title: Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models

abstract: Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics - a high-enrollment, high-impact, and core course that many engineering undergraduates are required to take. The four types of mathematical models include the multiple linear regression model, the multilayer perception network model, the radial basis function network model, and the support vector machine model. The inputs (i.e., predictor variables) of the models include student's cumulative GPA, grades earned in four pre-requisite courses (statics, calculus I, calculus II, and physics), and scores on three dynamics mid-term exams (i.e., the exams given to students during the semester and before the final exam). The output of the models is students' scores on the dynamics final comprehensive exam. A total of 2907 data points were collected from 323 undergraduates in four semesters. Based on the four types of mathematical models and six different combinations of predictor variables, a total of 24 predictive mathematical models were developed from the present study. The analysis reveals that the type of mathematical model has only a slight effect on the average prediction accuracy (APA, which indicates on average how well a model predicts the final exam scores of all students in the dynamics course) and on the percentage of accurate predictions (PAP, which is calculated as the number of accurate predictions divided by the total number of predictions). The combination of predictor variables has only a slight effect on the APA, but a profound effect on the PAP. In general, the support vector machine models have the highest PAP as compared to the other three types of mathematical models. The research findings from the present study imply that if the goal of the instructor is to predict the average academic performance of his/her dynamics class as a whole, the instructor should choose the simplest mathematical model, which is the multiple linear regression model, with student's cumulative GPA as the only predictor variable. Adding more predictor variables does not help improve the average prediction accuracy of any mathematical model. However, if the goal of the instructor is to predict the academic performance of individual students, the instructor should use the support vector machine model with the first six predictor variables as the inputs of the model, because this particular predictor combination increases the percentage of accurate predictions, and most importantly, allows sufficient time for the instructor to implement subsequent educational interventions to improve student learning.


title: Video content analysis of surgical procedures

abstract: Background ::: In addition to its therapeutic benefits, minimally invasive surgery offers the potential for video recording of the operation. The videos may be archived and used later for reasons such as cognitive training, skills assessment, and workflow analysis. Methods from the major field of video content analysis and representation are increasingly applied in the surgical domain. In this paper, we review recent developments and analyze future directions in the field of content-based video analysis of surgical operations.


title: Memory consolidation and the medial temporal lobe: a simple network model

abstract: Abstract ::: Some forms of memory have been shown to depend on a system of medial temporal lobe structures that includes the hippocampus and the adjacent cortical areas (entorhinal, perirhinal, and parahippocampal cortex). The role of this system is only temporary, however, as indicated by the fact that, after damage to the medial temporal lobe, recent memories are impaired but very remote memories are intact. Here we review the evidence that the medial temporal lobe memory system is involved in a process of consolidation: memories are initially dependent on this system but gradually become established in other areas of the brain. We then review some of the ideas that have been proposed about the phenomenon of consolidation and suggest a synthesis of these views. Finally, we describe a simple neural network model that captures some key features of consolidation.


title: Authentication Using Graphical Passwords: Basic Results

abstract: Access to computer systems is most often based on the use of alphanumeric passwords. However, users have difficulty remembering a password that is long and random-appearing. Instead, they create short, simple, and insecure passwords. Graphical passwords have been designed to try to make passwords more memorable and easier for people to use and, therefore, more secure. Using a graphical password, users click on images rather than type alphanumeric characters. We have designed a new and more secure graphical password system, called PassPoints. In this paper we describe the PassPoints system, its security characteristics, and the empirical study we carried out comparing PassPoints to alphanumeric passwords. In the empirical study participants learned either an alphanumeric or graphical password and subsequently carried out three longitudinal trials to input their passwords over a period of five weeks. The results show that the graphical group took longer and made more errors in learning the password, but that the difference was largely a consequence of just a few graphical participants who had difficulty learning to use graphical passwords. In the longitudinal trials the two groups performed similarly on memory of their password, but the graphical group took more time to input a password.


title: Non Data Aided Timing Recovery Algorithm for Digital Underwater Communications

abstract: Synchronization is a critical operation in an underwater acoustic data communication receiver. This paper proposes a comparison of two digital NDA (non data aided) timing recovery schemes, referenced in literature as the Gardner, and the Oerder and Meyr algorithms. We consider the context of a single QPSK carrier continuous transmission, where a timing shift has to be estimated continuously in order to track the optimum sample time. Simulations processed on real data taken from sea trials, collected by the GESMA in collaboration with SERCEL and ENST Bretagne, reveal that a large Doppler shift or a large jitter on the timing shift estimation can introduce cycle slips in the clock synchronizer, which generate a large burst of errors in the data receiver. These perturbations can dramatically affect the global performance of the transmission system.


title: E-governance and stage models: analysis of identified models and selected Eurasian experiences in digitising citizen service delivery

abstract: Despite 60 years of use, challenges in relation to information communication technology (ICT) in public administration prevail. Statistics in 18 Eurasian countries show a linear relationship between internet access and frequency of use, and between internet use and the propensity to use private sector services such as online banking, but that this does not automatically translate to public sector e-services use. The literature review, subsequently identify and evaluate 30 e-government stage models to see if they offer a solution or explanation to the lack of e-service take-up. The paper identifies three gaps in current e-government research: Which variables positively effects e-service use and whether this correlations be statistically proven; the degree with which governance and cooperation models ensure successful supply and use of e-services, and existing stage models for mapping e-government progress only address supply-side, technology and organisational issues. The paper concludes by outlining a proposal to address the identified research gaps.


title: Bringing secure Bitcoin transactions to your smartphone

abstract: To preserve the Bitcoin ledger's integrity, a node that joins the system must download a full copy of the entire Bitcoin blockchain if it wants to verify newly created blocks. At the time of writing, the blockchain weights 79 GiB and takes hours of processing on high-end machines. Owners of low-resource devices (known as thin nodes), such as smart-phones, avoid that cost by either opting for minimum verification or by depending on full nodes, which weakens their security model. In this work, we propose to harden the security model of thin nodes by enabling them to verify blocks in an adaptive manner, with regards to the level of targeted confidence, with low storage requirements and a short bootstrap time. Our approach exploits sharding within a distributed hash table (DHT) to distribute the storage load, and a few additional hashes to prevent attacks on this new system.


title: Alzheimer's disease classification based on combination of multi-model convolutional networks

abstract: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. It brings increasingly troubling in today's society. Its early diagnosis and warning are particularly important. Structural magnetic resonance images (MRI) play an important role to help understand the brain anatomical changes related to AD. Traditional methods are usually based on extraction of handcrafted features and training a classifier to distinguish AD from other groups. Motivated by the success of deep learning in image classification, this paper proposes a classification method based on combination of multimodel 3D convolutional networks to learn the various features from MR brain images. First, a deep 3D convolutional neural network (3D CNN) is built to hierarchically transform the MR image into more compact high-level features. Second, the multiscale 3D convolutional autoencoders (3D CAEs) are constructed to extract features from MR brain images. The features learned by these models are combined with the upper fully connected layers for image classification in AD diagnosis. The proposed method can automatically learn the generic features from the imaging data for classification without segmentations of brain tissues and regions. Our method is evaluated using T1-weighted MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 88.31% for AD classification and an AUC (area under the ROC curve) of 92.73%, demonstrating the promising classification performances.


title: Cardinality estimation and dynamic length adaptation for Bloom filters

abstract: Bloom filters are extensively used in distributed applications, especially in distributed databases and distributed information systems, to reduce network requirements and to increase performance. In this work, we propose two novel Bloom filter features that are important for distributed databases and information systems. First, we present a new approach to encode a Bloom filter such that its length can be adapted to the cardinality of the set it represents, with negligible overhead with respect to computation and false positive probability. The proposed encoding allows for significant network savings in distributed databases, as it enables the participating nodes to optimize the length of each Bloom filter before sending it over the network, for example, when executing Bloom joins. Second, we show how to estimate the number of distinct elements in a Bloom filter, for situations where the represented set is not materialized. These situations frequently arise in distributed databases, where estimating the cardinality of the represented sets is necessary for constructing an efficient query plan. The estimation is highly accurate and comes with tight probabilistic bounds. For both features we provide a thorough probabilistic analysis and extensive experimental evaluation which confirm the effectiveness of our approaches.


title: A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods

abstract: Multiple criteria decision-making (MCDM) research has developed rapidly and has become a main area of research for dealing with complex decision problems. The purpose of the paper is to explore the performance evaluation model. This paper develops an evaluation model based on the fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS, to help the industrial practitioners for the performance evaluation in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The proposed method enables decision analysts to better understand the complete evaluation process and provide a more accurate, effective, and systematic decision support tool.


title: Novel blood pressure and pulse pressure estimation based on pulse transit time and stroke volume approximation

abstract: AbstractBackgroundNon-invasive continuous blood pressure monitors are of great interest to the medical community due to their value in hypertension ::: management. Recently, studies have shown the potential of pulse pressure as a therapeutic target for hypertension, but not enough attention has been given to non-invasive continuous monitoring of pulse pressure. Although accurate pulse pressure estimation can be of direct value to hypertension management and indirectly to the estimation of systolic blood pressure, as it is the sum of pulse pressure and diastolic blood pressure, only a few inadequate methods of pulse pressure estimation have been proposed.MethodsWe present a novel, non-invasive blood pressure and pulse pressure estimation method based on pulse transit time and pre-ejection period. Pre-ejection period and pulse transit time were measured non-invasively using electrocardiogram, seismocardiogram, and photoplethysmogram measured from the torso. The proposed method used the 2-element Windkessel model to model pulse pressure with the ratio of stroke volume, approximated by pre-ejection period, and arterial compliance, estimated by pulse transit time. Diastolic blood pressure was estimated using pulse transit time, and systolic blood pressure was estimated as the sum of the two estimates. The estimation method was verified in 11 subjects in two separate conditions with induced cardiovascular response and the results were compared against a reference measurement and values obtained from a previously proposed method.ResultsThe proposed method yielded high agreement with the reference (pulse pressure correlation with reference R ≥ 0.927, diastolic blood pressure correlation with reference R ≥ 0.854, systolic blood pressure correlation with reference R ≥ 0.914) and high estimation accuracy in pulse pressure (mean root-mean-squared error ≤ 3.46 mmHg) and blood pressure (mean root-mean-squared error ≤ 6.31 mmHg for diastolic blood pressure and ≤ 8.41 mmHg for systolic blood pressure) over a wide range of hemodynamic changes.ConclusionThe proposed pulse pressure estimation method provides accurate estimates in situations with and without significant changes in stroke volume. The proposed method improves upon the currently available systolic blood pressure estimation methods by providing accurate pulse pressure estimates.


title: Small-objectness sensitive detection based on shifted single shot detector

abstract: We present a small object sensitive method for object detection. Our method is built based on SSD (Single Shot MultiBox Detector (Liu et al. 2016)), a simple but effective deep neural network for image object detection. The discrete nature of anchor mechanism used in SSD, however, may cause misdetection for the small objects located at gaps between the anchor boxes. SSD performs better for small object detection after circular shifts of the input image. Therefore, auxiliary feature maps are generated by conducting circular shifts over lower extra feature maps in SSD for small-object detection, which is equivalent to shifting the objects in order to fit the locations of anchor boxes. We call our proposed system Shifted SSD. Moreover, pinpoint accuracy of localization is of vital importance to small objects detection. Hence, two novel methods called Smooth NMS and IoU-Prediction module are proposed to obtain more precise locations. Then for video sequences, we generate trajectory hypothesis to obtain predicted locations in a new frame for further improved performance. Experiments conducted on PASCAL VOC 2007, along with MS COCO, KITTI and our small object video datasets, validate that both mAP and recall are improved with different degrees and the speed is almost the same as SSD.


title: MTC: A Fast and Robust Graph-Based Transductive Learning Method

abstract: Despite the great success of graph-based transductive learning methods, most of them have serious problems in scalability and robustness. In this paper, we propose an efficient and robust graph-based transductive classification method, called minimum tree cut (MTC), which is suitable for large-scale data. Motivated from the sparse representation of graph, we approximate a graph by a spanning tree. Exploiting the simple structure, we develop a linear-time algorithm to label the tree such that the cut size of the tree is minimized. This significantly improves graph-based methods, which typically have a polynomial time complexity. Moreover, we theoretically and empirically show that the performance of MTC is robust to the graph construction, overcoming another big problem of traditional graph-based methods. Extensive experiments on public data sets and applications on web-spam detection and interactive image segmentation demonstrate our method’s advantages in aspect of accuracy, speed, and robustness.


title: Automated mood-aware engagement prediction

abstract: Developing intelligent machines that recognize facial expressions, detect spontaneous emotions and infer affective states of an individual are all challenging problems. While significant amount of work in recent years has focussed on advancing machine learning techniques for affect recognition and affect classification, the prediction of mood from facial analysis and the usage of mood data have received less attention. Questionnaires for psychometric measurement of mood-states are common, but using them during interventions that target psychological well-being of people are arduous and may burden an already troubled population. In this work, we present mood prediction as a sequence learning problem that uses facial Action Units (AUs) as inputs to a Long Short-Term Memory (LSTM) machine. We create two separate automated LSTM models — a total mood disturbance predictor and a mood sub-scale predictor, and then use them to aid behavioral assessments of engagement. Our mood-aware engagement predictor uses total mood disturbance score, and our analysis compares both mood sub-scale predictors and an overall mood disturbance predictor for engagement prediction. We evaluate our mood models on a large scale dataset consisting of 8M+ frames from multiple videos collected from 110 subjects during a web-intervention for trauma recovery. Our experiments show that mood-aware engagement predictor using our novel visual analysis approach performs significantly better or on par with using self-reports.


title: Self Paced Deep Learning for Weakly Supervised Object Detection

abstract: In a weakly-supervised scenario object detectors need to be trained using image-level annotation alone. Since bounding-box-level ground truth is not available, most of the solutions proposed so far are based on an iterative, Multiple Instance Learning framework in which the current classifier is used to select the highest-confidence boxes in each image, which are treated as pseudo-ground truth in the next training iteration. However, the errors of an immature classifier can make the process drift, usually introducing many of false positives in the training dataset. To alleviate this problem, we propose in this paper a training protocol based on the self-paced learning paradigm. The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training. While in the past few years similar strategies have been adopted for SVMs and other classifiers, we are the first showing that a self-paced approach can be used with deep-network-based classifiers in an end-to-end training pipeline. The method we propose is built on the fully-supervised Fast-RCNN architecture and can be applied to similar architectures which represent the input image as a bag of boxes. We show state-of-the-art results on Pascal VOC 2007, Pascal VOC 2010 and ILSVRC 2013. On ILSVRC 2013 our results based on a low-capacity AlexNet network outperform even those weakly-supervised approaches which are based on much higher-capacity networks.


title: Learning text representation using recurrent convolutional neural network with highway layers

abstract: Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the input. The experiment shows that our model outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment analysis task. Besides, the analysis of how sequence length influences the RCNN with highway layers shows that our model could learn good representation for the long text.


title: Active terahertz metamaterial devices

abstract: The construction of a device that consists of a semiconductor substrate with an array of split-ring gold resonators demonstrates that metamaterials (objects with properties based on their structure instead of the materials they are composed of) can be designed to efficiently control terahertz waves in real time. The development of artificially structured electromagnetic materials, termed metamaterials, has led to the realization of phenomena that cannot be obtained with natural materials1. This is especially important for the technologically relevant terahertz (1 THz = 1012 Hz) frequency regime; many materials inherently do not respond to THz radiation, and the tools that are necessary to construct devices operating within this range—sources, lenses, switches, modulators and detectors—largely do not exist. Considerable efforts are underway to fill this ‘THz gap’ in view of the useful potential applications of THz radiation2,3,4,5,6,7. Moderate progress has been made in THz generation and detection8; THz quantum cascade lasers are a recent example9. However, techniques to control and manipulate THz waves are lagging behind. Here we demonstrate an active metamaterial device capable of efficient real-time control and manipulation of THz radiation. The device consists of an array of gold electric resonator elements (the metamaterial) fabricated on a semiconductor substrate. The metamaterial array and substrate together effectively form a Schottky diode, which enables modulation of THz transmission by 50 per cent, an order of magnitude improvement over existing devices10.


title: Tangential Biopsy Thickness versus Lesion Depth in Longitudinal Melanonychia: A Pilot Study

abstract: Longitudinal melanonychia can be caused by melanocyte activation (hypermelanosis) or proliferation (lentigo, nevus or melanoma). Histopathologic examination is mandatory for suspicious cases of melanomas. Tangential biopsy of the matrix is an elegant technique avoiding nail plate dystrophy, but it was unknown whether the depth of the sample obtained by this method is adequate for histopathologic diagnosis. Twenty-two patients with longitudinal melanonychia striata were submitted to tangential matrix biopsies described by Haneke. The tissue was stained with hematoxylin-eosin and the specimens were measured at 3 distinct points according to the total thickness: largest (A), intermediate (B) and narrowest (C) then divided into 4 groups according to the histopathologic diagnosis (G1: hypermelanosis; G2: lentigos; G3: nevus; G4: melanoma). The lesions were measured using the same method. The mean specimen/lesion thickness measure values for each group was: G1: 0,59/0,10 mm, G2: 0,67/0,08 mm, G3: 0,52/0,05 mm, G4: 0,58/0,10 mm. The general average thickness for all the specimens/lesions was 0,59/0,08 mm. We concluded that the tangential excision, for longitudinal melanonychia, provides an adequate material for histopathological diagnosis.


title: BlockChain: A Distributed Solution to Automotive Security and Privacy

abstract: Interconnected smart vehicles offer a range of sophisticated services that benefit the vehicle owners, transport authorities, car manufacturers, and other service providers. This potentially exposes smart vehicles to a range of security and privacy threats such as location tracking or remote hijacking of the vehicle. In this article, we argue that blockchain (BC), a disruptive technology that has found many applications from cryptocurrencies to smart contracts, is a potential solution to these challenges. We propose a BC-based architecture to protect the privacy of users and to increase the security of the vehicular ecosystem. Wireless remote software updates and other emerging services such as dynamic vehicle insurance fees are used to illustrate the efficacy of the proposed security architecture. We also qualitatively argue the resilience of the architecture against common security attacks.


title: The Evaluation Report of SHA-256 Crypt Analysis Hash Function

abstract: Abstract This paper describes the study of cryptographic hash functions, one of the most important classes of primitives used in recent techniques in cryptography. The main aim is the development of recent crypt analysis hash function. We present different approaches to defining security properties more formally and present basic attack on hash function. We recall Merkle-Damgard security properties of iterated hash function. The Main aim of this paper is the development of recent techniques applicable to crypt Analysis hash function, mainly from SHA family. Recent proposed attacks an MD5 & SHA motivate a new hash function design. It is designed not only to have higher security but also to be faster than SHA-256. The performance of the new hash function is at least 30% better than that of SHA-256 in software. And it is secure against any known cryptographic attacks on hash functions.


title: Origamics In Architecture: A Medium Of Inquiry Or Design In Architecture

abstract: The Japanese craft of “origami” has proved itself as being a valuable tool to develop various engineering and design applications in numerous fields. Several patterns developed by Dr. Nojima Taketoshi ranging from environmentally friendly containers (pet bottles, plastic containers, cans and et. al) to medical applications such as stents, catheters, from vehicle parts to new insulation material configurations, from robotics to education are sources of inspiration for many other research studies (Hagiwara 2008). These wide range of applications has been named by Ian Steward as “Origamics” demonstrating the interdisciplinary nature of these studies including mathematics, engineering, biology and many other possible disciplines which may use origamics (Stewart 2007). This paper aims to discuss the potentials of “origamics” in general, then in architecture, as an interface to gain cognitive experience on spatial transformations, computational design, form finding etc., and as a medium of inquiry for structural design in through the examples of kinetic or deployable structural designs in architecture.


title: Improved Radiation Performance of Low Profile Printed Slot Antenna Using Wideband Planar AMC Surface

abstract: In this paper, a novel design of low profile printed slot antenna using wideband artificial magnetic conductor (AMC) is presented with improved radiation performance. For this purpose, the radiating slots with three unequal arms fed by coplanar waveguide are employed for broadening the impedance bandwidth with three resonances. It achieves a wide impedance bandwidth in the measured frequency range of 7.96–12.56 GHz ( $S_{11}\leq -10$ dB) for X-band applications. By loading a novel wideband planar AMC surface as the antenna ground plane, the radiation properties of this printed slot antenna are enhanced. This design includes the measured −10 dB impedance bandwidth from 5.75 to 14.51 GHz (86.48%). The proposed wideband planar AMC design operates at the frequency of 10.15 GHz with ±90° reflection phase bandwidth of 8–12.38 GHz (43.15%). The AMC surface is designed with the $5\times 8$ array of periodic patches which are developed underneath the broadband printed slot antenna. In comparison with the proposed design without AMC, the printed slot antenna loaded with AMC introduces 62.82% size reduction, a bandwidth enhancement of 41.65%, and better impedance matching. It also contributes considerable features like a low profile antenna with unidirectional radiation patterns and an increased gain of more than 10.6 dBi.


title: Orthogonal Recurrent Neural Networks with Scaled Cayley Transform

abstract: Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs). We propose a simpler and novel update scheme to maintain orthogonal recurrent weight matrices without using complex valued matrices. This is done by parametrizing with a skew-symmetric matrix using the Cayley transform. Such a parametrization is unable to represent matrices with negative one eigenvalues, but this limitation is overcome by scaling the recurrent weight matrix by a diagonal matrix consisting of ones and negative ones. The proposed training scheme involves a straightforward gradient calculation and update step. In several experiments, the proposed scaled Cayley orthogonal recurrent neural network (scoRNN) achieves superior results with fewer trainable parameters than other unitary RNNs.


title: Hyperstaticity for ergonomie design of a wrist exoskeleton

abstract: Increasing the level of transparency in rehabilitation devices has been one of the main goals in robot-aided neurorehabilitation for the past two decades. This issue is particularly important to robotic structures that mimic the human counterpart's morphology and attach directly to the limb. Problems arise for complex joints such as the human wrist, which cannot be accurately matched with a traditional mechanical joint. In such cases, mechanical differences between human and robotic joint cause hyperstaticity (i.e. overconstraint) which, coupled with kinematic misalignments, leads to uncontrolled force/torque at the joint. This paper focuses on the prono-supination (PS) degree of freedom of the forearm. The overall force and torque in the wrist PS rotation is quantified by means of a wrist robot. A practical solution to avoid hyperstaticity and reduce the level of undesired force/torque in the wrist is presented, which is shown to reduce 75% of the force and 68% of the torque.


title: Compiling a Massive, Multilingual Dictionary via Probabilistic Inference

abstract: Can we automatically compose a large set of Wiktionaries and translation dictionaries to yield a massive, multilingual dictionary whose coverage is substantially greater than that of any of its constituent dictionaries? ::: ::: The composition of multiple translation dictionaries leads to a transitive inference problem: if word A translates to word B which in turn translates to word C, what is the probability that C is a translation of A? The paper introduces a novel algorithm that solves this problem for 10,000,000 words in more than 1,000 languages. The algorithm yields PanDictionary, a novel multilingual dictionary. PanDictionary contains more than four times as many translations than in the largest Wiktionary at precision 0.90 and over 200,000,000 pairwise translations in over 200,000 language pairs at precision 0.8.


title: On the Performance of Intel SGX

abstract: As cloud computing is widely used in various fields, more and more individuals and organizations are considering outsourcing data to the public cloud. However, the security of the cloud data has become the most prominent concern of many customers, especially those who possess a large volume of valuable and sensitive data. Although some technologies like Homomorphic Encryption were proposed to solve the problem of secure data, the result is still not satisfying. With the advent of Intel SGX processor, which aims to thoroughly eliminate the security concern of cloud environment in a hardware-assisted approach, it brings us a number of questions on its features and its practicability for the current cloud platform. To evaluate the potential impact of Intel SGX, we analyzed the current SGX programming mode and inferred some possible factors that may arise the overhead. To verify our performance hypothesis, we conducted a systematic study on SGX performance by a series of benchmark experiments. After analyzing the experiment result, we performed a workload characterization to help programmer better exploit the current availability of Intel SGX and identify feasible research directions.


title: Single cell studies on the antigen content of plasmablasts during the inductive phase of antibody formation

abstract: Rats were inoculated with125I-flagella and lymphoid cells were removed and studied during the earliest stages of primary and secondary immune responses. Antibody-forming cells were identified and subjected to quantitative autoradiography to determine their antigen content.


title: Dimensional inconsistencies in code and ROS messages: A study of 5.9M lines of code

abstract: This work presents a study of robot software using the Robot Operating System (ROS), focusing on detecting inconsistencies in physical unit manipulation. We discuss how dimensional analysis, the rules governing how physical quantities are combined, can be used to detect inconsistencies in robot software that are otherwise difficult to detect. Using a corpus of ROS software with 5.9M lines of code, we measure the frequency of these dimensional inconsistencies and find them in 6% (211 / 3,484) of repositories that use ROS. We find that the inconsistency type ‘Assigning multiple units to a variable’ accounts for 75% of inconsistencies in ROS code. We identify the ROS classes and physical units most likely to be involved with dimensional inconsistencies, and find that the ROS Message type geometry_msgs::Twist is involved in over half of all inconsistencies and is used by developers in ways contrary to Twist's intent. We further analyze the frequency of physical units used in ROS programs as a proxy for assessing how developers use ROS, and discuss the practical implications of our results including how to detect and avoid these inconsistencies.


title: A mathematical analysis of the R-MAT random graph generator

abstract: The R-MAT graph generator introduced by Chakrabarti et al (Int Conf Data Mining, 2004) offers a simple, fast method for generating very large directed graphs. These properties have made it a popular choice as a method of generating graphs for objects of study in a variety of disciplines, from social network analysis to high performance computing. We analyze the graphs generated by R-MAT and model the generator in terms of occupancy problems to prove results about the degree distributions of these graphs. We prove that the limiting degree distributions can be expressed as a mixture of normal distributions with means and variances that can be easily calculated from the R-MAT parameters. Additionally, this article offers an efficient computational technique for computing the exact degree distribution and concise expressions for a number of properties of R-MAT graphs. ©2011 Wiley Periodicals, Inc.*. NETWORKS, 2011 ::: ::: The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.


title: A Comprehensive Survey on Graph Neural Networks

abstract: Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.


title: Risk factors for brain metastases in patients with metastatic colorectal cancer

abstract: AbstractBackground: Brain metastases (BM) from colorectal cancer (CRC) are rare, but the incidence is suspected to rise as treatment of metastatic (m) CRC improves. The aim of this study was to identify possible biological and clinical characteristics at initial presentation of mCRC that could predict later risk of developing BM. Furthermore, we wished to estimate the incidence of BM in long-term surviving patients.Material and methods: We conducted a retrospective study on a Danish multicenter cohort of patients with mCRC who received cetuximab and irinotecan (CetIri) as third-line treatment. All patients had previously progression on 5-FU, irinotecan and oxaliplatin containing regimens and received CetIri treatment independent of RAS mutations status. We subsequently performed KRAS, NRAS, BRAF, PIK3CA, PTEN, ERBB2 and EGFR sequencing of DNA extracted from primary tumor tissue.Results: Totally, 480 patients were included in our study. BM were diagnosed in 42 [8.8%; 95% confidence interval (CI) 6.4–11.6%]...


title: Autoconvolution for Unsupervised Feature Learning

abstract: In visual recognition tasks, supervised learning shows excellent performance. On the other hand, unsupervised learning exploits cheap unlabeled data and can help to solve the same tasks more efficiently. We show that the recursive autoconvolutional operator, adopted from physics, boosts existing unsupervised methods to learn more powerful filters. We use a well established multilayer convolutional network and train filters layer-wise. To build a stronger classifier, we design a very light committee of SVM models. The total number of trainable parameters is also greatly reduced by using shared filters in higher layers. We evaluate our networks on the MNIST, CIFAR-10 and STL-10 benchmarks and report several state of the art results among other unsupervised methods.


title: Solving the"false positives"problem in fraud prediction

abstract: In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction. False positives plague the fraud prediction industry. It is estimated that only 1 in 5 declared as fraud are actually fraud and roughly 1 in every 6 customers have had a valid transaction declined in the past year. To address this problem, we use the Deep Feature Synthesis algorithm to automatically derive behavioral features based on the historical data of the card associated with a transaction. We generate 237 features (>100 behavioral patterns) for each transaction, and use a random forest to learn a classifier. We tested our machine learning model on data from a large multinational bank and compared it to their existing solution. On an unseen data of 1.852 million transactions, we were able to reduce the false positives by 54% and provide a savings of 190K euros. We also assess how to deploy this solution, and whether it necessitates streaming computation for real time scoring. We found that our solution can maintain similar benefits even when historical features are computed once every 7 days.


title: Attacks on WebView in the Android system

abstract: WebView is an essential component in both Android and iOS platforms, enabling smartphone and tablet apps to embed a simple but powerful browser inside them. To achieve a better interaction between apps and their embedded "browsers", WebView provides a number of APIs, allowing code in apps to invoke and be invoked by the JavaScript code within the web pages, intercept their events, and modify those events. Using these features, apps can become customized "browsers" for their intended web applications. Currently, in the Android market, 86 percent of the top 20 most downloaded apps in 10 diverse categories use WebView. The design of WebView changes the landscape of the Web, especially from the security perspective. Two essential pieces of the Web's security infrastructure are weakened if WebView and its APIs are used: the Trusted Computing Base (TCB) at the client side, and the sandbox protection implemented by browsers. As results, many attacks can be launched either against apps or by them. The objective of this paper is to present these attacks, analyze their fundamental causes, and discuss potential solutions.


title: Perianal Giant Condyloma Acuminatum—Buschke-Löwenstein Tumor: A Case Report

abstract: Condyloma acuminatum caused by Human Papillomavirus is the most commonly occurring sexually transmitted infection in the anogenital region. Buschke-Lowenstein tumor (BLT) known also as giant condyloma acuminatum is a rare disease. The disease, for which the most important treatment method is the surgical excision, differs from normal condyloma acuminatum cases with its high degree of malignancy. The purpose of this paper is to present the case that reached huge dimensions in the perianal region and that was treated with wide resection in the literature.


title: A Multi-theoretical Framework for Social Network-based Recommendation

abstract: Traditional recommender system research often explores customer demographics, product characteristics, and transactions in providing recommendations. This study investigates the recommendation problem based on social network information. In light of the social network theories on the formation of a social network and its impact on human behavior, we present a multi-theoretical framework to model multiple facets of social relations for recommendation. Taking a kernel-based framework, we design and select kernels describing individuals’ similarities projected by social network theories. Moreover, we employ a non-linear multiple kernel learning approach to combine the kernels to increase the dimension of models on assessing individuals’ opinions. We evaluate our proposed framework on a real-world movie review dataset. The experiments show that our framework provides more accurate recommendations than trust-based methods, the collaborative filtering approach, and individual kernels. Further analysis shows that kernels derived from contagion theory and homophily theory contribute a larger portion of the framework.


title: Active and passive control algorithm for an exoskeleton with bowden cable transmission for hand rehabilitation

abstract: This paper investigates the control algorithm of an exoskeleton for hand rehabilitation, which accomplishes both active and passive rehabilitation training. In the passive mode control the PID control algorithm is executed in the velocity mode of the driver. In the active mode control, control architecture is proposed to deal with in both free space and constraint space. A resistance compensation control method is proposed to reduce the resistance in free space which is caused by the friction of the Bowden cable as well as the moment of inertial. To realize the compensation, force sensors are used to measure the force exerted by the human fingertip. A commercial driver, which could switch between the two control modes by a programmable digital switch rather than changing the physical connection manually, guarantees the realization of the required functions. The experiments are conducted to verify the proposed method, and the results show that in the active control mode, the maximum finger-exerted force with compensation is about two fifths of the force without compensation which means the resistance is greatly reduced. And in the passive mode, the maximum joint position error is about 1.2 degree, which satisfies the requirement in hand rehabilitation application. The experimental results demonstrate the validity of the proposed method.


title: A hierarchical classification approach to automated essay scoring

abstract: a b s t r a c t This study evaluates the use of a hierarchical classification approach to automated assessment of essays. Automated essay scoring (AES) generally relies on machine learning techniques that compute essay scores using a set of text variables. Unlike previous studies that rely on regression models, this study computes essay scores using a hierarchical approach, analogous to an incremental algorithm for hierarchical classification. The corpus in this study consists of 1243 argumentative (persuasive) essays written on 14 different prompts, across 3 different grade levels (9th grade, 11th grade, college freshman), and four different time limits for writing or temporal conditions (untimed essays and essays written in 10, 15, and 25 minute increments). The features included in the analysis are computed using the automated tools, Coh-Metrix, the Writing Assessment Tool (WAT), and Linguistic Inquiry and Word Count (LIWC). Overall, the models developed to score all the essays in the data set report 55% exact accuracy and 92% adjacent accuracy between the predicted essay scores and the human scores. The results indicate that this is a promising approach to AES that could provide more specific feedback to writers and may be relevant to other natural language computations, such as the scoring of short answers in comprehension or knowledge assessments.


title: Multimodal cue integration through Hypotheses Verification for RGB-D object recognition and 6DOF pose estimation

abstract: This paper proposes an effective algorithm for recognizing objects and accurately estimating their 6DOF pose in scenes acquired by a RGB-D sensor. The proposed method is based on a combination of different recognition pipelines, each exploiting the data in a diverse manner and generating object hypotheses that are ultimately fused together in an Hypothesis Verification stage that globally enforces geometrical consistency between model hypotheses and the scene. Such a scheme boosts the overall recognition performance as it enhances the strength of the different recognition pipelines while diminishing the impact of their specific weaknesses. The proposed method outperforms the state-of-the-art on two challenging benchmark datasets for object recognition comprising 35 object models and, respectively, 176 and 353 scenes.


title: An open software architecture for virtual reality interaction

abstract: This article describes OpenTracker, an open software architecture that provides a framework for the different tasks involved in tracking input devices and processing multi-modal input data in virtual environments and augmented reality application. The OpenTracker framework eases the development and maintenance of hardware setups in a more flexible manner than what is typically offered by virtual reality development packages. This goal is achieved by using an object-oriented design based on XML, taking full advantage of this new technology by allowing to use standard XML tools for development, configuration and documentation. The OpenTracker engine is based on a data flow concept for multi-modal events. A multi-threaded execution model takes care of tunable performance. Transparent network access allows easy development of decoupled simulation models. Finally, the application developer's interface features both a time-based and an event based model, that can be used simultaneously, to serve a large range of applications. OpenTracker is a first attempt towards a "'write once, input anywhere"' approach to virtual reality application development. To support these claims, integration into an existing augmented reality system is demonstrated. We also show how a prototype tracking equipment for mobile augmented reality can be assembled from consumer input devices with the aid of OpenTracker. Once development is sufficiently mature, it is planned to make Open-Tracker available to the public under an open source software license.


title: A 5.8-GHz radar sensor chip in 0.18-μm CMOS for non-contact vital sign detection

abstract: This paper presents a 5.8-GHz radar sensor chip for non-contact vital sign detection. The sensor chip is designed and fabricated in TSMC 0.18 μm CMOS 1P6M process. Except for the low-noise amplifier, all the active and passive components of the radar system are fully integrated in a single CMOS chip. With packaging on the printed-circuit board and connecting transmitting and receiving antennas, the radar sensor chip has been successfully demonstrated to detect the respiration and heart beat rates of a human adult.


title: Towards modularity in biological networks while avoiding retroactivity

abstract: We approach the solution to the problem “are biological networks modular” using a systems theory approach. We propose a method to divide a particular family of gene regulatory networks into “modules” that are functionally isolated from each other, so that the behavior of a composite network of two or more modules can be predicted from the input-output characteristics of the individual modules. This method provides a platform for the creation of new foundational modules using which networks can be decomposed. We present our work for the deterministic case, while providing a well-known example from the literature to validate our approach.


title: Ransomware behavioural analysis on windows platforms

abstract: Abstract Ransomware infections have grown exponentially during the recent past to cause major disruption in operations across a range of industries including the government. Through this research, we present an analysis of 14 strains of ransomware that infect Windows platforms, and we do a comparison of Windows Application Programming Interface (API) calls made through ransomware processes with baselines of normal operating system behaviour. The study identifies and reports salient features of ransomware as referred through the frequencies of API calls.


title: Rolling shutter motion deblurring

abstract: Although motion blur and rolling shutter deformations are closely coupled artifacts in images taken with CMOS image sensors, the two phenomena have so far mostly been treated separately, with deblurring algorithms being unable to handle rolling shutter wobble, and rolling shutter algorithms being incapable of dealing with motion blur. We propose an approach that delivers sharp and undistorted output given a single rolling shutter motion blurred image. The key to achieving this is a global modeling of the camera motion trajectory, which enables each scanline of the image to be deblurred with the corresponding motion segment. We show the results of the proposed framework through experiments on synthetic and real data.


title: Fathers and the Separation Pathway: The Family Court of Australia in Dialogue with Men’s Groups

abstract: This article outlines initiatives being taken by the Family Court of Australia to improve the pathways for fathers through the separation process as part of its ongoing dialogue with clients and stakeholder groups. In particular, it highlights the process and outcomes of a recent cross-sectoral discussion forum, Fathers and the Family Court. The forum gave rise to constructive, nonadversarial dialogue on the issue and found strong commonality of views across sectors, with outcomes that are expected to lead to more satisfactory pathways through separation for all clients, including for fathers. It moved beyond the court processes to discuss the entire separation pathway.


title: Patient dignity in an acute hospital setting: a case study.

abstract: BACKGROUND ::: Nurses have a professional duty to respect patients' dignity. There is a dearth of research about patients' dignity in acute hospital settings. ::: ::: ::: OBJECTIVE ::: The study investigated the meaning of patient dignity, threats to patients' dignity, and how patient dignity can be promoted, in acute hospital settings. ::: ::: ::: DESIGN ::: A qualitative, triangulated single case study design (one acute hospital), with embedded cases (one ward and its staff, and 24 patients). ::: ::: ::: SETTING ::: The study was based on a 22-bedded surgical ward in an acute hospital in England. ::: ::: ::: PARTICIPANTS ::: Twenty-four patients, aged 34-92 years were purposively selected. There were 15 men and 9 women of varied socio-economic backgrounds. They could all communicate verbally and speak English. Twelve patients, who had stayed in the ward at least 2 days, were interviewed following discharge. The other 12 patients were observed and interviewed on the ward. The ward-based staff (26 registered nurses and healthcare assistants) were observed in practice. 13 were interviewed following observation. Six senior nurses were purposively selected for interviews. ::: ::: ::: METHODS ::: The data were collected during 2005. The Local Research Ethics Committee gave approval. Unstructured interviews using topic guides were conducted with the 24 patients, 13 ward-based staff and 6 senior nurses. Twelve 4-h episodes of participant observation were conducted. The data were analysed thematically using the framework approach. ::: ::: ::: FINDINGS ::: Patient dignity comprised feelings (feeling comfortable, in control and valued), physical presentation and behaviour. The environment, staff behaviour and patient factors impacted on patient dignity. Lack of environmental privacy threatened dignity. A conducive physical environment, dignity-promoting culture and other patients' support promoted dignity. Staff being curt, authoritarian and breaching privacy threatened dignity. Staff promoted dignity by providing privacy and interactions which made patients feel comfortable, in control and valued. Patients' impaired health and older age rendered them vulnerable to a loss of dignity. Patients promoted their own dignity through their attitudes (rationalisation, use of humour, acceptance), developing relationships with staff and retaining ability and control. ::: ::: ::: CONCLUSION ::: Patients are vulnerable to loss of dignity in hospital. Staff behaviour and the hospital environment can influence whether patients' dignity is lost or upheld.


title: Learning analytics and policy (LAP): international aspirations, achievements and constraints

abstract: The Learning Analytics and Policy (LAP) workshop explores and documents the ways in which policies at national and regional level are shaping the development of learning analytics. It brings together representatives from around the world who report on the circumstances in their own country. The workshop is preceded by an information gathering phase, and followed by the authoring of a report. The aspirations, achievements and constraints in the different countries are contrasted and documented, providing a valuable resource for the future development of learning analytics.


title: Collaborative Fashion Recommendation: A Functional Tensor Factorization Approach

abstract: With the rapid expansion of online shopping for fashion products, effective fashion recommendation has become an increasingly important problem. In this work, we study the problem of personalized outfit recommendation, i.e. automatically suggesting outfits to users that fit their personal fashion preferences. Unlike existing recommendation systems that usually recommend individual items, we suggest sets of items, which interact with each other, to users. We propose a functional tensor factorization method to model the interactions between user and fashion items. To effectively utilize the multi-modal features of the fashion items, we use a gradient boosting based method to learn nonlinear functions to map the feature vectors from the feature space into some low dimensional latent space. The effectiveness of the proposed algorithm is validated through extensive experiments on real world user data from a popular fashion-focused social network.


title: Gore Galore: Literary Theory and Computer Games

abstract: Computer games have not beenadequately theorized within the humanities. Inthis paper a brief history of computer games ispresented as a starting point for developing atopology of games and a theory of computergames as rhetorical artifacts suitable forcritical study. The paper addresses thequestion of why games should be treatedseriously and suggests a theoretical approachbased on Bakhtin's poetics of the novel wherethe experience of time and space (thechronotope) provides a framework of questionsfor discussing computer games.


title: News via Voldemort: Parody accounts in topical discussions on Twitter

abstract: Parody accounts are prevalent on Twitter, offering irreverent interpretations of public figures, fictional characters and more. These accounts post comments framed within the context of their fictional universes or stereotypes of their subjects, responding in-character to topical events. This article positions parody accounts as a ritualised social media practice, an extension of fan practices and irreverent Internet culture. By providing a typology of parody accounts and analysing the topicality of selected parody accounts’ tweets, the research examines how these accounts contribute to topical discussions. In-character framing of topical comments allows parody accounts to offer original interpretations of breaking news that receive more attention than their other tweets. The presence and longevity of parody accounts underline the importance of humour on social media, including within news and topical coverage.


Improving Data Quality by Leveraging Statistical Relational Learning


title: Inverse Kinematics for Industrial Robots using Conformal Geometric Algebra

abstract: This paper shows how the recently developed formulation of conformal geometric algebra can be used for analytic inverse kinematics of two six-link industrial manipulators with revolute joints. The paper demonstrates that the solution of the inverse kinematics in this framework relies on the intersection of geometric objects like lines, circles, planes and spheres, which provides the developer with valuable geometric intuition about the problem. It is believed that this will be very useful for new robot geometries and other mechanisms like cranes and topside drilling equipment. The paper extends previous results on inverse kinematics using conformal geometric algebra by providing consistent solutions for the joint angles for the dierent congurations depending on shoulder left or right, elbow up or down, and wrist ipped or not. Moreover, it is shown how to relate the solution to the Denavit-Hartenberg parameters of the robot. The solutions have been successfully implemented and tested extensively over the whole workspace of the manipulators.


title: Novel Dual-Broadband Planar Antenna and Its Array for 2G/3G/LTE Base Stations

abstract: A novel dual-broadband planar antenna is proposed for 2G/3G/LTE (4G) mobile communications. The proposed dual-broadband antenna consists of one element for the lower band and two elements for the upper band, making it possible to be arrayed without appearance of grating lobes in the upper band. The lower-band element comprises a pair of printed dipoles with a pair of parasitic elements for bandwidth enhancement. Two upper-band elements composed of two pairs of folded dipoles are nested inside the lower-band element, forming a compact arrayable antenna unit. The dual-broadband antenna achieves a bandwidth of 20% (800-980 MHz) for the lower band and a bandwidth of 60% (1540-2860 MHz) for the upper band. A dual-broadband antenna array that consists of 4 elements for the lower band and 8 elements for the upper band is developed for base station applications. The dual-broadband array achieves a bandwidth of 22% (780-980 MHz) for the lower band and a bandwidth of 68% (1470-3000 MHz) for the upper band, covering all the frequency bands for 2G/3G/LTE (4G) systems. Measured antenna gains for the array are ~12 dBi for the lower band and dBi for the upper band, suitable for potential applications in mobile communication base stations.


title: End-to-end video background subtraction with 3d convolutional neural networks

abstract: Background subtraction in videos is a highly challenging task by definition, as it lays on a pixel-wise classification level. Therefore, great attention to detail is essential. In this paper, we follow the success of Deep Learning in Computer Vision and present an end-to-end system for background subtraction in videos. Our model is able to track temporal changes in a video sequence by applying 3D convolutions to the most recent frames of the video. Thus, no background model is needed to be retained and updated. In addition, it can handle multiple scenes without further fine-tuning on each scene individually. We evaluate our system on the largest dataset for change detection, CDnet, with over 50 videos which span across 11 categories. Further evaluation is performed in the ESI dataset which features extreme and sudden illumination changes. Our model surpasses the state-of-the-art on both datasets according to the average ranking of the models over a wide range of metrics.


title: Acoustic source location in a three-dimensional space using crosspower spectrum phase

abstract: A microphone array can be used to locate a dominant acoustic source in a given environment. This capability is successfully employed to locate an active talker in teleconferencing or other multi-speaker applications. In this work the source location is obtained in two steps: (1) a time difference of arrival (TDOA) computation between the signals of the array; (2) an "optimal" source location based on the interchannel delay estimates and on a geometrical description of the sensor arrangement. The crosspower spectrum phase technique was used for TDOA estimation, while a maximum likelihood approach was followed to derive the source coordinates. Source location experiments in a three-dimensional space were performed by means of an array of 8 microphones. For this purpose both a loudspeaker and a real talker were used to collect data in a large noisy and reverberant room.


title: A Note on Structural Equation Modeling Estimates of Reliability

abstract: Reliability can be estimated using structural equation modeling (SEM). Two potential problems with this approach are that estimates may be unstable with small sample sizes and biased with misspecified models. A Monte Carlo study was conducted to investigate the quality of SEM estimates of reliability by themselves and relative to coefficient alpha. The SEM approach showed minimal bias when the model was correctly specified if items were relatively well defined by their underlying factor(s). They tended to demonstrate somewhat greater bias when the model was misspecified, particularly underspecified. Overall, SEM estimates were more stable than anticipated. Researchers are more likely to obtain accurate estimates of reliability using SEM by conducting large-sample studies with well-constructed scales and critically assessing model fit.


Combining Color Spaces for Human Skin Detection in Color Images using Skin Cluster Classifier


title: Information theoretic key frame selection for action recognition

abstract: This paper presents an approach for human action recognition by finding the discriminative key frames from a video sequence and representing them with the distribution of local motion features and their spatiotemporal arrangements. In this approach, the key frames of the video sequence are selected by their discriminative power and represented by the local motion features detected in them and integrated from their temporal neighbors. In the key frame’s representation, the spatial arrangements of the motion features are captured in a hierarchical spatial pyramid structure. By using frame by frame voting for the recognition, experiments have demonstrated improved performances over most of the other known methods on the popular benchmark data sets. Recognizinghumanactionfromimagesequencesis an appealingyet challengingproblem in computer vision with many applications including motion capture, human-computer interaction, environment control, and security surveillance. In this paper, we focus on recognizing the activities of a person in an image sequence from local motion features and their spatiotemporal arrangements. Our approach is motivated by the recent success of “bag-of-words” model for general object recognition in computer vision[21, 14]. This representation, which is adapted from the text retrieval literature, models the object by the distribution of words from a fixed visual code book, which is usually obtained by vector quantization of local image visual features. However, this method discards the spatial and the temporal relations among these visual features, which could be helpful in object recognition. Addressing this problem, our approach uses a hierarchical representation for the key frames of a given video sequence to integrate information from both the spatial and the temporal domains. We first apply a spatiotemporal feature detector to the video sequence and obtain the local motion features. Then we generate a visual word code book by quantization of the local motion features and assign a word label to each of them. Next we select key frames of the video sequence by their discriminative power. Then, for each key frame, we integrate the visual words from its nearby frames, divide the key frame spatially into finer subdivisions and compute in each cell the histograms of the visual words detected in this key frame and its temporal neighbors. Finally, we concatenate the histograms from all cells and use


MicroRNA-17-5p contributes to osteoarthritis progression by binding p62/SQSTM1


title: Denosumab versus zoledronic acid for treatment of bone metastases in men with castration-resistant prostate cancer: a randomised, double-blind study

abstract: Summary Background Bone metastases are a major burden in men with advanced prostate cancer. We compared denosumab, a human monoclonal antibody against RANKL, with zoledronic acid for prevention of skeletal-related events in men with bone metastases from castration-resistant prostate cancer. Methods In this phase 3 study, men with castration-resistant prostate cancer and no previous exposure to intravenous bisphosphonate were enrolled from 342 centres in 39 countries. An interactive voice response system was used to assign patients (1:1 ratio), according to a computer-generated randomisation sequence, to receive 120 mg subcutaneous denosumab plus intravenous placebo, or 4 mg intravenous zoledronic acid plus subcutaneous placebo, every 4 weeks until the primary analysis cutoff date. Randomisation was stratified by previous skeletal-related event, prostate-specific antigen concentration, and chemotherapy for prostate cancer within 6 weeks before randomisation. Supplemental calcium and vitamin D were strongly recommended. Patients, study staff, and investigators were masked to treatment assignment. The primary endpoint was time to first on-study skeletal-related event (pathological fracture, radiation therapy, surgery to bone, or spinal cord compression), and was assessed for non-inferiority. The same outcome was further assessed for superiority as a secondary endpoint. Efficacy analysis was by intention to treat. This study is registered with ClinicalTrials.gov, number NCT00321620, and has been completed. Findings 1904 patients were randomised, of whom 950 assigned to denosumab and 951 assigned to receive zoledronic acid were eligible for the efficacy analysis. Median duration on study at primary analysis cutoff date was 12·2 months (IQR 5·9–18·5) for patients on denosumab and 11·2 months (IQR 5·6–17·4) for those on zoledronic acid. Median time to first on-study skeletal-related event was 20·7 months (95% CI 18·8–24·9) with denosumab compared with 17·1 months (15·0–19·4) with zoledronic acid (hazard ratio 0·82, 95% CI 0·71–0·95; p=0·0002 for non-inferiority; p=0·008 for superiority). Adverse events were recorded in 916 patients (97%) on denosumab and 918 patients (97%) on zoledronic acid, and serious adverse events were recorded in 594 patients (63%) on denosumab and 568 patients (60%) on zoledronic acid. More events of hypocalcaemia occurred in the denosumab group (121 [13%]) than in the zoledronic acid group (55 [6%]; p vs 12 [1%]; p=0·09). Interpretation Denosumab was better than zoledronic acid for prevention of skeletal-related events, and potentially represents a novel treatment option in men with bone metastases from castration-resistant prostate cancer. Funding Amgen.


title: A Novel Wideband Slotline Antenna with Dual Resonances: Principle and Design Approach

abstract: A novel wideband slotline antenna is proposed using the multimode resonance concept. By symmetrically introducing two slot stubs along the slotline radiator near the nulls of electric-field distribution of the second odd-order mode, two radiation modes are excited in a single slotline resonator. With the help of the two stubs, the second odd-order mode gradually merges with its first counterpart and results into a wideband radiation with two resonances. Prototype antennas are then fabricated to experimentally validate the principle and design approach of the proposed slotline antenna. It is shown that the proposed slotline antenna’s impedance bandwidth could be effectively increased to 32.7% while keeping an inherent narrow slot structure.


title: Computing the Discrete Fr\'echet Distance in Subquadratic Time

abstract: The Frechet distance measures similarity between two curves $f$ and $g$ that takes into account the ordering of the points along the two curves: Informally, it is the minimum length of a leash required to connect a dog, walking along $f$, and its owner, walking along $g$, as they walk without backtracking along their respective curves from one endpoint to the other. The discrete Frechet distance replaces the dog and its owner by a pair of frogs that can only reside on $m$ and $n$ specific stones, respectively. The stones are in fact sequences of points, typically sampled from the respective curves $f$ and $g$. These frogs hop from one stone to the next without backtracking, and the discrete Frechet distance is the minimum length of a “leash” that connects the frogs and allows them to execute such a sequence of hops from the starting points to the terminal points of their sequences. The discrete Frechet distance can be computed in $O(mn)$ time by a straightforward dynamic programming algorithm. We present ...


title: Modeling and simulation of the communication networks in Smart grid

abstract: A reliable and secure communication network plays a significant role in Smart grid systems, which aims at coordinating generation, transmission, distribution, and consumption parts in power system. The scope of our work ranges from utility level to end consumption level. The major difficulties in this work can be summarized as follows: 1) Performance requirements from the viewpoint of network have not been clearly defined; 2) Model mapping from power system to communication networks is not straightforward. 3) Network performance has not been well-investigated. This paper proposes a communication network model for a typical program of smart gird. Moreover, application requirements, link capacity and traffic settings have been investigated. Simulation results validate the feasibility of this model and provide useful network performances which can satisfy both the non-real-time and real-time application requirements.


Abstract ADVANCING PERSONALIZED LEARNING VIA AN ADAPTIVE CONCEPT MAP


title: Mining multilingual topics from wikipedia

abstract: In this paper, we try to leverage a large-scale and multilingual knowledge base, Wikipedia, to help effectively analyze and organize Web information written in different languages. Based on the observation that one Wikipedia concept may be described by articles in different languages, we adapt existing topic modeling algorithm for mining multilingual topics from this knowledge base. The extracted 'universal' topics have multiple types of representations, with each type corresponding to one language. Accordingly, new documents of different languages can be represented in a space using a group of universal topics, which makes various multilingual Web applications feasible.


title: Compressive Spectral Imaging via Polar Coded Aperture

abstract: A compressive spectral imager based on a polar coded aperture and a continuous variable circular bandpass filter is proposed for spinning munitions. As the imager rotates with the munition, compressive projections are sequentially captured with embedded spatial and spectral modulation. The polar coded aperture design is introduced, aiming at optimizing the sensing process. Both discrete and continuous rotation models of the proposed imager are derived and used to characterize the compressive imager. Computer simulations validate the computational models and the reconstruction algorithm.


title: Attacking DDoS at the source

abstract: Distributed denial-of-service (DDoS) attacks present an Internet-wide threat. We propose D-WARD, a DDoS defense system deployed at source-end networks that autonomously detects and stops attacks originating from these networks. Attacks are detected by the constant monitoring of two-way traffic flows between the network and the rest of the Internet and periodic comparison with normal flow models. Mismatching flows are rate-limited in proportion to their aggressiveness. D-WARD offers good service to legitimate traffic even during an attack, while effectively reducing DDoS traffic to a negligible level. A prototype of the system has been built in a Linux router. We show its effectiveness in various attack scenarios, discuss motivations for deployment, and describe associated costs.


title: Optimize Routing in Hybrid SDN Network with Changing Traffic

abstract: Traffic Engineering is an efficient tool to balance the network flows and, thus improving the network performance with limited network resources. The goal of traffic engineering is to find an efficient and robust routing to balance the flows with changing traffic. Multiple traffic matrices are good representatives of the changing traffic. The emergence of Software Defined Networking (SDN) provides us a more flexible way to route the network flows with multiple traffic matrices. We expect to optimize the routing of the average case performance over multiple traffic matrices and, at the same time, bound the worst case performance for some unexpected traffic in hybrid SDN network. In this paper, we first formulate the problem of optimizing both the average case and worst case performance of the routing over multiple traffic matrices. Then, we prove the problem is NP-hard and propose a heuristic algorithm to solve it. Finally, we evaluate our algorithm with real traffic datasets. Through extensive experiments, we observe that the worst case performance of our routing can be dramatically improved by 32.15% with a little sacrifice of the average case performance by 2.01% and demonstrate the effectiveness of our algorithm in optimizing both the average case and worst case performance of routing.


title: Recursive Bayesian Control of Multichannel Acoustic Echo Cancellation

abstract: We present a novel recursive Bayesian method in the DFT-domain to address the multichannel acoustic echo cancellation problem. We model the echo paths between the loudspeakers and the near-end microphone as a multichannel random variable with a first-order Markov property. The incorporation of the near-end observation noise, in conjunction with the multichannel Markov model, leads to a multichannel state-space model. We derive a recursive Bayesian solution to the multichannel state-space model, which turns out to be well suited for input signals that are not only auto-correlated but also cross-correlated. We show that the resulting multichannel state-space frequency-domain adaptive filter (MCSSFDAF) can be efficiently implemented due to the submatrix-diagonality of the state-error covariance. The filter offers optimal tracking and robust adaptation in the presence of near-end noise and echo path variability.


title: Close Enough Traveling Salesman Problem: A Discussion of Several Heuristics

abstract: The use of radio frequency identification (RFID) allows utility companies to read meters from a distance. Thus a meter reader need not visit every customer on his route, but only get within a certain radius of each customer. In finding an optimal route — one that minimizes the distance the meter reader travels while servicing each customer on his route — this notion of only needing to be close enough changes the meter reading problem from a standard Traveling Salesperson Problem (TSP) into a variant problem: Close Enough TSP (CETSP). As a project for a graduate course in network optimization various heuristics for finding near optimal CETSP solutions were developed by six groups of students. In this paper we survey the heuristics and provide results for a diverse set of sample cases.


title: Would You Do That? – Understanding Social Acceptance of Gestural Interfaces

abstract: With gesture-based interactions in mobile settings becoming more popular, there is a growing concern regarding the social acceptance of these interaction techniques. In this paper we begin by examining the various definitions of social acceptance that have been proposed in the literature to synthesize a definition that is based on how the user feels about performing a particular interaction as well as how the bystanders perceive the user during this interaction. We then present the main factors that influence gestures' social acceptance including culture, time, interaction type and the user's position on the innovation adoption curve. Through a user study we show that an important factor in determining social acceptance of gesture-based interaction techniques is the user's perception of others ability to interpret the potential effect of a manipulation.


title: On Robust Gaussian Graphical Modeling

abstract: The objective of this exposition is to give an overview of the existing approaches to robust Gaussian graphical modeling. We start by thoroughly introducing Gaussian graphical models (also known as covariance selection models or concentration graph models) and then review the established, likelihood-based statistical theory (estimation, testing and model selection). Afterwards we describe robust methods and compare them to the classical approaches.


title: Design and control of a lightweight magnetic climbing robot for vessel inspection

abstract: The inspection of marine vessels is currently performed manually. Inspectors use sensors (e.g. cameras, devices for non-destructive testing) to detect damaged areas, cracks, and corrosion in large cargo holds, tanks, and other parts of a ship. Due to the size and complex geometry of most ships, ship inspection is time-consuming and expensive. The EU-funded project MINOAS develops concepts for a Marine Inspection Robotic Assistant System to improve and to automate ship inspections. As one example of a physical system implementation, we introduce our magnetic wall-climbing robot. This remotely operated lightweight system is able to climb a vessels steel frame and is able to deliver visual inspection data on-line. For any type of surveying process, the raw and meta data are mandatory in order to compare inspection data over time. In this paper, we describe our approach of how the magnetic climbing robot is localized and controlled. Additionally, we describe the design of the robot as well as the localization device which is able to provide a real-time 3D pose of the inspection robot.


title: Narcissistic self-sorting in self-assembled cages of rare Earth metals and rigid ligands.

abstract: Highly selective, narcissistic self-sorting can be achieved in the formation of self-assembled cages of rare earth metals with multianionic salicylhydrazone ligands. The assembly process is highly sensitive to the length of the ligand and the coordination geometry. Most surprisingly, high-fidelity sorting is possible between ligands of identical coordination angle and geometry, differing only in a single functional group on the ligand core, which is not involved in the coordination. Supramolecular effects allow discrimination between pendant functions as similar as carbonyl or methylene groups in a complex assembly process.


title: Interactive Tensor Field Design and Visualization on Surfaces

abstract: Designing tensor fields in the plane and on surfaces is a necessary task in many graphics applications, such as painterly rendering, pen-and-ink sketching of smooth surfaces, and anisotropic remeshing. In this article, we present an interactive design system that allows a user to create a wide variety of symmetric tensor fields over 3D surfaces either from scratch or by modifying a meaningful input tensor field such as the curvature tensor. Our system converts each user specification into a basis tensor field and combines them with the input field to make an initial tensor field. However, such a field often contains unwanted degenerate points which cannot always be eliminated due to topological constraints of the underlying surface. To reduce the artifacts caused by these degenerate points, our system allows the user to move a degenerate point or to cancel a pair of degenerate points that have opposite tensor indices. These operations provide control over the number and location of the degenerate points in the field. We observe that a tensor field can be locally converted into a vector field so that there is a one-to-one correspondence between the set of degenerate points in the tensor field and the set of singularities in the vector field. This conversion allows us to effectively perform degenerate point pair cancellation and movement by using similar operations for vector fields. In addition, we adapt the image-based flow visualization technique to tensor fields, therefore allowing interactive display of tensor fields on surfaces. We demonstrate the capabilities of our tensor field design system with painterly rendering, pen-and-ink sketching of surfaces, and anisotropic remeshing


title: The Formal Specification Language mCRL2

abstract: We introduce mCRL2, a specification language that can be used to specify and analyse the behaviour of distributed systems. This language is the successor of the mCRL specification language. The mCRL2 language extends a timed basic process algebra with the possibility to define and use abstract data types. The mCRL2 data language features predefined and higher-order data types. The process algebraic part of mCRL2 allows a faithful translation of coloured Petri nets and component based systems: we have introduced multiactions and we have separated communication and parallelism.


Open Forum: Legitimacy/Change/Power: Is a New Course in Italian Gender Studies Possible? A Response to Chiara Saraceno


title: Sequence-to-sequence Modelling for Categorical Speech Emotion Recognition Using Recurrent Neural Network

abstract: To model the categorical speech emotion recognition tasks in a sequential approach, the first challenge is how to transfer the categorical label for each utterance into a label sequence. To settle this, we make a hypothesis that an utterance is consisting of emotional and non-emotional segments alternatively, and these non-emotional segments correspond to silent regions, short pauses, transits between phonemes, fricative phonemes, etc. With this hypothesis, we propose to treat an utterance, ’s label sequence as a chain of two kinds of states: emotional states denoting emotional frames and Nulls denoting non-emotional frames. Then, we exploit a connectionist temporal classification based recurrent neural network (CTC-RNN) to automatically label and align an utterance’s emotional segments with emotional labels, while non-emotional segments with non-emotional labels. Experimental results on the IEMOCAP corpus demonstrate the effectiveness of our proposed method compared to state-of-the-art emotion recognition algorithms.


title: The future of grey-box fuzzing

abstract: Society are becoming more dependent on software, and more artifacts are being connected to the Internet each day [31]. This makes the work of tracking down vulnerabilities in software a moral oblig ...


title: Autopoiesis and Knowledge in Self-Sustaining Organizational Systems

abstract: Knowledge and the communication of knowledge are critical for self-sustaining organizations comprised of people and the tools and machines that extend peoples’ physical and cognitive capacities. Humberto Maturana and Francisco Varela proposed the concept of autopoiesis (“self” “production”) as a definition of life in the 1970s. Nicklas Luhmann extended this concept to establish a theory of social systems, where intangible human social systems were formed by recursive networks of communications. We show here that Luhmann fundamentally misunderstood Maturana and Varela’s autopoiesis by thinking that the self-observation necessary for self-maintenance formed a paradoxically vicious circle. Luhmann tried to resolve this apparent paradox by placing the communication networks on an imaginary plane orthogonal to the networked people. However, Karl Popper’s evolutionary epistemology and the theory of hierarchically complex systems turns what Luhmann thought was a vicious circle into a virtuous spiral of organizational learning and knowledge. There is no closed circle that needs to be explained via Luhmann’s extraordinarily paradoxical linguistic contortions.


title: Knowledge-based Sales Advisory: Experiences and Future Directions

abstract: This paper summarizes our experiences gained from several industrial advisory applications that were devel- oped with the knowledge-based ADVISOR SUITE framework over the last years and gives an outlook on future extensions of the presented system. In the 'experiences' section of the paper, we first address aspects related to the development of such appli- cations, such as knowledge engineering, software maintenance, or testing. In addition, we describe the main requirements for such an advisory application to be perceived as an intelligent, value-adding service by the end users and finally summarize the results of an industrial study on how advisory applications are able to influence the buying behavior of online shoppers. The second part of the paper discusses current and future extensions of our system. The main lines of research addressed in this section are 'Extended debugging support', 'Automated extraction of product data from web sources', 'Log mining and advanced data analysis', and 'Community-adapted advisory systems'.


title: Utilizing Microblogs for Automatic News Highlights Extraction

abstract: Story highlights form a succinct single-document summary consisting of 3-4 highlight sentences that reflect the gist of a news article. Automatically producing news highlights is very challenging. We propose a novel method to improve news highlights extraction by using microblogs. The hypothesis is that microblog posts, although noisy, are not only indicative of important pieces of information in the news story, but also inherently “short and sweet” resulting from the artificial compression effect due to the length limit. Given a news article, we formulate the problem as two rank-then-extract tasks: (1) we find a set of indicative tweets and use them to assist the ranking of news sentences for extraction; (2) we extract top ranked tweets as a substitute of sentence extraction. Results based on our news-tweets pairing corpus indicate that the method significantly outperform some strong baselines for single-document summarization.


title: On saliency enhancement of salient pole wound field synchronous machines

abstract: Salient pole wound field synchronous machines (WFSMs) share similar torque characteristics as permanent magnet (PM) machines, but are free of permanent magnet material since a wound rotor winding instead generates the required field flux. In addition to cost advantages, the rotor field flux is readily varied making these machines attractive for traction and generator applications. This paper introduces flux barrier structure on rotor design to improve the saliency and thus the torque production of a conventional synchronous machine. Analysis are done using both dq-equivalent circuit model and finite element (FE) calculations. Flux barrier width is carefully tuned in FE analysis. Comparison is made to a conventional salient pole structure whereby a steady state torque improvement is achieved at rated to high stator current, and total loss is reduced in field weakening region.


title: Iot-based smart cities: A survey

abstract: Due to the growing developments in advanced metering and digital technologies, smart cities have been equipped with different electronic devices on the basis of Internet of Things (IoT), therefore becoming smarter than before. The aim of this article is that of providing a comprehensive review on the concepts of smart cities and on their motivations and applications. Moreover, this survey describes the IoT technologies for smart cities and the main components and features of a smart city. Furthermore, practical experiences over the world and the main challenges are explained.


title: Effects of sexual dimorphism on facial attractiveness

abstract: Testosterone-dependent secondary sexual characteristics in males may signal immunological competence1 and are sexually selected for in several species2,3. In humans, oestrogen-dependent characteristics of the female body correlate with health and reproductive fitness and are found attractive4,5,6. Enhancing the sexual dimorphism of human faces should raise attractiveness by enhancing sex-hormone-related cues to youth and fertility in females5,7,8,9,10,11, and to dominance and immunocompetence in males5,12,13. Here we report the results of asking subjects to choose the most attractive faces from continua that enhanced or diminished differences between the average shape of female and male faces. As predicted, subjects preferred feminized to average shapes of a female face. This preference applied across UK and Japanese populations but was stronger for within-population judgements, which indicates that attractiveness cues are learned. Subjects preferred feminized to average or masculinized shapes of a male face. Enhancing masculine facial characteristics increased both perceived dominance and negative attributions (for example, coldness or dishonesty) relevant to relationships and paternal investment. These results indicate a selection pressure that limits sexual dimorphism and encourages neoteny in humans.


title: ENVIRONMENTAL INTERFACES IN TEACHING STATISTICS

abstract: The objective of this article is, based on the Critical Statistics Education assumptions, to value some environmental interfaces in teaching Statistics by modeling projects. Due to this, we present a practical case, one in which we address an environmental issue, placed in the context of the teaching of index numbers, within the Statistics discipline in an undergraduate course in Economic Sciences. In this project, we discuss the Human Development Index (HDI) and we propose the creation of an environmental index in order to evaluate the countries concern level in following some ecological and/or preservation practices.


title: Quality Assessment of Deblocked Images

abstract: We study the efficiency of deblocking algorithms for improving visual signals degraded by blocking artifacts from compression. Rather than using only the perceptually questionable PSNR, we instead propose a block-sensitive index, named PSNR-B, that produces objective judgments that accord with observations. The PSNR-B modifies PSNR by including a blocking effect factor. We also use the perceptually significant SSIM index, which produces results largely in agreement with PSNR-B. Simulation results show that the PSNR-B results in better performance for quality assessment of deblocked images than PSNR and a well-known blockiness-specific index.


title: Image processing with complex wavelets

abstract: We first review how wavelets may be used for multi–resolution image processing, describing the filter–bank implementation of the discrete wavelet transform (DWT) and how it may be extended via separable filtering for processing images and other multi–dimensional signals. We then show that the condition for inversion of the DWT (perfect reconstruction) forces many commonly used wavelets to be similar in shape, and that this shape produces severe shift dependence (variation of DWT coefficient energy at any given scale with shift of the input signal). It is also shown that separable filtering with the DWT prevents the transform from providing directionally selective filters for diagonal image features. Complex wavelets can provide both shift invariance and good directional selectivity, with only modest increases in signal redundancy and computation load. However, development of a complex wavelet transform (CWT) with perfect reconstruction and good filter characteristics has proved difficult until recently. We now propose the dual–tree CWT as a solution to this problem, yielding a transform with attractive properties for a range of signal and image processing applications, including motion estimation, denoising, texture analysis and synthesis, and object segmentation.


title: A novel data mining mechanism considering bio-signal and environmental data with applications on asthma monitoring

abstract: Chronic asthmatic sufferers need to be constantly observed to prevent sudden attacks. In order to improve the efficiency and effectiveness of patient monitoring, we proposed in this paper a novel data mining mechanism for predicting attacks of chronic diseases by considering of both bio-signals of patients and environmental factors. We proposed two data mining methods, namely Pattern Based Decision Tree (PBDT) and Pattern Based Class-Association Rule (PBCAR). Both methods integrate the concepts of sequential pattern mining to extract features of asthma attacks, and then build classifiers with the concepts of decision tree mining and rule-based method respectively. Besides the general clinical data of patients, we considered environmental factors, which are related to many chronic diseases. For experimental evaluations, we adopted the children asthma allergic dataset collated from a hospital in Taiwan as well as the environmental factors like weather and air pollutant data. The experimental results show that PBCAR delivers 86.89% of accuracy and 84.12% of recall, and PBDT shows 87.52% accuracy and 85.59 of recall. These results also indicate that our methods can perform high accuracy and recall on predictions of chronic disease attacks. The readable rules of both classifiers can provide patients and healthcare workers with insights on essential illness related information. At the same time, additional environmental factors of input data are also proven to be valuable in predicting attacks.


title: An Incremental Type-2 Meta-Cognitive Extreme Learning Machine

abstract: Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.