The films exhibited a high transmittance in the mid-infrared region (≈ 90% at 2.5 μm). Additional annealing of the films at 400 °C in a mixture of Ar and hydrogen (10%) resulted in the increase in carrier concentration and mobility and in the reduction of film resistivity.


title: Control of a Visual Keyboard Using an Electrocorticographic Brain–Computer Interface

abstract: Objective. Brain–computer interfaces (BCIs) are devices that enable severely disabled people to communicate and interact with their environments using their brain waves. Most studies investigating BCI in humans have used scalp EEG as the source of electrical signals and focused on motor control of prostheses or computer cursors on a screen. The authors hypothesize that the use of brain signals obtained directly from the cortical surface will more effectively control a communication/spelling task compared to scalp EEG. Methods. A total of 6 patients with medically intractable epilepsy were tested for the ability to control a visual keyboard using electrocorticographic (ECOG) signals. ECOG data collected during a P300 visual task paradigm were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. Results. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in 5 of the 6 people te...


A comparison of classification methods for gene prediction in metagenomics


title: EXPLORING THE CRITICAL SUCCESS FACTORS FOR CUSTOMER RELATIONSHIP MANAGEMENT AND ELECTRONIC CUSTOMER RELATIONSHIP MANAGEMENT SYSTEMS

abstract: Both customer relationship management (CRM) and electronic customer relationship management (eCRM) systems have unique characteristics that support customer-business interactions and are linked to internal business processes and systems across different areas for operational and analytical purposes. Such characteristics may imply that different critical success factors are required for both to be successfully implemented. This exploratory study identifies the factors and the interrelationships associated with the success of CRM and eCRM, compares the differences between CRM and eCRM, and discusses the reasons of the differences. Since there are only a few cases of CRM or eCRM systems fully implemented across marketing, technology, people, and business processes, an exploratory multiple-case study is conducted. The current status of CRM research and future research direction are discussed.


title: An efficient and secure privacy-preserving approach for outsourced data of resource constrained mobile devices in cloud computing

abstract: Outsourcing of data into cloud has become an effective trend in modern day computing due to its ability to provide low-cost, pay-as-you-go IT services. Although cloud based services offer many advantages, privacy of the outsourced data is a big concern. To mitigate this concern, it is desirable to outsource sensitive data in an encrypted form but cost of encryption process would increase the heavy computational overhead on thin clients such as resource-constrained mobile devices. Recently, several keyword searchable encryption schemes have been described in the literature. However, these schemes are not effective for resource-constrained mobile devices, because the adopted encryption system should not only support keyword search over the encrypted data but also offer high performance. In this paper, we propose an efficient and secure privacy-preserving approach for outsourced data of resource-constrained mobile devices in the cloud computing. Our approach employs probabilistic public key encryption algorithm for encrypting the data and invoke ranked keyword search over the encrypted data to retrieve the files from the cloud. We aim to achieve an efficient system for data encryption without sacrificing the privacy of data. Further, our ranked keyword search greatly improves the system usability by enabling ranking based on relevance score for search result, sends top most relevant files instead of sending all files back, and ensures the file retrieval accuracy. As a result, data privacy ensures and computation, communication overheads in reduction. Thorough security and performance analysis, we prove that our approach is semantically secure and efficient.


title: Software development process from natural language specification

abstract: A process to derive incrementally a formal specification from an informal specification written in natural-language is presented. This process consists of two major activities, "design" and "elaborate". Through the design activity, the structure of software modules based on an object oriented model is interactively extracted from the informal English description. Each word such as nouns and verbs in the natural-language sentences is associated with a software concept, e.g. class, attribute, and method. Especially we concentrate on types of verb patterns occurring in the sentences to increase applicability to dynamic systems. The elaborate activity is one to refine and rewrite the informal specification more precisely based on the derived mod- ule design document. We can complete the formal specification through the elaborate - design activity cycle. Each document created in our process is called product. The integration man- agement of the various kinds of the products are supported by a hypertext system. Furthermore we discuss the whole of soft- ware development process in which the design process and the elaborate one are embedded.


title: A multimodal interface to control a robot arm via the web: a case study on remote programming

abstract: In this paper, we present the user interface and the system architecture of an Internet-based telelaboratory, which allows researchers and students to remotely control and program two educational online robots. In fact, the challenge has been to demonstrate that remote programming combined with an advanced multimedia user interface for remote control is very suitable, flexible, and profitable for the design of a telelaboratory. The user interface has been designed by using techniques based on augmented reality and nonimmersive virtual reality, which enhance the way operators get/put information from/to the robotic scenario. Moreover, the user interface provides the possibility of letting the operator manipulate the remote environment by using multiple ways of interaction (i.e., from the simplification of the natural language to low-level remote programming). In fact, the paper focuses on the lowest level of interaction between the operator and the robot, which is remote programming. As explained in the paper, the system architecture permits any external program (i.e., remote experiment, speech-recognition module, etc.) to have access to almost every feature of the telelaboratory (e.g., cameras, object recognition, robot control, etc.). The system validation was performed by letting 40 Ph.D. students within the "European Robotics Research Network Summer School on Internet and Online Robots for Telemanipulation" workshop (Benica/spl grave/ssim, Spain, 2003) program several telemanipulation experiments with the telelaboratory. Some of these experiments are shown and explained in detail. Finally, the paper focuses on the analysis of the network performance for the proposed architecture (i.e., time delay). In fact, several configurations are tested through various networking protocols (i.e., Remote Method Invocation, Transmission Control Protocol/IP, User Datagram Protocol/IP). Results show the real possibilities offered by these remote-programming techniques, in order to design experiments intended to be performed from both home and the campus.


Internet of Things


title: Protonated adenine: tautomers, solvated clusters, and dissociation mechanisms.

abstract: Ab initio and density functional theory calculations at the B3-MP2 and CCSD(T)/6-311 + G(3df,2p) levels of theory are reported that address the protonation of adenine in the gas phase, water clusters, and bulk aqueous solution. The calculations point to N-1-protonated adenine (1+) as the thermodynamically most stable cationic tautomer in the gas phase, water clusters, and bulk solution. This strongly indicates that electrospray ionization of adenine solutions produces tautomer 1+ with a specificity calculated as 97-90% in the 298-473 K temperature range. The mechanisms for elimination of hydrogen atoms and ammonia from 1+ have also been studied computationally. Ion 1+ is calculated to undergo fast migrations of protons among positions N-1, C-2, N-3, N-10, N-7, and C-8 that result in an exchange of five hydrogens before loss of a hydrogen atom forming adenine cation radical at 415 kJ mol(-1) dissociation threshold energy. The elimination of ammonia is found to be substantially endothermic requiring 376-380 kJ mol(-1) at the dissociation threshold and depending on the dissociation pathway. The overall dissociation is slowed by the involvement of ion-molecule complexes along the dissociation pathways. The competing isomerization of 1+ proceeds by a sequence of ring opening, internal rotations, imine flipping, ring closures, and proton migrations to effectively exchange the N-1 and N-10 atoms in 1+, so that either can be eliminated as ammonia. This mechanism explains the previous N-1/N-10 exchange upon collision-induced dissociation of protonated adenine.


title: Computer-generated residential building layouts

abstract: We present a method for automated generation of building layouts for computer graphics applications. Our approach is motivated by the layout design process developed in architecture. Given a set of high-level requirements, an architectural program is synthesized using a Bayesian network trained on real-world data. The architectural program is realized in a set of floor plans, obtained through stochastic optimization. The floor plans are used to construct a complete three-dimensional building with internal structure. We demonstrate a variety of computer-generated buildings produced by the presented approach.


title: Path planning for complex 3D multilevel environments

abstract: The continuous development of graphics hardware is contributing to the creation of 3D virtual worlds with high level of detail, from models of large urban areas, to complete infrastructures, such as residential buildings, stadiums, industrial settings or archaeological sites, to name just a few. Adding virtual humans or avatars adds an extra touch to the visualization providing an enhanced perception of the spaces, namely adding a sense of scale, and enabling simulations of crowds. Path planning for crowds in a meaningful way is still an open research field, particularly when it involves an unknown polygonal 3D world. Extracting the potential paths for navigation in a non automated fashion is no longer a feasible option due to the dimension and complexity of the virtual environments available nowadays. This implies that we must be able to automatically extract information from the geometry of the unknown virtual world to define potential paths, determine accessibilities, and prepare a navigation structure for real time path planning and path finding. A new image based method is proposed that deals with arbitrarily a priori unknown complex virtual worlds, namely those consisting of multilevel passages (e.g. over and below a bridge). The algorithm is capable of extracting all the information required for the actual navigation of avatars, creating a hierarchical data structure to help both high level path planning and low level path finding decisions. The algorithm is image based, hence it is tessellation independent, i.e. the algorithm does not rely on the underlying polygonal structure of the 3D world. Therefore, the number of polygons does not have a significant impact on the performance, and the topology has no weight on the results.


title: Facebook immune system

abstract: Popular Internet sites are under attack all the time from phishers, fraudsters, and spammers. They aim to steal user information and expose users to unwanted spam. The attackers have vast resources at their disposal. They are well-funded, with full-time skilled labor, control over compromised and infected accounts, and access to global botnets. Protecting our users is a challenging adversarial learning problem with extreme scale and load requirements. Over the past several years we have built and deployed a coherent, scalable, and extensible realtime system to protect our users and the social graph. This Immune System performs realtime checks and classifications on every read and write action. As of March 2011, this is 25B checks per day, reaching 650K per second at peak. The system also generates signals for use as feedback in classifiers and other components. We believe this system has contributed to making Facebook the safest place on the Internet for people and their information. This paper outlines the design of the Facebook Immune System, the challenges we have faced and overcome, and the challenges we continue to face.


title: Multi-Timescale Active and Reactive Power-Coordinated Control of Large-Scale Wind Integrated Power System for Severe Wind Speed Fluctuation

abstract: Wind speed fluctuations are extremely easy to cause the voltage change frequently. The existing voltage control methods are lacking in adjustment range and response speed. When wind speed fluctuates substantially or rapidly, voltage exceeding limit and the resulting grid safety problems cannot be avoided. Therefore, reasonably distributing and utilizing the reactive power of variable speed wind turbines in the whole network are an inevitable choice. However, the controllable reactive capacity of wind turbines is limited by converter capacity and active power, and thus, the grid demand may not be met. A new idea of active and reactive power-coordinated control for preventing voltage exceeding limit due to wind speed fluctuations was proposed. The effects of wind speed fluctuations on voltage were analyzed, and the controllable reactive capacity of variable speed wind turbines under wind speed fluctuations was studied. The relationship between voltage and the active and reactive power of wind farms was deduced. The theory and method of active and reactive power coordinated control before wind speed fluctuations were proposed based on the model predictive control theory. The control model based on multi-timescale was established. Finally, it was verified that the method can solve the voltage problems and maximize the system economy.


A Roadmap for a Rigorous Science of Interpretability


title: Identifying the tool-tissue force in robotic laparoscopic surgery using neuro-evolutionary fuzzy systems and a synchronous self-learning hyper level supervisor

abstract: In this paper, two different hybrid intelligent systems are applied to develop practical soft identifiers for modeling the tool-tissue force as well as the resulted maximum local stress in laparoscopic surgery. To conduct the system identification process, a 2D model of an in vivo porcine liver was built for different probing tasks. Based on the simulation, three different geometric features, i.e. maximum deformation angle, maximum deformation depth and width of displacement constraint of the reconstructed shape of the deformed body are extracted. Thereafter, two different fuzzy inference paradigms are proposed for the identification task. The first identifier is an adaptive co-evolutionary fuzzy inference system (ACFIS) which takes advantage of bio-inspired supervisors to be reconciled to the characteristics of the problem at hand. To learn the fuzzy machine, the authors propose a co-evolutionary technique which uses a modified optimizer called scale factor local search differential evolution (SFLSDE) as the core metaheuristic. The concept of co-evolving is implemented through a consequential optimization procedure in which the degree of optimality of the ACFIS architecture is evaluated by sharing the characteristics of both antecedent and consequent parts between two different SFLSDEs. The second identifier is an adaptive neuro-fuzzy inference system (ANFIS) which is based on the use of some well-known neuro computing concepts, i.e. back-propagation learning and synaptic nodal computing, for tuning the construction of the fuzzy identifier. The two proposed techniques are used to identify the force and maximum local stress of tool-tissue. Based on the experiments, the authors have observed that each of the identifiers have their own advantages and disadvantages. However, both ACFIS and ANFIS succeed to identify the model outputs precisely. Moreover, to ascertain the veracity of the derived systems, the authors adopt a Pareto-based hyper-level heuristic approach called synchronous self-learning Pareto strategy (SSLPS). This technique provides the authors with good information regarding the optimum controlling parameters of both ACFIS and ANFIS identifiers.


title: Mammaplasty: Indications, technique, and complications

abstract: Summary 1.A report of 347 consecutive cases of breast reduction is given as regards classification, type of operations, complications, and results. 2.A modification of the Biesenberger operation is presented in detail. 3.H˦matoma is the most serious complication and is probably responsible for most of the surgical disasters in breast reduction. 4.Controlled hypotension has improved the cosmetic result and reduced the time of operation. It has also decreased the number of complications in this type of surgery.


title: Addressing Cold Start for Next-song Recommendation

abstract: The cold start problem arises in various recommendation applications. In this paper, we propose a tensor factorization-based algorithm that exploits content features extracted from music audio to deal with the cold start problem for the emerging application next-song recommendation. Specifically, the new algorithm learns sequential behavior to predict the next song that a user would be interested in based on the last song the user just listened to. A unique characteristic of the algorithm is that it learns and updates the mapping between the audio feature space and the item latent space each time during the iterations of the factorization process. This way, the content features can be better exploited in forming the latent features for both users and items, leading to more effective solutions for cold-start recommendation. Evaluation on a large-scale music recommendation dataset shows that the recommendation result of the proposed algorithm exhibits not only higher accuracy but also better novelty and diversity, suggesting its applicability in helping a user explore new items in next-item recommendation. Our implementation is available at https://github.com/fearofchou/ALMM.


title: Adjusting mixture weights of gaussian mixture model via regularized probabilistic latent semantic analysis

abstract: Mixture models, such as Gaussian Mixture Model, have been widely used in many applications for modeling data. Gaussian mixture model (GMM) assumes that data points are generated from a set of Gaussian models with the same set of mixture weights. A natural extension of GMM is the probabilistic latent semantic analysis (PLSA) model, which assigns different mixture weights for each data point. Thus, PLSA is more flexible than the GMM method. However, as a tradeoff, PLSA usually suffers from the overfitting problem. In this paper, we propose a regularized probabilistic latent semantic analysis model (RPLSA), which can properly adjust the amount of model flexibility so that not only the training data can be fit well but also the model is robust to avoid the overfitting problem. We conduct empirical study for the application of speaker identification to show the effectiveness of the new model. The experiment results on the NIST speaker recognition dataset indicate that the RPLSA model outperforms both the GMM and PLSA models substantially. The principle of RPLSA of appropriately adjusting model flexibility can be naturally extended to other applications and other types of mixture models.


title: ColLoc: A collaborative location and tracking system on WirelessHART

abstract: Localization in wireless sensor networks is an important functionality that is required for tracking personnel and assets in industrial environments, especially for emergency response. Current commercial localization systems such as GPS suffer from the limitations of either high cost or low availability in many situations (e.g., indoor environments that exclude direct line-of-sight signal reception). The development of industrial wireless sensor networks such as WirelessHART provides an alternative. In this article, we present the design and implementation of ColLoc: a collaborative location and tracking system on WirelessHART as an industrially viable solution. This solution is built upon several technological advances. First, ColLoc adds the roaming functionality to WirelessHART and thus provides a means for keeping mobile WirelessHART devices connected to the network. Second, ColLoc employs a collaborative framework to integrate different types of distance measurements into the location estimation algorithm by weighing them according to their precision levels. ColLoc adopts several novel techniques to improve distance estimation accuracy and decreases the RSSI presurvey cost. These techniques include introducing distance error range constraints to the measurements, judiciously selecting the initial point in location estimation and online updating the signal propagation models in the anchor nodes, integrating Extended Kalman Filter (EKF) with trilateration to track moving objects. Our implementation of ColLoc can be applied to any WirelessHART-conforming network because no modification is needed on the WirelessHART field devices. We have implemented a complete ColLoc system to validate both the design and the effectiveness of our localization algorithm. Our experiments show that the mobile device never drops out of the WirelessHART network while moving around; with the help of even one dependable anchor, using RSSI can yield at least 75p of distance errors below 5 meters, which is quite acceptable for many typical industrial automation applications.


Enhanced Security for Online Exams Using Group Cryptography


title: Towards a Framework for Evaluating Mobile Mental Health Apps.

abstract: INTRODUCTION ::: Mobile phones are ubiquitous in society and owned by a majority of psychiatric patients, including those with severe mental illness. Their versatility as a platform can extend mental health services in the areas of communication, self-monitoring, self-management, diagnosis, and treatment. However, the efficacy and reliability of publicly available applications (apps) have yet to be demonstrated. Numerous articles have noted the need for rigorous evaluation of the efficacy and clinical utility of smartphone apps, which are largely unregulated. Professional clinical organizations do not provide guidelines for evaluating mobile apps. ::: ::: ::: MATERIALS AND METHODS ::: Guidelines and frameworks are needed to evaluate medical apps. Numerous frameworks and evaluation criteria exist from the engineering and informatics literature, as well as interdisciplinary organizations in similar fields such as telemedicine and healthcare informatics. ::: ::: ::: RESULTS ::: We propose criteria for both patients and providers to use in assessing not just smartphone apps, but also wearable devices and smartwatch apps for mental health. Apps can be evaluated by their usefulness, usability, and integration and infrastructure. Apps can be categorized by their usability in one or more stages of a mental health provider's workflow. ::: ::: ::: CONCLUSIONS ::: Ultimately, leadership is needed to develop a framework for describing apps, and guidelines are needed for both patients and mental health providers.


title: Improving Small Object Proposals for Company Logo Detection

abstract: Many modern approaches for object detection are two-staged pipelines. The first stage identifies regions of interest which are then classified in the second stage. Faster R-CNN is such an approach for object detection which combines both stages into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by its weak performance on small object instances, we examine in detail both the proposal and the classification stage with respect to a wide range of object sizes. We investigate the influence of feature map resolution on the performance of those stages. Based on theoretical considerations, we introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the FlickrLogos dataset improving the RPN performance from 0.52 to 0.71 (MABO) and the detection performance from 0.52 to 0.67 (mAP).


title: Different models for model matching: An analysis of approaches to support model differencing

abstract: Calculating differences between models is an important and challenging task in Model Driven Engineering. Model differencing involves a number of steps starting with identifying matching model elements, calculating and representing their differences, and finally visualizing them in an appropriate way. In this paper, we provide an overview of the fundamental steps involved in the model differencing process and summarize the advantages and shortcomings of existing approaches for identifying matching model elements. To assist potential users in selecting one of the existing methods for the problem at stake, we investigate the trade-offs these methods impose in terms of accuracy and effort required to implement each one of them.


Obstacle and Lane Detection on the ARGO Autonomous Vehicle


title: ContextPlayer: learning contextual music preferences for situational recommendations

abstract: Music listening is a very personal and situational behaviour, which suggests that contextual information could be used to greatly enhance music recommendation experience. However, making such use of mobile context, while learning user profiles, is a challenging problem. This case study presents a system for collecting context and usage data from mobile devices, but targeted at recommending music via unsupervised learning of user profiles and relevant situations. The developed data flow system supports both short enough response times and longer asynchronous reasoning on the collected data; furthermore, the mobile phone acts not only as sensor, but the mobile app is directly tied to the effectiveness of the music service user experience (UX). This work describes our system design and discusses issues related to the problem space and to usability tests on such systems, based on an international user trial.


title: A Third Method of Generation and Detection of Single-Sideband Signals

abstract: This paper presents a third method of generation and detection of a single-sideband signal. The method is basically different from either the conventional filter or phasing method in that no sharp cutoff filters or wide-band 90° phase-difference networks are needed. This system is especially suited to keeping the signal energy confined to the desired bandwidth. Any unwanted sideband occupies the same band as the desired sideband, and the unwanted sideband in the usual sense is not present.


title: Microcontroller Based Wireless Temperature And Heart Beat Read-Out

abstract: In this paper, we propose a Simple Wireless transmission System using common approach Sensor Platform called The wireless based Patient Sensor platform (WSP, Sensor Node) which has remote access capability. The goals of the WSP are to establish: Standard sensor node (System on module), a common software .The proposed platform architecture (Sensor Node) offers flexibility, easy customization for different vital parameter collecting and sending. An prototype has been established based on wireless communication channel. Wireless lan (IEEE .802.15.4)) has been used as communication channel on our prototype (Sensor node). Desire sensor information (vital parameter) can be viewed remotely, and also vital parameter can be adjusted to meet demand.


title: Protecting privacy using the decentralized label model

abstract: Stronger protection is needed for the confidentiality and integrity of data, because programs containing untrusted code are the rule rather than the exception. Information flow control allows the enforcement of end-to-end security policies, but has been difficult to put into practice. This article describes the decentralized label model, a new label model for control of information flow in systems with mutual distrust and decentralized authority. The model improves on existing multilevel security models by allowing users to declassify information in a decentralized way, and by improving support for fine-grained data sharing. It supports static program analysis of information flow, so that programs can be certified to permit only acceptable information flows, while largely avoiding the overhead of run-time checking. The article introduces the language Jif, an extension to Java that provides static checking of information flow using the decentralized label model.


title: Clustering Convolutional Kernels to Compress Deep Neural Networks

abstract: In this paper, we propose a novel method to compress CNNs by reconstructing the network from a small set of spatial convolution kernels. Starting from a pre-trained model, we extract representative 2D kernel centroids using k-means clustering. Each centroid replaces the corresponding kernels of the same cluster, and we use indexed representations instead of saving whole kernels. Kernels in the same cluster share their weights, and we fine-tune the model while keeping the compressed state. Furthermore, we also suggest an efficient way of removing redundant calculations in the compressed convolutional layers. We experimentally show that our technique works well without harming the accuracy of widely-used CNNs. Also, our ResNet-18 even outperforms its uncompressed counterpart at ILSVRC2012 classification task with over 10x compression ratio.


title: Adolescent Development

abstract: This chapter identifies the most robust conclusions and ideas about adolescent development and psychological functioning that have emerged since Petersen's 1988 review. We begin with a discussion of topics that have dominated recent research, including adolescent problem behavior, parent-adolescent relations, puberty, the development of the self, and peer relations. We then identify and examine what seem to us to be the most important new directions that have come to the fore in the last decade, including research on diverse populations, contextual influences on development, behavioral genetics, and siblings. We conclude with a series of recommendations for future research on adolescence.


title: Extracting Appraisal Expressions

abstract: Sentiment analysis seeks to characterize opinionated or evaluative aspects of natural language text. We suggest here that appraisal expression extraction should be viewed as a fundamental task in sentiment analysis. An appraisal expression is a textual unit expressing an evaluative stance towards some target. The task is to find and characterize the evaluative attributes of such elements. This paper describes a system for effectively extracting and disambiguating adjectival appraisal expressions in English outputting a generic representation in terms of their evaluative function in the text. Data mining on appraisal expressions gives meaningful and non-obvious insights.


title: Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges

abstract: Wideband microwave imaging is of interest wherever optical opaque scenarios need to be analyzed, as these waves can penetrate biological tissues, many building materials, or industrial materials. One of the challenges of microwave imaging is the computation of the image from the measurement data because of the need to solve extensive inverse scattering problems due to the sometimes complicated wave propagation. The inversion problem simplifies if only spatially limited objects—point objects, in the simplest case—with temporally variable scattering properties are of interest. Differential imaging uses this time variance by observing the scenario under test over a certain time interval. Such problems exist in medical diagnostics, in the search for surviving earthquake victims, monitoring of the vitality of persons, detection of wood pests, control of industrial processes, and much more. This paper gives an overview of imaging methods for point-like targets and discusses the impact of target variations onto the radar data. Because the target variations are very weak in many applications, a major issue of differential imaging concerns the suppression of random effects by appropriate data processing and concepts of radar hardware. The paper introduces related methods and approaches, and some applications illustrate their performance.


title: FFTs for the 2-Sphere-Improvements and Variations

abstract: Earlier work by Driscoll and Healy has produced an efficient algorithm for computing the Fourier transform of band-limited functions on the 2-sphere. In this article we present a reformulation and variation of the original algorithm which results in a greatly improved inverse transform, and consequent improved convolution algorithm for such functions. All require at most O(N log2 N) operations where N is the number of sample points. We also address implementation considerations and give heuristics for allowing reliable and computationally efficient floating point implementations of slightly modified algorithms. These claims are supported by extensive numerical experiments from our implementation in C on DEC, HP, SGI and Linux Pentium platforms. These results indicate that variations of the algorithm are both reliable and efficient for a large range of useful problem sizes. Performance appears to be architecture-dependent. The article concludes with a brief discussion of a few potential applications.


A REVIEW ON K-mean ALGORITHM AND IT’S DIFFERENT DISTANCE MATRICS


Metalearning - A Tutorial


title: Hybrid Silicon Photonic Integrated Circuit Technology

abstract: In this paper, we review the current status of the hybrid silicon photonic integration platform with emphasis on its prospects for increased integration complexity. The hybrid silicon platform is maturing fast as increasingly complex circuits are reported with tens of integrated components including on-chip lasers. It is shown that this platform is well positioned and holds great potential to address future needs for medium-scale photonic integrated circuits.


title: Gummy smile and botulinum toxin: a new approach based on the gingival exposure area.

abstract: BACKGROUND ::: Gummy smile (GS) is an aesthetic disorder for some patients, which can be corrected by injection of botulinum toxin. ::: ::: ::: OBJECTIVE ::: We sought to classify GS according to the area of gingival exposure and the respective muscles involved in order to perfect the botulinum toxin injection technique for each patient. ::: ::: ::: METHODS ::: Sixteen patients with GS were evaluated before receiving botulinum toxin injections. Based on the area of excessive gum displayed and identification of the muscles involved, 4 different types of GS were identified: anterior, posterior, mixed, and asymmetric. AbobotulinumtoxinA (Dysport, Ipsen Biopharm Limited, Wrexham, UK) was injected using a different injection technique for each type of GS, based on the main muscles involved. With the aid of two computer programs, the area of gum exposed was measured before and after the application of abobotulinumtoxinA, to evaluate the level of improvement. ::: ::: ::: RESULTS ::: There was a decrease in the degree of gum display in all patients. The general average improvement achieved was 75.09%. Two patients showed slight adverse effects that were easily corrected with additional doses of abobotulinumtoxinA. ::: ::: ::: LIMITATIONS ::: For this study, there was no sample size calculation and no statistical analysis of the cases. ::: ::: ::: CONCLUSION ::: The authors conclude that it is important to identify the type of GS and therefore the main muscles involved, so that the correct injection technique can be used. AbobotulinumtoxinA was shown to be effective and safe for use in all types of GS in the present sample.


title: Alopex: A Correlation-Based Learning Algorithm for Feedforward and Recurrent Neural Networks

abstract: We present a learning algorithm for neural networks, called Alopex. Instead of error gradient, Alopex uses local correlations between changes in individual weights and changes in the global error measure. The algorithm does not make any assumptions about transfer functions of individual neurons, and does not explicitly depend on the functional form of the error measure. Hence, it can be used in networks with arbitrary transfer functions and for minimizing a large class of error measures. The learning algorithm is the same for feedforward and recurrent networks. All the weights in a network are updated simultaneously, using only local computations. This allows complete parallelization of the algorithm. The algorithm is stochastic and it uses a “temperature” parameter in a manner similar to that in simulated annealing. A heuristic “annealing schedule” is presented that is effective in finding global minima of error surfaces. In this paper, we report extensive simulation studies illustrating these advantages and show that learning times are comparable to those for standard gradient descent methods. Feedforward networks trained with Alopex are used to solve the MONK's problems and symmetry problems. Recurrent networks trained with the same algorithm are used for solving temporal XOR problems. Scaling properties of the algorithm are demonstrated using encoder problems of different sizes and advantages of appropriate error measures are illustrated using a variety of problems.


title: A Hybrid Machine Learning System for Stock Market Forecasting

abstract: INTRODUCTION Stock market prediction is regarded as a challenging task in financial time-series forecasting. This is primarily because of the uncertainties involved in the movement of the market. Many factors interact in the stock market including political events, general economic conditions, and traders 'expectations. Therefore, predicting market price movements is quite difficult. Increasingly, according to academic investigations, movements in market prices are not random. Rather, they behave in a highly non-linear, dynamic manner. Also, the ability to predict the direction and not the exact value of the future stock prices is the most important factor in making money using financial prediction. All the investor needs to know to make a buying or selling decision is the expected direction of the stock. Studies have also shown that predicting direction as compared to value can generate higher profit (Chen, Leung, & Daouk, 2003). TIME SERIES ANALYSIS Forecasting, or predicting, is an essential tool in any decision-making process. Its uses vary from determining inventory requirements for a local shoe store to estimating the annual sales of video games. The quality of the forecasts management can make is strongly related to the information that can be extracted and used from past data. Time-series analysis is one quantitative method we used to determine patterns in data collected over time. Time-series analysis is used to detect patterns of change in statistical information over regular intervals of time. We project these patterns to arrive at an estimate for the future. Thus Time-series analysis helps us cope with uncertainty about the future. GENETIC ALGORITHM Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. They combine survival of the fittest among string structures with a structured yet randomized information exchange to form a search algorithm with some of the innovative flair of human search. In every generation, a new set of artificial creatures (strings) is created using bits and pieces of the fittest of the old; an occasional new part is tried for good measure. While randomized, genetic algorithms are no simple random walk. They efficiently exploit historical information to speculate on new search points with expected improved performance. Generally, genetic operations include: " crossover" , "mutation" and " selection" . "Crossover" operator: Suppose S1={s11, s12, ... , s1n}, S2={s21, s22, ... , s2n}, are two chromosomes, select a random integer number 0 r n, S3, S4 are offspring of crossover(S1,S2), S3={si | if i r, si S1, else si S2}, S4={si | if i r, si S2, else si S1}. "Mutation" operator: Suppose a chromosome S1={s11, s12, ... , s1n}, select a random integer number 0 r n, S3 is a mutation of S1, S3={ si | if i r, then si=s1 i, else si =random(s1 i)}. "Selection" operator: Suppose there are m individuals, If m is even: no change If m is odd: i. take mean of the dataset. ii. Now, mutate each individual value with this mean iii. Match each newly generated population to the mean value if the bit value (up to r-random value used for mutation), then reject this data set. i.e. Consider other one. In the proposed system, we use the single point crossover and flip bit(bit string) mutation. Single Point Crossover: A single crossover point on both parents' organism strings is selected. All data beyond that point in either organism string is swapped between the two parent organisms. The resulting organisms are the children: [FIGURE 1 OMITTED] Flip-Bit Mutation: The mutation of bit strings ensue through bit flips at random positions. Example: 1 0 10 0 10 [down arrow] 11 01 01 1 0 The probability of a mutation of bits is I, where 1/l is the length of the binary vector. …


title: Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization

abstract: Differential evolution (DE) is a simple, yet efficient, evolutionary algorithm for global numerical optimization, which has been widely used in many areas. However, the choice of the best mutation strategy is difficult for a specific problem. To alleviate this drawback and enhance the performance of DE, in this paper, we present a family of improved DE that attempts to adaptively choose a more suitable strategy for a problem at hand. In addition, in our proposed strategy adaptation mechanism (SaM), different parameter adaptation methods of DE can be used for different strategies. In order to test the efficiency of our approach, we combine our proposed SaM with JADE, which is a recently proposed DE variant, for numerical optimization. Twenty widely used scalable benchmark problems are chosen from the literature as the test suit. Experimental results verify our expectation that the SaM is able to adaptively determine a more suitable strategy for a specific problem. Compared with other state-of-the-art DE variants, our approach performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate. Finally, we validate the powerful capability of our approach by solving two real-world optimization problems.


title: A Joint Model for Sentence Semantic Similarity Learning

abstract: Sentence similarity is a fundamental task to measure the degree of the likelihood between two sentences. It plays an important role in both NLP and IR communities. The current sentence semantic similarity measurements can be divided into two categories: the model based on feature engineering and the model based on deep learning. These two categories all have their own advantages and disadvantages. The feature captured based on deep learning may not get the deep meaning of the sentence. And the feature exacted based on feature engineering may not be comprehensive. The algorithm proposed recently usually use one of these two models. In this paper, we proposed a new model that combine the deep learning model and the feature engineering model together. Our model enables two methods to complement each other, making the feature extraction more comprehensive: not only take the global semantic information into account, but also further refine the semantic information at the word level. The experiment shows that our model performs better than models only use one category.


title: Efficient Optimization of Convolutional Neural Networks Using Particle Swarm Optimization

abstract: This work presents methods to automatically find optimal parameter settings for convolutional neural networks (CNNs) by using an evolutionary algorithm called particle swarm optimization (PSO). Even though the parameter space is extremely large (> 10 20), we experimentally show that a better parameter setting can be found for Alexnet configuration for five different image datasets. We have also developed two candidate pruning algorithms for efficient evolutionary process. In the experiments, we achieved 0.7-5.7% improvements from the original parameter sets in Caffe, while requiring only 2-4% of processing cost of the naive PSO-based approach.


title: Deep belief nets for natural language call-routing

abstract: This paper considers application of Deep Belief Nets (DBNs) to natural language call routing. DBNs have been successfully applied to a number of tasks, including image, audio and speech classification, thanks to the recent discovery of an efficient learning technique. DBNs learn a multi-layer generative model from unlabeled data and the features discovered by this model are then used to initialize a feed-forward neural network which is fine-tuned with backpropagation. We compare a DBN-initialized neural network to three widely used text classification algorithms; Support Vector machines (SVM), Boosting and Maximum Entropy (MaxEnt). The DBN-based model gives a call-routing classification accuracy that is equal to the best of the other models even though it currently uses an impoverished representation of the input.


title: Parallel Algorithms for Hierarchical Clustering

abstract: Hierarchical clustering is common method used to determine clusters of similar data points in multi-dimensional spaces. $O(n^2)$ algorithms, where $n$ is the number of points to cluster, have long been known for this problem. This paper discusses parallel algorithms to perform hierarchical clustering using various distance metrics. I describe $O(n)$ time algorithms for clustering using the single link, average link, complete link, centroid, median, and minimum variance metrics on an $n$ node CRCW PRAM and $O(n \log n)$ algorithms for these metrics (except average link and complete link) on $\frac{n}{\log n}$ node butterfly networks or trees. Thus, optimal efficiency is achieved for a significant number of processors using these distance metrics. A general algorithm is given that can be used to perform clustering with the complete link and average link metrics on a butterfly. While this algorithm achieves optimal efficiency for the general class of metrics, it is not optimal for the specific cases of complete link and average link clustering.


1 GIVING AND RECEIVING AWARDS


title: Fast Deep Convolutional Face Detection in the Wild Exploiting Hard Sample Mining

abstract: Abstract Face detection constitutes a key visual information analysis task in Machine Learning. The rise of Big Data has resulted in the accumulation of a massive volume of visual data which requires proper and fast analysis. Deep Learning methods are powerful approaches towards this task as training with large amounts of data exhibiting high variability has been shown to significantly enhance their effectiveness, but often requires expensive computations and leads to models of high complexity. When the objective is to analyze visual content in massive datasets, the complexity of the model becomes crucial to the success of the model. In this paper, a lightweight deep Convolutional Neural Network (CNN) is introduced for the purpose of face detection, designed with a view to minimize training and testing time, and outperforms previously published deep convolutional networks in this task, in terms of both effectiveness and efficiency. To train this lightweight deep network without compromising its efficiency, a new training method of progressive positive and hard negative sample mining is introduced and shown to drastically improve training speed and accuracy. Additionally, a separate deep network was trained to detect individual facial features and a model that combines the outputs of the two networks was created and evaluated. Both methods are capable of detecting faces under severe occlusion and unconstrained pose variation and meet the difficulties of large scale real-world, real-time face detection, and are suitable for deployment even in mobile environments such as Unmanned Aerial Vehicles (UAVs).


CP-consensus: a Blockchain Protocol Based on Synchronous Timestamps of Compass Satellite.


title: Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

abstract: Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when control- ling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.


title: Energy-Aware Scheduling of MapReduce Jobs for Big Data Applications

abstract: The majority of large-scale data intensive applications executed by data centers are based on MapReduce or its open-source implementation, Hadoop. Such applications are executed on large clusters requiring large amounts of energy, making the energy costs a considerable fraction of the data center’s overall costs. Therefore minimizing the energy consumption when executing each MapReduce job is a critical concern for data centers. In this paper, we propose a framework for improving the energy efficiency of MapReduce applications, while satisfying the service level agreement (SLA). We first model the problem of energy-aware scheduling of a single MapReduce job as an Integer Program. We then propose two heuristic algorithms, called energy-aware MapReduce scheduling algorithms (EMRSA-I and EMRSA-II), that find the assignments of map and reduce tasks to the machine slots in order to minimize the energy consumed when executing the application. We perform extensive experiments on a Hadoop cluster to determine the energy consumption and execution time for several workloads from the HiBench benchmark suite including TeraSort, PageRank, and K-means clustering, and then use this data in an extensive simulation study to analyze the performance of the proposed algorithms. The results show that EMRSA-I and EMRSA-II are able to find near optimal job schedules consuming approximately 40 percent less energy on average than the schedules obtained by a common practice scheduler that minimizes the makespan.


title: Maestro: quality-of-service in large disk arrays

abstract: Provisioning storage in disk arrays is a difficult problem because many applications with different workload characteristics and priorities share resources provided by the array. Currently, storage in disk arrays is statically partitioned, leading to difficult choices between over-provisioning to meet peak demands and resource sharing to meet efficiency targets. In this paper, we present Maestro, a feedback controller that can manage resources on large disk arrays to provide performance differentiation among multiple applications. Maestro monitors the performance of each application and dynamically allocates the array resources so that diverse performance requirements can be met without static partitioning. It supports multiple performance metrics (e.g., latency and throughput) and application priorities so that important applications receive better performance in case of resource contention. By ensuring that high-priority applications sharing storage with other applications obtain the performance levels they require, Maestro makes it possible to use storage resources efficiently. We evaluate Maestro using both synthetic and real-world workloads on a large, commercial disk array. Our experiments indicate that Maestro can reliably adjust the allocation of disk array resources to achieve application performance targets.


title: A Comprehensive Framework for Image Inpainting

abstract: Inpainting is the art of modifying an image in a form that is not detectable by an ordinary observer. There are numerous and very different approaches to tackle the inpainting problem, though as explained in this paper, the most successful algorithms are based upon one or two of the following three basic techniques: copy-and-paste texture synthesis, geometric partial differential equations (PDEs), and coherence among neighboring pixels. We combine these three building blocks in a variational model, and provide a working algorithm for image inpainting trying to approximate the minimum of the proposed energy functional. Our experiments show that the combination of all three terms of the proposed energy works better than taking each term separately, and the results obtained are within the state-of-the-art.


Trading the Bond-CDS Basis- The Role of Credit Risk and Liquidity


title: Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine

abstract: The determination of the energy characteristics of a Kaplan hydraulic turbine is based on numerous measuring points during extensive and expensive experimental model tests in laboratory and on-site prototype tests at the hydropower plant. The results of those experimental researches are valuable insofar as they are detailed and comprehensive. In order to reduce the number of modes, in which the double-regulated turbine has to be tested with the aim of obtaining the off-cam energy characteristics in unknown operating modes, the application of contemporary artificial neural networks models is presented in the paper. The rationalization of the turbine test conditions may not be at the expense of the quality of the obtained characteristics. Two types of neural networks, feedforward neural networks and adaptive network-based fuzzy inference system with different partitioning methods, were used. The reliability of applied method was considered by analyzing and validating the predicted turbine energy parameters with the results obtained in the highly sophisticated laboratory.


title: Sustainability and cities: extending the metabolism model

abstract: The use of the metabolism concept, expanded to include aspects of livability, is applied to cities to demonstrate the practical meaning of sustainability. Its application in industrial ecology, urban ecology, urban demonstration projects, business plans and city comparisons are used to illustrate its potential.


title: Optimizing Pairwise Box Intersection Checking on GPUs for Large-Scale Simulations

abstract: Box intersection checking is a common task used in many large-scale simulations. Traditional methods cannot provide fast box intersection checking with large-scale datasets. This article presents a parallel algorithm to perform Pairwise Box Intersection checking on Graphics processing units (PBIG). The PBIG algorithm consists of three phases: planning, mapping and checking. The planning phase partitions the space into small cells, the sizes of which are determined to optimize performance. The mapping phase maps the boxes into the cells. The checking phase examines the box intersections in the same cell. Several performance optimizations, including load-balancing, output data compression/encoding, and pipelined execution, are presented for the PBIG algorithm. The experimental results show that the PBIG algorithm can process large-scale datasets and outperforms three well-performing algorithms.


title: Theoretical Foundations of Association Rules

abstract: In this paper we describe a formal framework for the problem of mining association rules. The theoretical foundation is based on the field of formal concept analysis. A concept is composed of closed subsets of attributes (itemsets) and objects (transactions). We show that all frequent itemsets are uniquely determined by the frequent concepts. We further show how this lattice-theoretic framework can be used to find a small rule generating set, from which one can infer all other association rules.


title: A novel approach for load balancing in cloud data center

abstract: In a large-scale cloud computing environment the cloud data centers and end users are geographically distributed across the globe. The biggest challenge for cloud data centers is how to handle and service the millions of requests that are arriving very frequently from end users efficiently and correctly. In cloud computing, load balancing is required to distribute the dynamic workload evenly across all the nodes. Load balancing helps to achieve a high user satisfaction and resource utilization ratio by ensuring an efficient and fair allocation of every computing resource. Proper load balancing aids in minimizing resource consumption, implementing fail-over, enabling scalability, avoiding bottlenecks and over-provisioning etc. In this paper, we propose “Central Load Balancer” a load balancing algorithm to balance the load among virtual machines in cloud data center. Results show that our algorithm can achieve better load balancing in a large-scale cloud computing environment as compared to previous load balancing algorithms.


title: Robust Driving Path Detection in Urban and Highway Scenarios Using a Laser Scanner and Online Occupancy Grids

abstract: Many driver assistant and safety systems depend on an accurate environmental model containing the positions of stationary objects, the states of dynamic objects and information about valid driving corridors. Therefore, a robust differentiation between moving and stationary objects is required. This is challenging for laser scanners, because these sensors are not able to measure the velocity of objects directly. Therefore, an advanced occupancy grid approach, the online map, is introduced, which enables the robust separation of moving and stationary objects. The online map is used for the robust detection of the road boundaries for the determination of driving corridors in urban and highway scenarios. An algorithm for the detection of arbitrary moving objects using the online map is proposed.


title: TyTAN: tiny trust anchor for tiny devices

abstract: Embedded systems are at the core of many security-sensitive and safety-critical applications, including automotive, industrial control systems, and critical infrastructures. Existing protection mechanisms against (software-based) malware are inflexible, too complex, expensive, or do not meet real-time requirements. We present TyTAN, which, to the best of our knowledge, is the first security architecture for embedded systems that provides (1) hardware-assisted strong isolation of dynamically configurable tasks and (2) real-time guarantees. We implemented TyTAN on the Intel® Siskiyou Peak embedded platform and demonstrate its efficiency and effectiveness through extensive evaluation.


title: Elastic models: A comparative study applied to retinal images

abstract: In this work various methods of parametric elastic models are compared, namely the classical snake, the gradient vector field snake (GVF snake) and the topology-adaptive snake (t-snake), as well as the method of self-affine mapping system as an alternative to elastic models. We also give a brief overview of the methods used. The self-affine mapping system is implemented using an adapting scheme and minimum distance as optimization criterion, which is more suitable for weak edges detection. All methods are applied to glaucomatic retinal images with the purpose of segmenting the optical disk. The methods are compared in terms of segmentation accuracy and speed, as these are derived from cross-correlation coefficients between real and algorithm extracted contours and segmentation time, respectively. As a result, the method of self-affine mapping system presents adequate segmentation time and segmentation accuracy, and significant independence from initialization.


title: Gaze Stripes: Image-Based Visualization of Eye Tracking Data

abstract: We present a new visualization approach for displaying eye tracking data from multiple participants. We aim to show the spatio-temporal data of the gaze points in the context of the underlying image or video stimulus without occlusion. Our technique, denoted as gaze stripes, does not require the explicit definition of areas of interest but directly uses the image data around the gaze points, similar to thumbnails for images. A gaze stripe consists of a sequence of such gaze point images, oriented along a horizontal timeline. By displaying multiple aligned gaze stripes, it is possible to analyze and compare the viewing behavior of the participants over time. Since the analysis is carried out directly on the image data, expensive post-processing or manual annotation are not required. Therefore, not only patterns and outliers in the participants' scanpaths can be detected, but the context of the stimulus is available as well. Furthermore, our approach is especially well suited for dynamic stimuli due to the non-aggregated temporal mapping. Complementary views, i.e., markers, notes, screenshots, histograms, and results from automatic clustering, can be added to the visualization to display analysis results. We illustrate the usefulness of our technique on static and dynamic stimuli. Furthermore, we discuss the limitations and scalability of our approach in comparison to established visualization techniques.


title: Three Classes of Deep Learning Architectures and Their Applications: A Tutorial Survey

abstract: In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference (Deng, 2011) are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of nonlinear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures --deep auto-encoder, deep stacking network, and deep neural network (pre-trained with deep belief network) --one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed.