title: A Deep Visual Correspondence Embedding Model for Stereo Matching Costs

abstract: This paper presents a data-driven matching cost for stereo matching. A novel deep visual correspondence embedding model is trained via Convolutional Neural Network on a large set of stereo images with ground truth disparities. This deep embedding model leverages appearance data to learn visual similarity relationships between corresponding image patches, and explicitly maps intensity values into an embedding feature space to measure pixel dissimilarities. Experimental results on KITTI and Middlebury data sets demonstrate the effectiveness of our model. First, we prove that the new measure of pixel dissimilarity outperforms traditional matching costs. Furthermore, when integrated with a global stereo framework, our method ranks top 3 among all two-frame algorithms on the KITTI benchmark. Finally, cross-validation results show that our model is able to make correct predictions for unseen data which are outside of its labeled training set.


title: MARBLE: Mobile Augmented Reality Using a Distributed BLE Beacon Infrastructure

abstract: This paper describes MARBLE, which is a mobile augmented reality system that uses a cluster of off-the-shelf, low power, storage and bandwidth constrained Bluetooth Low Energy (BLE) beacons as an infrastructure. MARBLE efficiently stores and broadcasts minimal visual information of 3D objects and help localize a mobile viewer, who receives, renders, and experiences those 3D virtual objects while walking in the environment, wearing an augmented reality headset or viewing it through a smartphone. Compared to other common indoor AR systems, MARBLE consumes less computation resource, stores and broadcasts 3D objects and shapes data over a very long period, does not require a pre-defined texture pattern to be placed in the scene for camera pose estimation, and is less sensitive to camera capture quality. We conduct a user study to demonstrate that MARBLE is capable of capturing freehand gestures, and when replayed back, a user sees a virtual avatar performing the gestures in the 3D environment in real-time.


Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking


title: KNIME for Open-Source Bioimage Analysis: A Tutorial

abstract: The open analytics platform KNIME is a modular environment that enables easy visual assembly and interactive execution of workflows. KNIME is already widely used in various areas of research, for instance in cheminformatics or classical data analysis. In this tutorial the KNIME Image Processing Extension is introduced, which adds the capabilities to process and analyse huge amounts of images. In combination with other KNIME extensions, KNIME Image Processing opens up new possibilities for inter-domain analysis of image data in an understandable and reproducible way.


title: Combination of Time Series, Decision Tree and Clustering: A Case Study in Aerology Event Prediction

abstract: Predictive systems use historical and other available data to predict an event. In this paper we propose a general framework to predict the Aerology events with time series streams and events stream using combination of K-means clustering algorithm and Decision Tree C5 algorithm. Firstly, we find the closest time series record for any events; therefore, we have gathered different parameters value when an event is occurring. Using K-means we add a field to data set which determines the cluster of each record after that by using C5 algorithm we predict events. C5 Decision Tree Algorithm is one of the well-known Decision Tree Algorithms. This framework and time series model can predict future events efficiently. We gathered 1961 until 2005 data of aerology organization for Tehran Mehrabad Station. This data contains some fields such as wet bulb, relative humidity, amount of cloud, wind speed and etc. This data set includes 17 types of events. Time series models can predict next time series parameters value and by using this Framework the closest event can be predicted. The C5 method is able to predict Events with Correct 74.11 percent and Wrong 25.89 percent. But with the aims of K-means clustering algorithm the prediction increase to 85 percent and wrong to 15 percent. 90 percent of data was used for training set and 10 percent for test set. We use 10-fold cross validation to evaluate our prediction rate. This framework is the first estimation in the area of event prediction for a huge data set of aerology and can be extended in many different data sets in any other environments.


title: Amphetamine exposure enhances habit formation

abstract: Performance of instrumental actions in rats is initially sensitive to postconditioning changes in reward value, but after more extended training, behavior comes to be controlled by stimulus–response (S-R) habits that are no longer goal directed. To examine whether sensitization of dopaminergic systems leads to a more rapid transition from action–outcome processes to S-R habits, we examined performance of amphetamine-sensitized rats in an instrumental devaluation task. Animals were either sensitized (7 d, 2 mg/kg/d) before training (experiment 1) or sensitized between training and testing (experiment 2). Rats were trained to press a lever for a reward (three sessions) and were then given a test of goal sensitivity by devaluation of the instrumental outcome before testing in extinction. Control animals showed selective sensitivity to devaluation of the instrumental outcome. However, amphetamine sensitization administered before training caused the animals’ responding to persist despite the changed value of the reinforcer. This deficit resulted from an inability to use representations of the outcome to guide behavior, because a reacquisition test confirmed that all of the animals had acquired an aversion to the reinforcer. In experiment 2, post-training sensitization did not disrupt normal goal-directed behavior. These findings indicate that amphetamine sensitization leads to a rapid progression from goal-directed to habit-based responding but does not affect the performance of established goal-directed actions.


title: A single stage single switch valley switching Flyback-Forward converter with regenerative snubber and PFC for LED light source system

abstract: This paper proposes a single-stage single-switch Flyback-Forward converter as a LED driver lighting circuit. The converter is an improvement of a typical flyback converter, when the switch is turned on, the energy can be outputted, when the switch is turned off, also energy can be inputted. The output power is up to 150W-200W. And add a boost inductor in the input of the converter, to make it has the function of power factor correction. In order to improve the efficiency of the converter, design a control loop to make the switch valley switching, to reduce switching losses. And design a transformer leakage inductance energy recovery circuit, when the switch turns off, the surge oscillation can be reduced and the switch voltage is lower than 280V, the leakage inductance energy is stored in the clamp capacitance. At the next switch turn-on, the energy in the clamp capacitor is recovered to the bulk capacitor. Finally, a prototype 150W (36V/4.2A) AC/ DC LED driver and lighting circuits are implemented. The efficiency is above 90%, higher than the efficiency of 85% in the industry.


title: Modular Design of Master-Slave Surgical Robotic System with Reliable Real-Time Control Performance

abstract: This paper describes a master-slave surgical robotic system with software modular system design, which integrates various independently developed surgical devices. When we add new functions to the developed modular system, there is no need for time-consuming redesign of the entire system. However, we cannot evaluate the computational system load exactly because the system configuration changes according to the components included. A master-slave system should be a stable and reliable control system with high sampling frequency to enable human visual feedback control. Moreover, it requires consistent master-slave operation on the integrated component. To maintain reliable real-time performance and consistent operation, even with changes in system configuration, we introduced a software system consisting of; A position registration system to realize consistent operation on the integrated component, modular asynchronous system to avoid performance changes caused by the introduction of various subsystems and a management system to arbitrate inconsistencies between systems. An optical tracking system is used to integrate position and motion information of the subsystems to simplify setup in an operating theater. We evaluated the feasibility of the modular master-slave system by integrating independently developed components into the system. The results of the evaluation experiments showed that we could control all the subsystems consistently as an entire master-slave surgical system on an integrated coordinate system with stable real-time response


title: Toward a Cognitive Neuroscience of Metacognition

abstract: The relationship between metacognition and executive control is explored. According to an analysis by Fernandez-Duque, Baird, and Posner (this issue), metacognitive regulation involves attention, conflict resolution, error correction, inhibitory control, and emotional regulation. These aspects of metacognition are presumed to be mediated by a neural circuit involving midfrontal brain regions. An evaluation of the proposal by Fernandez-Duque et al. is made, and it is suggested that there is considerable convergence of issues associated with metacognition, executive control, working memory, and frontal lobe function. By integrating these domains and issues, significant progress could be made toward a cognitive neuroscience of metacognition. ≈ 2000 Academic Press Metacognition refers to evaluation and control of one’s cognitive processes. In this way, metacognition often suggests conscious or volitional control of thoughts, memories, and actions. Early research in this area focused on metamemory—the evaluation of memory processes (e.g., tip-of-the-tongue phenomenon, feeling of knowing) and awareness of mnemonic strategies that could facilitate remembering (see Nelson, 1992). Much of this work was sparked by developmental studies in which the ability to evaluate one’s memory processes and to be aware of mnemonic strategies improved during the course of early childhood (Brown, 1978; Flavell & Wellman, 1977). Over the years, metacognitive research has blossomed to include a variety of issues and domains—including life-span approaches, cognitive neuropsychology, theory of mind, and educational psychology (for review, see Mazzoni & Nelson, 1998; Metcalfe & Shimamura, 1994; Reder, 1996). Fernandez-Duque, Baird, and Posner (this issue) offer a useful framework for the analysis of metacognition. Metacognition is viewed in terms of executive control processes, such as those involved in selective attention, conflict resolution, error detection, and inhibitory control. By relating metacognition to executive control, Fernandez-Duque et al. bring together an extraordinarily rich and useful pool of data not otherwise considered important for the analysis of metacognition (for similar conceptualizations see Shimamura, 1996; Umilta & Stablum, 1998). FernandezDuque et al. (this issue) review findings from basic cognitive psychology, cognitive neuroscience, and developmental psychology. These findings suggest a strong relationship between metacognitive regulation and executive control. They emphasize the biological bases of metacognition and suggest that midfrontal brain regions are part of a neural circuit that enables metacognitive regulation.


Network effects and psychological effects: a theory of urban movement


title: A Hybrid Tabu Search Algorithm for the Nurse Rostering Problem

abstract: This paper deals with the problem of nurse rostering in Belgian hospitals. This is a highly constrained real world problem that was (until the results of this research were applied) tackled manually. The problem basically concerns the assignment of duties to a set of people with different qualifications, work regulations and preferences. ::: ::: Constraint programming and linear programming techniques can produce feasible solutions for this problem. However, the reality in Belgian hospitals forced us to use heuristics to deal with the over constrained schedules. An important reason for this decision is the calculation time, which the users prefer to reduce. The algorithms presented in this paper are a commercial nurse rostering product developed for the Belgian hospital market, entitled Plane.


title: An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy

abstract: To better understand how individual differences influence the use of information technology (IT), this study models and tests relationships among dynamic, IT-specific individual differences (i.e., computer self-efficacy and computer anxiety), stable, situation-specific traits (i.e., personal innovativeness in IT) and stable, broad traits (i.e., trait anxiety and negative affectivity). When compared to broad traits, the model suggests that situation-specific traits exert a more pervasive influence on IT situation-specific individual differences. Further, the model suggests that computer anxiety mediates the influence of situation-specific traits (i.e., personal innovativeness) on computer self-efficacy. Results provide support for many of the hypothesized relationships. From a theoretical perspective, the findings help to further our understanding of the nomological network among individual differences that lead to computer self-efficacy. From a practical perspective, the findings may help IT managers design training programs that more effectively increase the computer self-efficacy of users with different dispositional characteristics.


title: Statistical region merging

abstract: This paper explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose segmentation error is, as we show, limited from both the qualitative and quantitative standpoints. This approach can be efficiently approximated in linear time/space, leading to a fast segmentation algorithm tailored to processing images described using most common numerical pixel attribute spaces. The conceptual simplicity of the approach makes it simple to modify and cope with hard noise corruption, handle occlusion, authorize the control of the segmentation scale, and process unconventional data such as spherical images. Experiments on gray-level and color images, obtained with a short readily available C-code, display the quality of the segmentations obtained.


title: On the Comprehension of Program Comprehension

abstract: Research in program comprehension has evolved considerably over the past decades. However, only little is known about how developers practice program comprehension in their daily work. This article reports on qualitative and quantitative research to comprehend the strategies, tools, and knowledge used for program comprehension. We observed 28 professional developers, focusing on their comprehension behavior, strategies followed, and tools used. In an online survey with 1,477 respondents, we analyzed the importance of certain types of knowledge for comprehension and where developers typically access and share this knowledge. We found that developers follow pragmatic comprehension strategies depending on context. They try to avoid comprehension whenever possible and often put themselves in the role of users by inspecting graphical interfaces. Participants confirmed that standards, experience, and personal communication facilitate comprehension. The team size, its distribution, and open-source experience influence their knowledge sharing and access behavior. While face-to-face communication is preferred for accessing knowledge, knowledge is frequently shared in informal comments. Our results reveal a gap between research and practice, as we did not observe any use of comprehension tools and developers seem to be unaware of them. Overall, our findings call for reconsidering the research agendas towards context-aware tool support.


title: Evaluating and Combining Name Entity Recognition Systems

abstract: Name entity recognition (NER) is an important subtask in natural language processing. Various NER systems have been developed in the last decade. They may target for different domains, employ different methodologies, work on different languages, detect different types of entities, and support different inputs and output formats. These conditions make it difficult for a user to select the right NER tools for a specific task. Motivated by the need of NER tools in our research work, we select several publicly available and well-established NER tools to validate their outputs against both Wikipedia gold standard corpus and a small set of manually annotated documents. All the evaluations show consistent results on the selected tools. Finally, we constructed a hybrid NER tool by combining the best performing tools for the domains of our interest.


title: CircStat: A MATLAB Toolbox for Circular Statistics

abstract: Directional data is ubiquitious in science. Due to its circular nature such data cannot be analyzed with commonly used statistical techniques. Despite the rapid development of specialized methods for directional statistics over the last fifty years, there is only little software available that makes such methods easy to use for practioners. Most importantly, one of the most commonly used programming languages in biosciences, MATLAB, is currently not supporting directional statistics. To remedy this situation, we have implemented the CircStat toolbox for MATLAB which provides methods for the descriptive and inferential statistical analysis of directional data. We cover the statistical background of the available methods and describe how to apply them to data. Finally, we analyze a dataset from neurophysiology to demonstrate the capabilities of the CircStat toolbox.


title: WeiSTARS: A weighted trust-aware relay selection scheme for VANET

abstract: Considered as a primordial component of Cooperative Intelligent Transportation Systems (C-ITS), Vehicular Ad Hoc Network (VANET) plays a weighty role to facilitate different on-road applications, most of which primary rely on multi hop communications. To ensure reliability and security of such communication, it is very pertinent to guarantee that the most proper and trustworthy vehicles are selected as relays. This paper takes up this challenge by introducing a weighted probabilistic and trust-aware strategy called WeiSTARS, to ensure high delivery ratios within reduced delays. Simulation results conducted using NS2 tool depicted that our technique enhances the delivery ratio by more than 8% with around 30% less delay compared to both GytAR and GPSR routing protocols even in the presence of high ratios of dishonest vehicles.


title: Evaluation of bottom-up interventions targeting community-dwelling frail older people in Belgium: methodological challenges and lessons for future comparative effectiveness studies

abstract: Optimizing the organization of care for community-dwelling frail older people is an important issue in many Western countries. In Belgium, a series of complex, innovative, bottom-up interventions was recently designed and implemented to help frail older people live at home longer. As the effectiveness of these interventions may vary between different population groups according to their long-term care needs, they must be evaluated by comparison with a control group that has similar needs. The goal was to identify target groups for these interventions and to establish control groups with similar needs and to explore, per group, the extent to which the utilization of long-term care is matched to needs. We merged two databases: a clinical prospective database and the routine administrative database for healthcare reimbursements. Through Principal Component Analysis followed by Clustering, the intervention group was first stratified into disability profiles. Per profile, comparable control groups for clinical variables were established, based on propensity scores. Using chi-squared tests and logistic regression analysis, long-term care utilization at baseline was then compared per profile and group studied. Stratification highlighted five disability profiles: people with low-level limitations; people with limitations in instrumental activities of daily life and low-level of cognitive impairment; people with functional limitations; people with functional and cognitive impairments; and people with functional, cognitive, and behavioral problems. These profiles made it possible to identify long-term care needs. For instance, at baseline, those who needed more assistance with hygiene tasks also received more personal nursing care (P < 0.05). However, there were some important discrepancies between the need for long-term care and its utilization: while 21% of patients who were totally dependent for hygiene tasks received no personal nursing care, personal nursing care was received by 33% of patients who could perform hygiene tasks. The disability profiles provide information on long-term care needs but not on the extent to which those needs are met. To assess the effectiveness of interventions, controls at baseline should have similar disability profiles and comparable long-term care utilization. To allow for large comparative effectiveness studies, these dimensions should ideally be available in routine databases.


title: Using one-class and two-class SVMs for multiclass image annotation

abstract: We propose using one-class, two-class, and multiclass SVMs to annotate images for supporting keyword retrieval of images. Providing automatic annotation requires an accurate mapping of images' low-level perceptual features (e.g., color and texture) to some high-level semantic labels (e.g., landscape, architecture, and animals). Much work has been performed in this area; however, there is a lack of ability to assess the quality of annotation. In this paper, we propose a confidence-based dynamic ensemble (CDE), which employs a three-level classification scheme. At the base-level, CDE uses one-class support vector machines (SVMs) to characterize a confidence factor for ascertaining the correctness of an annotation (or a class prediction) made by a binary SVM classifier. The confidence factor is then propagated to the multiclass classifiers at subsequent levels. CDE uses the confidence factor to make dynamic adjustments to its member classifiers so as to improve class-prediction accuracy, to accommodate new semantics, and to assist in the discovery of useful low-level features. Our empirical studies on a large real-world data set demonstrate CDE to be very effective.


title: Probabilistic word pre-activation during language comprehension inferred from electrical brain activity

abstract: Despite the numerous examples of anticipatory cognitive processes at micro and macro levels in many animal species, the idea that anticipation of specific words plays an integral role in real-time language processing has been contentious. Here we exploited a phonological regularity of English indefinite articles ('an' precedes nouns beginning with vowel sounds, whereas 'a' precedes nouns beginning with consonant sounds) in combination with event-related brain potential recordings from the human scalp to show that readers' brains can pre-activate individual words in a graded fashion to a degree that can be estimated from the probability that each word is given as a continuation for a sentence fragment offline. These findings are evidence that readers use the words in a sentence (as cues to their world knowledge) to estimate relative likelihoods for upcoming words.


title: Securing Proof-of-Stake Blockchain Protocols

abstract: Proof-of-Stake (PoS) protocols have been actively researched for the past five years. PoS finds direct applicability in open blockchain platforms and has been seen as a strong candidate to replace the largely inefficient Proof of Work mechanism that is currently plugged in most existing open blockchains. Although a number of PoS variants have been proposed, these protocols suffer from a number of security shortcomings; for instance, most existing PoS variants suffer from the nothing at stake and the long range attacks which considerably degrade security in the blockchain.


title: Skeletal anchorage in orthodontics--a review of various systems in animal and human studies.

abstract: PURPOSE ::: The aim of the present investigation was to review and evaluate the current literature on skeletal bone anchorage in orthodontics with regard to success rates of the various systems. ::: ::: ::: MATERIALS AND METHODS ::: MEDLINE, PubMed, and Cochrane searches (period January 1966 to January 2006, English language) of animal and human studies using skeletal anchorage during orthodontic treatment were scrutinized. A total of 50 relevant articles were identified which investigated various types of implants. ::: ::: ::: RESULTS ::: Two types of anchorage systems are used in orthodontics: (1) osseointegrated dental implants, including temporary mid-palatal implants. These systems were associated with a wide variety of success rates in animal studies. In human studies, the systems were shown to be reliable, with success rates between 85% and 100% (ie, systems still functioning at the end of the orthodontic treatment). (2) Nonosseointegrated mini-plates and mini-screw anchorage systems. Titanium miniplates were associated with 100% success in animals, and hardly any loss of these mini-plates (bone anchors) were lost due to infection in human studies, with success rates between 91% and 100%. Few long-term studies on nonosseointegrated mini-screws were found, but in animal studies, success rates ranged from 90% to 100%. A success rate of more than 75% in human studies is considered favorable for these orthodontic implants, which confirms the clinical applicability of this type of immediate loading anchor support in orthodontics. ::: ::: ::: CONCLUSIONS ::: Both animal and human studies revealed that mesiodistal and intrusion movements can be reliably carried out by means of skeletal anchorage devices. A drawback is that animal studies do not reflect the real orthodontic clinical situation; thus, the outcome of these studies should be interpreted with caution. Human studies, however, show that orthodontic forces between 100 and 400 grams can be applied successfully to skeletal anchorage devices. Appropriate treatment strategies need to be confirmed by randomized prospective clinical trials. (More than 50 references).


title: Words Are Important: Improving Sentiment Analysis in the Persian Language by Lexicon Refining

abstract: Lexicon-based sentiment analysis (SA) aims to address the problem of extracting people’s opinions from their comments on the Web using a predefined lexicon of opinionated words. In contrast to the machine learning (ML) approach, lexicon-based methods are domain-independent methods that do not need a large annotated training corpus and hence are faster. This makes the lexicon-based approach prevalent in the SA community. However, the story is different for the Persian language. In contrast to English, using the lexicon-based method in Persian is a new discipline. There are rather limited resources available for SA in Persian, making the accuracy of the existing lexicon-based methods lower than other languages. In the current study, first an exhaustive investigation of the lexicon-based method is performed. Then two new resources are introduced to address the problem of resource scarcity for SA in Persian: a carefully labeled lexicon of sentiment words, PerLex, and a new handmade dataset of about 16,000 rated documents, PerView. Moreover, a new hybrid method using both ML and the lexicon-based approach is presented in which PerLex words are used to train the ML algorithm. Experiments are carried out on our new PerView dataset. Results indicate that the accuracy of PerLex is higher than the existing CNRC, Adjectives, SentiStrength, PerSent, and LexiPers lexicons. In addition, the results show that using PerLex significantly decreases the execution time of the proposed system in comparison to the above-mentioned lexicons. Moreover, the results demonstrate the excellence of using opinionated lexicon terms followed by bigrams as the features employed in the ML method.


title: Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

abstract: Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost---The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution---We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.


title: Generative Adversarial Networks for Parallel Vision

abstract: Video image dataset is playing an essential role in design and evaluation of traffic vision methods. However, there is a longstanding difficulty that manually collecting and annotating large-scale diversified dataset from real scenes is time-consuming and prone to error. In 2016, we proposed the parallel vision methodology to tackle the issues of conventional vision computing approach in data collection, model learning and evaluation. We built the ParallelEye dataset with virtual reality and the scene-specific virtual pedestrian dataset with augmented reality. In the dataset compiling process, the graphics rendering engine was used to render the artificial scenes and generate virtual images. However, the fidelity of virtual images is not satisfactory due to limitation of rendering engine, so that there is a distribution gap between virtual data and real data. In our opinion, Generative Adversarial Networks (GANs) can generate more realistic images for parallel vision research. We introduce some GANs and explain their utility in parallel vision.


Tax transition in developing countries : Do value added tax and excises really work ?


title: Make it stand: balancing shapes for 3D fabrication

abstract: Imbalance suggests a feeling of dynamism and movement in static objects. It is therefore not surprising that many 3D models stand in impossibly balanced configurations. As long as the models remain in a computer this is of no consequence: the laws of physics do not apply. However, fabrication through 3D printing breaks the illusion: printed models topple instead of standing as initially intended. We propose to assist users in producing novel, properly balanced designs by interactively deforming an existing model. We formulate balance optimization as an energy minimization, improving stability by modifying the volume of the object, while preserving its surface details. This takes place during interactive editing: the user cooperates with our optimizer towards the end result. We demonstrate our method on a variety of models. With our technique, users can produce fabricated objects that stand in one or more surprising poses without requiring glue or heavy pedestals.


title: Fully Binary Neural Network Model and Optimized Hardware Architectures for Associative Memories

abstract: Brain processes information through a complex hierarchical associative memory organization that is distributed across a complex neural network. The GBNN associative memory model has recently been proposed as a new class of recurrent clustered neural network that presents higher efficiency than the classical models. In this article, we propose computational simplifications and architectural optimizations of the original GBNN. This work leads to significant complexity and area reduction without affecting neither memorizing nor retrieving performance. The obtained results open new perspectives in the design of neuromorphic hardware to support large-scale general-purpose neural algorithms.


Climate change is a health issue


title: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification

abstract: MRI patterns were examined together with cerebrospinal fluid (CSF) biomarkers in serial scans of ADNI participants with mild cognitive impairment (MCI). The SPARE-AD score, summarizing brain atrophy patterns, was tested as predictor of short-term conversion to AD. MCI individuals that converted to AD (MCI-C) had mostly positive baseline SPARE-AD and atrophy in temporal lobe grey (GM) and white (WM) matter, posterior cingulate/precuneous, insula. MCI-C had mostly AD-like baseline CSF biomarkers. MCI non-converters (MCI-NC) had mixed baseline SPARE-AD and CSF values, suggesting that some MCI-NC subjects may later convert. Those MCI-NC with most negative baseline SPARE-AD scores (normal brain structure) had significantly higher baseline MMSE scores (28.67) than others, and relatively low annual rate of MMSE decrease (−0.25). MCI-NC with mid-level baseline SPARE-AD displayed faster annual rates of SPARE-AD increase (indicating progressing atrophy). SPARE-AD and CSF combination improved prediction over individual values. In summary, both SPARE-AD and CSF biomarkers showed high baseline sensitivity, however, many MCI-NC had abnormal baseline SPARE-AD and CSF biomarkers. Longer follow-up will elucidate the specificity of baseline measurements.


title: Open-Ended Coaxial Probe Technique for Dielectric Measurement of Biological Tissues: Challenges and Common Practices

abstract: Electromagnetic (EM) medical technologies are rapidly expanding worldwide for both diagnostics and therapeutics. As these technologies are low-cost and minimally invasive, they have been the focus of significant research efforts in recent years. Such technologies are often based on the assumption that there is a contrast in the dielectric properties of different tissue types or that the properties of particular tissues fall within a defined range. Thus, accurate knowledge of the dielectric properties of biological tissues is fundamental to EM medical technologies. Over the past decades, numerous studies were conducted to expand the dielectric repository of biological tissues. However, dielectric data is not yet available for every tissue type and at every temperature and frequency. For this reason, dielectric measurements may be performed by researchers who are not specialists in the acquisition of tissue dielectric properties. To this end, this paper reviews the tissue dielectric measurement process performed with an open-ended coaxial probe. Given the high number of factors, including equipment- and tissue-related confounders, that can increase the measurement uncertainty or introduce errors into the tissue dielectric data, this work discusses each step of the coaxial probe measurement procedure, highlighting common practices, challenges, and techniques for controlling and compensating for confounders.


title: Continuous Signed Distance Functions for 3D Vision

abstract: We explore the use of continuous signed distance functions as an object representation for 3D vision. Popularized in procedural computer graphics, this representation defines 3D objects as geometric primitives combined with constructive solid geometry and transformed by nonlinear deformations, scaling, rotation or translation. Unlike its discretized counterpart, that has become important in dense 3D reconstruction, the continuous distance function is not stored as a sampled volume, but as a closed mathematical expression. We argue that this representation can have several benefits for 3D vision, such as being able to describe many classes of indoor and outdoor objects at the order of hundreds of bytes per class, getting parametrized shape variations for free. As a distance function, the representation also has useful computational aspects by defining, at each point in space, the direction and distance to the nearest surface, and whether a point is inside or outside the surface.


title: Gaze Direction based Mobile Application for Quadriplegia Wheelchair Control System

abstract: People with quadriplegia recruit the interest of researchers in introducing automated movement systems for adopted special purpose wheelchairs. These systems were introduced for easing the movement of such type of disabled people independently. This paper proposed a comprehensive control system that can control the movement of Quadriplegia wheelchairs using gaze direction and blink detection. The presented system includes two main parts. The first part consists of a smartphone that applies the propose gaze direction detection based mobile application. It then sends the direction commands to the second part via Wi-Fi connection. The second part is a prototype representing the quadriplegia wheelchair that contains robotic car (two-wheel driving car), Raspberry Pi III and ultrasound sensors. The gaze direction commands, sent from the smartphone, are received by the Raspberry Pi for processing and producing the control signals. The ultrasound sensors are fixed at the front and back of the car for performing the emergency stop when obstacles are detected. The proposed system is based on gaze tracking and direction detection without the requirement of calibration with additional sensors and instruments. The obtained results show the superior performance of the proposed system that proves the claim of authors. The accuracy ratio is ranged between 66% and 82% depending on the environment (indoor and outdoor) and surrounding lighting as well as the smart phone type.


Thresholds as spaces of potentiality: Negotiating the supervision relationship in a non-traditional art and design PhD candidature


title: On resolving schematic heterogeneity in multidatabase systems

abstract: The objective of a multidatabase system is to provide a single uniform interface to accessing multiple independent databases being managed by multiple independent, and possibly heterogeneous, database systems. One crucial element in the design of a multidatabase system is the design of a data definition language for specifying a schema that represents the integration of the schemas of multiple independent databases. The design of such a language in turn requires a comprehensive classification of the conflicts (i.e., discrepancies) among the schemas of the independent databases and development of techniques for resolving (i.e., homogenizing) all of the conflicts in the classification. An earlier paper provided a comprehensive classification of schematic conflicts that may arise when integrating multiple independent relational database (RDB) schemas into a single multidatabase (MDB) schema. In this paper, we provide a comprehensive classification of techniques for resolving the schematic conflicts that may arise when integrating multiple RDB schemas, or RDB schemas and object-oriented database (OODB) schemas, or multiple OODB schemas. The classification of conflict resolution techniques includes not only those necessary for resolving schematic conflicts identified in the earlier paper, but also additional conflicts that arise when OODBs become part of the databases to be integrated. Most of the conflict resolution techniques discussed in the paper have already been incorporated into SQL/M, a multidatabase language implemented in UniSQL/M, a commercially available multidatabase system from UniSQL, Inc. which integrated SQL-based relational database systems and the UniSQL/X unified relational and object-oriented database system.


REACTIVELY PROCESSED POLYAMIDE-6 STRUCTURAL COMPOSITES FOR AUTOMOTIVE APPLICATIONS


title: Effects of Antecedent Food Intake Pattern on Hepatic Lipogenesis

abstract: Liver slices of ATG obese mice, of rats trained to eat their 24-hour ration in 1 hour and of rats fed a high carbohydrate diet after a 48-hour period of fasting were incubated with acetate-1-C14 an...


title: Prevalence and Factors Associated with Walking and Bicycling for Transport Among Young Adults in Two Low-Income Localities of Bogotá, Colombia

abstract: Background: Utilitarian physical activity confers health benefits, but little is known about experiences in developing countries. The objective was to examine the prevalence and factors associated with walking and bicycling for transport in adults from Bogota. Methods: A cross-sectional study including 1464 adults age 18 to 29 y during the year 2002. Results: 16.7% reported bicycling for at least 10 min during the last week and 71.7% reported walking for at least 90 min during the last week. Bicycling was more likely among adults living in Tunjuelito (flat terrain), who use the “ciclovia” (car-roads for recreational bicycling on holidays/Sundays) or reporting physical activity during leisure-time and less likely among women, or adults with college education. Walking was more likely among adults reporting physical activity during leisure time and less likely among housewives/househusbands or those living in Tunjuelito. Conclusion: Programs that promote walking or bicycling in Bogota should consider differe...


title: Histologic and Histometric Study of Experimental Occlusal Trauma in Rats

abstract: TH E role of occlusal trauma in the genesis of periodontal lesions has been extensively studied from many angles. Clinico-radiographic studies have established the signs and symptoms attributable to trauma from occlusion. Increased mobility of the involved tooth, widening of the periodontal membrane and loss of the cortical layer of bone are among the changes produced.1 -4 Microscopic studies of human material have been done in autopsied jaws.5' In these studies tissue response to local irritation, occlusal trauma and systemic factors is difficult to separate.


title: Fast and Accurate Template Matching Using Pixel Rearrangement on the GPU

abstract: A GPU (Graphics Processing Unit) is a specialized processor for graphics processing. GPUs have the ability to perform high-speed parallel processing using its many processing cores. To utilize the powerful computing ability, GPUs are widely used for general purpose processing. The main contribution of this paper is to show a new template matching algorithm using pixel rearrangement. Template Matching is a technique for finding small parts of an image which match a template image. The feature of our proposed algorithm is that using pixel rearrangement, multiple low-resolution images are generated and template matching for the low-resolution images is performed to reduce the computing time. Also, we implemented our algorithm on a GPU system. The experimental results show that, for an input image with size of 4096 $\times$ 4096 and a template image with size of 256 $\times$ 256, our implementation can achieve a speedup factor of approximately 78 times over the conventional sequential implementation.


Review of Spatial Clustering Methods


Aalborg Universitet Time Series Management Systems : A Survey


title: Meta Networks

abstract: Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training data while preserving performances on previously learned ones still presents a significant challenge to neural network models. In this work, we introduce a novel meta learning method, Meta Networks (MetaNet), that learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. When evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve a near human-level performance and outperform the baseline approaches by up to 6% accuracy. We demonstrate several appealing properties of MetaNet relating to generalization and continual learning.


title: The Internet of Hackable Things

abstract: The Internet of Things makes possible to connect each everyday object to the Internet, making computing pervasive like never before. From a security and privacy perspective, this tsunami of connectivity represents a disaster, which makes each object remotely hackable. We claim that, in order to tackle this issue, we need to address a new challenge in security: education.


An Ensemble Method to Predict Student Performance in an Online Math Learning Environment


title: The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions

abstract: In this work, we draw attention to a connection between skill-based models of game outcomes and Gaussian process classification models. The Gaussian process perspective enables a) a principled way of dealing with uncertainty and b) rich models, specified through kernel functions. Using this connection, we tackle the problem of predicting outcomes of football matches between national teams. We develop a player kernel that relates any two football matches through the players lined up on the field. This makes it possible to share knowledge gained from observing matches between clubs (available in large quantities) and matches between national teams (available only in limited quantities). We evaluate our approach on the Euro 2008, 2012 and 2016 final tournaments.


title: Distributed Power System Automation With IEC 61850, IEC 61499, and Intelligent Control

abstract: This paper presents a new approach to power system automation, based on distributed intelligence rather than traditional centralized control. The paper investigates the interplay between two international standards, IEC 61850 and IEC 61499, and proposes a way of combining of the application functions of IEC 61850-compliant devices with IEC 61499-compliant “glue logic,” using the communication services of IEC 61850-7-2. The resulting ability to customize control and automation logic will greatly enhance the flexibility and adaptability of automation systems, speeding progress toward the realization of the smart grid concept.


title: Human emotion and memory: interactions of the amygdala and hippocampal complex

abstract: The amygdala and hippocampal complex, two medial temporal lobe structures, are linked to two independent memory systems, each with unique characteristic functions. In emotional situations, these two systems interact in subtle but important ways. Specifically, the amygdala can modulate both the encoding and the storage of hippocampal-dependent memories. The hippocampal complex, by forming episodic representations of the emotional significance and interpretation of events, can influence the amygdala response when emotional stimuli are encountered. Although these are independent memory systems, they act in concert when emotion meets memory.


title: Towards effective research recommender systems for repositories

abstract: In this paper, we argue why and how the integration of recommender systems for research can enhance the functionality and user experience in repositories. We present the latest technical innovations in the CORE Recommender, which provides research article recommendations across the global network of repositories and journals. The CORE Recommender has been recently redeveloped and released into production in the CORE system and has also been deployed in several third-party repositories. We explain the design choices of this unique system and the evaluation processes we have in place to continue raising the quality of the provided recommendations. By drawing on our experience, we discuss the main challenges in offering a state-of-the-art recommender solution for repositories. We highlight two of the key limitations of the current repository infrastructure with respect to developing research recommender systems: 1) the lack of a standardised protocol and capabilities for exposing anonymised user-interaction logs, which represent critically important input data for recommender systems based on collaborative filtering and 2) the lack of a voluntary global sign-on capability in repositories, which would enable the creation of personalised recommendation and notification solutions based on past user interactions.


title: Introduction to Security and Privacy on the Blockchain

abstract: The blockchain has fueled one of the most enthusiastic bursts of activity in applied cryptography in years, but outstanding problems in security and privacy research must be solved for blockchain technologies to go beyond the hype and reach their full potential. At the first IEEE Privacy and Security on the Blockchain Workshop (IEEE S&B), we presented peer-reviewed papers bringing together academia and industry to analyze problems ranging from deploying newer cryptographic primitives on Bitcoin to enabling usecases like privacy-preserving file storage. We overview not only the larger problems the workshop has set out to tackle, but also outstanding unsolved issues that will require further cooperation between academia and the blockchain community.


title: Superior Mesenteric Artery Syndrome in association with Abdominal Tuberculosis: An Eye Opener

abstract: Superior Mesenteric Artery Syndrome (SMAS) is a rare clinical entity presenting as acute or chronic upper gastrointestinal obstruction. It occurs due to compression of third part of duodenum between abdominal aorta and overlying superior mesenteric artery caused by a decrease in angle between the two vessels. Rapid loss of retroperitoneal fat, in conditions leading to severe weight loss is the main factor responsible for this disorder. Superior mesenteric artery syndrome in association with abdominal tuberculosis has not been reported earlier to the best of our knowledge. Therefore, an unknown cause (SMAS) of upper gastrointestinal obstruction in a patient of abdominal tuberculosis is being presented for the first time through this case report. An imaging diagnosis of SMAS was made on contrast enhanced CT abdomen which also confirmed the clinical suspicion of abdominal tuberculosis in the patient. The patient was managed conservatively and recovered without requiring any surgical intervention for the obstructive symptoms.


title: Analysis on Rectifier Diode Stress Issue of the DCM Active Clamped Dual-Switch Forward Converter and its Solutions

abstract: This paper illustrates the rectifier stress issue of the active clamped dual switch forward converters operating on discontinuous current mode (DCM), and analyzes the additional reverse voltage on the rectifier diode of active clamped dual switch forward converter at DCM operation, which does not appear in continuous current mode (CCM). The additional reverse voltage stress, plus its spikes, definitely causes many difficulties in designing high performance power supplies. In order to suppress this voltage spike to an acceptable level and improve the working conditions for the rectifier diode, this paper carefully explains and presents the working principles of active clamped dual switch forward converter in DCM operation, and theoretically analyzes the causes of the additional reverse voltage and its spikes. For conquering these difficulties, this paper also innovate active clamped snubber (ACS) cell to solve this issue. Furthermore, experiments on a 270W active clamped dual switch forward converter prototype were designed to validate the innovation. Finally, based on the similarities of the rectifier network in forward-topology based converters, this paper also extents the utility of this idea into even wider dc-dc converters.


title: Crop Yield Assessment from Remote Sensing

abstract: Monitoring crop condition and production estimates at the state and county level is of great interest to the U.S. Department of Agriculture. The National Agricultural Statistical Service (NASS) of the U.S. Department of Agriculture conducts field interviews with sampled farm operators and obtains crop cuttings to make crop yield estimates at regional and state levels. NASS needs supplemental spatial data that provides timely information on crop condition and potential yields. In this research, the crop model EPIC (Erosion Productivity Impact Calculator) was adapted for simulations at regional scales. Satellite remotely sensed data provide a real-time assessment of the magnitude and variation of crop condition parameters, and this study investigates the use of these parameters as an input to a crop growth model. This investigation was conducted in the semi-arid region of North Dakota in the southeastern part of the state. The primary objective was to evaluate a method of integrating parameters. retrieved from satellite imagery in a crop growth model to simulate spring wheat yields at the sub-county and county levels. The input parameters derived from remotely sensed data provided spatial integrity, as well as a real-time calibration of model simulated parameters during the season, to ensure that the modeled and observed conditions agree. A radiative transfer model, SAIL (Scattered by Arbitrary Inclined Leaves), provided the link between the satellite data and crop model. The model parameters were simulated in a geographic information system grid, which was the platform for aggregating yields at local and regional scales. A model calibration was performed to initialize the model parameters. This calibration was performed using Landsat data over three southeast counties in North Dakota. The model was then used to simulate crop yields for the state of North Dakota with inputs derived from NOAA AVHRR data. The calibration and the state level simulations are compared with spring wheat yields reported by NASS objective yield surveys.


title: An improved centroid classifier for text categorization

abstract: In the context of text categorization, Centroid Classifier has proved to be a simple and yet efficient method. However, it often suffers from the inductive bias or model misfit incurred by its assumption. In order to address this issue, we propose a novel batch-updated approach to enhance the performance of Centroid Classifier. The main idea behind this method is to take advantage of training errors to successively update the classification model by batch. The technique is simple to implement and flexible to text data. The experimental results indicate that the technique can significantly improve the performance of Centroid Classifier.


title: A SURVEY OF TEXT CLASSIFICATION ALGORITHMS

abstract: The problem of classification has been widely studied in the data mining, machine learning, database, and information retrieval communities with applications in a number of diverse domains, such as target marketing, medical diagnosis, news group filtering, and document organization. In this paper we will provide a survey of a wide variety of text classification algorithms.


title: Active Shape Models-Their Training and Application

abstract: !, Model-based vision is firmly established as a robust approach to recognizing and locating known rigid objects in the presence of noise, clutter, and occlusion. It is more problematic to apply modelbased methods to images of objects whose appearance can vary, though a number of approaches based on the use of flexible templates have been proposed. The problem with existing methods is that they sacrifice model specificity in order to accommodate variability, thereby compromising robustness during image interpretation. We argue that a model should only be able to deform in ways characteristic of the class of objects it represents. We describe a method for building models by learning patterns of variability from a training set of correctly annotated images. These models can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes). The key difference is that our Active Shape Models can only deform to fit the data in ways consistent with the training set. We show several practical examples where we have built such models and used them to locate partially occluded objects in noisy, cluttered images. Q 199s A&& prrss, IN.


title: Integrated cognitive architectures: a survey

abstract: This article aims to present an account of the state of the art research in the field of integrated cognitive architectures by providing a review of six cognitive architectures, namely Soar, ACT-R, ICARUS, BDI, the subsumption architecture and CLARION. We conduct a detailed functional comparison by looking at a wide range of cognitive components, including perception, memory, goal representation, planning, problem solving, reasoning, learning, and relevance to neurobiology. In addition, we study the range of benchmarks and applications that these architectures have been applied to. Although no single cognitive architecture has provided a full solution with the level of human intelligence, important design principles have emerged, pointing to promising directions towards generic and scalable architectures with close analogy to human brains.


title: Impact of Corpora Quality on Neural Machine Translation

abstract: Large parallel corpora that are automatically obtained from the web, documents or elsewhere often exhibit many corrupted parts that are bound to negatively affect the quality of the systems and models that learn from these corpora. This paper describes frequent problems found in data and such data affects neural machine translation systems, as well as how to identify and deal with them. The solutions are summarised in a set of scripts that remove problematic sentences from input corpora.


title: Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric and Circulant Weight Matrices

abstract: Currently, the biaffine classifier has been attracting attention as a method to introduce an attention mechanism into the modeling of binary relations. For instance, in the field of dependency parsing, the Deep Biaffine Parser by Dozat and Manning has achieved state-of-the-art performance as a graph-based dependency parser on the English Penn Treebank and CoNLL 2017 shared task. On the other hand, it is reported that parameter redundancy in the weight matrix in biaffine classifiers, which has O(n^2) parameters, results in overfitting (n is the number of dimensions). In this paper, we attempted to reduce the parameter redundancy by assuming either symmetry or circularity of weight matrices. In our experiments on the CoNLL 2017 shared task dataset, our model achieved better or comparable accuracy on most of the treebanks with more than 16% parameter reduction.


title: Challenge AI Mind: A Crowd System for Proactive AI Testing

abstract: Artificial Intelligence (AI) has burrowed into our lives in various aspects; however, without appropriate testing, deployed AI systems are often being criticized to fail in critical and embarrassing cases. Existing testing approaches mainly depend on fixed and pre-defined datasets, providing a limited testing coverage. In this paper, we propose the concept of proactive testing to dynamically generate testing data and evaluate the performance of AI systems. We further introduce Challenge.AI, a new crowd system that features the integration of crowdsourcing and machine learning techniques in the process of error generation, error validation, error categorization, and error analysis. We present experiences and insights into a participatory design with AI developers. The evaluation shows that the crowd workflow is more effective with the help of machine learning techniques. AI developers found that our system can help them discover unknown errors made by the AI models, and engage in the process of proactive testing.


title: Harvesting facts from textual web sources by constrained label propagation

abstract: There have been major advances on automatically constructing large knowledge bases by extracting relational facts from Web and text sources. However, the world is dynamic: periodic events like sports competitions need to be interpreted with their respective timepoints, and facts such as coaching a sports team, holding political or business positions, and even marriages do not hold forever and should be augmented by their respective timespans. This paper addresses the problem of automatically harvesting temporal facts with such extended time-awareness. We employ pattern-based gathering techniques for fact candidates and construct a weighted pattern-candidate graph. Our key contribution is a system called PRAVDA based on a new kind of label propagation algorithm with a judiciously designed loss function, which iteratively processes the graph to label good temporal facts for a given set of target relations. Our experiments with online news and Wikipedia articles demonstrate the accuracy of this method.


title: Vitamin D for health: a global perspective.

abstract: It is now generally accepted that vitamin D deficiency is a worldwide health problem that affects not only musculoskeletal health but also a wide range of acute and chronic diseases. However, there remains cynicism about the lack of randomized controlled trials to support the association studies regarding the nonskeletal health benefits of vitamin D. This review was obtained by searching English-language studies published up to April 1, 2013, in PubMed, MEDLINE, and the Cochrane Central Register of Controlled Trials (search terms: vitamin D and supplementation) and focuses on recent challenges regarding the definition of vitamin D deficiency and how to achieve optimal serum 25-hydroxyvitamin D concentrations from dietary sources, supplements, and sun exposure. The effect of vitamin D on fetal programming epigenetics and gene regulation could potentially explain why vitamin D has been reported to have such wide-ranging health benefits throughout life. There is potentially a great upside to increasing the vitamin D status of children and adults worldwide for improving musculoskeletal health and reducing the risk of chronic illnesses, including some cancers, autoimmune diseases, infectious diseases, type 2 diabetes mellitus, neurocognitive disorders, and mortality.


title: Process Invariant Biasing of Ring Amplifiers Using Deadzone Regulation Circuit

abstract: In this paper, process invariant biasing is proposed for robust operation of ring amplifiers. The ring amplifier is an efficient solution for high accuracy amplification in sub-micron CMOS process. A traditional ring amplifier structure requires an external control voltage, defined as the deadzone voltage, to set the optimum quiescent current at the output stage. Other structures of ring amplifiers use on-chip resistors or current starved inverters to set the deadzone voltage. Since the deadzone voltage is a function of the threshold voltage of transistors in the output stage inverter, ring amplifiers are susceptible to process variations. In this paper, a deadzone regulation circuit is proposed that utilizes a constant current source and a negative feedback loop to regulate the quiescent current of the output stage inverter across process corners. Transistor level simulations are used to validate the operation of the proposed deadzone regulation technique across FF, TT and SS corners in a 65nm CMOS process. The design example uses a current starved inverter based ring amplifier in a switched capacitor amplifier to achieve a closed loop gain of 4 with a settling accuracy of ≤ 0.05% and operated at a sampling rate of 125MHz.


title: Identifying Satisfaction Factors in Tertiary Education: The case of an Information Systems Program

abstract: This paper introduces a modified Kano approach to analysing and classifying quality attributes that drive student satisfaction in tertiary education. The approach provides several benefits over the traditional Kano approach. Firstly, it uses existing student evaluations of subjects in the educational institution instead of purpose-built surveys as the data source. Secondly, since the data source includes qualitative comments and feedback, it has the exploratory capability to identify emerging and unique attributes. Finally, since the quality attributes identified could be tied directly to students’ detailed feedback, the approach enables practitioners to easily translate the results into concrete action plans. In this paper, the approach is applied to analysing 26 subjects in the information systems school of an Australia university. The approach has enabled the school to uncover new quality attributes and paves the way for other institutions to use their student evaluations to continually understand and addressed students’ changing needs.


title: Experiments on local positioning with Bluetooth

abstract: This paper presents the design and implementation of the Bluetooth local positioning application. Positioning is based on received power levels, which are converted to distance estimates according to a simple propagation model. The extended Kalman filter computes a 3D position estimate on the basis of distance estimates. With the used Bluetooth hardware, the mean absolute error of positioning was measured to be 3.76 m. The accuracy can be improved if Bluetooth devices are able to measure received power levels more precisely.


title: Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization

abstract: One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization. One of the most widely-used methods is the Kalman filter, which is both extremely simple and general. However, Kalman filters require a motion model and measurement model to be specified a priori, which burdens the modeler and simultaneously demands that we use explicit models that are often only crude approximations of reality. For example, in the pose-estimation tasks mentioned above, it is common to use motion models that assume constant velocity or constant acceleration, and we believe that these simplified representations are severely inhibitive. In this work, we propose to instead learn rich, dynamic representations of the motion and noise models. In particular, we propose learning these models from data using long short term memory, which allows representations that depend on all previous observations and all previous states. We evaluate our method using three of the most popular pose estimation tasks in computer vision, and in all cases we obtain state-of-the-art performance.


title: A classification-based methodology for planning audit strategies in fraud detection

abstract: Planning adequate audit strategies is a key success factor in a posterion’ fraud detection, e.g., in the fiscal and insurance domains, where audits are intended to detect tax evasion and fraudulent claims. A case study is presented in this paper, which illustrates how techniques based on classification can be used to support the task of planning audit strategies. The proposed approach is sensible to some conflicting issues of audit planning, e.g., the trade-off between maximizing audit benefits vs. minimizing audit costs. A methodological scenario, common to a whole class of similar applications, is then abstracted away from the case study. The limitations of available systems to support the identified overall KDD process, bring us to point out the key aspects of a logic-based database language, integrated with mining mechanisms, which is used to provide a uniform, highly expressive environment for the various steps in the construction of the considered case-study.


title: Preventing Parastomal Hernia Using a Modified Sugarbaker Technique With Composite Mesh During Laparoscopic Abdominoperineal Resection

abstract: OBJECTIVE ::: The aim of this study was to assess the reduction in the incidence of parastomal hernia (PH) after placement of prophylactic synthetic mesh using a modified Sugarbaker technique when a permanent end-colostomy is needed. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: Prevention of PH formation is crucial given the high prevalence of PH and difficulties in the surgical repair of PH. ::: ::: ::: METHODS ::: A randomized, prospective, double-blind, and controlled trial. Rectal cancer patients undergoing laparoscopic abdominoperineal resection with permanent colostomy were randomized (1 : 1) to the mesh and nonmesh arms. In the mesh group, a large-pore lightweight composite mesh was placed in the intraperitoneal/onlay fashion using a modified Sugarbaker technique. PH was detected by computed tomography (CT) after a minimum follow-up of 12 months. Analysis was per-protocol. ::: ::: ::: RESULTS ::: The mesh group included 24 patients and the control group 28. Preoperative data, surgical time, and postoperative morbidity were similar. The median follow-up was 26 months. After CT examination, 6 of 24 PHs (25%) were observed in the mesh group compared with 18 of 28 (64.3%) in the nonmesh group (odds ratio 0.39, 95% confidence interval 0.18-0.82; P = 0.005). The Kaplan-Meier curves showed significant differences in favor of the mesh group (long-rank = 4.21, P = 0.04). The number needed to treat was 2.5, which confirmed the effectiveness of the intervention. ::: ::: ::: CONCLUSIONS ::: Placement of a prosthetic mesh by the laparoscopic approach following the modified Sugarbaker technique is safe and effective in the prevention of PH, reducing significantly the incidence of PH.


title: Coronally advanced flap with and without a xenogenic collagen matrix in the treatment of multiple recessions: a randomized controlled clinical study.

abstract: Multiple adjacent recession defects were treated in 32 patients using a coronally advanced flap (CAF) with or without a collagen matrix (CM). The percentage of root coverage was 81.49% ± 23.45% (58% complete root coverage) for CAF sites (control) and 93.25% ± 10.01% root coverage (72% complete root coverage) for CM plus CAF sites (test). The results achieved in the test group were significantly greater than in the control group, indicating that CM plus CAF is a suitable option for the treatment of multiple adjacent gingival recessions.


title: Three techniques for integrating data in mixed methods studies

abstract: Techniques designed to combine the results of qualitative and quantitative studies can provide researchers with more knowledge than separate analysis


STEREO IMAGE POINT CLOUD AND LIDAR POINT CLOUD FUSION FOR THE 3 D STREET MAPPING


title: Hijacking Bitcoin: Routing Attacks on Cryptocurrencies

abstract: As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) can intercept and manipulate a large fraction of Bitcoin traffic. ::: This paper presents the first taxonomy of routing attacks and their impact on Bitcoin, considering both small-scale attacks, targeting individual nodes, and large-scale attacks, targeting the network as a whole. While challenging, we show that two key properties make routing attacks practical: (i) the efficiency of routing manipulation; and (ii) the significant centralization of Bitcoin in terms of mining and routing. Specifically, we find that any network attacker can hijack few (<100) BGP prefixes to isolate ~50% of the mining power---even when considering that mining pools are heavily multi-homed. We also show that on-path network attackers can considerably slow down block propagation by interfering with few key Bitcoin messages. ::: We demonstrate the feasibility of each attack against the deployed Bitcoin software. We also quantify their effectiveness on the current Bitcoin topology using data collected from a Bitcoin supernode combined with BGP routing data. ::: The potential damage to Bitcoin is worrying. By isolating parts of the network or delaying block propagation, attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of exploits such as double spending. To prevent such effects in practice, we provide both short and long-term countermeasures, some of which can be deployed immediately.


title: e-Book on Closteroviridae

abstract: This e-Book on the virus family Closteroviridae provides an overview on some representative members of this family. Most articles are reviews on different fundamental and applied aspects, but a few are original contributions focused on more specific issues. Even if biased toward closteroviruses, and more explicitly toward Citrus tristeza virus (CTV) due to its economic relevance, this compilation altogether results an attractive blend that we hope will attract the attention of the aficionados of this fascinating virus family.


title: Mining program workflow from interleaved traces

abstract: Successful software maintenance is becoming increasingly critical due to the increasing dependence of our society and economy on software systems. One key problem of software maintenance is the difficulty in understanding the evolving software systems. Program workflows can help system operators and administrators to understand system behaviors and verify system executions so as to greatly facilitate system maintenance. In this paper, we propose an algorithm to automatically discover program workflows from event traces that record system events during system execution. Different from existing workflow mining algorithms, our approach can construct concurrent workflows from traces of interleaved events. Our workflow mining approach is a three-step coarse-to-fine algorithm. At first, we mine temporal dependencies for each pair of events. Then, based on the mined pair-wise tem-poral dependencies, we construct a basic workflow model by a breadth-first path pruning algorithm. After that, we refine the workflow by verifying it with all training event traces. The re-finement algorithm tries to find out a workflow that can interpret all event traces with minimal state transitions and threads. The results of both simulation data and real program data show that our algorithm is highly effective.


Interdisciplinary Course Design


title: Ridges and Valleys Detection in Images using Difference of Rotating Half Smoothing Filters

abstract: In this paper we propose a new ridge/valley detection method in images based on the difference of rotating Gaussian semi filters. The novelty of this approach resides in the mixing of ideas coming both from directional filters and DoG method. We obtain a new ridge/valley anisotropic DoG detector enabling very precise detection of ridge/valley points. Moreover, this detector performs correctly at crest lines even if highly bended, and is precise on junctions. This detector has been tested successfully on various image types presenting difficult problems for classical ridges/valleys detection methods.


title: Computing inversion-free mappings by simplex assembly

abstract: We present a novel method, called Simplex Assembly, to compute inversion-free mappings with low or bounded distortion on simplicial meshes. Our method involves two steps: simplex disassembly and simplex assembly. Given a simplicial mesh and its initial piecewise affine mapping, we project the affine transformation associated with each simplex into the inversion-free and distortion-bounded space. The projection disassembles the input mesh into disjoint simplices. The disjoint simplices are then assembled to recover the original connectivity by minimizing the mapping distortion and the difference of the disjoint vertices with respect to the piecewise affine transformations, while the piecewise affine mapping is restricted inside the feasible space. Due to the use of affine transformations as variables, our method explicitly guarantees that no inverted simplex occurs, and that the mapping distortion is below the bound during the optimization. Compared with existing methods, our method is robust to an initialization with many inverted elements and positional constraints. We demonstrate the efficiency and robustness of our method through a variety of geometric processing tasks.


title: A morphological adaptation approach to path planning inspired by slime mould

abstract: Path planning is a classic problem in computer science and robotics which has recently been implemented in unconventional computing substrates such as chemical reaction–diffusion computers. These novel computing schemes utilise the parallel spatial propagation of information and often use a two-stage method involving diffusive propagation to discover all paths and a second stage to highlight or visualise the path between two particular points in the arena. The true slime mould Physarum polycephalum is known to construct efficient transport networks between nutrients in its environment. These networks are continuously remodelled as the organism adapts its body plan to changing spatial stimuli. It can be guided towards attractant stimuli (nutrients, warm regions) and it avoids locations containing hazardous stimuli (light irradiation, repellents, or regions occupied by predatory threats). Using a particle model of slime mould we demonstrate scoping experiments which explore how path planning may be performed by morphological adaptation. We initially demonstrate simple path planning by a shrinking blob of virtual plasmodium between two attractant sources within a polygonal arena. We examine the case where multiple paths are required and the subsequent selection of a single path from multiple options. Collision-free paths are implemented via repulsion from the borders of the arena. Finally, obstacle avoidance is implemented by repulsion from obstacles as they are uncovered by the shrinking blob. These examples show proof-of-concept results of path planning by morphological adaptation which complement existing research on path planning in novel computing substrates.


title: Consistency-based characterization for IC Trojan detection

abstract: A Trojan attack maliciously modifies, alters, or embeds unplanned components inside the exploited chips. Given the original chip specifications, and process and simulation models, the goal of Trojan detection is to identify the malicious components. This paper introduces a new Trojan detection method based on nonintrusive external IC quiescent current measurements. We define a new metric called consistency. Based on the consistency metric and properties of the objective function, we present a robust estimation method that estimates the gate properties while simultaneously detecting the Trojans. Experimental evaluations on standard benchmark designs show the validity of the metric, and demonstrate the effectiveness of the new Trojan detection.


title: On practical selective jamming of Bluetooth Low Energy advertising

abstract: Bluetooth Low Energy (BLE) Advertising is an important component of BLE as it is used to broadcast connection information to other devices nearby, usually smartphones. Furthermore, it is the foundation of a variety of services, such as indoor localization. We present a selective jammer against BLE beacons. The jammer selectively jams only BLE advertising beacons for which it was configured (API) on just the BLE advertising channels actually being used (narrowband). Such a jammer is hard to detect and hence requires a sophisticated Intrusion Detection System (IDS). The prototypic implementation uses off-the-shelf embedded hardware which is low-cost, small-size and very power efficient. Experiment results demonstrate the feasibility of the proposed solution and reveal the impact of the distance between jammer and receiver on the advertising success rate. At short distances the jammer is able to successfully corrupt all BLE advertisement beacon frames making the BLE beacon source undiscoverable.


title: Pixel Recurrent Neural Networks

abstract: Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent.


title: Long range, safe power transmission using iteratively-tuned rectification

abstract: A long range wireless power system operating at 2.45 GHz is developed. The system is comprised of: 1) high gain horn antennas, 2) iteratively tuned matching/filtering network, 3) high efficiency rectifier, and 4) optimized load. An iterative technique is then used to match the antenna to the rectifier to greatly improve the efficiency. The rectifier is built around a single shunt diode rectifier topology. The output of the rectifier is fed to an optimized load, producing efficiency above 45% at 0 dBm. The overall system is able to operate across half a meter wirelessly.


title: An Auto-Scaling Cloud Controller Using Fuzzy Q-Learning - Implementation in OpenStack

abstract: Auto-scaling, i.e., acquiring and releasing resources automatically, is a central feature of cloud platforms. The key problem is how and when to add/remove resources in order to meet agreed service-level agreements. Many commercial solutions use simple approaches such as threshold-based ones. However, providing good thresholds for auto-scaling is challenging. Recently, machine learning approaches have been used to complement and even replace expert knowledge. We propose a dynamic learning strategy based on a fuzzy logic algorithm, which learns and modifies fuzzy scaling rules at runtime without requiring prior knowledge. The proposed algorithm is implemented and evaluated as an extension to the OpenStack cloud platform, integrating it with the Heat and Ceilometer components for orchestration and monitoring, respectively, using Heat Orchestration Templates. We specifically focus on implementation and experimentation aspects here. Our auto-scaling approach can handle various load traffic situations, delivering resources on demand while reducing infrastructure and management costs. The experimentals show promising performance in terms of resource adjustment to optimize SLA compliance (response time) while reducing cloud provider’s costs.


title: An improved KNN text classification algorithm based on density

abstract: Text classification has gained booming interest over the past few years. As a simple, effective and nonparametric classification method, KNN method is widely used in document classification. However, the uneven distribution in training set will affect the KNN classified result negatively. Moreover, the uneven distribution phenomenon of text is very common in documents on the Web. To tackling on this, this paper proposes an improved KNN method denoted by DBKNN. Experimental results show that the DBKNN algorithm can better serve classification requests for large sets of unevenly distributed documents.


title: Structure of X-ray emitting jets close to the launching site: from embedded to disk-bearing sources

abstract: Several observations of stellar jets show evidence of X-ray emitting shocks close to the launching site. In some cases, the shocked features appear to be stationary, also for YSOs at different stages of evolution. We study the case of HH 154, the jet originating from the embedded binary Class 0/I protostar IRS 5, and the case of the jet associated to DG Tau, a more evolved Class II disk-bearing source or Classical T Tauri star (CTTS), both located in the Taurus star-forming region. We aim at investigating the effect of perturbations in X-ray emitting stationary shocks in stellar jets; the stability and detectability in X-rays of these shocks; and explore the differences in jets from Class 0 to Class II sources. We performed a set of 2.5-dimensional magnetohydrodynamic numerical simulations that modelled supersonic jets ramming into a magnetized medium. The jet is formed by two components: a continously driven component that forms a quasi-stationary shock at the base of the jet; and a pulsed component constituted by blobs perturbing the shock. We explored different parameters for both components. We studied two cases: a jet less dense than the ambient medium (light jet), representing the case of HH 154; and a jet denser than the ambient (heavy jet), associated with DG Tau. We synthesized the count rate from the simulations and compared with available Chandra observations. Our model explains the formation of X-ray emitting quasi-stationary shocks observed at the base of jets in a natural way, being able to reproduce the observed jet properties at different evolutionary phases (in particular, for HH 154 and DG Tau). The jet is collimated by the magnetic field forming a quasi-stationary shock at the base which emits in X-rays even when perturbations formed by a train of blobs are present. We found similar collimation mechanisms dominating in both heavy and light jets...


title: Stress Assessment While Listening to Quran Recitation

abstract: Stress and anxiety are one of the most widespread problems presently, stress treatment has been featured in many researches. The use of Quran offers a substantial help in treating stress. The purpose of this study is to examine the various aspects and perspectives of human emotions while listening to Quran Recitation. This study aims to identify and select verses that have more psychological impact than other verses and to identify the most Quran reciter that respondents believe his voice brings calmness and tranquility to their mind. Quantitative and qualitative methods were adopted. An online distributed questionnaire was sent to the academic staffs of all the Islamic faculties in the Malaysian public universities, five subjects participated in EEG experiment to identify their emotions while listening to these Quran verses. Five Quran verses and one reciter were identified, while experiment indicates that the subjects are more relieved and relaxed when listening to these Quran verses.


title: Video sharing in online social networks: measurement and analysis

abstract: Online social networks (OSNs) have become popular destinations for connecting friends and sharing information. Recent statistics suggest that OSN users regularly share contents from video sites, and a significant amount of requests of the video sites are indeed from them nowadays. These behaviors have substantially changed the workload of online video services. To better understand this paradigm shift, we conduct a long-term and extensive measurement of video sharing in RenRen, the largest Facebook-like OSN in China. In this paper, we focus on the video popularity distribution and evolution. In particular, we find that the video popularity distribution exhibits perfect power-law feature (while videos in YouTube exhibit a power-law waist with a long truncated tail). Moreover, we observe that the requests for the new published videos generally experience two or three days latency to reach the peak value, and then change dynamically with a series of unpredictable bursts (while in YouTube, videos reach the global peak immediately after introduction to the system, and then the accesses generally decrease overtime, except possibly on some special days). These differences can raise new challenges to content providers. For example, the video popularity is now hard to predict based on their historical requests. We further develop a simple yet effective model to simulate user requests process across videos in OSNs. Trace-based simulation shows that it can well capture the observed features.


title: Syntax Annotation for the GENIA Corpus

abstract: Linguistically annotated corpus based on texts in biomedical domain has been constructed to tune natural language processing (NLP) tools for biotextmining. As the focus of information extraction is shifting from "nominal" information such as named entity to "verbal" information such as function and interaction of substances, application of parsers has become one of the key technologies and thus the corpus annotated for syntactic structure of sentences is in demand. A subset of the GENIA corpus consisting of 500 MEDLINE abstracts has been annotated for syntactic structure in an XMLbased format based on Penn Treebank II (PTB) scheme. Inter-annotator agreement test indicated that the writing style rather than the contents of the research abstracts is the source of the difficulty in tree annotation, and that annotation can be stably done by linguists without much knowledge of biology with appropriate guidelines regarding to linguistic phenomena particular to scientific texts.


title: Towards Parallel Spatial Query Processing for Big Spatial Data

abstract: In recent years, spatial applications have become more and more important in both scientific research and industry. Spatial query processing is the fundamental functioning component to support spatial applications. However, the state-of-the-art techniques of spatial query processing are facing significant challenges as the data expand and user accesses increase. In this paper we propose and implement a novel scheme (named VegaGiStore) to provide efficient spatial query processing over big spatial data and numerous concurrent user queries. Firstly, a geography-aware approach is proposed to organize spatial data in terms of geographic proximity, and this approach can achieve high aggregate I/O throughput. Secondly, in order to improve data retrieval efficiency, we design a two-tier distributed spatial index for efficient pruning of the search space. Thirdly, we propose an "indexing + MapReduce'' data processing architecture to improve the computation capability of spatial query. Performance evaluations of the real-deployed VegaGiStore system confirm its effectiveness.


title: A 4-GHz All Digital PLL With Low-Power TDC and Phase-Error Compensation

abstract: This paper presents a 4-GHz all-digital fractional-N PLL with a low-power TDC operating at low-rate retimed reference clocks, a compensator preventing big phase-error downfalls, and a loop settling monitor. Two retimed reference clocks, nCKR and pCKR, are employed in the TDC to estimate the fractional phase error between the low-rate reference and high-rate oscillator clocks. Applying the retimed reference clocks does not only reduce a dynamic power in its delay chain, but simplify a fractional phase-error correction. The phase-error compensator is introduced to avoid big phase-error downfalls caused by large output glitches originating from a high-speed accumulator. In addition, a loop-settling monitor is invented to allow the DCO operation mode to be shifted seamlessly and fast. By consuming 9.6 mW, the ADPLL achieves -97 dBc in-band phase noise, - 38 dBc/Hz integrated noise, and 740 ns settling time.


title: Local energy-based shape histogram feature extraction technique for breast cancer diagnosis

abstract: Local energy-based shape histogram features for abnormality detection (mammograms).Experimentation on INbreast and MIAS datasets.Study impact of selecting subset of the features upon classification performance.Achieved a higher classification accuracy of 100.00% with the SVM linear. This paper proposes a novel local energy-based shape histogram (LESH) as the feature set for recognition of abnormalities in mammograms. It investigates the implication of this technique on mammogram datasets of the Mammographic Image Analysis Society and INbreast. In the evaluation, regions of interest were extracted from the mammograms, their LESH features calculated, and fed to support vector machine (SVM) classifiers. In addition, the impact of selecting a subset of LESH features on classification performance was also observed and benchmarked against a state-of-the-art wavelet based feature extraction method. The proposed method achieved a higher classification accuracy of 99.00?0.50, as well as an Az value of 0.9900?0.0050 with multiple SVM kernels, where a linear kernel performed with 100% accuracy for distinguishing between the abnormalities (masses vs. microcalcifications). Hence, the general capability of the proposed method was established, in which it not only distinguishes between malignant and benign cases for any type of abnormality but also among different types of abnormalities. It is therefore concluded that LESH features are an excellent choice for extracting significant clinical information from mammogram images with significant potential for application to 3-D MRI images.


title: / Olgu Sunumu 21 Postpartum Spinal Osteoporoza Ba ¤ l › Vertebra K › r › klar › : Bel A ¤ r › s › n › n Nadir Bir Nedeni

abstract: Postpartum spinal osteoporoz (PPSO) s›rt/bel a¤r›s› ile bulgu veren ve gec gebelik veya puerperal donemde osteoporozla karakterize nadir bir hastal›kt›r. Genellikle bir veya daha fazla vertebra k›r›¤›na neden olur. Burada postpartum donemde PPSO’a ba¤l› multipl vertebral kompresyon frakturleri olan genc bir kad›n hasta sunulmufltur. (Turk Osteoporoz Dergisi 2011;17:21-3) Anahtar kelimeler: Osteoporoz, gebelik, postpartum, bel a¤r›s›, vertebra k›r›klar› Vertebral Fractures Due to Postpartum Spinal Osteoporosis: an Unusual Cause of Low Back Pain


title: Knowledge Based Decision Support Systems: A Survey on Technologies and Application Domains

abstract: Knowledge-Based Decision Support Systems (KBDSS) have evolved greatly over the last few decades. The key technologies underpinning the development of KBDSS can be classified into two categories: technologies for knowledge modelling and representation, and the technologies for reasoning and inference. This paper provides a review on the recent advances in the two types of technologies, as well as the main application domains of KBDSS. Based on the examination of literature, future research directions are recommended for the development of KBDSS in general and in particular to support group decision making.


title: A survey on multi-output regression

abstract: In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This study provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks. WIREs Data Mining Knowl Discov 2015, 5:216-233. doi: 10.1002/widm.1157


title: Dense 3D semantic mapping of indoor scenes from RGB-D images

abstract: Dense semantic segmentation of 3D point clouds is a challenging task. Many approaches deal with 2D semantic segmentation and can obtain impressive results. With the availability of cheap RGB-D sensors the field of indoor semantic segmentation has seen a lot of progress. Still it remains unclear how to deal with 3D semantic segmentation in the best way. We propose a novel 2D-3D label transfer based on Bayesian updates and dense pairwise 3D Conditional Random Fields. This approach allows us to use 2D semantic segmentations to create a consistent 3D semantic reconstruction of indoor scenes. To this end, we also propose a fast 2D semantic segmentation approach based on Randomized Decision Forests. Furthermore, we show that it is not needed to obtain a semantic segmentation for every frame in a sequence in order to create accurate semantic 3D reconstructions. We evaluate our approach on both NYU Depth datasets and show that we can obtain a significant speed-up compared to other methods.


title: Prevention of credit card fraud detection based on HSVM

abstract: Specific crime in the banking system is credit card fraud. Credit card usage has been increased due to the rapid growth of E-commerce techniques. Credit card fraud also increased at the same time. Prevention is better than detection. So the existing system prevented the credit card fraud by identifying fraud in the application of the Credit card. Due to the limitation of the existing system, this paper proposed new algorithm along with the existing algorithm. Scalability issues, extreme imbalanced class and time constraints are the limitation of existing systems. Those limitations are overcome by hybrid support vector machine (HSVM) along with communal and spike detection for credit card application fraud detection. HSVM is the most used method for the pattern recognition and classification.