Miracle-dz/UniFAR-FTUs huggingface dataset:
Anxiety in Chronic Moderate to Severe Traumatic Brain Injury: Measurement and Relation to Hippocampal Volume Loss

Moderate to severe traumatic brain injury (TBI) can result in chronic and progressive atrophy of the hippocampus (HPC), a bilateral brain structure housed in the medial temporal lobes. As demonstrated in a recent study of individuals with amnestic Mild Cognitive Impairment (MCI) and early Alzheimer’s disease (AD), anxiety is a candidate mechanism underlying atrophy of the medial temporal lobes in neurological populations. This thesis investigates (1) the validity of the Beck Anxiety Inventory (BAI) for measuring anxiety in chronic TBI and (2) the role of anxiety in HPC volume loss in this population. First, evidence for the validity of the BAI in chronic TBI is provided. Second, it is shown that higher anxiety on the BAI at 5 and 12 months post-injury predicts subsequent atrophy of the right HPC complex, particularly of the right HPC head.
Efficient parallel partition-based algorithms for similarity search and join with edit distance constraints

The quantity of data in real-world applications is growing significantly while the data quality is still a big problem. Similarity search and similarity join are two important operations to address the poor data quality problem. Although many similarity search and join algorithms have been proposed, they did not utilize the abilities of modern hardware with multi-core processors. It calls for new parallel algorithms to enable multi-core processors to meet the high performance requirement of similarity search and join on big data. To this end, in this paper we propose parallel algorithms to support efficient similarity search and join with edit-distance constraints. We adopt the partition-based framework and extend it to support parallel similarity search and join on multi-core processors. We also develop two novel pruning techniques. We have implemented our algorithms and the experimental results on two real datasets show that our parallel algorithms achieve high performance and obtain good speedup.
Hierarchical semisupervised transfer AdaBoost

We propose a hierarchical and semisupervised transfer AdaBoost (HissTrAdaBoost) algorithm to address over-fitting and generalization problem in TrAdaBoost, which is one of the state-of-the-art instance based transfer learning algorithm. Specifically, the samples in the source domain which have larger difference from the target domain are removed, and then the unlabeled instances in the target domain are hierarchically imported to the classifiers. In this way, the generalization error is reduced by extra constraints provided by the semi-supervised classifiers of the unlabeled data. Experimental results conducted on the public data sets confirm the effectiveness of the proposed method, for the classification accuracy has been improved by 1% to 3%.
Anomaly detection of Internet traffic using robust feature selection based on kernel density estimation

Anomaly detection of Internet traffic is a network service of primary importance, given the constant threats that impinge on Internet security. From a statistical perspective, traffic anomalies can be considered outliers, and must be handled through effective outlier detection methods, for which feature selection is an important pre-processing step. Feature selection removes the redundant and irrelevant features from the detection process, increasing its performance. In this work, we consider outlier detection based on principal component analysis, and feature selection based on mutual information. Moreover, we address the use of kernel density estimation (KDE) to estimate themutual information, which is designed for continuous features, and avoids the discretization step of histograms. Our results, obtained using a high-quality ground-truth, clearly show the usefulness of feature selection and the superiority of KDE to estimate the mutual information, in the context of Internet traffic anomaly detection.
Performance evaluation of secure routing in ad-hoc network environment

We have considered the problem of incorporating security mechanisms in routing protocols for ad hoc networks. There have been various secure routing protocols proposed for the mobile ad hoc network. Secured Ad hoc On Demand Vector Routing (SAODV) protocol considered as one of the popular existing secure routing protocol which have used digital signature and hash chain techniques to secure Ad hoc On Demand Vector Routing (AODV) protocol. Most of these protocols were analyzed by two standard techniques: simulation and security analysis. We have used simulative approach for the performance comparisons between AODV and SAODV routing protocols using Glomosim simulator. The packet deliver fraction, average end-to-end delay, throughput and number of dropped packets metrics have been chosen for comparison. The results have shown that packet delivery fraction decreased in the case of SOADV. Routing overhead and throughput increased in case of SAODV. The simulation result have shown that network performance degrade when security counter measure are taken. Secure routing protocols available today (such as SAODV) still need further optimizations to minimize the processing overhead, delays and to maximize the routing throughputs.
The effects of prototypicality and gender salience on liking and friendship potential of a female interlocutor

Drawing on self-categorization theory, this article examines the indirect effects of gender salience and prototypicality on friendship potential through increasing liking of a female interlocutor. We manipulated biographies of the fictitious interlocutor to change perceptions of prototypicality. For women, gender salience interacted with prototypicality to directly predict liking and the desire to become friends with the interlocutor indirectly through liking. Specifically, there was an interaction between prototypicality and gender salience, such that as gender salience increased, the prototypical interlocutor was liked significantly more and had higher friendship potential. For men, the same relationships did not appear. We discuss the implications of our study as well as directions for future research on intragroup communication and intergroup contexts with regard to power asymmetry.
All Uses and Statement Coverage: A Controlled Experiment

This article presents a controlled experiment that compares the behavior of the testing techniques Statement Coverage and All Uses. The design of this experiment is typical for a factor with two alternatives. A total of 14 subjects carry out tests on a single program. The results indicate that there is enough statistical ev- idence to state that the cost of executing All Uses is higher than that of executing Statement Coverage - a result that we expected to find. However, no statistical differences were found as regards the effectiveness of the techniques.
A Fault-Tolerant Routing Protocol in 2D Torus Based on Positive-First and Negative-First Turn Models

A kind of routing scheme with the ability to tolerate the faults is necessary in the massively parallel multiprocessors.In this paper, we would present a fault-tolerant wormhole routing scheme, called Positive-first-Negative-first scheme, in the torus network with Orthogonal Convex Polygon faults model, this fault-tolerant routing scheme is motivated by the PFNF routing algorithms that with positive-first and negative-first turn restriction implement in two virtual networks. Because of the mutual-switch of turn schemes between the two virtual networks, the routing algorithm is nearly fully adaptive without deadlock, so it can convenience and efficient applied in the fault-tolerant routing by employing the algorithm selfproperty. The reason why we choose the Orthogonal Convex Polygon faulty model is that it is convenient to design a simple and efficient tolerant-tolerant routing algorithm, at the same time the number of disabled healthy nodes in the fault block is reduced at the moderate level. Sufficient simulation results are presented to demonstrate the effectiveness of the proposed algorithm. Keywordstorus network; fault-tolerant; turn models
Reactive relay selection in underlay cognitive networks with fixed gain relays

Best relay selection is a bandwidth efficient technique for multiple relay environments without compromising the system performance. The problem of relay selection is more challenging in underlay cognitive networks due to strict interference constraints to the primary users. Generally, relay selection is done on the basis of maximum end-to-end signal to noise ratio (SNR). However, it requires large amounts of channel state information (CSI) at different network nodes. In this paper, we present and analyze a reactive relay selection scheme in underlay cognitive networks where the relays are operating with fixed gains near a primary user. The system model minimizes the amount of CSI required at different nodes and the destination selects the best relay on the basis of maximum relay to destination SNR. We derive close form expressions for the received SNR statistics, outage probability, bit error probability and average channel capacity of the system. Simulation results are also presented to confirm the validity of the derived expressions.
Aerosol and precipitation chemistry in a remote site in Central Amazonia: the role of biogenic contribution

Aerosol and precipitation chemistry in a remote site in Central Amazonia: the role of biogenic contribution T. Pauliquevis, L. L. Lara, M. L. Antunes, and P. Artaxo Instituto Nacional de Pesquisas da Amazônia (INPA), Av. André Araújo 2936, campus II (LBA), CEP 69060-001, Manaus, AM, Brazil Instituto de Fı́sica, Universidade de São Paulo, Rua do Matão, Travessa R, 187, CEP 05508-900, São Paulo, S.P., Brazil Unidade Diferenciada Sorocaba/Iperó – UNESP, Universidade Estadual Paulista, Sorocaba, SP, Brazil Received: 30 May 2007 – Accepted: 19 July 2007 – Published: 3 August 2007 Correspondence to: T. Pauliquevis (theotonio@gmail.com)
The Research on the Multiple Kernel Learning-based Face Recognition inPattern Matching

The paper analyses the multiple kernel learning-based face recognition in pattern matching area. Based on the analysis of the basic theory of multiple kernel SVM, this thesis focuses on the multiple kernel SVM algorithm based on semi-infinite linear program (SILP), including SILP based on column generation (CG) and SILP based on chunking algo- rithm (CA). The two SILP improved algorithms are applied to several classification problems, including UCI binary clas- sification problem datasets and multi-classification problem datasets. Furthermore, the two SILP improved algorithms are applied to the actual problems of face recognition. The experiment data shows that with the multiple kernel learning-based method, the performance of face recognition can be obviously improved.
Contextual Hidden Markov Models

Multiple works have proposed extensions of HMMs for handling variability. We focus here on the design of HMMs whose probability distribution on sequences depends on additional external variables that we call the context, which may stand for emotion features in speech recognition, physical features in gesture recognition, etc. We show experimentally the relevance of the method for handwriting recognition.
Unsupervised Induction of Tree Substitution Grammars for Dependency Parsing

Inducing a grammar directly from text is one of the oldest and most challenging tasks in Computational Linguistics. Significant progress has been made for inducing dependency grammars, however the models employed are overly simplistic, particularly in comparison to supervised parsing models. In this paper we present an approach to dependency grammar induction using tree substitution grammar which is capable of learning large dependency fragments and thereby better modelling the text. We define a hierarchical non-parametric Pitman-Yor Process prior which biases towards a small grammar with simple productions. This approach significantly improves the state-of-the-art, when measured by head attachment accuracy.
A study of the healing-over in the cardiac muscle using suction electrodes.

Suction electrodes were used to investigate the phenomenon of the healing-over in preparations of rabbit, cat and dog ventricular muscles. At least two processes were shown to participate in the process of the healing-over: a rapid one (time constant approximately 1 min) and a slow one (time constant approximately 10 min). Procion Yellow dye was used to determine the size of the injured zone under the suction electrode tip. Larger tips resulted in larger zones injured. The specific resistance of the border formed during the healing over was estimated to be in the range of approx. 1 k omega X cm2, i.e. much smaller than that of the intact surface membrane.
Anna Freud and Observation: Memoirs of Her Colleagues from the Hampstead War Nurseries

ABSTRACT Initially, Anna Freud did not wish to carry out systematic observations; rather, she wanted to be in the field while maintaining an analytical attitude with a free-floating attention. Based on memories of her employees Manna Friedmann, Anneliese Schnurman, Hansi Kennedy, and Joyce Robertson, this article discusses the way in which Anna Freud taught them to observe the children. She also observed those very colleagues who were observing, in order to be able to convey to them, a psychoanalytic view of the children.
Adaptive noise removal approach for restoration of digital images corrupted by multimodal noise

Data smoothing algorithms are commonly applied to reduce the level of noise and eliminate the weak textures contained in digital images. Anisotropic diffusion algorithms form a distinct category of noise removal approaches that implement the smoothing process locally in agreement with image features such as edges that are typically determined by applying diverse partial differential equation (PDE) models. While this approach is opportune since it allows the implementation of feature-preserving data smoothing strategies, the inclusion of the PDE models in the formulation of the data smoothing process compromises the performance of the anisotropic diffusion schemes when applied to data corrupted by non-Gaussian and multimodal image noise. In this study the authors first evaluate the positive aspects related to the inclusion of a multi-scale edge detector based on the generalisation of the Di Zenzo operator into the formulation of the anisotropic diffusion process. Then, a new approach that embeds vector median filtering into discrete implementation of the anisotropic diffusion is introduced to improve the performance of the noise removal algorithm when applied to multimodal noise suppression. To evaluate the performance of the proposed data smoothing strategy, a large number of experiments on various types of digital images corrupted by multimodal noise were conducted.
Identity Interruption on Social Media

This study aims to understand how online identity interruption can impact psychological distress and how psychological distress drives avoidance intention in using online social networks. Results of the experiment indicate that the effect of content dispersion on feelings of betrayal is amplified when network similarity is low but the effect is nullified when network similarity is high. More interestingly, results reveal that the interaction between content dispersion and network similarity on feelings of betrayal is moderated by social readership. Responses from the participants also reveal that higher feelings of betrayal lead to higher avoidance intention. Implications for research and practice are discussed.
Survey of Text Categorization Techniques

On the internet huge data are in the uncategorized form. Big information is hidden behind this uncategorized scene of data. If classification of these internet documents done, then it will be helpful in many cases. All the documents related to a single class can be found at the single location. This paper considers the different text categorization systems. These systems are using different classification algorithms for he classification of the text documents.
Predict Scooter's Stopping Event Using Smartphone as the Sensing Device

Researches show that most of deadly crashes involve one or more unsafe driving behaviors typically associated with careless driving. Many researchers try to develop intelligent transportation system (ITS) or machine learning model to detect these potential risks, to make alert, and to prevent driver from traffic accident. For example, intentionally or carelessly inappropriate stopping or not stopping a vehicle may cause traffic violation or vehicle accident. However, to the best of our knowledge so far, there exist no research of ITS dedicated to collecting scooter's driving profile and improving driving safety of scooter rider, given the fact of that riding scooter is one of the most important transportation means in Taiwan - every 1.56 persons in Taiwan own a scooter. In this work, taking advantages of machine learning technique, we propose a model to predict whether scooter is going to stop or not, by collecting data of various sensors using smart phone, a popular and relative cheap device, set on the handler of scooter. Experiments shows that by carefully concerning the characteristics and tendencies differ from drivers to drivers, from locations to locations, our model can detect stop event of scooter with at most 90% accuracy, such that it can provide significant information to prevent traffic violation, ex: red-light running, or car accident.
A Problog Model for Analyzing Gene Regulatory Networks

Transcriptional regulation play an important role in every cellular decision. Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic-based regulation models based on state-of-the-art work on statistical relational learning, to show that network hypotheses can be generated from existing gene expression data for use by experimental biologists.
THE RELATIONSHIP BETWEEN WORKLOAD AND STUDENTS ’ DISRUPTIVE BEHAVIOURS WITH TURNOVER INTENTION AMONG ACADEMICIANS OF PRIVATE HIGHER EDUCATION INSTITUTIONS : BOREDOM AT WORKPLACE AS MEDIATOR

Boredom is widespread contagious bacterial experienced by all occupational levels at increasing trend and that influences employees’ affection, cognition, and well-being. However, limited studies are carried out to investigate the influence of boredom at workplace on turnover intention. In academia, academicians are mentally and physically overloaded due to heavy workload and student disruptive behaviour. Therefore, this study investigates the relationship between workload, student disruptive behaviour, and turnover intention among academicians. A quantitative study was conducted to obtain the data from 279 academicians working for 20 private higher education institutions (PHEIs) in Sarawak. The results showed that workload was negatively associated with turnover intention, while student disruptive behaviours were positively associated with turnover intention. A structural mediation model showed that workload was negatively associated with boredom which led to positive association with turnover intention. In contrast, student disruptive behaviour was positively associated with boredom and which resulted in turnover intention. After performing bootstrapping, the result confirmed the presence of boredom as mediator. PHEIs should establish technologies and facilities to reduce the academicians’ workload. The academicians should also be provided with trainings related to class management and learning tools in order to minimize their boredom and turnover intention.
Active Object Recognition via Monte Carlo Tree Search

—This paper considers object recognition with a camera, whose viewpoint can be controlled in order to improve the recognition results. The goal is to choose a multi-view camera trajectory in order to minimize the probability of having misclassiﬁed objects and incorrect orientation estimates. Instead of using ofﬂine dynamic programming, the resulting stochastic optimal control problem is addressed via an online Monte Carlo tree search algorithm, which can handle various constraints and provides exceptional performance in large state spaces. A key insight is to use an active hypothesis testing policy to select camera viewpoints during the rollout stage of the tree search.
Semantic Activity Recognition

Extracting automatically the semantics from visual data is a real challenge. We describe in this paper how recent work in cognitive vision leads to significative results in activity recognition for visualsurveillance and video monitoring. In particular we present work performed in the domain of video understanding in our PULSAR team at INRIA in Sophia Antipolis. Our main objective is to analyse in real-time video streams captured by static video cameras and to recognize their semantic content. We present a cognitive vision approach mixing 4D computer vision techniques and activity recognition based on a priori knowledge. Applications in visualsurveillance and healthcare monitoring are shown. We conclude by current issues in cognitive vision for activity recognition.
Spotting Trees with Few Leaves

We show two results related to the Hamiltonicity and \(k\) -Path algorithms in undirected graphs by Bjorklund [FOCS’10], and Bjorklund et al., [arXiv’10]. First, we demonstrate that the technique used can be generalized to finding some \(k\)-vertex tree with \(l\) leaves in an \(n\)-vertex undirected graph in \(O^*(1.657^k2^{l/2})\) time. It can be applied as a subroutine to solve the \(k\) -Internal Spanning Tree (\(k\)-IST) problem in \(O^*({\text {min}}(3.455^k, 1.946^n))\) time using polynomial space, improving upon previous algorithms for this problem. In particular, for the first time, we break the natural barrier of \(O^*(2^n)\). Second, we show that the iterated random bipartition employed by the algorithm can be improved whenever the host graph admits a vertex coloring with few colors; it can be an ordinary proper vertex coloring, a fractional vertex coloring, or a vector coloring. In effect, we show improved bounds for \(k\) -Path and Hamiltonicity in any graph of maximum degree \(\Delta =4,\ldots ,12\) or with vector chromatic number at most \(8\).
Learning to detect task boundaries of query session

To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement.