Query: On matching latent fingerprints

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The coming paradigm shift in forensic identification science. <=SEP=> Converging legal and scientific forces are pushing the traditional forensic identification sciences toward fundamental change. The assumption of discernible uniqueness that resides at the core of these fields is weakened by evidence of errors in proficiency testing and in actual cases. Changes in the law pertaining to the admissibility of expert evidence in court, together with the emergence of DNA typing as a model for a scientifically defensible approach to questions of shared identity, are driving the older forensic sciences toward a new scientific paradigm.

Combining minutiae descriptors for fingerprint matching <=SEP=> A novel minutiae-based fingerprint matching algorithm is proposed. A minutiae matching algorithm has to solve two problems: correspondence and similarity computation. For the correspondence problem, we assign each minutia two descriptors: texture-based and minutiae-based descriptors, and use an alignment-based greedy matching algorithm to establish the correspondences between minutiae. For the similarity computation, we extract a 17-D feature vector from the matching result, and convert the feature vector into a matching score using support vector classifier. The proposed algorithm is tested on FVC2002 databases and compared to all participators in FVC2002. According to equal error rate, the proposed algorithm ranks 1st on DB3, the most difficult database in FVC2002, and on the average ranks 2nd on all 4 databases. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Handbook of Fingerprint Recognition <=SEP=> label(s) Label Class c) b) a) 7.3 Integration Strategies 241 Combination strategy A combination strategy (also called combination scheme) is a technique used to combine the output of the individual classifiers. The most popular combination strategies at the abstract level are based on majority vote rules, which simply assign an input pattern to the most voted class (see Section 7.2). When two classifiers are combined, either a logical AND or a logical OR operator is typically used. When more than two classifiers are integrated, the AND/OR rules can be combined. For example, a biometric system may work on “fingerprint OR (face AND hand geometry)”; that is, it requires a user to present either a fingerprint or both face and hand geometry for recognition. Class set reduction, logistic regression, and Borda counts are the most commonly used approaches in combining classifiers based on the rank labels (Ho, Hull, and Srihari, 1994). In class set reduction, a subset of the classes is selected with the aim that the subset be as small as possible and still contain the true class. Multiple subsets from multiple modalities are typically combined using either a union or an intersection of the subsets. The logistic regression and Borda count methods are collectively called the class set reordering methods. The objective here is to derive a consensus ranking of the given classes such that the true class is ranked at the top. Rank labels are very useful for integration in an indexing/retrieval system. A biometric retrieval system typically outputs an ordered list of candidates (most likely matches). The top element of this ordered list is the most likely to be a correct match and the bottom of the list is the least likely match. The most popular combination schemes for combining confidence values from multiple modalities are sum, mean, median, product, minimum, and maximum rules. Kittler et al. (1998) have developed a theoretical framework in an attempt to understand the underlying mathematical basis of these popular schemes. Their experiments demonstrated that the sum or mean scheme typically performs very well in practice. A problem in using the sum rule is that the confidences (or scores) from different modalities should be normalized. This normalization typically involves mapping the confidence measures from different modalities into a common domain. For example, a biometric system may output a distance score (the lower the score, the more similar the patterns) whereas another may output a similarity score (the higher the score, the more similar the patterns) and thus the scores cannot be directly combined using the sum rule. In its simplest form, this normalization may only include inverting the sign of distance scores such that a higher score corresponds to a higher similarity. In a more complex form, the normalization may be non-linear which can be learned from training data by estimating distributions of confidence values from each modality. The scores are then translated and scaled to have zero mean, unit variance, and then remapped to a fixed interval of (0,1) using a hyperbolic tangent function. Note that it is tempting to parameterize the estimated distributions for normalization. However, such parameterization of distributions should be used with care, because the error rates of biometric systems are typically very small and a small error in estimating the tails of the distributions may result in a significant change in the error estimates (see Figure 7.3). Another common practice is to compute different scaling factors (weights) for each modality from training data, such that the accuracy of the combined classifier is maxi7 Multimodal Biometric Systems 242 mized. This weighted sum rule is expected to work better than the simple sum rule when the component classifiers have different strengths (i.e., different error rates). Figure 7.3. a) Genuine and impostor distributions for a fingerprint verification system (Jain et al., 2000) and a Normal approximation for the impostor distribution. Visually, the Normal approximation seems to be good, but causes significant decrease in performance compared to the non-parametric estimate as shown in the ROCs in b), where FMR is referred to as FAR (False Acceptance Rate) and (1-FNMR) as Genuine Acceptance Rate. ©Elsevier. Some schemes to combine multiple modalities in biometric systems have also been studied from a theoretical point of view. Through a theoretical analysis, Daugman (1999b) showed that if a strong biometric and a weak biometric are combined with an abstract level combination using either the AND or the OR voting rules, the performance of the combination will be worse than the better of the two individual biometrics. Hong, Jain, and Pankanti’s (1999) theoretical analysis that AND/OR voting strategies can improve performance only when certain conditions are satisfied confirmed Daugman’s findings. Their analysis further showed that a confidence level fusion is expected to significantly improve overall performance even in the case of combining a weak and a strong biometric. Kittler et al. (1998) introduced a sensitivity analysis to explain why the sum (or average) rule outperforms the other rules. They showed that the sum rule is less sensitive than the other similar rules (such as the “product” rule) to the error rates of individual classifiers in estimating posterior probabilities (confidence values). They claim that the sum rule is the most appropriate for combining different estimates of the same posterior probabilities (e.g., resulting from different classifier initializations). Prabhakar and Jain (2002) compared the sum and the product rules with the Neyman−Pearson combination scheme and showed that the product rule is worse than the sum rule when combining correlated features and both the sum rule and the product rules are inferior to the Neyman− Pearson combination scheme when combining weak and strong classifiers. 0 20 40 60 80 100 0 1 2 3 4 5 6 7 Normalized Matching Score Pe rc en ta ge ( % ) Imposter Genuine Nonparametric Imposter Distribution Normal Imposter Distribution Genuine Distribution 0 1 2 3 4 5 50 55 60 65 70 75 80 85 90 95 100 False Acceptance Rate (%) G en ui ne A cc ep ta nc e R at e (% ) Using Nonparametric Imposter Distribution Using Normal Imposter Distribution

Fingerprint matching by thin-plate spline modelling of elastic deformations <=SEP=> This paper presents a novel minutiae matching method that describes elastic distortions in 4ngerprints by means of a thin-plate spline model, which is estimated using a local and a global matching stage. After registration of the 4ngerprints according to the estimated model, the number of matching minutiae can be counted using very tight matching thresholds. For deformed 4ngerprints, the algorithm gives considerably higher matching scores compared to rigid matching algorithms, while only taking 100 ms on a 1 GHz P-III machine. Furthermore, it is shown that the observed deformations are di6erent from those described by theoretical models proposed in the literature. ? 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

A Real-Time Matching System for Large Fingerprint Databases <=SEP=> With the current rapid growth in multimedia technology, there is an imminent need for efficient techniclues to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated iri a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexincl large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-,4SIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the

Review of Farm Management Information Systems ( FMIS ) <=SEP=> There have been considerable advancements in the field of MIS over the years, and it continues to grow and develop in response to the changing needs of the business and marketing environment. Professionals and academicians are contributing and serving the field of MIS through the dissemination of their knowledge and ideas in professional journals. Thus, changes and trends that likely have an impact on MIS concepts, processes, and implementation can be determined by reviewing the articles published in journals. Content of the articles published in journals can also give us an idea about the types of research and themes that are popular during a given period. To see the evolutionary change in the field of MIS, the present study examined the content of articles published in business and marketing journals. [New York Science Journal 2010;3(5):87-95]. (ISSN 1554 – 0200).

Is it really about me?: message content in social awareness streams <=SEP=> In this work we examine the characteristics of social activity and patterns of communication on Twitter, a prominent example of the emerging class of communication systems we call "social awareness streams." We use system data and message content from over 350 Twitter users, applying human coding and quantitative analysis to provide a deeper understanding of the activity of individuals on the Twitter network. In particular, we develop a content-based categorization of the type of messages posted by Twitter users, based on which we examine users' activity. Our analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.

Advanced Gate Drive Unit With Closed-Loop $di_{{C}}/dt$  Control <=SEP=> This paper describes the design and the experimental investigation of a gate drive unit with closed-loop control of the collector current slope diC/dt for multichip insulated-gate bipolar transistors (IGBTs). Compared to a pure resistive gate drive, the proposed diC/dt control offers the ability to adjust the collector current slope freely which helps to find an optimized relation between switching losses and secure operation of the freewheeling diode for every type of IGBT. Based on the description of IGBT's switching behavior, the design and the realization of the gate drive are presented. The test setup and the comparison of switching tests with and without the proposed diC/dt control are discussed.

DC Motor Speed Control using PID Controllers <=SEP=> An Implementation of PID controllers for the speed control of a DC motor is given in this report. The motor is modeled as a first order system and its response is studied. The speed control using PI and PID control modes is explained and an implementation of the controller using OP-AMPs is given. The response of the controller to load variations is looked at.

Echo: An Edge-Centric Code Offloading System With Quality of Service Guarantee <=SEP=> Code offloading is a promising way to accelerate mobile applications and reduce the energy consumption of mobile devices by shifting some computation to the cloud. However, existing code offloading systems suffer from a long communication delay between mobile devices and the cloud. To address this challenge, in this paper, we consider to deploy edge nodes in close proximity to mobile devices and study how they benefit code offloading. We design an edge-centric code offloading system, called Echo, over a three-layer computing hierarchy consisting of mobile devices, the edge, and the cloud. A critical problem needs to be addressed by Echo is to decide which methods should be offloaded to which computing platform (the edge or the cloud). Different from existing offloading systems that let mobile devices individually make offloading decisions, Echo implements a centralized decision engine at the edge. This edge-centric design can fully exploit limited hardware resources at the edge to provide offloading services with the quality-of-service guarantee. Furthermore, we propose some novel mechanisms, e.g., lazy object transmission and differential object update, to further improve system performance. The results of a small-scale real deployment and trace-driven simulations show that Echo significantly outperforms existing code offloading systems at both execution time and energy consumption.

A Load Balancing Model based for Cloud Computing using Public Cloud <=SEP=> It is difficult to maintain all the resources and services on single system. So by using Load balancing strategy we distribute load on single system to multiple system providing efficient response to user, resulting in improve user satisfaction. Load balancing in the cloud computing has an important impact on the performance. To improve user satisfaction there is a need of efficient cloud computing for that we need a good load balancing algorithm. This article shows

An international qualitative study of ability and disability in ADHD using the WHO-ICF framework <=SEP=> This is the third in a series of four cross-cultural empirical studies designed to develop International Classification of Functioning, Disability and Health (ICF, and Children and Youth version, ICF(-CY) Core Sets for Attention-Deficit Hyperactivity Disorder (ADHD). To explore the perspectives of individuals diagnosed with ADHD, self-advocates, immediate family members and professional caregivers on relevant areas of impairment and functional abilities typical for ADHD across the lifespan as operationalized by the ICF(-CY). A qualitative study using focus group discussions or semi-structured interviews of 76 participants, divided into 16 stakeholder groups. Participants from five countries (Brazil, India, Saudi Arabia, South Africa and Sweden) were included. A deductive qualitative content analysis was conducted to extract meaningful functioning and disability concepts from verbatim material. Extracted concepts were then linked to ICF(-CY) categories by independent researchers using a standardized linking procedure. In total, 82 ICF(-CY) categories were identified, of which 32 were related to activities and participation, 25 to environmental factors, 23 to body functions and 2 to body structures. Participants also provided opinions on experienced positive sides to ADHD. A high level of energy and drive, creativity, hyper-focus, agreeableness, empathy, and willingness to assist others were the most consistently reported strengths associated with ADHD. Stakeholder perspectives highlighted the need to appraise ADHD in a broader context, extending beyond diagnostic criteria into many areas of ability and disability as well as environmental facilitators and barriers. This qualitative study, along with three other studies (comprehensive scoping review, expert survey and clinical study), will provide the scientific basis to define ICF(-CY) Core Sets for ADHD, from which assessment tools can be derived for use in clinical and research setting, as well as in health care administration.

Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems <=SEP=> In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the HHH matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix H increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes HTHx, where x is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the HTH matrix. We also propose a simple estimation scheme which directly obtains an estimate of HTH (instead of an estimate of H), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of H. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.

A unified distance measurement for orientation coding in palmprint verification <=SEP=> Orientation coding based palmprint verification methods, such as competitive code, palmprint orientation code and robust line orientation code, are state-of-the-art verification algorithms with fast matching speeds. Orientation code makes use of two types of distance measure, SUM_XOR (angular distance) and OR_XOR (Hamming distance), yet little is known about the similarities and differences both SUM_XOR and OR_XOR can be regarded as special cases, and provide some principles for determining the parameters of the unified distance. Experimental results show that, using the same feature extraction and coding methods, the unified distance measure gets lower equal error rates than the original distance measures. & 2009 Elsevier B.V. All rights reserved.

Co-evolution of Fitness Maximizers and Fitness Predictors <=SEP=> We introduce an estimation of distribution algorithm (EDA) based on co-evolution of fitness maximizers and fitness predictors for improving the performance of evolutionary search when evaluations are prohibitively expensive. Fitness predictors are lightweight objects which, given an evolving individual, heuristically approximate the true fitness. The predictors are trained by their ability to correctly differentiate between good and bad solutions using reduced computation. We apply co-evolving fitness prediction to symbolic regression and measure its impact. Our results show that the additional computational investment in training the co-evolving fitness predictors can greatly enhance both speed and convergence of individual solutions while overall reducing the number of evaluations. In application to symbolic regression, the advantage of using fitness predictors grows as the complexity of models increases.

Group recommendations with rank aggregation and collaborative filtering <=SEP=> The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for personal usage but for a group; e.g., a DVD could be watched by a group of friends. In order to generate effective recommendations for a group the system must satisfy, as much as possible, the individual preferences of the group's members.
 This paper analyzes the effectiveness of group recommendations obtained aggregating the individual lists of recommendations produced by a collaborative filtering system. We compare the effectiveness of individual and group recommendation lists using normalized discounted cumulative gain. It is observed that the effectiveness of a group recommendation does not necessarily decrease when the group size grows. Moreover, when individual recommendations are not effective a user could obtain better suggestions looking at the group recommendations. Finally, it is shown that the more alike the users in the group are, the more effective the group recommendations are.

Lightweight Monitoring of Distributed Streams <=SEP=> As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that—as long as they are maintained—some desirable global condition holds.
 Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated.
 Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB’s superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.

Comparison of vision-based and sensor-based systems for joint angle gait analysis <=SEP=> Gait analysis has become recently a popular research field and been widely applied to clinical diagnosis of neurodegenerative diseases. Various low-cost sensor-based and vision-based systems are developed for capturing the hip and knee joint angles. However, the performances of these systems have not been validated and compared between each other. The purpose of this study is to set up an experiment and compare the performances of a sensor-based system with multiple inertial measurement units (IMUs), a vision-based gait analysis system with marker detection, and a markerless vision-based system on capturing the hip and knee joint angles during normal walking. The obtained measurements were validated with the data acquired from goniometers as ground truth measurement. The results indicate that the IMUs-based sensor system gives excellent performance with small errors, while vision systems produce acceptable results with slightly larger errors.

Speed, Data, and Ecosystems: The Future of Software Engineering <=SEP=> An evaluation of recent industrial and societal trends revealed three key factors driving software engineering's future: speed, data, and ecosystems. These factors' implications have led to guidelines for companies to evolve their software engineering practices. This article is part of a special issue on the Future of Software Engineering.

Convolutional Neural Network for Brain MR Imaging Extraction Using Silver-Standards Masks <=SEP=> Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required for the training stage. Usually, manual annotation is considered to be the “gold-standard’. However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. Moreover, the single-rater manual annotation is often used in data-driven approaches making the algorithm optimal for one guideline only. In this work, we propose a convolutional neural network (CNN) for brain magnetic resonance (MR) imaging extraction, fully trained with what we refer to as silver-standard masks. To the best of our knowledge, our approach is the first deep learning approach that is fully trained using silver-standards masks, having an optimal generalization. Furthermore, regarding the Dice coefficient, we outperform the current skull-stripping (SS) state-of-the-art method in three datasets. Our method consists of 1) a dataset with silver-standard masks as input, 2) a tri-planar method using parallel 2D U-Net-based CNNs, which we refer to as CONSNet, and 3) an auto-context implementation of CONSNet. CONSNet refers to our integrated approach, i.e., training with silver-standard masks and us∗Corresponding author Email address: oeslle@dca.fee.unicamp.br (Oeslle Lucena) URL: miclab.fee.unicamp.br (Oeslle Lucena) Preprint submitted to Medical Image Analysis April 13, 2018 ar X iv :1 80 4. 04 98 8v 1 [ cs .C V ] 1 3 A pr 2 01 8 ing a CNN architecture. Masks are generated by forming a consensus from a set of publicly available automatic segmentation methods using the simultaneous truth and performance level estimation (STAPLE) algorithm. The CNN architecture is robust, with dropout and batch normalization as regularizers. It was inspired by the 2D U-Net model published by the RECOD group. We conducted our analysis using three publicly available datasets: the Calgary-Campinas-359 (CC-359 ), the LONI Probabilistic Brain Atlas (LPBA40), and the Open Access Series of Imaging Studies (OASIS). Five performance metrics were used in our experiments: Dice coefficient, sensitivity, specificity, Hausdorff distance, and symmetric surface-to-surface mean distance. Our results also demonstrate that we have comparable overall performance versus current methods, including recently proposed deep learningbased strategies that were trained using manually annotated masks. Our usage of silver-standard masks, however, reduced the cost of manual annotation, diminished inter-/intra-rater variability, and avoided CNN segmentation super-specialization towards one specific SS method found when mask generation is done through an agreement among many automatic methods. Moreover, usage of silver-standard masks enlarges the volume of input annotated data since we can generate annotation from unlabelled data. Also, our method has the advantage that, once trained, it takes only a few seconds to process a typical brain image volume using a modern graphics processing unit (GPU). In contrast, many of the other competitive consensus-building methods have processing times on the order of minutes.

Evidence for sensory prediction deficits in schizophrenia. <=SEP=> OBJECTIVE
Patients with schizophrenia experiencing delusions and hallucinations can misattribute their own actions to an external source. The authors test the hypothesis that patients with schizophrenia have defects in their ability to predict the sensory consequences of their actions.


METHOD
The authors measured sensory attenuation of self-produced stimuli by patients with schizophrenia and by healthy subjects.


RESULTS
Patients with schizophrenia demonstrated significantly less sensory attenuation than healthy subjects.


CONCLUSIONS
Patients with a diagnosis of schizophrenia have a dysfunction in their predictive mechanisms.

Implementing an Effective Test Automation Framework <=SEP=> Testing automation tools enable developers and/or testers to easily automate the entire process of testing in software development. Nevertheless, adopting automated testing is not easy and unsuccessful due to a lack of key information and skills. In order to help solve such problems, we have designed a new framework to support a clear overview of the test design and/or plan for automating tests in distributed environments. Those that are new to testing do not need to delve into complex automation tools or test scripts. This framework allows a programmer or tester to graphically specify the granularity of execution control and aids communication between various stakeholders and software developers. It also enables them to perform the automated Continuous Integration environment. In this paper, we describe details on experiences and usage of the proposed framework.

Synonym set extraction from the biomedical literature by lexical pattern discovery <=SEP=> Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst [1], but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way. We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous. We then model this result as a probability graph to find synonym sets, which is equivalent to the well-studied problem of finding an optimal set cover. We achieved 73.2% precision and 29.7% recall by our method, out-performing hand-made resources such as MeSH and Wikipedia. We conclude that automatic methods can play a practical role in developing new thesauri or expanding on existing ones, and this can be done with only a small amount of training data and no need for resources such as parsers. We also concluded that the accuracy can be improved by grouping into synonym sets.

Age differences in the neural systems supporting human allocentric spatial navigation <=SEP=> Age-related declines in spatial navigation are well-known in human and non-human species. Studies in non-human species suggest that alteration in hippocampal and other neural circuitry may underlie behavioral deficits associated with aging but little is known about the neural mechanisms of human age-related decline in spatial navigation. The purpose of the present study was to examine age differences in functional brain activation during virtual environment navigation. Voxel-based analysis of activation patterns in young subjects identified activation in the hippocampus and parahippocampal gyrus, retrosplenial cortex, right and left lateral parietal cortex, medial parietal lobe and cerebellum. In comparison to younger subjects, elderly participants showed reduced activation in the hippocampus and parahippocampal gyrus, medial parietal lobe and retrosplenial cortex. Relative to younger participants elderly subjects showed increased activation in anterior cingulate gyrus and medial frontal lobe. These results provide evidence of age specific neural networks supporting spatial navigation and identify a putative neural substrate for age-related differences in spatial memory and navigational skill.

Advanced LSTM: A Study About Better Time Dependency Modeling in Emotion Recognition <=SEP=> Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However, conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based weighted pooling RNN can also complement the state-of-the-art emotion classification framework. This shows the advantage of A-LSTM.

Carpenter: finding closed patterns in long biological datasets <=SEP=> The growth of bioinformatics has resulted in datasets with new characteristics. These datasets typically contain a large number of columns and a small number of rows. For example, many gene expression datasets may contain 10,000-100,000 columns but only 100-1000 rows.Such datasets pose a great challenge for existing (closed) frequent pattern discovery algorithms, since they have an exponential dependence on the average row length. In this paper, we describe a new algorithm called CARPENTER that is specially designed to handle datasets having a large number of attributes and relatively small number of rows. Several experiments on real bioinformatics datasets show that CARPENTER is orders of magnitude better than previous closed pattern mining algorithms like CLOSET and CHARM.

Incorporating intra-class variance to fine-grained visual recognition <=SEP=> Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.

Social-information-processing factors in reactive and proactive aggression in children's peer groups. <=SEP=> We examined social-information-processing mechanisms (e.g., hostile attributional biases and intention-cue detection deficits) in chronic reactive and proactive aggressive behavior in children's peer groups. In Study 1, a teacher-rating instrument was developed to assess these behaviors in elementary school children (N = 259). Reactive and proactive scales were found to be internally consistent, and factor analyses partially supported convergent and discriminant validities. In Study 2, behavioral correlates of these forms of aggression were examined through assessments by peers (N = 339). Both types of aggression related to social rejection, but only proactively aggressive boys were also viewed as leaders and as having a sense of humor. In Study 3, we hypothesized that reactive aggression (but not proactive aggression) would occur as a function of hostile attributional biases and intention-cue detection deficits. Four groups of socially rejected boys (reactive aggressive, proactive aggressive, reactive-proactive aggressive, and nonaggressive) and a group of average boys were presented with a series of hypothetical videorecorded vignettes depicting provocations by peers and were asked to interpret the intentions of the provocateur (N = 117). Only the two reactive-aggressive groups displayed biases and deficits in interpretations. In Study 4, attributional biases and deficits were found to be positively correlated with the rate of reactive aggression (but not proactive aggression) displayed in free play with peers (N = 127). These studies supported the hypothesis that attributional biases and deficits are related to reactive aggression but not to proactive aggression.

n ensemble learning framework for anomaly detection in building nergy consumption <=SEP=> During building operation, a significant amount of energy is wasted due to equipment and humanrelated faults. To reduce waste, today’s smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energysaving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework that combines several anomaly detection classifiers

The Other Side of the Coin: A Framework for Detecting and Analyzing Web-based Cryptocurrency Mining Campaigns <=SEP=> Mining for crypto currencies is usually performed on high-performance single purpose hardware or GPUs. However, mining can be easily parallelized and distributed over many less powerful systems. Cryptojacking is a new threat on the Internet and describes code included in websites that uses a visitor's CPU to mine for crypto currencies without the their consent. This paper introduces MiningHunter, a novel web crawling framework which is able to detect mining scripts even if they obfuscate their malicious activities. We scanned the Alexa Top 1 million websites for cryptojacking, collected more than 13,400,000 unique JavaScript files with a total size of 246 GB and found that 3,178 websites perform cryptocurrency mining without their visitors' consent. Furthermore, MiningHunter can be used to provide an in-depth analysis of cryptojacking campaigns. To show the feasibility of the proposed framework, three of such campaigns are examined in detail. Our results provide the most comprehensive analysis to date of the spread of cryptojacking on the Internet.