Miracle-dz/UniFAR-FTUs huggingface dataset:
A New MFI Mining Algorithm with effective Pruning Mechanisms

Mining of frequent patterns is a basic problem in data mining applications. Frequent Itemset Mining is considered to be an important research oriented task in data mining, due to its large applicability in real world applications. In this paper, a new Maximal Frequent Itemset mining algorithm with effective pruning mechanism is proposed. The proposed algorithm takes vertical tidset representation of the database and removes all the non-maximal frequent item-sets to get exact set of MFI directly. Pruning is done for both search space reduction and minimizing the number of frequency computations. It works efficiently when the number of item-sets and tid-sets are more. The proposed approach has been compared with Mafia algorithm for mushroom dataset and the results shows that the proposed algorithm performs effectively and generates frequent patterns faster. In order to understand the algorithm easily, an example is provided in detail.
An auto-encoder based approach to unsupervised learning of subword units

In this paper we propose an auto encoder-based method for the unsupervised identification of subword units. We experiment with different types and architectures of auto encoders to assess what auto encoder properties are most important for this task. We first show that the encoded representation of speech produced by standard auto encoders is more effective than Gaussian posteriorgrams in a spoken query classification task. Finally we evaluate the subword inventories produced by the proposed method both in terms of classification accuracy in a word classification task (with lexicon size up to 263 words) and in terms of consistency between subword transcription of different word examples of a same word type. The evaluation is carried out on Italian and American English datasets.
Multivoxel Object Representations in Adult Human Visual Cortex Are Flexible: An Associative Learning Study

Abstract Learning associations between co-occurring events enables us to extract structure from our environment. Medial-temporal lobe structures are critical for associative learning. However, the role of the ventral visual pathway (VVP) in associative learning is not clear. Do multivoxel object representations in the VVP reflect newly formed associations? We show that VVP multivoxel representations become more similar to each other after human participants learn arbitrary new associations between pairs of unrelated objects (faces, houses, cars, chairs). Participants were scanned before and after 15 days of associative learning. To evaluate how object representations changed, a classifier was trained on discriminating two nonassociated categories (e.g., faces/houses) and tested on discriminating their paired associates (e.g., cars/chairs). Because the associations were arbitrary and counterbalanced across participants, there was initially no particular reason for this cross-classification decision to tend toward either alternative. Nonetheless, after learning, cross-classification performance increased in the VVP (but not hippocampus), on average by 3.3%, with some voxels showing increases of up to 10%. For example, a chair multivoxel representation that initially resembled neither face nor house representations was, after learning, classified as more similar to that of faces for participants who associated chairs with faces and to that of houses for participants who associated chairs with houses. Additionally, learning produced long-lasting perceptual consequences. In a behavioral priming experiment performed several months later, the change in cross-classification performance was correlated with the degree of priming. Thus, VVP multivoxel representations are not static but become more similar to each other after associative learning.
A Model of Homework's Influence on the Performance Evaluations of Elementary School Students

Abstract This study was the first to test a model of the influence of homework on classroom performance using a sample of elementary school students. A total of 28 teachers in Grades 2 and 4 took part in the study, along with 428 students and parents. The authors used structural equation modeling to examine relationships among variables. Student norms were positively related to the elimination of distractions from homework by parents. Positive student norms, higher student ability, and positive parent attitudes toward homework were all related to greater parent facilitation. Student's attitude toward homework was unrelated to home and community factors but was related positively to parent attitudes toward homework. Classroom grades were unrelated to student's attitude toward homework but were predicted by how much homework the student completed (even after the use of homework in grading was controlled), by student ability, and by the amount of parent facilitation. More generally, parent facilitation was an important mediator of the relation between student norms, student ability, and parent attitudes toward homework, and the outcome of classroom grades.
Social Cloud Computing: A Vision for Socially Motivated Resource Sharing

Online relationships in social networks are often based on real world relationships and can therefore be used to infer a level of trust between users. We propose leveraging these relationships to form a dynamic "Social Cloud,” thereby enabling users to share heterogeneous resources within the context of a social network. In addition, the inherent socially corrective mechanisms (incentives, disincentives) can be used to enable a cloud-based framework for long term sharing with lower privacy concerns and security overheads than are present in traditional cloud environments. Due to the unique nature of the Social Cloud, a social market place is proposed as a means of regulating sharing. The social market is novel, as it uses both social and economic protocols to facilitate trading. This paper defines Social Cloud computing, outlining various aspects of Social Clouds, and demonstrates the approach using a social storage cloud implementation in Facebook.
Regional‐specific regulation of BDNF and trkB correlates with nigral dopaminergic cell sprouting following unilateral nigrostriatal axotomy

Axotomy is a powerful stimulus of axon growth and plastic changes. We investigated the potential role of BDNF/trkB signaling in the sprouting of dopaminergic nigral axons in response to axotomy of the medial forebrain bundle. Tyrosine hydroxylase immunohistochemistry revealed the existence of sprouting mechanisms in the axotomized substantia nigra (SN). Time‐course changes of trkB mRNA expression demonstrated a robust increase in an area projecting from the rostral tip of the SN to the glial scar, which coincided with evidence of nigral dopaminergic sprouting. In addition, we found an early loss of this messenger in areas related to the knife cut, which recovered by 7 days postlesion. TrkB down‐regulation appeared to be associated to the lesion‐induced local damage, as it was restricted to an area showing Fluoro‐Jade B– and TUNEL positive cells. In trkB‐depleted areas, an inverse correlation between mRNA expressions of BDNF and trkB was apparent. Specific induction of BDNF mRNA was mostly seen in border of areas devoid of trkB mRNA. In contrast, in the areas exhibiting trkB mRNA expression, no BDNF mRNA was detected. We suggest that trkB levels could be a determinant element in regulating BDNF expression. Finally, the search for molecules involved in either promoting or inhibiting axonal growth, demonstrated up‐regulation of GAP‐43 and Nogo‐A mRNA at sites close to the knife transections as early as 1 day postlesion. However, overall, Nogo‐A induction was more robust than that seen for GAP‐43. © 2008 Wiley‐Liss, Inc.
FOCUS ARTICLE: Separating Stable From Variable Individuals in Longitudinal Studies by Mixture Distribution Models

After a short overview of the metatrait approach in personality psychology and of several related concepts (intraindividual variability, individual reactivity, hardiness, resilience), the consequences of these approaches for the measurement of individuals over time are discussed. Based on the implications of the metatrait approach for psychometric modeling, several extensions of a latent state-trait model for categorical response variables (Eid & Langeheine, 1999) are described. These extensions are mixture distributions variants of the basic model suitable for separating stable (traited) from variable (untraited, less traited) individuals in longitudinal studies. An empirical application of the models to the measurement of momentary negative mood on 4 occasions of measurement (N = 494) revealed that a model with 2 latent chains fits the data well. The first chain (size: 24%) consists of individuals that are perfectly stable (traited) on the latent level, whereas for the second chain (size: 76 %), a latent state-trait model with Markov structure (less traited individuals) explains variability and stability appropriately. The consequences of the results of the study for the measurement of individuals in contexts as well as the psychometric modeling of change processes are discussed.
Number : WUCSE-2004-82 2004-1204 Link Buffer Sizing : a New Look at the Old Problem

In this paper, we revisit the question of how much buffer an IP router should allocate for its output link. For a long time, the intuitive answer of setting the buffer size to the bitrate-delay product has been widely regarded as reasonable. Recent studies of interaction between queueing at IP routers and TCP congestion control proposed alternative answers. First, we expose and explain contradictions between existing guidelines for link buffer sizing. Then, we argue that the problem of link buffer sizing needs a different formulation. In particular, the chosen buffer size should accommodate not only common versions of TCP but also UDP traffic. Besides, our new formulation of the problem contains an explicit constraint of not engaging IP routers in any additional signaling. We conclude the paper by outlining a promising direction for solving the reformulated problem. Link Buffer Sizing: a New Look at the Old Problem Sergey Gorinsky Anshul Kantawala Jonathan Turner Applied Research Laboratory Department of Computer Science and Engineering Washington University in St. Louis St. Louis, MO 63130-4899, USA gorinsky,anshul,jst @arl.wustl. edu
A step towards sequence-to-sequence alignment

The paper presents an approach for establishing correspondences in time and in space between two different video sequences of the same dynamic scene, recorded by stationary uncalibrated video cameras. The method simultaneously estimates both spatial alignment as well as temporal synchronization (temporal alignment) between the two sequences, using all available spatio-temporal information. Temporal variations between image frames (such as moving objects or changes in scene illumination) are powerful cues for alignment, which cannot be exploited by standard image-to-image alignment techniques. We show that by folding spatial and temporal cues into a single alignment framework, situations which are inherently ambiguous for traditional image-to-image alignment methods, are often uniquely resolved by sequence-to-sequence alignment. We also present a "direct" method for sequence-to-sequence alignment. The algorithm simultaneously estimates spatial and temporal alignment parameters directly from measurable sequence quantities, without requiring prior estimation of point correspondences, frame correspondences, or moving object detection. Results are shown on real image sequences taken by multiple video cameras.
Parallel Multi-task Learning

In this paper, we develop parallel algorithms for a family of regularized multi-task methods which can model task relations under the regularization framework. Since those multi-task methods cannot be parallelized directly, we use the FISTA algorithm, which in each iteration constructs a surrogate function of the original problem by utilizing the Lipschitz structure of the objective function based on the solution in the last iteration, to solve it. Specifically, we investigate the dual form of the objective function in those methods by adopting the hinge, e-insensitive, and square losses to deal with multi-task classification and regression problems, and then utilize the Lipschitz structure to construct the surrogate function for the dual forms. The surrogate functions constructed in the FISTA algorithm are founded to be decomposable, leading to parallel designs for those multi-task methods. Experiments on several benchmark datasets show that the convergence of the proposed algorithms is as fast as that of SMO-style algorithms and the parallel design can speedup the computation.
Improvement of Spatial Resolution Using Block-Matching Based Motion Estimation and Frame Integration

High frame-rate images typically possess a high temporal correlation between their motion vectors. By use of this characteristic, high accuracy motion estimation techniques for high frame-rate images were proposed in our previous work. Compared with conventional methods, our proposed methods are relatively resistant to spatial aliasing errors. However these methods are still inadequate for resistant to spatial aliasing errors. This paper proposes a new matching based motion estimation method that further improves spatial resolution. The proposed motion estimation method is more resistant to spatial aliasing errors and is suitable for reconstructing a high spatial resolution image using multiple high frame-rate images. The method uses multiple previous frames for motion estimation. First, motion vectors are independently estimated between the current frame and multiple previous frames using the basic block matching method and parabola fitting. Next, a timeaxially-averaged motion vector is approximated from multiple motion vectors using the least-squares method. Finally, the motion vector in the current frame is calculated by the approximation straight line. The results of motion estimation experiments conducted using images with spatial aliasing errors show that the proposed method improves accuracy. Further, the results of super-resolution experiments conducted using high frame-rate images show that the proposed method also improves spatial resolution.
TeJaS: retrofitting type systems for JavaScript

JavaScript programs vary widely in functionality, complexity, and use, and analyses of these programs must accommodate such variations. Type-based analyses are typically the simplest such analyses, but due to the language's subtle idioms and many application-specific needs---such as ensuring general-purpose type correctness, security properties, or proper library usage---we have found that a single type system does not suffice for all purposes. However, these varied uses still share many reusable common elements. In this paper we present TeJaS, a framework for building type systems for JavaScript. TeJaS has been engineered modularly to encourage experimentation. Its initial type environment is reified, to admit easy modeling of the various execution contexts of JavaScript programs, and its type language and typing rules are extensible, to enable variations of the type system to be constructed easily. The paper presents the base TeJaS type system, which performs traditional type-checking for JavaScript. Because JavaScript demands complex types, we explain several design decisions to improve user ergonomics. We then describe TeJaS's modular structure, and illustrate it by reconstructing the essence of a very different type system for JavaScript. Systems built from TeJaS have been applied to several real-world, third-party JavaScript programs.
UVisP: User-centric Visualization of Data Provenance with Gestalt Principles

The need to understand and track files (and inherently, data) in cloud computing systems is in high demand. Over the past years, the use of logs and data representation using graphs have become the main method for tracking and relating information to the cloud users. While being used, tracking related information with 'data provenance' (i.e. series of chronicles and the derivation history of data on metadata) is the new trend for cloud users. However, there is still much room for improving data activity representation in cloud systems for end-users. We propose "User-centric Visualization of data provenance with Gestalt (UVisP)", a novel user-centric visualization technique for data provenance. This technique aims to facilitate the missing link between data movements in cloud computing environments and the end-users uncertain queries over their files security and life cycle within cloud systems. The proof of concept for the UVisP technique integrates an open-source visualization API with Gestalt's theory of perception to provide a range of user-centric provenance visualizations. UVisP allows users to transform and visualize provenance (logs) with implicit prior knowledge of 'Gestalt's theory of perception. ' We presented the initial development of the UVisP technique and our results show that the integration of Gestalt and 'perceptual key(s)' in provenance visualization allows end-users to enhance their visualizing capabilities, to extract useful knowledge and understand the visualizations better.
Methodological issues in using Kahle's list of values scale for Japanese tourism behaviour

The literature clearly provides support and empirical evidence linking personal values and consumption, and acknowledges cross-cultural differences in these values. Personal values have been shown to be efficient, measurable sets of variables that are less numerous, more centrally held and more closely related to motivations than demographic and psychographic measures. The list of values (LOV) is a widely used scale for the measurement of values in a variety of consumer behaviour contexts. It has been used for the purposes of cross-cultural comparison and in tourism behaviour research. Kahle has suggested the instrument is a viable candidate as a widely accepted measure for cross-cultural comparison of values. This paper examines the reliability and validity of the LOV in the context of Japanese tourism behaviour. Results suggest several problems with the scale in this context, and the paper discusses some philosophical and methodological issues in cross-cultural values research in tourism.
Fingerprinting Web Browser for Tracing Anonymous Web Attackers

As web attackers hide themselves by using multi-step springboard (e.g., VPN, encrypted proxy) or anonymous network (i.e. Tor network), it raises a big obstacle for traceability and forensics. Furthermore, traditional forensics methods based on traffic and log analysis are just useful for analyzing attack events but useless for fingerprinting an attacker. Because of this, the browser fingerprinting technique which makes use of slight differences among different browsers was come up with. However, although this technique is effective for tracing attackers, countermeasures have been proposed, such as blocking extensions, spoofing extensions and Blink (a dynamic reconfiguration tool). These countermeasures will lead to changes of fingerprints. To solve the instability of browser fingerprints, we present an enhanced solution aiming at tracing attackers continuously even if the fingerprint changes within a particular period of time. By introducing secondary attributes, employing browser storage mechanisms and designing correlation algorithms, we implement the prototype system to examine the accuracy of our approach. Experimental results show that our proposed solution has the ability to associate different fingerprints from a single platform and the accuracy of tracing anonymous web attackers increases by 24.5% than traditional fingerprinting techniques.
Optimal Decision Trees

We propose an Extreme Point Tabu Search (EPTS) algorithm that constructs globally optimal decision trees for classiication problems. Typically, decision tree algorithms are greedy. They optimize the misclassiication error of each decision sequentially. Our non-greedy approach minimizes the misclassiication error of all the decisions in the tree concurrently. Using Global Tree Optimization (GTO), we can optimize existing decision trees. This capability can be used in classiication and data mining applications to avoid overrtting, transfer knowledge, incorporate domain knowledge , and maintain existing decision trees. Our method works by xing the structure of the decision tree and then representing it as a set of disjunctive linear inequalities. An optimization problem is constructed that minimizes the errors within the disjunctive linear inequalities. To reduce the misclassiication error, a nonlinear error function is minimized over a polyhedral region. We show that it is suucient to restrict our search to the extreme points of the polyhedral region. A new EPTS algorithm is used to search the extreme points of the polyhedral region for an optimal solution. Promising computational results are given for both randomly generated and real-world problems.
A Piagetian Scale for the Measurement of Ethical Competence in Politics

Politicians' ethical competence is defined as the use of a cognitive ability, independent of particular political, ideological, or moral values. Accordingly, an assessment instrument-the Ethical Competence Questionnaire-Political (ECQ-P)-measuring ethical autonomy was constructed, based on Piaget's theory of moral development. The administration of ECQ-P to politicians with a high degree of political engagement, and on ordinary citizens and simple party members, demonstrated satisfying psychometric properties. The former group obtained a higher score on ECQ-P. A confirmatory factor analysis showed that the measurement model is compatible with the data. These findings indicate the adequacy of ECQ-P as an instrument measuring ethical competence.