title: Equivalent Lumped Element Network Synthesis for Distributed Passive Microwave Circuits

abstract: In this paper, two compact lumped element ::: models synthesis methods for distributed passive ::: electromagnetic structures are presented. The first ::: method is based on the canonical Foster representation ::: while the second method uses the Brune’s synthesis ::: procedure which is a more general approach capable ::: to account for lossy structures. Both methods, used in ::: combination with a system identification approach, generate ::: equivalent network models that can be analyzed ::: using circuit simulators. In addition, two decomposition ::: techniques, allow to reduce complexity in the network ::: model synthesis by exploiting the structure’s symmetry ::: are presented. The accuracy and efficiency of the proposed ::: methods are demonstrated on two examples: a ::: lossless branch-line directional


title: Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

abstract: Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the “tilt” quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.


title: Pramipexole. A review of its use in the management of early and advanced Parkinson's disease.

abstract: UNLABELLED ::: Pramipexole is an orally active non-ergoline dopamine agonist with selective activity at dopamine receptors belonging to the D2 receptor subfamily (D2, D3, D4 receptor subtypes) and with preferential affinity for the D3 receptor subtype. It is approved as monotherapy in early Parkinson's disease and as adjunctive therapy to levodopa in patients with advanced disease experiencing motor effects because of diminished response to levodopa. The potential neuroprotective effects of pramipexole have been shown in animal and in vitro studies. Data from relatively long term (10- or 31-week) studies suggest that pramipexole monotherapy (0.375 to 6.0 mg/day) can improve activities of daily living and motor symptoms in patients with early Parkinson's disease. Pramipexole (0.375 to 4.5 mg/day for 31 or 36 weeks), as an adjunct to levodopa in advanced disease, improved activities of daily living and motor symptoms, reduced the duration and severity of 'off' periods and allowed a reduction in levodopa dosage. Mentation, behaviour and mood [Unified Parkinson's Disease Rating Scale (UPDRS) part I], and timed walking test were not significantly improved. The extent of disability improved according to the UPDRS parts II and III but, when assessed by secondary efficacy parameters, it is unclear whether disability or the severity of disease improved. No significant differences were observed in patients randomised to pramipexole or bromocriptine according to a secondary hypothesis in a prospective study in which both drugs were better than placebo. Some quality-of-life measures improved with active treatment relative to placebo. Further studies comparing pramipexole with other dopamine agonists and levodopa in patients with early and advanced Parkinson's disease would be useful. In pramipexole recipients with early disease, the most commonly experienced adverse events were nausea, dizziness, somnolence, insomnia, constipation, asthenia and hallucinations. The most commonly reported adverse events in pramipexole recipients with advanced disease were orthostatic hypotension, dyskinesias, extrapyramidal syndrome (defined as a worsening of the Parkinson's disease), dizziness, hallucinations, accidental injury, dream abnormalities, confusion, constipation, asthenia, somnolence, dystonia, gait abnormality, hypertonia, dry mouth, amnesia and urinary frequency. The incidence of some adverse events did not greatly differ between pramipexole and placebo recipients. ::: ::: ::: CONCLUSIONS ::: Pramipexole is effective as adjunctive therapy to levodopa in patients with advanced Parkinson's disease. However, the potential beneficial effects of pramipexole on disease progression need to be confirmed in clinical studies. The efficacy of pramipexole monotherapy in patients with early disease has also been demonstrated, although the use of dopamine agonists in early Parkinson's disease remains controversial.


title: Powertrace: Network-level Power Profiling for Low-power Wireless Networks

abstract: Low-power wireless networks are quickly becoming a critical part of our everyday infrastructure. Power consumption is a critical concern, but power measurement and estimation is a challenge. We present Powertrace, ::: which to the best of our knowledge is the first system for network-level power profiling of low-power wireless systems. Powertrace uses power state tracking to estimate system power consumption and a structure called energy capsules to attribute energy consumption to activities such as packet transmissions and receptions. With Powertrace, the power consumption of a system can be broken down into individual activities which allows us to answer questions such as “How much energy is spent forwarding packets for node X?”, “How much energy ::: is spent on control traffic and how much on critical data?”, and “How much energy does application X account for?”. Experiments show that Powertrace is accurate to 94% of the energy consumption of a device. To ::: demonstrate the usefulness of Powertrace, we use it to experimentally analyze the power behavior of the proposed IETF standard IPv6 RPL routing protocol and a sensor network data collection protocol. Through using Powertrace, we find the highest power consumers and are ::: able to reduce the power consumption of data collection with 24%. It is our hope that Powertrace will help the community to make empirical energy evaluation a widely used tool in the low-power wireless research community toolbox.


title: Measurement of the mechanical properties of the handball, volleyball, and basketball using DIC method: a combination of experimental, constitutive, and viscoelastic models

abstract: Many players and audiences nowadays are involved with hand sports, such as handball, volleyball, and basketball. The most important equipment of these games is ball. Each ball has its own characteristics and has to be made according to the rules of its federation. Many characteristics were defined for an approved handball, volleyball, and basketball by IHF, FIBA, and FIVB, respectively. These are including material properties, mass, pressure, etc. These characteristics so far have been well defined by many researchers to get the most efficient performance from a ball. Nonetheless, the mechanical properties of these balls so far have not been comprehensively studied. Therefore, this study was aimed to experimentally measure the compressive mechanical properties, including linear elastic, nonlinear hyperelastic, and viscoelastic (Prony series), of the handball, volleyball, and basketball using digital image correlation technique. To do this, 26 of each approved balls were mounted on a testing machine, and a set of quasi-static compressive and stress-relaxation loading were applied to them. The stress–strain and reduced relaxation function of the samples were calculated and compared. The results revealed the highest elastic modulus for the basketball (591 kPa), while the lowest one was observed for the handball (17 kPa). In addition, volleyball showed to be able to bear 5.941 and 1.615 times more stress compared to the handball and basketball, respectively. The hyperelastic material models, namely Mooney-Rivlin and Ogden, were employed to address the nonlinear mechanical behavior of the soccer balls. Finite element model of each ball was established as well and the obtained numerical data were found to be in good agreement with that of experimental ones. Many injuries to the human head have been attributed to these games due to the balls’ high speed. However, so far these injuries to the human eye have not been studied. The findings of this study, therefore, may have implications not only for understanding the mechanical properties of the balls, but also for providing raw data to investigate the injury that can be occurred for the human body by these balls, especially the eye.


title: An Efficient Constraint Handling Method for Genetic Algorithms

abstract: Many real-world search and optimization problems involve inequality and/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using genetic algorithms (GAs) or classical optimization methods, penalty function methods have been the most popular approach, because of their simplicity and ease of implementation. However, since the penalty function approach is generic and applicable to any type of constraint (linear or nonlinear), their performance is not always satisfactory. Thus, researchers have developed sophisticated penalty functions specific to the problem at hand and the search algorithm used for optimization. However, the most difficult aspect of the penalty function approach is to find appropriate penalty parameters needed to guide the search towards the constrained optimum. In this paper, GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter. Careful comparisons among feasible and infeasible solutions are made so as to provide a search direction towards the feasible region. Once sufficient feasible solutions are found, a niching method (along with a controlled mutation operator) is used to maintain diversity among feasible solutions. This allows a real-parameter GA's crossover operator to continuously find better feasible solutions, gradually leading the search near the true optimum solution. GAs with this constraint handling approach have been tested on nine problems commonly used in the literature, including an engineering design problem. In all cases, the proposed approach has been able to repeatedly find solutions closer to the true optimum solution than that reported earlier.


From gan-zi-gun-nu to anandamide and 2-arachidonoylglycerol: the ongoing story of cannabis


title: The Limits of Partisan Prejudice

abstract: Partisanship increasingly factors into the behavior of Americans in both political and nonpolitical situations, yet the bounds of partisan prejudice are largely unknown. In this paper, we systematically evaluate the limits of partisan prejudice using a series of five studies situated within a typology of prejudice. We find that partisan prejudice predicts suppression of hostile rhetoric toward one’s own party, avoidance of members of the opposition, and a desire for preferential treatment for one’s own party. While these behaviors may cause incidental or indirect harm to the opposition, we find that even the most affectively polarized—those with the strongest disdain for the opposition—are no more likely to intentionally harm the opposition than those with minimal levels of affective polarization.


title: Explicit B-spline regularization in diffeomorphic image registration

abstract: Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g. Gaussian smoothing of the vector fields (a la Thirion's Demons \citep{thirion1998}). In the context of the original Demons' framework, the so-called {\it directly manipulated free-form deformation} \citep{tustison2009} can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline ``flavored'' diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm \citep{avants2008}, implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.


title: Three-Dimensional Radiofrequency Tissue Tightening: A Proposed Mechanism and Applications for Body Contouring

abstract: The use of radiofrequency energy to produce collagen matrix contraction is presented. Controlling the depth of energy delivery, the power applied, the target skin temperature, and the duration of application of energy at various soft tissue levels produces soft tissue contraction, which is measurable. This technology allows precise soft tissue modeling at multiple levels to enhance the result achieved over traditional suction-assisted lipectomy as well as other forms of energy such as ultrasonic and laser-generated lipolysis.


title: Interfacing with a speller using EOG glasses

abstract: Bio-signal based human computer interface (HCI) systems are a good alternative to standard touch based interfaces, offering subjects with motor impairments an alternative means of communication. This work investigates the use of electrooculography (EOG) to interface with a speller application. The use of a wireless EOG glasses currently on the market, known as JINS MEME, comprising only three dry electrodes, is compared to the standard two-pair EOG electrode configuration using wet electrodes. A blink accuracy of 97.63% and a saccade accuracy of 73.38% was obtained using a novel thresholding algorithm on the EOG data collected through the MEME glasses and the results were shown to be comparable to those obtained using wet surface electrodes. A real-time menu driven keyboard is also proposed and tested using the different eye movement recording techniques. In this case an average writing speed of 7.11 letters per minute, with a classification accuracy of 90.59% was obtained using signals recorded from the MEME glasses, showing that this new technology offers an ergonomic system that can easily be used in eye-based assistive applications.


title: Transformational and Transactional Leadership: Association With Attitudes Toward Evidence-Based Practice

abstract: Objective: Leadership in organizations is important in shaping workers’ perceptions, responses to organizational change, and acceptance of innovations, such as evidence-based practices. Transformational leadership inspires and motivates followers, whereas transactional leadership is based more on reinforcement and exchanges. Studies have shown that in youth and family service organizations, mental health providers’ attitudes toward adopting an evidence-based practice are associated with organizational context and individual provider differences. The purpose of this study was to expand these findings by examining the association between leadership and mental health providers’ attitudes toward adopting evidence-based practice. Methods: Participants were 303 public-sector mental health service clinicians and case managers from 49 programs who were providing mental health services to children, adolescents, and their families. Data were gathered on providers’ characteristics, attitudes toward evidence-based practices, and perceptions of their supervisors’ leadership behaviors. Zero-order correlations and multilevel regression analyses were conducted that controlled for effects of service providers’ characteristics. Results: Both transformational and transactional leadership were positively associated with providers’ having more positive attitudes toward adoption of evidence-based practice, and transformational leadership was negatively associated with providers’ perception of difference between the providers’ current practice and evidence-based practice. Conclusions: Mental health service organizations may benefit from improving transformational and transactional supervisory leadership skills in preparation for implementing evidence-based practices. (Psychiatric Services 57:1162–1169, 2006)


title: A high speed configurable FPGA architecture for k-mean clustering

abstract: This paper presents a high speed configurable FPGA architecture for k-means clustering. The proposed architecture is highly pipelined, parallel and fully configurable. It can achieve an operating frequency of 400 MHz, which is at least three times faster than prior works. The proposed architecture addresses the high speed and throughput requirements of machine vision, multi-media and data mining applications.


title: Conformance Checking Using Cost-Based Fitness Analysis

abstract: The growing complexity of processes in many organizations stimulates the adoption of business process analysis techniques. Typically, such techniques are based on process models and assume that the operational processes in reality conform to these models. However, experience shows that reality often deviates from hand-made models. Therefore, the problem of checking to what extent the operational process conforms to the process model is important for process management, process improvement, and compliance. In this paper, we present a robust replay analysis technique that is able to measure the conformance of an event log for a given process model. The approach quantifies conformance and provides intuitive diagnostics (skipped and inserted activities). Our technique has been implemented in the ProM 6framework. Comparative evaluations show that the approach overcomes many of the limitations of existing conformance checking techniques.


title: Predicting Stock Market Trends by Recurrent Deep Neural Networks

abstract: Investors make decisions based on various factors, including consumer price index, price-earnings ratio, and also miscellaneous events reported by newspapers. In order to assist their decisions in a timely manner, many studies have been conducted to automatically analyze those information sources in the last decades. However, the majority of the efforts was made for utilizing numerical information, partly due to the difficulty to process natural language texts and to make sense of their temporal properties. This study sheds light on this problem by using deep learning, which has been attracting much attention in various areas of research including pattern mining and machine learning for its ability to automatically construct useful features from a large amount of data. Specifically, this study proposes an approach to market trend prediction based on a recurrent deep neural network to model temporal effects of past events. The validity of the proposed approach is demonstrated on the real-world data for ten Nikkei companies.


title: Mediating roles of self-presentation desire in online game community commitment and trust behavior of Massive Multiplayer Online Role-Playing Games

abstract: Massive Multiplayer Online Role-Playing Games (MMORPGs), which allow simultaneous participation of several gamers, have attracted a great deal of attention recently. Since MMORPGs can be categorized as a type of online community, the behavior of MMORPGs users needs to be considered as the general behavior in online communities. However, previous studies of online communities did not pay enough attention to MMORPGs, in which users can express themselves by interacting actively through games and game avatars. Understanding the characteristics of MMORPGs as online game communities where users communicate and interact will allow games to be vitalized and users to be immersed in games in a more positive way. Hence, using self-presentation theory and social identity theory, this study examined the factors influencing self-presentation desire and the mediating role of self-presentation desire examined in terms of trust of and commitments to online game communities. The results showed that the interactivity in the spaces of MMORPGs had the biggest impacts on self-presentation desire; personal innovativeness and game design quality also was influential. The results also indicated that self-presentation desire caused trust of online games and eventually led to even stronger commitments to gamers.


title: Dataset-driven research for improving recommender systems for learning

abstract: In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or Each-Movie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for learning. We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to improve the performance of recommendation algorithms.


title: Zero-knowledge from secure multiparty computation

abstract: We present a general construction of a zero-knowledge proof for an NP relation R(x,w) which only makes a black-box use of a secure protocol for a related multi-partyfunctionality f. The latter protocol is only required to be secure against a small number of "honest but curious" players. As an application, we can translate previous results on the efficiency of secure multiparty computation to the domain of zero-knowledge, improving over previous constructions of efficient zero-knowledge proofs. In particular, if verifying R on a witness of length m can be done by a circuit C of size s, and assuming one-way functions exist, we get the following types of zero-knowledge proof protocols. Approaching the witness length. If C has constant depth over ∧,∨,⊕, - gates of unbounded fan-in, we get a zero-knowledge protocol with communication complexity m·poly(k)·polylog(s), where k is a security parameter. Such a protocol can be implemented in either the standard interactive model or, following a trusted setup, in a non-interactive model. "Constant-rate" zero-knowledge. For an arbitrary circuit C of size s and a bounded fan-in, we geta zero-knowledge protocol with communication complexity O(s)+poly(k). Thus, for large circuits, the ratio between the communication complexity and the circuit size approaches a constant. This improves over the O(ks) complexity of the best previous protocols.


title: Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory

abstract: Damage to the hippocampal system disrupts recent memory but leaves remote memory intact. Our account of this suggests that memories are first stored via synaptic changes in the hippocampal system; that these changes support reinstatement of recent memories in the neocortex; that neocortical synapses change a little on each reinstatement; and that remote memory is based on accumulated neocortical changes. Models that learn via adaptive changes to connections help explain this organization. These models discover the structure in ensembles of items if learning of each item is gradual and interleaved with learning about other items. This suggests that neocortex learns slowly to discover the structure in ensembles of experiences. The hippocampal system permits rapid learning of new items without disrupting this structure, and reinstatement of new memories interleaves them with others to integrate them into structured neocortical memory systems. Psychological Review, in press


title: The Machine Vision Toolbox: a MATLAB toolbox for vision and vision-based control

abstract: The article described an open-source toolbox for machine vision called Machine Vision Toolbox (MVT). MVT includes more than 60 functions including image file reading and writing, acquisition, display, filtering, blob, point and line feature extraction, mathematical morphology, homographies, visual Jacobians, camera calibration, and color space conversion. MVT can be used for research into machine vision but is also versatile enough to be usable for real-time work and even control. MVT, combined with MATLAB and a model workstation computer, is a useful and convenient environment for the investigation of machine vision algorithms. The article illustrated the use of a subset of toolbox functions for some typical problems and described MVT operations including the simulation of a complete image-based visual servo system.


title: Refactoring Test Code

abstract: Two key aspects of extreme programming (XP) are unit testing and merciless refactoring. Given the fact that the ideal test code / production code ratio approaches 1:1, it is not surprising that unit tests are being refactored. We found that refactoring test code is different from refactoring production code in two ways: (1) there is a distinct set of bad smells involved, and (2) improving test code involves additional test-specific refactorings. To share our experiences with other XP practitioners, we describe a set of bad smells that indicate trouble in test code, and a collection of test refactorings to remove these smells.


title: Spinal Instability Neoplastic Score: An Analysis of Reliability and Validity From the Spine Oncology Study Group

abstract: Purpose Standardized indications for treatment of tumor-related spinal instability are hampered by the lack of a valid and reliable classification system. The objective of this study was to determine the interobserver reliability, intraobserver reliability, and predictive validity of the Spinal Instability Neoplastic Score (SINS). Methods Clinical and radiographic data from 30 patients with spinal tumors were classified as stable, potentially unstable, and unstable by members of the Spine Oncology Study Group. The median category for each patient case (consensus opinion) was used as the gold standard for predictive validity testing. On two occasions at least 6 weeks apart, each rater also scored each patient using SINS. Each total score was converted into a three-category data field, with 0 to 6 as stable, 7 to 12 as potentially unstable, and 13 to 18 as unstable. Results The statistics for interobserver reliability were 0.790, 0.841, 0.244, 0.456, 0.462, and 0.492 for the fields of location, pain, bone quality, alignment, vertebral body collapse, and posterolateral involvement, respectively. The statistics for intraobserver reliability were 0.806, 0.859, 0.528, 0.614, 0.590, and 0.662 for the same respective fields. Intraclass correlation coefficients for inter- and intraobserver reliability of total SINS score were 0.846 (95% CI, 0.773 to 0.911) and 0.886 (95% CI, 0.868 to 0.902), respectively. The statistic for predictive validity was 0.712 (95% CI, 0.676 to 0.766). Conclusion SINS demonstrated near-perfect inter- and intraobserver reliability in determining three clinically relevant categories of stability. The sensitivity and specificity of SINS for potentially unstable or unstable lesions were 95.7% and 79.5%, respectively. J Clin Oncol 29:3072-3077. © 2011 by American Society of Clinical Oncology


title: On Providing Downlink Services in Collocated Spectrum-Sharing Macro and Femto Networks

abstract: Femtocells have been considered by the wireless industry as a cost-effective solution not only to improve indoor service providing, but also to unload traffic from already overburdened macro networks. Due to spectrum availability and network infrastructure considerations, a macro network may have to share spectrum with overlaid femtocells. In spectrum-sharing macro and femto networks, inter-cell interference caused by different transmission powers of macrocell base stations (MBSs) and femtocell access points (FAPs), in conjunction with potentially densely deployed femtocells, may create dead spots where reliable services cannot be guaranteed to either macro or femto users. In this paper, based on a thorough analysis of downlink (DL) outage probabilities (OPs) of collocated spectrum-sharing orthogonal frequency division multiple access (OFDMA) based macro and femto networks, we devise a decentralized strategy for an FAP to self-regulate its transmission power level and usage of radio resources depending on its distance from the closest MBS. Simulation results show that the derived closed-form lower bounds of DL OPs are tight, and the proposed decentralized femtocell self-regulation strategy is able to guarantee reliable DL services in targeted macro and femto service areas while providing superior spatial reuse, for even a large number of spectrum-sharing femtocells deployed per cell site.


title: Increasing Acceptance of Free-Floating Car Sharing Systems Using Smart Relocation Strategies: A Survey Based Study of car2go Hamburg

abstract: Rapid growth rates seem to indicate that recently introduced free-floating car sharing (FFCS) can mean a breakthrough for the car sharing idea. Like other vehicle sharing systems, FFCS systems face significant regional demand fluctuations. Thus, providers might have to reposition empty vehicles to not lose potential customers. However, this aspect has been hardly examined in research and practice. This study aims to investigate appropriate relocation strategies for this new type of car sharing systems regarding the case of car2go. For this purpose a survey based evaluation was conducted, new relocation strategies were developed, and a discrete-event simulation model for the evaluation with real time data from car2go was drafted. The results indicate that availability of cars is crucial for customer acceptance, but customers are open for smart relocation strategies. Furthermore, the developed simulation approach provides the basis for an in-depth analysis of various strategies with different objectives using a realistic demand forecast.


title: Incremental Learning With Saliency Map for Moving Object Detection

abstract: Moving object detection is a key to intelligent video analysis. On the one hand, what moves are not only interesting objects but also noise and cluttered background. On the other hand, moving objects without rich texture are prone to not be detected. Therefore, there are undesirable false alarms and missed alarms in the results of many algorithms of moving object detection. To reduce the false alarms and missed alarms, in this paper we propose to incorporate a saliency map into an incremental subspace analysis framework in which the saliency map makes the estimated background have less of a chance than the foreground (i.e., moving objects) to contain salient objects. The proposed objective function systematically takes into account the properties of sparsity, low rank, connectivity, and saliency. An alternative minimization algorithm is proposed to seek the optimal solutions. The experimental results on both the Perception Test Images Sequences data set and Wallflower data set demonstrate that the proposed method is effective in reducing false alarms and missed alarms.


title: Effect of homeopathic Lycopodium clavatum on memory functions and cerebral blood flow in memory-impaired rats

abstract: Background: Lycopodium clavatum (Lyc) is a widely used homeopathic medicine for the liver, urinary and digestive disorders. Recently, acetyl cholinesterase (AchE) inhibitory activity has been found inLycalkaloid extract, whichcouldbe beneficialin dementia disorder. However, the effect of Lyc has not yet been explored in animal model of memory impairment and on cerebral blood flow. Aim: The present study was planned to explore the effect of Lyc on learning and memory function and cerebral blood flow (CBF) in intracerebroventricularly (ICV) administered streptozotocin (STZ) induced memory impairment in rats. Materials and methods: Memory deficit was induced by ICV administration of STZ (3 mg/kg) in rats on 1st and 3rd day. Male SD rats were treated with Lyc Mother Tincture (MT) 30, 200 and 1000 for 17 days. Learning and memory was evaluated by Morris water maze test on 14th, 15th and 16th day. CBF was measured by Laser Doppler flow meter on 17th day. Results: STZ (ICV) treated rats showed impairment in learning and memory along with reduced CBF. Lyc MT and 200 showed improvement in learning and memory. There was increased CBF in STZ (ICV) treated rats at all the potencies of Lyc studied. Conclusion: The above study suggests that Lyc may be used as a drug of choice in condition of memory impairment due to its beneficial effect on CBF. Homeopathy (2015) 104 ,2 4e28.


title: Kineograph: taking the pulse of a fast-changing and connected world

abstract: Kineograph is a distributed system that takes a stream of incoming data to construct a continuously changing graph, which captures the relationships that exist in the data feed. As a computing platform, Kineograph further supports graph-mining algorithms to extract timely insights from the fast-changing graph structure. To accommodate graph-mining algorithms that assume a static underlying graph, Kineograph creates a series of consistent snapshots, using a novel and efficient epoch commit protocol. To keep up with continuous updates on the graph, Kineograph includes an incremental graph-computation engine. We have developed three applications on top of Kineograph to analyze Twitter data: user ranking, approximate shortest paths, and controversial topic detection. For these applications, Kineograph takes a live Twitter data feed and maintains a graph of edges between all users and hashtags. Our evaluation shows that with 40 machines processing 100K tweets per second, Kineograph is able to continuously compute global properties, such as user ranks, with less than 2.5-minute timeliness guarantees. This rate of traffic is more than 10 times the reported peak rate of Twitter as of October 2011.


title: Geometric calibration of a mobile C-arm for intraoperative cone-beam CT.

abstract: A geometric calibration method that determines a complete description of source-detector geometry was adapted to a mobile C-arm for cone-beam computed tomography (CBCT). The non-iterative calibration algorithm calculates a unique solution for the positions of the source (X(s), Y(s), Z(s)), detector (X(d), Y(d), Z(d)), piercing point (U(o), V(o)), and detector rotation angles (phi, theta, eta) based on projections of a phantom consisting of two plane-parallel circles of ball bearings encased in a cylindrical acrylic tube. The prototype C-arm system was based on a Siemens PowerMobil modified to provide flat-panel CBCT for image-guided interventions. The magnitude of geometric nonidealities in the source-detector orbit was measured, and the short-term (approximately 4 h) and long-term (approximately 6 months) reproducibility of the calibration was evaluated. The C-arm exhibits large geometric nonidealities due to mechanical flex, with maximum departures from the average semicircular orbit of deltaU(o) = 15.8 mm and deltaV(o) = 9.8 mm (for the piercing point), deltaX and deltaY = 6-8 mm and deltaZ = 1 mm (for the source and detector), and deltaphi approximately 2.9 degrees, deltatheta approximately 1.9 degrees, and delta eta approximately 0.8 degrees (for the detector tilt/rotation). Despite such significant departures from a semicircular orbit, these system parameters were found to be reproducible, and therefore correctable by geometric calibration. Short-term reproducibility was < 0.16 mm (subpixel) for the piercing point coordinates, < 0.25 mm for the source-detector X and Y, < 0.035 mm for the source-detector Z, and < 0.02 degrees for the detector angles. Long-term reproducibility was similarly high, demonstrated by image quality and spatial resolution measurements over a period of 6 months. For example, the full-width at half-maximum (FWHM) in axial images of a thin steel wire increased slightly as a function of the time (delta) between calibration and image acquisition: FWHM=0.62, 0.63, 0.66, 0.71, and 0.72 mm at delta = 0 s, 1 h, 1 day, 1 month, and 6 months, respectively. For ongoing clinical trials in CBCT-guided surgery at our institution, geometric calibration is conducted monthly to provide sufficient three-dimensional (3D) image quality while managing time and workflow considerations of the calibration and quality assurance process. The sensitivity of 3D image quality to each of the system parameters was investigated, as was the tolerance to systematic and random errors in the geometric parameters, showing the most sensitive parameters to be the piercing point coordinates (U(o), V(o)) and in-plane positions of the source (X(s), Y(s)) and detector (X(d), Y(d)). Errors in the out-of-plane position of the source (Z(s)) and detector (Z(d)) and the detector angles (phi, theta, eta) were shown to have subtler effects on 3D image quality.


title: Adoption of the cloud business model in Indonesia: triggers, benefits, and challenges

abstract: The objective of this paper is to identify the current state of cloud business model adoption in Indonesia. We use an exploratory case study approach, since the number of publications on this topic is limited. We conducted 12 case studies with cloud service providers in Indonesia, which consisted of interviews and document studies. The analysis of our interviews revealed that the cloud business model adoption in Indonesia is mainly initiated by the local IT services providers. We also found that market demand was the main triggering factor for adopting the cloud business model. One of the key benefits that was mentioned, is the positive trend in revenue gains. The most encountered challenge for Indonesian cloud service providers is the low speed of internet connection in rural areas. The findings revealed from our study can serve as reference for IT practitioners and researchers who look for information and understanding on cloud business model adoption in Indonesia.


title: Representations in Children With Mathematical Learning Disability

abstract: Children with a mathematical learning disability (MLD, n = 19) and low achieving (LA, n = 43) children were identified using mathematics achievement scores below the 11th percentile and between the 11th and 25th percentiles, respectively. A control group of typically achieving (TA, n = 50) children was also identified. Number line and speed of processing tasks were administered in 1st and 2nd grade and a working memory battery in 1st grade. In both grades, the MLD children were less accurate in their number line placements and more reliant on a natural number-magnitude representational system to make these placements than were TA children. The TA children were more reliant on the school-taught linear system in both grades. The performance of the LA children was similar to that of the MLD children in first grade and to the TA children in second. The central executive component of working memory contributed to across-grade improvements in number line performance and to group differences in this performance.


title: Word Embedding-based Antonym Detection using Thesauri and Distributional Information

abstract: This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, however, cannot capture antonyms since they depend on the distributional hypothesis. Our approach utilizes supervised synonym and antonym information from thesauri, as well as distributional information from large-scale unlabelled text data. The evaluation results on the GRE antonym question task show that our model outperforms the state-of-the-art systems and it can answer the antonym questions in the F-score of 89%.


title: Social influence on the use of Clinical Decision Support Systems: Revisiting the Unified Theory of Acceptance and Use of Technology by the fuzzy DEMATEL technique

abstract: The aim of study is to examine whether social influence affects medical professionals' behavioral intention to use while introducing a new Clinical Decision Support System (CDSS). The series of Technology Acceptance Models (TAMs) have been widely applied to examine new technology acceptance by scholars; nevertheless, these models omit system diversity and the user's profession. On the other hand, causal analysis greatly affects the efficiency of decision-making, and it is usually analyzed by Structural Equation Modeling (SEM); however, the method is often misapplied. This research applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to explore the causal relationship between the significant Unified Theory of Acceptance and Use of Technology (UTAUT) variables. Fuzzy concept is applied to illustrate human vague judgment. It is significant that, in contrary with UTAUT, this study found that social influence does not matter in the behavioral intention to use the CDSS for medical professionals.


title: Cloud Computing for Agent-Based Urban Transportation Systems

abstract: Agent-based traffic management systems can use the autonomy, mobility, and adaptability of mobile agents to deal with dynamic traffic environments. Cloud computing can help such systems cope with the large amounts of storage and computing resources required to use traffic strategy agents and mass transport data effectively. This article reviews the history of the development of traffic control and management systems within the evolving computing paradigm and shows the state of traffic control and management systems based on mobile multiagent technology.


title: Caglioti E.: A plagiarism detection procedure in three steps: selection, matches and 'squares

abstract: We present a detailed description of an algorithm tailored to detect external plagiarism in PAN-09 competition. The algorithm is divided into three steps: a first reduction of the size of the problem by a selection of ten suspicious plagiarists using a n-gram distance on properly recoded texts. A search for matches after T9-like recoding. A “joining algorithm” that merges selected matches and is able to detect obfuscated plagiarism. The results are briefly discussed.


title: Multiple intraosseous lipomatosis: a case report

abstract: IntroductionIntraosseous lipoma is an uncommon entity that presents almost exclusively as a solitary lesion. Multiple intraosseous lipomas are exceedingly rare with only a few cases being reported in the literature.Case presentationWe present a case of 43-year-old African American female who presented with left leg and left wrist pain. The initial radiographs revealed well-defined radiolucent lesions in multiple bones involving the left wrist and the left lower limb. The magnetic resonance demonstrated multiple lesions, which showed high signal on the T1 and low signal on the fat suppressed T2 images. This favored the diagnosis of intraosseous lipomatosis that was confirmed by biopsy.ConclusionMultiple intraosseous lipomatosis is an uncommon but important differential for multiple radiolucent lesions on the plain radiographs. This condition can lead to pathological fractures. Magnetic resonance imaging can aid in providing an accurate diagnosis. The awareness of this condition can help the clinician in guiding the correct diagnosis and management.


title: A STUDY ON THE IMPORTANCE OF IMAGE PROCESSING AND ITS APLLICATIONS

abstract: A systematic study on importance of image processing and its applications to the field of computer vision is carried out in this paper. An image is defined as an array, or a matrix, of square pixels (elements of picture) arranged in rows and columns. Image processing is a procedure of converting an image into digital form and carry out some operation on it, in order to get an improved image and take out several helpful information from it. Mathematically image processing is defined as the processing of a two dimensional picture by a computer i.e., an image is defined as a function of two real variables, like t(x, y) with an amplitude such as brightness of an image at the coordinate point (a, b). The outcome of image processing can be an image or a set of features or characteristics related to the image. Most image processing methods treats an image as a two-dimensional signal and implementing standard signalprocessing techniques to it. The goal of this operation can be divided into 3 categories. Firstly image processing in which input is an image and output is also an image; secondly image analysis in which input is an image and output are the dimensions or measurements. Finally image understanding in which input is an image and output is the standard description of an image. Some of the important applications of image processing in the field of science and technology include computer vision, remote sensing, feature extraction, face detection, forecasting, optical character recognition, finger-print detection, optical sorting, argument reality, microscope imaging, lane departure caution system, Non-photorealistic representation, medical image processing, and morphological imaging.


title: High-Sensitivity Metamaterial-Inspired Sensor for Microfluidic Dielectric Characterization

abstract: A new metamaterial-inspired microwave microfluidic sensor is proposed in this paper. The main part of the device is a microstrip coupled complementary split-ring resonator (CSRR). At resonance, a strong electric field will be established along the sides of CSRR producing a very sensitive area to a change in the nearby dielectric material. A micro-channel is positioned over this area for microfluidic sensing. The liquid sample flowing inside the channel modifies the resonance frequency and peak attenuation of the CSRR resonance. The dielectric properties of the liquid sample can be estimated by establishing an empirical relation between the resonance characteristics and the sample complex permittivity. The designed microfluidic sensor requires a very small amount of sample for testing since the cross-sectional area of the sensing channel is over five orders of magnitude smaller than the square of the wavelength. The proposed microfluidic sensing concept is compatible with lab-on-a-chip platforms owing to its compactness.


title: Cross-project defect prediction using a connectivity-based unsupervised classifier

abstract: Defect prediction on projects with limited historical data has attracted great interest from both researchers and practitioners. Cross-project defect prediction has been the main area of progress by reusing classifiers from other projects. However, existing approaches require some degree of homogeneity (e.g., a similar distribution of metric values) between the training projects and the target project. Satisfying the homogeneity requirement often requires significant effort (currently a very active area of research). An unsupervised classifier does not require any training data, therefore the heterogeneity challenge is no longer an issue. In this paper, we examine two types of unsupervised classifiers: a) distance-based classifiers (e.g., k-means); and b) connectivity-based classifiers. While distance-based unsupervised classifiers have been previously used in the defect prediction literature with disappointing performance, connectivity-based classifiers have never been explored before in our community. We compare the performance of unsupervised classifiers versus supervised classifiers using data from 26 projects from three publicly available datasets (i.e., AEEEM, NASA, and PROMISE). In the cross-project setting, our proposed connectivity-based classifier (via spectral clustering) ranks as one of the top classifiers among five widely-used supervised classifiers (i.e., random forest, naive Bayes, logistic regression, decision tree, and logistic model tree) and five unsupervised classifiers (i.e., k-means, partition around medoids, fuzzy C-means, neural-gas, and spectral clustering). In the within-project setting (i.e., models are built and applied on the same project), our spectral classifier ranks in the second tier, while only random forest ranks in the first tier. Hence, connectivity-based unsupervised classifiers offer a viable solution for cross and within project defect predictions.


title: The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

abstract: The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3′-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.


title: Trusting the Looks of Others: Gaze Effects of Faces in Social Settings

abstract: Eye gaze is an important social cue in human communication that serves as a predictor of changes in attention, indicates social interest, and even affects the evaluation of objects that have been looked at. However, it needs to be shown how this strong nonverbal signal affects social and aesthetic evaluations in social settings. In this study, we presented natural scenes with two faces in three gaze conditions showing different directions of gaze. Participants evaluated faces that were looked at or not, that showed direct or averted gaze, for attractiveness (aesthetic) as well as trustworthiness (social). In Experiment 1, faces looking directly at the perceiver were rated as more attractive and as more trustworthy. In Experiment 2, when the direct gaze condition was omitted, faces that were looked at by another face were judged as more trustworthy. In Experiment 3a, participants did not remember the directions of gaze of two faces, demonstrating the dependence of the judgement on the actual situation and ...


title: Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning

abstract: Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it. Traditionally, such algorithms make decisions, i.e., select actions to execute, at every single time step of the agent-environment interactions. In this paper, we propose a novel framework, Fine Grained Action Repetition (FiGAR), which enables the agent to decide the action as well as the time scale of repeating it. ::: FiGAR can be used for improving any Deep Reinforcement Learning algorithm which maintains an explicit policy estimate by enabling temporal abstractions in the action space and implicitly enabling planning through sequences of repetitive macro-actions. ::: We empirically demonstrate the efficacy of our framework by showing performance improvements on top of three policy search algorithms in different domains: Asynchronous Advantage Actor Critic in the Atari 2600 domain, Trust Region Policy Optimization in Mujoco domain and Deep Deterministic Policy Gradients in the TORCS car racing domain.


title: Formalization of Weak Emergence in Multiagent Systems

abstract: Emergence becomes a distinguishing system feature as system complexity grows with the number of components, interactions, and connectivities. Examples of emergent behaviors include the flocking of birds, traffic jams, and hubs in social networks, among others. Despite significant research interest in recent years, there is a lack of formal methods to understand, identify, and predict emergent behavior in multiagent systems. Existing approaches either require detailed prior knowledge about emergent behavior or are computationally infeasible. This article introduces a grammar-based approach to formalize and identify the existence and extent of emergence without the need for prior knowledge of emergent properties. Our approach is based on weak (basic) emergence that is both generated and autonomous from the underlying agents. We employ formal grammars to capture agent interactions in the forms of words written on a common tape. Our formalism captures agents of diverse types and open systems. We propose an automated approach for the identification of emergent behavior and show its benefits through theoretical and experimental analysis. We also propose a significant reduction of state-space explosion through the use of our proposed degree of interaction metrics. Our experiments using the boids model show the feasibility of our approach but also highlight future avenues of improvement.


title: Stereological studies of the effects of sodium benzoate or ascorbic acid on rats’ cerebellum

abstract: OBJECTIVES ::: To evaluate the cerebellar structure in sodium benzoate (NaB) or ascorbic acid (AA) treated rats. ::: ::: ::: METHODS ::: This experimental study was conducted between May and September 2013 in the Laboratory Animal Center of Shiraz University of Medical Sciences, Shiraz, Iran. The rats received distilled either water, NaB (200mg/kg/day), AA (100mg/kg/day), or NaB+AA. The hemispheres were removed after 28 days and underwent quantitative study. ::: ::: ::: RESULTS ::: The total volume of the cerebellar hemisphere, its cortex, intracerebellar nuclei; the total number of the Purkinje, Bergman, granule, neurons, and glial cells of the molecular layer; and neurons and glial cells of the intracerebellar nuclei reduced by 21-52% in the NaB-treated rats compared with the distilled water group (p=0.004). The total number of the Purkinje, Bergman, Golgi, and granule cells was 29-45% higher in the AA-treated rats compared with the distilled water group (p=0.05). However, these measures reduced by 17-50% in the NaB+AA-treated rats compared with the distilled water group (p=0.004). The NaB+AA group did not induce any significant structural changes in comparison with the NaB group (p>0.05). ::: ::: ::: CONCLUSIONS ::: The NaB exposure with or without AA treatment could alter the cerebellum. Yet, AA could prevent the loss of some cells in the cerebellum.


title: doi:10.3906/elk-1102-1015 Abstract feature extraction for text classification

abstract: Feature selection and extraction are frequently used solutions to overcome the curse of dimensionality in text classification problems. We introduce an extraction method that summarizes the features of the document samples, where the new features aggregate information about how much evidence there is in a document, for each class. We project the high dimensional features of documents onto a new feature space having dimensions equal to the number of classes in order to form the abstract features. We test our method on 7 different text classification algorithms, with different classifier design approaches. We examine performances of the classifiers applied on standard text categorization test collections and show the enhancements achieved by applying our extraction method. We compare the classification performance results of our method with popular and well-known feature selection and feature extraction schemes. Results show that our summarizing abstract feature extraction method encouragingly enhances classification performances on most of the classifiers when compared with other methods.


title: Triple Scissor Extender Robot Arm: A Solution to the Last One Foot Problem of Manipulation

abstract: Final assembly, inspection, and maintenance of aircrafts, automobiles, and industrial equipment often requires a robot to reach confined areas that are difficult to access. While a robot can move freely in open space, the last one foot to a target site in a manufacturing environment is typically cluttered with complex obstacles. Here, we present a novel solution to the last one foot problem. Inspired by plant stem growth, a new design concept for an expandable arm, the triple scissor extender robot arm (TSERA), is developed to access a confined area through a narrow channel. The robot consists of a series of expandable segments each of which is able to extend and tilt. Thus, the robot can elongate its structure along a narrow, winding space. The novel design concept is accompanied by a new algorithm for path planning, the sequential expansion algorithm. Unlike traditional algorithms for articulated robot path planning, TSERA's sequential expansion algorithm reduces the otherwise computationally expensive multibody path planning problem to a single rigid body kinematics problem. Exploiting the serial nature of expandable structure, we can compute arm motion by sequentially solving a simple inverse kinematics problem for each segment.


title: BREAKING OF DORMANCY IN RHUBARB (RHEUM RIBES L.)

abstract: Freshly harvested seeds of Rhubarb exhibited a high degree of dormancy when germination has been studied in different temperatures in distilled water. In the present paper, different treatments including moist - chilling, immersing in NaOCl, acid scarification, surgical procedure, and treatment by CaCl 2 and KNO 3 were performed to determine the most effective method for removing dormancy in Rhubarb (Rheum ribes L.). When seed coats (pericarp + testa) were completely removed, the root and hypocotyls of excised embryo from dormant seeds did not elongate. This indicates that dormancy is imposed by the embryo and those inhibitors in seed embryo may be responsible for the dormancy. Germination was observed only in CaCl 2 and KNO 3 treatments. Optimum temperature for germination was 10 °C and dormancy was partially removed when seeds were germinated in different concentrations of CaCl 2. Moreover, CaCl 2 solutions promoted germination more than KNO 3.


title: Leveraging Virtual and Real Person for Unsupervised Person Re-identification

abstract: Person re-identification (re-ID) is a challenging problem especially when no labels are available for training. Although recent deep re-ID methods have achieved great improvement, it is still difficult to optimize deep re-ID model without annotations in training data. To address this problem, this study introduces a novel approach for unsupervised person re-ID by leveraging virtual and real data. Our approach includes two components: virtual person generation and training of deep re-ID model. For virtual person generation, we learn a person generation model and a camera style transfer model using unlabeled real data to generate virtual persons with different poses and camera styles. The virtual data is formed as labeled training data, enabling subsequently training deep re-ID model in supervision. For training of deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data. The final re-ID model is achieved by iterating between step 2 and step 3 until convergence. Experimental results on two large-scale datasets, Market-1501 and DukeMTMC-reID, demonstrate the effectiveness of our approach and shows that the state of the art is achieved in unsupervised person re-ID.


title: Medical applications: a database and characterization of apps in Apple iOS and Android platforms

abstract: BackgroundMedical applications (apps) for smart phones and tablet computers are growing in number and are commonly used in healthcare. In this context, there is a need for a diverse community of app users, medical researchers, and app developers to better understand the app landscape.MethodsIn mid-2012, we undertook an environmental scan and classification of the medical app landscape in the two dominant platforms by searching the medical category of the Apple iTunes and Google Play app download sites. We identified target audiences, functions, costs and content themes using app descriptions and captured these data in a database. We only included apps released or updated between October 1, 2011 and May 31, 2012, with a primary “medical” app store categorization, in English, that contained health or medical content. Our sample of Android apps was limited to the most popular apps in the medical category.ResultsOur final sample of Apple iOS (n = 4561) and Android (n = 293) apps illustrate a diverse medical app landscape. The proportion of Apple iOS apps for the public (35%) and for physicians (36%) is similar. Few Apple iOS apps specifically target nurses (3%). Within the Android apps, those targeting the public dominated in our sample (51%). The distribution of app functions is similar in both platforms with reference being the most common function. Most app functions and content themes vary considerably by target audience. Social media apps are more common for patients and the public, while conference apps target physicians.ConclusionsWe characterized existing medical apps and illustrated their diversity in terms of target audience, main functions, cost and healthcare topic. The resulting app database is a resource for app users, app developers and health informatics researchers.


title: A mode-matching 130-kHz ring-coupled gyroscope with 225 ppm initial driving/sensing mode frequency splitting

abstract: A degenerate mode 130-kHz ring-coupled gyroscope with auxiliary transducer array is designed to enhance the sensitivity as well as the mode-matching feature. The proof-of-concept device with 3 µm transducer's gap is fabricated using a conventional (100) silicon-on-insulator (SOI) wafer process with only two lithography steps. The auxiliary parallel-plate transducer array is located at the maximum displacement of the vibrating ring to enhance the electromechanical coupling while reducing the sensing noise. The in-plane trefoil mode (n=3) is adopted to alleviate the initial frequency splitting in (100) crystalline silicon device layer. The average frequency split for the drive/sense modes over multiple tested devices is only 225 ppm with the mean resonance frequency of 130 kHz. The measured Q-factor is 50 in atmospheric pressure and up to 10,000 in vacuum. Owing to the larger transduction area benefitting from the transducer array design, a low dc-bias voltage (V P ) of 3 V in vacuum (21 V in air) is sufficient to sustain the driving loop oscillation. As integrated with the sensing circuits to operate the proposed gyroscope, a scale factor of 2.2 mV/°/s and resolution of 0.26 °/s, respectively, are characterized in atmospheric pressure.


title: Power Electronics and Motor Drives in Electric, Hybrid Electric, and Plug-In Hybrid Electric Vehicles

abstract: With the requirements for reducing emissions and improving fuel economy, automotive companies are developing electric, hybrid electric, and plug-in hybrid electric vehicles. Power electronics is an enabling technology for the development of these environmentally friendlier vehicles and implementing the advanced electrical architectures to meet the demands for increased electric loads. In this paper, a brief review of the current trends and future vehicle strategies and the function of power electronic subsystems are described. The requirements of power electronic components and electric motor drives for the successful development of these vehicles are also presented.


title: Advanced Cooling Methods for High-Speed Electrical Machines

abstract: High-speed electrical machines are gaining increasing attention, as they enable higher power densities in several applications such as micromachining spindles and turbo compressors. This brings along an important challenge in thermal management due to the higher loss densities in the machine. Therefore, a careful thermal analysis is required along with the electromagnetic and mechanical considerations during the design phase of the machines. In this paper, different forced cooling options are compared for a slotless-type high-speed permanent-magnet machine. Fast, yet sufficiently accurate thermal models are derived for analyzing these cooling concepts. This enables their coupling with electromagnetic models and incorporation into the machine optimization procedure, which would not be feasible when using computationally very intensive methods such as three-dimensional finite element method or computational fluid dynamics. The developed thermal models are first verified on mechanically simplified stator designs (in which no rotor coupling is possible), and later on fully functional high-speed electrical machine prototypes. Using an integrated cooling method instead of a standard cooling jacket, the power density can be nearly doubled while keeping the maximum winding temperature below 80 °C, without altering the rotor or the stator core geometries.


title: The Application of Non-parametric Method and Time Series Analysis to Predict the Machine Repair Times

abstract: This study focuses on the utilization of non-parametric to assess the distribution of repair times of a machine part as well as the prediction of the future values. There are two folds of objectives, namely, the distribution assessment of the repair times. The diagnostic graph, i.e., histogram and normal probability plot, as well as a non-parametric test, Kolmogorov-Smirnov (KS) method, is utilized to assess the distribution of data. According to the KS test, it can be used effectively to test the distribution of the repair times of a machine which are selected as a case study. Another objective is the prediction of the future repair time required to fix a designated part. The time series analysis based on autoregressive integrated moving average (ARIMA) model is deployed in order to forecast the repair times. It turned out that one of the simplest models of ARIMA, ARIMA (0, 1, 0) or random walk, is the most appropriate model for the prediction and this indicates that the pattern of repair times is non-stationary.


title: Supporting mobility in 5G: A comparison between massive MIMO and continuous ultra dense networks

abstract: This paper proposes an approach for providing 5G services to mobile users that is based on continuous ultra dense networks (C-UDNs). The proposed approach outperforms the widely accepted solution based on macro cells and massive MIMO systems (M-MIMO). In particular, we show that the performance of M-MIMO systems deployed on macro cells is significantly limited by channel aging. The proposed mobility solution based on uplink beacons overcomes the handover problem often linked to UDNs. Dense networks make it possible for users to transmit at a power lower than that of macro cells, thus making C-UDN more robust to pilot contamination and allowing for lower Channel State Information (CSI) latencies due to shorter reuse distance of UL pilots. Extensive numerical results from detailed systemlevel simulations are provided in order to compare and highlight the benefits of the proposed C-UDN mobility solution to that of a macro M-MIMO deployment.


title: Interactive Occlusion and Collision of Real and Virtual Objects in Augmented Reality

abstract: We present several techniques for interactively performing occlusion and collision detection between static real objects and dynamic virtual objects in augmented reality. Computer vision algorithms are used to acquire data that model aspects of the real world. Either geometric models may be registered to real objects, or a depth map of the real scene may be extracted with computer vision algorithms. The computer vision-derived data are mapped into algorithms that exploit the power of graphics workstations, in order to interactively produce new effects in augmented reality. By combining live video from a calibrated camera with real-time renderings of the real-world data from graphics hardware, dynamic virtual objects occlude and are occluded by static real objects. As a virtual object is interactively manipulated collisions with real objects are detected, and the motion of the virtual object is constrained. Simulated gravity may then be produced by automatically moving the virtual object in the direction of a gravity vector until it encounters a collision with a real object.


title: Steganography for inserting message on digital image using least significant bit and AES cryptographic algorithm

abstract: Along with advances in information technology, cybercrime techniques also increased. There are several forms of attacks on data and information, such as hackers, crackers, Trojans, etc. The Symantec Intelligence report edition on August 2012 indicated that the attacker selected the target of attacks. The type of data is valuable and confidential. The Hackers selected the target to attack or steal interest information the first and they did not just taking random from a large amount of data. This indication worried because hackers stealing the data more planned. Therefore, today many systems reinforced with various efforts to maintain data security and overcome these attacks. Necessary methods to secure electronic messages that do not fall on those who are not authorized. One alternative is steganography. Cryptography and Steganography are the two major techniques for secret communication. Cryptography converts information from its original form (plaintext) into unreadable form (cipher text); where as in steganography is the art of hiding messages within other data without changing the data to it attaches, so data before and after the process of hiding almost look like the same. There are many different techniques are available for cryptography and steganography. The cryptography suspicion against disguised message is easily recognizable, because of the message disguised by changing the original message becomes as if illegible. While further reduce suspicion steganography disguised as a message hidden in the file. The research designed the application of steganography using Least Significant Bit (LSB) in which the previous message is encrypted using the Advanced Encryption Standard algorithm (AES) and it can restore the previously hidden data. The messages in this form application and hidden text on media digital image so as not to arouse suspicion. The result of research shown the steganography is expected to hide the secret message, so the message is not easy to know other people who are not eligible.


title: Peer Norms and Susceptibility Mediate the Effect of Movie Alcohol Exposure on Alcohol Initiation in Adolescents

abstract: Movie alcohol exposure is a known and consistent predictor of adolescent alcohol use initiation and escalation. Nearly 90% of the top U.S. movies contain alcohol content. Social-cognitive theory posits reciprocal links among environmental influences (e.g., movie alcohol exposure), social-cognitive processes (e.g., perceived norms, susceptibility, alcohol outcome expectancies), and behavior (alcohol use), but these links have been tested in only 1 direction. In the current study, we assessed movie alcohol exposure, alcohol cognitions, and alcohol initiation among adolescents (N = 1,023; 52% female, mean age = 13.7 years) in 4 annual survey waves. Cross-lagged panel models tested bidirectional relations between cognitions and movie alcohol exposure. Finally, we tested bidirectional mediation effects in the prospective prediction of alcohol initiation. Movie alcohol exposure prospectively predicted increases in descriptive norms, injunctive norms, and susceptibility but not positive or negative alcohol outcome expectancies. All factors predicted subsequent alcohol initiation. Close friend injunctive and descriptive norms, peer descriptive norms, and susceptibility mediated the effect of earlier movie alcohol exposure on subsequent alcohol initiation. Movie alcohol exposure mediated the effect of earlier close friend descriptive norms on subsequent alcohol initiation. Movie alcohol exposure and social-cognitive processes are interrelated facets that impact alcohol initiation. Permissive cognitions infrequently predicted higher subsequent exposure to movie alcohol. Clinical and preventative implications of this work are discussed. (PsycINFO Database Record


title: Incentive Problems in China's Urban Pension Reform

abstract: The urban pension system of China has undergone a series of re- forms, but reforms so far have ignored the crucial problem of incentive of participa- tion. As a result, deficits in the pension system have been rising, the expansion of coverage has made little progress, and the enforcement of payments by existing par- ticipants has been ineffective. The fundamental reason for the lack of participation incentives is the redistributive nature of the existing system- Most contributions go to the public pillar instead of individual accounts, individual accounts are notional. rates of return to investment in individual accounts are too low, etc. This paper analyzes the evolution of urban pension system in China, rationales for the current arrangements, and inevitableness and ramifications of non-payment and resistance to participation in the existing system. We further demonstrate that given proper incentives, the required pension contribution can be drastically reduced from the current level.


title: Study of RF inter satellite links

abstract: Intersatellite links also known as cross links provide the straight connectivity between satellites which eliminates the need for intermediate ground stations when sending data. So for satellite constellation mission involving earth observation and communication, intersatellite link have been considered. Some important requirements for this link are intersatellite link selection, frequency band selection, and multiple access and modulation type. So here, in this paper we explained the fundamental idea of Radio link and simulation results are provided to deal with the study of RF intersatellite links (ISLs) connecting two or more satellites at V band (60 GHz).


title: A survey of vision-based methods for action representation, segmentation and recognition

abstract: Action recognition has become a very important topic in computer vision, with many fundamental applications, in robotics, video surveillance, human-computer interaction, and multimedia retrieval among others and a large variety of approaches have been described. The purpose of this survey is to give an overview and categorization of the approaches used. We concentrate on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and we categorize them according to how they represent the spatial and temporal structure of actions; how they segment actions from an input stream of visual data; and how they learn a view-invariant representation of actions.


title: Supervisory routing control for dynamic load balancing in low data rate wireless sensor networks

abstract: Routing protocols for Wireless Sensor Networks (WSN) are designed to select parent nodes so that data packets can reach their destination in a timely and efficient manner. Typically neighboring nodes with strongest connectivity are more selected as parents. This Greedy Routing approach can lead to unbalanced routing loads in the network. Consequently, the network experiences the early death of overloaded nodes causing permanent network partition. Herein, we propose a framework for load balancing of routing in WSN. In-network path tagging is used to monitor network traffic load of nodes. Based on this, nodes are identified as being relatively overloaded, balanced or underloaded. A mitigation algorithm finds suitable new parents for switching from overloaded nodes. The routing engine of the child of the overloaded node is then instructed to switch parent. A key future of the proposed framework is that it is primarily implemented at the Sink and so requires few changes to existing routing protocols. The framework was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The algorithm increased the lifetime of the network by 41 % as recorded in the testbed experiment. The Packet Delivery Ratio was also improved from 85.97 to 99.47 %. Finally a comparative study was performed using the proposed framework with various existing routing protocols.


title: Micropower non-contact EEG electrode with active common-mode noise suppression and input capacitance cancellation

abstract: A non-contact EEG electrode with input capacitance neutralization and common-mode noise suppression circuits is presented. The coin sized sensor capacitively couples to the scalp without direct contact to the skin. To minimize the effect of signal attenuation and channel gain mismatch, the input capacitance of each sensor is actively neutralized using positive feedback and bootstrapping. Common-mode suppression is achieved through a single conductive sheet to establish a common mode reference. Each sensor electrode provides a differential gain of 60dB. Signals are transmitted in a digital serial daisy-chain directly from a local 16-bit ADC, minimizing the number of wires required to establish a high density EEG sensor network. The micropower electrode consumes only 600μW from a single 3.3V supply.


title: Learning to Count Objects in Natural Images for Visual Question Answering

abstract: Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to soft attention in these models as a cause. To circumvent this problem, we propose a neural network component that allows robust counting from object proposals. Experiments on a toy task show the effectiveness of this component and we obtain state-of-the-art accuracy on the number category of the VQA v2 dataset without negatively affecting other categories, even outperforming ensemble models with our single model. On a difficult balanced pair metric, the component gives a substantial improvement in counting over a strong baseline by 6.6%.


title: 3M syndrome: an easily recognizable yet underdiagnosed cause of proportionate short stature.

abstract: OBJECTIVE ::: To characterize, via clinical and molecul criteria, a cohort of patients with 3M syndrome and thereby increase awareness of this syndrome as a recognizable cause of proportionate short stature. ::: ::: ::: STUDY DESIGN ::: We conducted a case series of patients referred to clinical genetics for proportionate short stature. CUL7, OBSL1, and CCDC8 genes were clinically phenotyped and sequenced. ::: ::: ::: RESULTS ::: In 6 Saudi families with 3M syndrome, we identified three CUL7, one OBSL1, and one CCDC8 novel mutations, which we show result in a remarkably similar clinical phenotype. Despite their typical and easily discernible clinical phenotype, all these patients have been extensively investigated for alternative causes of their short stature and received erroneous diagnoses. ::: ::: ::: CONCLUSION ::: Increased awareness about this syndrome among pediatricians and endocrinologists is needed to avoid a costly and unnecessary diagnostic odyssey.


title: Permanent magnet axial flux disc generator for small wind turbines

abstract: In the paper a permanent magnet axial flux disc generator (AFPMG) with fractional slot pitch winding for small wind energy applications is proposed. Its main features are high specific power, very low torque ripple and a quasi-sinusoidal output voltage. A preliminary design can be carried out by a simplified analytical procedure. Afterwards, an automated sequence of magnetostatic FEM analyses on rectified 2D AFPMG models enabled to examine more in detail the influence of parametric variation with a considerable reduction of calculation time with respect to 3D analyses, to improve the generator performance, fulfilling the design constraints. The comparison with the results of some 3D FEM analyses confirmed the good accuracy of the proposed design approach. Furthermore, the results related to the simulation of an AFPMG-rectifier drive will be presented.


title: Chatbol, a Chatbot for the Spanish “La Liga”

abstract: This work describes the development of a social chatbot for the football domain. The chatbot, named chatbol, aims at answering a wide variety of questions related to the Spanish football league “La Liga”. Chatbol is deployed as a Slack client for text-based input interaction with users. One of the main Chatbol’s components, a NLU block, is trained to extract the intents and associated entities related to user’s questions about football players, teams, trainers and fixtures. The information for the entities is obtained by making sparql queries to Wikidata site in real time. Then, the retrieved data is used to update the specific chatbot responses. As a fallback strategy, a retrieval-based conversational engine is incorporated to the chatbot system. It allows for a wider variety and freedom of responses, still football oriented, for the case when the NLU module was unable to reply with high confidence to the user. The retrieval-based response database is composed of real conversations collected both from a IRC football channel and from football-related excerpts picked up across movie captions, extracted from the OpenSubtitles database.


Anomaly Detection for Univariate Time-Series Data


title: Analysis of ECG signal by chaos principle to help automatic diagnosis of myocardial infarction

abstract: Chaotic behavior of electrocardiogram (ECG) signal of myocardial and non-myocardial infarctions is differentiated using neuro-GA approach, incorporating heuristically chosen phase space fractal dimension (PSFD) of ECG data. A remarkable improvement of diagnostic efficiency, sensitivity and specificity was observed in case study.


title: Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images

abstract: Recently, several machine learning methods for gender classi- fication from frontal facial images have been proposed. Their variety suggests that there is not a unique or generic solution to this problem. In addition to the diversity of methods, there is also a diversity of benchmarks used to assess them. This gave us the motivation for our work: to select and compare in a concise but reliable way the main state-of-the-art methods used in automatic gender recognition. As expected, there is no overall winner. The winner, based on the accuracy of the classification, depends on the type of benchmarks used.


title: Mechanical behaviour and structure of light-cured special tray materials.

abstract: Abstract Objectives: This study relates some structural characteristics of representative special tray visible light cured (VLC) resin composites to their creep-recovery, hardness and flexural strength properties. VLC prosthodontic materials have not yet achieved the same acceptance as the VLC resin composites, perhaps because less is known of their structural and performance characteristics. Methods: The VLC resin composites used were Triad, Convertray and Palatray. The inorganic composition was analysed quantitatively by heating known amounts of resin composite to 800 °C, and weighing the ash. Elemental composition was determined using X-ray microanalysis. Mechanical properties, including hardness, flexural behaviour and creep recovery, were obtained by standard methods. Results: The inorganic filler content for Palatray was higher than that of Convertray or Triad, and the flexural modulus and flexural strength of Palatray were superior to that of Triad or Convertray. The mean maximum creep strain for the Palatray specimens was only 2.45% after 5 h loading under 37 MPa and there was a permanent set of 1.5% after 7 h recovery. Conclusions: Palatray exhibited superior overall properties and this evidently related primarily to the high volume fraction of filler.


title: Low-Profile Broadband Dual-Polarized Integrated Patch Subarray for X-Band Synthetic Aperture Radar Payload on Small Satellite

abstract: A low-profile broadband dual-polarized patch subarray is designed in this letter for a highly integrated X-band synthetic aperture radar payload on a small satellite. The proposed subarray is lightweight and has a low profile due to its tile structure realized by a multilayer printed circuit board process. The measured results confirm that the subarray yields 14-dB bandwidths from 9.15 to 10.3 GHz for H-pol and from 9.35 to 10.2 GHz for V-pol. The isolation remains better than 40 dB. The average realized gains are approximately 13 dBi for both polarizations. The sidelobe levels are 25 dB for H-pol and 20 dB for V-pol. The relative cross-polarization levels are 30 dB within the half-power beamwidth range.


title: Practical lattice-based cryptography: A signature scheme for embedded systems

abstract: Nearly all of the currently used and well-tested signature schemes (e.g. RSA or DSA) are based either on the factoring assumption or the presumed intractability of the discrete logarithm problem. Further algorithmic advances on these problems may lead to the unpleasant situation that a large number of schemes have to be replaced with alternatives. In this work we present such an alternative --- a signature scheme whose security is derived from the hardness of lattice problems. It is based on recent theoretical advances in lattice-based cryptography and is highly optimized for practicability and use in embedded systems. The public and secret keys are roughly 12000 and 2000 bits long, while the signature size is approximately 9000 bits for a security level of around 100 bits. The implementation results on reconfigurable hardware (Spartan/Virtex 6) are very promising and show that the scheme is scalable, has low area consumption, and even outperforms some classical schemes.


title: Fusion of Multiple Tracking Algorithms for Robust People Tracking

abstract: This paper shows how the output of a number of detection and tracking algorithms can be fused to achieve robust tracking of people in an indoor environment. The new tracking system contains three co-operating parts: i) an Active Shape Tracker using a PCA-generated model of pedestrian outline shapes, ii) a Region Tracker, featuring region splitting and merging for multiple hypothesis matching, and iii) a Head Detector to aid in the initialisation of tracks. Data from the three parts are fused together to select the best tracking hypotheses.The new method is validated using sequences from surveillance cameras in a underground station. It is demonstrated that robust realtime tracking of people can be achieved with the new tracking system using standard PC hardware.


title: Automobile Driver Fingerprinting

abstract: Abstract Today’s automobiles leverage powerful sensors and embedded computers to optimize efficiency, safety, and driver engagement. However the complexity of possible inferences using in-car sensor data is not well understood. While we do not know of attempts by automotive manufacturers or makers of after-market components (like insurance dongles) to violate privacy, a key question we ask is: could they (or their collection and later accidental leaks of data) violate a driver’s privacy? In the present study, we experimentally investigate the potential to identify individuals using sensor data snippets of their natural driving behavior. More specifically we record the in-vehicle sensor data on the controllerarea- network (CAN) of a typical modern vehicle (popular 2009 sedan) as each of 15 participants (a) performed a series of maneuvers in an isolated parking lot, and (b) drove the vehicle in traffic along a defined ~ 50 mile loop through the Seattle metropolitan area. We then split the data into training and testing sets, train an ensemble of classifiers, and evaluate identification accuracy of test data queries by looking at the highest voted candidate when considering all possible one-vs-one comparisons. Our results indicate that, at least among small sets, drivers are indeed distinguishable using only incar sensors. In particular, we find that it is possible to differentiate our 15 drivers with 100% accuracy when training with all of the available sensors using 90% of driving data from each person. Furthermore, it is possible to reach high identification rates using less than 8 minutes of training data. When more training data is available it is possible to reach very high identification using only a single sensor (e.g., the brake pedal). As an extension, we also demonstrate the feasibility of performing driver identification across multiple days of data collection


title: Explaining structured queries in natural language

abstract: Many applications offer a form-based environment for naive users for accessing databases without being familiar with the database schema or a structured query language. User interactions are translated to structured queries and executed. However, as a user is unlikely to know the underlying semantic connections among the fields presented in a form, it is often useful to provide her with a textual explanation of the query. In this paper, we take a graph-based approach to the query translation problem. We represent various forms of structured queries as directed graphs and we annotate the graph edges with template labels using an extensible template mechanism. We present different graph traversal strategies for efficiently exploring these graphs and composing textual query descriptions. Finally, we present experimental results for the efficiency and effectiveness of the proposed methods.


title: Types of software evolution and software maintenance

abstract: The past two decades have seen increasing sophistication in software work. Now and in the future, the work of both practitioners and researchers would be helped by a more objective and finer granularity recognition of types of software evolution and software maintenance activities as actually done. To these ends, this paper proposes a clarifying redefinition of the types of software evolution and software maintenance. The paper bases the proposed classification not on people's intentions but upon objective evidence of maintainers' activities ascertainable from observation of activities and artifacts, and/or a before and after comparison of the software documentation. The classification includes taking into account in a semi-hierarchical manner evidence of the change or lack thereof in: (1) the software, (2) the documentation, (3) the properties of the software, and (4) the customer-experienced functionality. A comparison is made with other classifications and typologies. The paper provides a classified list of maintenance activities and a condensed decision tree as a summary guide to the proposed evidence-based classification of the types of software evolution and software maintenance. Copyright © 2001 John Wiley & Sons, Ltd.


title: Transparent Protection of Commodity OS Kernels Using Hardware Virtualization

abstract: Kernel rootkits are among the most insidious threats to computer security today. By employing various code injection techniques, they are able to maintain an omnipotent presence in the compromised OS kernels. Existing preventive countermeasures typically employ virtualization technology as part of their solutions. However, they are still limited in either (1) requiring modifying the OS kernel source code for the protection or (2) leveraging software-based virtualization techniques such as binary translation with a high overhead to implement a Harvard architecture (which is robust to various code injection techniques used by kernel rootkits). In this paper, we introduce hvmHarvard, a hardware virtualization-based Harvard architecture that transparently protects commodity OS kernels from kernel rootkit attacks and significantly reduces the performance overhead. Our evaluation with a Xen-based prototype shows that it can transparently protect legacy OS kernels with rootkit resistance while introducing < 5% performance overhead.


title: Model selection for linear classifiers using Bayesian error estimation

abstract: Regularized linear models are important classification methods for high dimensional problems, where regularized linear classifiers are often preferred due to their ability to avoid overfitting. The degree of freedom of the model dis determined by a regularization parameter, which is typically selected using counting based approaches, such as K-fold cross-validation. For large data, this can be very time consuming, and, for small sample sizes, the accuracy of the model selection is limited by the large variance of CV error estimates. In this paper, we study the applicability of a recently proposed Bayesian error estimator for the selection of the best model along the regularization path. We also propose an extension of the estimator that allows model selection in multiclass cases and study its efficiency with L1 regularized logistic regression and L2 regularized linear support vector machine. The model selection by the new Bayesian error estimator is experimentally shown to improve the classification accuracy, especially in small sample-size situations, and is able to avoid the excess variability inherent to traditional cross-validation approaches. Moreover, the method has significantly smaller computational complexity than cross-validation. HighlightsWe propose to use the Bayesian error estimator (BEE) for classifier model selection.We show that the BEE rule speeds up model selection by an order of magnitude.We show that the BEE rule selects a better model than cross-validation.We propose an approximation rule of the BEE for multi-label classification problems.


title: Trading Credit Default Swaps via Interdealer Brokers

abstract: Credit default swaps (CDSs) are among the most successful financial innovations of recent years, which is reflected in the rapidly expanding market. CDS trading occurs in the over-the-counter market, which relies heavily on broker intermediation to arrange trades. We provide empirical evidence that liquidity in the voice brokered market varies with the particulars of the CDS contracts and that the differences in market structure is reflected in the costs of liquidity. Moreover, the brokered and direct interdealer trading markets seem to be well integrated; thus the higher liquidity costs in the brokered market may reflect the value of intermediation. Hybrid market structures, which combine voice brokerage with an electronic platform, are discussed as a viable alternative to fully automated trading systems.


3 Ontology-based Information Visualization : Towards Semantic Web Applications


title: Cyberbullying: Labels, Behaviours and Definition in Three European Countries.

abstract: This study aims to examine students' perception of the term used to label cyberbullying, the perception of different forms and behaviours (written, verbal, visual, exclusion and impersonation) and the perception of the criteria used for its definition (imbalance of power, intention, repetition, anonymity and publicity) in three different European countries: Italy, Spain and Germany. Seventy adolescents took part in nine focus groups, using the same interview guide across countries. Thematic analysis focused on three main themes related to: (1) the term used to label cyberbullying, (2) the different behaviours representing cyberbullying, (3) the three traditional criteria of intentionality, imbalance of power and repetition and the two new criteria of anonymity and publicity. Results showed that the best word to label cyberbullying is 'cybermobbing' (in Germany), 'virtual' or 'cyber-bullying' (in Italy), and 'harassment' or 'harassment via Internet or mobile phone' (in Spain). Impersonation cannot be considered wholly as cyberbullying behaviour. In order to define a cyberbullying act, adolescents need to know whether the action was done intentionally to harm the victim, the effect on the victim and the repetition of the action (this latter criterion evaluated simultaneously with the publicity). Information about the anonymity and publicity contributes to better understand the nature and the severity of the act, the potential effects on the victim and the intentionality.


Heuristic-based Approach for Phishing Site Detection Using URL Features


title: Cultural Humility: Measuring Openness to Culturally Diverse Clients

abstract: Building on recent theory stressing multicultural orientation, as well as the development of virtues and dispositions associated with multicultural values, we introduce the construct of cultural humility, defined as having an interpersonal stance that is other-oriented rather than self-focused, characterized by respect and lack of superiority toward an individual’s cultural background and experience. In 4 studies, we provide evidence for the estimated reliability and construct validity of a client-rated measure of a therapist’s cultural humility, and we demonstrate that client perceptions of their therapist’s cultural humility are positively associated with developing a strong working alliance. Furthermore, client perceptions of their therapist’s cultural humility were positively associated with improvement in therapy, and this relationship was mediated by a strong working alliance. We consider implications for research, practice, and training.