title: Reliable ABC model choice via random forests

abstract: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques.Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets.Availability and implementation: The proposed methodology is implemented in the R package abcrf available on the CRAN.


title: Experimental Study on Low Power Wide Area Networks (LPWAN) for Mobile Internet of Things

abstract: In the past decade, we have witnessed explosive growth in the number of low-power embedded and Internet-connected devices, reinforcing the new paradigm, Internet of Things (IoT). The low power wide area network (LPWAN), due to its long-range, low-power and low-cost communication capability, is actively considered by academia and industry as the future wireless communication standard for IoT. However, despite the increasing popularity of `mobile IoT', little is known about the suitability of LPWAN for those mobile IoT applications in which nodes have varying degrees of mobility. To fill this knowledge gap, in this paper, we conduct an experimental study to evaluate, analyze, and characterize LPWAN in both indoor and outdoor mobile environments. Our experimental results indicate that the performance of LPWAN is surprisingly susceptible to mobility, even to minor human mobility, and the effect of mobility significantly escalates as the distance to the gateway increases. These results call for development of new mobility-aware LPWAN protocols to support mobile IoT.


title: Securing configuration management and migration of virtual network functions using blockchain

abstract: The integration of network function visualization (NFV) and service function chaining (SFC) adds intelligence to the core of the network. The programmability of the network core, however, raises new vulnerabilities and increases the number of victims, since a simple modification in the core can affect multiple network users. Thus, the provision of secure virtual network service functions (VNFs) is mandatory to guarantee a correct chaining of network functions. This paper proposes a blockchain-based architecture for secure management, configuration and migration of VNFs, which ensures: (i) immutability, non-repudiation, and auditability of the configuration update history; (ii) integrity and consistency of stored information; and (iii) the anonymity of VNFs, tenants, and configuration information. Furthermore, the proposed architecture guarantees the secure update and migration of configurations at the core of the network. A prototype of the proposed architecture using the Open Platform for NFV (OPNFV) indicates parameter trade-offs and performance bottlenecks.


Excision of Mucocele Using Diode Laser in Lower Lip


title: A Hybrid ICT-Solution for Smart Meter Data Analytics

abstract: Smart meters are increasingly used worldwide. Smart meters are the advanced meters capable of measuring energy consumption at a fine-grained time interval, e.g., every 15 min. Smart meter data are typically bundled with social economic data in analytics, such as meter geographic locations, weather conditions and user information, which makes the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analyzing the so-called big data possible. This paper proposes an innovative ICT-solution to streamline smart meter data analytics. The proposed solution offers an information integration pipeline for ingesting data from smart meters, a scalable platform for processing and mining big data sets, and a web portal for visualizing analytics results. The implemented system has a hybrid architecture of using Spark or Hive for big data processing, and using the machine learning toolkit, MADlib, for doing in-database data analytics in PostgreSQL database. This paper evaluates the key technologies of the proposed ICT-solution, and the results show the effectiveness and efficiency of using the system for both batch and online analytics.


The convenient myth of Thomas Szasz


title: CodeAttention: translating source code to comments by exploiting the code constructs

abstract: Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension. However, due to the great cost of authoring with the comments, many code projects do not contain adequate comments. Automatic comment generation techniques have been proposed to generate comments from pieces of code in order to alleviate the human efforts in annotating the code. Most existing approaches attempt to exploit certain correlations (usually manually given) between code and generated comments, which could be easily violated if coding patterns change and hence the performance of comment generation declines. In addition, recent approaches ignore exploiting the code constructs and leveraging the code snippets like plain text. Furthermore, previous datasets are also too small to validate the methods and show their advantage. In this paper, we propose a new attention mechanism called CodeAttention to translate code to comments, which is able to utilize the code constructs, such as critical statements, symbols and keywords. By focusing on these specific points, CodeAttention could understand the semantic meanings of code better than previous methods. To verify our approach in wider coding patterns, we build a large dataset from open projects in GitHub. Experimental results in this large dataset demonstrate that the proposed method has better performance over existing approaches in both objective and subjective evaluation. We also perform ablation studies to determine effects of different parts in CodeAttention.


title: A Photonic In-Memory Computing primitive for Spiking Neural Networks using Phase-Change Materials

abstract: Spiking Neural Networks (SNNs) offer an event-driven and more biologically realistic alternative to standard Artificial Neural Networks based on analog information processing. This can potentially enable energy-efficient hardware implementations of neuromorphic systems which emulate the functional units of the brain, namely, neurons and synapses. Recent demonstrations of ultra-fast photonic computing devices based on phase-change materials (PCMs) show promise of addressing limitations of electrically driven neuromorphic systems. However, scaling these standalone computing devices to a parallel in-memory computing primitive is a challenge. In this work, we utilize the optical properties of the PCM, Ge\textsubscript{2}Sb\textsubscript{2}Te\textsubscript{5} (GST), to propose a Photonic Spiking Neural Network computing primitive, comprising of a non-volatile synaptic array integrated seamlessly with previously explored `integrate-and-fire' neurons. The proposed design realizes an `in-memory' computing platform that leverages the inherent parallelism of wavelength-division-multiplexing (WDM). We show that the proposed computing platform can be used to emulate a SNN inferencing engine for image classification tasks. The proposed design not only bridges the gap between isolated computing devices and parallel large-scale implementation, but also paves the way for ultra-fast computing and localized on-chip learning.


title: A study of DES and Blowfish encryption algorithm

abstract: With the rapid growing of internet and networks applications, data security becomes more important than ever before. Encryption algorithms play a crucial role in information security systems. In this paper, we have a study of the two popular encryption algorithms: DES and Blowfish. We overviewed the base functions and analyzed the security for both algorithms. We also evaluated performance in execution speed based on different memory sizes and compared them. The experimental results show the relationship between function run speed and memory size.


Guidelines for Depicting Emotions in Storyboard Scenarios


Using Dynamic Programming for Path Planning of a Spherical Mobile Robot


title: HealthyTogether: exploring social incentives for mobile fitness applications

abstract: A crucial element in many mobile fitness applications is gamification that makes physical activities fun. While many methods focus on competition and individual users' interaction with the game, the aspect of social interaction and how users play games together in a group remains an open subject. To investigate these issues, we developed a mobile game, HealthyTogether, to understand how users interact in different group gamification settings: competition, cooperation, or hybrid. We describe the results of a user study involving 18 dyads (N=36) over a period of two weeks. Results show that users significantly enhanced physical activities using HealthyTogether compared with when they exercised alone by up to 15%. Among the group settings, cooperation (21% increase) and hybrid (18% increase) outperformed competition (8% increase). Additionally, users sent significantly more messages in cooperation setting than hybrid and competition. Furthermore, physical activities are positively correlated with the number of messages they exchanged. Based on the findings, we derive design implications for practitioners.


title: Image recognition via two-dimensional random projection and nearest constrained subspace

abstract: We consider the problem of image recognition via two-dimensional random projection and nearest constrained subspace. First, image features are extracted by a two-dimensional random projection. The two-dimensional random projection for feature extraction is an extension of the 1D compressive sampling technique to 2D and is computationally more efficient than its 1D counterpart and 2D reconstruction is guaranteed. Second, we design a new classifier called NCSC (Nearest Constrained Subspace Classifier) and apply it to image recognition with the 2D features. The proposed classifier is a generalized version of NN (Nearest Neighbor) and NFL (Nearest Feature Line), and it has a close relationship to NS (Nearest Subspace). For large datasets, a fast NCSC, called NCSC-II, is proposed. Experiments on several publicly available image sets show that when well-tuned, NCSC/NCSC-II outperforms its rivals including NN, NFL, NS and the orthonormal l2l2-norm classifier. NCSC/NCSC-II with the 2D random features also shows good classification performance in noisy environment.


title: Exploiting Social Relations and Sentiment for Stock Prediction

abstract: In this paper we first exploit cash-tags ("$" fol- lowed by stocks' ticker symbols) in Twitter to build a stock network, where nodes are stocks connected by edges when two stocks co-occur frequently in tweets. We then employ a labeled topic model to jointly model both the tweets and the network structure to assign each node and each edge a topic respectively. This Semantic Stock Network (SSN) summarizes discussion topics about stocks and stock relations. We fur- ther show that social sentiment about stock (node) topics and stock relationship (edge) topics are predictive of each stock's market. For predic- tion, we propose to regress the topic-sentiment time-series and the stock's price time series. Ex- perimental results demonstrate that topic senti- ments from close neighbors are able to help im- prove the prediction of a stock markedly.


title: A Distributed Snapshot Protocol for Efficient Artificial Intelligence Computation in Cloud Computing Environments

abstract: Many artificial intelligence applications often require a huge amount of computing resources. As a result, cloud computing adoption rates are increasing in the artificial intelligence field. To support the demand for artificial intelligence applications and guarantee the service level agreement, cloud computing should provide not only computing resources but also fundamental mechanisms for efficient computing. In this regard, a snapshot protocol has been used to create a consistent snapshot of the global state in cloud computing environments. However, the existing snapshot protocols are not optimized in the context of artificial intelligence applications, where large-scale iterative computation is the norm. In this paper, we present a distributed snapshot protocol for efficient artificial intelligence computation in cloud computing environments. The proposed snapshot protocol is based on a distributed algorithm to run interconnected multiple nodes in a scalable fashion. Our snapshot protocol is able to deal with artificial intelligence applications, in which a large number of computing nodes are running. We reveal that our distributed snapshot protocol guarantees the correctness, safety, and liveness conditions.


title: A Fast-Charging Switching-Based Charger With Adaptive Hybrid Duty Cycle Control for Multiple Batteries

abstract: To reduce circuit complexity and better control the charging current for parallel charging, a switching-based charger with an adaptive hybrid duty cycle control (AHDCC) for multiple batteries is presented. Depending on the voltage difference of the batteries, the AHDCC mechanism automatically delivers a different amount of energy to each battery to realize battery voltage balance and reduce power losses. Since an intermittent charging method is adopted in the constant current (CC) mode, the effect of voltage drop across the battery's parasitic resistor can be removed, and thus the charging time in CC mode is prolonged. AHDCC adjusts the charging current, prolongs the charging period in CC mode, and entirely turns on the auxiliary switches in the constant voltage mode. As a result, the functionalities of fast charging and battery voltage balance can be realized concurrently. The experimental results demonstrate that the peak efficiency of the charger is up to 89.4% and the total charging time of two batteries is slightly smaller than that of a single battery.


title: Autonomous precision agriculture through integration of wireless underground sensor networks with center pivot irrigation systems

abstract: Abstract Precision agriculture (PA) refers to a series of practices and tools necessary to correctly evaluate farming needs. The accuracy and effectiveness of PA solutions are highly dependent on accurate and timely analysis of the soil conditions. In this paper, a proof-of-concept towards an autonomous precision irrigation system is provided through the integration of a center pivot (CP) irrigation system with wireless underground sensor networks (WUSNs). This Wireless Underground Sensor-Aided Center Pivot (WUSA-CP) system will provide autonomous irrigation management capabilities by monitoring the soil conditions in real time using wireless underground sensors. To this end, field experiments with a hydraulic drive and continuous-move center pivot irrigation system are conducted. The results are used to evaluate empirical channel models for soil-air communications. The experiment results show that the concept of WUSA-CP is feasible. Through the design of an underground antenna, communication ranges can be improved by up to 400% compared to conventional antenna designs. The results also highlight that the wireless communication channel between soil and air is significantly affected by many spatio-temporal aspects, such as the location and burial depth of the sensors, soil texture and physical properties, soil moisture, and the vegetation canopy height. To the best of our knowledge, this is the first work on the development of an autonomous precision irrigation system with WUSNs.


title: Mecanum wheeled motion system with three wheels

abstract: Omni-directional wheeled motion system with its flexible steering, stable structure and reliable control precision, become the basis of the mobile robot system which used indoor. In this paper two kinds of Omni-directions platform which wheeled by three Mecanum wheels are built up, by setting up the dynamical simulations about the two model based in simulation software, and according to the dynamic simulation analysis, we try to find the affections of the platform motion caused by the drive force on Mecanum wheels, and choose the better model. All of the work will also make the basis for the control theory which is needed by the next step.


title: Internet Traffic Forecasting using Neural Networks

abstract: The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a neural network ensemble (NNE) for the prediction of TCP/IP traffic using a time series forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA).


title: Exposing the Hidden Web: An Analysis of Third-Party HTTP Requests on 1 Million Websites

abstract: This article provides a quantitative analysis of privacy-compromising mechanisms on 1 million popular websites. Findings indicate that nearly 9 in 10 websites leak user data to parties of which the user is likely unaware; more than 6 in 10 websites spawn third-party cookies; and more than 8 in 10 websites load Javascript code from external parties onto users' computers. Sites that leak user data contact an average of nine external domains, indicating that users may be tracked by multiple entities in tandem. By tracing the unintended disclosure of personal browsing histories on the Web, it is revealed that a handful of U.S. companies receive the vast bulk of user data. Finally, roughly 1 in 5 websites are potentially vulnerable to known National Security Agency spying techniques at the time of analysis.


title: An Ensemble Adjustment Kalman Filter for Data Assimilation

abstract: Abstract A theory for estimating the probability distribution of the state of a model given a set of observations exists. This nonlinear filtering theory unifies the data assimilation and ensemble generation problem that have been key foci of prediction and predictability research for numerical weather and ocean prediction applications. A new algorithm, referred to as an ensemble adjustment Kalman filter, and the more traditional implementation of the ensemble Kalman filter in which “perturbed observations” are used, are derived as Monte Carlo approximations to the nonlinear filter. Both ensemble Kalman filter methods produce assimilations with small ensemble mean errors while providing reasonable measures of uncertainty in the assimilated variables. The ensemble methods can assimilate observations with a nonlinear relation to model state variables and can also use observations to estimate the value of imprecisely known model parameters. These ensemble filter methods are shown to have significant advantag...


title: Biopsy of peripheral lung nodules utilizing cone beam computer tomography with and without trans bronchial access tool: a retrospective analysis

abstract: Background: Currently there are several techniques for endoscopic diagnosis of parenchymal lung abnormalities. Electromagnetic navigation with or without endobronchial ultrasound for diagnosis of the above has been well described. Bronchoscopic Trans Bronchial Access Tool is a novel endoscopic technique that creates a virtual pathway to the lesion and is less limited by location of the airway. The CrossCountryTM Transbronchial Access Tool (CovidienTM, Plymouth, MN, USA) is a Food and Drug Administration (FDA) approved off airway device that utilizes a catheter equipped guide sheath for a trans-parenchymal approach to a distal lesion. Cone beam computer tomography (CBCT) is a real-time onsite extrathoracic navigational modality used in the bronchoscopy suite that allows for an open working channel. All three of the above modalities can have reasonable diagnostic yields when used independently. While utilizing the above tools we frequently found ourselves in situations where one technique was not enough, prompting the use of a combination of modalities to obtain the most efficient and accurate diagnosis. We are reporting the feasibility and safety of utilizing these three modalities in conjunction with one another. Methods: Patients with peripheral pulmonary nodules on chest computed tomography underwent a navigation bronchoscopy under general anesthesia. CBCT and radial ultrasound was used in every case to confirm navigation to the target lesion. Lesions without definitive airways leading to them were accessed with the transbronchial access tool (TBAT). Results: Electromagnetic bronchoscopy using CBCT and radial US was performed on 22 patients from April 2016 to September 2016. The TBAT tool was used in 7 patients. The overall diagnostic yield was 77.2% (17 of 22). Diagnostic yield of with use TBAT was 100% (7 of 7). There were no complications. Average case length was 79.95 (range, 50–124) minutes and average fluoroscopy time was 10.39 (1–21.7) minutes. Conclusions: TBAT is a useful and safe tool when accessing peripheral pulmonary nodules and is used in conjunctions with electromagnetic navigation and CBCT.


title: Security Policies and Security Models

abstract: We assune that the reader is familiar with the ubiquity of information in the modern world and is sympathetic with the need for restricting rights to read, add, modify, or delete information in specific contexts. This need is particularly acute for systems having computers as significant components.


title: Relational Leadership Theory: Exploring the Social Processes of Leadership and Organizing

abstract: While the concept of relationship-oriented behavior has been around since the earliest formal studies of leadership in organizations (Stogdill and Coons, 1957), the term relational leadership is surprisingly new (Brower et al., 2000; Drath, 2001; Murrell, 1997; Uhl-Bien, 2003, 2005). Because of this, its meaning is still uncertain.


title: Vision-Based Real-Time Lane Marking Detection and Tracking

abstract: Detection and tracking of lane marking is essential for driving safety and intelligent vehicle. In this paper, an algorithm is presented which allows detection and tracking of multiple lane markings. Edge points cue is used to detect the lane marking and a road orientation estimation method is used to delete the edge lines which are impossible attribute to lane markings. In order to select the candidate lane marking, a confidence measures method is proposed. Then a finite-state machine decides whether or not a lane marking is really detected by fusion multi-frame detection results. Specifically, a particle filter is used to predict the future values of the lane marking model parameters, based on past observations. With particle filtering and confidence measures method, lane markings on various road scenes are detected and tracked. Experimental in different conditions, including illumination, weather and road, demonstrates its effectiveness and robustness. The algorithm runs in real-time at rates of about 30 Hz.


title: Molecular studies in olive (Olea europaea L.): overview on DNA markers applications and recent advances in genome analysis

abstract: Olive (Olea europaea L.) is one of the oldest agricultural tree crops worldwide and is an important source of oil with beneficial properties for human health. This emblematic tree crop of the Mediterranean Basin, which has conserved a very wide germplasm estimated in more than 1,200 cultivars, is a diploid species (2n = 2x = 46) that is present in two forms, namely wild (Olea europaea subsp. europaea var. sylvestris) and cultivated (Olea europaea subsp. europaea var. europaea). In spite of its economic and nutritional importance, there are few data about the genetic of olive if compared with other fruit crops. Available molecular data are especially related to the application of molecular markers to the analysis of genetic variability in Olea europaea complex and to develop efficient molecular tools for the olive oil origin traceability. With regard to genomic research, in the last years efforts are made for the identification of expressed sequence tag, with particular interest in those sequences expressed during fruit development and in pollen allergens. Very recently the sequencing of chloroplast genome provided new information on the olive nucleotide sequence, opening the olive genomic era. In this article, we provide an overview of the most relevant results in olive molecular studies. A particular attention was given to DNA markers and their application that constitute the most part of published researches. The first important results in genome analysis were reported.


title: Design of High Power Permanent Magnet Motor With Segment Rectangular Copper Wire and Closed Slot Opening on Electric Vehicles

abstract: Brushless permanent magnet DC motors are being manufactured and used increasingly in everything from home appliances to automobiles due to their inherent advantages. The merits of these motors could be enhanced further according to another design approach method which alters the typically used winding parts for low to medium power range motors. This paper deals with the high power brushless DC motor used for traction in a 48 V golf cart system. The stator is designed with segment rectangular copper winding to maximize output power within its limited dimensions. In the design process, the closed slot opening and slot-pole combination are investigated and compared in terms of efficiency and input current density. The developed motor obtained about 120% greater power density per volume compared to a commercial motor with round winding coils at similar efficiency and variable speed range.


title: Compact Disc Monopole Antennas for Current and Future Ultrawideband (UWB) Applications

abstract: Circular disc monopole antennas are investigated for current and future ultrawideband (UWB) applications. The studied antennas are compact and of small size (25 mm × 35 mm × 0.83 mm) with a 50-Ω feed line and offer a very simple geometry suitable for low cost fabrication and straightforward printed circuit board integration. More specifically, the impedance matching of the classic printed circular disc UWB monopole is improved by introducing transitions between the microstrip feed line and the printed disc. Two particular designs are examined using a dual and single microstrip transition. By using this simple antenna matching technique, respective impedance bandwidths (|Sn | <; -10 dB) from 2.5 to 11.7 GHz and 3.5 to 31.9 GHz are obtained. Results are also compared to a classic UWB monopole with no such matching network transitions. Measured and simulated reflection coefficient curves are provided along with beam patterns, gain and group delay values as a function of frequency. The transient behavior of the studied antennas is also examined in the time domain.


abstract: Abstract Introduction Vulvar and vaginal atrophy (VVA) affects up to two thirds of postmenopausal women, but most symptomatic women do not receive prescription therapy. Aim To evaluate postmenopausal women's perceptions of VVA and treatment options for symptoms in the Women's EMPOWER survey. Methods The Rose Research firm conducted an internet survey of female consumers provided by Lightspeed Global Market Insite. Women at least 45 years of age who reported symptoms of VVA and residing in the United States were recruited. Main Outcome Measures Survey results were compiled and analyzed by all women and by treatment subgroups. Results Respondents (N = 1,858) had a median age of 58 years (range = 45–90). Only 7% currently used prescribed VVA therapies (local estrogen therapies or oral selective estrogen receptor modulators), whereas 18% were former users of prescribed VVA therapies, 25% used over-the-counter treatments, and 50% had never used any treatment. Many women (81%) were not aware of VVA or that it is a medical condition. Most never users (72%) had never discussed their symptoms with a health care professional (HCP). The main reason for women not to discuss their symptoms with an HCP was that they believed that VVA was just a natural part of aging and something to live with. When women spoke to an HCP about their symptoms, most (85%) initiated the discussion. Preferred sources of information were written material from the HCP's office (46%) or questionnaires to fill out before seeing the HCP (41%).The most negative attributes of hormonal products were perceived risk of systemic absorption, messiness of local creams, and the need to reuse an applicator. Overall, HCPs only recommended vaginal estrogen therapy to 23% and oral hormone therapies to 18% of women. When using vaginal estrogen therapy, less than half of women adhered to and complied with posology; only 33% to 51% of women were very to extremely satisfied with their efficacy. Conclusion The Women's EMPOWER survey showed that VVA continues to be an under-recognized and under-treated condition, despite recent educational initiatives. A disconnect in education, communication, and information between HCPs and their menopausal patients remains prevalent. Kingsberg S, Krychman M, Graham S, et al. The Women's EMPOWER Survey: Identifying Women's Perceptions on Vulvar and Vaginal Atrophy and Its Treatment. J Sex Med 2017;14:413–424.


title: Speech enhancement based on wavelet thresholding the multitaper spectrum

abstract: It is well known that the "musical noise" encountered in most frequency domain speech enhancement algorithms is partially due to the large variance estimates of the spectra. To address this issue, we propose in this paper the use of low-variance spectral estimators based on wavelet thresholding the multitaper spectra for speech enhancement. A short-time spectral amplitude estimator is derived which incorporates the wavelet-thresholded multitaper spectra. Listening tests showed that the use of multitaper spectrum estimation combined with wavelet thresholding suppressed the musical noise and yielded better quality than the subspace and MMSE algorithms.


Dirichlet Reputation Systems


title: D-band grid-array antenna integrated in the lid of a surface-mountable chip-package

abstract: A packaging solution for single-chip millimeter-wave transceivers is presented. It integrates an antenna into the lid of the chip package. Multi-layer organic materials are used as antenna substrate. A prototype demonstrates the feasibility of the packaging process and the antenna performance. It consists of three parts: A package base; a MMIC dummy that allows measuring the antenna including the flip chip interconnect; and a 122-GHz grid array antenna. The complete antenna-in-package prototype is measured using a probe based measurement system.


title: Predicting readers' sarcasm understandability by modeling gaze behavior

abstract: Sarcasm understandability or the ability to understand textual sarcasm depends upon readers' language proficiency, social knowledge, mental state and attentiveness. We introduce a novel method to predict the sarcasm understandability of a reader. Presence of incongruity in textual sarcasm often elicits distinctive eye-movement behavior by human readers. By recording and analyzing the eye-gaze data, we show that eye-movement patterns vary when sarcasm is understood vis-a`-vis when it is not. Motivated by our observations, we propose a system for sarcasm understandability prediction using supervised machine learning. Our system relies on readers' eye-movement parameters and a few textual features, thence, is able to predict sarcasm understandability with an F-score of 93%, which demonstrates its efficacy. ::: ::: The availability of inexpensive embedded-eye-trackers on mobile devices creates avenues for applying such research which benefits web-content creators, review writers and social media analysts alike.


title: Emotion Regulation Predicts Imagery Ability

abstract: This study explored the relationship between athletes’ emotion regulation and imagery ability. A total of 648 athletes (57% female; Mage = 20.79 years, SD = 4.36) completed the Sport Imagery Ability Questionnaire (SIAQ) and Emotion Regulation Questionnaire (ERQ). Structural equation modeling supported the hypothesized model in which reappraisal positively predicted all SIAQ subscales. However, suppression had no significant association with imagery ability despite being predicted to be negatively associated. Results support the revised applied model of deliberate imagery use that individual characteristics will influence the imagery experience. Specifically, athletes who reappraise their emotions more frequently find it easier to image sport related content.


title: Classification of red blood cells disease using fuzzy logic theory

abstract: Blood cell classification is the initial process for detecting diseases; the diseases can be carried if it is detected at early stage. For solving such problems. Quantitative processing of digital images based on fuzzy technique is applied for classification of red blood cells. There are various features consist of shape, size and colour based features that based on statistical analysis (i.e. Mean, Standard Deviation, Variance, Roundness, Skewness, Kurtosis) have been extracted. The classification results indicated that these features highly signification and can be used for classification of red cells to the normal and up normal cells. The obtained result successfully identified 98% of red blood cells.


title: Deep learning approaches for video-based anomalous activity detection

abstract: The pervasive use of cameras at indoor and outdoor premises on account of recording the activities has resulted into deluge of long video data. Such surveillance videos are characterized by single or multiple entities (persons, objects) performing sequential/concurrent activities. It is often interesting to detect suspicious behavior of such entities in an automated manner without any intervention of human personnel, and to this end, anomalous activity detection from surveillance videos is an important research domain in Computer Vision. Detecting the anomalous activities from videos is very challenging due to equivocal nature of anomalies, context at which events took place, lack of ample size of anomalous ground truth training data and also other factors associated with variation in environment conditions, illumination conditions and working status of capturing cameras. Though automated visual surveillance is one of the highly sought-after research domains, use of deep learning techniques for anomalous activity detection is still in nascent stage. Deep learning models like convolution neural networks, auto-encoders, Long Short Term Memory network models have achieved remarkable performance on different domains like image classification, object detection, speech processing, and expediting towards achieving excellence in anomaly detection tasks. This paper aims at studying and analyzing deep learning techniques for video-based anomalous activity detection. As outcome of the study, the graphical taxonomy has been put forth based on kinds of anomalies, level of anomaly detection, and anomaly measurement for anomalous activity detection. The focus has been given on various anomaly detection frameworks having deep learning techniques as their core methodology. Deep learning approaches from both the perspectives of accuracy oriented anomaly detection and real-time processing oriented anomaly detection are compared. This paper also sheds light upon research issues and challenges, application domains, benchmarked datasets and future directions in the domain of deep learning based anomaly detection.


title: A Divide-And-Rule Scheme For Shot Boundary Detection Based on SIFT

abstract: Shot boundary detection is an important fundamental process toward automatic video indexing, retrieval, editing, etc. After a critical review of most approaches seeking to solve this problem, we propose a novel shot boundary detection. To improve the performance of the algorithm and reduce the computational cost, frames that are clearly not shot boundaries are first removed from the original video. After that, a novel SIFT keypoint matching algorithm based on SVM is proposed, which is used to capture the changing statistics of different kinds of shot transitions so as to identify, not only abrupt transitions, but also gradual transitions(fade, dissolve, wipe) accordingly. At last, our system use different algorithms for different kinds of shot transitions to help us to get a better solution for shot boundary detection problem. Numerical experiments in the evaluation of TRECVID and a variety of film videos demonstrate that our method is capable of accurately detecting shot transitions, and could greatly reduce the computational cost.


title: Mining the World Wide Web - Methods, Applications, and Perspectives

abstract: The World Wide Web has become, over the last years, a major source of information, and at the same time a significant platform for commerce. Both aspects make it an interesting target for data mining applications. In this survey, we will discuss different facets of data mining on the Web, and illustrate its methods by typical application areas. These areas will be highlighted in a more detailed descriptions in the subsequent contributions to this special issue of the KI Journal on Web Mining. As internet based applications become more and more intertwined, we will equally consider related domains like email and newsgroups here. The contributions of the special issue indicate new trends in web mining research. Although not specifically requested in the call for papers, they all focus on one of two issues: the detection of upcoming topics and trends, or the detection and support of online communities. We discuss in this paper that the emergence of these application domains goes together with two technical developments: the Semantic Web for explicitly representing knowledge in the Web, and the Web 2.0 as an effort for facilitating user participation in the Web. We will argue that the convergence of these two areas – one being an academic, top-down and the other a grass-roots, bottom-up approach – will be a major research challenge for the next years, where web mining will play a significant role.


title: Classification of lower limb motor imagery using K Nearest Neighbor and Naïve-Bayesian classifier

abstract: For development of foot prosthetics driven by brain computer interface (BCI) for lower limb amputees, the primary requirement is the classification of right and left lower limb motor imagery movement from brain signals. It is important to detect best possible combination of feature extraction and classification algorithms efficiently and accurately recognize left and right lower limb motor imagery from Electroencephalogram (EEG) signals in minimum time possible. An optimal choice has to be reached to select a feature extraction and classification technique with highest accuracy in minimum time. Thus, in this study we direct our attention towards finding the best feature extraction technique and classifier. Preprocessing of the EEG signals are done and relevant features are extracted. The extracted features are then used to classify left and right imagery movement by k-Nearest Neighbor (kNN) and Naive-Bayesian classifier. The best classification accuracy of 90% is obtained by kNN for power spectral density feature set requiring a time of 0.0531 sec. Thus, in future, it can be applied in real time classification to obtain best results in minimum time.


title: Getting precise and pragmatic about the assessment of bullying: The development of the California Bullying Victimization Scale

abstract: Accurate assessment of bullying is essential to intervention planning and evaluation. Limitations to many currently available self-report measures of bullying victimization include a lack of psychometric information, use of the emotionally laden term "bullying" in definition-first approaches to self-report surveys, and not assessing all components of the definition of bullying (chronicity, intentionality, and imbalance of power) in behavioral-based self-report methods. To address these limitations, we developed the California Bullying Victimization Scale (CBVS), which is a self-report scale that measures the three-part definition of bullying without the use of the term bully. We examined test-retest reliability and the concurrent and predictive validity of the CBVS across students in Grades 5-12 in four central California schools. Concurrent validity was assessed by comparing the CBVS with a common, definition-based bullying victimization measure. Predictive validity was examined through the co-administration of measures of psychological well-being. Analysis by grade and gender are included. Results support the test-retest reliability of the CBVS over a 2-week period. The CBVS was significantly, positively correlated with another bullying assessment and was related in expected directions to measures of well-being. Implications for differentiating peer victimization and bullying victimization via self-report measures are discussed. Aggr. Behav. 37:1-14, 2011. © 2011 Wiley-Liss, Inc. Language: en


title: An Unsupervised Method for Uncovering Morphological Chains

abstract: Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations. We use log-linear models with morpheme and word-level features to predict possible parents, including their modifications, for each word. The limited set of candidate parents for each word render contrastive estimation feasible. Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.


title: Decentralized blockchain-based electronic marketplaces

abstract: In a decentralized marketplace, buyers and sellers transact directly, without manipulation by intermediary platforms.


title: Effective hate-speech detection in Twitter data using recurrent neural networks

abstract: This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users’ tendency towards racism or sexism. This data is fed as input to the above classifiers along with the word frequency vectors derived from the textual content. We evaluate our approach on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state-of-the-art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.


title: Legal Determinants of the Return on Equity

abstract: Recent work documents that better legal institutions are associated with broader equity markets. We investigate whether international differences in legal institutions also help explain the international cross-section of expected stock returns. We document three main regularities. First, total stock market returns are positively correlated with overall measures of the quality of institutions, such as judicial efficiency and rule of law, but have no relationship with measures of shareholder rights, controlling for risk. Second, dividend yields and earning-price ratios also correlate positively with judicial efficiency and rule of law, but negatively with shareholder rights' protection, controlling for risk and expected earnings growth. Thirdly, the excess return on new issues is negatively associated with the quality of accounting standards. We interpret the positive effect of the overall quality of institutions on equity returns as capturing the resulting curtailment of private benefits and increase of profitability, under imperfect international integration of stock markets. The negative impact of shareholders' legal protection and of accounting standards can instead be seen as resulting from the implied reduction in shareholders' auditing and monitoring costs.


title: Using Supervised Bigram-based ILP for Extractive Summarization

abstract: In this paper, we propose a bigram based supervised method for extractive document summarization in the integer linear programming (ILP) framework. For each bigram, a regression model is used to estimate its frequency in the reference summary. The regression model uses a variety of indicative features and is trained discriminatively to minimize the distance between the estimated and the ground truth bigram frequency in the reference summary. During testing, the sentence selection problem is formulated as an ILP problem to maximize the bigram gains. We demonstrate that our system consistently outperforms the previous ILP method on different TAC data sets, and performs competitively compared to the best results in the TAC evaluations. We also conducted various analysis to show the impact of bigram selection, weight estimation, and ILP setup.


title: Temporal Multimodal Learning in Audiovisual Speech Recognition

abstract: In view of the advantages of deep networks in producing useful representation, the generated features of different modality data (such as image, audio) can be jointly learned using Multimodal Restricted Boltzmann Machines (MRB-M). Recently, audiovisual speech recognition based the M-RBM has attracted much attention, and the MRBM shows its effectiveness in learning the joint representation across audiovisual modalities. However, the built networks have weakness in modeling the multimodal sequence which is the natural property of speech signal. In this paper, we will introduce a novel temporal multimodal deep learning architecture, named as Recurrent Temporal Multimodal RB-M (RTMRBM), that models multimodal sequences by transforming the sequence of connected MRBMs into a probabilistic series model. Compared with existing multimodal networks, it's simple and efficient in learning temporal joint representation. We evaluate our model on audiovisual speech datasets, two public (AVLetters and AVLetters2) and one self-build. The experimental results demonstrate that our approach can obviously improve the accuracy of recognition compared with standard MRBM and the temporal model based on conditional RBM. In addition, RTMRBM still outperforms non-temporal multimodal deep networks in the presence of the weakness of long-term dependencies.


title: Computing clothoid segments for trajectory generation

abstract: A new, simple, and fast method to compute the sharpness and curvature of a clothoid segment of a continuous curvature path is presented. When generating continuous curvature paths, clothoid segments are needed to connect straight segments to perfect arc segments. This algorithm is part of a new trajectory generator and motion planner that generates smooth, natural, drivable paths, using a minimal amount of steering to reach the desired ending position similar to the way a human would drive.


title: Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco

abstract: This study aims to shed light on the process of building an effective smart city by integrating various practical perspectives with a consideration of smart city characteristics taken from the literature. We developed a framework for conducting case studies examining how smart cities were being implemented in San Francisco and Seoul Metropolitan City. The study's empirical results suggest that effective, sustainable smart cities emerge as a result of dynamic processes in which public and private sector actors coordinate their activities and resources on an open innovation platform. The different yet complementary linkages formed by these actors must further be aligned with respect to their developmental stage and embedded cultural and social capabilities. Our findings point to eight ‘stylized facts’, based on both quantitative and qualitative empirical results that underlie the facilitation of an effective smart city. In elaborating these facts, the paper offers useful insights to managers seeking to improve the delivery of smart city developmental projects.


title: FC/battery power management for electric vehicle based interleaved DC-DC boost converter topology

abstract: Due to the fact that the environmental issues have become more serious recently, interest in renewable energy systems, such as, fuel-cells (FCs) has increased steadfastly. Among many types of FCs, proton exchange membrane FC (PEMFC) is one of the most promising power sources due to its advantages, such as, low operation temperature, high power density and low emission. However, using only PEMFC for electric vehicle may not be feasible to satisfy the peak demand changes especially during accelerations and braking. So, hybridizing PEMFC and an energy storage system (ESS) decreases the FC cost and improves its performance and life. Battery (B) appears to be the most powerful candidate to hybridize with PEMFC for vehicular applications. Therefore, the performance of PEMFC/B hybridization is limited considerably by the performance of the converter. Thus, a suitable dc-dc converter topology is required. Various isolated and nonisolated converter topologies for FC applications have been proposed in literature. The objective of this study is to design and simulate a fuel cell - interleaved boost dc-dc converter (FC-IBC) for hybrid power systems in electric vehicle application, in order to decrease the FC current ripple. Therefore Energetic efficiency can also be improved. A control strategy capable of determining the desired FC power and keeps the dc voltage around its nominal value by supplying propulsion power and recuperating braking energy is designed and tested with an urbane electric vehicle model.


title: Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition

abstract: Abstract —An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.


title: System modelling and performance evaluation of a three-tier Cloud of Things

abstract: Abstract The emergent paradigm of fog computing advocates that the computational resources can be extended to the edge of the network, so that the transmission latency and bandwidth burden caused by cloud computing can be effectively reduced. Moreover, fog computing can support and facilitate some kinds of applications that do not cope well with some features of cloud computing, for instance, applications that require low and predictable latency, and geographically distributed applications. However, fog computing is not a substitute but instead a powerful complement to the cloud computing. This paper focuses on studying the interplay and cooperation between the edge (fog) and the core (cloud) in the context of the Internet of Things (IoT). We first propose a three-tier system architecture and mathematically characterize each tier in terms of energy consumption and latency. After that, simulations are performed to evaluate the system performance with and without the fog involvement. The simulation results show that the three-tier system outperforms the two-tier system in terms of the assessed metrics.


title: Big Graph Mining: Frameworks and Techniques

abstract: Abstract Big graph mining is an important research area and it has attracted considerable attention. It allows to process, analyze, and extract meaningful information from large amounts of graph data. Big graph mining has been highly motivated not only by the tremendously increasing size of graphs but also by its huge number of applications. Such applications include bioinformatics, chemoinformatics and social networks. One of the most challenging tasks in big graph mining is pattern mining in big graphs. This task consists on using data mining algorithms to discover interesting, unexpected and useful patterns in large amounts of graph data. It aims also to provide deeper understanding of graph data. In this context, several graph processing frameworks and scaling data mining/pattern mining techniques have been proposed to deal with very big graphs. This paper gives an overview of existing data mining and graph processing frameworks that deal with very big graphs. Then it presents a survey of current researches in the field of data mining/pattern mining in big graphs and discusses the main research issues related to this field. It also gives a categorization of both distributed data mining and machine learning techniques, graph processing frameworks and large scale pattern mining approaches.


title: Application of Tikhonov Regularization to Super-Resolution Reconstruction of Brain MRI Images

abstract: This paper presents an image super-resolution method that enhances spatial resolution of MRI images in the slice-select direction. The algorithm employs Tikhonov regularization, using a standard model of imaging process and reformulating the reconstruction as a regularized minimization task. Our experimental result shows improvements in both signal-to-noise ratio and visual quality.


title: Evaluating visualizations: Do expert reviews work

abstract: Visualization research generates beautiful images and impressive interactive systems. Emphasis on evaluating visualizations is growing. Researchers have successfully used alternative evaluation techniques in human-computer interaction (HCI), including focus groups, field studies, and expert reviews. These methods tend to produce qualitative results and require fewer participants than controlled experiments. In this article, we focus on expert reviews that we used for the applications. We commonly use expert reviews to assess interface usability. Expert reviews can generate valuable feedback on visualization tools. We recommend i) including experts with experience in data display as well as usability, and ii) developing heuristics based on visualization guidelines as well as usability guidelines. Expert reviews should not be used exclusively, since experts might not hilly predict end-user actions. Furthermore, we encourage more experimentation with this technique, particularly to develop a good set of visualization heuristics and to compare it with other methods.


title: Symmetric Offset Stack Balun in Standard 0.13-$\mu{\hbox {m}}$ CMOS Technology for Three Broadband and Low-Loss Balanced Passive Mixer Designs

abstract: This paper presents symmetric offset stack Marchand single and dual baluns that are designed, analyzed, and implemented in a 0.18-μm CMOS process to verify the feasibility. Both single and dual baluns achieve measured bandwidths (BWs) of over 110% and 90%, and insertion losses of less than 4.4 and 7.4 dB at 38 GHz. The amplitude imbalance and phase imbalance of single and dual baluns are less than 1 dB and 5° from 10 to 67 and 11 to 50 GHz, respectively. The baluns were used in three broadband balanced passive mixers, i.e., a single-balanced resistive mixer (SBRM), a star mixer, and a subharmonic gate pumped resistive mixer (SHPRM) design in a 0.13-μm CMOS technology. These mixers exhibit wide BWs over 14-45 GHz (105%), 18-54 GHz (100%), and 28-50 GHz (56%). The 14-45-GHz SBRM achieves a conversion loss of better than 12 dB at 7 dBm of local oscillator (LO) power. The LO to RF and LO to IF isolations are better than 30 dB. The chip area is 0.53 mm2. The star mixer achieves a conversion loss of better than 12 dB from 18 to 54 GHz, and LO to RF, LO to IF, and RF to IF isolations better than 35 dB at LO frequencies spanning 10-60 GHz. The chip area is 0.6 mm2. The SHPRM has a conversion loss of better than 11 dB from 28 to 50 GHz. The isolations are better than 31 dB and occupy a chip area of 0.61 mm2.


title: Multi-Perspective Detail+Overview Visualization for 3D Building Exploration

abstract: Virtual 3D building models, as key elements of virtual 3D city models, are used in a growing number of application domains, such as geoanalysis, disaster management and architectural planning. Visualization systems for such building models often rely on perspective or orthogonal projections using a single viewpoint. Therefore, the exploration of a complete model requires a user to change the viewpoint multiple times and to memorize the content of each view to obtain a comprehensive mental model. Since this is usually a time-consuming task, which implies context switching, current visualization systems use multiple viewports to simultaneously depict an object from different perspectives. Our approach extends the idea of multiple viewports by combining two linked views for the interactive exploration of virtual 3D buildings model and their facades. In contrast to traditional approaches, we automatically generate a multi-perspective view that simultaneously depicts all facades of the building in one overview image. This facilitates the process of obtaining overviews and supports fast and direct navigation to various points-of-interest. We describe the concept and implementations of our Multiple-Center-of-Projection camera model for real-time multi-perspective image synthesis. Further, we provide insights into different interaction techniques for linked multi-perspective views and outline approaches of future work.


title: A Globally Optimal k-Anonymity Method for the De-Identification of Health Data

abstract: Background: Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified. ::: ::: Objective: The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets. ::: ::: Design: Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. ::: ::: Measurement: Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated. ::: ::: Results: The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution. ::: ::: Conclusions: For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.


title: Integration of agent-based and Cloud Computing for the smart objects-oriented IoT

abstract: In the future Internet of Things (IoT), smart objects will be the fundamental building blocks for the creation of cyber-physical smart pervasive systems in a great variety of application domains ranging from health-care to transportation, from logistics to smart grid and cities. The implementation of a smart objects-oriented IoT is a complex challenge as distributed, autonomous, and heterogeneous IoT components at different levels of abstractions and granularity need to cooperate among themselves, with conventional networked IT infrastructures, and also with human users. In this paper, we propose the integration of two complementary mainstream paradigms for large-scale distributed computing: Agents and Cloud. Agent-based computing can support the development of decentralized, dynamic, cooperating and open IoT systems in terms of multi-agent systems. Cloud computing can enhance the IoT objects with high performance computing capabilities and huge storage resources. In particular, we introduce a cloud-assisted and agent-oriented IoT architecture that will be realized through ACOSO, an agent-oriented middleware for cooperating smart objects, and BodyCloud, a sensor-cloud infrastructure for large-scale sensor-based systems.


title: A meta-analytic review of gender differences in perceptions of sexual harassment.

abstract: Research on gender differences in perceptions of sexual harassment informs an ongoing legal debate regarding the use of a reasonable person standard instead of a reasonable woman standard to evaluate sexual harassment claims. The authors report a meta-analysis of 62 studies of gender differences in harassment perceptions. An earlier quantitative review combined all types of social-sexual behaviors for a single meta-analysis; the purpose of this study was to investigate whether the magnitude of the female-male difference varies by type of behavior. An overall standardized mean difference of 0.30 was found, suggesting that women perceive a broader range of social-sexual behaviors as harassing. However, the meta-analysis also found that the female-male difference was larger for behaviors that involve hostile work environment harassment, derogatory attitudes toward women, dating pressure, or physical sexual contact than sexual propositions or sexual coercion.


title: Remote QR code recognition based on HOG and SVM classifiers

abstract: QR codes have become useful and efficient data storage tools which are exploited in many commercial applications including product tracking, website redirection, etc. A QR code is a 2-dimensional barcode localised through three finder patterns (three squares characterised by a series of alternative black and white modules at ratios 1∶1∶3∶1∶1) placed in its three corners. QR codes are generally placed in different environments with complex backgrounds (overlapping text, pictures, etc.), and are often captured under unfavourable conditions such as poor lighting. These factors can significantly affect the recognition ability and thus may hinder correct QR code localisation and identification. In order to appropriately address these issues, in this paper, we present a QR code recognition algorithm based on histogram of oriented gradients (HOG) features combined with support vector machine (SVM) classifiers. Using HOG, we extract gradient features of each extracted pattern. Subsequently, the obtained features are passed to two linear SVM classifiers, one trained with finder patterns and one trained with alignment patterns, to remove irrelevant patterns. QR codes are then conveniently localised according to a pattern closeness constraint. In the last stage, the captured code is enhanced by applying a perspective correction followed by image binarisation and morphological processing. Finally, the patterns are decoded using an accurate 2-d barcode decoder. Our proposed approach is designed for an embedded systems using a Raspberry Pi equipped with a HD camera and a small robot carrying the equipment.


title: SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

abstract: Synthesizing SQL queries from natural language is a long-standing open problem and has been attracting considerable interest recently. Toward solving the problem, the de facto approach is to employ a sequence-to-sequence-style model. Such an approach will necessarily require the SQL queries to be serialized. Since the same SQL query may have multiple equivalent serializations, training a sequence-to-sequence-style model is sensitive to the choice from one of them. This phenomenon is documented as the "order-matters" problem. Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations. However, we observe that the improvement from reinforcement learning is limited. In this paper, we propose a novel approach, i.e., SQLNet, to fundamentally solve this problem by avoiding the sequence-to-sequence structure when the order does not matter. In particular, we employ a sketch-based approach where the sketch contains a dependency graph, so that one prediction can be done by taking into consideration only the previous predictions that it depends on. In addition, we propose a sequence-to-set model as well as the column attention mechanism to synthesize the query based on the sketch. By combining all these novel techniques, we show that SQLNet can outperform the prior art by 9% to 13% on the WikiSQL task.


title: Why reduce? Phonological neighborhood density and phonetic reduction in spontaneous speech

abstract: Frequent or contextually predictable words are often phonetically reduced, i.e. shortened and produced with articulatory undershoot. Explanations for phonetic reduction of predictable forms tend to take one of two approaches: Intelligibility-based accounts hold that talkers maximize intelligibility of words that might otherwise be difficult to recognize; production-based accounts hold that variation reflects the speed of lexical access and retrieval in the language production system. Here we examine phonetic variation as a function of phonological neighborhood density, capitalizing on the fact that words from dense phonological neighborhoods tend to be relatively difficult to recognize, yet easy to produce. We show that words with many phonological neighbors tend to be phonetically reduced (shortened in duration and produced with more centralized vowels) in connected speech, when other predictors of phonetic variation are brought under statistical control. We argue that our findings are consistent with the predictions of production-based accounts of pronunciation variation.


title: Steering Opinion Dynamics in Information Diffusion Networks.

abstract: Our opinion is influenced by those of others we connect to in social networks. How to model opinion dynamics across social networks and can we steer the public opinion to a desired state? Answers to these questions are critically important for understanding the vulnerabilities of online social networks and increasing their resilience to rumor and false information. Recently there has been much work on modeling the dynamics in information diffusion, but few work has integrated these models into a systematic control framework. In this paper, we propose a unified multivariate jump diffusion process framework for modeling opinion dynamics over networks and determining the control over such networks. Our method can handle noisy and partially observed data, and over networks with time-varying and node birth processes. Using synthetic and real world networks, we showed that our framework is robust, able to control both stable and unstable dynamics systems with fast convergence speed, less variance and low control cost.


title: The fastest carry lookahead adder

abstract: Adder is a very basic component in a central processing unit. The speed of compute becomes the most considerable condition for a designer. The carry lookahead adder is the highest speed adder nowadays. In this paper, a new method for modifying the carry lookahead adder is proposed. Based on the analysis of gate delay and simulation, the proposed modified carry lookahead adder is faster than the carry lookahead adder.


title: Learning Prices for Repeated Auctions with Strategic Buyers

abstract: Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism. We model the buyer as a strategic agent, whose goal is to maximize her long-term surplus, and we are interested in mechanisms that maximize the seller's long-term revenue. We define the natural notion of strategic regret — the lost revenue as measured against a truthful (non-strategic) buyer. We present seller algorithms that are no-(strategic)-regret when the buyer discounts her future surplus — i.e. the buyer prefers showing advertisements to users sooner rather than later. We also give a lower bound on strategic regret that increases as the buyer's discounting weakens and shows, in particular, that any seller algorithm will suffer linear strategic regret if there is no discounting.


title: A RDF and OWL-Based Temporal Context Reasoning Model for Smart Home

abstract: In the ubiquitous computing environment, context reasoning is an important issue of context-awareness. It is used to deduce desired or higher-level context and then to provide suitable services automatically. The previous context-reasoning approaches are mainly non-temporal. The reasoning is according to the real-time contexts without time information. However, temporal contexts are very important information for context-awareness. Therefore, a temporal context reasoning model (TempCRM) based on resource description framework (RDF) and Web ontology language (OWL) is proposed in this paper. TempCRM is used for inferring the dangerous level of a smart home. In a home environment, a potential dangerous situation is caused by a series of temporal events. A temporal event is represented as a RDF-based temporal context. A smart home ontology is defined for the terms and relationships used in the temporal context. Then, a set of reasoning rules can be defined for inferring and computing the dangerous level. In the simulation study, a script with dangerous situations is designed to evaluate the dangerous level generated by TempCRM. The result illustrates that TempCRM is useful to alarm the inhabitant and thus prevent the occurrence of an incident from the temporal contexts.


title: Adding depth to cartoons using sparse depth (in)equalities

abstract: This paper presents a novel interactive approach for adding depth information into hand-drawn cartoon images and animations. In comparison to previous depth assignment techniques our solution requires minimal user effort and enables creation of consistent pop-ups in a matter of seconds. Inspired by perceptual studies we formulate a custom tailored optimization framework that tries to mimic the way that a human reconstructs depth information from a single image. Its key advantage is that it completely avoids inputs requiring knowledge of absolute depth and instead uses a set of sparse depth (in)equalities that are much easier to specify. Since these constraints lead to a solution based on quadratic programming that is time consuming to evaluate we propose a simple approximative algorithm yielding similar results with much lower computational overhead. We demonstrate its usefulness in the context of a cartoon animation production pipeline including applications such as enhancement, registration, composition, 3D modelling and stereoscopic display.


title: Dual composite right/left-handed (D-CRLH) leaky-wave antenna with low beam squinting and tunable group velocity

abstract: The propagation and radiation properties of dual composite right/left-handed (D-CRLH) transmission line metamaterials are presented, and validated experimentally with a 1D microstrip prototype. In its fast-wave regime, this structure operates as a leaky-wave antenna capable of backfire-to-endfire radiation scanning with beam squinting smaller than that of conventional CRLH (C-CRLH) antennas. It is shown that this DCRLH beam squinting can be further reduced by increasing the group velocity but that this can occur, close (and possibly beyond) to luminal velocities, only at the expense of losses produced by parasitic effects, a fundamental limitation which is demonstrated via the Bloch impedance concept.


title: Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach

abstract: Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings one seeks to adapt to multiple, but somewhat similar, target domains. Applying pairwise adaptation approaches to this setting may be suboptimal, as they fail to leverage shared information among multiple domains. In this work, we propose an information theoretic approach for domain adaptation in the novel context of multiple target domains with unlabeled instances and one source domain with labeled instances. Our model aims to find a shared latent space common to all domains, while simultaneously accounting for the remaining private, domain-specific factors. Disentanglement of shared and private information is accomplished using a unified information-theoretic approach, which also serves to establish a stronger link between the latent representations and the observed data. The resulting model, accompanied by an efficient optimization algorithm, allows simultaneous adaptation from a single source to multiple target domains. We test our approach on three challenging publicly-available datasets, showing that it outperforms several popular domain adaptation methods.


title: Tests for equivalence or non‐inferiority for paired binary data

abstract: Assessment of therapeutic equivalence or non-inferiority between two medical diagnostic procedures often involves comparisons of the response rates between paired binary endpoints. The commonly used and accepted approach to assessing equivalence is by comparing the asymptotic confidence interval on the difference of two response rates with some clinical meaningful equivalence limits. This paper investigates two asymptotic test statistics, a Wald-type (sample-based) test statistic and a restricted maximum likelihood estimation (RMLE-based) test statistic, to assess equivalence or non-inferiority based on paired binary endpoints. The sample size and power functions of the two tests are derived. The actual type I error and power of the two tests are computed by enumerating the exact probabilities in the rejection region. The results show that the RMLE-based test controls type I error better than the sample-based test. To establish an equivalence between two treatments with a symmetric equivalence limit of 0.15, a minimal sample size of 120 is needed. The RMLE-based test without the continuity correction performs well at the boundary point 0. A numerical example illustrates the proposed procedures. Copyright © 2002 John Wiley & Sons, Ltd.


title: Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees

abstract: Maximally Stable Extremal Regions (MSERs) have achieved great success in scene text detection. However, this low-level pixel operation inherently limits its capability for handling complex text information efficiently (e. g. connections between text or background components), leading to the difficulty in distinguishing texts from background components. In this paper, we propose a novel framework to tackle this problem by leveraging the high capability of convolutional neural network (CNN). In contrast to recent methods using a set of low-level heuristic features, the CNN network is capable of learning high-level features to robustly identify text components from text-like outliers (e.g. bikes, windows, or leaves). Our approach takes advantages of both MSERs and sliding-window based methods. The MSERs operator dramatically reduces the number of windows scanned and enhances detection of the low-quality texts. While the sliding-window with CNN is applied to correctly separate the connections of multiple characters in components. The proposed system achieved strong robustness against a number of extreme text variations and serious real-world problems. It was evaluated on the ICDAR 2011 benchmark dataset, and achieved over 78% in F-measure, which is significantly higher than previous methods.


title: Word Association Thesaurus As a Resource for Building WordNet

abstract: The goal of the present paper is to report on the on-going research for applying psycholinguistic resources to building a WordNet-like lexicon of the Russian language. We are to survey different kinds of the linguistic data that can be extracted from a Word Association Thesaurus, a resource representing the results of a large- scaled free association test. In addition, we will give a comparison of Word Association Thesaurus and other language resources applied to wordnet constructing (e.g. text corpora, explanatory dictionaries) from the viewpoint of the quality and quantity of information they supply the researcher with.


title: SSVDAGs: symmetry-aware sparse voxel DAGs

abstract: Voxelized representations of complex 3D scenes are widely used nowadays to accelerate visibility queries in many GPU rendering techniques. Since GPU memory is limited, it is important that these data structures can be kept within a strict memory budget. Recently, directed acyclic graphs (DAGs) have been successfully introduced to compress sparse voxel octrees (SVOs), but they are limited to sharing identical regions of space. In this paper, we show that a more efficient lossless compression of geometry can be achieved, while keeping the same visibility-query performance, by merging subtrees that are identical through a similarity transform, and by exploiting the skewed distribution of references to shared nodes to store child pointers using a variabile bit-rate encoding. We also describe how, by selecting plane reflections along the main grid directions as symmetry transforms, we can construct highly compressed GPU-friendly structures using a fully out-of-core method. Our results demonstrate that state-of-the-art compression and real-time tracing performance can be achieved on high-resolution voxelized representations of real-world scenes of very different characteristics, including large CAD models, 3D scans, and typical gaming models, leading, for instance, to real-time GPU in-core visualization with shading and shadows of the full Boeing 777 at sub-millimetric precision.


title: Images Features Extraction of Tobacco Leaves

abstract: Images features extraction is very important for the grading process of flue-cured tobacco leaves. In this paper, a machine vision techniques base system is proposed for the automatic inspection of flue-cured tobacco leaves. Machine vision techniques are used in this system to solve problems of features extraction and analysis of tobacco leaves, which include features of color, size, shape andsurface texture. The experimental results show that this system is a viable way for the features extraction of tobacco leaves, and can be used for the automatic classification of tobacco leaves.


title: Passive detection of image forgery using DCT and local binary pattern

abstract: With the development of easy-to-use and sophisticated image editing software, the alteration of the contents of digital images has become very easy to do and hard to detect. A digital image is a very rich source of information and can capture any event perfectly, but because of this reason, its authenticity is questionable. In this paper, a novel passive image forgery detection method is proposed based on local binary pattern (LBP) and discrete cosine transform (DCT) to detect copy–move and splicing forgeries. First, from the chrominance component of the input image, discriminative localized features are extracted by applying 2D DCT in LBP space. Then, support vector machine is used for detection. Experiments carried out on three image forgery benchmark datasets demonstrate the superiority of the method over recent methods in terms of detection accuracy.


title: Underwater Acoustic Sensor Networks: Research Challenges

abstract: Underwater sensor nodes will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. Moreover, unmanned or autonomous underwater vehicles (UUVs, AUVs), equipped with sensors, will enable the exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area. In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the characteristics of the underwater channel are detailed. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed and a cross-layer approach to the integration of all communication functionalities is suggested. Furthermore, open research issues are discussed and possible solution approaches are outlined. � 2005 Published by Elsevier B.V.


title: Compiling PCRE to FPGA for accelerating SNORT IDS

abstract: Deep Payload Inspection systems like SNORT and BRO utilize regular expression for their rules due to their high expressibility and compactness. The SNORT IDS system uses the PCRE Engine for regular expression matching on the payload. The software based PCRE Engine utilizes an NFA engine based on certain opcodes which are determined by the regular expression operators in a rule. Each rule in the SNORT ruleset is translated by PCRE compiler into an unique regular expression engine. Since the software based PCRE engine can match the payload with a single regular expression at a time, and needs to do so for multiple rules in the ruleset, the throughput of the SNORT IDS system dwindles as each packet is processed through a multitude of regular expressions. In this paper we detail our implementation of hardware based regular expression engines for the SNORT IDS by transforming the PCRE opcodes generated by the PCRE compiler from SNORT regular expression rules. Our compiler generates VHDL code corresponding to the opcodes generated for the SNORT regular expression rules. We have tuned our hardware implementation to utilize an NFA based regular expression engine, using greedy quantifiers, in much the same way as the software based PCRE engine. Our system implements a regular expression only once for each new rule in the SNORT ruleset, thus resulting in a fast system that scales well with new updates. We implement two hundred PCRE engines based on a plethora of SNORT IDS rules, and use a Virtex-4 LX200 FPGA, on the SGI RASC RC 100 Blade connected to the SGI ALTIX 4700 supercomputing system as a testbed. We obtain an interface through-put of (12.9 GBits/s) and also a maximum speedup of 353X over software based PCRE execution.


title: Development of a New Low-Cost and Reliable Core-Less Current Probe for Conductor With Reduced Access

abstract: A new coreless non-intrusive current probe is presented in this paper. Most of the existing current sensors require 360° access to the wire, while the proposed probe accurately measures the current even if the access to the wire is limited to a small angle. Another new feature of the probe is that its output is not sensitive to the variation in the gap between the wire and the probe, which is a major advantage as it helps to provide reliable measurement over time. The proposed probe employs two sets of high sensitive and lightweight Hall effect or giant magneto-resistance-based sensors. Results from the experimental studies conducted showed that the proposed configuration is insensitive to the variation in the gap between the wire and the probe. This feature ensures that the output is independent of the insulation thickness, vibration, thermal expansion, etc. Two prototypes of the proposed probes have been developed and tested. The results obtained established the feasibility of the proposed low-cost current sensor.


title: Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data

abstract: Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We demonstrate the quality of the produced flow fields on synthetic and real-world datasets. Finally, we collect a novel challenging optical flow dataset by applying our technique on data from a high-speed camera and analyze the performance of the state-of-the-art in optical flow under various levels of motion blur.


title: Social Spammer and Spam Message Co-Detection in Microblogging with Social Context Regularization

abstract: The popularity of microblogging platforms, such as Twitter, makes them important for information dissemination and sharing. However, they are also recognized as ideal places by spammers to conduct social spamming. Massive social spammers and spam messages heavily hurt the user experience and hinder the healthy development of microblogging systems. Thus, effectively detecting the social spammers and spam messages in microblogging is of great value. Existing studies mainly regard social spammer detection and spam message detection as two separate tasks. However, social spammers and spam messages have strong connections, since social spammers tend to post more spam messages and spam messages have high probabilities to be posted by social spammers. Combining social spammer detection with spam message detection has the potential to boost the performance of each task. In this paper, we propose a unified framework for social spammer and spam message co-detection in microblogging. Our framework utilizes the posting relations between users and messages to combine social spammer detection with spam message detection. In addition, we extract the social relations between users as well as the connections between messages, and incorporate them into our framework as regularization terms over the prediction results. Besides, we introduce an efficient optimization method to solve our framework. Extensive experiments on a real-world microblog dataset demonstrate that our framework can significantly and consistently improve the performance of both social spammer detection and spam message detection.


title: An inductive peaking technology for high-speed MIPI receiver bandwidth expanding in a 90 nm CMOS process

abstract: An inductive peaking technology is proposed in this paper for improving receiver bandwidth. A N/PMOS cross biasing active inductor and negative Miller capacitance are introduced to provide inductive peaking around the amplifier bandwidth. According to the simulation and experimental results within a MIPI receiver under 1.2V CMOS 90nm process, the inductive peaking technology increases the bandwidth of the preamplifier in the MIPI PHY circuit from 1.05GHz to 2.09GHz. As a results, the PHY circuit which employs the inductive peaking amplifier consumes only 4mW power for transferring 1Gbps data signal with 70mV differential amplitude to 1.2V CMOS level.


PROTEINURIA : A RISK FACTOR FOR PREGNANCY-RELATED RENAL FUNCTION DECLINE IN PRIMARY GLOMERULAR DISEASE?


title: Balancing repair and tolerance of DNA damage caused by alkylating agents

abstract: Alkylating agents can both induce tumorigenesis and be used to treat cancer patients. However, responses to alkylating chemotherapeutic drugs are variable. This Review discusses the molecular responses to alkylating agents and the causes for such variation.


title: A 5.8nW, 45ppm/°C on-chip CMOS wake-up timer using a constant charge subtraction scheme

abstract: This work presents an ultra-low power oscillator designed for wake-up timers in compact wireless sensors. A constant charge subtraction scheme removes continuous comparator delay from the oscillation period, which is the source of temperature dependence in conventional RC relaxation oscillators. This relaxes comparator design constraints, enabling low power operation. In 0.18µm CMOS, the oscillator consumes 5.8nW at room temperature with temperature stability of 45ppm/°C (-10°C to 90°C) and 1%V line sensitivity.


title: When science is nonscientific

abstract: This paper addresses the connection that science has to philosophy and the use of presuppositions. The author critiques Slawinski's paper titled "Electromagnetic Radiation And The Afterlife" by pointing out a variety of flaws in Slawinski's reasoning.


title: Brain Knowledge and the Prevalence of Neuromyths among Prospective Teachers in Greece

abstract: Although very often teachers show a great interest in introducing findings from the field of neuroscience in their classrooms, there is there is growing concern about the lack of academic instruction on neuroscience on teachers' curricula because this has led to a proliferation of neuromyths. We surveyed 468 undergraduate (mean age = 19.60 years, SD = 2.29) and 86 postgraduate students (mean age = 28.52 years, SD = 7.16) enrolled in the Departments of Education at the University of Thessaly and the National and Kapodistrian University of Athens. We used a 70-item questionnaire aiming to explore general knowledge on the brain, neuromyths, the participants’ attitude towards neuroeducation as well as their reading habits. Prospective teachers were found to believe that neuroscience knowledge is useful for teachers (90.3% agreement), to be somewhat knowledgeable when it comes to the brain (47.33% of the assertions were answered correctly), but to be less well informed when it comes to neuroscientific issues related to special education (36.86% correct responses). Findings further indicate that general knowledge about the brain was found to be the best safeguard against believing in neuromyths. Based on our results we suggest that prospective teachers can benefit from academic instruction on neuroscience. We propose that such instruction takes place in undergraduate courses of Departments of Education and that emphasis is given in debunking neuromyths, enhancing critical reading skills, and dealing with topics relevant to special education.


An Empirical Study on Fine-Grained Named Entity Recognition


title: Coherence-Enhancing Shock Filters

abstract: Shock filters are based in the idea to apply locally either a dilation or an erosion process, depending on whether the pixel belongs to the influence zone of a maximum or a minimum. They create a sharp shock between two influence zones and produce piecewise constant segmentations. In this paper we design specific shock filters for the enhancement of coherent flow-like structures. They are based on the idea to combine shock filtering with the robust orientation estimation by means of the structure tensor. Experiments with greyscale and colour images show that these novel filters may outperform previous shock filters as well as coherence-enhancing diffusion filters.


title: Dynamic Blocking and Collapsing for Gibbs Sampling

abstract: In this paper, we investigate combining blocking and collapsing - two widely used strategies for improving the accuracy of Gibbs sampling - in the context of probabilistic graphical models (PGMs). We show that combining them is not straight-forward because collapsing (or eliminating variables) introduces new dependencies in the PGM and in computation-limited settings, this may adversely affect blocking. We therefore propose a principled approach for tackling this problem. Specifically, we develop two scoring functions, one each for blocking and collapsing, and formulate the problem of partitioning the variables in the PGM into blocked and collapsed subsets as simultaneously maximizing both scoring functions (i.e., a multi-objective optimization problem). We propose a dynamic, greedy algorithm for approximately solving this intractable optimization problem. Our dynamic algorithm periodically updates the partitioning into blocked and collapsed variables by leveraging correlation statistics gathered from the generated samples and enables rapid mixing by blocking together and collapsing highly correlated variables. We demonstrate experimentally the clear benefit of our dynamic approach: as more samples are drawn, our dynamic approach significantly outperforms static graph-based approaches by an order of magnitude in terms of accuracy.


title: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing

abstract: Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.


title: An iCRISPR platform for rapid, multiplexable, and inducible genome editing in human pluripotent stem cells.

abstract: Human pluripotent stem cells (hPSCs) offer a unique platform for elucidating the genes and molecular pathways that underlie complex traits and diseases. To realize this promise, methods for rapid and controllable genetic manipulations are urgently needed. By combining two newly developed gene-editing tools, the TALEN and CRISPR/Cas systems, we have developed a genome-engineering platform in hPSCs, which we named iCRISPR. iCRISPR enabled rapid and highly efficient generation of biallelic knockout hPSCs for loss-of-function studies, as well as homozygous knockin hPSCs with specific nucleotide alterations for precise modeling of disease conditions. We further demonstrate efficient one-step generation of double- and triple-gene knockout hPSC lines, as well as stage-specific inducible gene knockout during hPSC differentiation. Thus the iCRISPR platform is uniquely suited for dissection of complex genetic interactions and pleiotropic gene functions in human disease studies and has the potential to support high-throughput genetic analysis in hPSCs.


Self-organization in multi-agent systems


Credit Card Fraud Detection Using Hidden Markov Model


title: Exploiting self-similarity in geometry for voxel based solid modeling

abstract: Voxel-based modeling techniques are known for their robustness and flexibility. However, they have three major shortcomings: (1) Memory intensive, since a large number of voxels are needed to represent high-resolution models (2) Computationally expensive, since a large number of voxels need to be visited (3) Computationally expensive isosurface extraction is needed to visualize the results. We describe techniques which alleviate these by taking advantage of self-similarity in the data making voxel-techniques practical and attractive. We describe algorithms for MEMS process emulation, isosurface extraction and visualization which utilize these techniques.