title: Influence of filler loading on dielectric properties of epoxy-ZnO nanocomposites

abstract: Experimental investigations into the dielectric properties of epoxy-ZnO nanocomposites at different filler loadings reveal few unique behaviors (at certain filler loadings) and also advantageous characteristics in contrast to the properties obtained for the corresponding microcomposites. Results demonstrate that in nanocomposites, it is possible to achieve lower values of permittivity and tandelta with respect to unfilled epoxy over a wide frequency range. Analysis of the results attributes this interesting observation to the interaction dynamics between the epoxy chains and the ZnO nanoparticles at the interfacial area. The dc volume resistivities and ac dielectric strengths of nanocomposites were also experimentally determined in the present study and the obtained characteristics are found to be different as compared to the results obtained for microcomposites. The volume fraction and nature of the interfaces in the bulk of the composites seem to influence this difference in the examined dielectric properties of the nanocomposites.


title: An ontology-driven framework towards building enterprise semantic information layer

abstract: Enterprises Information Systems (EIS) have been applied for decades in Computer-Aided Engineering (CAE) and Computer-Aided Design (CAD), where huge amount of increasing data is stored in the heterogeneous and distributed systems. As systems evaluating, system redesign and reengineering are demanded. A facing challenge is how to interoperate among different systems by overcoming the gap of conceptual heterogeneity. In this article, an enlarged data representation called semantic information layer (SIL) is described for facilitating heterogeneous systems interoperable. SIL plays a role as mediation media and knowledge representation among heterogeneous systems. The SIL building process is based on ontology engineering, including ontology extraction from relational database (RDB), ontology enrichment and ontology alignment. Mapping path will maintain the links between SIL and data source, and query implementation and user interface are for retrieving data and interacting with end users. We described fully a practical ontology-driven framework for building SIL and introduced extensively relevant standards and techniques for implementing the framework. In the core part of ontology development, a dynamic multi-strategies ontology alignment with automatic matcher selection and dynamic similarity aggregation is proposed. A demonstration case study in the scenario of mobile phone industry is used to illustrate the proposed framework.


title: A deep learning approach for detecting malicious JavaScript code

abstract: Malicious JavaScript code in webpages on the Internet is an emergent security issue because of its universality and potentially severe impact. Because of its obfuscation and complexities, detecting it has a considerable cost. Over the last few years, several machine learning-based detection approaches have been proposed; most of them use shallow discriminating models with features that are constructed with artificial rules. However, with the advent of the big data era for information transmission, these existing methods already cannot satisfy actual needs. In this paper, we present a new deep learning framework for detection of malicious JavaScript code, from which we obtained the highest detection accuracy compared with the control group. The architecture is composed of a sparse random projection, deep learning model, and logistic regression. Stacked denoising auto-encoders were used to extract high-level features from JavaScript code; logistic regression as a classifier was used to distinguish between malicious and benign JavaScript code. Experimental results indicated that our architecture, with over 27 000 labeled samples, can achieve an accuracy of up to 95%, with a false positive rate less than 4.2% in the best case. Copyright © 2016 John Wiley & Sons, Ltd.


title: An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)

abstract: The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.


title: Learning human motion intention with 3D Convolutional Neural Network

abstract: In this paper, we present an end-to-end learning approach for human motion inference from 3D point cloud data. Examples of human motion to be learned are collected as point cloud data through a 3D sensor, mapped into 3D occupancy grids and then used as supervised learning samples for a 3D Convolutional Neural Network (3D CNN). The 3D CNN model is able to learn spatiotemporal features from time steps of occupancy grids and predict human motion intentions with an accuracy of 83% within 60% of the motion performed. We demonstrate the performance of this model in real time by predicting the intention of a human arm motion for some predetermined targets, and furthermore generalise the model to new users whose data were not used in the training of the model. This approach is useful for human-robot interaction and human-computer interaction applications that need human motion learning without explicitly modelling the dynamics of the human motion.


title: A Multi-Modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling

abstract: While deep convolutional neural networks have shown a remarkable success in image classification, the problems of inter-class similarities, intra-class variances, the effective combination of multi-modal data, and the spatial variability in images of objects remain to be major challenges. To address these problems, this paper proposes a novel framework to learn a discriminative and spatially invariant classification model for object and indoor scene recognition using multi-modal RGB-D imagery. This is achieved through three postulates: 1) spatial invariance $-$ this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations, rotations, and scale changes, 2) high discriminative capability $-$ this is achieved by introducing Fisher encoding within the CNN architecture to learn features which have small inter-class similarities and large intra-class compactness, and 3) multi-modal hierarchical fusion $-$ this is achieved through the regularization of semantic segmentation to a multi-modal CNN architecture, where class probabilities are estimated at different hierarchical levels (i.e., image- and pixel-levels), and fused into a Conditional Random Field (CRF)-based inference hypothesis, the optimization of which produces consistent class labels in RGB-D images. Extensive experimental evaluations on RGB-D object and scene datasets, and live video streams (acquired from Kinect) show that our framework produces superior object and scene classification results compared to the state-of-the-art methods.


title: A functional representation for aiding biomimetic and artificial inspiration of new ideas

abstract: Inspiration is useful for exploration and discovery of new solution spaces. Systems in natural and artificial worlds and their functionality are seen as rich sources of inspiration for idea generation. However, unlike in the artificial domain where existing systems are often used for inspiration, those from the natural domain are rarely used in a systematic way for this purpose. Analogy is long regarded as a powerful means for inspiring novel idea generation. One aim of the work reported here is to initiate similar work in the area of systematic biomimetics for product development, so that inspiration from both natural and artificial worlds can be used systematically to help develop novel, analogical ideas for solving design problems. A generic model for representing causality of natural and artificial systems has been developed, and used to structure information in a database of systems from both the domains. These are implemented in a piece of software for automated analogical search of relevant ideas from the databases to solve a given problem. Preliminary experiments at validating the software indicate substantial potential for the approach.


title: LogTree: A Framework for Generating System Events from Raw Textual Logs

abstract: Modern computing systems are instrumented to generate huge amounts of system logs and these data can be utilized for understanding and complex system behaviors. One main fundamental challenge in automated log analysis is the generation of system events from raw textual logs. Recent works apply clustering techniques to translate the raw log messages into system events using only the word/term information. In this paper, we first illustrate the drawbacks of existing techniques for event generation from system logs. We then propose Log Tree, a novel and algorithm-independent framework for events generation from raw system log messages. Log Tree utilizes the format and structural information of the raw logs in the clustering process to generate system events with better accuracy. In addition, an indexing data structure, Message Segment Table, is proposed in Log Tree to significantly improve the efficiency of events creation. Extensive experiments on real system logs demonstrate the effectiveness and efficiency of Log Tree.


title: Catch your attention: Quality-retaining power saving on mobile OLED displays

abstract: Organic light-emitting diode (OLED) technology is considered as a promising alternative to mobile displays. This paper explores how to reduce the OLED power consumption by exploiting visual attention. First, we model the problem of OLED image scaling optimization, with the objective of minimizing the power required to display an image without adversely impacting the user's visual experience. Then, we propose an algorithm to solve the fundamental problem, and prove its optimality even without the accurate power model. Finally, based on the algorithm, we consider implementation issues and realize two application scenarios on a commercial OLED mobile tablet. The results of experiments conducted on the tablet with real images demonstrate that the proposed methodology can achieve significant power savings while retaining the visual quality.


title: Integrated CMOS transmit-receive switch using LC-tuned substrate bias for 2.4-GHz and 5.2-GHz applications

abstract: CMOS transmit-receive (T/R) switches have been integrated in a 0.18-/spl mu/m standard CMOS technology for wireless applications at 2.4 and 5.2 GHz. This switch design achieves low loss and high linearity by increasing the substrate impedance of a MOSFET at the frequency of operation using a properly tuned LC tank. The switch design is asymmetric to accommodate the different linearity and isolation requirements in the transmit and receive modes. In the transmit mode, the switch exhibits 1.5-dB insertion loss, 28-dBm power, 1-dB compression point (P/sub 1dB/), and 30-dB isolation, at 2.4 and 5.2 GHz. In the receive mode, the switch achieves 1.6-dB insertion loss, 11.5-dBm P/sub 1dB/, and 15-dB isolation, at 2.4 and 5.2 GHz. The linearity obtained in the transmit mode is the highest reported to date in a standard CMOS process. The switch passes the 4-kV Human Body Model electrostatic discharge test. These results show that the switch design is suitable for narrow-band applications requiring a moderate-high transmitter power level (<1 W).


title: Audience Involvement in Advertising: Four Levels

abstract: The effectiveness of advertising messages is widely believed to be moderated by audience involvement. In this paper, psychological theories of attention and levels of processing are used to establish a framework that can accommodate the major consumer behavior theories of audience involvement. Four levels of involvement are identified (in order from low to high) as preattention, focal attention, comprehension, and elaboration. These levels allocate increasing attentional capacity to a message source, as needed for analysis of the message by increasingly abstract—and qualitatively distinct—representational systems. Lower levels use relatively little capacity and extract information needed to determine whether higher levels will be invoked. The higher levels require greater capacity and result in increasingly durable cognitive and attitudinal effects.


title: Learning Word-Like Units from Joint Audio-Visual Analysis

abstract: Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word 'lighthouse' within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.


Full Bridge Resonant Inverter For Induction Heating Applications


title: Differentially Private Histogram Publishing through Lossy Compression

abstract: Differential privacy has emerged as one of the most promising privacy models for private data release. It can be used to release different types of data, and, in particular, histograms, which provide useful summaries of a dataset. Several differentially private histogram releasing schemes have been proposed recently. However, most of them directly add noise to the histogram counts, resulting in undesirable accuracy. In this paper, we propose two sanitization techniques that exploit the inherent redundancy of real-life datasets in order to boost the accuracy of histograms. They lossily compress the data and sanitize the compressed data. Our first scheme is an optimization of the Fourier Perturbation Algorithm (FPA) presented in \cite{RN10}. It improves the accuracy of the initial FPA by a factor of 10. The other scheme relies on clustering and exploits the redundancy between bins. Our extensive experimental evaluation over various real-life and synthetic datasets demonstrates that our techniques preserve very accurate distributions and considerably improve the accuracy of range queries over attributed histograms.


title: Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion

abstract: In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular-mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular-mechanical fusion and a support vector machine (SVM) algorithm produced 99% or higher accuracy in the stance phase and 95% accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular-mechanical fusion for neural control of prosthetic legs.


title: A battery-free RFID-based indoor acoustic localization platform

abstract: The ability to precisely localize RFID tags with low latency and low cost is desirable for applications such as inventory, asset tracking and robotic-manipulation of tagged items. However, because of multipath propagation effects, traditional RFID positioning methods based on RF Received Signal Strength Indication (RSSI) have limited accuracy (meter-scale) or require large numbers of reference tags and readers, which increases both latency and cost. Acoustic localization systems can be more precise, but typically require a battery-powered sensor platform, which both increases tag size, weight and limits tag lifetime. In addition, conventional acoustic localization systems are not integrated with existing RFID infrastructure. This paper presents a working prototype of a RFID-based system that localizes a custom battery-free, EPC Gen2-compatible UHF tag. The system uses the RFID communication channel for synchronization and inventory, and acoustic propagation delays for distance measurement. The system consists of a custom passive tag (WISP) with acoustic tone-detector that receives and times ultrasound signals, an off-the-shelf EPC Gen2 UHF RFID reader, and an array of ultrasonic beacons. By measuring the Time of Arrival (ToA) of the ultrasound, the passive WISP tag can determine its location relative to the ultrasonic beacons. Time synchronization between the tag being tracked and the ultrasonic beacons is accomplished by using a “spy WISP.” The spy WISP listens to the RFID communication traffic between the reader and the tracked tag and triggers the ultrasonic beacons in a fashion that is synchronous with the RFID traffic. The localization performance of the prototype is characterized, and the tradeoffs among power consumption, precision, latency and range are discussed.


title: Relation between drug utilization and morbidity pattern: antihypertensive drugs.

abstract: . Drug utilization studies are a valuable tool in health services research aiming to improve quality of health care and use of resources. We first present a simple model for drug utilization studies and then give practical examples from our studies in Norway, mainly dealing with the treatment of hypertension. There are marked differences in drug utilization between hospitals, counties, and the five Nordic countries. The medical consequences of these differences are probably small, but they have important economic implications. The marked regional differences in drug utilization argue for better organization of health care, for standardized treatment programs, and for continuing education of doctors.


title: Breeding technologies to increase crop production in a changing world.

abstract: To feed the several billion people living on this planet, the production of high-quality food must increase with reduced inputs, but this accomplishment will be particularly challenging in the face of global environmental change. Plant breeders need to focus on traits with the greatest potential to increase yield. Hence, new technologies must be developed to accelerate breeding through improving genotyping and phenotyping methods and by increasing the available genetic diversity in breeding germplasm. The most gain will come from delivering these technologies in developing countries, but the technologies will have to be economically accessible and readily disseminated. Crop improvement through breeding brings immense value relative to investment and offers an effective approach to improving food security.


1 The Effect of Peppermint on Memory Performance


Data Mining Techniques for ( Network ) Intrusion Detection Systems


title: Building better passwords using probabilistic techniques

abstract: Password creation policies attempt to help users generate strong passwords but are generally not very effective and tend to frustrate users. The most popular policies are rule based which have been shown to have clear limitations. In this paper we consider a new approach that we term analyze-modify that ensures strong user passwords while maintaining usability. In our approach we develop a software system called AMP that first analyzes whether a user proposed password is weak or strong by estimating the probability of the password being cracked. AMP then modifies the password slightly (to maintain usability) if it is weak to create a strengthened password. We are able to estimate the strength of the password appropriately since we use a probabilistic password cracking system and associated probabilistic context-free grammar to model a realistic distribution of user passwords. In our experiments we were able to distinguish strong passwords from weak ones with an error rate of 1.43%. In one of a series of experiments, our analyze-modify system was able to strengthen a set of weak passwords, of which 53% could be easily cracked to a set of strong passwords of which only 0.27% could be cracked with only a slight modification to the passwords. In our work, we also show how to compute and use various entropy measures from the grammar and show that our system remains effective with continued use through a dynamic updating capability.


title: Tempo Induction Using Filterbank Analysis and Tonal Features

abstract: This paper presents an algorithm that extracts the tempo of a musical excerpt. The proposed system assumes a constant tempo and deals directly with the audio signal. A sliding window is applied to the signal and two feature classes are extracted. The first class is the log-energy of each band of a mel-scale triangular filterbank, a common feature vector used in various MIR applications. For the second class, a novel feature for the tempo induction task is presented; the strengths of the twelve western musical tones at all octaves are calculated for each audio frame, in a similar fashion with Pitch Class Profile. The timeevolving feature vectors are convolved with a bank of resonators, each resonator corresponding to a target tempo. Then the results of each feature class are combined to give the final output. The algorithm was evaluated on the popular ISMIR 2004 Tempo Induction Evaluation Exchange Dataset. Results demonstrate that the superposition of the different types of features enhance the performance of the algorithm, which is in the current state-of-the-art algorithms of the tempo induction task.


title: Epigenetic regulation of cardiac fibrosis.

abstract: Abstract Cardiac fibrosis is characterized by excessive extracellular matrix accumulation that ultimately destroys tissue architecture and eventually abolishes normal function. In recent years, despite the underlying mechanisms of cardiac fibrosis are still unknown, numerous studies suggest that epigenetic modifications impact on the development of cardiac fibrosis. Epigenetic modifications control cell proliferation, differentiation, migration, and so on. Epigenetic modifications contain three main processes: DNA methylation, histone modifications, and silencing by microRNAs. We here outline the recent work pertaining to epigenetic changes in cardiac fibrosis. This review focuses on the epigenetic regulation of cardiac fibrosis.


title: "I Love All the Bits": The Materiality of Boardgames

abstract: This paper presents findings from a study of boardgamers which stress the importance of the materiality of modern boardgames. It demonstrates that materiality is one of four significant factors in the player experience of tabletop gaming and describes four domains of materiality in boardgaming settings. Further, building on understanding of non-use in HCI, it presents boardgames as a unique situation of parallel use, in which users simultaneously engage with a single game in both digital and material, non-digital environments.


title: Effectiveness of a multi‐layer foam dressing in preventing sacral pressure ulcers for the early acute care of patients with a traumatic spinal cord injury: comparison with the use of a gel mattress

abstract: Individuals with spinal cord injury are at risk of sacral pressure ulcers due to, among other reasons, prolonged immobilisation. The effectiveness of a multi-layer foam dressing installed pre-operatively in reducing sacral pressure ulcer occurrence in spinal cord injured patients was compared to that of using a gel mattress, and stratified analyses were performed on patients with complete tetraplegia and paraplegia. Socio-demographic and clinical data were collected from 315 patients admitted in a level-I trauma centre following a spinal cord injury between April 2010 and March 2016. Upon arrival to the emergency room and until surgery, patients were transferred on a foam stretcher pad with a viscoelastic polymer gel mattress (before 1 October 2014) or received a multi-layer foam dressing applied to their sacral-coccygeal area (after 1 October 2014). The occurrence of sacral pressure ulcer during acute hospitalisation was similar irrespective of whether patients received the dressing or the gel mattress. It was found that 82% of patients with complete tetraplegia receiving the preventive dressing developed sacral ulcers as compared to only 36% of patients using the gel mattress. Although multi-layer dressings were suggested to improve skin protection and decrease pressure ulcer occurrence in critically ill patients, such preventive dressings are not superior to gel mattresses in spinal cord injured patients and should be used with precaution, especially in complete tetraplegia.


Peak Torque and Rate of Torque Development Influence on Repeated Maximal Exercise Performance: Contractile and Neural Contributions


title: Laparoscopic versus open gastrectomy for gastric cancer.

abstract: BACKGROUND ::: Gastric cancer is the third most common cause of cancer-related mortality in the world. Currently there are two surgical options for potentially curable patients (i.e. people with non-metastatic gastric cancer), laparoscopic and open gastrectomy. However, it is not clear whether one of these options is superior. ::: ::: ::: OBJECTIVES ::: To assess the benefits and harms of laparoscopic gastrectomy or laparoscopy-assisted gastrectomy versus open gastrectomy for people with gastric cancer. In particular, we planned to investigate the effects by patient groups, such as cancer stage, anaesthetic risk, and body mass index (BMI), and by intervention methods, such as method of anastomosis, type of gastrectomy and laparoscopic or laparoscopically-assisted gastrectomy. ::: ::: ::: SEARCH METHODS ::: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, Science Citation Index, ClinicalTrials.gov and the WHO ICTRP (World Health Organization International Clinical Trials Registry Platform) until September 2015. We also screened reference lists from included trials. ::: ::: ::: SELECTION CRITERIA ::: Two review authors independently selected references for further assessment by going through all titles and abstracts. Further selection was based on review of full text articles for selected references. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Two review authors independently extracted study data. We calculated the risk ratio (RR) with 95% confidence interval (CI) for binary outcomes, the mean difference (MD) or the standardised mean difference (SMD) with 95% CI for continuous outcomes and the hazard ratio (HR) for time-to-event outcomes. We performed meta-analyses where it was meaningful. ::: ::: ::: MAIN RESULTS ::: In total, 2794 participants were randomised in 13 trials included in this review. All the trials were at unclear or high risk of bias. One trial (which included 53 participants) did not contribute any data to this review. A total of 213 participants were excluded in the remaining trials after randomisation, leaving a total of 2528 randomised participants for analysis, with 1288 undergoing laparoscopic gastrectomy and 1240 undergoing open gastrectomy. All the participants were suitable for major surgery.There was no difference in the proportion of participants who died within thirty days of treatment between laparoscopic gastrectomy (7/1188: adjusted proportion = 0.6% (based on meta-analysis)) and open gastrectomy (4/1447: 0.3%) (RR 1.60, 95% CI 0.50 to 5.10; risk difference 0.00, 95% CI -0.01 to 0.01; participants = 2335; studies = 11; I(2) = 0%; low quality evidence). There were no events in either group for short-term recurrence (participants = 103; studies = 3), proportion requiring blood transfusion (participants = 66; studies = 2), and proportion with positive margins at histopathology (participants = 28; studies = 1). None of the trials reported health-related quality of life, time to return to normal activity or time to return to work. The differences in long-term mortality (HR 0.94, 95% CI 0.70 to 1.25; participants = 195; studies = 3; I(2) = 0%; very low quality evidence), serious adverse events within three months (laparoscopic gastrectomy (7/216: adjusted proportion = 3.6%) versus open gastrectomy (13/216: 6%) (RR 0.60, 95% CI 0.27 to 1.34; participants = 432; studies = 8; I(2) = 0%; very low quality evidence), long-term recurrence (HR 0.95, 95% CI 0.70 to 1.30; participants = 162; studies = 4; very low quality evidence), adverse events within three months (laparoscopic gastrectomy (204/268: adjusted proportion = 16.1%) versus open gastrectomy (253/1222: 20.7%) (RR 0.78, 95% CI 0.60 to 1.01; participants = 2490; studies = 11; I(2) = 38%; very low quality evidence), quantity of perioperative blood transfused (SMD 0.05, 95% CI -0.27 to 0.38; participants = 143; studies = 2; I(2) = 0%; very low quality evidence), length of hospital stay (MD -1.82 days, 95% CI -3.72 to 0.07; participants = 319; studies = 6; I(2) = 83%; very low quality evidence), and number of lymph nodes harvested (MD -0.63, 95% CI -1.51 to 0.25; participants = 472; studies = 9; I(2) = 40%; very low quality evidence) were imprecise. There was no alteration in the interpretation of the results in any of the subgroups. ::: ::: ::: AUTHORS' CONCLUSIONS ::: Based on low quality evidence, there is no difference in short-term mortality between laparoscopic and open gastrectomy. Based on very low quality evidence, there is no evidence for any differences in short-term or long-term outcomes between laparoscopic and open gastrectomy. However, the data are sparse, and the confidence intervals were wide, suggesting that significant benefits or harms of laparoscopic gastrectomy cannot be ruled out. Several trials are currently being conducted and interim results of these trials have been included in this review. These trials need to perform intention-to-treat analysis to ensure that the results are reliable and report the results according to the CONSORT Statement.


Machine Learning Methods for Malware Detection and Classification


title: Analysing differences between algorithm configurations through ablation

abstract: Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated parameter tuning and algorithm configuration tools, and consequently often create solvers with many parameters and vast configuration spaces. However, there has been very little work to help these algorithm developers answer questions about the high-quality configurations produced by these tools, specifically about which parameter changes contribute most to improved performance. In this work, we present an automated technique for answering such questions by performing ablation analysis between two algorithm configurations. We perform an extensive empirical analysis of our technique on five scenarios from propositional satisfiability, mixed-integer programming and AI planning, and show that in all of these scenarios more than 95 % of the performance gains between default configurations and optimised configurations obtained from automated configuration tools can be explained by modifying the values of a small number of parameters (1---4 in the scenarios we studied). We also investigate the use of our ablation analysis procedure for producing configurations that generalise well to previously-unseen problem domains, as well as for analysing the structure of the algorithm parameter response surface near and between high-performance configurations.


title: Risk factors related to the presence and severity of hot flushes in mid-aged Ecuadorian women.

abstract: BACKGROUND ::: Several studies drawn from the Ecuadorian population have previously reported that more than half of mid-aged women present hot flushes, which can impair their quality of life. However up-to-date risk factors for their presence and severity have not been assessed. ::: ::: ::: OBJECTIVE ::: To assess hot flush frequency and intensity and related risk factors among middle-aged Ecuadorian women. ::: ::: ::: METHODS ::: In this cross-sectional study, 1154 healthy women aged 40-59 years, visiting healthcare centers of eight main cities of Ecuador with more than 100,000 inhabitants, were assessed with the first item of the Menopause Rating Scale (MRS) and a questionnaire containing female and partner socio-demographic data. ::: ::: ::: RESULTS ::: Mean age of the entire sample was 48.8+/-5.6 years (median 48), a 48.7% had 12 or less years of schooling, 52.8% were postmenopausal, 43.6% lived at high altitude, 56.8% were married and 10% were on hormonal therapy (HT). Hot flushes accounted for 56% (n=646) of the whole sample, of which 29.1% and 9.1% were respectively graded as severe and very severe. Logistic regression determined that female sedentarism (OR: 2.42, CI 95% [1.63-3.59]), accessing a free healthcare system (OR: 1.96, CI 95% [1.30-2.96]), living at high altitude (OR: 1.82, CI 95% [1.14-2.90]) and having a partner abusing alcohol (OR: 1.92, CI 95% [1.09-3.35]) were significant risk factors related to the presence of hot flushes. The regression model also determined that among women with hot flushes (n=646), sedentarism (OR: 1.73, CI 95% [1.14-2.62]) and having a partner with erectile dysfunction (OR: 2.57, CI 95% [1.44-4.59]) were significant risk factors related to severe/very severe hot flushes whereas married status (OR: 0.53, CI 95% [0.32-0.86]), living at high altitude (OR: 0.46, CI 95% [0.26-0.78]) and partner healthiness (OR: 0.59, CI 95% [0.36-0.95]) were not. ::: ::: ::: CONCLUSION ::: To the best of our knowledge this is the first and largest study assessing hot flushes in a mid-aged Ecuadorian population. We found that the presence and severity were not significantly related to age and hormonal status yet to other individual female/male characteristics and the demography of the studied population.


title: Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment

abstract: Rotation estimation is a fundamental step for various robotic applications such as automatic control of ground/aerial vehicles, motion estimation and 3D reconstruction. However it is now well established that traditional navigation equipments, such as global positioning systems (GPSs) or inertial measurement units (IMUs), suffer from several disadvantages. Hence, some vision-based works have been proposed recently. Whereas interesting results can be obtained, the existing methods have non-negligible limitations such as a difficult feature matching (e.g. repeated textures, blur or illumination changes) and a high computational cost (e.g. analyze in the frequency domain). Moreover, most of them utilize conventional perspective cameras and thus have a limited field of view. In order to overcome these limitations, in this paper we present a novel rotation estimation approach based on the extraction of vanishing points in omnidirectional images. The first advantage is that our rotation estimation is decoupled from the translation computation, which accelerates the execution time and results in a better control solution. This is made possible by our complete framework dedicated to omnidirectional vision, whereas conventional vision has a rotation/translation ambiguity. Second, we propose a top-down approach which maintains the important constraint of vanishing point orthogonality by inverting the problem: instead of performing a difficult line clustering preliminary step, we directly search for the orthogonal vanishing points. Finally, experimental results on various data sets for diverse robotic applications have demonstrated that our novel framework is accurate, robust, maintains the orthogonality of the vanishing points and can run in real-time.


title: Inductorless Wideband CMOS Low-Noise Amplifiers Using Noise-Canceling Technique

abstract: Two inductorless wideband low-noise amplifiers (LNAs) fabricated in a 65-nm CMOS process are presented. By using the gain-enhanced noise-canceling technique, the gain at noise-cancelling condition is increased, while the input matching is maintained. The first work is a common-source LNA with resistive shunt feedback. It achieves a maximum power gain of 10.5 dB, a bandwidth of 10 GHz, a noise figure (NF) of 2.7-3.3 dB, and an IIP3 of -3.5 dBm. The power consumption is 13.7 mW from a 1-V supply, and the area is 0.02 mm 2. The second work is a common-gate LNA. It achieves a maximum power gain of 10.7 dB, a bandwidth of 5.2 GHz, a NF of 2.9-5.4 dB, and an IIP3 of -6 dBm. The power consumption is 7 mW from a 1-V supply, and the area is 0.03 mm 2. Experimental results demonstrate that the first LNA shows the largest bandwidth, and the second LNA has the lowest power consumption among the inductorless wideband LNAs.


title: SVM-based indoor localization in Wireless Sensor Networks

abstract: The need to locate objects and to be situated in the space, whether inside or outside, has long been the focus of a substantial amount of research. Especially in Wireless Sensor Networks, indoor localization has become an important issue in many fields of applications. In this paper, we propose an indoor location solution based on Support Vector Machine (SVM). SVM is a class of learning algorithms defined to resolve discrimination and regression problems. In fact, with many works, it turned out that it is very difficult to properly locate a target with only the RSSI measurements. Thus, the idea is to use multi-class SVM with RSSI measurements to propose a zoning localization approach. The performed experiments using different datasets, collected from two real world environments in both a hospital and a laboratory building, and the comparison with Artificial Neural Networks (ANN) confirm the effectiveness of our SVM-based localization proposal. Experimental results show that the system achieves a correct classification rate of around 90% with misclassification is in rooms where there is no wall separating them.


title: An image conveys a message: A brief survey on image description generation

abstract: Image caption generation is the problem of generating a descriptive sentence of an image. Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. This paper presents a brief survey of some technical aspects and methods for description-generation of images. As there has been great interest in research community, to come up with automatic ways to retrieve images based on content. There are numbers of techniques, that, have been used to solve this problem, and purpose of this paper is to have an overview of many of these approaches and databases used for description generation purpose. Finally, we discuss open challenges and future directions for upcoming researchers.


title: An Accurate Multi-Row Panorama Generation Using Multi-Point Joint Stitching

abstract: Most of the existing panorama generation tools require the input images to be captured along one direction, and yield a narrow strip panorama. To generate a large viewing field panorama, this paper proposes a multi-row panorama generation (MRPG) method. For a pan/tilt camera whose scanning path covers a wide range of horizontal and vertical views, the image frames in different views correspond to different coordinate benchmarks and different projections. And the image frame should not only be aligned with the continuous frames in timeline but also be aligned with other frames in spatial neighborhood even with long time intervals. For these problems, MRPG first designs an optimal scanning path to cover the large viewing field, and chooses the center frame as the reference frame to start to stitch. The stitching order of multi-frame is arranged in first-column and second-row to ensure a small alignment error. Moreover, MRPG proposes a multi-point joint stitching method to eliminate the seams and correct the distortions, which makes the current frame accurately integrated into the panoramic canvas from all directions. Experimental results show that MRPG can generate a more accurate panorama than other state-of-the-art image stitching methods, and give a better visual effect for a large viewing field panorama.


title: Navigation algorithm for autonomous mobile robots in indoor environments

abstract: Autonomous mobile robots are expected to provide a major role in manufacturing, logistics and service industry. The objective of this research is to develop an improved obstacle avoidance and navigation algorithms for navigation in an unknown indoor environment. Ultrasonic sensors are used to detect obstacles, rotational odometry is used to localize the robot position and reach the target. A Bluetooth interface is used to establish communication between the laptop and robot and the same is used to program the robot and feed starting and target positions. An improvised collision avoidance algorithm based on the type Bug-2 algorithm is developed. An experimental robot is developed by modifying the commercially available Kobuki robot base. The proposed improved navigation algorithm is tested experimentally in a typical laboratory setting and experimental results are presented.


title: Generic Related-key Attacks for HMAC

abstract: In this article we describe new generic distinguishing and forgery attacks in the related-key scenario (using only a single related-key) for the HMAC construction. When HMAC uses a k-bit key, outputs an n-bit MAC, and is instantiated with an l-bit inner iterative hash function processing m-bit message blocks where m=k, our distinguishing-R attack requires about 2n/2 queries which improves over the currently best known generic attack complexity 2l/2 as soon as l>n. This means that contrary to the general belief, using wide-pipe hash functions as internal primitive will not increase the overall security of HMAC in the related-key model when the key size is equal to the message block size. We also present generic related-key distinguishing-H, internal state recovery and forgery attacks. Our method is new and elegant, and uses a simple cycle-size detection criterion. The issue in the HMAC construction (not present in the NMAC construction) comes from the non-independence of the two inner hash layers and we provide a simple patch in order to avoid this generic attack. Our work finally shows that the choice of the opad and ipad constants value in HMAC is important.


Subpixel Rendering of Bayer-Patterned Images


The Transaction Graph for Modeling Blockchain Semantics


Text-Mining and Pattern-Matching based Prediction Models for Detecting Vulnerable Files in Web Applications


title: Surfacing role of probiotics in cancer prophylaxis and therapy: A systematic review.

abstract: Cancers figure among the most important causes of morbidity and mortality worldwide. Cancer and its associated infections are always complicated even when specific cancer regimens are available. It is well proved that Lactobacillus and other probiotic bacteria can modulate-ameliorate specific mechanisms against various infections including cancers. The present systematic review is intended to focus on the 'cellular and molecular mechanisms' of probiotic bacteria in the prevention and treatment of various cancers. The clinical and experimental findings of various studies explain the mechanisms such as apoptosis, antioxidant activity, immune response and epigenetics and illustrate the role of probiotics in cancer management and prophylaxis. In addition, the present review also discusses the safety aspects of probiotics when they are used in therapeutic and nutritional diet management. However, further investigations are required to reveal the effectiveness of probiotics in cancer treatment in clinical settings.


title: System-Level ESD Protection for Automotive Electronics by Co-Design of TVS and CAN Transceiver Chips

abstract: This paper proposed a co-packaged methodology using transient voltage suppressor (TVS) chips and a controller area network (CAN) bus transceiver to ensure IEC 61000-4-2 system-level ESD protection. The design methodology is verified in a high-voltage silicon-on-insulator process for CAN transceiver chip and an 0.8- ${\mu }\text{m}$ bipolar process for TVS chips. The ${I}$ – ${V}$ curves of the TVS and CAN transceiver chip are evaluated by the transmission line pulsing system with the pulse width of 100 ns. The breakdown voltage of the bi-directional TVS chips and CAN transceiver chip are higher than ±80 V compared with the ±70 V fault voltage tolerance requirement. The clamping voltage of the TVS device is lower than ±130 V to discharge ESD current before the CAN transceiver chip breakdown for effective ESD protection. The design target of parasitic inductance by bonding wires is calculated to meet the clamping voltage requirement. The CAN bus transceiver IC with TVS chips co-packaged has been evaluated to pass the IEC61000-4-2 contact ±15-kV stress without any hardware damages and latch-up issues.


Learning Theory : Applying Kolb ’ s Learning Style Inventory with Computer Based Training


title: Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens

abstract: We explore the use of social comparison theory as a natural ::: mechanism to increase contributions to an online movie ::: recommendation community by investigating the effects of social ::: information on user behavior in an online field experiment. We ::: find that, after receiving behavioral information about ::: the median user's total number of movie ratings, users below the ::: median demonstrate a 530% increase in the number of monthly movie ::: ratings, while those above the median decrease their monthly ::: ratings by 62%. Movements from both ends converge towards the ::: median, indicating conformity towards a newly-established social ::: norm in a community where such a norm had been absent. ::: Furthermore, the social information has a more dramatic effect on ::: those below the median, suggesting an interaction between ::: conformity and competitive preferences. When given ::: outcome information about the average user's net benefit ::: score from the system, consistent with social preference theory, ::: users with net benefit scores above average contribute 94% of the ::: new updates in the database. In both treatments, we find a highly ::: significant Red Queen Effect.


title: Building Commitment, Attachment, and Trust in Strategic Decision-Making Teams: The Role of Procedural Justice

abstract: This study examined how decision-making procedures can facilitate the positive attitudes necessary for cooperative relations in decisionmaking teams. We hypothesized that consideration of member input and members' influence on a decision affect their perceptions of procedural fairness and consequently, their commitment to the decision, attachment to the group, and trust in its leader. An experiment involving intact management teams supported these hypotheses and indicated that perceived fairness partially mediated the impact of procedures on commitment, attachment, and trust.


title: Logical models of argument

abstract: Logical models of arguement formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas that characterize different logical models of argument. It presents the formal features of a few features of a few main approaches to the modeling of argumentation. We trace the evolution of argumentation from the mid-1980s, when argument systems emerged as an alternative to nonmonotonic formalisms based on classical logic, to the present, as argument in embedded in different complex systems for real-world applications, and allow more formal work to be done in different areas, such as AI and Law, case-based reasoning and negotiation among intelligent agents.


title: Integrated control of a multi-fingered hand and arm using proximity sensors on the fingertips

abstract: In this study, we propose integrated control of a robotic hand and arm using only proximity sensing from the fingertips. An integrated control scheme for the fingers and for the arm enables quick control of the position and posture of the arm by placing the fingertips adjacent to the surface of an object to be grasped. The arm control scheme enables adjustments based on errors in hand position and posture that would be impossible to achieve by finger motions alone, thus allowing the fingers to grasp an object in a laterally symmetric grasp. This can prevent grasp failures such as a finger pushing the object out of the hand or knocking the object over. Proposed control of the arm and hand allowed correction of position errors on the order of several centimeters. For example, an object on a workbench that is in an uncertain positional relation with the robot, with an inexpensive optical sensor such as a Kinect, which only provides coarse image data, would be sufficient for grasping an object.


Assessment of fluoride concentrations in commercially available mouthrinses in central Saudi Arabia


title: POSTER: Locally Virtualized Environment for Mitigating Ransomware Threat

abstract: Ransomware is one of the rising malwares in the crimeware family. It encrypts the user files and demands extortion money. From the perspective of an enterprise it is very crucial to detect and stop a ransomware attack. A well studied technique is to monitor file system behavior for suspicious activity. In this work we will show the gap in the existing state of art and describe a dynamic system which learns new behavior while under attack.


Annotated Corpus for Telugu Sentiment Analysis


HOW TO SELECT A HEAT SINK


title: A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities

abstract: Technologies to support the Internet of Things are becoming more important as the need to better understand our environments and make them smart increases. As a result it is predicted that intelligent devices and networks, such as WSNs, will not be isolated, but connected and integrated, composing computer networks. So far, the IP-based Internet is the largest network in the world; therefore, there are great strides to connect WSNs with the Internet. To this end, the IETF has developed a suite of protocols and open standards for accessing applications and services for wireless resource constrained networks. However, many open challenges remain, mostly due to the complex deployment characteristics of such systems and the stringent requirements imposed by various services wishing to make use of such complex systems. Thus, it becomes critically important to study how the current approaches to standardization in this area can be improved, and at the same time better understand the opportunities for the research community to contribute to the IoT field. To this end, this article presents an overview of current standards and research activities in both industry and academia.


COLABA : Arabic Dialect Annotation and Processing


title: Style Finder: Fine-Grained Clothing Style Detection and Retrieval

abstract: With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel fine-grained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women's Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.


title: Conceptual Survey on Data Stream Processing Systems

abstract: The present paper gives an overview about the state of the art technology within the area of data stream processing systems. Although the area of stream processing systems is not new, it is receiving a greater interest in the light of current business trends like the Internet of Things (IoT). The comparison of systems thereby includes several aspects such as a look into their architectures as well as into the responsibilities of the corresponding system components. A ranking or recommendations for one or more system(s) is not part of the work.


title: Deep Matching Autoencoders

abstract: Increasingly many real world tasks involve data in multiple modalities or views. This has motivated the development of many effective algorithms for learning a common latent space to relate multiple domains. However, most existing cross-view learning algorithms assume access to paired data for training. Their applicability is thus limited as the paired data assumption is often violated in practice: many tasks have only a small subset of data available with pairing annotation, or even no paired data at all. In this paper we introduce Deep Matching Autoencoders (DMAE), which learn a common latent space and pairing from unpaired multi-modal data. Specifically we formulate this as a cross-domain representation learning and object matching problem. We simultaneously optimise parameters of representation learning auto-encoders and the pairing of unpaired multi-modal data. This framework elegantly spans the full regime from fully supervised, semi-supervised, and unsupervised (no paired data) multi-modal learning. We show promising results in image captioning, and on a new task that is uniquely enabled by our methodology: unsupervised classifier learning.


title: The genus Allochromatium (Chromatiales Chromatiaceae) revisited: a study on its intragenic structure based on multilocus sequence analysis (MLSA) and DNA-DNA hybridization (DDH).

abstract: In this study, the taxonomic status of anoxygenic photosynthetic bacteria belonging to the genus Allochromatium is revisited. The inter- and intraspecies relationship of seven Allochromatium strains, including a set of well described type strains, were examined by DNA-DNA hybridization (DDH) and multilocus sequence analysis (MLSA) using segments of seven protein-coding genes. The re-sequencing of the 16S rRNA, the internal transcriber spacer (ITS), multi-gene analysis and DDH comparison indicated that both type strains Allochromatium vinosum DSM 180(T) and Allochromatium minutissimum DSM 1376(T) are closely related to each other forming an independent cluster together with the strains A. vinosum DSM 183 and DSM 1686. The internal comparison of members of this A. vinosum phylogroup showed values of DDH relatedness above 80% and concatenated sequence similarities (4744bp) above 98%. In contrast, the MLSA scheme has identified A. vinosum strain BH-2 as a separate lineage. Strain BH-2 was first classified as a member of the species A. vinosum based on DDH comparison. However, this strain showed the lowest similarity values of the 16S rRNA gene and concatenated sequences, as well as amino acid identity (AAI) when compared to other Allochromatium strains, suggesting that strain BH-2 may represent a new species.


title: Peek-a-Boo, I Still See You: Why Efficient Traffic Analysis Countermeasures Fail

abstract: We consider the setting of HTTP traffic over encrypted tunnels, as used to conceal the identity of websites visited by a user. It is well known that traffic analysis (TA) attacks can accurately identify the website a user visits despite the use of encryption, and previous work has looked at specific attack/countermeasure pairings. We provide the first comprehensive analysis of general-purpose TA countermeasures. We show that nine known countermeasures are vulnerable to simple attacks that exploit coarse features of traffic (e.g., total time and bandwidth). The considered countermeasures include ones like those standardized by TLS, SSH, and IPsec, and even more complex ones like the traffic morphing scheme of Wright et al. As just one of our results, we show that despite the use of traffic morphing, one can use only total upstream and downstream bandwidth to identify -- with 98% accuracy - which of two websites was visited. One implication of what we find is that, in the context of website identification, it is unlikely that bandwidth-efficient, general-purpose TA countermeasures can ever provide the type of security targeted in prior work.


title: Particle filtering for lane-level map-matching at road bifurcations

abstract: Estimating the map-matched position of a vehicle on a digital map is a key technology for modern navigation and advanced driver assistance systems (ADAS). Inaccurate sensor measurements and a simplified road network representation cause uncertainties in the map-matched position. This paper describes a particle filter method for finding a robust and stable solution for the map-matching problem at the lane-level in ambiguous situations. The proposed approach fuses low-cost sensor data including camera detections of the lane markings with commercial map data. A new spatio-temporal filtering algorithm is introduced for estimating the hypotheses for the map-matched positions together with a confidence measure from the set of weighted particles. The results of tests at road bifurcations indicate that this method produces stable decisions for the correct road segment. It was found that the integration of lane based features improves the performance of the map-matcher at the road level.


AIMS CDT Project Report : Towards One-Shot Learning From Demonstration via Reinforcement Learning


title: COT: contextual operating tensor for context-aware recommender systems

abstract: With rapid growth of information on the internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information can be used as a significant factor in modeling user behavior, various context-aware recommendation methods are proposed. However, the state-of-the-art context modeling methods treat contexts as other dimensions similar to the dimensions of users and items, and cannot capture the special semantic operation of contexts. On the other hand, some works on multi-domain relation prediction can be used for the context-aware recommendation, but they have problems in generating recommendation under a large amount of contextual information. In this work, we propose Contextual Operating Tensor (COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector. Then, to model the semantic operation of a context combination, we generate contextual operating matrix from the contextual operating tensor and latent vectors of contexts. Thus latent vectors of users and items can be operated by the contextual operating matrices. Experimental results show that the proposed COT model yields significant improvements over the competitive compared methods on three typical datasets, i.e., Food, Adom and Movielens-1M datasets.


title: Multiple-output ZCS resonant inverter for multi-coil induction heating appliances

abstract: This paper presents a multiple-output resonant inverter for multi-coil systems featuring high efficiency and flexible output power control for modern induction heating appliances. By adopting a matrix structure, the number of controlled devices can be reduced to the square root of the number of controlled induction heating loads. Therefore, an elevated component count reduction can be achieved compared to classical multi-outputs solutions. The proposed converter is analyzed and verified experimentally, proving the feasibility of this proposal and benefits compared with previous alternatives.


title: DeepSense: A GPU-based Deep Convolutional Neural Network Framework on Commodity Mobile Devices

abstract: Recently, a branch of machine learning algorithms called deep learning gained huge attention to boost up accuracy of a variety of sensing applications. However, execution of deep learning algorithm such as convolutional neural network on mobile processor is non-trivial due to intensive computational requirements. In this paper, we present our early design of DeepSense - a mobile GPU-based deep convolutional neural network (CNN) framework. For its design, we first explored the differences between server-class and mobile-class GPUs, and studied effectiveness of various optimization strategies such as branch divergence elimination and memory vectorization. Our results show that DeepSense is able to execute a variety of CNN models for image recognition, object detection and face recognition in soft real time with no or marginal accuracy tradeoffs. Experiments also show that our framework is scalable across multiple devices with different GPU architectures (e.g. Adreno and Mali).


Reasoning for Semantic Error Detection in Text


title: Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering

abstract: This paper investigates the problem of image classification with limited or no annotations, but abundant unlabeled data. The setting exists in many tasks such as semi-supervised image classification, image clustering, and image retrieval. Unlike previous methods, which develop or learn sophisticated regularizers for classifiers, our method learns a new image representation by exploiting the distribution patterns of all available data for the task at hand. Particularly, a rich set of visual prototypes are sampled from all available data, and are taken as surrogate classes to train discriminative classifiers; images are projected via the classifiers; the projected values, similarities to the prototypes, are stacked to build the new feature vector. The training set is noisy. Hence, in the spirit of ensemble learning we create a set of such training sets which are all diverse, leading to diverse classifiers. The method is dubbed Ensemble Projection (EP). EP captures not only the characteristics of individual images, but also the relationships among images. It is conceptually simple and computationally efficient, yet effective and flexible. Experiments on eight standard datasets show that: (1) EP outperforms previous methods for semi-supervised image classification; (2) EP produces promising results for self-taught image classification, where unlabeled samples are a random collection of images rather than being from the same distribution as the labeled ones; and (3) EP improves over the original features for image clustering. The code of the method is available on the project page.


title: Notice of Violation of IEEE Publication PrinciplesA 0.16-2.55-GHz CMOS active clock deskewing PLL using analog phase interpolation

abstract: This paper presents a multigigahertz active clock deskewing architecture that uses analog phase interpolation to replace the area-consuming capacitively controlled delay lines used in regional clock deskewing delay-locked loops. It provides a small phase step that is uniform and process-independent over the entire 2/spl pi/ phase deskew range, which reduces the intra-die clock skew. The phase interpolators have virtually zero latency, which leads to a very fast clock deskewing process and allows for the tracking of fast dynamic variations. A bandgap referencing technique was used to provide a tracking mechanism between the phase-locked loop (PLL) time constants to achieve a process-independent PLL damping factor and pole-zero separation. Both feedback and input divider modulus independence of the damping factor was achieved through a combined current and capacitor switching architecture, that provides an optimal compromise between area, power, and spurs. A reset-generation charge-pump architecture was introduced to minimize the dead-zone avoidance pulse width in order to improve the PLL jitter and reference spurs performance. The power supply partitioning exploits the dual gate oxide transistors offered by the current deep-submicron CMOS technologies. The biasing scheme is comprised of a 1.5-V supply for the ring oscillator and digital circuitry for speed considerations, while the analog front-end is biased from a 2.5-V supply to ensure more voltage headroom as required by low-gain oscillators and low-noise loop filters.


title: Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

abstract: Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well.


title: CHARACTERIZATION OF SILICON CARBIDE NANOTUBE SYNTHESIZED USING MICROWAVE HEATING

abstract: Currently available conventional methods for the synthesis of silicon carbide nanotubes are highly demanding in terms of the energy consumption, temperature and reaction duration. Synthesis of silicon carbide nanotube through microwave irradiation is thought to be more efficient considering shorter time and lower energy consumption is needed. In this paper, for the first time the synthesis of silicon carbide nanotubes was reported through microwave heating of mixture of carbon nanotubes and silicon dioxide in argon atmosphere at temperature of 1400°C. Silicon dioxide and carbon nanotube in the ratio of 1:3 as suggested by previous study were mixed in ultrasonic bath for 2 hour using ethanol as medium. The mixture was dried on hot plate and cold pressed into a 3mm pellet. The pressed pellet was then placed in an alumina crucible filled with silica sand acted as heat insulator and SiC susceptor. The pellet was heated to 1400°C at heating rate of 30°C/min and hold at 1400°C for 40 minute. X-ray diffraction analysis verified that silicon carbide nanotubes were produced after the mixture was subjected to the microwave irradiation. Scanning electron microscopy analysis revealed the tubular structure of carbon nanotubes was retained after the microwave irradiation by formation of silicon carbide nanotubes which have tubular structure.Fourier transform infrared (FTIR) spectroscopy show that Si=C bond has the absorption bands around 900cm -1 to 700cm -1 and band gap of silicon carbide nanotube was found out to be 2.76 eV.


title: Graph-based anomaly detection

abstract: Anomaly detection is an area that has received much attention in recent years. It has a wide variety of applications, including fraud detection and network intrusion detection. A good deal of research has been performed in this area, often using strings or attribute-value data as the medium from which anomalies are to be extracted. Little work, however, has focused on anomaly detection in graph-based data. In this paper, we introduce two techniques for graph-based anomaly detection. In addition, we introduce a new method for calculating the regularity of a graph, with applications to anomaly detection. We hypothesize that these methods will prove useful both for finding anomalies, and for determining the likelihood of successful anomaly detection within graph-based data. We provide experimental results using both real-world network intrusion data and artificially-created data.


title: PeachPy meets Opcodes: direct machine code generation from Python

abstract: We introduce Opcodes, a Python package which presents x86 and x86-64 instruction sets as a set of high-level objects. Opcodes provides information about instruction names, implicit and explicit operands, and instruction encoding. We use the Opcodes package to auto-generate instruction classes for PeachPy, an x86-64 assembler embedded in Python, and enable new functionality. The new PeachPy functionality lets low-level optimization experts write high-performance assembly kernels in Python, load them as callable Python functions, test the kernels using numpy and generate object files for Windows, Linux, and Mac OS X entirely within Python. Additionally, the new PeachPy can generate and run assembly code inside Chromium-based browsers by leveraging Native Client technology. Beyond that, PeachPy gained ability to target Google Go toolchain, by generating either source listing for Go assembler, or object files that can be linked with Go toolchain. With backends for Windows, Linux, Mac OS X, Native Client, and Go, PeachPy is the most portable way to write high-performance kernels for x86-64 architecture.


title: A counting algorithm for multiple objects using an IR-UWB radar system

abstract: In this paper, a counting algorithm is proposed based on the signal's local maximum values of power signals and positions. After background subtraction process to eliminate clutter signals, several positions and values of the signal's local maximum values are searched for some prefixed number of frames. After sufficient values are stored in a buffer, an average of the values is calculated. Among the values, the number of values exceeding the thresholds is counted and stored as 1-st decided targets' number. To improve the performance we vote with some 1-st decided targets' number. We prove this algorithm's performance by some experiments. Simulation results are included experimented in the 6 m by 6 m indoor environment.


Idlak Tangle: An Open Source Kaldi Based Parametric Speech Synthesiser Based on DNN.


title: Using machine learning algorithms for housing price prediction

abstract: Housing price valuation is one of most important trading decisions.This study uses machine learning to develop housing price prediction models.This study analyzes the housing data of 5359 townhouses in Fairfax County, VA.The 10-fold cross-validation was applied to C4.5, RIPPER, Bayesian, and AdaBoost.RIPPER outperformed these other housing price prediction models in all tests. House sales are determined based on the Standard & Poor's Case-Shiller home price indices and the housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US housing market. In addition to these housing price indices, the development of a housing price prediction model can greatly assist in the prediction of future housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a housing price prediction model. To improve the accuracy of housing price prediction, this paper analyzes the housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naive Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of housing price prediction.


title: Comparison of vision-based and sensor-based systems for joint angle gait analysis

abstract: Gait analysis has become recently a popular research field and been widely applied to clinical diagnosis of neurodegenerative diseases. Various low-cost sensor-based and vision-based systems are developed for capturing the hip and knee joint angles. However, the performances of these systems have not been validated and compared between each other. The purpose of this study is to set up an experiment and compare the performances of a sensor-based system with multiple inertial measurement units (IMUs), a vison-based gait analysis system with marker detection, and a markerless vision-based system on capturing the hip and knee joint angles during normal walking. The obtained measurements were validated with the data acquired from goniometers as ground truth measurement. The results indicate that the IMUs-based sensor system gives excellent performance with small errors, while vision systems produce acceptable results with slightly larger errors.


title: FireDroid: hardening security in almost-stock Android

abstract: Malware poses a serious threat to Android smartphones. Current security mechanisms offer poor protection and are often too inflexible to quickly mitigate new exploits. In this paper we present FireDroid, a policy-based framework for enforcing security policies by interleaving process system calls. The main advantage of FireDroid is that it is completely transparent to the applications as well as to the Android OS. FireDroid enforces security policies without modifying either the Android OS or its applications. FireDroid is able to perform security checks on third-party and pre-installed applications, as well as malicious native code. We have implemented a novel mechanism that is able to attach, identify, monitor and enforce polices for any process spawned by the Android's mother process Zygote. We have tested the effectiveness of FireDroid against real malware. Moreover, we show how FireDroid can be used as a swift solution for blocking OS and application vulnerabilities before patches are available. Finally, we provide an experimental evaluation of our approach showing that it has only a limited overhead. Given these facts, FireDroid represents a practical solution for strengthening security on Android smartphones.


title: A Rawlsian algorithm for autonomous vehicles

abstract: Autonomous vehicles must be programmed with procedures for dealing with trolley-style dilemmas where actions result in harm to either pedestrians or passengers. This paper outlines a Rawlsian algorithm as an alternative to the Utilitarian solution. The algorithm will gather the vehicle's estimation of probability of survival for each person in each action, then calculate which action a self-interested person would agree to if he or she were in an original bargaining position of fairness. I will employ Rawls' assumption that the Maximin procedure is what self-interested agents would use from an original position, and then show how the Maximin procedure can be operationalized to produce unique outputs over probabilities of survival.


title: Word cloud of online hotel reviews in Chiang Mai for customer satisfaction analysis

abstract: This study aims to examine the satisfied and unsatisfied of hotel customers by utilizing a word cloud approach to evaluate online reviews. As a pilot test, online commends of 1,752 hotel guests were collected from TripAdvisor.com for 5 selected hotels in Chiang Mai, Thailand. The research results revealed some common features that are identified in both satisfied and dissatisfied of customer reviews; including staff service skills, hotel environment and facilities and a quality of room and bathroom. On the other hand, the findings shown that dissatisfied customers pointed out more frequently on the booking systems of the hotel. Therefore, this article's results suggests some clearer managerial implications pertaining to understanding of customer satisfaction level through the utilization of world cloud technique via review online platforms.


title: Forgiveness, Forbearance, and Time: The Temporal Unfolding of Transgression-Related Interpersonal Motivations

abstract: The investigators proposed that transgression-related interpersonal motivations result from 3 psychological parameters: forbearance (abstinence from avoidance and revenge motivations, and maintenance of benevolence), trend forgiveness (reductions in avoidance and revenge, and increases in benevolence), and temporary forgiveness (transient reductions in avoidance and revenge, and transient increases in benevolence). In 2 studies, the investigators examined this 3-parameter model. Initial ratings of transgression severity and empathy were directly related to forbearance but not trend forgiveness. Initial responsibility attributions were inversely related to forbearance but directly related to trend forgiveness. When people experienced high empathy and low responsibility attributions, they also tended to experience temporary forgiveness. The distinctiveness of each of these 3 parameters underscores the importance of studying forgiveness temporally.


title: Learning Deep Networks from Noisy Labels with Dropout Regularization

abstract: Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is overdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.


title: Using meta ethnography to synthesise qualitative research: a worked example

abstract: Objectives: To demonstrate the benefits of applying meta ethnography to the synthesis of qualitative research, by means of a worked example. Methods: Four papers about lay meanings of medicines were arbitrarily chosen. Noblit and Hare's seven-step process for conducting a meta ethnography was employed: getting started; deciding what is relevant to the initial interest; reading the studies; determining how the studies are related; translating the studies into one another; synthesising translations; and expressing the synthesis. Results: Six key concepts were identified: adherence/compliance; self-regulation; aversion; alternative coping strategies; sanctions; and selective disclosure. Four second-order interpretations (derived from the chosen papers) were identified, on the basis of which four third-order interpretations (based on the key concepts and second-order interpretations) were constructed. These were all linked together in a line of argument that accounts for patients' medicine-taking behaviour and communication with health professionals in different settings. Third-order interpretations were developed which were not only consistent with the original results but also extended beyond them. Conclusions: It is possible to use meta ethnography to synthesise the results of qualitative research. The worked example has produced middle-range theories in the form of hypotheses that could be tested by other researchers.


title: Sparsity induced similarity measure for label propagation

abstract: Graph-based semi-supervised learning has gained considerable interests in the past several years thanks to its effectiveness in combining labeled and unlabeled data through label propagation for better object modeling and classification. A critical issue in constructing a graph is the weight assignment where the weight of an edge specifies the similarity between two data points. In this paper, we present a novel technique to measure the similarities among data points by decomposing each data point as an L 1 sparse linear combination of the rest of the data points. The main idea is that the coefficients in such a sparse decomposition reflect the point's neighborhood structure thus providing better similarity measures among the decomposed data point and the rest of the data points. The proposed approach is evaluated on four commonly-used data sets and the experimental results show that the proposed Sparsity Induced Similarity (SIS) measure significantly improves label propagation performance. As an application of the SIS-based label propagation, we show that the SIS measure can be used to improve the Bag-of-Words approach for scene classification.


title: One‐to‐one mobile technology in high school physics classrooms: Understanding its use and outcome.

abstract: Abstract: This study examined ways in which high school students used mobile devices in physics classrooms and after school, and the impact of in‐class and after‐school mobile technology use on their physics learning performance and interest. We collected data from 803 high school freshmen in China after they had used mobile devices for over five months. A fixed‐effects model was employed to control the undetected variances. Results indicated that the students frequently used their mobile devices for physics learning in class and after school. Students also perceived the mobile devices as very useful for their physics learning, and their perception of usefulness was positively correlated with their frequency of use. Both the in‐class and after‐school duration and frequency of mobile technology use positively influenced students' physics learning achievement and interest. However, we also found that mobile technology was mainly used to augment rather than to transform instructional practices in physics classrooms. The paper concludes with a discussion of implications and limitations of these results for research and practice. [ABSTRACT FROM AUTHOR]


title: Physicochemical properties of alkali activated GGBS and fly ash geopolymeric recycled concrete

abstract: Abstract Reuse of recycled aggregates (RA), which are from construction and demolition wastes, and fly ash and ground granulated blast furnace slag (GGBS), which are from industry by-products, in geopolymeric recycled aggregate concrete (GRAC) could provide a sustainable solution to deal with the increasing construction waste and to reduce the CO2 emission from OPC production. This work aims to investigate the workability, mechanical performance and hydration mechanism of GGBS and fly ash based alkali activated GRAC. In this study, the RA were used to replace different levels (0%, 30%, 50%, 70% and 100%) of natural aggregates, and GGBS and fly ash based geopolymer was used to replace 100% of OPC. A series of slump, setting time, compression, XRD and SEM tests were conducted to evaluate the effects of RA content and water-binder ratio on the physicochemical properties of this green concrete. It is found that the combination of fly ash and GGBS based geopolymer and RA can provide an excellent compression resistance in concrete. The compressive failure of GRAC was dominated by the defects in RA and the ITZ. However, the denser geopolymer matrix could modify these shortages in RA and the ITZ, resulting in that GRAC have better mechanical properties than conventional concrete.


title: Compact Planar Multistandard MIMO Antenna for IoT Applications

abstract: A novel compact single-substrate planar multiband five-element multiple-input multiple-output (MIMO) antenna system is presented in this paper. The tunable two-element folded meandered MIMO antenna covers the long-term evolution frequency bands below 1 GHz (687–813 MHz) and radio frequency identification bands centered around 2.4 and 5.8 GHz. The other two-element compact MIMO antennas operate over 754–971 MHz, 1.65–1.83 GHz, 2–3.66 GHz, and 5.1–5.6 GHz frequency bands. Furthermore, the proposed antenna elements are integrated with a wideband sensing antenna for the spectrum sensing in 0.668–1.94 and 3–4.6 GHz, which also acts as the ground plane for the MIMO elements in the cognitive radio application environment. The antenna is fabricated on a 65 mm $\times \,\, 120$ mm $\times \,\, 1.56$ mm low-cost FR-4 substrate. The antenna’s radiation characteristics are experimentally verified, and the results are in agreement with the full-wave simulation. The 3-D radiation pattern-based envelope correlation coefficient of the MIMO antennas is also experimentally verified which is below the desired value of 0.5. Finally, to show its utility at the Internet-of-Things platform, the antenna is tested in the realistic application environment.


title: On the detection and recognition of television commercials

abstract: TV commercials are interesting in many respects: advertisers and psychologists are interested in their influence on human purchasing habits, while parents might be interested in shielding their children from their influence. In this paper, two methods for detecting and extracting commercials in digital videos are described. The first method is based on statistics of measurable features and enables the detection of commercial blocks within TV broadcasts. The second method performs detection and recognition of known commercials with high accuracy. Finally, we show how both approaches can be combined into a self-learning system. Our experimental results underline the practicality of the methods.


title: The /spl phi/ accrual failure detector

abstract: The detection of failures is a fundamental issue for fault-tolerance in distributed systems. Recently, many people have come to realize that failure detection ought to be provided as some form of generic service, similar to IP address lookup or time synchronization. However, this has not been successful so far; one of the reasons being the fact that classical failure detectors were not designed to satisfy several application requirements simultaneously. We present a novel abstraction, called accrual failure detectors, that emphasizes flexibility and expressiveness and can serve as a basic building block to implementing failure detectors in distributed systems. Instead of providing information of a binary nature (trust vs. suspect), accrual failure detectors output a suspicion level on a continuous scale. The principal merit of this approach is that it favors a nearly complete decoupling between application requirements and the monitoring of the environment. In this paper, we describe an implementation of such an accrual failure detector, that we call the /spl phi/ failure detector. The particularity of the /spl phi/ failure detector is that it dynamically adjusts to current network conditions the scale on which the suspicion level is expressed. We analyzed the behavior of our /spl phi/ failure detector over an intercontinental communication link over a week. Our experimental results show that if performs equally well as other known adaptive failure detection mechanisms, with an improved flexibility.


title: A Survey on Automatic Test Data Generation

abstract: In order to reduce the high cost of manual software testing and at the same time to increase the reliability of the testing processes researchers and practitioners have tried to automate it. One of the most important components in a testing environment is an automatic test data generator — a system that automatically generates test data for a given program. Through the years several attempts in automatic test data generations have been made. The focus of this article is program-based generation, where the generation starts from the actual programs. Thus, techniques such as GUI-based and syntax-based test data generation are not an issue in this article. In this article I present a survey on automatic test data generation techniques that can be found in current literature. Basic concepts and notions of test data generation as well as how a test data generator system works are described. Problems of automatic generation are identified and explained. Finally important and challenging future research topics are presented.


title: Brand trust and brand extension acceptance: the relationship

abstract: Purpose – This UK‐based research aims to build on the US‐based work of Keller and Aaker, which found a significant association between “company credibility” (via a brand's “expertise” and “trustworthiness”) and brand extension acceptance, hypothesising that brand trust, measured via two correlate dimensions, is significantly related to brand extension acceptance.Design/methodology/approach – Discusses brand extension and various prior, validated influences on its success. Focuses on the construct of trust and develops hypotheses about the relationship of brand trust with brand extension acceptance. The hypotheses are then tested on data collected from consumers in the UK.Findings – This paper, using 368 consumer responses to nine, real, low involvement UK product and service brands, finds support for a significant association between the variables, comparable in strength with that between media weight and brand share, and greater than that delivered by the perceived quality level of the parent brand.Origi...


title: Obstacle negotiation learning for a compliant wheel-on-leg robot

abstract: Generic control of wheel-on-leg robots on arbitrary uneven terrains is a challenging task due to the complexity of the robot dynamics, surface interactions, and environmental structures. This paper deals with the control of a wheel-on-leg robot with passive and active internal compliance that enables estimation of wheel-ground interaction forces. The proposed method is based on a continuous state space Q-learning approach that uses the contact forces estimates to learn, through trial and error, the appropriate control policy from a set of predefined behaviors. Without any prior knowledge of the ground geometry, the robot is able to react to unanticipated obstacles. The learned policy proves to be generic and allows the robot to negotiate complex obstacles that had not been considered during learning phase.


title: Bearing fault detection via autoregressive stator current modeling

abstract: This paper proposes a method for detecting developing bearing faults via stator current. Current-based condition monitoring offers significant economic savings and implementation advantages over vibration-based techniques. This method begins by filtering the stator current to remove most of the significant frequency content unrelated to bearing faults. Afterwards, the filtered stator current is used to train an autoregressive signal model. This model is first trained while the bearings are healthy, and a baseline spectrum is computed. As bearing health degrades, the modeled spectrum deviates from its baseline value; the mean spectral deviation is then used as the fault index. This fault index is able to track changes in machine vibration due to developing bearing faults. Due to the initial filtering process, this method is robust to many influences including variations in supply voltage, cyclical load torque variations, and other (nonbearing) fault sources. Experimental results from ten different bearings are used to verify the proficiency of this method.


title: An Affine Invariant Interest Point Detector

abstract: This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of the neighbourhood of an interest point. Our approach allows to solve for these problems simultaneously. It is based on three key ideas : 1) The second moment matrix computed in a point can be used to normalize a region in an affine invariant way (skew and stretch). 2) The scale of the local structure is indicated by local extrema of normalized derivatives over scale. 3) An affine-adapted Harris detector determines the location of interest points. A multi-scale version of this detector is used for initialization. An iterative algorithm then modifies location, scale and neighbourhood of each point and converges to affine invariant points. For matching and recognition, the image is characterized by a set of affine invariant points; the affine transformation associated with each point allows the computation of an affine invariant descriptor which is also invariant to affine illumination changes. A quantitative comparison of our detector with existing ones shows a significant improvement in the presence of large affine deformations. Experimental results for wide baseline matching show an excellent performance in the presence of large perspective transformations including significant scale changes. Results for recognition are very good for a database with more than 5000 images.


title: Computer animation of knowledge-based human grasping

abstract: The synthesis of human hand motion and grasping of arbitrary shaped objects is a very complex problem. Therefore high-level control is needed to perform these actions. In order to satisfy the kinematic and physical constraints associated with the human hand and to reduce the enormous search space associated with the problem of grasping objects, a knowledge based approach is used. A three-phased scheme is presented which incorporates the role of the hand, the object, the environment and the animator. The implementation of a hand simulation system HANDS is discussed.


title: Automatic ontology generation for data mining using fca and clustering

abstract: Motivated by the increased need for formalized representations of the domain of Data Mining, the success of using Formal Concept Analysis (FCA) and Ontology in several Computer Science fields, we present in this paper a new approach for automatic generation of Fuzzy Ontology of Data Mining (FODM), through the fusion of conceptual clustering, fuzzy logic, and FCA. In our approach, we propose to generate ontology taking in consideration another degree of granularity into the process of generation. Indeed, we suggest to define an ontology between classes resulting from a preliminary classification on the data. We prove that this approach optimize the definition of the ontology, offered a better interpretation of the data and optimized both the space memory and the execution time for exploiting this data.


title: Mean and Variance of the Sampling Distribution of Particle Swarm Optimizers During Stagnation

abstract: Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterization of the sampling distribution of the particle swarm optimizer (PSO). In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO sampling distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the sampling distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.


Geometric Range Searching and Its Relatives