title: Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark

abstract: Recent deep learning based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition (HCCR) by learning discriminative representations directly from raw data. Nevertheless, we believe that the long-and-well investigated domain-specific knowledge should still help to boost the performance of HCCR. By integrating the traditional normalization-cooperated direction-decomposed feature map (directMap) with the deep convolutional neural network (convNet), we are able to obtain new highest accuracies for both online and offline HCCR on the ICDAR-2013 competition database. With this new framework, we can eliminate the needs for data augmentation and model ensemble, which are widely used in other systems to achieve their best results. This makes our framework to be efficient and effective for both training and testing. Furthermore, although directMap+convNet can achieve the best results and surpass human-level performance, we show that writer adaptation in this case is still effective. A new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer. The adaptation process can be efficiently and effectively implemented in an unsupervised manner. By adding the adaptation layer into the pre-trained convNet, it can adapt to the new handwriting styles of particular writers, and the recognition accuracy can be further improved consistently and significantly. This paper gives an overview and comparison of recent deep learning based approaches for HCCR, and also sets new benchmarks for both online and offline HCCR.


title: Cell-aware Stacked LSTMs for Modeling Sentences

abstract: We propose a method of stacking multiple long short-term memory (LSTM) layers for modeling sentences. In contrast to the conventional stacked LSTMs where only hidden states are fed as input to the next layer, the suggested architecture accepts both hidden and memory cell states of the preceding layer and fuses information from the left and the lower context using the soft gating mechanism of LSTMs. Thus the architecture modulates the amount of information to be delivered not only in horizontal recurrence but also in vertical connections, from which useful features extracted from lower layers are effectively conveyed to upper layers. We dub this architecture Cell-aware Stacked LSTM (CAS-LSTM) and show from experiments that our models bring significant performance gain over the standard LSTMs on benchmark datasets for natural language inference, paraphrase detection, sentiment classification, and machine translation. We also conduct extensive qualitative analysis to understand the internal behavior of the suggested approach.


title: Power analysis attack based on the MLP in DPA Contest v4

abstract: Power analysis represents extremely effective and successful way of side-channels attacks on so far confidential cryptographic algorithms and cryptographic devices. One of the widespread countermeasures against these power analysis attacks is the masking approach. In 2012, Nassar et al. presented a new lightweight masking countermeasure to protect the AES (Advanced Encryption Standard) implementation. This masking scheme represents the target algorithm of the DPA Contest v4. In this article, we present successful attack based on MLP (Multilayer Perceptron) aimed on this masking countermeasure. For the first time, the MLP attack is used to reveal secret key from masked implementation of cryptographic algorithm. The article describes in detail every step of the attack implemented including the results achieved. Our attack reveal each byte of the secret key of the masked AES only with 23 power traces.


title: An energy-efficient cell planning strategy for heterogeneous network based on realistic traffic data

abstract: With the exponential growth of wireless network data traffic and mobile users, traditional single layer homogeneous network cannot meet the increasing high-speed traffic demand. As a solution, heterogeneous networks (HetNet) are considered as a promising architecture to fulfill the challenge. At the same time, energy consumption problem has become one of the most challenging problems due to the fast expansion of wireless network scale. Traditionally, network operators are more concerned about the coverage and capacity problem when planning cellular networks, rather than the energy efficiency of the network. In this paper, we propose an energy-efficient micro base station (BS) planning strategy based on realistic traffic data in HetNet architecture. A genetic algorithm (GA) is introduced to solve the optimization problem. By carefully selecting algorithm parameters, the algorithm can produce a set of near optimum solutions. The simulation results show that the energy efficiency and traffic coverage ratio are both improved by the deployment of micro BSs.


title: The influence of national culture on dissolution communication strategies in Western versus Asian business relationships: a theoretical model

abstract: Identifies the communication strategies available to companies when dissolving cross‐cultural inter‐organisational relationships to achieve effective (cooperative) outcomes. First, addresses the importance of communication dissolution, and proposes a typology of available communication strategies. Second, emphasises the importance of understanding cultural diversity in business relationships in general and dissolution in particular. Third, proposes two related theoretical frameworks. The first addresses different conflict management styles that bridge the gap between dissolution communication strategies and the cultural context in which the actors are embedded. The second is a theoretical model for analysing dissolution process in a cross‐cultural business relationship context. Proposes the independent variable, culture, as providing a frame of reference by which meaning and intent are assigned by the foreign company to the communications of the terminating company (disengager), thus affecting the choice of dissolution strategy.


Modeling of the physicochemical properties of aliphatic alcohols using topological indices and quantitative structure-property relationship


title: Recursive Feature Generation for Knowledge-based Learning

abstract: When humans perform inductive learning, they often enhance the process with background knowledge. With the increasing availability of well-formed collaborative knowledge bases, the performance of learning algorithms could be significantly enhanced if a way were found to exploit these knowledge bases. In this work, we present a novel algorithm for injecting external knowledge into induction algorithms using feature generation. Given a feature, the algorithm defines a new learning task over its set of values, and uses the knowledge base to solve the constructed learning task. The resulting classifier is then used as a new feature for the original problem. We have applied our algorithm to the domain of text classification using large semantic knowledge bases. We have shown that the generated features significantly improve the performance of existing learning algorithms.


title: Multi-Class Ground Truth Inference in Crowdsourcing with Clustering

abstract: Due to low quality of crowdsourced labelers, the integrated label of each example is usually inferred from its multiple noisy labels provided by different labelers. This paper proposes a novel algorithm, Ground Truth Inference using Clustering (GTIC), to improve the quality of integrated labels for multi-class labeling. For a K labeling case, GTIC utilizes the multiple noisy label sets of examples to generate features. Then, it uses a K-Means algorithm to cluster all examples into K different groups, each of which is mapped to a specific class. Examples in the same cluster are assigned a corresponding class label. We compare GTIC with four existing multi-class ground truth inference algorithms, majority voting (MV), Dawid & Skene's (DS), ZenCrowd (ZC) and Spectral DS (SDS), on one synthetic and eight real-world datasets. Experimental results show that the performance of GTIC is significantly superior to the others in terms of both accuracy and M-AUC. Besides, the running time of GTIC is about twenty times faster than EM-based complicated inference algorithms.


title: A Dynamic Slack Management Technique for Real-Time Distributed Embedded Systems

abstract: This work presents a novel slack management technique, the service-rate-proportionate (SRP) slack distribution, for real-time distributed embedded systems to reduce energy consumption. The proposed SRP-based slack distribution technique has been considered with EDF and rate-based scheduling schemes that are most commonly used with embedded systems. A fault-tolerant mechanism has also been incorporated into the proposed technique in order to utilize the available dynamic slack to maintain checkpoints and provide for rollbacks on faults. Results show that, in comparison to contemporary techniques, the proposed SRP slack distribution technique achieves about 29 percent more performance/overhead improvement benefits when validated with random and real-world benchmarks.


title: Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly

abstract: A new method of physical activity monitoring is presented, which is able to detect body postures (sifting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking." 8.2% for sifting, and 1.3% for lying.


title: TFET-based cellular neural network architectures

abstract: It is well known that CMOS scaling trends are now accompanied by less desirable byproducts such as increased energy dissipation. To combat the aforementioned challenges, solutions are sought at both the device and architectural levels. With this context, this work focuses on embedding a low voltage device, a Tunneling Field Effect Transistor (TFET) within a Cellular Neural Network (CNN) -- a low power analog computing architecture. Our study shows that TFET-based CNN systems, aside from being fully functional, also provide significant power savings when compared to the conventional resistor-based CNN. Our initial studies suggest that power savings are possible by carefully engineering lower voltage, lower current TFET devices without sacrificing performance. Moreover, TFET-based CNN reduces implementation footprints by eliminating the hardware required to realize output transfer functions. Application dynamics are verified through simulations. We conclude the paper with a discussion of desired device characteristics for CNN architectures with enhanced functionality.


title: Review of road traffic control strategies

abstract: Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. After illustrating the main reasons for infrastructure deterioration due to traffic congestion, a comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway networks, and route guidance. Selected application results, obtained from either simulation studies or field implementations, are briefly outlined to illustrate the impact of various control actions and strategies. The paper concludes with a brief discussion of future needs in this important technical area.


title: Gastrointestinal disorders in pregnancy.

abstract: Gastrointestinal disorders constitute one of the most frequent complaints of pregnancy. An understanding of the mode of presentation and the incidence of the various gastrointestinal disorders will optimize care in obstetric patients. Disorders of the esophagus, stomach, duodenum, ilium, jejunum, colon, rectum, and appendix are individually discussed with reference to physiologic changes in pregnancy, infectious diseases, autoimmune disease, and ulcer formation.


title: Web-based monitoring and control system for aeroponics growing chamber

abstract: This paper presents a design and implementation of a system prototype for plant water and nutrients distribution. Furthermore, it has been implemented to support the optimal application of aeroponics system. It is based on a monitoring system which was used to observe the aeroponics growing chamber's parameters such as temperature, light, and pH. Meanwhile, the control system was used to manage actuators i.e. mist maker and fan for delivering water moisture. Sensor's data are transmitted via internet into server in order to facilitate easier monitoring for users. The prototype of the system is successfully implemented and provide a series of sensor's data.


title: Success Measurement of Enterprise Social Networks

abstract: Especially in the knowledge intensive service industry and times of virtual teams, there is a high need to support collaboration and information ex- change between employees with IT. Whereas many organizations have recog- nized the great potential of Enterprise Social Networks (ESN) in this context, there is still a lack of well-founded and applicable approaches to make this po- tential visible i.e. to measure the success of ESN. To alleviate this drawback we propose a novel approach to measure ESN success covering the dimensions us- age and business value. To illustrate the practical benefit and applicability of the novel approach, we provide an extensive real-world example from the ser- vice industry. In cooperation with a large financial services provider, the ap- proach was successfully applied and led to an improved decision support for different stakeholder groups including system administrators and executive management.


title: FaceTube: predicting personality from facial expressions of emotion in online conversational video

abstract: The advances in automatic facial expression recognition make possible to mine and characterize large amounts of data, opening a wide research domain on behavioral understanding. In this paper, we leverage the use of a state-of-the-art facial expression recognition technology to characterize users of a popular type of online social video, conversational vlogs. First, we propose the use of several activity cues to characterize vloggers based on frame-by-frame estimates of facial expressions of emotion. Then, we present results for the task of automatically predicting vloggers' personality impressions using facial expressions and the Big-Five traits. Our results are promising, specially for the case of the Extraversion impression, and in addition our work poses interesting questions regarding the representation of multiple natural facial expressions occurring in conversational video.


title: The Asymmetric Spatial Effects for Eastern and Western Regions of Russia

abstract: The purpose of this study is to identify the spatial effects of the main macroeconomic indicators of the eastern and western regions of Russia. These regions differ significantly in population density and the distances between cities. The main research question we are interested in is the following: how are events occurring in one of the western regions, such as economic growth or a decrease in the unemployment rate, effecting similar indicators in other western and eastern regions. The spatial effects of the western and eastern regions, when considered separately, may differ both qualitatively and with of the ‘flow on effect’. The determinants of the same macro-economic indicators in the eastern and western regions may also differ. In order to test the hypothesis of a possible difference in the spatial effects and determinants for these regions, we have developed a special class of model with four spatial matrices (west-west, east-east, west-east, and east-west) and a double set of control variables (one for each type of region). As the macroeconomic indicators monitor the rate of unemployment in the region, the real regional wage and GRP growth for the year were chosen for our models. We controlled the variables describing the socio-demographic situation in the region, migration processes, economic development, and export-import activity in the region. The models were estimated by the Arellano-Bond method on panel data for Russian regions over 2000-2010. Our analysis revealed, 1) a positive spatial correlation of the main macroeconomic indicators for the western regions, 2) both positive and negative externalities for the eastern regions and 3) the asymmetric influence of eastern and western regions on each other. Usually “impulses” from the western regions have a positive effect on the eastern regions, but the “impulses” from the eastern regions usually do not affect the western regions.


title: Measuring motor activity in major depression: The association between the Hamilton Depression Rating Scale and actigraphy

abstract: Abstract Despite the use of actigraphy in depression research, the association of depression ratings and quantitative motor activity remains controversial. In addition, the impact of recurring episodes on motor activity is uncertain. In 76 medicated inpatients with major depression (27 with a first episode, 49 with recurrent episodes), continuous wrist actigraphy for 24h and scores on the Hamilton Depression Rating Scale (HAMD) were obtained. In addition, 10 subjects of the sample wore the actigraph over a period of 5 days, in order to assess the reliability of a 1-day measurement. Activity levels were stable over 5 consecutive days. Actigraphic parameters did not differ between patients with a first or a recurrent episode, and quantitative motor activity failed to correlate with the HAMD total score. However, of the motor-related single items of the HAMD, the item activities was associated with motor activity parameters, while the items agitation and retardation were not. Actigraphy is consistent with clinical observation for the item activities . Expert raters may not correctly rate the motor aspects of retardation and agitation in major depression.


title: Grounding Strategic Conversation: Using Negotiation Dialogues to Predict Trades in a Win-Lose Game

abstract: This paper describes a method that predicts which trades players execute during a winlose game. Our method uses data collected from chat negotiations of the game The Settlers of Catan and exploits the conversation to construct dynamically a partial model of each player’s preferences. This in turn yields equilibrium trading moves via principles from game theory. We compare our method against four baselines and show that tracking how preferences evolve through the dialogue and reasoning about equilibrium moves are both crucial to success.


title: A wideband circularly polarized antenna for low mutual coupling Ka-band phased arrays

abstract: in this paper, we propose a wideband circularly polarized antenna with low mutual coupling for Ka-band phased array. The antenna element is composed of a square-ring integrated square patch radiator and a parasitic square patch with a coaxial feeding probe. The antenna element is with size of 7.0mm × 7.0mm × 1.0mm and achieves a impedance bandwidth (10-dB return-loss) of 6.85GHz (27.0GHz–33.9GHz) and circular polarization bandwidth (3-dB axial ratio) of 3.2GHz (27.7GHz– 30.9GHz), and boresight gain of greater than 7.2dBic. By partially removing the substrate of a stacked antenna element, the surface waves are able to be suppressed so that the interelement coupling in an antenna array can be reduced. A 2×2 array antenna exhibits a mutual coupling of less than −20 dB with element spacing (distance between the centers of the elements) of 0.48λ 0 at 29GHz.


title: Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

abstract: While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.


title: Self-Biased 215MHz Magnetoelectric NEMS Resonator for Ultra-Sensitive DC Magnetic Field Detection

abstract: High sensitivity magnetoelectric sensors with their electromechanical resonance frequencies < 200 kHz have been recently demonstrated using magnetostrictive/piezoelectric magnetoelectric heterostructures. In this work, we demonstrate a novel magnetoelectric nano-electromechanical systems (NEMS) resonator with an electromechanical resonance frequency of 215 MHz based on an AlN/(FeGaB/Al2O3) × 10 magnetoelectric heterostructure for detecting DC magnetic fields. This magnetoelectric NEMS resonator showed a high quality factor of 735, and strong magnetoelectric coupling with a large voltage tunable sensitivity. The admittance of the magnetoelectric NEMS resonator was very sensitive to DC magnetic fields at its electromechanical resonance, which led to a new detection mechanism for ultra-sensitive self-biased RF NEMS magnetoelectric sensor with a low limit of detection of DC magnetic fields of ~300 picoTelsa. The magnetic/piezoelectric heterostructure based RF NEMS magnetoelectric sensor is compact, power efficient and readily integrated with CMOS technology, which represents a new class of ultra-sensitive magnetometers for DC and low frequency AC magnetic fields.


title: The Deep-Space Network Telecommunication CubeSat Antenna: Using the deployable Ka-band mesh reflector antenna.

abstract: In the near future, CubeSats will be deployed beyond low-Earth orbit (LEO) to perform scientific tasks in deep space. To do so, these CubeSats will need high-gain antennas (HGAs) that fit in a highly confined volume. In this article, we present an innovative, deployable Ka-band antenna that folds in a 1.5-U (10 ? 10 ? 15 cm3) stowage volume suitable for 6U (10 ? 20 ? 30 cm3)-class CubeSats. This antenna is designed for telecommunication and is compatible with NASA's deep-space network (DSN) at Ka-band frequencies (i.e., uplink: 34.2-34.7 GHz; downlink: 31.8-32.3 GHz). Calculations and measurements show that 42.0-dBi gain and 57% aperture efficiency are obtained at 32 GHz. We thoroughly describe the mechanical deployment mechanism because it is a critical component of the deployable Cube-Sat antenna. This challenging new design evolved from our previous design that only provided linear polarization and a single-frequency band.


title: Review of the evidence base for treatment of childhood psychopathology: Externalizing disorders.

abstract: This article reviews the empirical literature on psychosocial, psychopharmacological, and adjunctive treatments for children between the ages of 6 and 12 with internalizing disorders. The aim of this review was to identify interventions that have potential to prevent substance use disorders in adolescence by treating internalizing disorders in childhood. Results suggest that a variety of behavioral, cognitive-behavioral, and pharmacological interventions are effective in reducing symptoms of childhood depression, phobias, and anxiety disorders. None of the studies reviewed included substance abuse outcomes. Thus, little can be said about the relationship between early treatment and the prevention of later substance use. The importance of evaluating the generalizability of research-supported interventions to community settings is highlighted and recommendations for future research are offered.


title: Activity and Fitness in Spinal Cord Injury: Review and Update

abstract: The primary aims of this report are to provide a review of current literature pertaining to fitness and health-related outcomes following spinal cord injury (SCI), to identify the common and relevant fitness assessment techniques, to describe key evidence-based intervention strategies for improving health-related components of fitness among persons with SCI and to identify emerging and future trends in health-related fitness programming for rehabilitation and research. SCI results in a partial or total loss of neural signal transmission at and below the level of injury, characterized by motor and sensory loss. About 250,000–400,000 individuals have a SCI in the USA with approximately 12,000 new injuries occurring annually. The loss of somatic and autonomic control results in a reduction of physical activity and blunted cardiovascular response to exercise. The consequences of this reduction in physical activity are significant physical deconditioning, altered body composition, and development of detrimental metabolic profiles leading to poorer health outcomes in this population. Over the past decade, much attention has been devoted to understanding the relationship between exercise participation, physical activity, and physical fitness and their impact on health-related outcomes following SCI. Physical fitness interventions are of great importance to the SCI consumer and clinicians whose goals are to halt the deconditioning process, increase functional capacity, and decrease secondary health complications associated with chronic SCI. Exercise risk evaluation and individualized fitness assessments for body composition, muscular strength/endurance, and cardiorespiratory fitness should precede implementation of exercise interventions. Current evidence indicates that both electrically stimulated and volitional exercise strategies have the potential to improve components of fitness, reduce the risk of secondary health complications, and positively impact overall health for those with chronic physical disability. However, given the complexity of SCI with respect to level and severity of injury, a clear consensus on the optimal frequency, duration, and intensity of exercise needed to ensure lasting effects on health-related outcomes has yet to be determined. In addition, as is the case in non-injured populations, consideration should be given to both diet and exercise, not exercise alone, when considering the optimal strategy for improving body composition and cardiovascular health for persons with SCI.


title: Author Profiling with Doc2vec Neural Network-Based Document Embeddings

abstract: To determine author demographics of texts in social media such as Twitter, blogs, and reviews, we use doc2vec document embeddings to train a logistic regression classifier. We experimented with age and gender identification on the PAN author profiling 2014–2016 corpora under both single- and cross-genre conditions. We show that under certain settings the neural network-based features outperform the traditional features when using the same classifier. Our method outperforms existing state of the art under some settings, though the current state-of-the-art results on those tasks have been quite weak.


title: On being the object of attention: implications for self–other consciousness

abstract: Joint attention to an external object at the end of the first year is typically believed to herald the infant's discovery of other people's attention. I will argue that mutual attention in the first months of life already involves an awareness of the directedness of attention. The self is experienced as the first object of this directedness followed by gradually more distal 'objects'. This view explains early infant affective self-consciousness within mutual attention as emotionally meaningful, rather than as bearing only a spurious similarity to that in the second and third years of life. Such engagements precede and must inform, rather than derive from, conceptual representations of self and other, and can be better described as self-other conscious affects.


title: Capturing Topology in Graph Pattern Matching

abstract: Graph pattern matching is often defined in terms of subgraph isomorphism, an NP-complete problem. To lower its complexity, various extensions of graph simulation have been considered instead. These extensions allow pattern matching to be conducted in cubic-time. However, they fall short of capturing the topology of data graphs, i.e., graphs may have a structure drastically different from pattern graphs they match, and the matches found are often too large to understand and analyze. To rectify these problems, this paper proposes a notion of strong simulation, a revision of graph simulation, for graph pattern matching. (1) We identify a set of criteria for preserving the topology of graphs matched. We show that strong simulation preserves the topology of data graphs and finds a bounded number of matches. (2) We show that strong simulation retains the same complexity as earlier extensions of simulation, by providing a cubic-time algorithm for computing strong simulation. (3) We present the locality property of strong simulation, which allows us to effectively conduct pattern matching on distributed graphs. (4) We experimentally verify the effectiveness and efficiency of these algorithms, using real-life data and synthetic data.


title: The social media manifesto: a comprehensive review of the impact of social media on emergency management.

abstract: Over the past five years, social media have impacted emergency management and disaster response in numerous ways. The emergency management professional must begin to accept this impact not as an arbitrary consequence of an uncontrolled disaster, but rather as a tool to help coordinate, manage and facilitate a safe and expected response during emergencies and disasters. This paper will explain the power and purpose of social media as well as how social media systems have equalised capabilities for all levels and sizes of government. Moreover, this paper will also highlight the social media systems that are being used as operational tools as well as what the future holds. Lastly, common implementation challenges will be discussed through a look at systematic approaches to applying social media in emergency management as a positive and valuable tool. Language: en


title: Body Image and Self-Esteem in Lower-Limb Amputees

abstract: Background ::: Limb amputation is often an inevitable procedure in the advanced condition of various diseases and poses a dramatic impact on a patient's life. The aim of the present study is to analyze the impact of lower-limb amputations on aesthetic factors such as body image and self-esteem as well as quality of life (QoL). ::: ::: ::: Methods ::: 298 patients (149 uni- or bilateral lower-limb amputees and 149 controls) were included in this cross-sectional study in three centers. Demographic data was collected and patients received a 118-item questionnaire including the Multidimensional Body-Self Relations Questionnaire (MBSRQ), the Rosenberg Self-esteem (RSE) scale and the SF-36 Health Survey (QoL). ANOVA and student's t-test were used for statistical analysis. ::: ::: ::: Results ::: Unilateral lower-limb amputees showed a significant lower MBSRQ score of 3.07±0.54 compared with 3.41±0.34 in controls (p<0.001) and a lower score in the RSE compared to controls (21.63±4.72 vs. 21.46±5.86). However, differences were not statistically significant (p = 0.36). Patients with phantom pain sensation had a significantly reduced RSE (p = 0.01). The SF-36 health survey was significantly lower in patients with lower-limb amputation compared to controls (42.17±14.47 vs. 64.05±12.39) (p<0.001). ::: ::: ::: Conclusion ::: This study showed that lower-limb amputations significantly influence patients' body image and QoL. Self-esteem seems to be an independent aspect, which is not affected by lower-limb amputation. However, self-esteem is influenced significantly by phantom pain sensation.


title: Wireless dual channel human body temperature measurement device

abstract: Human body temperature is influenced by many factors such as cognitive, emotional and psychological states. Temperature measurements can be done using analogue or digital electrical sensors. High sensitivity and high accuracy in measurements are usually provided by the analogue sensors that require sophisticated circuits for linearization/calibration and also for digitization. When compared with the analogue ones, digital sensors can also provide high sensitivity but lower accuracy although good enough for several medical applications. In this study, a portable device with wireless computer connectivity that provides noninvasive dual channel digital body temperature measurements has been developed. Trials with the device show that it is quite beneficial in measuring body temperature from ear canal. It may also be used to perform temperature measurements from mouth, armpit and anus.


title: Incorporating Personalized Contextual Information in Item-based Collaborative Filtering Recommendation

abstract: After reviewing the prior work and problem of collaborative filtering recommendation approaches, an approach incorporating personalized contextual information in item-based collaborative filtering is proposed to solve the problem. The approach provides recommendations based on user personalized contextual information besides the typical information on users and items used in most of the current recommendation systems. In this paper, several approaches are proposed to calculate context-based item differences, learn personalized contextual information for every user and predict ratings based on well-known item-based collaborative filtering Slope One. Finally, we experimentally evaluate our approach and compare it to Slope One. The experiment al results show that our approach provide s more precision recommendations than Slope One.


title: A Review of Optical NDT Technologies

abstract: Optical non-destructive testing (NDT) has gained more and more attention in recent years, mainly because of its non-destructive imaging characteristics with high precision and sensitivity. This paper provides a review of the main optical NDT technologies, including fibre optics, electronic speckle, infrared thermography, endoscopic and terahertz technology. Among them, fibre optics features easy integration and embedding, electronic speckle focuses on whole-field high precision detection, infrared thermography has unique advantages for tests of combined materials, endoscopic technology provides images of the internal surface of the object directly, and terahertz technology opens a new direction of internal NDT because of its excellent penetration capability to most of non-metallic materials. Typical engineering applications of these technologies are illustrated, with a brief introduction of the history and discussion of recent progress.


title: Insomnia and Sleep Duration in a Large Cohort of Patients With Major Depressive Disorder and Anxiety Disorders

abstract: Objective: Disturbed sleep has a high impact on daily functioning and has been correlated with psychopathology. We investigated the extent to which insomnia and sleep duration were associated with both current and remitted depressive and anxiety disorders in a large-scale epidemiologic study, taking sociodemographics, health factors, and medication use into account. Method: Data of 2,619 individuals from the Netherlands Study of Depression and Anxiety (NESDA) were analyzed. Psychopathology was classified as no, current, or remitted DSM-IV-based diagnosis of major depressive or anxiety disorder. Outcome measures were insomnia (Women's Health Initiative Insomnia Rating Scale score ≥ 9) and sleep duration (≤ 6 hours, 7-9 hours, ≥ 10 hours). Baseline measurement was conducted between September 2004 and February 2007. Results: Both current and remitted depressive disorder and current anxiety disorder were associated with insomnia and short sleep duration with odds ratios (ORs) for insomnia ranging from 1.42 to 3.23 and for short sleep duration ranging from 1.41 to 2.53. Associations were stronger for current than for remitted diagnoses and stronger for depressive than for anxiety disorders. Also long sleep duration was associated with current depressive disorder and anxiety disorders (OR range, 1.53-2.66). Sociodemographic factors, health indicators, and psychotropic medication use did contribute to sleep outcomes but could not explain much of the psychopathology and sleep associations. Conclusion: Depressive disorder - but also anxiety disorder - is strongly associated with sleep disturbances. Insomnia and short sleep duration persist after remittance of these disorders, suggesting that these are residual symptoms or possibly trait markers. Also, long sleep duration is associated with current depressive or anxiety disorders. © Copyright 2010 Physicians Postgraduate Press, Inc.


title: A New Multilevel Inverter Topology With Self-Balancing Level Doubling Network

abstract: A new multilevel inverter (MLI) topology is proposed using a level doubling network (LDN). The LDN takes the form of a half-bridge inverter to almost double the number of output voltage levels. The concept (of the proposed LDN) has the capability of self-balancing during positive and negative cycles without any closed-loop control/algorithm, and it does not consume or supply any power. The topology uses a symmetric cascaded H-bridge MLI but offers an equivalent performance of an asymmetric topology in terms of the number of levels. Also, it maintains the merit of uniform loading of the individual cell for a symmetric MLI. The topology is implemented by connecting only a three-arm H-bridge (only two switches per phase) with the entire three-phase inverter to double the number of levels. Thus, it significantly improves the power quality, reduces the switching frequency, and reduces the cost and size of the power filter. Operation of the circuit is verified by simulation result and experiments from a laboratory prototype.


title: Efficacy and safety of oral magnesium supplementation in the treatment of depression in the elderly with type 2 diabetes: a randomized, equivalent trial.

abstract: To evaluate the efficacy and safety of oral magnesium supplementation, with magnesium chloride (MgCl 2), in the treatment of newly diagnosed depression in the elderly with type 2 diabetes and hypomagnesemia. Twenty-three elderly patients with type 2 diabetes and hypomagnesemia were enrolled and randomly allocated to receive either 50 mL of MgCl 2 5% solution equivalent to 450 mg of elemental magnesium or Imipramine 50 mg daily during 12 weeks. Widowhood or divorce in the last six months, alcoholism, degenerative illnesses of the nervous central system, recent diagnosis of diabetes, previous or current treatment with antidepressants, chronic diarrhea, use of diuretics, and reduced renal function were exclusion criteria. Hypomagnesemia was defined by serum magnesium levels < 1.8 mg/dL and depression by Yasavage and Brink score ≥ 11 points. The primary trial end point was the improvement of depression symptoms. At baseline, there were no differences by age (69 ± 5.9 and 66.4 ± 6.1 years, p = 0.39), duration of diabetes (11.8 ± 7.9 and 8.6 ± 5.7 years, p = 0.33), serum magnesium levels (1.3 ± 0.04 and 1.4 ± 0.04 mg/dL, p = 0.09), and Yasavage and Brink Score (17.9 ± 3.9 and 16.1 ± 4.5 point, p = 0.34) in the groups with MgCl 2 and imipramine, respectively. At end of follow-up, there were no significant differences in the Yasavage and Brink score (11.4 ± 3.8 and 10.9 ± 4.3, p = 0.27) between the groups in study; whereas serum magnesium levels were significantly higher in the group with MgCl 2 (2.1 ± 0.08 mg/dL) than in the subjects with imipramine (1.5 ± 0.07 mg/dL), p < 0.0005. In conclusion, MgCl 2 is as effective in the treatment of depressed elderly type 2 diabetics with hypomagnesemia as imipramine 50 mg daily.


title: Control method for myoelectric hand using convolutional neural network to simplify learning of EMG signals

abstract: By employing learning, myoelectric hands can demonstrate high performance, but learning takes a long time. This problem reduces the motivation of a myoelectric hand wearer and introduces an adverse effect on the mastery of myoelectric hand operation. Thus, we propose a control method for myoelectric hands that uses convolutional neural network without relearning when a wearer reseat the myoelectric hand. As a result of the experiments using a healthy subject, the proposed method was shown to maintain a better classification rate than the traditional method even if the methods learn using datasets measured in other days. This advantage will reduce the relearning process of myoelectric hands when a wearer reseat the hand.


title: Resistant groups in enterprise system implementations: a Q-methodology examination

abstract: This paper is an initial investigation into types of user resistance and the management strategy expectations of users in a mandatory adoption setting. Despite its relationship to adoption, relatively little is known about user resistance. User resistance is investigated in the Enterprise System (ES) environment because the complexity and richness of ES leads users to manifest a large range of resistant behaviors and beliefs. Using Concourse Theory and Q-methodology, ES users are interviewed followed by the development of a Q-sort questionnaire, which was distributed to ES users. The results reveal eight user groups and address the management strategies preferred by each group. The results have implications for both research in the field of user resistance and adoption, and practitioners involved in system implementation.


title: Design of a K-band fast hopping frequency synthesizer

abstract: The design and implementation of a K-band fast hopping frequency synthesizer based on DDS and PLL is described. Main technical specifications are as follows: frequency ranges from 24GHz to 27GHz, step frequency is 50MHz and lock time is less than 150ns. The parameters of loop filter and the analyses of phase noise and lock time are provided by MATLAB simulation to verify the feasibility of the design. Final test results of the circuits proves that the targets of performance are achieved.


title: Assessing and Improving the Dimensionality of the Computer Anxiety Rating Scale

abstract: Using data from a sample of 776 university employees, this study uses confirmatory factor analysis to assess and then improve the unidimensionality of the two constructs underlying the Computer Anxiety Rating Scale. The process resulted in more parsimonious constructs that demonstrate acceptable reliabilities while retaining their original meanings.


title: Early reading acquisition and its relation to reading experience and ability 10 years later.

abstract: A group of lst-graders who were administered a battery of reading tasks in a previous study were followed up as 1 lth graders. Ten years later, they were administered measures of exposure to print, reading comprehension, vocabulary, and general knowledge. First-grade reading ability was a strong predictor of all of the 1 lth-grade outcomes and remained so even when measures of cognitive ability were partialed out. First-grade reading ability (as well as 3rd- and 5th-grade ability) was reliably linked to exposure to print, as assessed in the 1 lth grade, even after 1 lth-grade reading comprehension ability was partialed out, indicating that the rapid acquisition of reading ability might well help develop the lifetime habit of reading, irrespective of the ultimate level of reading comprehension ability that the individual attains. Finally, individual differences in exposure to print were found to predict differences in the growth in reading comprehension ability throughout the elementary grades and thereafter.


title: A Distributed Canny Edge Detector: Algorithm and FPGA Implementation

abstract: The Canny edge detector is one of the most widely used edge detection algorithms due to its superior performance. Unfortunately, not only is it computationally more intensive as compared with other edge detection algorithms, but it also has a higher latency because it is based on frame-level statistics. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-level Canny algorithm. Directly applying the original Canny algorithm at the block-level leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a distributed Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. In addition, the new algorithm uses a nonuniform gradient magnitude histogram to compute block-based hysteresis thresholds. The resulting block-based algorithm has a significantly reduced latency and can be easily integrated with other block-based image codecs. It is capable of supporting fast edge detection of images and videos with high resolutions, including full-HD since the latency is now a function of the block size instead of the frame size. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images. Finally, this algorithm is implemented using a 32 computing engine architecture and is synthesized on the Xilinx Virtex-5 FPGA. The synthesized architecture takes only 0.721 ms (including the SRAM read/write time and the computation time) to detect edges of \(512\times 512\) images in the USC SIPI database when clocked at 100 MHz and is faster than existing FPGA and GPU implementations.


title: Potter's Wheel: An Interactive Data Cleaning System

abstract: Cleaning data of errors in structure and content is important for data warehousing and integration. Current solutions for data cleaning involve many iterations of data “auditing” to find errors, and long-running transformations to fix them. Users need to endure long waits, and often write complex transformation scripts. We present Potter’s Wheel, an interactive data cleaning system that tightly integrates transformation and discrepancy detection. Users gradually build transformations to clean the data by adding or undoing transforms on a spreadsheet-like interface; the effect of a transform is shown at once on records visible on screen. These transforms are specified either through simple graphical operations, or by showing the desired effects on example data values. In the background, Potter’s Wheel automatically infers structures for data values in terms of user-defined domains, and accordingly checks for constraint violations. Thus users can gradually build a transformation as discrepancies are found, and clean the data without writing complex programs or enduring long delays.


Mining Social Networks for Viral Marketing


title: Anxiety, Self-Efficacy, and College Exam Grades.

abstract: A student's level of self-efficacy and test anxiety directly impacts their academic success (Abdi, Bageri, Shoghi, Goodarzi, & Hosseinzadeh, 2012; Hassanzadeh, Ebrahimi, & Mahdinejad, 2012). When a student doubts themself and their own ability to test well, the students' sole focus becomes worrying about poor grades and cannot focus on academics (Bandura, 1993). But, little is understood about how test-anxiety and self-efficacy affect short-term success in the classroom. Specifically, how test anxiety and level of self-efficacy directly preceding an exam will affect the exam score. Pre-and post-questionnaires assessing anxiety and self-efficacy immediately before and after a single college exam was completed by 110 college students and exam grades were obtained from the instructor. Results showed a strong relationship between both test anxiety and exam grades, and self-efficacy and exam grades. Further, multiple linear regression analyses showed that exam grade could be predicted by test anxiety and self-efficacy level, and that self-efficacy moderated the effects of anxiety.


title: The Parking Lot Problem

abstract: If there is queueing for an underpriced good, the queueing can eat up the entire surplus, eliminating the social value of the good. An implication is that there is a discontinuity in social welfare between "enough" and "not enough" for certain goods such as parking spaces. This implies that if there is uncertainty in the number of demanders, the amount of the good should be set well in excess of the mean demand


title: 1kW, 400V/12V high step-down DC/DC converter: Comparison between phase-shifted full-bridge and LLC resonant converters

abstract: Phase-shifted full bridge (PSFB) and LLC resonant converters with center-tapped rectifier are two popular solutions for auxiliary power module (APM) in the electrical system of electric vehicles (EVs). The high output current and the requirement for a wide gain range due to the high-voltage battery variable voltage bring unique challenges in the design of APMs. This paper provides a comprehensive analysis of the operational principle of these two topologies. Two 1kW prototypes are designed and developed to provide a realistic comparison. The manuscript provides basic guidelines about the topology selection between these two converters for high step-down ratio and high output current applications.


title: Single-Switch Quasi-Resonant Converter

abstract: A single-switch quasi-resonant converter is proposed to obtain high efficiency. Using a variable switching frequency control, this converter is continuously operated at the critical conduction mode for soft switching of the power semiconductor switch. By the resonance, the proposed converter reduces the turn-on loss of the switch without additional active switches and alleviates the reverse-recovery losses of the rectifying diodes. Furthermore, the voltage stress of the rectifying diodes due to the output series resonant circuit is clamped to the output voltage. Experimental results for the 48-V/160-W converter at the variable switching frequency are obtained to show the performance of the proposed converter.


title: Medical and Disaster Preparedness of US Marathons.

abstract: BACKGROUND: Despite the events that occurred at the 2013 Boston Marathon (Boston, Massachusetts USA), there are currently no evidence-based guidelines or published data regarding medical and disaster preparedness of marathon races in the United States. PURPOSE To determine the current state of medical disaster preparedness of marathons in the US and to identify potential areas for improvement. METHODS: A cross-sectional, questionnaire-based study was conducted from January through May of 2014. The questionnaire was distributed to race directors of US road and trail marathons, as identified by a comprehensive internet database. RESULTS: One hundred twenty-three questionnaires were available for analysis (19% usable response rate). Marathon races from all major regions of the US were represented. Runner medical information was not listed on race bibs in 53% of races. Only 45% of races held group training and planning sessions prior to race day. Automated external defibrillators (AEDs) were immediately available on 50% of courses, and medications such as albuterol (30%), oxygen (33%), and IV fluids (34%) were available less frequently. Regarding medical emergencies, 55% of races did not have protocols for the assessment of dehydration, asthma, chest pain, syncope, or exercise-induced cramping. With regard to disaster preparedness, 50% of races did not have protocols for the management of disasters, and 21% did not provide security personnel at start/finish lines, aid stations, road crossings, and drop bag locations. CONCLUSIONS: Areas for improvement in the preparedness of US marathons were identified, such as including printed medical information on race bibs, increasing pre-race training and planning sessions for volunteers, ensuring the immediate availability of certain emergency equipment and medications, and developing written protocols for specific emergencies and disasters. Glick J Rixe JA Spurkeland N Brady J Silvis M Olympia RP. Medical and disaster preparedness of US marathons. Prehosp Disaster Med. 2015;30(4):1-7. Language: en


title: Enhancing Sentiment Analysis of Financial News by Detecting Negation Scopes

abstract: Sentiment analysis refers to the extraction of the polarity of source materials, such as financial news. However, measuring positive tone requires the correct classification of sentences that are negated, i.e. The negation scopes. For example, around 4.74% of all sentences in German ad hoc announcements contain negations. To predict the corresponding negation scope, related literature commonly utilizes two approaches, namely, rule-based algorithms and machine learning. Nevertheless, a thorough comparison is missing, especially for the sentiment analysis of financial news. To close this gap, this paper uses German ad hoc announcements as a common example of financial news in order to pursue a two-sided evaluation. First, we compare the predictive performance using a manually-labeled dataset. Second, we examine how detecting negation scopes can improve the accuracy of sentiment analysis. In this instance, rule-based algorithms produce superior results, resulting in an improvement of up to 9.80% in the correlation between news sentiment and stock market returns.


title: Enhanced User Security and Privacy Protection in 4G LTE Network

abstract: Although the Evolved Packet System Authentication and Key Agreement (EPS-AKA) provides security and privacy enhancements in 3rd Generation Partnership Project (3GPP), the International Mobile Subscriber Identity (IMSI) is sent in clear text in order to obtain service. Various efforts to provide security mechanisms to protect this unique private identity have not resulted in methods implemented to protect the disclosure of the IMSI. The exposure of the IMSI brings risk to user privacy, and knowledge of it can lead to several passive and active attacks targeted at specific IMSI's and their respective users. Further, the Temporary Mobile Subscribers Identity (TMSI) generated by the Authentication Center (AuC) have been found to be prone to rainbow and brute force attacks, hence an attacker who gets hold of the TMSI can be able to perform social engineering in tracing the TMSI to the corresponding IMSI of a User Equipment (UE). This paper proposes a change to the EPS-AKA authentication process in 4G Long Term Evolution (LTE) Network by including the use of Public Key Infrastructure (PKI). The change would result in the IMSI never being released in the clear in an untrusted network.


title: Successful Treatment of Depressed, Distensible Acne Scars Using Autologous Fibroblasts: A Multi-Site, Prospective, Double Blind, Placebo-Controlled Clinical Trial

abstract: BACKGROUND ::: A previous clinical trial evaluating autologous fibroblasts (human dermal) injections for the treatment of facial contour deformities found significantly greater improvements in wrinkle and acne scar appearance than with placebo treatment. ::: ::: ::: OBJECTIVE ::: To compare the efficacy and safety of autologous fibroblast treatment of moderate to severe, depressed, distensible facial acne scars with that of vehicle control. ::: ::: ::: METHODS ::: This was a randomized multicenter, double-blind, placebo-controlled trial in subjects with bilateral moderate to severe acne scarring; subjects served as their own controls. Skin biopsies were obtained from randomized subjects for fibroblast production. Subjects (n = 99) underwent three intradermal injection sessions with 2 mL of autologous fibroblast suspension (10-20 million cells/mL) on one cheek and vehicle control (cell culture medium) on the other at 14-day intervals. Efficacy was based on the blinded subject's, evaluator's, and independent photographic viewer's (IPR) assessment of acne scarring 1 to 4 months after the last treatment. ::: ::: ::: RESULTS ::: Autologous fibroblast treatment was associated with significantly greater treatment success than vehicle control for the subject (43% vs.18%), evaluator (59% vs. 2%), and IPR assessments. Autologous fibroblast injections were well tolerated, without permanent adverse effects. ::: ::: ::: CONCLUSIONS ::: Autologous fibroblast injections safely and effectively improved the appearance of depressed distensible acne scars.


title: Control of an aerial robot with multi-link arm for assembly tasks

abstract: This paper deals with aerial manipulators consisting of an unmanned aerial vehicle equipped with a robotic multi-link arm. The paper presents methods for the control of the aerial platform taking into account the motion of the arm. It shows how a Variable Parameter Integral Backstepping controller outperforms the results obtained by using PID controllers. The paper presents a quadrotor with a new arm designed for assembly tasks and the implementation of the proposed control methods. Simulations and outdoor experiments confirm the validity of the proposed approach.


title: DVQA: Understanding Data Visualizations via Question Answering

abstract: Bar charts are an effective way to convey numeric information, but today's algorithms cannot parse them. Existing methods fail when faced with even minor variations in appearance. Here, we present DVQA, a dataset that tests many aspects of bar chart understanding in a question answering framework. Unlike visual question answering (VQA), DVQA requires processing words and answers that are unique to a particular bar chart. State-of-the-art VQA algorithms perform poorly on DVQA, and we propose two strong baselines that perform considerably better. Our work will enable algorithms to automatically extract numeric and semantic information from vast quantities of bar charts found in scientific publications, Internet articles, business reports, and many other areas.


title: A high-frequency comb-actuated resonant MEMS scanner for microdisplays

abstract: A high-frequency novel torsional MEMS scanner is developed for high resolution microdisplays employing a multi-frame geometry. For the torsional mirror, 26.7° and 36.1° total-optical-scan-angle are achieved at resonance, at atmospheric pressure and vacuum respectively.


title: Quantitative Measurements of the Bulbous Tip in Ethnic Rhinoplasty

abstract: Background Refinement of the bulbous nasal tip continues to be one of the more challenging aspects of ethnic rhinoplasty. Better objective measures are necessary to quantitatively assess changes in surgery of the bulbous tip. We propose the use of a new nasal anatomic landmark in the subnasal vertex view, designated c1, in conjunction with previously described landmarks to offer an improved means of characterizing the bulbous tip. Together, these landmarks form a pentagon, allowing for measurement of its angles and distances to quantitatively evaluate the nasal tip in the subnasal vertex view. We hypothesize that this method will provide a means of demonstrating a decrease in tip bulbosity postoperatively. Methods An institutional review board–approved retrospective analysis of 44 nonwhite patients undergoing ethnic rhinoplasty was undertaken and preoperative and postoperative photographs in the subnasal vertex view were subjected to the proposed analysis. All patients underwent an open approach rhinoplasty with a combination of tip defatting and cartilage graft placement. Using the polygonal construct, several angles, distances, and areal indices were measured from the preoperative and postoperative photographs and comparison was made for statistical significance. Results All but 1 of the 8 parameters examined demonstrated statistical significance comparing preoperative and postoperative values. These measures, thus, allow the surgeon to objectively assess and ascertain a reduction in tip bulbosity of postoperative ethnic rhinoplasty patients. Conclusions This article presents a new anatomic landmark c1 seen from a subnasal view of the tip. This landmark can be combined with previously established landmarks to create a polygon that allows for a simple yet objective method for measuring the reduction and contouring of the ethnic bulbous tip.


title: An open-source toolkit for mining wikipedia

abstract: The online encyclopedia Wikipedia is a vast, constantly evolving tapestry of interlinked articles. For developers and researchers it represents a giant multilingual database of concepts and semantic relations, a potential resource for natural language processing and many other research areas. This paper introduces the Wikipedia Miner toolkit, an open-source software system that allows researchers and developers to integrate Wikipedia@?s rich semantics into their own applications. The toolkit creates databases that contain summarized versions of Wikipedia@?s content and structure, and includes a Java API to provide access to them. Wikipedia@?s articles, categories and redirects are represented as classes, and can be efficiently searched, browsed, and iterated over. Advanced features include parallelized processing of Wikipedia dumps, machine-learned semantic relatedness measures and annotation features, and XML-based web services. Wikipedia Miner is intended to be a platform for sharing data mining techniques.


title: A Data-Mining Approach to Monitoring Wind Turbines

abstract: The rapid expansion of wind farms has generated interest in operations and maintenance. An operating wind turbine undergoes various state changes, including transformation from a normal to a fault mode. Condition-based maintenance tools are needed to identify potential faults in the system. The prediction of turbine fault modes is of particular interest. In this research, data-mining algorithms are employed to construct prediction models for wind turbine faults. A three-stage prediction process is followed: 1) prediction of a fault of any kind; 2) prediction of specific faults of the system; and 3) identification on unseen faults. A comparative analysis of various data-mining algorithms is reported based on the data collected at a large wind farm. Random forest algorithm models provided the best accuracy among all algorithms tested. The robustness of the predictive model is validated for faults that have occurred at turbines with previously unseen data. The research results discussed in this paper have been derived from data collected at 17 wind turbines.


GROUND WATER VULNERABILITY MAPS: A REVIEW


title: Electromagnetic sizing of axial-field flux switching permanent magnet machine

abstract: This paper presents a general sizing procedure of axial-field flux switching permanent magnet (AF-FSPM) machine for various topologies. A comprehensive approach is used for design of AF-FSPM machine and the design flowchart is presented. A three phase, 12/10 single-stage AF-FSPM machine is designed and three dimensional finite element analyses are performed to validate the design procedure. The magnetic flux distribution, induced EMF, and cogging torque has been computed. It is found that the results confirm the presented procedure.


title: Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

abstract: We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, we introduce additional sparse depth samples, which are either collected from a low-resolution depth sensor or computed from SLAM, to attain a higher level of robustness and accuracy. We propose the use of a single regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on prediction accuracy. Our experiments show that, as compared to using only RGB images, the addition of 100 spatially random depth samples reduces the prediction root-mean-square error by half in the NYU-Depth-v2 indoor dataset. It also boosts the percentage of reliable prediction from 59% to 92% on the more challenging KITTI driving dataset. We demonstrate two applications of the proposed algorithm: serving as a plug-in module in SLAM to convert sparse maps to dense maps, and creating much denser point clouds from low-resolution LiDARs. Codes and video demonstration are publicly available.


title: Specific inhibition of DNMT1 by antisense oligonucleotides induces re-expression of estrogen receptor-alpha (ER) in ER-negative human breast cancer cell lines.

abstract: Recent studies have shown that changes in epigenetic regulation, such as DNA methylation and histone acetylation, are associated with silencing of the estrogen receptor _ (ER) gene in ER-negative human breast cancer cells. Treatment of these cells with the general DNMT inhibitor, 5-aza-2’deoxycytidine, led to reactivation of functional ER protein. This study addresses the hypothesis that specific inhibition of the maintenance DNA methyltransferase, DNMT1, by antisense oligonucleotides (DNMT1 ASO) is sufficient to re-express the ER gene in ER-negative human breast cancer cell lines. MDA-MB-231 and Hs578t cells were transfected with 100 nM and 150 nM DNMT1 ASO respectively for three consecutive days and evidence of DNMT1 down-regulation and functional ER re-expression was sought. Significant growth reduction was observed within 48 hr and persisted after 96 hr. DNMT1 expression was blocked after exposure to DNMT1 ASO as detected by RT-PCR, Western blot and enzymatic assay whereas a mutant DNMT1 ASO had littl...


title: Ontological technologies for user modelling

abstract: This paper brings together research from two different fields – user modelling and web ontologies – in attempt to demonstrate how recent semantic trends in web development can be combined with the modern technologies of user modelling. Over the last several years, a number of user-adaptive systems have been exploiting ontologies for the purposes of semantics representation, automatic knowledge acquisition, domain and user model visualisation and creation of interoperable and reusable architectural solutions. Before discussing these projects, we first overview the underlying user modelling and ontological technologies. As an example of the project employing ontology-based user modelling, we present an experiment design for translation of overlay student models for relative domains by means of ontology mapping.


title: Architecture Design of the Vehicle Tracking System Based on RFID

abstract: Vehicle tracking plays more important roles in modern transportation and logistics operation. This paper deals with a new approach to track vehicles based on RFID (Radio Frequency Identification) technology. The vehicle tracking system is designed overallly supported by Axiomatic Design theory.The basic steps of vehicle tracking based on RFID are developed and a six-layered architecture for the vehicle tracking system integrating databases, RFID tags, RFID readers, data centers, networks and user interface is presented, where the positions of vehicles are attained directly by compared the RFID readers collecting vehicle RFID tags their position information in the database. It is shown that this vehicle tracking method uses RFID more fully, the architecture with clear levels, adapts to the future technical and practical requirements, and can help fuse more complicated applications like intelligent charging and cargo tracking. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2606


title: A Cross-cultural Study: Effect of Robot Appearance and Task

abstract: This study investigates the effects of culture, robot appearance and task on human-robot interaction. We propose a model with culture (Chinese, Korean and German), robot appearance (anthropomorphic, zoomorphic and machinelike) and task (teaching, guide, entertainment and security guard) as factors, and analyze these factors’ effects on the robot’s likeability, and people’s active response to, engagement with, trust in and satisfaction with the robot. We conducted a laboratory experiment with 108 participants to test the model and performed Repeated ANOVA and Kruskal Wallis Test on the data. The results show that cultural differences exist in participants’ perception of likeability, engagement, trust and satisfaction; a robot’s appearance affects its likeability, while the task affects participants’ active response and engagement. We found the participants expected the robot appearance to match its task only in the interview but not in the subjective ratings. Interaction between culture and task indicates that participants from low-context cultures may have significantly decreased engagement when the sociability of a task is lowered. We found strong and positive correlations between interaction performance (active response and engagement) and preference (likeability, trust and satisfaction) in the human-robot interaction.


title: High Accuracy Attack Provenance via Binary-based Execution Partition

abstract: An important aspect of cyber attack forensics is to understand the provenance of suspicious events, as it discloses the root cause and ramifications of cyber attacks. Traditionally, this is done by analyzing audit log. However, the presence of long running programs makes a live process receiving a large volume of inputs and produce many outputs and each output may be causally related to all the preceding inputs, leading to dependence explosion and making attack investigations almost infeasible. We observe that a long running execution can be partitioned into individual units by monitoring the execution of the program’s event-handling loops, with each iteration corresponding to the processing of an independent input/request. We reverse engineer such loops from application binaries. We also reverse engineer instructions that could cause workflows between units. Detecting such a workflow is critical to disclosing causality between units. We then perform selective logging for unit boundaries and unit dependences. Our experiments show that our technique, called BEEP, has negligible runtime overhead (< 1.4%) and low space overhead (12.28% on average). It is effective in capturing the minimal causal graph for every attack case we have studied, without any dependence explosion.


title: Localized Deep Extreme Learning Machines for Efficient RGB-D Object Recognition

abstract: Existing RGB-D object recognition methods either use channel specific handcrafted features, or learn features with deep networks. The former lack representation ability while the latter require large amounts of training data and learning time. In real-time robotics applications involving RGB-D sensors, we do not have the luxury of both. In this paper, we propose Localized Deep Extreme Learning Machines (LDELM) that efficiently learn features from RGB-D data. By using localized patches, not only is the problem of data sparsity solved, but the learned features are robust to occlusions and viewpoint variations. LDELM learns deep localized features in an unsupervised way from random patches of the training data. Each image is then feed-forwarded, patch-wise, through the LDELM to form a cuboid of features. The cuboid is divided into cells and pooled to get the final compact image representation which is then used to train an ELM classifier. Experiments on the benchmark Washington RGB-D and 2D3D datasets show that the proposed algorithm not only is significantly faster to train but also outperforms state-of-the-art methods in terms of accuracy and classification time.


title: Examining Multiple Features for Author Profiling

abstract: Authorship analysis aims at classifying texts based on the stylistic choices of their authors. The idea is to discover characteristics of the authors of the texts. This task has a growing importance in forensics, security, and marketing. In this work, we focus on discovering age and gender from blog authors. With this goal in mind, we analyzed a large number of features -- ranging from Information Retrieval to Sentiment Analysis. This paper reports on the usefulness of these features. Experiments on a corpus of over 236K blogs show that a classifier using the features explored here have outperformed the state-of-the art. More importantly, the experiments show that the Information Retrieval features proposed in our work are the most discriminative and yield the best class predictions.


title: In-vehicle camera traffic sign detection and recognition

abstract: In this paper, we discuss theoretical foundations and a practical realization of a real-time traffic sign detection, tracking and recognition system operating on board of a vehicle. In the proposed framework, a generic detector refinement procedure based on mean shift clustering is introduced. This technique is shown to improve the detection accuracy and reduce the number of false positives for a broad class of object detectors for which a soft response’s confidence can be sensibly estimated. The track of an already established candidate is maintained over time using an instance-specific tracking function that encodes the relationship between a unique feature representation of the target object and the affine distortions it is subject to. We show that this function can be learned on-the-fly via regression from random transformations applied to the image of the object in known pose. Secondly, we demonstrate its capability of reconstructing the full-face view of a sign from substantial view angles. In the recognition stage, a concept of class similarity measure learned from image pairs is discussed and its realization using SimBoost, a novel version of AdaBoost algorithm, is analyzed. Suitability of the proposed method for solving multi-class traffic sign classification problems is shown experimentally for different feature representations of an image. Overall performance of our system is evaluated based on a prototype C++ implementation. Illustrative output generated by this demo application is provided as a supplementary material attached to this paper.


title: Operating System Transactions

abstract: Applications must be able to synchronize accesses to operating system resources in order to ensure correctness in the face of concurrency and system failures. System transactions allow the programmer to specify updates to heterogeneous system resources with the OS guaranteeing atomicity, consistency, isolation, and durability (ACID). System transactions efficiently and cleanly solve persistent concurrency problems that are difficult to address with other techniques. For example, system transactions eliminate security vulnerabilities in the file system that are caused by time-of-check-to-time-of-use (TOCTTOU) race conditions. System transactions enable an unsuccessful software installation to roll back without disturbing concurrent, independent updates to the file system. This paper describes TxOS, a variant of Linux 2.6.22 that implements system transactions. TxOS uses new implementation techniques to provide fast, serializable transactions with strong isolation and fairness between system transactions and non-transactional activity. The prototype demonstrates that a mature OS running on commodity hardware can provide system transactions at a reasonable performance cost. For instance, a transactional installation of OpenSSH incurs only 10% overhead, and a non-transactional compilation of Linux incurs negligible overhead on TxOS. By making transactions a central OS abstraction, TxOS enables new transactional services. For example, one developer prototyped a transactional ext3 file system in less than one month.


title: SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents

abstract: We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.


Universal Steganalysis Techniques Based on the Feature Extraction in Transform Domain


title: Generation of nodding, head tilting and eye gazing for human-robot dialogue interaction

abstract: Head motion occurs naturally and in synchrony with speech during human dialogue communication, and may carry paralinguistic information, such as intentions, attitudes and emotions. Therefore, natural-looking head motion by a robot is important for smooth human-robot interaction. Based on rules inferred from analyses of the relationship between head motion and dialogue acts, this paper proposes a model for generating head tilting and nodding, and evaluates the model using three types of humanoid robot (a very human-like android, "Geminoid F", a typical humanoid robot with less facial degrees of freedom, "Robovie R2", and a robot with a 3-axis rotatable neck and movable lips, "Telenoid R2"). Analysis of subjective scores shows that the proposed model including head tilting and nodding can generate head motion with increased naturalness compared to nodding only or directly mapping people's original motions without gaze information. We also find that an upwards motion of a robot's face can be used by robots which do not have a mouth in order to provide the appearance that utterance is taking place. Finally, we conduct an experiment in which participants act as visitors to an information desk attended by robots. As a consequence, we verify that our generation model performs equally to directly mapping people's original motions with gaze information in terms of perceived naturalness.


title: Shadow removal of single texture region using local histogram matching

abstract: Shadows have long been a problem to computer vision algorithms. Removing shadow can significantly improve the performance of several vision task such as object detection and image segmentation. Various methods of shadow removal in images had been developed. In this paper, we discussed the characteristic of shadowed region with single texture statistically, based on an illumination model, and then developed a simple and fast way of removal shadow using local histogram matching in different illumination reduction level.


title: Intelligent agent supporting human-multi-robot team collaboration

abstract: The number of multi-robot systems deployed in field applications has risen dramatically over the years. Nevertheless, supervising and operating multiple robots at once is a difficult task for a single operator to execute. In this paper we propose a novel approach for utilizing advising automated agents when assisting an operator to better manage a team of multiple robots in complex environments. We introduce the Myopic Advice Optimization (MYAO) Problem and exemplify its implementation using an agent for the Search And Rescue (SAR) task. Our intelligent advising agent was evaluated through extensive field trials, with 44 non-expert human operators and 10 low-cost mobile robots, in simulation and physical deployment, and showed a significant improvement in both team performance and the operator's satisfaction.


title: N-Acetylcysteine—a safe antidote for cysteine/glutathione deficiency

abstract: Loss of balance between the antioxidant defence and oxidant production in the cells, which commonly occurs as a secondary feature in many human diseases, is loosely termed “oxidative stress”. This balance is important because the intracellular redox environment must be more reducing than oxidative to maintain optimal cell function. Four major inter-dependent redox couples – GSH/GSSG, NADHP/NADP+, NADH/NAD+ and thioredoxin [Trx (SH)2/TrxS-S] – interact to regulate this redox environment [1]. However, the loss of antioxidant capacity in an oxidatively stressed cell is mainly due to decrease in GSH and/or increase in GSSG, because glutathione (GSH) is the most abundant intracellular free thiol. Thus, oxidative stress in vivo mainly translates to deficiency of GSH and/or its precursor, cysteine. ::: ::: Antioxidant supplementation has been studied extensively as a method to counter disease-associated oxidative stress. Several antioxidants have been used with varying degrees of success. However, although the commonly used antioxidants, which include vitamin C, vitamin K and lipoic acid, can directly neutralize free radicals, they cannot replenish the cysteine required for GSH synthesis and replenishment [2]. Thus, not surprisingly, the cysteine prodrug N-acetylcysteine, which supplies the cysteine necessary for GSH synthesis, has proven more effective in treating disease-associated oxidative stress. NAC been used in the clinic to treat a variety of conditions including drug toxicity (acetaminophen toxicity) [3•], human immunodeficiency virus/AIDS [4,5], Cystic Fibrosis (CF) [6,7], chronic obstructive pulmonary disease (COPD) [8], diabetes [9] etc. ::: ::: In this review, we summarize the biochemical and pharmacological aspects of NAC that make it a “wise choice” to treat cysteine/GSH deficiencies. We then focus on the various NAC formulations that are currently available. Comprehensive reviews of placebo-controlled trials with NAC have been published previously [10••-14]. Here, we briefly comment on the various clinical trials with NAC with special reference to acetaminophen toxicity, HIV and CF, in which our laboratory has a special interest.


title: High-Order Contrasts for Independent Component Analysis

abstract: This article considers high-order measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations are discussed. We compare the proposed approaches with gradient-based techniques from the algorithmic point of view and also on a set of biomedical data.


title: Cold Fusion: Training Seq2Seq Models Together with Language Models

abstract: Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.


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title: Training Energy-Based Models for Time-Series Imputation

abstract: Imputing missing values in high dimensional time-series is a difficult problem. This paper presents a strategy for training energy-based graphical models for imputation directly, bypassing difficulties probabilistic approaches would face. The training strategy is inspired by recent work on optimization-based learning (Domke, 2012) and allows complex neural models with convolutional and recurrent structures to be trained for imputation tasks. In this work, we use this training strategy to derive learning rules for three substantially different neural architectures. Inference in these models is done by either truncated gradient descent or variational mean-field iterations. In our experiments, we found that the training methods outperform the Contrastive Divergence learning algorithm. Moreover, the training methods can easily handle missing values in the training data itself during learning. We demonstrate the performance of this learning scheme and the three models we introduce on one artificial and two real-world data sets.


title: MOSKIT: Motion sickness analysis platform for VR games

abstract: This paper introduces MOSKIT, a motion sickness analysis platform for VR gaming content. Using the proposed platform, software developers will be able to significantly reduce MS without looking to hardware for updates. The paper has investigated three known main contributors to motion sickness: system configuration, device orientation, and interaction among objects. Our findings strongly suggest that adapting the analysis of MOSKIT to the software's production pipeline enables developers to identify and effectively eliminate any main source of motion sickness in their gaming content.


title: Toward Supervised Anomaly Detection

abstract: Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement. Although being intrinsically non-convex, we further show that the optimization problem has a convex equivalent under relatively mild assumptions. Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, we observe that the proposed learning methodology requires much less labeled data than the state-of-the-art, while achieving higher detection accuracies.


title: Marginal noise removal of document images

abstract: Marginal noise is a common phenomenon in document analysis which results from the scanning of thick documents or skew documents. It usually appears in the front of a large and dark region around the margin of document images. Marginal noise might cover meaningful document objects, such as text, graphics and forms. The overlapping of marginal noise with meaningful objects makes it difficult to perform the task of segmentation and recognition of document objects. This paper proposes a novel approach to remove marginal noise. The proposed approach consists of two steps which are marginal noise detection and marginal noise deletion. Marginal noise detection will reduce an original document image into a smaller image, and then find marginal noise regions according to the shape length and location of the split blocks. After the detection of marginal noise regions, different removal methods are performed. A local thresholding method is proposed for the removal of marginal noise in gray-scale document images, whereas a region growing method is devised for binary document images. Experimenting with a wide variety of test samples reveals the feasibility and effectiveness of our proposed approach in removing marginal noises.


title: Acquisition vs. outsourcing decisions modeling process applied on an international transport company by means of the decision tree method

abstract: In this paper, we emphasize the simulation of a decision making process applied to an international transport company from Romania. Our goal was to establish the most efficient decision, having in view three alternatives: acquisition of one additional truck, acquisition of two additional trucks and the outsourcing supplementary requests of transport services, over the trucks fleet capacity of the company, to other specialized transport companies. In view to determine mathematically the best decision, we applied the decision tree method, in the conditions in which the company's manager offered us a feasibility analysis concerning these alternatives. We also reveal in this paper the opportunity provided by WinQSB software functions, which reduce the time involved by solving this decision problem.


title: Applications of Support Vector Machines for Pattern Recognition: A Survey

abstract: In this paper, we present a comprehensive survey on applications of Support Vector Machines (SVMs) for pattern recognition. Since SVMs show good generalization performance on many real-life data and the approach is properly motivated theoretically, it has been applied to wide range of applications. This paper describes a brief introduction of SVMs and summarizes its numerous applications.


title: Robot homing based on corner tracking in a sequence of panoramic images

abstract: In robotics, homing can be defined as that behavior which enables a robot to return to its initial (home) position, after traveling a certain distance along an arbitrary path. Odometry has traditionally been used for the implementation of such a behavior, but it has been shown to be an unreliable source of information. In this work, a novel method for visual homing is proposed, based on a panoramic camera. As the robot departs from its initial position, it tracks characteristic features of the environment (corners). As soon as homing is activated, the robot selects intermediate target positions on the original path. These intermediate positions (IPs) are then visited sequentially, until the home position is reached. For the robot to move between two consecutive IPs, it is only required to establish correspondence among at least three corners. This correspondence is obtained through a feature tracking mechanism. The proposed homing scheme is based on the extraction of very low-level sensory information, namely the bearing angles of corners, and has been implemented on a robotic platform. Experimental results show that the proposed scheme achieves homing with a remarkable accuracy, which is not affected by the distance traveled by the robot.


title: Identification of isometric contractions based on High Density EMG maps.

abstract: Identification of motion intention and muscle activation strategy is necessary to control human-machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve pattern-recognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it. The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion-extension and supination-pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the "isolated masses" method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation.


title: Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering

abstract: Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work e ectively and e ciently in highdimensional space, which is due to the so-called "curse of dimensionality". In addition, the high-dimensional data often contains a signi cant amount of noise which causes additional e ectiveness problems. In this paper, we review and compare the existing algorithms for clustering highdimensional data and show the impact of the curse of dimensionality on their e ectiveness and e ciency. The comparison reveals that condensation-based approaches (such as BIRCH or STING) are the most promising candidates for achieving the necessary e ciency, but it also shows that basically all condensation-based approaches have severe weaknesses with respect to their e ectiveness in highdimensional space. To overcome these problems, we develop a new clustering technique called OptiGrid which is based on constructing an optimal grid-partitioning of the data. The optimal grid-partitioning is determined by calculating the best partitioning hyperplanes for each dimension (if such a partitioning exists) using certain projections of the data. The advantages of our new approach are (1) it has a rm mathematical basis (2) it is by far more e ective than existing clustering algorithms for highdimensional data (3) it is very e cient even for large data sets of high dimensionality. To demonstrate the e ectiveness and e ciency of our new approach, we perform a series of experiments on a number of di erent data sets including real data sets from CAD and molecular biology. A comparison with one of the best known algorithms (BIRCH) shows the superiority of our new approach. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999.