title: Joint Sequential Shape Classification and Piecewise Elastic Motion Estimation

abstract: This paper proposes a novel motion model for classifying general non-rigid motion into piecewise elastic motion, so as to achieve the non-rigid motion estimation without any priori shape models. Three interrelated sub-problems have to be addressed: classifying the whole motion sequence, estimating motion inside each segment and connecting the piecewise motions. In this paper, a Markov chain is used to classify the motion into a pre-defined number of classes, at the same time the estimation of elastic motion in individual segments of the sequence, and then all piecewise motions are connected with the correspondence between neighbor frames. Finally, the experiment results on human motions show the capability and robustness of proposed algorithm.


title: Fine motor skills and early comprehension of the world: Two new school readiness indicators.

abstract: Abstract Duncan et al. (2007) presented a new methodology for identifying kindergarten readiness factors and quantifying their importance by determining which of children's developing skills measured around kindergarten entrance would predict later reading and math achievement. This article extends Duncan et al.'s work to identify kindergarten readiness factors with 6 longitudinal data sets. Their results identified kindergarten math and reading readiness and attention as the primary long-term predictors but found no effects from social skills or internalizing and externalizing behavior. We incorporated motor skills measures from 3 of the data sets and found that fine motor skills are an additional strong predictor of later achievement. Using one of the data sets, we also predicted later science scores and incorporated an additional early test of general knowledge of the social and physical world as a predictor. We found that the test of general knowledge was by far the strongest predictor of science and reading and also contributed significantly to predicting later math, making the content of this test another important kindergarten readiness indicator. Together, attention, fine motor skills, and general knowledge are much stronger overall predictors of later math, reading, and science scores than early math and reading scores alone.


title: 79GHz-band radar cross section measurement for pedestrian detection

abstract: This paper presents radar cross section (RCS) measurement for pedestrian detection in 79GHz-band radar system. For a human standing at 6.2 meters, the RCS distribution's median value is -11.1 dBsm and the 90 % of RCS fluctuation is between -20.7 dBsm and -4.8 dBsm. Other measurement results (human body poses beside front) are shown. And we calculated the coefficient values of the Weibull distribution fitting to the human body RCS distribution.


SHORT-TERM FORECASTING OF WEATHER CONDITIONS IN PALESTINE USING ARTIFICIAL NEURAL NETWORKS


title: Duplication of chromosome band 12q24.11q24.23 results in apparent Noonan syndrome.

abstract: Noonan syndrome is an autosomal dominant disorder with an estimated incidence of 1 in 1,000 to 1 in 2,500 live births. It is characterized by postnatal-onset short stature, characteristic facial changes, webbed neck, pectus carinatum, or excavatum, congenital heart defects, and bleeding abnormalities. Gain-of-function mutations in the PTPN11, KRAS, SOS1, and RAF1 genes that are components of the RAS/MEPK signaling pathway are identified in about 70-85% of individuals with Noonan syndrome. We report here a case of duplication of chromosome region 12q24.11q24.23 identified by array comparative genomic hybridization (aCGH) that includes the PTPN11 gene in a 3-year-old girl with apparent Noonan syndrome. The patient presented with postnatal-onset failure-to-thrive, developmental delay, microcephaly, velopalatal incompetence, pectus excavatum, coarctation of aorta, atrial and ventricular septal defects, decreased muscle tone, and minor facial anomalies consistent with Noonan syndrome. At 3 years of age her speech, gross and fine motor development were at the level of a 12-18 month old child. This degree of developmental delay was atypical for an individual with Noonan syndrome, raising concerns for a chromosomal abnormality. Array-CGH showed an interstitial duplication of 10 Mb including the PTPN11 gene. Sequencing of PTPN11, KRAS, SOS1 and the coding region of RAF1 did not identify mutations. The increased gene dosage of the PTPN11 gene in the form of duplication is expected to have the same consequence as gain-of-function mutations seen in Noonan syndrome. We propose that at least some of the 15-30% of individuals with Noonan syndrome who do not have a mutation by sequencing may have a gain in copy number of PTPN11 and recommend that comprehensive testing for Noonan syndrome should include analysis for copy number changes of PTPN11.


title: Feature Selection Using a Piecewise Linear Network

abstract: We present an efficient feature selection algorithm for the general regression problem, which utilizes a piecewise linear orthonormal least squares (OLS) procedure. The algorithm 1) determines an appropriate piecewise linear network (PLN) model for the given data set, 2) applies the OLS procedure to the PLN model, and 3) searches for useful feature subsets using a floating search algorithm. The floating search prevents the "nesting effect." The proposed algorithm is computationally very efficient because only one data pass is required. Several examples are given to demonstrate the effectiveness of the proposed algorithm


title: Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey

abstract: Abstract The cloud phenomenon is quickly becoming an important service in Internet computing. Infrastructure as a Service (IaaS) in cloud computing is one of the most significant and fastest growing field. In this service model, cloud providers offer resources to users/machines that include computers as virtual machines, raw (block) storage, firewalls, load balancers, and network devices. One of the most pressing issues in cloud computing for IaaS is the resource management. Resource management problems include allocation, provisioning, requirement mapping, adaptation, discovery, brokering, estimation, and modeling. Resource management for IaaS in cloud computing offers following benefits: scalability, quality of service, optimal utility, reduced overheads, improved throughput, reduced latency, specialized environment, cost effectiveness and simplified interface. This paper focuses on some of the important resource management techniques such as resource provisioning, resource allocation, resource mapping and resource adaptation. It brings out an exhaustive survey of such techniques for IaaS in cloud computing, and also put forth the open challenges for further research.


title: Why men commit crimes (and why they desist

abstract: Hirschi and Gottfredson (1983) claim that the relationship between age and crime is similar in all social and cultural conditions and that no current sociological or criminological theory can account for this similarity. We introduce the new field of evolutionary psychology and extend Daly and Wilson's (1988) work on homicide to construct a general theory of male criminality, which explains why men commit violent and property crimes. The theory can also explain the age-crime curve. It might also account for some empirical anomalies such as why physically smaller boys are more delinquent, and why violent criminals desist more slowly.


title: Software defect association mining and defect correction effort prediction

abstract: Much current software defect prediction work focuses on the number of defects remaining in a software system. In this paper, we present association rule mining based methods to predict defect associations and defect correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results show that, for defect association prediction, the accuracy is very high and the false-negative rate is very low. Likewise, for the defect correction effort prediction, the accuracy for both defect isolation effort prediction and defect correction effort prediction are also high. We compared the defect correction effort prediction method with other types of methods - PART, C4.5, and Naive Bayes - and show that accuracy has been improved by at least 23 percent. We also evaluated the impact of support and confidence levels on prediction accuracy, false-negative rate, false-positive rate, and the number of rules. We found that higher support and confidence levels may not result in higher prediction accuracy, and a sufficient number of rules is a precondition for high prediction accuracy.


title: On the control of planar cable-driven parallel robot via classic controllers and tuning with intelligent algorithms

abstract: This paper presents different classical control approaches for planar cable-driven parallel robots. For the proposed robot, PD and PID controllers are designed based on the concept of pole placement method. In order to optimize and tune the controller parameters of planar cable-driven parallel robot, Differential Evaluation, Particle Swarm Optimization and Genetic algorithms are applied as optimization techniques. The simulation results of Genetic algorithm, Particle Swarm Optimization and Differential Evaluation algorithms reveal that the output results of tunes controllers with Particle Swarm Optimization and Differential Evaluation algorithms are more similar than Genetic algorithm and the processing time of Differential Evaluation is less than Genetic algorithm and Particle Swarm Optimization. Moreover, performance of the Particle Swarm Optimization and Differential Evaluation algorithms are better than Genetic algorithm for tuning the controllers parameters.


title: The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding

abstract: In this paper we present the first large-scale scene attribute database. First, we perform crowdsourced human studies to find a taxonomy of 102 discriminative attributes. We discover attributes related to materials, surface properties, lighting, affordances, and spatial layout. Next, we build the "SUN attribute database" on top of the diverse SUN categorical database. We use crowdsourcing to annotate attributes for 14,340 images from 707 scene categories. We perform numerous experiments to study the interplay between scene attributes and scene categories. We train and evaluate attribute classifiers and then study the feasibility of attributes as an intermediate scene representation for scene classification, zero shot learning, automatic image captioning, semantic image search, and parsing natural images. We show that when used as features for these tasks, low dimensional scene attributes can compete with or improve on the state of the art performance. The experiments suggest that scene attributes are an effective low-dimensional feature for capturing high-level context and semantics in scenes.


title: Understanding Low- and High-Level Contributions to Fixation Prediction

abstract: Understanding where people look in images is an important problem in computer vision. Despite significant research, it remains unclear to what extent human fixations can be predicted by low-level (contrast) compared to highlevel (presence of objects) image features. Here we address this problem by introducing two novel models that use different feature spaces but the same readout architecture. The first model predicts human fixations based on deep neural network features trained on object recognition. This model sets a new state-of-the art in fixation prediction by achieving top performance in area under the curve metrics on the MIT300 hold-out benchmark (AUC = 88%, sAUC = 77%, NSS = 2.34). The second model uses purely low-level (isotropic contrast) features. This model achieves better performance than all models not using features pretrained on object recognition, making it a strong baseline to assess the utility of high-level features. We then evaluate and visualize which fixations are better explained by lowlevel compared to high-level image features. Surprisingly we find that a substantial proportion of fixations are better explained by the simple low-level model than the stateof- the-art model. Comparing different features within the same powerful readout architecture allows us to better understand the relevance of low- versus high-level features in predicting fixation locations, while simultaneously achieving state-of-the-art saliency prediction.


Collaborative Service Blueprinting for Design Thinking: Evaluation of a Digital Prototype


title: Policy-based routing and load balancing for EVPN-based data center interconnections

abstract: The Ethernet VPN (EVPN) technology has emerged as a key solution for the interconnection of geo-distributed Data Centers (DCs) over provider-managed MPLS networks. Such interconnections need to satisfy service-level agreements, which can be achieved by enforcing Traffic Engineering (TE) policies. However, deploying an effective TE policy is challenging and complex. This stems from the fact that network administrators should have a detailed insight into the network status and protocol specifics. Software-Defined Networking (SDN) may facilitate both the policy definition and deployment based on its comprehensive network view and existing integration with DC management systems, such as OpenStack. This paper presents an SDN-based framework for policy-driven DC interconnections that are built around EVPN. The framework is designed to translate routing and other TE policies, which are defined for EVPN instances, into appropriate low-level network actions to meet the policy goals. A generic programming interface allows an SDN controller to load different TE strategies so as to implement the policy, without the need to hard-code it. Moreover, our evaluations illustrate how clients might benefit from specific TE strategies and what is their impact on network performance.


title: Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification

abstract: Recently, very high resolution (VHR) panchromatic and multispectral (MS) remote-sensing images can be acquired easily. However, it is still a challenging task to fuse and classify these VHR images. Generally, there are two ways for the fusion and classification of panchromatic and MS images. One way is to use a panchromatic image to sharpen an MS image, and then classify a pan-sharpened MS image. Another way is to extract features from panchromatic and MS images, respectively, and then combine these features for classification. In this paper, we propose a superpixel-based multiple local convolution neural network (SML-CNN) model for panchromatic and MS images classification. In order to reduce the amount of input data for the CNN, we extend simple linear iterative clustering algorithm for segmenting MS images and generating superpixels. Superpixels are taken as the basic analysis unit instead of pixels. To make full advantage of the spatial-spectral and environment information of superpixels, a superpixel-based multiple local regions joint representation method is proposed. Then, an SML-CNN model is established to extract an efficient joint feature representation. A softmax layer is used to classify these features learned by multiple local CNN into different categories. Finally, in order to eliminate the adverse effects on the classification results within and between superpixels, we propose a multi-information modification strategy that combines the detailed information and semantic information to improve the classification performance. Experiments on the classification of Vancouver and Xi’an panchromatic and MS image data sets have demonstrated the effectiveness of the proposed approach.


title: Criminal Liability: A Grammar

abstract: Include: The elements of criminal liability. The structure of criminal liability. The defences to a criminal charge. The nature of excuses and justifications. Derivative, inchoate and strict liability and causation.


title: MicroRNAs: A Puzzling Tool in Cancer Diagnostics and Therapy.

abstract: MicroRNAs (miRNAs) constitute a dominating class of small RNAs that regulate diverse cellular functions. Due the pivotal role of miRNAs in biological processes, a deregulated miRNA expression is likely involved in human cancers. MicroRNAs possess tumor suppressor capability, as well as display oncogenic characteristics. Interestingly, miRNAs exist in various biological fluids as circulating entities. Changes in the profile of circulating miRNAs are indicative of pathophysiological conditions in human cancer. This concept has led to consider circulating miRNAs valid biomarkers in cancer diagnostics. Furthermore, current research promotes the use of miRNAs as a target in cancer therapy. However, miRNAs are an evolving research field. Although miRNAs have been demonstrated to be potentially valuable tools both in cancer diagnosis and treatment, a greater effort should be made to improve our understanding of miRNAs biology. This review describes the biology of microRNAs, emphasizing on the use of miRNAs in cancer diagnostics and therapy.


title: Expect the unexpected: event-related brain response to morphosyntactic violations

abstract: Arguments about the existence of language-specific neural systems and particularly arguments about the independence of syntactic and semantic processing have recently focused on differences between the event-related potentials (ERPs) elicited by violations of syntactic structure (e.g. the P600) and those elicited by violations of semantic expectancy (e.g. the N400). However, the scalp distribution of the P600 component elicited by syntactic violations appears to resemble that elicited by rare categorical events (ldquo;odd-balls”) in non-linguistic contexts, frequently termed the P3b. The relationship between the P600 and the P3b was explored by manipulating the grammaticality of sentences read for comprehension, as well as two factors known to influence P3b amplitude: odd-ball probability and event saliency. Oddball probability was manipulated by varying the frequency of morphosyntactic violations within blocks of sentences, and event saliency was manipulated by using two types of morphosyntactic violatio...


title: Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction

abstract: Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.


title: Enhanced strain of InAs quantum dots by an InGaAs ternary layer in a GaAs matrix

abstract: The present work demonstrates via numerical analysis that the presence of a thin InGaAs ternary layer around InAs quantum dots (QDs) reinforces the in-plane (err) and vertical (ezz) strain components of InAs quantum dots as compared to the QDs embedded directly in GaAs matrix, contrary to the general belief of strain relief. It has been further shown that such reinforced err and ezz states yields a decreased band-gap energy, i.e., the experimentally observed redshift in the literature.


title: Know Rob 2.0 — A 2nd Generation Knowledge Processing Framework for Cognition-Enabled Robotic Agents

abstract: In this paper we present KnowRob2, a second generation knowledge representation and reasoning framework for robotic agents. KnowRob2 is an extension and partial redesign of KnowRob, currently one of the most advanced knowledge processing systems for robots that has enabled them to successfully perform complex manipulation tasks such as making pizza, conducting chemical experiments, and setting tables. The knowledge base appears to be a conventional first-order time interval logic knowledge base, but it exists to a large part only virtually: many logical expressions are constructed on demand from data structures of the control program, computed through robotics algorithms including ones for motion planning and solving inverse kinematics problems, and log data stored in noSQL databases. Novel features and extensions of KnowRob2 substantially increase the capabilities of robotic agents of acquiring open-ended manipulation skills and competence, reasoning about how to perform manipulation actions more realistically, and acquiring commonsense knowledge.


BlinkML : Approximate Machine Learning with Probabilistic Guarantees


title: Power factor improvement of a transverse flux machine with high torque density

abstract: Design of a transverse flux machine (TFM) with high power factor and high torque density is presented. Detailed analysis and verification of the benefit of TFMs over conventional radial flux machines, in terms of torque production capability, is described. A comprehensive investigation of the ideal torque production capability, power factor, and deviation from ideal cases is explained. Factors contributing to performance degradation are identified and a robust method is proposed to design and improve the machine performance. Simulation and experimental results are provided to validate the innovation and the robustness of the design process.


title: The Hard-CoRe Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution

abstract: We introduce a new benchmark task for coreference resolution, Hard-CoRe, that targets common-sense reasoning and world knowledge. Previous coreference resolution tasks have been overly vulnerable to systems that simply exploit the number and gender of the antecedents, or have been handcrafted and do not reflect the diversity of sentences in naturally occurring text. With these limitations in mind, we present a resolution task that is both challenging and realistic. We demonstrate that various coreference systems, whether rule-based, feature-rich, graphical, or neural-based, perform at random or slightly above-random on the task, whereas human performance is very strong with high inter-annotator agreement. To explain this performance gap, we show empirically that state-of-the art models often fail to capture context and rely only on the antecedents to make a decision.


title: Internet of Things (IoT) communication protocols: Review

abstract: Internet of Things (IoT) consists of smart devices that communicate with each other. It enables these devices to collect and exchange data. Besides, IoT has now a wide range of life applications such as industry, transportation, logistics, healthcare, smart environment, as well as personal, social gaming robot, and city information. Smart devices can have wired or wireless connection. As far as the wireless IoT is the main concern, many different wireless communication technologies and protocols can be used to connect the smart device such as Internet Protocol Version 6 (IPv6), over Low power Wireless Personal Area Networks (6LoWPAN), ZigBee, Bluetooth Low Energy (BLE), Z-Wave and Near Field Communication (NFC). They are short range standard network protocols, while SigFox and Cellular are Low Power Wide Area Network (LPWAN).standard protocols. This paper will be an attempt to review different communication protocols in IoT. In addition, it will compare between commonly IoT communication protocols, with an emphasis on the main features and behaviors of various metrics of power consumption security spreading data rate, and other features. This comparison aims at presenting guidelines for the researchers to be able to select the right protocol for different applications.


title: Grammatical Error Correction with Neural Reinforcement Learning

abstract: We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.


title: Towards autonomous reinforcement learning: Automatic setting of hyper-parameters using Bayesian optimization

abstract: With the increase of machine learning usage by industries and scientific communities in a variety of tasks such as text mining, image recognition and self-driving cars, automatic setting of hyper-parameter in learning algorithms is a key factor for achieving satisfactory performance regardless of user expertise in the inner workings of the techniques and methodologies. In particular, for a reinforcement learning algorithm, the efficiency of an agent learning a control policy in an uncertain environment is heavily dependent on the hyper-parameters used to balance exploration with exploitation. In this work, an autonomous learning framework that integrates Bayesian optimization with Gaussian process regression to optimize the hyper-parameters of a reinforcement learning algorithm, is proposed. Also, a bandits-based approach to achieve a balance between computational costs and decreasing uncertainty about the Q-values, is presented. A gridworld example is used to highlight how hyper-parameter configurations of a learning algorithm (SARSA) are iteratively improved based on two performance functions.


title: Supervised Sequential Classification Under Budget Constraints

abstract: In this paper we develop a framework for a sequential decision making under budget constraints for multi-class classification. In many classification systems, such as medical diagnosis and homeland security, sequential decisions are often warranted. For each instance, a sensor is first chosen for acquiring measurements and then based on the available information one decides (rejects) to seek more measurements from a new sensor/modality or to terminate by classifying the example based on the available information. Different sensors have varying costs for acquisition, and these costs account for delay, throughput or monetary value. Consequently, we seek methods for maximizing performance of the system subject to budget constraints. We formulate a multi-stage multi-class empirical risk objective and learn sequential decision functions from training data. We show that reject decision at each stage can be posed as supervised binary classification. We derive bounds for the VC dimension of the multi-stage system to quantify the generalization error. We compare our approach to alternative strategies on several multi-class real world datasets.


title: Safe Reinforcement Learning via Shielding

abstract: Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield is introduced in the traditional learning process in two alternative ways, depending on the location at which the shield is implemented. In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions. In the second way, the shield is introduced after the learning agent. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner. Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.


title: Codebook based Audio Feature Representation for Music Information Retrieval

abstract: Digital music has become prolific in the web in recent decades. Automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience. When manual annotations and user preference data is lacking (e.g. for new artists) these systems must rely on content based methods. Besides powerful machine learning tools for classification and retrieval, a key component for successful recommendation is the audio content representation. Good representations should capture informative musical patterns in the audio signal of songs. These representations should be concise, to enable efficient (low storage, easy indexing, fast search) management of huge music repositories, and should also be easy and fast to compute, to enable real-time interaction with a user supplying new songs to the system. Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations. We experiment with different encoding methods, namely the LASSO, vector quantization (VQ) and cosine similarity (CS). We evaluate the representations' quality in two music information retrieval applications: query-by-tag and query-by-example. Our results show that concise representations can be used for successful performance in both applications. We recommend using top-τ VQ encoding, which consistently performs well in both applications, and requires much less computation time than the LASSO.


title: Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment

abstract: Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends called r-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method for vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall, the obtained qualitative and quantitative results show effectiveness in both segmentation and subsequent OD parameterization for glaucoma assessment.


title: Where third wave HCI meets HRI: report from a workshop on user-centred design of robots

abstract: In this report we discuss some of the challenges when applying a user-centred design approach in the field of human-robot interaction (HRI). The discussion is based on a one-day workshop at the NordiCHI'08 conference, investigating how methods, techniques and perspectives from the field of Human Computer Interaction (HCI) could contribute to and learn from recent developments in the area of HRI. Emphasis was put on topics that are infrequent in mainstream HCI such as machine movement, autonomy, anthropomorphism, physical interaction, environmental issues and issues concerned more generally with cultural notions of robots.


title: Intrusion of Overerupted Molars using Miniscrews and TMA Spring: A Case Report

abstract: Praveen Prakash1, Kuttapa Nishanth2, Nikul Jasani1, Aneesh Katyal1, US Krishna Nayak3 1Post Graduate Student, Department of Orthodontics & Dentofacial Orthopaedics, A.B. Shetty Memorial Institute of Dental Sciences, Mangalore, Karnataka, India, 2Professor, Department of Orthodontics & Dentofacial Orthopaedics, A.B. Shetty Memorial Institute of Dental Sciences, Mangalore, Karnataka, India, 3Dean Academics, Head of Department, Department of Orthodontics & Dentofacial Orthopaedics, A.B. Shetty Memorial Institute of Dental Sciences, Mangalore, Karnataka, India


title: Interval Estimation for a Binomial Proportion

abstract: We revisit the problem of interval estimation of a binomial proportion. The erratic behavior of the coverage probability of the stan- d ardWaldconfid ence interval has previously been remarkedon in the literature (Blyth andStill, Agresti andCoull, Santner andothers). We begin by showing that the chaotic coverage properties of the Waldinter- val are far more persistent than is appreciated. Furthermore, common textbook prescriptions regarding its safety are misleading and defective in several respects andcannot be trusted . This leads us to consideration of alternative intervals. A number of natural alternatives are presented, each with its motivation and con- text. Each interval is examinedfor its coverage probability andits length. Basedon this analysis, we recommendthe Wilson interval or the equal- tailedJeffreys prior interval for small n andthe interval suggestedin Agresti andCoull for larger n. We also provide an additional frequentist justification for use of the Jeffreys interval.


title: Unsupervised Document Embedding With CNNs.

abstract: We propose a new model for unsupervised document embedding. Leading existing approaches either require complex inference or use recurrent neural networks (RNN) that are difficult to parallelize. We take a different route and develop a convolutional neural network (CNN) embedding model. Our CNN architecture is fully parallelizable resulting in over 10x speedup in inference time over RNN models. Parallelizable architecture enables to train deeper models where each successive layer has increasingly larger receptive field and models longer range semantic structure within the document. We additionally propose a fully unsupervised learning algorithm to train this model based on stochastic forward prediction. Empirical results on two public benchmarks show that our approach produces comparable to state-of-the-art accuracy at a fraction of computational cost.


title: Design of a Blockchain-Based Lottery System for Smart Cities Applications

abstract: Among the smart cities applications, optimizing lottery games is one of the urgent needs to ensure their fairness and transparency. The emerging blockchain technology shows a glimpse of solutions to fairness and transparency issues faced by lottery industries. This paper presents the design of a blockchain-based lottery system for smart cities applications. We adopt the smart contracts of blockchain technology and the cryptograph blockchain model, Hawk [8], to design the blockchain-based lottery system, FairLotto, for future smart cities applications. Fairness, transparency, and privacy of the proposed blockchain-based lottery system are discussed and ensured.


title: Big Data Pre-Processing: Closing the Data Quality Enforcement Loop

abstract: In the Big Data Era, data is the core for any governmental, institutional, and private organization. Efforts were geared towards extracting highly valuable insights that cannot happen if data is of poor quality. Therefore, data quality (DQ) is considered as a key element in Big data processing phase. In this stage, low quality data is not penetrated to the Big Data value chain. This paper, addresses the data quality rules discovery (DQR) after the evaluation of quality and prior to Big Data pre-processing. We propose a DQR discovery model to enhance and accurately target the pre-processing activities based on quality requirements. We defined, a set of pre-processing activities associated with data quality dimensions (DQD's) to automatize the DQR generation process. Rules optimization are applied on validated rules to avoid multi-passes pre-processing activities and eliminates duplicate rules. Conducted experiments showed an increased quality scores after applying the discovered and optimized DQR's on data.


title: A Portable FMCW Interferometry Radar With Programmable Low-IF Architecture for Localization, ISAR Imaging, and Vital Sign Tracking

abstract: This paper presents a portable radar system for short-range localization, inverse synthetic aperture radar imaging, and vital sign tracking. The proposed sensor incorporates frequency-modulated continuous-wave (FMCW) and interferometry (Doppler) modes, which enable this radar system to obtain both absolute range information and tiny vital signs (i.e., respiration and heartbeat) of human targets. These two different operation modes can be switched through an on-board microcontroller. To simplify the system, the proposed radar utilizes the audio card of a laptop to sample the baseband signal. The FMCW mode of the radar uses operational-amplifier-based circuits to generate an analog sawtooth signal and a reference pulse sequence (RPS). The RPS is locked to the sawtooth signal to obtain coherence for the radar system. For the interferometry mode, a low-intermediate-frequency modulation method is implemented to avoid the slow vital signs from being distorted by the high-pass filter of the audio card. Several experiments were carried out to reveal the capability and distinct operational features of the proposed portable hybrid radar. The experiments also showed that the system can easily detect glass, which is usually difficult to identify for optical-based sensors. In addition, 2-D scanning in a complex environment revealed that the proposed radar was able to differentiate human targets from other objects. Moreover, ISAR images were used to isolate moving human targets from surrounding clutter. Finally, the proposed radar also demonstrated its ability to accurately measure vital signs when a human subject sits still.


title: Effects Of OTA's Nonidealities On Bandpass Otabased Tow-thomas Biquad And Compensation Via Simulated Annealing Algorithm

abstract: In this work, the practical response of a very compact version of OTA-based TT biquad which is not suitable to compensate with regular methods, is compensated by the use of SA algorithm. For effective compensation, the OTA nonidealities are precisely defined based on the commercially available OTA, LM13700 from National Semiconductor. The initial solution is obtained via the ordinary method of an analogue filter design based on the utilization of ideal-OTA-based transfer function which provides the response that is much deviated from the desired bandpass response especially in the pole frequency closing to the OTA's finite bandwidth. The Matlab is used as the tool for implementing the compensation process, while the results which are the circuit parameters are being verified by the OrCAD PSPICE simulation. Using the SSE based cost function; the simulation displays the significant improvement of the compensated response compared to the initial solution but still not acceptable. To retrieve the remarkable response, the weighted SSE is utilized while maintaining others parameters


title: Syntactic Simplification and Text Cohesion

abstract: Syntactic simplification is the process of reducing the grammatical complexity of a text, while retaining its information content and meaning. The aim of syntactic simplification is to make text easier to comprehend for human readers, or process by programs. In this paper, we formalise the interactions that take place between syntax and discourse during the simplification process. This is important because the usefulness of syntactic simplification in making a text accessible to a wider audience can be undermined if the rewritten text lacks cohesion. We describe how various generation issues like sentence ordering, cue-word selection, referring-expression generation, determiner choice and pronominal use can be resolved so as to preserve conjunctive and anaphoric cohesive relations during syntactic simplification and present the results of an evaluation of our syntactic simplification system.


title: Hardware design for Hash functions

abstract: Due to its cryptographic and operational key features such as the one-way function property, high speed and a fixed output size independent of input size the hash algorithm is one of the most important cryptographic primitives. A critical drawback of most cryptographic algorithms is the large computational overhead. This is getting more critical since the data amount to process or communicate is increasing a lot. In many cases, a proper use of the hash algorithm reduces the computational overhead. Digital signature generation and the message authentication are the most common applications of the hash algorithms. The increasing data size also motivates hardware designers to have a throughput optimal architecture for a given hash algorithm. In this chapter, some popular hash algorithms and their cryptanalysis are briefly introduced, and a design methodology for throughput optimal architectures of MD4-based hash algorithms is described in detail.


title: Characterization of Neck Strength in Healthy Young Adults

abstract: BACKGROUND ::: The role of cervical muscle (neck) strength in traumatic brain and spine injury and chronic neck pain disorders is an area of active research. Characterization of the normal ranges of neck strength in healthy young adults is essential to designing future investigations of how strength may act as a modifier for risk and progression in head and neck disorders. ::: ::: ::: OBJECTIVE ::: To develop a normative reference database of neck strength in a healthy young adult population, and to evaluate the relationship of neck strength to anthropometric measurements. ::: ::: ::: DESIGN ::: Cross-sectional. ::: ::: ::: SETTING ::: An academic medical center research institution. ::: ::: ::: PARTICIPANTS ::: A total of 157 healthy young adults (18-35 years of age) had their neck strength measured with fixed frame dynamometry (FFD) during 1 visit to establish a normative neck strength database. ::: ::: ::: INTERVENTIONS ::: Not applicable. ::: ::: ::: MAIN OUTCOME MEASUREMENTS ::: Peak and average strength of the neck muscles were measured in extension, forward flexion, and right and left lateral flexion using FFD. The ranges of peak and average neck strength were characterized and correlated with anthropometric characteristics. ::: ::: ::: RESULTS ::: In all, 157 subjects (84 male, 73 female; average age 27 years) were included in the normative sample. Neck strength ranged from 38 to 383 Newtons in men and from 15 to 223 Newtons in women. Normative data are provided for each gender in all 4 directions. Weight, body mass index, neck circumference, and estimated neck muscle volume were modestly correlated with neck strength in multiple directions (correlation coefficients < .4). In a multivariate regression model, weight in women and neck volume in men were significant predictors of neck strength. ::: ::: ::: CONCLUSIONS ::: Neck strength in healthy young adults exhibits a broad range, is significantly different in men from that in women, and correlates only modestly with anthropometric characteristics. ::: ::: ::: LEVEL OF EVIDENCE ::: Not applicable.


Chess Q & A : Question Answering on Chess Games


title: Emotional Uses of Facebook and Twitter: Its Relation With Empathy, Narcissism, and Self-Esteem in Adolescence

abstract: Facebook and Twitter have change interpersonal relationships. Adolescents are the sector of the population who use most these networks. They use them in an emotional way, to express their emotions and to comment on those of others. Empathy, narcissism, and self-esteem may play an important role in the use of these networks. Using a sample of 503 Spanish adolescents (272 males, 231 females), this work studies the relationship between the Basic Empathy Scale, the Narcissistic Personality Inventory, the Rosenberg Self-Esteem Scale, and emotional and empathic use of Facebook and Twitter. The results showed that those who use Facebook and Twitter have higher scores in empathy. It has been observed that certain ways of using these two social networks are associated with narcissism. Greater use of Facebook and Twitter is associated with lower self-esteem.


title: Prediction of User Mobility Pattern on a Network Traffic Analysis Platform

abstract: The mobile Internet brings tremendous opportunities for researchers to analyze user mobility pattern, which is of great importance for Internet Service Providers (ISP) to provide better location-based services. This paper focuses on predicting user mobility patterns based on their different mobility characteristics. For that, we collect real-world data from Long Term Evolution (LTE) mobile network by a specially developed network traffic analysis platform followed by clustering the user into stationary one or mobile one with a location-entropy-based method for distinguishing groups with distinct mobility characteristics, and then we present the tailored Intelligent Time Division (ITD) method and Time-Based Markov (TBM) predictor for the location prediction of stationary and mobile users respectively. Extensive experiments demonstrate the effectiveness and better performance of our proposed methods compared with the baselines, as well as the adaptabilities of different predictors according to individual's mobility characteristics.


title: An Algorithmic Game Theory Primer ∗

abstract: We give a brief and biased survey of the past, present, and future of research on the interface of theoretical computer science and game theory. By the end of the 20th century, the widespread adoption of the Internet and the emergence of the Web had changed fundamentally society’s relationship with computers. The primary role of a computer evolved from a stand-alone, well-understood machine for executing software to a conduit for global communication, content-dissemination, and commerce. Two consequences of this phase transition were inevitable: theoretical computer science would respond by formulating novel problems, goals, and design and analysis techniques relevant for Internet applications; and game theory, with its deep and beautiful study of interaction between competing or cooperating individuals, would play a crucial role. Research on the interface of theoretical computer science and game theory, an area now known as algorithmic game theory (AGT), has exploded phenomenally


title: Multicasting over Emerging 5G Networks: Challenges and Perspectives

abstract: The number and variety of mobile multicast applications are growing at an unprecedented and unanticipated pace. Mobile network providers are in front of a dramatic increase in multicast traffic load, and this growth is forecasted to continue in fifth-generation (5G) networks. The major challenges come from the fact that multicast traffic not only targets groups of end-user devices; it also involves machine-type communications (MTC) for the Internet of Things (IoT). The increase in the MTC load, predicted for 5G, calls into question the effectiveness of the current multimedia broadcast multicast service (MBMS). The aim of this paper is to provide a survey of 5G challenges in the view of effective management of multicast applications, and to identify how to enhance the mobile network architecture to enable multicast applications in future 5G scenarios. By accounting for the presence of both human and machine-related traffic, strengths and weaknesses of the state-of-the-art achievements in multicasting are critically analyzed to provide guidelines for future research on 5G networks and more conscious design choices.


title: Geometry-oblivious FMM for compressing dense SPD matrices

abstract: We present GOFMM (geometry-oblivious FMM), a novel method that creates a hierarchical low-rank approximation, or "compression," of an arbitrary dense symmetric positive definite (SPD) matrix. For many applications, GOFMM enables an approximate matrix-vector multiplication in N log N or even N time, where N is the matrix size. Compression requires N log N storage and work. In general, our scheme belongs to the family of hierarchical matrix approximation methods. In particular, it generalizes the fast multipole method (FMM) to a purely algebraic setting by only requiring the ability to sample matrix entries. Neither geometric information (i.e., point coordinates) nor knowledge of how the matrix entries have been generated is required, thus the term "geometry-oblivious." Also, we introduce a shared-memory parallel scheme for hierarchical matrix computations that reduces synchronization barriers. We present results on the Intel Knights Landing and Haswell architectures, and on the NVIDIA Pascal architecture for a variety of matrices.


title: Gene selection from microarray data for cancer classification—a machine

abstract: A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, nave Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.


title: On the capacity of spatially correlated MIMO Rayleigh-fading channels

abstract: In this paper, we investigate the capacity distribution of spatially correlated, multiple-input-multiple-output (MIMO) channels. In particular, we derive a concise closed-form expression for the characteristic function (c.f.) of MIMO system capacity with arbitrary correlation among the transmitting antennas or among the receiving antennas in frequency-flat Rayleigh-fading environments. Using the exact expression of the c.f., the probability density function (pdf) and the cumulative distribution function (CDF) can be easily obtained, thus enabling the exact evaluation of the outage and mean capacity of spatially correlated MIMO channels. Our results are valid for scenarios with the number of transmitting antennas greater than or equal to that of receiving antennas with arbitrary correlation among them. Moreover, the results are valid for an arbitrary number of transmitting and receiving antennas in uncorrelated MIMO channels. It is shown that the capacity loss is negligible even with a correlation coefficient between two adjacent antennas as large as 0.5 for exponential correlation model. Finally, we derive an exact expression for the mean value of the capacity for arbitrary correlation matrices.


title: Parameter-Free Fast Pixelwise Non-Local Means Denoising

abstract: This article proposes a fast and open-source implementation of the well-known Non-Local Means (NLM) denoising algorithm, in its original pixelwise formulation. The fast implementation is based on the computation of patch distances using sums of lines that are invariant under a patch shift. The optimal parameters of NLM (in the average peak signal to noise ratio - PSNR - sense) are computed from an image database, thereby leading to a parameter-free NLM implementation. Comparison is performed with the parameter-free blockwise NLM implementation already proposed in IPOL journal by Buades, Coll and Morel. As expected the blockwise implementation oers better PSNR, at least when the noise standard deviation is large enough, but there is no signicant dierence in quality when performing visual inspection. The highlight is that the proposed parameter-free pixelwise NLM implementation is faster than the patchwise one by a factor of 6 to 49. Source Code The reviewed source code and documentation for the parameter-free fast pixelwise NLM algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.txt le of the archive.


Autovectorization in GCC


title: Secure and light IoT protocol (SLIP) for anti-hacking

abstract: In the elemental technologies, it is necessary to realize the Internet service of things (IoT), sensors and devices, network, platform (hardware platforms, open software platform, such as specific OS platforms). Web services, data analysis and prediction, big data processing, such as security and privacy protection technology, there are a variety of techniques. These elements technology provide a specific function. The element technology is integrated with each other. However, by several techniques are integrated, it can be problems with integration of security technologies that existed for each element technology. Even if individual technologies basic security features are constituting Internet Services (CIA: Confidentiality, integrity, authentication or authorization). It also offers security technology not connected to each other. Therefore, I will look at the security technology and proposes a lightweight routing protocol indispensable for realizing a secure Internet services things.


title: Smart farm monitoring using Raspberry Pi and Arduino

abstract: This study aimed to investigate an establishment using an Intelligent System which employed an Embedded System and Smart Phone for chicken farming management and problem solving using Raspberry Pi and Arduino Uno. An experiment and comparative analysis of the intelligent system was applied in a sample chicken farm in this study. The findings of this study found that the system could monitor surrounding weather conditions including humidity, temperature, climate quality, and also the filter fan switch control in the chicken farm. The system was found to be comfortable for farmers to use as they could effectively control the farm anywhere at anytime, resulting in cost reduction, asset saving, and productive management in chicken farming.


title: Turning CARTwheels: an alternating algorithm for mining redescriptions

abstract: We present an unusual algorithm involving classification trees---CARTwheels---where two trees are grown in opposite directions so that they are joined at their leaves. This approach finds application in a new data mining task we formulate, called redescription mining. A redescription is a shift-of-vocabulary, or a different way of communicating information about a given subset of data; the goal of redescription mining is to find subsets of data that afford multiple descriptions. We highlight the importance of this problem in domains such as bioinformatics, which exhibit an underlying richness and diversity of data descriptors (e.g., genes can be studied in a variety of ways). CARTwheels exploits the duality between class partitions and path partitions in an induced classification tree to model and mine redescriptions. It helps integrate multiple forms of characterizing datasets, situates the knowledge gained from one dataset in the context of others, and harnesses high-level abstractions for uncovering cryptic and subtle features of data. Algorithm design decisions, implementation details, and experimental results are presented.


title: Bayesian Sampling Using Stochastic Gradient Thermostats

abstract: Dynamics-based sampling methods, such as Hybrid Monte Carlo (HMC) and Langevin dynamics (LD), are commonly used to sample target distributions. Recently, such approaches have been combined with stochastic gradient techniques to increase sampling efficiency when dealing with large datasets. An outstanding problem with this approach is that the stochastic gradient introduces an unknown amount of noise which can prevent proper sampling after discretization. To remedy this problem, we show that one can leverage a small number of additional variables to stabilize momentum fluctuations induced by the unknown noise. Our method is inspired by the idea of a thermostat in statistical physics and is justified by a general theory.


title: Visualization of Diversity in Large Multivariate Data Sets

abstract: Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.


title: Design optimisation of a transverse flux, short stroke, linear generator

abstract: This study entails the design optimisation of a tubular, transverse flux, reciprocating linear electric generator for short stroke, high frequency applications — more specifically with resonant free-piston Stirling engines. Gradient-based iterative dimensional optimisation was performed using a static three-dimensional finite element analysis in order to determine the most suitable design. The electromagnetically active mass of the generator was minimised while maintaining certain power output and efficiency levels. Three consecutive optimisations were performed with increasing degrees of freedom to investigate the influence thereof on the mass of the generator. The final design is considered to be an improvement on previous efforts.


title: Handwritten Arabic text line segmentation using affinity propagation

abstract: In this paper, we present a novel graph-based method for extracting handwritten text lines in monochromatic Arabic document images. Our approach consists of two steps - Coarse text line estimation using primary components which define the line and assignment of diacritic components which are more difficult to associate with a given line. We first estimate local orientation at each primary component to build a sparse similarity graph. We then, use a shortest path algorithm to compute similarities between non-neighboring components. From this graph, we obtain coarse text lines using two estimates obtained from Affinity propagation and Breadth-first search. In the second step, we assign secondary components to each text line. The proposed method is very fast and robust to non-uniform skew and character size variations, normally present in handwritten text lines. We evaluate our method using a pixel-matching criteria, and report 96% accuracy on a dataset of 125 Arabic document images. We also present a proximity analysis on datasets generated by artificially decreasing the spacings between text lines to demonstrate the robustness of our approach.


title: ONTOLOGY ALIGNMENT USING MACHINE LEARNING TECHNIQUES

abstract: In the semantic web, ontology plays an important role to provide formal definitions of concepts and relationships. Therefore, communicating similar ontologies becomes essential to provide ontologies interpretability and extendibility. Thus, it is inevitable to have similar but not the same ontologies in a particular domain since there might be several definitions for a given concept. This paper presents a method to combine similarity measures of different categories without having ontology instances or any user feedback in regard with alignment of two given ontologies. To align different ontologies efficiently, K Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT) and AdaBoost classifiers are investigated. Each classifier is optimized based on the lower cost and better classification rate. Experimental results demonstrate that the F-measure criterion improves up to 99% using feature selection and combination of AdaBoost and DT classifiers, which is highly comparable, and outperforms the previous reported F-measures.


title: Smoothing depth maps for improved steroscopic image quality

abstract: A technique to improve the image quality of stereoscopic pictures generated from depth maps (depth image based rendering or DIBR) is examined. In general, there are two fundamental problems with DIBR: a depth map could contain artifacts (e.g., noise or "blockiness") and there is no explicit information on how to render newly exposed regions ("holes") in the rendered image as a result of new virtual camera positions. We hypothesized that smoothing depth maps before rendering will not only minimize the effects of noise and distortions in the depth maps but will also reduce areas of newly exposed regions where potential artifacts can arise. A formal subjective assessment of four stereoscopic sequences of natural scenes was conducted with 23 viewers. The stereoscopic sequences consisted of source images for the left-eye view and rendered images for the right-eye view. The depth maps were smoothed with a Gaussian blur filter at different levels of strength before depth image based rendering. Results indicated that ratings of perceived image quality improved with increasing levels of smoothing of the depth maps. Even though the depth maps were smoothed, a negative effect on ratings of overall perceived depth quality was not found.


title: Cancer Hallmark Text Classification Using Convolutional Neural Networks

abstract: The first author is funded by the Commonwealth Scholarship and the Cambridge Trust. This work is supported by Medical Research Council grant MR/M013049/1 and the Google Faculty Award.


title: Haze removal and fuzzy based enhancement of image

abstract: The quality of outdoor captured images by camera may be degraded due to the occurrence of haze in the atmosphere. The process of enhancing the image by removing the haze is call dehazing. In this paper, dark channel prior and fuzzy enhancement based method is applied to remove haze from a hazy image. The performance of the proposed method is evaluated by comparing average information content and natural image quality evaluator with two state-of-the-art algorithms. A comparative study and quantitative evaluation with existing algorithms demonstrate that similar even better quality results can be obtained by the proposed methods. The experimental result demonstrates that the proposed method removes the haze from a hazy image and also enhances it by maintaining the contrast.


Study and Analysis of Various Approaches for Malware Detection and Identification


title: A Sign-Component-Based Framework for Chinese Sign Language Recognition Using Accelerometer and sEMG Data

abstract: Identification of constituent components of each sign gesture can be beneficial to the improved performance of sign language recognition (SLR), especially for large-vocabulary SLR systems. Aiming at developing such a system using portable accelerometer (ACC) and surface electromyographic (sEMG) sensors, we propose a framework for automatic Chinese SLR at the component level. In the proposed framework, data segmentation, as an important preprocessing operation, is performed to divide a continuous sign language sentence into subword segments. Based on the features extracted from ACC and sEMG data, three basic components of sign subwords, namely the hand shape, orientation, and movement, are further modeled and the corresponding component classifiers are learned. At the decision level, a sequence of subwords can be recognized by fusing the likelihoods at the component level. The overall classification accuracy of 96.5% for a vocabulary of 120 signs and 86.7% for 200 sentences demonstrate the feasibility of interpreting sign components from ACC and sEMG data and clearly show the superior recognition performance of the proposed method when compared with the previous SLR method at the subword level. The proposed method seems promising for implementing large-vocabulary portable SLR systems.


title: Cell-level Production-Logistics Synchronization for multi-variety and small-batch Production: A step toward industry 4.0

abstract: Industry 4.0 is a strategy that has been proposed to satisfy the requirements of the individual customer, while the factory will become the Smart Factory. In Smart Factory, enterprises will use device-level flexible production system to deal with the enormous variety of customer orders. However, nowadays which called Industry 3.X stage in this paper, customer requirements are still in the order customization stage, on the other hand, enterprises have to deal with the diversified and small-batch orders with Cell-level static collaborative production systems, which is not enough. Under Industry 3.X stage, in this paper, a cell-level Production-Logistics Synchronized (PLS) management approach for multi-variety and small-batch Production putted forward to solve the problem that it is difficult to deal with the dynamic problem effectively with limited resource in the static cooperative process.


title: A multistage dataflow implementation of a Deep Convolutional Neural Network based on FPGA for high-speed object recognition

abstract: Deep Neural Networks (DNNs) have progressed significantly in recent years. Novel DNN methods allow tasks such as image and speech recognition to be conducted easily and efficiently, compared with previous methods that needed to search for valid feature values or algorithms. However, DNN computations typically consume a significant amount of time and high-performance computing resources. To facilitate high-speed object recognition, this article introduces a Deep Convolutional Neural Network (DCNN) accelerator based on a field-programmable gate array (FPGA). Our hardware takes full advantage of the characteristics of convolutional calculation; this allowed us to implement all DCNN layers, from image input to classification, in a single chip. In particular, the dateflow from input to classification is uninterrupted and paralleled. As a result, our implementation achieved a speed of 409.62 giga-operations per second (GOPS), which is approximately twice as fast as the latest reported result. Furthermore, we used the same architecture to implement a Recurrent Convolutional Neural Network (RCNN), which can, in theory, provide better recognition accuracy.


title: Design Patterns in FPS Levels

abstract: Level designers create gameplay through geometry, AI scripting, and item placement. There is little formal understanding of this process, but rather a large body of design lore and rules of thumb. As a result, there is no accepted common language for describing the building blocks of level design and the gameplay they create. This paper presents level design patterns for first-person shooter (FPS) games, providing cause-effect relationships between level design elements and gameplay. These patterns allow designers to create more interesting and varied levels.


title: Two replicable suppressor situations in personality research

abstract: Suppressor situations occur when the simultaneous inclusion of two predictors improves one or both validities. A common allegation is that suppressor effects rarely replicate and have little substantive import. We present substantive examples from two established research domains to counter this skepticism. In the first domain, we show how measures of guilt and shame act consistently as mutual suppressors: Adding shame into a regression equation increases the negative association between guilt and aggression, whereas adding guilt increases the positive association between shame and aggression. In the second domain, we show how the effects of self-esteem and narcissism operate consistently as mutual suppressors: That is, adding narcissism into a regression equation increases the negative association between self-esteem and antisocial behavior, whereas adding self-esteem increases the positive association between narcissism and antisocial behavior. Discussion addresses the different implications for suppres...


title: Modeling and circuit-based simulation of photovoltaic arrays

abstract: This paper presents an easy and accurate method of modeling photovoltaic arrays. The method is used to obtain the parameters of the array model using information from the datasheet. The photovoltaic array model can be simulated with any circuit simulator. The equations of the model are presented in details and the model is validated with experimental data. Finally, simulation examples are presented. This paper is useful for power electronics designers and researchers who need an effective and straightforward way to model and simulate photovoltaic arrays.


title: Optimized Enterprise Risk Management

abstract: As the result of the increasing costs of risk and compliance activities, enterprises are beginning to integrate compliance and risk management into a comprehensive enterprise risk management function and thus proactively address all sorts of risk, including operational risk and the risk of noncompliance. We present the IBM Research enterprise risk management framework, designed to address risk and compliance management in a strategic, integrated, and comprehensive manner. We demonstrate how enterprises evolve along an enterprise-risk-management maturity continuum from a state of mere penalty avoidance through a state of improvement until they finally reach a state of continuous, risk-based transformation. We then explain our high-level model of the enterprise and its environment and describe the central issues, systems, models, and technologies involved. We conclude by presenting the tactical steps necessary to successfully launch enterprise risk management in accordance with our framework.


title: A 32mW 1.25GS/s 6b 2b/step SAR ADC in 0.13μm CMOS

abstract: ADCs with 6b resolution and gigahertz sampling frequency are widely used in serial links, magnetic recording systems and UWB receivers. Flash ADCs have been dominantly used for these applications. This paper presents an ADC that takes advantage of the high-speed digital logic and highly matched small capacitors in deep-submicron digital CMOS processes to achieve similar performance, but with lower power consumption than flash ADCs. Unlike many previously published low-power high-speed ADCs based on time-interleaved SAR, this ADC has only 2 clock-cycle latency (1.6ns at 1.25GS/s) and achieves 6b performance without any digital post-processing or off-line calibration, making it a plug- in replacement for conventional flash ADCs in many applications.


title: Color Orchestra: Ordering Color Palettes for Interpolation and Prediction

abstract: Color theme or color palette can deeply influence the quality and the feeling of a photograph or a graphical design. Although color palettes may come from different sources such as online crowd-sourcing, photographs and graphical designs, in this paper, we consider color palettes extracted from fine art collections, which we believe to be an abundant source of stylistic and unique color themes. We aim to capture color styles embedded in these collections by means of statistical models and to build practical applications upon these models. As artists often use their personal color themes in their paintings, making these palettes appear frequently in the dataset, we employed density estimation to capture the characteristics of palette data. Via density estimation, we carried out various predictions and interpolations on palettes, which led to promising applications such as photo-style exploration, real-time color suggestion, and enriched photo recolorization. It was, however, challenging to apply density estimation to palette data as palettes often come as unordered sets of colors, which make it difficult to use conventional metrics on them. To this end, we developed a divide-and-conquer sorting algorithm to rearrange the colors in the palettes in a coherent order, which allows meaningful interpolation between color palettes. To confirm the performance of our model, we also conducted quantitative experiments on datasets of digitized paintings collected from the Internet and received favorable results.


Hepatoprotective Effect and Chemical Assessment of a Selected Egyptian Chickpea Cultivar


title: Security analysis and enhancements of 3GPP authentication and key agreement protocol

abstract: This paper analyzes the authentication and key agreement protocol adopted by Universal Mobile Telecommunication System (UMTS), an emerging standard for third-generation (3G) wireless communications. The protocol, known as 3GPP AKA, is based on the security framework in GSM and provides significant enhancement to address and correct real and perceived weaknesses in GSM and other wireless communication systems. In this paper, we first show that the 3GPP AKA protocol is vulnerable to a variant of the so-called false base station attack. The vulnerability allows an adversary to redirect user traffic from one network to another. It also allows an adversary to use authentication vectors corrupted from one network to impersonate all other networks. Moreover, we demonstrate that the use of synchronization between a mobile station and its home network incurs considerable difficulty for the normal operation of 3GPP AKA. To address such security problems in the current 3GPP AKA, we then present a new authentication and key agreement protocol which defeats redirection attack and drastically lowers the impact of network corruption. The protocol, called AP-AKA, also eliminates the need of synchronization between a mobile station and its home network. AP-AKA specifies a sequence of six flows. Dependent on the execution environment, entities in the protocol have the flexibility of adaptively selecting flows for execution, which helps to optimize the efficiency of AP-AKA both in the home network and in foreign networks.


title: Mutations in the PLEKHG5 gene is relevant with autosomal recessive intermediate Charcot-Marie-Tooth disease

abstract: BACKGROUND ::: Mutations in the Pleckstrin homology domain-containing, family G member 5 (PLEKHG5) gene has been reported in a family harboring an autosomal recessive lower motor neuron disease (LMND). However, the PLEKHG5 mutation has not been described to cause Charcot-Marie-Tooth disease (CMT). ::: ::: ::: METHODS ::: To identify the causative mutation in an autosomal recessive intermediate CMT (RI-CMT) family with childhood onset, whole exome sequencing (WES), histopathology, and lower leg MRIs were performed. Expression and activity of each mutant protein were analyzed. ::: ::: ::: RESULTS ::: We identified novel compound heterozygous (p.Thr663Met and p.Gly820Arg) mutations in the PLEKHG5 gene in the present family. The patient revealed clinical manifestations of sensory neuropathy. Fatty replacements in the distal lower leg muscles were more severe than in the thigh muscles. Although the symptoms and signs of this patient harboring slow nerve conduction velocities suggested the possibility of demyelinating neuropathy, a distal sural nerve biopsy was compatible with axonal neuropathy. Immunohistochemical analysis revealed that the patient has a low level of PLEKHG5 in the distal sural nerve and an in vitro assay suggested that the mutant proteins have a defect in activating the NF-κB signaling pathway. ::: ::: ::: CONCLUSIONS ::: This study identifies compound heterozygous PLEKHG5 mutations as the cause of RI-CMT. We suggest that PLEKHG5 might play a role in the peripheral motor and sensory nervous system. This study expands the phenotypic spectrum of PLEKHG5 mutations.


title: A new, evidence-based estimate of patient harms associated with hospital care.

abstract: OBJECTIVES ::: Based on 1984 data developed from reviews of medical records of patients treated in New York hospitals, the Institute of Medicine estimated that up to 98,000 Americans die each year from medical errors. The basis of this estimate is nearly 3 decades old; herein, an updated estimate is developed from modern studies published from 2008 to 2011. ::: ::: ::: METHODS ::: A literature review identified 4 limited studies that used primarily the Global Trigger Tool to flag specific evidence in medical records, such as medication stop orders or abnormal laboratory results, which point to an adverse event that may have harmed a patient. Ultimately, a physician must concur on the findings of an adverse event and then classify the severity of patient harm. ::: ::: ::: RESULTS ::: Using a weighted average of the 4 studies, a lower limit of 210,000 deaths per year was associated with preventable harm in hospitals. Given limitations in the search capability of the Global Trigger Tool and the incompleteness of medical records on which the Tool depends, the true number of premature deaths associated with preventable harm to patients was estimated at more than 400,000 per year. Serious harm seems to be 10- to 20-fold more common than lethal harm. ::: ::: ::: CONCLUSIONS ::: The epidemic of patient harm in hospitals must be taken more seriously if it is to be curtailed. Fully engaging patients and their advocates during hospital care, systematically seeking the patients' voice in identifying harms, transparent accountability for harm, and intentional correction of root causes of harm will be necessary to accomplish this goal.


title: Slotted substrate integrated waveguide phase shifter

abstract: The slotted substrate integrated waveguide (SSIW) phase shifter with periodic loading varactors is presented. With appropriate numbers of varactors placed on the SIW's surface, the phase of two-port device could be varied with the varactors' capacitance, which is inversely proportional to the bias voltage. To prove it, a phase shifter installed seventeen varactors has been designed. There are good agreements between simulated results and experimental ones. In the frequency bandwidth of 2.5 to 3.45 GHz (about 31.9% relative bandwidth), the return loss and inserted loss are mostly better than 10 dB and 5 dB, respectively. At the center frequency of 3GHz, the maximum phase range of 80° is obtained.


title: A system for algorithm animation

abstract: A software environment is described which provides facilities at a variety of levels for “animating” algorithms: exposing properties of programs by displaying multiple dynamic views of the program and associated data structures. The system is operational on a network of graphics-based, personal workstations and has been used successfully in several applications for teaching and research in computer science and mathematics. In this paper, we outline the conceptual framework that we have developed for animating algorithms, describe the system that we have implemented, and give several examples drawn from the host of algorithms that we have animated.


title: Portfolio selection using neural networks

abstract: In this paper we apply a heuristic method based on artificial neural networks (NN) in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz mean-variance model which includes cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We present some experimental results obtained with the NN heuristic and we compare them to those obtained with three previous heuristic methods. The portfolio selection problem is an instance from the family of quadratic programming problems when the standard Markowitz mean-variance model is considered. But if this model is generalized to include cardinality and bounding constraints, then the portfolio selection problem becomes a mixed quadratic and integer programming problem. When considering the latter model, there is not any exact algorithm able to solve the portfolio selection problem in an efficient way. The use of heuristic algorithms in this case is imperative. In the past some heuristic methods based mainly on evolutionary algorithms, tabu search and simulated annealing have been developed. The purpose of this paper is to consider a particular neural network (NN) model, the Hopfield network, which has been used to solve some other optimisation problems and apply it here to the portfolio selection problem, comparing the new results to those obtained with previous heuristic algorithms.


title: Algorithm for image reconstruction in multi-slice helical CT

abstract: Efforts are being made to develop a new type of CT system that can scan volumes over a large range within a short time with thin slice images. One of the most promising approaches is the combination of helical scanning with multi-slice CT, which involves several detector arrays stacked in the z direction. However, the algorithm for image reconstruction remains one of the biggest problems in multi-slice CT. Two helical interpolation methods for single-slice CT, 360LI and 180LI, were used as starting points and extended to multi-slice CT. The extended methods, however, had a serious image quality problem due to the following three reasons: (1) excessively close slice positions of the complementary and direct data, resulting in a larger sampling interval; (2) the existence of several discontinuous changeovers in pairs of data samples for interpolation; and (3) the existence of cone angles. Therefore we have proposed a new algorithm to overcome the problem. It consists of the following three parts: (1) optimized sampling scan; (2) filter interpolation; and (3) fan-beam reconstruction. Optimized sampling scan refers to a special type of multi-slice helical scan developed to shift the slice position of complementary data and to acquire data with a much smaller sampling interval in the z direction. Filter interpolation refers to a filtering process performed in the z direction using several data. The normal fan-beam reconstruction technique is used. The section sensitivity profile (SSP) and image quality for four-array multi-slice CT were investigated by computer simulations. Combinations of three types of optimized sampling scan and various filter widths were used. The algorithm enables us to achieve acceptable image quality and spatial resolution at a scanning speed that is about three times faster than that for single-slice CT. The noise characteristics show that the proposed algorithm efficiently utilizes the data collected with optimized sampling scan. The new algorithm allows suitable combinations of scan and filter parameters to be selected to meet the purpose of each examination.


Classical Test Theory ( CTT ) VS Item Response Theory ( IRT ) : AN EVALUATION OF THE COMPARABILITY OF ITEM ANALYSIS RESULTS BY Prof


title: An Introduction to the Five-Factor Model and Its Applications

abstract: ABSTRACT The five-factor model of personality is a hierarchical organization of personality traits in terms of five basic dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience. Research using both natural language adjectives and theoretically based personality questionnaires supports the comprehensiveness of the model and its applicability across observers and cultures. This article summarizes the history of the model and its supporting evidence; discusses conceptions of the nature of the factors; and outlines an agenda for theorizing about the origins and operation of the factors. We argue that the model should prove useful both for individual assessment and for the elucidation of a number of topics of interest to personality psychologists.


title: The Shazam music recognition service

abstract: Guided by a user's query-by-example music sample, it delivers the matching song, as well as related music information, of immediate interest to the user.


title: Flextream: Adaptive Compilation of Streaming Applications for Heterogeneous Architectures

abstract: Increasing demand for performance and efficiency has driven the computer industry toward multicore systems. These systems have become the industry standard in almost all segments of the computer market from high-end servers to handheld devices. In order to efficiently use these systems, an extensive amount of research and industry support has been devoted to developing explicitly parallel programming paradigms, such as streaming models, and new compiler techniques. One important challenge that arises in multicore systems is the ability to dynamically adapt a running application to a target architecture in the face of changes in resource availability (e.g., number of cores, available memory or bandwidth). In this paper, we focus on the increasingly important area of streaming computing and introduce Flextream as a flexible compilation framework that can dynamically adapt applications to the changing characteristics of the underlying architecture. We believe this is an important contribution as software developers grapple with the details of parallelism in a rapidly changing architecture landscape. Flextream achieves its goals through a combination of static compilation and dynamic adaptation techniques. Our results indicate that Flextream’s approach can achieve high-performance resource allocations that are within an average of 9% of the optimal solution with low overhead for a wide range of streaming applications


title: An Open, Secure and Flexible Platform Based on Internet of Things and Cloud Computing for Ambient Aiding Living and Telemedicine

abstract: Currently Internet of Things (IoT) and multimedia technologies have entered the healthcare field through ambient aiding living and telemedicine. However there are still several obstacles blocking in the way, the toughest ones among which are IoT interoperability, system security, streaming Quality of Service (QoS) and dynamic increasing storage. The major contribution of this paper is proposing an open, secure and flexible platform based on IoT and Cloud computing, on which several mainstream short distant ambient communication protocols for medical purpose are discussed to address interoperability; Secure Sockets Layer (SSL), authentication and auditing are taken into consideration to solve the security issue; an adaptive streaming QoS model is utilized to improve streaming quality in dynamic environment; and an open Cloud computing infrastructure is adopted to support elastic Electronic Health Record (EHR) archiving in the backend. Finally an integrated reference implementation is introduced to demonstrate feasibility.


title: Analyzing Twitter sentiments through big data

abstract: The rise of social media has generated tremendous interest among Internet users today. Data from these social networking sites can be used for a number of purposes, like prediction, marketing or sentiment analysis. Twitter is a widely used social media site for posting comments through short statuses. The millions of tweets received every year could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a tedious task to take up. The current Analytics tools and models used that are available in the market are not sufficient to manage big data. Therefore, there is a need to use a Cloud storage for such type of applications. So we have utilized Hadoop for intelligent analysis and storage of big data. In this paper, we propose a method that does sentiment analysis on tweets in Cloud environment.


title: Recommending new movies: even a few ratings are more valuable than metadata

abstract: The Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by finding a linear transformation that transforms user or item descriptions so that they are as close as possible to the feature vectors generated by MF for CF. We propose methods for explicit feedback that are able to handle 140,000 features when feature vectors are very sparse. With movie metadata collected for the NP movies we show that the prediction performance of the methods is comparable to that of CF, and can be used to predict user preferences on new movies. We also investigate the value of movie metadata compared to movie ratings in regards of predictive power. We compare our solely CBF approach with a simple baseline rating-based predictor. We show that even 10 ratings of a new movie are more valuable than its metadata for predicting user ratings.


title: A bandgap voltage reference design for high power supply

abstract: Based on the CSMC 0.6um 40V BCD process and the bandgap principle a reference circuit used in high voltage chip is designed. The simulation results show that a temperature coefficient of 26.5ppm/°C in the range of 3.5∼40V supply, the output voltage is insensitive to the power supply, when the supply voltage rages from 3.5∼40V, the output voltage is equal to 1.2558V to 1.2573V at room temperature. The circuit we designed has high precision and stability, thus it can be used as stability reference voltage in power management IC.


title: Eigenfaces vs. fisherfaces vs. ICA for face recognition; a comparative study

abstract: Face recognition issue gained more interest recently due to its various applications and the demand of high security. Some researches with contradicting results were published concerning this issue. This paper compared three popular face recognition projection methods: (eigenfaces), (fisherfaces), and ICA. We also applied some data transformations: (discrete wavelet and cosine transforms) preceding methods to see their effect. Most researches based their results on the FERET database. AR and AT & T databases were used here to see if the same results apply. We also compared the results of two sets of experiments with the second set using half the training images used in the first to observe if the results may change. Overall conclusion is it canpsilat be stated that specific algorithm outperforms others, though ICA and eigenfaces respectively showed better results than fisherfaces for both experiments sets and both databases. Preceding algorithms with transformations yield better results for some algorithms.


title: Dengue Fever in Perspective of Clustering Algorithms

abstract: Dengue fever is a disease which is transmitted and caused by Aedes Aegypti mosquitos. Dengue has become a serious health issue in all over the world especially in those countries who are situated in tropical or subtropical regions because rain is an important factor for growth and increase in the population of dengue transmitting mosquitos. For a long time, data mining algorithms have been used by the scientists for the diagnosis and prognosis of different diseases which includes dengue as well. This was a study to analyses the attack of dengue fever in different areas of district Jhelum, Pakistan in 2011. As per our knowledge, we are unaware of any kind of research study in the area of district Jhelum for diagnosis or analysis of dengue fever. According to our information, we are the first one researching and analyzing dengue fever in this specific area. Dataset was obtained from the office of Executive District Officer EDO (health) District Jhelum. We applied DBSCAN algorithm for the clustering of dengue fever. First we showed overall behavior of dengue in the district Jhelum. Then we explained dengue fever at tehsil level with the help of geographical pictures. After that we have elaborated comparison of different clustering algorithms with the help of graphs based on our dataset. Those algorithms include k-means, K-mediods, DBSCAN and OPTICS.


title: Simulink model for Zigbee transceiver using OQPSK modulation under fading channels

abstract: Wireless sensor networks are meant for short range communications. To make the transceiver performance more efficient we need to follow IEEE 802.15.4 standard to address the needs of low data rate, low power consumption and low cost sensor networks with improved performance. In this paper PHY layer of Zigbee transceiver using orthogonal quadrature phase shift keying (OQPSK) modulation scheme with half sine pulse shaping and direct sequence spread spectrum (DSSS) technique is designed, and its performance under rayleigh fading channel is analyzed. Matlab Simulink is used to develop the system level design and its performance is analysed with bit error rate (BER) of the transceiver by increasing signal to noise ratio (SNR).


Multimodal interfaces for dynamic interactive maps


title: Performance evaluation of object serialization libraries in XML, JSON and binary formats

abstract: This paper compares twelve libraries of object serialization from qualitative and quantitative aspects. Those are object serialization in XML, JSON and binary formats. Using each library, a common example is serialized to a file. The size of the serialized file and the processing time are measured during the execution to compare all object serialization libraries. Some libraries show the performance penalty. But it is clear that there is no best solution. Each library makes good in the context it was developed.


A Taxonomy for Denial-of-Service Attacks in Wireless Sensor Networks


title: Dummynet: a simple approach to the evaluation of network protocols

abstract: Network protocols are usually tested in operational networks or in simulated environments. With the former approach it is not easy to set and control the various operational parameters such as bandwidth, delays, queue sizes. Simulators are easier to control, but they are often only an approximate model of the desired setting, especially for what regards the various traffic generators (both producers and consumers) and their interaction with the protocol itself.In this paper we show how a simple, yet flexible and accurate network simulator - dummynet - can be built with minimal modifications to an existing protocol stack, allowing experiments to be run on a standalone system. dummynet works by intercepting communications of the protocol layer under test and simulating the effects of finite queues, bandwidth limitations and communication delays. It runs in a fully operational system, hence allowing the use of real traffic generators and protocol implementations, while solving the problem of simulating unusual environments. With our tool, doing experiments with network protocols is as simple as running the desired set of applications on a workstation.A FreeBSD implementation of dummynet, targeted to TCP, is available from the author. This implementation is highly portable and compatible with other BSD-derived systems, and takes less than 300 lines of kernel code.


title: Incremental Author Name Disambiguation for Scientific Citation Data

abstract: Name disambiguation is a perennial challenge for any large and growing dataset but is particularly significant for scientific publication data where documents and ideas are linked through citations and depend on highly accurate authorship. Differentiating personal names in scientific publications is a substantial problem as many names are not sufficiently distinct due to the large number of researchers active in most academic disciplines today. As more and more documents and citations are published every year, any system built on this data must be continually retrained and reclassified to remain relevant and helpful. Recently, some incremental learning solutions have been proposed, but most of these have been limited to small-scale simulations and do not exhibit the full heterogeneity of the millions of authors and papers in real world data. In our work, we propose a probabilistic model that simultaneously uses a rich set of metadata and reduces the amount of pairwise comparisons needed for new articles. We suggest an approach to disambiguation that classifies in an incremental fashion to alleviate the need for retraining the model and re-clustering all papers and uses fewer parameters than other algorithms. Using a published dataset, we obtained the highest K-measure which is a geometric mean of cluster and author-class purity. Moreover, on a difficult author block from the Clarivate Analytics Web of Science, we obtain higher precision than other algorithms.