title: A new paradigm of human gait analysis with Kinect

abstract: Analysis of human gait helps to find an intrinsic gait signature through which ubiquitous human identification and medical disorder problems can be investigated in a broad spectrum. The gait biometric provides an unobtrusive feature by which video gait data can be captured at a larger distance without prior awareness of the subject. In this paper, a new technique has been addressed to study the human gait analysis with Kinect Xbox device. It ensures us to minimize the segmentation errors with automated background subtraction technique. The closely similar human skeleton model can be generated from background subtracted gait images, altered by covariate conditions, such as change in walking speed and variations in clothing type. The gait signatures are captured from joint angle trajectories of left hip, left knee, right hip and right knee of subject's skeleton model. The experimental verification on Kinect gait data has been compared with our in-house development of sensor based biometric suit, Intelligent Gait Oscillation Detector (IGOD). An endeavor has been taken to investigate whether this sensor based biometric suit can be altered with a Kinect device for the proliferation of robust gait identification system. The Fisher discriminant analysis has been applied on training gait signature to look into the discriminatory power of feature vector. The Naive Bayesian classifier demonstrates an encouraging classification result with estimation of errors on limited dataset captured by Kinect sensor.


title: Millimeter-Wave Reflective-Type Phase Shifter in CMOS Technology

abstract: The design and measurement of a compact, wide-band reflective-type phase shifter in 90 nm CMOS technology in V-band frequency is presented. This phase shifter has a fractional bandwidth of 26% and an average insertion loss of 6 dB over all phase states. The chip area is only 0.08 mm 2. Measurement results show that the developed phase shifter provides 90° continuous phase shift over the frequency range of 50-65 GHz. The measured return loss is greater than 12 dB at 50 GHz. The output power is linear up to at least 4 dBm input power.


title: Analysis of the Yokeless And Segmented Armature Machine

abstract: This paper presents a new type of axial flux motor, the yokeless and segmented armature (YASA) topology. The YASA motor has no stator yoke, a high fill factor and short end windings which all increase torque density and efficiency of the machine. Thus, the topology is highly suited for high performance applications. The LIFEcar project is aimed at producing the world's first hydrogen sports car, and the first YASA motors have been developed specifically for the vehicle. The stator segments have been made using powdered iron material which enables the machine to be run up to 300 Hz. The iron in the stator of the YASA motor is dramatically reduced when compared to other axial flux motors, typically by 50%, causing an overall increase in torque density of around 20%. A detailed Finite Element analysis (FEA) analysis of the YASA machine is presented and it is shown that the motor has a peak efficiency of over 95%.


title: An endoscopic continuum testbed for finalizing system characteristics of a surgical robot for NOTES procedures

abstract: Natural Orifice Translumenal Endoscopic Surgery (NOTES) has recently attracted lots of attention, promising surgical procedures with fewer complications, better cosmesis, lower pains and faster recovery. Several robotic systems were developed aiming to enable abdominal surgeries in a NOTES manner. Although these robotic systems demonstrated the surgical concept, characteristics which could fully enable NOTES procedures remain unclear. This paper presents the development of an endoscopic continuum testbed for finalizing system characteristics of a surgical robot for NOTES procedures, which include i) deployability (the testbed can be deployed in a folded endoscope configuration and then be unfolded into a working configuration), ii) adequate workspace, iii) sufficient distal dexterity (e.g. suturing capability), and iv) desired mechanics properties (e.g. enough load carrying capability). Continuum mechanisms were implemented in the design and a diameter of 12mm of this testbed in its endoscope configuration was achieved. Results of this paper could be used to form design references for future development of NOTES robots.


title: SEAME: a Mandarin-English code-switching speech corpus in south-east asia.

abstract: In Singapore and Malaysia, people often speak a mixture of Mandarin and English within a single sentence. We call such sentences intra-sentential code-switch sentences. In this paper, we report on the development of a Mandarin-English codeswitching spontaneous speech corpus: SEAME. The corpus is developed as part of a multilingual speech recognition project and will be used to examine how Mandarin-English codeswitch speech occurs in the spoken language in South-East Asia. Additionally, it can provide insights into the development of large vocabulary continuous speech recognition (LVCSR) for code-switching speech. The corpus collected consists of intra-sentential code-switching utterances that are recorded under both interview and conversational settings. This paper describes the corpus design and the analysis of collected corpus.


A Fast , Adaptive Variant of the Goemans-Williamson Scheme for the Prize-Collecting Steiner Tree Problem


title: Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach

abstract: In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: (a) the development of a ‘good AI society’; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.


title: Prioritizing and optimizing risk factors in agile software development

abstract: To ensure success and quality of a software, early identification and prioritization of the risk is necessary. Risk impacts the cost and duration for of a software. As agile practices of software development prevail over traditional software development, so they are used in present scenario. This paper proposes an Agile based Risk Rank (AR-Rank) method for the prioritization of risk factors in agile software development. To reduce the impact of risks, the proposed method provides precedence ranking of risk factors from high to low. Therefore, the goal of proposed method is to provide minimum risk-free software on time with varying degree of flexibility. For optimization of risk factors, the Particle Swarm Optimization (PSO) is applied as an iterative approach. The proposal is compared proposed with various prevalent approaches as proposed in past.


title: Security and Privacy in Device-to-Device (D2D) Communication: A Review

abstract: Device-to-device (D2D) communication presents a new paradigm in mobile networking to facilitate data exchange between physically proximate devices. The development of D2D is driven by mobile operators to harvest short range communications for improving network performance and supporting proximity-based services. In this paper, we investigate two fundamental and interrelated aspects of D2D communication, security and privacy, which are essential for the adoption and deployment of D2D. We present an extensive review of the state-of-the-art solutions for enhancing security and privacy in D2D communication. By summarizing the challenges, requirements, and features of different proposals, we identify lessons to be learned from existing studies and derive a set of “best practices.” The primary goal of our work is to equip researchers and developers with a better understanding of the underlying problems and the potential solutions for D2D security and privacy. To inspire follow-up research, we identify open problems and highlight future directions with regard to system and communication design. To the best of our knowledge, this is the first comprehensive review to address the fundamental security and privacy issues in D2D communication.


title: A 4.2-W 10-GHz GaN MMIC Doherty Power Amplifier

abstract: This paper describes the design and performance of an X-band GaN monolithic microwave integrated circuit (MMIC) Doherty power amplifier (DPA) in a 0.15 µm gate length GaN on SiC process. The measured output power is greater than 36dBm at peak PAE of 47% at 10 GHz. Gain flatness of ±0.1 dB around 9.2 dB is obtained at up to 25dBm input power. The PAE at 6 and 10 dB backoff is 41% and 31% respectively. For a 10-Mbps OQPSK signal, the ACPR at 10MHz is >30 dBc at maximum output power and >33 dBc at 10 dB backoff. This combination of linearity and efficiency at backoff represents state of the art for an X-band DPA. Limitations of supply modulation in DPAs to further extend output power back-off are highlighted in the discussion.


title: Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks

abstract: Convolutional neural network (CNN) is well known for its capability of feature learning and has made revolutionary achievements in many applications, such as scene recognition and target detection. In this paper, its capability of feature learning in hyperspectral images is explored by constructing a five-layer CNN for classification (C-CNN). The proposed C-CNN is constructed by including recent advances in deep learning area, such as batch normalization, dropout, and parametric rectified linear unit (PReLU) activation function. In addition, both spatial context and spectral information are elegantly integrated into the C-CNN such that spatial-spectral features are learned for hyperspectral images. A companion feature-learning CNN (FL-CNN) is constructed by extracting fully connected feature layers in this C-CNN. Both supervised and unsupervised modes are designed for the proposed FL-CNN to learn sensor-specific spatial-spectral features. Extensive experimental results on four benchmark data sets from two well-known hyperspectral sensors, namely airborne visible/infrared imaging spectrometer (AVIRIS) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed C-CNN outperforms the state-of-the-art CNN-based classification methods, and its corresponding FL-CNN is very effective to extract sensor-specific spatial-spectral features for hyperspectral applications under both supervised and unsupervised modes.


title: A Computational Theory for Life-Long Learning of Semantics

abstract: Semantic vectors are learned from data to express semantic relationships between elements of information, for the purpose of solving and informing downstream tasks. Other models exist that learn to map and classify supervised data. However, the two worlds of learning rarely interact to inform one another dynamically, whether across types of data or levels of semantics, in order to form a unified model. We explore the research problem of learning these vectors and propose a framework for learning the semantics of knowledge incrementally and online, across multiple mediums of data, via binary vectors. We discuss the aspects of this framework to spur future research on this approach and problem.


SEMANTIC ROLE LABELING OF MALAYALAM WEB DOCUMENTS IN CRICKET DOMAIN


title: Open information extraction as an intermediate semantic structure for Persian text summarization

abstract: Semantic applications typically exploit structures such as dependency parse trees, phrase-chunking, semantic role labeling or open information extraction. In this paper, we introduce a novel application of Open IE as an intermediate layer for text summarization. Text summarization is an important method for providing relevant information in large digital libraries. Open IE is referred to the process of extracting machine-understandable structural propositions from text. We use these propositions as a building block to shorten the sentence and generate a summary of the text. The proposed system offers a new form of summarization that is able to break the structure of the sentence and extract the most significant sub-sentential elements. Other advantages include the ability to identify and eliminate less important sections of the sentence (such as adverbs, adjectives, appositions or dependent clauses), or duplicate pieces of sentences which in turn opens up the space for entering more sentences in the summary to enhance the coverage and coherency of it. The proposed system is localized for Persian language; however, it can be adopted to other languages. Experiments performed on a standard data set “Pasokh” with a standard comparison tool showed promising results for the proposed approach. We used summaries produced by the system in a real-world application in the virtual library of Shahid Beheshti University and received good feedbacks from users.


Assessing trends over time in performance, costs, and energy use for servers


GRADING OF BEEF MARBLING BASED ON IMAGE PROCESSING AND SUPPORT VECTOR MACHINE


The Solubility of Silver Chloride and the Formation of Complexes in Chloride Solution1


title: New technology in dietary assessment: a review of digital methods in improving food record accuracy

abstract: Methods for conducting dietary assessment in the United States date back to the early twentieth century. Methods of assessment encompassed dietary records, written and spoken dietary recalls, FFQ using pencil and paper and more recently computer and internet applications. Emerging innovations involve camera and mobile telephone technology to capture food and meal images. This paper describes six projects sponsored by the United States National Institutes of Health that use digital methods to improve food records and two mobile phone applications using crowdsourcing. The techniques under development show promise for improving accuracy of food records.


title: Big forensic data reduction: digital forensic images and electronic evidence

abstract: An issue that continues to impact digital forensics is the increasing volume of data and the growing number of devices. One proposed method to deal with the problem of "big digital forensic data": the volume, variety, and velocity of digital forensic data, is to reduce the volume of data at either the collection stage or the processing stage. We have developed a novel approach which significantly improves on current practice, and in this paper we outline our data volume reduction process which focuses on imaging a selection of key files and data such as: registry, documents, spreadsheets, email, internet history, communications, logs, pictures, videos, and other relevant file types. When applied to test cases, a hundredfold reduction of original media volume was observed. When applied to real world cases of an Australian Law Enforcement Agency, the data volume further reduced to a small percentage of the original media volume, whilst retaining key evidential files and data. The reduction process was applied to a range of real world cases reviewed by experienced investigators and detectives and highlighted that evidential data was present in the data reduced forensic subset files. A data reduction approach is applicable in a range of areas, including: digital forensic triage, analysis, review, intelligence analysis, presentation, and archiving. In addition, the data reduction process outlined can be applied using common digital forensic hardware and software solutions available in appropriately equipped digital forensic labs without requiring additional purchase of software or hardware. The process can be applied to a wide variety of cases, such as terrorism and organised crime investigations, and the proposed data reduction process is intended to provide a capability to rapidly process data and gain an understanding of the information and/or locate key evidence or intelligence in a timely manner.


title: Deep metric learning using Triplet network

abstract: Deep learning has proven itself as a successful set of models for learning useful semantic representations of data. These, however, are mostly implicitly learned as part of a classification task. In this paper we propose the triplet network model, which aims to learn useful representations by distance comparisons. A similar model was defined by Wang et al. (2014), tailor made for learning a ranking for image information retrieval. Here we demonstrate using various datasets that our model learns a better representation than that of its immediate competitor, the Siamese network. We also discuss future possible usage as a framework for unsupervised learning.


title: On blockchain security and relevant attacks

abstract: The blockchain technology witnessed a wide adoption and a swift growth in recent years. This ingenious distributed peer-to-peer design attracted several businesses and solicited several communities beyond the financial market. There are also multiple use cases built around its ecosystem. However, this backbone introduced a lot of speculation and has been criticized by several researchers. Moreover, the lack of legislations perceived a lot of attention. In this paper, we are concerned in analyzing blockchain networks and their development, focusing on their security challenges. We took a holistic approach to cover the involved mechanisms and the limitations of Bitcoin, Ethereum and Hyperledger networks. We expose also numerous possible attacks and assess some countermeasures to dissuade vulnerabilities on the network. For occasion, we simulated the majority and the re-entrancy attacks. The purpose of this paper is to evaluate Blockchain security summarizing its current state. Thoroughly showing threatening flaws, we are not concerned with favoring any particular blockchain network.


title: PhishEye: Live Monitoring of Sandboxed Phishing Kits

abstract: Phishing is a form of online identity theft that deceives unaware users into disclosing their confidential information. While significant effort has been devoted to the mitigation of phishing attacks, much less is known about the entire life-cycle of these attacks in the wild, which constitutes, however, a main step toward devising comprehensive anti-phishing techniques. In this paper, we present a novel approach to sandbox live phishing kits that completely protects the privacy of victims. By using this technique, we perform a comprehensive real-world assessment of phishing attacks, their mechanisms, and the behavior of the criminals, their victims, and the security community involved in the process -- based on data collected over a period of five months. Our infrastructure allowed us to draw the first comprehensive picture of a phishing attack, from the time in which the attacker installs and tests the phishing pages on a compromised host, until the last interaction with real victims and with security researchers. Our study presents accurate measurements of the duration and effectiveness of this popular threat, and discusses many new and interesting aspects we observed by monitoring hundreds of phishing campaigns.


title: Text steganography through Indian languages using feature coding method

abstract: Steganography is the method to hide a message inside another message without drawing any suspicion to the others and that the message can only be detected by its intended recipient. With the other steganography methods such as Image, Audio, Video, a number of text steganography algorithms have been introduced. This paper presents a new approach for steganography in Indian Languages. Considering the availability of more feature code able characters and flexible grammar structure of Indian Languages this approach is able to hide a secret message in the text. The text, after replacing feature code able characters with the transformed versions, can be used as cover media. Similarly by applying a reverse method to the cover file, we show how easily the secret message can be recovered by the intended receiver.


title: A Bibliometric Model for Identifying Emerging Research Topics

abstract: Detecting emerging research topics is essential, not only for research agencies but also for individual researchers. Previous studies have created various bibliographic indicators for the identific ...


title: Exploiting Scene Context for Image Captioning

abstract: This paper presents a framework for image captioning by exploiting the scene context. To date, most of the captioning models have been relying on the combination of Convolutional Neural Networks (CNN) and the Long-Short Term Memory (LSTM) model, trained in an end-to-end fashion. Recently, there has been extensive research towards improving the language model and the CNN architecture, utilizing attention mechanisms, and improving the learning techniques in such systems. A less studied area is the contribution of the scene context in the captioning. In this work, we study the role of the scene context, consisting of the scene type and objects. To this end, we augment the CNN features with scene context features, including scene detectors, objects and their localization, and their combinations. We use the scene context features as an initialization feature at the zeroth time step in a LSTM model with deep residual connections. In subsequent time steps, the model, however, uses the original CNN features. The proposed language model, contrary to more conventional ones, thus has access to visual features through the whole process of sentence generation. We demonstrate that the scene context features affect the language formation and improve the captioning results in the proposed framework. We also report results from the Microsoft COCO benchmark, where our model achieves the state-of-the-art performance on the test set.


title: Survey on artificial intelligence for additive manufacturing

abstract: Additive manufacturing of three-dimensional objects are now more and more realised through 3D printing, known as an evolutional paradigm in the manufacturing industry. Artificial intelligence is currently finding wide applications to 3D printing for an intelligent, efficient, high quality, mass customised and service-oriented production process. This paper presents a comprehensive survey of artificial intelligence in 3D printing. Before a printing task begins, the printability of given 3D objects can be determined through a printability checker using machine learning. The prefabrication of slicing is accelerated through parallel slicing algorithms and the path planning is optimised intelligently. In the aspect of service and security, intelligent demand matching and resource allocation algorithms enable a Cloud service platform and evaluation model to provide clients with an on-demand service and access to a collection of shared resources. We also present three machine learning algorithms to detect product defects in the presence of cyber-attacks. Based on the reviews on various applications, printability with multi-indicators, reduction of complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of good opportunities for further research, especially in the era of Industry 4.0.


title: Change in hippocampal theta oscillation associated with multiple lever presses in a bimanual two-lever choice task for robot control in rats

abstract: Hippocampal theta oscillations have been implicated in working memory and attentional process, which might be useful for the brain-machine interface (BMI). To further elucidate the properties of the hippocampal theta oscillations that can be used in BMI, we investigated hippocampal theta oscillations during a two-lever choice task. During the task body-restrained rats were trained with a food reward to move an e-puck robot towards them by pressing the correct lever, ipsilateral to the robot several times, using the ipsilateral forelimb. The robot carried food and moved along a semicircle track set in front of the rat. We demonstrated that the power of hippocampal theta oscillations gradually increased during a 6-s preparatory period before the start of multiple lever pressing, irrespective of whether the correct lever choice or forelimb side were used. In addition, there was a significant difference in the theta power after the first choice, between correct and incorrect trials. During the correct trials the theta power was highest during the first lever-releasing period, whereas in the incorrect trials it occurred during the second correct lever-pressing period. We also analyzed the hippocampal theta oscillations at the termination of multiple lever pressing during the correct trials. Irrespective of whether the correct forelimb side was used, the power of hippocampal theta oscillations gradually decreased with the termination of multiple lever pressing. The frequency of theta oscillation also demonstrated an increase and decrease, before and after multiple lever pressing, respectively. There was a transient increase in frequency after the first lever press during the incorrect trials, while no such increase was observed during the correct trials. These results suggested that hippocampal theta oscillations reflect some aspects of preparatory and cognitive neural activities during the robot controlling task, which could be used for BMI.


title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

abstract: Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89.0\% and 82.1\% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at \url{https://github.com/tensorflow/models/tree/master/research/deeplab}.


title: Predictors of micro-costing components in liver transplantation

abstract: OBJECTIVES: Although liver transplantation procedures are common and highly expensive, their cost structure is still poorly understood. This study aimed to develop models of micro-costs among patients undergoing liver transplantation procedures while comparing the role of individual clinical predictors using tree regression models. METHODS: We prospectively collected micro-cost data from patients undergoing liver transplantation in a tertiary academic center. Data collection was conducted using an Intranet registry integrated into the institution’s database for the storing of financial and clinical data for transplantation cases. RESULTS: A total of 278 patients were included and accounted for 300 procedures. When evaluating specific costs for the operating room, intensive care unit and ward, we found that in all of the sectors but the ward, human resources were responsible for the highest costs. High cost supplies were important drivers for the operating room, whereas drugs were among the top four drivers for all sectors. When evaluating the predictors of total cost, a MELD score greater than 30 was the most important predictor of high cost, followed by a Donor Risk Index greater than 1.8. CONCLUSION: By focusing on the highest cost drivers and predictors, hospitals can initiate programs to reduce cost while maintaining high quality care standards.


title: A gene module associated with dysregulated TCR signaling pathways in CD4 + T cell subsets in rheumatoid arthritis

abstract: We analyzed the transcriptome of detailed CD4+ T cell subsets including them after abatacept treatment, and examined the difference among CD4+ T cell subsets and identified gene sets that are closely associated disease activity and abatacept treatment. Seven CD4+ T cell subsets (naive, Th1, Th17, Th1/17, nonTh1/17, Tfh and Treg) were sorted from PBMCs taken from 10 RA patients and 10 healthy controls, and three RA patients donated samples before and 6 months after abatacept treatment. Paired-end RNA sequencing was performed using HiSeq 2500. A total of 149 samples except for 12 outliers were analyzed. Overview of expression pattern of RA revealed that administration of abatacept exerts a large shift toward the expression pattern of HC. Most of differentially expressed gene (DEG) upregulated in RA (n = 1776) were downregulated with abatacept treatment (n = 1349). Inversely, most of DEG downregulated in RA (n = 1860) were upregulated with abatacept treatment (n = 1294). This DEG-based analysis revealed shared pathway changes in RA CD4+ T cell subsets. Knowledge-based pathway analysis revealed the upregulation of activation-related pathways in RA that was substantially ameliorated by abatacept. Weighted gene co-expression network analysis (WGCNA) evaluated CD4+ T cells collectively and identified a gene module that consisted of 227 genes and was correlated with DAS28-CRP (Spearman's rho = 0.46, p = 4 × 10-9) and abatacept administration (Spearman's rho = -0.91, p = 5 × 10-57). The most highly connected 30 genes of this module included ZAP70 and JAK3, and pathway analysis of this module revealed dysregulation of the TCR signaling pathway network, which was ameliorated by abatacept.


title: Simple to Complex Cross-modal Learning to Rank

abstract: The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model's robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the state-of-the-arts in this literature.


title: Recent methods and databases in vision-based hand gesture recognition

abstract: The paper surveys RGB and RGB-D sensors based hand gesture recognition methods.Dynamic as well as static gesture (posture/pose) recognition methods are reviewed.Qualitative as well as quantitative comparison of algorithms is provided.Twenty-six publicly available hand gesture/posture databases are also reviewed.Discussion on unresolved issues and future research directions is provided. Successful efforts in hand gesture recognition research within the last two decades paved the path for natural human-computer interaction systems. Unresolved challenges such as reliable identification of gesturing phase, sensitivity to size, shape, and speed variations, and issues due to occlusion keep hand gesture recognition research still very active. We provide a review of vision-based hand gesture recognition algorithms reported in the last 16 years. The methods using RGB and RGB-D cameras are reviewed with quantitative and qualitative comparisons of algorithms. Quantitative comparison of algorithms is done using a set of 13 measures chosen from different attributes of the algorithm and the experimental methodology adopted in algorithm evaluation. We point out the need for considering these measures together with the recognition accuracy of the algorithm to predict its success in real-world applications. The paper also reviews 26 publicly available hand gesture databases and provides the web-links for their download.


title: Scale-Space Theory in Computer Vision

abstract: A basic problem when deriving information from measured data, such as images, originates from the fact that objects in the world, and hence image structures, exist as meaningful entities only over ...


title: Collection statistics for fast duplicate document detection

abstract: We present a new algorithm for duplicate document detection thatuses collection statistics. We compare our approach with thestate-of-the-art approach using multiple collections. Thesecollections include a 30 MB 18,577 web document collectiondeveloped by Excite@Home and three NIST collections. The first NISTcollection consists of 100 MB 18,232 LA-Times documents, which isroughly similar in the number of documents to theExciteaHome collection. The other two collections are both 2GB and are the 247,491-web document collection and the TREC disks 4and 5---528,023 document collection. We show that our approachcalled I-Match, scales in terms of the number of documents andworks well for documents of all sizes. We compared our solution tothe state of the art and found that in addition to improvedaccuracy of detection, our approach executed in roughly one-fifththe time.


title: Big data machine learning using apache spark MLlib

abstract: Artificial intelligence, and particularly machine learning, has been used in many ways by the research community to turn a variety of diverse and even heterogeneous data sources into high quality facts and knowledge, providing premier capabilities to accurate pattern discovery. However, applying machine learning strategies on big and complex datasets is computationally expensive, and it consumes a very large amount of logical and physical resources, such as data file space, CPU, and memory. A sophisticated platform for efficient big data analytics is becoming more important these days as the data amount generated in a daily basis exceeds over quintillion bytes. Apache Spark MLlib is one of the most prominent platforms for big data analysis which offers a set of excellent functionalities for different machine learning tasks ranging from regression, classification, and dimension reduction to clustering and rule extraction. In this contribution, we explore, from the computational perspective, the expanding body of the Apache Spark MLlib 2.0 as an open-source, distributed, scalable, and platform independent machine learning library. Specifically, we perform several real world machine learning experiments to examine the qualitative and quantitative attributes of the platform. Furthermore, we highlight current trends in big data machine learning research and provide insights for future work.


title: Indoor positioning system based on sensor fusion for the Blind and Visually Impaired

abstract: There are over 1.2 million Australians registered as having vision impairment. In most cases, vision impairment severely affects the mobility and orientation of the person, resulting in loss of independence and feelings of isolation. GPS technology and its applications have now become omnipresent and are used daily to improve and facilitate the lives of many. Although a number of products specifically designed for the Blind and Vision Impaired (BVI) and relying on GPS technology have been launched, this domain is still a niche and ongoing R&D is needed to bring all the benefits of GPS in terms of information and mobility to the BVI. The limitations of GPS indoors and in urban canyons have led to the development of new systems and signals that bridge the gap and provide positioning in those environments. Although still in their infancy, there is no doubt indoor positioning technologies will one day become as pervasive as GPS. It is therefore important to design those technologies with the BVI in mind, to make them accessible from scratch. This paper will present an indoor positioning system that has been designed in that way, examining the requirements of the BVI in terms of accuracy, reliability and interface design. The system runs locally on a mid-range smartphone and relies at its core on a Kalman filter that fuses the information of all the sensors available on the phone (Wi-Fi chipset, accelerometers and magnetic field sensor). Each part of the system is tested separately as well as the final solution quality.


title: Share First, Save Later: Performance of Self through Snapchat Stories

abstract: As the third most popular social network among millennials, Snapchat is well known for its picture and video messaging system that deletes content after it is viewed. However, the Stories feature of Snapchat offers a different perspective of ephemeral content sharing, with pictures and videos that are available for friends to watch an unlimited number of times for 24 hours. We conduct-ed an in-depth qualitative investigation by interviewing 18 participants and reviewing 14 days of their Stories posts. We identify five themes focused on how participants perceive and use the Stories feature, and apply a Goffmanesque metaphor to our analysis. We relate the Stories medium to other research on self-presentation and identity curation in social media.


title: Exploring the context of locations for personalized location recommendations

abstract: Conventional location recommendation models rely on users' visit history, geographical influence, temporal influence, etc., to infer users' preferences for locations. However, systematically modeling a location's context (i.e., the set of locations visited before or after this location) is relatively unexplored. In this paper, by leveraging the Skipgram model, we learn the latent representation for a location to capture the influence of its context. A pair-wise ranking loss that considers the confidences of observed user preferences for locations is then proposed to learn users' latent representations for personalized top-N location recommendations. Moreover, we also extend our model by taking into account temporal influence. Stochastic gradient descent based optimization algorithms are developed to fit the models. We conduct comprehensive experiments over four real datasets. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art location recommendation methods.


title: Diplomat: using delegations to protect community repositories

abstract: Community repositories, such as Docker Hub, PyPI, and RubyGems, are bustling marketplaces that distribute software. Even though these repositories use common software signing techniques (e.g., GPG and TLS), attackers can still publish malicious packages after a server compromise. This is mainly because a community repository must have immediate access to signing keys in order to certify the large number of new projects that are registered each day. ::: ::: This work demonstrates that community repositories can offer compromise-resilience and real-time project registration by employing mechanisms that disambiguate trust delegations. This is done through two delegation mechanisms that provide flexibility in the amount of trust assigned to different keys. Using this idea we implement Diplomat, a software update framework that supports security models with different security / usability tradeoffs. By leveraging Diplomat, a community repository can achieve near-perfect compromise-resilience while allowing real-time project registration. For example, when Diplomat is deployed and configured to maximize security on Python's community repository, less than 1% of users will be at risk even if an attacker controls the repository and is undetected for a month. Diplomat is being integrated by Ruby, CoreOS, Haskell, OCaml, and Python, and has already been deployed by Flynn, LEAP, and Docker.


title: Wireless IoT based metering system for energy efficient smart cites

abstract: The emerging of IoT concept recently in our lives, has offered the chance to establish energy efficient smart devices, systems and cities. Due to the urging need for conserving energy, this paper proposes an IoT based energy efficient wireless smart metering system design. It competes with the existing meters as being a low cost completely integrated metering system. It offers an easily operated Android application for users as well as a Website and database for the electricity supplier company. The proposed system design has an accuracy level of 97% and it is about 25% lower cost than its peer in the global market. The proposed design reduced the power consumption by 16%.


title: Application of Proteus virtual system modelling (VSM) in teaching of microcontroller

abstract: Microcontrollers are widely applied in the field of industry to solve engineering control problems. The increased use of microcontrollers makes microcontroller course necessary for electricity related speciality. A new simulation software package for microcontroller, Proteus virtual system modelling(VSM), is introduced for industrial and educational use. Proteus VSM combines circuit simulation, animated components and microprocessor models to facilitate co-simulation of complete microcontroller based designs. Proteus VSM makes it possible to develop and test designs before a physical prototype is constructed. The functions and advantages of Proteus VSM are introduced. The comparisons of Proteus VSM lab against hardware lab are deeply discussed. Proteus VSM is uniquely suited to teaching students about the design and operation of microcontroller-based systems.


title: Illusion and Dazzle: Adversarial Optical Channel Exploits Against Lidars for Automotive Applications

abstract: With the advancement in computing, sensing, and vehicle electronics, autonomous vehicles are being realized. For autonomous driving, environment perception sensors such as radars, lidars, and vision sensors play core roles as the eyes of a vehicle; therefore, their reliability cannot be compromised. In this work, we present a spoofing by relaying attack, which can not only induce illusions in the lidar output but can also cause the illusions to appear closer than the location of a spoofing device. In a recent work, the former attack is shown to be effective, but the latter one was never shown. Additionally, we present a novel saturation attack against lidars, which can completely incapacitate a lidar from sensing a certain direction. The effectiveness of both the approaches is experimentally verified against Velodyne’s VLP-16.


title: Exploring Heterogeneous Algorithms for Accelerating Deep Convolutional Neural Networks on FPGAs

abstract: Convolutional neural network (CNN) finds applications in a variety of computer vision applications ranging from object recognition and detection to scene understanding owing to its exceptional accuracy. There exist different algorithms for CNNs computation. In this paper, we explore conventional convolution algorithm with a faster algorithm using Winograd's minimal filtering theory for efficient FPGA implementation. Distinct from the conventional convolution algorithm, Winograd algorithm uses less computing resources but puts more pressure on the memory bandwidth. We first propose a fusion architecture that can fuse multiple layers naturally in CNNs, reusing the intermediate data. Based on this fusion architecture, we explore heterogeneous algorithms to maximize the throughput of a CNN. We design an optimal algorithm to determine the fusion and algorithm strategy for each layer. We also develop an automated toolchain to ease the mapping from Caffe model to FPGA bitstream using Vivado HLS. Experiments using widely used VGG and AlexNet demonstrate that our design achieves up to 1.99× performance speedup compared to the prior fusion-based FPGA accelerator for CNNs.


title: A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes

abstract: Outlier detection has attracted substantial attention in many applications and research areas; some of the most prominent applications are network intrusion detection or credit card fraud detection. Many of the existing approaches are based on calculating distances among the points in the dataset. These approaches cannot easily adapt to current datasets that usually contain a mix of categorical and continuous attributes, and may be distributed among different geographical locations. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose a fast distributed outlier detection strategy intended for datasets containing mixed attributes. The proposed method takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and the number of attributes in the dataset. Experimental results show that the proposed outlier detection method compares very favorably with other state-of-the art outlier detection strategies proposed in the literature and that the speedup achieved by its distributed version is very close to linear.


title: Design Optimal PID Controller for Quad Rotor System

abstract: rotor aerial vehicles are one of the most flexible and adaptable platforms for undertaking aerial research. Quad rotor in simplicity is rotorcraft that has four lift-generation propellers (four rotors), two rotors rotate in clockwise and the other two rotate anticlockwise, the speed and direction of Quad rotor can be controlled by varying the speed of these rotors. This paper describes the PID controller has been used for controlling attitude, Roll, Pitch and Yaw direction. In addition, optimal PID controller has been achieving by using particle swarm optimization (PSO), Bacterial Foraging optimization (BFO) and the BF-PSO optimization. Finally, the Quad rotor model has been simulating for several scenarios of testing. Keywordsad rotor, PID controller, particle swarm (PSO), Bacterial Foraging optimization (BFO) and the BF-PSO optimization.


title: A Low-Power OFDM-Based Wake-Up Mechanism for IoE Applications

abstract: This brief presents a wake-up mechanism for orthogonal frequency division multiplexing (OFDM) modulation-based systems by coding a low data-rate wake-up signal in transmitter without extra circuitry overhead. It is achieved by controlling data pattern for each OFDM subcarrier in one symbol of frame to produce an equivalent amplitude-modulated (AM) signal as a wake-up query without contaminating protocol integrity. A low-power wake-up receiver is used to demodulate this signal and interpret the wake-up query. A system using proposed wake-up mechanism has been built based on IEEE 802.11ah standard. Measurement results demonstrate that an AM-type wake-up signal with a data-rate of 31.25 Kb/s is generated through the proposed method. Moreover, an envelope detector with −35-dBm sensitivity at 900 MHz has been implemented in 0.13- ${\mu }\text{m}$ CMOS technology for wake-up signal detection and consumes 120-nW power.


title: Why Testing Improves Memory: Mediator Effectiveness Hypothesis

abstract: A wealth of research has established that practice tests improve memory for the tested material. Although the benefits of practice tests are well documented, the mechanisms underlying testing effects are not well understood. We propose the mediator effectiveness hypothesis, which states that more-effective mediators (that is, information linking cues to targets) are generated during practice involving tests with restudy versus during restudy only. Effective mediators must be retrievable at time of test and must elicit the target response. We evaluated these two components of mediator effectiveness for learning foreign language translations during practice involving either test-restudy or restudy only. Supporting the mediator effectiveness hypothesis, test-restudy practice resulted in mediators that were more likely to be retrieved and more likely to elicit targets on a final test.


title: Urdu Natural Scene Character Recognition using Convolutional Neural Networks

abstract: In this paper we investigate the challenging problem of cursive text recognition in natural scene images. In particular, we have focused on isolated Urdu character recognition in natural scenes that could not be handled by tradition Optical Character Recognition (OCR) techniques developed for Arabic and Urdu scanned documents. We also present a dataset of Urdu characters segmented from images of signboards, street scenes, shop scenes and advertisement banners containing Urdu text. A variety of deep learning techniques have been proposed by researchers for natural scene text detection and recognition. In this work, a Convolutional Neural Network (CNN) is applied as a classifier, as CNN approaches have been reported to provide high accuracy for natural scene text detection and recognition. A dataset of manually segmented characters was developed and deep learning based data augmentation techniques were applied to further increase the size of the dataset. The training is formulated using filter sizes of 3x3, 5x5 and mixed 3x3 and 5x5 with a stride value of 1 and 2. The CNN model is trained with various learning rates and state-of-the-art results are achieved.


title: PRICING AND HEDGING DERIVATIVE SECURITIES IN MARKETS WITH UNCERTAIN VOLATILITIES

abstract: We present a model for pricing and hedging derivative securities and option portfolios in an environment where the volatility is not known precisely, but is assumed instead to lie between two extreme values σminand σmax. These bounds could be inferred from extreme values of the implied volatilities of liquid options, or from high-low peaks in historical stock- or option-implied volatilities. They can be viewed as defining a confidence interval for future volatility values. We show that the extremal non-arbitrageable prices for the derivative asset which arise as the volatility paths vary in such a band can be described by a non-linear PDE, which we call the Black-Scholes-Barenblatt equation. In this equation, the 'pricing' volatility is selected dynamically from the two extreme values, σmin, σmax, according to the convexity of the value-function. A simple algorithm for solving the equation by finite-differencing or a trinomial tree is presented. We show that this model captures the importance of diversification in managing derivatives positions. It can be used systematically to construct efficient hedges using other derivatives in conjunction with the underlying asset.


Artificial Immune Systems and Their Applications


title: Classification of DDoS attack tools and its handling techniques and strategy at application layer

abstract: Computer networks basically consist of seven layers in all at different levels. The seventh layer i.e. application layer is responsible to fulfill the user's requests. Distributed Denial of Service Attack (DDoS) is a condition in which upper three layers of any computer network generally stop their jobs to fulfill the request of clients. DDoS attacks at seventh layer have become highly complex to solve. Various companies hire attack developers or purchase attacking tools to pull down the business of their competitors. DDoS attack's developers are continuously adding new features in this weapon which makes application detector unable to identify. This paper deals with the classification of DDoS threats based on abnormal behavior at application layer and provides summarized information about various DDoS Tools. Moreover, it categorizes DDoS attack handling techniques based on monitoring, preventing, detecting, and mitigating concepts. Hence this paper aims to handle the DDoS attack issues at application layer and therefore providing information about loopholes of handling techniques, and hence better understanding for future advancements in this area.


title: Scalable Recommendation with Hierarchical Poisson Factorization

abstract: We develop hierarchical Poisson matrix factorization (HPF), a novel method for providing users with high quality recommendations based on implicit feedback, such as views, clicks, or purchases. In contrast to existing recommendation models, HPF has a number of desirable properties. First, we show that HPF more accurately captures the long-tailed user activity found in most consumption data by explicitly considering the fact that users have finite attention budgets. This leads to better estimates of users' latent preferences, and therefore superior recommendations, compared to competing methods. Second, HPF learns these latent factors by only explicitly considering positive examples, eliminating the often costly step of generating artificial negative examples when fitting to implicit data. Third, HPF is more than just one method— it is the simplest in a class of probabilistic models with these properties, and can easily be extended to include more complex structure and assumptions. We develop a variational algorithm for approximate posterior inference for HPF that scales up to large data sets, and we demonstrate its performance on a wide variety of real-world recommendation problems—users rating movies, listening to songs, reading scientific papers, and reading news articles.


title: Reinforced Continual Learning

abstract: Most artificial intelligence models have limiting ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion. In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies. We name it as Reinforced Continual Learning. Our method not only has good performance on preventing catastrophic forgetting but also fits new tasks well. The experiments on sequential classification tasks for variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach outperforms existing continual learning alternatives for deep networks.


title: Domain Adaptive Classification

abstract: We propose an unsupervised domain adaptation method that exploits intrinsic compact structures of categories across different domains using binary attributes. Our method directly optimizes for classification in the target domain. The key insight is finding attributes that are discriminative across categories and predictable across domains. We achieve a performance that significantly exceeds the state-of-the-art results on standard benchmarks. In fact, in many cases, our method reaches the same-domain performance, the upper bound, in unsupervised domain adaptation scenarios.


title: High resolution automotive radar measurements of vulnerable road users – pedestrians & cyclists

abstract: Several years after successful entry into automotive market radar sensors conquers more and more complex fields of automotive applications like Lane Change Assist or Automatic Emergency Braking systems. Future sensor generations will be challenged by constantly demanding resolution requirements not only determined by consumer rating organizations like EuroNCAP, but also by the challenges induced by highly automated driving features. To satisfy these needs detailed and more accurate information of objects in the vehicle's surrounding and their multi-dimensional extent will be mandatory. To gain information about radar characteristics of pedestrians and cyclists this paper provides measurements captured with a high resolution experimental radar setup. The results are analyzed and the found radar characteristics are discussed in detail, to serve as a basis for future development of sensor hardware and signal processing algorithms.


title: Unhealthy region of citrus leaf detection using image processing techniques

abstract: Producing agricultural products are difficult task as the plant comes to an attack from various micro-organisms, pests and bacterial diseases. The symptoms of the attacks are generally distinguished through the leaves, steams or fruit inspection. The present paper discusses the image processing techniques used in performing early detection of plant diseases through leaf features inspection. The objective of this work is to implement image analysis and classification techniques for extraction and classification of leaf diseases. Leaf image is captured and then processed to determine the status of each plant. Proposed framework is model into four parts image preprocessing including RGB to different color space conversion, image enhancement; segment the region of interest using K-mean clustering for statistical usage to determine the defect and severity areas of plant leaves, feature extraction and classification. texture feature extraction using statistical GLCM and color feature by means of mean values. Finally classification achieved using SVM. This technique will ensure that chemicals only applied when plant leaves are detected to be effected with the disease.


title: A Sequential Model For Multi-Class Classification

abstract: Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach -- a sequential learning model that utilizes classifiers to sequentially restrict the number of competing classes while maintaining, with high probability, the presence of the true outcome in the candidates set. Some theoretical and computational properties of the model are discussed and we argue that these are important in NLP-like domains. The advantages of the model are illustrated in an experiment in part-of-speech tagging.


title: Interactive cluster analysis of diverse types of spatiotemporal data

abstract: We suggest an approach to exploratory analysis of diverse types of spatiotemporal data with the use of clustering and interactive visual displays. We can apply the same generic clustering algorithm to different types of data owing to the separation of the process of grouping objects from the process of computing distances between the objects. In particular, we apply the densitybased clustering algorithm OPTICS to events (i.e. objects having spatial and temporal positions), trajectories of moving entities, and spatial distributions of events or moving entities in different time intervals. Distances are computed in a specific way for each type of objects; moreover, it may be useful to have several different distance functions for the same type of objects. Thus, multiple distance functions available for trajectories support different analysis tasks. We demonstrate the use of our approach by example of two datasets from the VAST Challenge 2008: evacuation traces (trajectories of moving entities) and landings and interdictions of migrant boats (events).


title: Measurement of visual channels by contrast adaptation

abstract: Inspection of a high-contrast grating pattern affects our ability to detect patterns that are similar. This technique can be used to infer the underlying mechanisms of the visual system. By using this technique, measurements of the bandwidth of orientation channels are taken for different levels of adapting contrast and adapting duration. If the threshold elevation is plotted as the difference between the unadapted and adapted threshold in decibels, then the orientation bandwidth is invariant if taken at some fraction of the maximum elevation. This results from the fact that, as the orientation difference between the adapting and test patterns increases, the function relating threshold elevation to adapting contrast reduces in slope. These data contradict the often-used 'equivalent contrast transformation' (in which the fall off in the adaptation effect with respect to orientation is expressed in terms of an equivalent reduction in adapting contrast) as this would produce quite different bandwidths at different adapting contrasts. The data also address the issue of the neuronal mechanisms of adaptation.


title: PCA-based feature selection scheme for machine defect classification

abstract: The sensitivity of various features that are characteristic of a machine defect may vary considerably under different operating conditions. Hence it is critical to devise a systematic feature selection scheme that provides guidance on choosing the most representative features for defect classification. This paper presents a feature selection scheme based on the principal component analysis (PCA) method. The effectiveness of the scheme was verified experimentally on a bearing test bed, using both supervised and unsupervised defect classification approaches. The objective of the study was to identify the severity level of bearing defects, where no a priori knowledge on the defect conditions was available. The proposed scheme has shown to provide more accurate defect classification with fewer feature inputs than using all features initially considered relevant. The result confirms its utility as an effective tool for machine health assessment.


title: A maturity model for information governance

abstract: Information Governance (IG) as defined by Gartner is the “specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of information. Includes the processes, roles, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals”. In this paper, we present how to create an IG maturity model based on existing reference documents. The process is based on existing maturity model development methods. These methods allow for a systematic approach to maturity model development backed up by a well-known and proved scientific research method called Design Science Research. Then, based on the maturity model proposed in this paper, an assessment is conducted and the results are presented, this assessment was conducted as a self-assessment in the context of the EC-funded E-ARK project for the seven pilots of the project. The main conclusion from this initial assessment is that there is much room for improvement with most pilots achieving results between maturity level two and three. As future work, the goal is to analyze other references from different domains, such as, records management. These references will enhance, detail and help develop the maturity model making it even more valuable for all types of organization that deal with information governance.


title: Mental health in Swedish children living in joint physical custody and their parents' life satisfaction: A cross-sectional study

abstract: This study compared the psychological symptoms of 129 children in joint physical custody with children in single care and nuclear families, using a nationally representative 2011 survey of 1,297 Swedish children aged between four and 18 years. The outcome measure was the Strengths and Difficulties Questionnaire (SDQ) and its association with three dimensions of parental life satisfaction was investigated. Linear regression analyses showed higher SDQ-scores for children in joint physical custody (B = 1.4, p < 0.001) and single care (B = 2.2, p < 0.001) than in nuclear families, after adjustment for socio-demographic variables. The estimates decreased to 1.1 and 1.3, respectively, after being adjusted for parental life satisfaction ( p < 0.01). Our findings confirm previous research that showed lower symptom scores for children in nuclear families than children in single care and joint physical custody. Parental life satisfaction should be investigated further as a possible explanation of differences in symptom load between children in different living arrangements.


title: An Evaluation Study on Log Parsing and Its Use in Log Mining

abstract: Logs, which record runtime information of modern systems, are widely utilized by developers (and operators) in system development and maintenance. Due to the ever-increasing size of logs, data mining models are often adopted to help developers extract system behavior information. However, before feeding logs into data mining models, logs need to be parsed by a log parser because of their unstructured format. Although log parsing has been widely studied in recent years, users are still unaware of the advantages of different log parsers nor the impact of them on subsequent log mining tasks. Thus they often re-implement or even re-design a new log parser, which would be time-consuming yet redundant. To address this issue, in this paper, we study four log parsers and package them into a toolkit to allow their reuse. In addition, we obtain six insightful findings by evaluating the performance of the log parsers on five datasets with over ten million raw log messages, while their effectiveness on a real-world log mining task has been thoroughly examined.


title: Automation of the Arm-Aided Climbing Maneuver for Tracked Mobile Manipulators

abstract: Surmounting terrain elevations, such as terraces, is useful to increase the reach of mobile robots operating in disaster areas, construction sites, and natural environments. This paper proposes an autonomous climbing maneuver for tracked mobile manipulators with the help of the onboard arm. The solution includes a fast 3-D scan processing method to estimate a simple set of geometric features for the ascent: three lines that correspond to the low and high edges, and the maximum inclination axis. Furthermore, terraces are classified depending on whether they are reachable through a slope or an abrupt step. In the proposed maneuver, the arm is employed both for shifting the center of gravity of the robot and as an extra limb that can be pushed against the ground. Feedback during climbing can be obtained through an inertial measurement unit, joint absolute encoders, and pressure sensors. Experimental results are presented for terraces of both kinds on rough terrain with the hydraulic mobile manipulator Alacrane.


title: Workload analysis and efficient OpenCL-based implementation of SIFT algorithm on a smartphone

abstract: Feature detection and extraction are essential in computer vision applications such as image matching and object recognition. The Scale-Invariant Feature Transform (SIFT) algorithm is one of the most robust approaches to detect and extract distinctive invariant features from images. However, high computational complexity makes it difficult to apply the SIFT algorithm to mobile applications. Recent developments in mobile processors have enabled heterogeneous computing on mobile devices, such as smartphones and tablets. In this paper, we present an OpenCL-based implementation of the SIFT algorithm on a smartphone, taking advantage of the mobile GPU. We carefully analyze the SIFT workloads and identify the parallelism. We implemented major steps of the SIFT algorithm using both serial C++ code and OpenCL kernels targeting mobile processors, to compare the performance of different workflows. Based on the profiling results, we partition the SIFT algorithm between the CPU and GPU in a way that best exploits the parallelism and minimizes the buffer transferring time to achieve better performance. The experimental results show that we are able to achieve 8.5 FPS for keypoints detection and 19 FPS for descriptor generation without reducing the number and the quality of the keypoints. Moreover, the heterogeneous implementation can reduce energy consumption by 41% compared to an optimized CPU-only implementation.


title: The Role of the Gut Microbiota in Childhood Obesity

abstract: Abstract Background: Childhood and adolescent obesity has reached epidemic proportions worldwide. The pathogenesis of obesity is complex and multifactorial, in which genetic and environmental contributions seem important. The gut microbiota is increasingly documented to be involved in the dysmetabolism associated with obesity. Methods: We conducted a systematic search for literature available before October 2015 in the PubMed and Scopus databases, focusing on the interplay between the gut microbiota, childhood obesity, and metabolism. Results: The review discusses the potential role of the bacterial component of the human gut microbiota in childhood and adolescent-onset obesity, with a special focus on the factors involved in the early development of the gut bacterial ecosystem, and how modulation of this microbial community might serve as a basis for new therapeutic strategies in combating childhood obesity. A vast number of variables are influencing the gut microbial ecology (e.g., the host genetics, de...


title: How Many Samples are Needed to Estimate a Convolutional Neural Network?

abstract: A widespread folklore for explaining the success of Convolutional Neural Networks (CNNs) is that CNNs use a more compact representation than the Fully-connected Neural Network (FNN) and thus require fewer training samples to accurately estimate their parameters. We initiate the study of rigorously characterizing the sample complexity of estimating CNNs. We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\epsilon$ is $\widetilde{O}(m/\epsilon^2)$ whereas the sample-complexity for its FNN counterpart is lower bounded by $\Omega(d/\epsilon^2)$ samples. Since, in typical settings $m \ll d$, this result demonstrates the advantage of using a CNN. We further consider the sample complexity of estimating a one-hidden-layer CNN with linear activation where both the $m$-dimensional convolutional filter and the $r$-dimensional output weights are unknown. For this model, we show that the sample complexity is $\widetilde{O}\left((m+r)/\epsilon^2\right)$ when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are a localized empirical process analysis and a new lemma characterizing the convolutional structure. We believe that these tools may inspire further developments in understanding CNNs.


title: Approximate Sum-of-Products Designs Based on Distributed Arithmetic

abstract: Approximate circuits provide high performance and require low power. Sum-of-products (SOP) units are key elements in many digital signal processing applications. In this brief, three approximate SOP (ASOP) models which are based on the distributed arithmetic are proposed. They are designed for different levels of accuracy. First model of ASOP achieves an improvement up to 64% on area and 70% on power, when compared with conventional unit. Other two models provide an improvement of 32% and 48% on area and 54% and 58% on power, respectively, with a reduced error rate compared with the first model. Third model achieves the mean relative error and normalized error distance as low as 0.05% and 0.009%, respectively. Performance of approximate units is evaluated with a noisy image smoothing application, where the proposed models are capable of achieving higher peak signal-to-noise ratio than the existing state-of-the-art techniques. It is shown that the proposed approximate models achieve higher processing accuracy than existing works but with significant improvements in power and performance.


title: Making by making strange: Defamiliarization and the design of domestic technologies

abstract: This article argues that because the home is so familiar, it is necessary to make it strange, or defamiliarize it, in order to open its design space. Critical approaches to technology design are of both practical and social importance in the home. Home appliances are loaded with cultural associations such as the gendered division of domestic labor that are easy to overlook. Further, homes are not the same everywhere---even within a country. Peoples' aspirations and desires differ greatly across and between cultures. The target of western domestic technology design is often not the user, but the consumer. Web refrigerators that create shopping lists, garbage cans that let advertisers know what is thrown away, cabinets that monitor their contents and order more when supplies are low are central to current images of the wireless, digital home of the future. Drawing from our research in the United States, the United Kingdom, and Asia, we provide three different narratives of defamiliarization. A historical reading of American kitchens provides a lens with which to scrutinize new technologies of domesticity, an ethnographic account of an extended social unit in England problematizes taken-for-granted domestic technologies, and a comparative ethnography of the role of information and communication technologies in the daily lives of urban Asia's middle classes reveals the ways in which new technologies can be captured and domesticated in unexpected ways. In the final section of the article, we build on these moments of defamiliarization to suggest a broad set of challenges and strategies for design in the home.


title: A Unified Deep Neural Network for Speaker and Language Recognition

abstract: Learned feature representations and sub-phoneme posteriors from Deep Neural Networks (DNNs) have been used separately to produce significant performance gains for speaker and language recognition tasks. In this work we show how these gains are possible using a single DNN for both speaker and language recognition. The unified DNN approach is shown to yield substantial performance improvements on the the 2013 Domain Adaptation Challenge speaker recognition task (55% reduction in EER for the out-of-domain condition) and on the NIST 2011 Language Recognition Evaluation (48% reduction in EER for the 30s test condition).


title: Cancer Stem Cell Immunology: Key to Understanding Tumorigenesis and Tumor Immune Escape?

abstract: Cancer stem cell (CSC) biology and tumor immunology have shaped our understanding of tumorigenesis. However, we still do not fully understand why tumors can be contained but not eliminated by the immune system and whether rare CSCs are required for tumor propagation. Long latency or recurrence periods have been described for most tumors. Conceptually, this requires a subset of malignant cells which is capable of initiating tumors, but is neither eliminated by immune cells nor able to grow straight into overt tumors. These criteria would be fulfilled by CSCs. Stem cells are pluripotent, immune-privileged and long-living, but depend on specialized niches. Thus, latent tumors may be maintained by a niche-constrained reservoir of long-living CSCs that are exempt from immunosurveillance while niche-independent and more immunogenic daughter cells are constantly eliminated. The small subpopulation of CSCs is often held responsible for tumor initiation, metastasis and recurrence. Experimentally, this hypothesis was supported by the observation that only this subset can propagate tumors in NOD/scid mice which lack T and B cells. Yet, the concept was challenged when an unexpectedly large proportion of melanoma cells were found to be capable of seeding complex tumors in mice which further lack NK cells. Moreover, the link between stem cell-like properties and tumorigenicity was not sustained in these highly immunodeficient animals. In humans, however, tumor propagating cells must also escape from immune-mediated destruction. The ability to persist and to initiate neoplastic growth in the presence of immunosurveillance could hence be a decisive criterion for CSCs - which would be lost in a maximally immunodeficient animal model. Consequently, integrating scientific insight from stem cell biology and tumor immunology to build a new concept of “cancer stem cell immunology” may help to reconcile the outlined contradictions and to improve our understanding of tumorigenesis.


title: Large Scale Cloudlets Deployment for Efficient Mobile Cloud Computing

abstract: Interest in using Mobile Cloud Computing (MCC) for compute intensive mobile devices jobs such as multimedia applications has been growing. In fact, many new research efforts were invested to maximize the mobile intensive jobs offloading to the cloud within the cloud system coverage area. However, a large scale MCC deployment challenges and limitations are not considered. In this paper, a large scale Cloudlet-based MCC system deployment is introduced, aiming at reducing the power consumption and the network delay of multimedia applications while using MCC. The proposed deployment offers a large coverage area that can be used by mobile devices while they are moving from one location to another to reduce broadband communication needs. Practical experimental results and simulated results using show that using the proposed model reduces the power consumption of the mobile devices as well as reducing the communication latency when the mobile device requests a job to be performed remotely while satisfying the high quality of service requirements of mobile users.


title: Geographical linked data: a Spanish use case

abstract: We present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to administrative, hydrographic, and statistical domains. Our application aims at analysing existing relations between the Spanish coastal area and different statistical variables such as unemployment, population, dwelling, industry, and building trade. Moreover, we provide an important innovation with respect to other similar processes followed in other initiatives by dealing with the geometrical information of features.


title: Determinants of Mobile Apps' Success: Evidence from the App Store Market

abstract: Mobile applications markets with app stores have introduced a new approach to define and sell software applications with access to a large body of heterogeneous consumer population. This research examines key seller- and app-level characteristics that impact success in an app store market. We tracked individual apps and their presence in the top-grossing 300 chart in Apple's App Store and examined how factors at different levels affect the apps' survival in the top 300 chart. We used a generalized hierarchical modeling approach to measure sales performance, and confirmed the results with the use of a hazard model and a count regression model. We find that broadening app offerings across multiple categories is a key determinant that contributes to a higher probability of survival in the top charts. App-level attributes such as free app offers, high initial ranks, investment in less-popular (less-competitive) categories, continuous quality updates, and high-volume and high-user review scores have positive e...


Fast Fingerprint Classification with Deep Neural Networks


title: A framework for multi-document abstractive summarization based on semantic role labelling

abstract: We have proposed a framework for multi-document abstractive summarization based on semantic role labeling (SRL). To the best of our knowledge, SRL has not been employed for abstractive summarization.The integration of genetic algorithm with SRL based framework for abstractive summarization results gives improved summarization results.My study focus on two highlights and discussion is based on these two highlights. We propose a framework for abstractive summarization of multi-documents, which aims to select contents of summary not from the source document sentences but from the semantic representation of the source documents. In this framework, contents of the source documents are represented by predicate argument structures by employing semantic role labeling. Content selection for summary is made by ranking the predicate argument structures based on optimized features, and using language generation for generating sentences from predicate argument structures. Our proposed framework differs from other abstractive summarization approaches in a few aspects. First, it employs semantic role labeling for semantic representation of text. Secondly, it analyzes the source text semantically by utilizing semantic similarity measure in order to cluster semantically similar predicate argument structures across the text; and finally it ranks the predicate argument structures based on features weighted by genetic algorithm (GA). Experiment of this study is carried out using DUC-2002, a standard corpus for text summarization. Results indicate that the proposed approach performs better than other summarization systems.


title: Density-Aware Single Image De-raining Using a Multi-stream Dense Network

abstract: Single image rain streak removal is an extremely challenging problem due to the presence of non-uniform rain densities in images. We present a novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining. The proposed method enables the network itself to automatically determine the rain-density information and then efficiently remove the corresponding rain-streaks guided by the estimated rain-density label. To better characterize rain-streaks with different scales and shapes, a multi-stream densely connected de-raining network is proposed which efficiently leverages features from different scales. Furthermore, a new dataset containing images with rain-density labels is created and used to train the proposed density-aware network. Extensive experiments on synthetic and real datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method. The code can be downloaded at https://github.com/hezhangsprinter/DID-MDN


title: A transformer-based broadband I/O matching-balun-T/R switch front-end combo scheme in standard CMOS

abstract: A transformer-based broadband front-end combo scheme (I/O matching, balun, and T/R switch) is proposed and achieves both high isolation and linearity performance. A design example is prototyped in a standard 90nm CMOS process covering a 1dB-bandwidth of 2GHz from 5GHz to 7GHz based on the total insertion loss. In the transmitting (TX) mode, the complete front-end combo achieves 2.77dB insertion loss, +45.7dBm IIP3, and −42dB antenna-receiver isolation. In the receiving (RX) mode, the front-end combo demonstrates an insertion loss of 2.62dB and +44.2dBm IIP3 with −42dB transmitter-antenna isolation. The front-end combo occupies a core area of 0.5mm2 including on-chip balun, which is conducive to broadband transceiver system-on-chip (SOC) integration.


title: Finding Deceptive Opinion Spam by Any Stretch of the Imagination

abstract: Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.


title: Variable Computation in Recurrent Neural Networks

abstract: Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data. Much of this progress has been achieved through devising recurrent units and architectures with the flexibility to capture complex statistics in the data, such as long range dependency or localized attention phenomena. However, while many sequential data (such as video, speech or language) can have highly variable information flow, most recurrent models still consume input features at a constant rate and perform a constant number of computations per time step, which can be detrimental to both speed and model capacity. In this paper, we explore a modification to existing recurrent units which allows them to learn to vary the amount of computation they perform at each step, without prior knowledge of the sequence's time structure. We show experimentally that not only do our models require fewer operations, they also lead to better performance overall on evaluation tasks.


title: Lexical inference as a spatial reasoning problem

abstract: Humans are often able to draw plausible conclusions from incomplete information based on background knowledge about the world. As a substantial part of this background knowledge is of a lexical nature, a crucial challenge in automating plausible reasoning consists in learning how different words are semantically related. This paper argues that most of the lexical relations that we need for plausible reasoning can be identified with qualitative spatial relations in semantic spaces, i.e. highdimensional Euclidean spaces in which words are represented as geometric objects. This leads us to treat lexical inference as a qualitative spatial reasoning problem, and allows us to combine distributional representations with relation extraction methods and existing lexical resources.


Innovations in Practice Learning from Online Video Lectures


title: The impact of a technology-based mathematics after-school program using ALEKS on student's knowledge and behaviors

abstract: The effectiveness of using the Assessment and LEarning in Knowledge Spaces (ALEKS) system, an Intelligent Tutoring System for mathematics, as a method of strategic intervention in after-school settings to improve the mathematical skills of struggling students was examined using a randomized experimental design with two groups. As part of a 25-week program, student volunteers were randomly assigned to either a teacher-led classroom or a classroom in which students interacted with ALEKS while teachers were present. Student's math performance, conduct, involvement, and assistance was needed to complete tasks were investigated to determine overall impact of the two programs. Students assigned to the ALEKS classrooms performed at the same level as students taught by expert teachers on the Tennessee Comprehensive Assessment Program (TCAP), which is given annually to all Tennessee students. Furthermore, student's conduct and involvement remained at the same levels in both conditions. However, students in the ALEKS after-school classrooms required significantly less assistance in mathematics from teachers to complete their daily work.


title: Integrating healthcare and research genetic data empowers the discovery of 28 novel developmental disorders

abstract: De novo mutations (DNMs) in protein-coding genes are a well-established cause of developmental disorders (DD). However, known DD-associated genes only account for a minority of the observed excess of such DNMs. To identify novel DD-associated genes, we integrated healthcare and research exome sequences on 31,058 DD parent-offspring trios, and developed a simulation-based statistical test to identify gene-specific enrichments of DNMs. We identified 285 significantly DD-associated genes, including 28 not previously robustly associated with DDs. Despite detecting more DD-associated genes than in any previous study, much of the excess of DNMs of protein-coding genes remains unaccounted for. Modelling suggests that over 1,000 novel DD-associated genes await discovery, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of dominant DDs.


title: MATLAB simulink model of fuzzy logic controller with PSS and its performance analysis

abstract: In this paper, the operation of a Fuzzy Logic Controller with PSS controller is analysed in simulink environment. The development of a Fuzzy Controller with power system stabilizer in order to maintain stability and enhance the performance of a power system is described widely. The application of the Fuzzy Controller with PSS controller is investigated by means of simulation studies on a single machine infinite bus system. The functional blocks of Fuzzy Controller with PSS are developed in Simulink and simulation studies are carried out. A study case for the validation of the proposed simulink mechanism is presented and analysed with control application for a synchronous generator excitation system. The superior performance of this stabilizer in comparison to PSS and without PSS proves the efficiency of this new Fuzzy Controller with PSS controller. The comparison studies carried out for various results such as speed deviation, field voltage, rotor angle and load angle in Simulink based MATLAB environment.


title: Paraphilic Diagnoses in DSM-5

abstract: Background: The DSM-5 has been under revision since 1999 and is scheduled for publication in 2013. This article will review the major proposed modifications of the Paraphilias. Method: The information reviewed was obtained from PubMed, PsychInfo, the DSM-5.org website and other sources and reviewed. Results: Pedohebephilia, Hypersexual Disorder and Paraphilic Coercive Disorder are new proposed diagnoses. Paraphilias have been assigned their own chapter in DSM5 and a distinction has been made between Paraphilias and Paraphilic Disorders. Victim numbers have been included in diagnosis of paraphilias that involve victims and remission and severity measures have been added to all paraphilias. Transvestic Disorder can apply to males or females, Fetishistic Disorder now includes partialism, and Sexual Masochism Disorder has Asphyxiophilia as a specifier. Limitations: This study is based on a literature review and influenced by the knowledge and biases of the authors. Conclusions: The Paraphilic Disorders Section of the DSM-5 represents a significant departure from DSMIV-TR.


title: Ideological Segregation Online and Offline *

abstract: We use individual and aggregate data to ask how the Internet is changing the ideological segregation of the American electorate. Focusing on online news consumption, offline news consumption, and face-to-face social interactions, we define ideological segregation in each domain using standard indices from the literature on racial segregation. We find that ideological segregation of online news consumption is low in absolute terms, higher than the segregation of most offline news consumption, and significantly lower than the segregation of face-to-face interactions with neighbors, co-workers, or family members. We find no evidence that the Internet is becoming more segregated over time.


title: A new hierarchical method for music genre classification

abstract: Large collections of music bring new challenges for people to choose the favorite songs. Music genre explicitly defined can help people to solve this problem. However, classifying the music genre automatically is a challenging problem since many genres do not have any special features. This paper presents a new music genre classification method which utilizes hierarchical analysis of the spectrograms features extracted from the audio signals. First support vector machines are used to build the classification tree, then K nearest neighbors are implemented to improve the accuracy of the classification. The GTZAN genre collection music database is used to evaluate the proposed model, and the results show that this model can get comparable results compared with some other existing music genre classification methods.


title: Experimental economics for web mining

abstract: This paper offers a step towards research infrastructure, which makes data from experimental economics efficiently usable for analysis of web data. We believe that regularities of human behavior found in experimental data also emerge in real world web data. A format for data from experiments is suggested, which enables its publication as open data. Once standardized datasets of experiments are available on-line, web mining can take advantages from this data. Further, the questions about the order of causalities arisen from web data analysis can inspire new experiment setups.


title: Enhanced (PC) 2 A for face recognition with one training image per person

abstract: At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. projection-combined principal component analysis, (PC)2A, is proposed. (PC)2A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)2A achieves 3-5% higher accuracy than the standard eigenface technique through using 10-15% fewer eigenfaces.


A Natural-language-based Simulation Modelling Approach


title: A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade

abstract: The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.


title: Combinatorial Multi-armed Bandits for Real-Time Strategy Games

abstract: Games with large branching factors pose a significant challenge for game tree search algorithms. In this paper, we address this problem with a sampling strategy for Monte Carlo Tree Search (MCTS) algorithms called {\em na\"{i}ve sampling}, based on a variant of the Multi-armed Bandit problem called {\em Combinatorial Multi-armed Bandits} (CMAB). We analyze the theoretical properties of several variants of {\em na\"{i}ve sampling}, and empirically compare it against the other existing strategies in the literature for CMABs. We then evaluate these strategies in the context of real-time strategy (RTS) games, a genre of computer games characterized by their very large branching factors. Our results show that as the branching factor grows, {\em na\"{i}ve sampling} outperforms the other sampling strategies.


title: AQUA: An Amphibious Autonomous Robot

abstract: AQUA, an amphibious robot that swims via the motion of its legs rather than using thrusters and control surfaces for propulsion, can walk along the shore, swim along the surface in open water, or walk on the bottom of the ocean. The vehicle uses a variety of sensors to estimate its position with respect to local visual features and provide a global frame of reference