Miracle-dz/UniFAR-FTUs huggingface dataset:
Incremental Ensemble Classifier Addressing Non-stationary Fast Data Streams

Classification of data points in a data stream is a fundamentally different set of challenges than data mining on static data. While streaming data is often placed into the context of "Big Data" (or more specifically "Fast Data") wherein one-pass algorithms are used, true data streams offer additional hurdles due to their dynamic, evolving, and non-stationary nature. During the stream, the available labels (or concepts) often change, and a concept's definition in the feature space can also evolve (or drift) over time. The core issue is that the hidden generative function of the data is not a constant function, but rather evolves over time. This is known as a non-stationary distribution. In this paper, we describe a new approach to using ensembles for stream classification. While the core method is straightforward, it is specifically designed to adapt quickly with very little overhead to the dynamic and evolving nature of data streams generated from non-stationary functions. Our method, M3, is based on a weighted majority ensemble of heterogeneous model types where model weights are updated on-line using Reinforcement Learning techniques. We compare our method with current leading algorithms as implemented in the Massive Online Analysis (MOA) framework using UCI benchmark and synthetic stream generator data sets, and find that our method shows particularly strong gain over the baseline method when ground truth is of limited availability to the classifiers.
Identification and Management of Sessions Generated by Instant Messaging and Peer-to-Peer Systems

Sessions generated by Instant Messaging and Peer-to-Peer systems (IM/P2Ps) not only consume considerable bandwidth and computing resources but also dramatically change the characteristics of data flows affecting both the operation and performance of networks. Most IM/P2Ps have known security loopholes and vulnerabilities making them an ideal platform for the dissemination of viruses, worms, and other malware. The lack of access control and weak authentication on shared resources further exacerbates the situation. Should IM/P2Ps be deployed in production environments, performance of conventional applications may significantly deteriorate and enterprise data may be contaminated. It is therefore imperative to identify, monitor and finally manage IM/P2P traffic. Unfortunately, this task cannot be easily attained as IM/P2Ps resort to advanced techniques to hide their traces including multiple channels to deliver services, port hopping, message encapsulation and encryption. In this paper, we propose an extensible framework that not only helps to identify and classify IM/P2P-generated sessions in real time but also assists in the manipulation of such traffic. Consisting of four modules namely, session manager, traffic assembler, IM/P2P dissector, and traffic arbitrator, our proposed framework uses multiple techniques to improve its traffic classification accuracy and performance. Through fine-tuned splay and interval trees that help organize IM/P2P sessions and packets in data streams, we accomplish stateful inspection, traffic re-assembly, data stream correlation, and application layer analysis that combined will boost the framework's identification precision. More importantly, we introduce IM/P2Ps "plug-and-play" protocol analyzers that inspect data streams according to their syntax and semantics; these analyzers render our framework easily extensible. Identified IM/P2P sessions can be shaped, blocked, or disconnected, and corresponding traffic can be stored for forensic analysis and threat evaluation. Experiments with our prototype show high IM/P2Ps detection accuracy rates under diverse settings and excellent overall performance in both controlled and real-world environments.
Separating Optimism and Pessimism: a Robust Psychometric Analysis of the Revised Life Orientation Test (lot–r) the Internal Structure of the Revised Life Orientation Test (lot

1994) was analyzed in a sample of 46,133 participants who ranged in age from 18 years to 103 years. Confirmatory factor analysis showed that dispositional optimism, as measured by the LOT–R, is bidimensional, consisting of an Optimism and a Pessimism factor. Consistent with previous results, there were small to moderate negative correlations between Optimism and Pessimism, but the strength of the association continuously decreased with age. The relative independence of the 2 dimensions occurred in both genders and across different age groups of patients with different medical disorders. The most frequently used measure of dispositional optimism is Despite its wide usage, a number of critical issues have been raised about the test (e.ers, 1992). One of them, the focus of this article, is whether the test measures one dimension (optimism) or two dimensions (optimism and pessimism). Empirical findings do not convincingly support the authors' original assumption that optimism and pessimism are polar opposites on a unidimensional continuum. Analyzing the LOT–R with confirmatory factor analysis (CFA), Scheier et al. (1994) concluded " that the two-factor solution was superior " (p. 1074). Nevertheless, in an attempt to defend unidimensionality, the authors included correlated errors among the positively worded items and then reevaluated the models. The resulting nonsignificant chi-square difference between the one-and the two-factor models led them to conclude that the LOT–R should be treated as unidi-mensional. Using a similar strategy, Mehrabian and Ljunggren (1997) demonstrated the potential unidimensionality of the LOT–R only when setting correlations between Items 1 and 4 and between Items 7 and 9. These procedures do not ensure unidimen-sionality, because any single-factor model with correlated errors reflects multidimensionality and can alternatively be specified by means of multiple latent factors. There is more evidence suggesting that the LOT consists of partially independent Optimism and Pessimism factors Bartum (2002) demonstrated that, in a sample of students, the LOT–R was bidimensional rather than unidimensional and that there was little shared variance between optimism and pessimism (r ϭ .16). However, it needs to be established that two-dimensionality is not an artifact of the methodology. Kubzansky, Kubzansky, and Maselko (2004) assessed whether the two-factor structure occurs through methodological bias or reflects a substantive difference. The method artifact model posits that items cluster together because they are similarly framed, whereas the bidimensional model posits that items cluster together because they share substantive content. Reversing the framing of only half of the items on each …
Digitized by the Internet Archive in 2011 with Funding from Boston Library Consortium Iviember Libraries Competition and Efficiency in Congested Markets Competition and Efficiency in Congested Markets^

We study the efSciency of oligopoly equilibria in congested markets. The motivating examples are the allocation of network flows in a communication network or of traffic in a transportation network. We show that increasing competition among oligopoUsts can reduce efficiency, measured as the difference between users' willingness to pay and delay costs. We characterize a tight bound of 5/6 on efficiency in pure strategy equilibria when there is zero latency at zero flow and a tight bound of 2v^ — 2 with positive latency at zero flow. These bounds are tight even when the numbers of routes and ohgopohsts are arbitrarily large. two anonymous referees and participants at various seminars and conferences for useful comments.
The Research of Modified Collaborative Filtering Recommendation Algorithm

Collaborative filtering is a kind of data mining algorithm. It has been widely used in commercial recommender system. In this paper, we discuss the problems of traditional collaborative filtering algorithm and propose improvement ideas. As the judging criteria of the recommendation algorithm, we introduce Mean Absolute Error (MAE). On the basis of data mining platform, we build user application layer of the prototype system by PHP language. We use the MovieLens data sets to conduct comparisons with the traditional and the improved collaborative filtering algorithm which is based on the item, then obtain different experimental data from two aspects, and evaluate the effect of the recommended result by MAE. Finally it concluded that the improved collaborative filtering recommendation algorithm is slightly better than the traditional collaborative filtering algorithm in the same situation.
Dynamic Contact Angle Analysis of Protein Adsorption on Polysaccharide Multilayer's Films for Biomaterial Reendothelialization

Atherosclerosis is a major cardiovascular disease. One of the side effects is restenosis. The aim of this work was to study the coating of stents by dextran derivates based polyelectrolyte's multilayer (PEM) films in order to increase endothelialization of injured arterial wall after stent implantation. Films were composed with diethylaminoethyl dextran (DEAE) as polycation and dextran sulphate (DS) as polyanion. One film was composed with 4 bilayers of (DEAE-DS)4 and was labeled D−. The other film was the same as D− but with an added terminal layer of DEAE polycation: (DEAE-DS)4-DEAE (labeled D+). The dynamic adsorption/desorption of proteins on the films were characterized by dynamic contact angle (DCA) and atomic force microscopy (AFM). Human endothelial cell (HUVEC) adhesion and proliferation were quantified and correlated to protein adsorption analyzed by DCA for fibronectin, vitronectin, and bovine serum albumin (BSA). Our results showed that the endothelial cell response was optimal for films composed of DS as external layer. Fibronectin was found to be the only protein to exhibit a reversible change in conformation after desorption test. This behavior was only observed for (DEAE-DS)4 films. (DEAE-DS)4 films could enhance HUVEC proliferation in agreement with fibronectin ability to easily change from conformation.
Disentangling speaker and channel effects in speaker verification

We show how a joint factor analysis of inter-speaker and intra-speaker variability in a training database which contains multiple recordings for each speaker can be used to construct likelihood ratio statistics for speaker verification which take account of intra-speaker variation and channel variation in a principled way. We report the results of experiments on the NIST 2001 cellular one speaker detection task carried out by applying this type of factor analysis to Switchboard Cellular Part I. The evaluation data for this task is contained in Switchboard Cellular Part I so these results cannot be taken at face value but they indicate that the factor analysis model can perform extremely well if it is perfectly estimated.
Video Inpainting using Nearest Neighbor Field Algorithm

Video inpainting or completion is a vital video improvement technique used to repair or editing of digital videos. In this paper, we are proposing video inpainting algorithm for repairing damaged frames in video films, focusing on maintaining good spatiotemporal continuity. It has been used all over the world to convert cultural artifacts such as vintage videos and films into digital form. However, such types of videos are usually very poor in quality and most of the time it contain unstable luminance and damaged content. Experiment on many frames of videos show the benefits of our proposed approach which lead to natural looking videos with less annoying artifacts.
3D Shape Reconstruction from Autostereograms and Stereo

We study the problem of shape reconstruction from stereo images based on a weighted area minimization process of a depth function. As a simple example we present an efficient shape reconstruction from computer generated autostereograms. A minimal surface area based correlation is applied to accurately reconstruct the surface structure embedded first in one autostereogram image and next in two or more stereo images. The minimal area approach proved itself as a useful geometric measure in recent reconstruction and enhancement applications in computer vision and image processing. Here we develop a simplified version for the O. Faugeras and R. Keriven (1998, IEEE Trans. Image Process.7, 336? 344) stereo reconstruction model and apply a weighted area measure as part of a solution to the correspondence extraction in the shape from stereo and the shape from autostereogram problems. The proposed schemes are computationally efficient and yield accurate 3D reconstructions for smooth as well as nonsmooth surfaces.
Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters

Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository.
Exploiting the advantages of connectionless and connection-oriented networks

This paper proposes a scheme for taking advantage of the properties of both connectionless (CL) and connection-oriented (CO) networks to carry traffic from existing applications. Specifically, it proposes a solution for allowing data generated by endpoints on a CL (IP) network to be redirected to CO networks if there is an advantage from the user or service provider perspective. The advantage to a user is that the user can ask for and receive a guaranteed quality of service for a specific flow. The advantage to a service provider is that bandwidth utilization in a packet-switched CO network is better than in a CL network with precomputed routes since bandwidth can be dynamically allocated to flows on an as-needed basis. These advantages only exist when the CO network is operated in a switched mode (i.e., some connection " setup " actions are performed for each arriving flow) rather than in a provisioned mode (where all these actions are performed a priori). This paper addresses the problem of how to internetwork a CL (IP) network with a switched CO network. Our solution is based on interworking routing schemes of the two networks, " halting " or " turning around " datagrams during connection setup, and performing protocol conversion rather than encapsulation (on the user plane) to reduce overheads, which results in a further improvement of bandwidth utilization. The CO network can be an MPLS (MultiProtocol Label Switching) or RSVP (Resource reSerVation Protocol) based IP network, a WDM (Wavelength Division Multiplexed) network , an ATM (Asynchronous Transfer Mode) network, or an STM (Synchronous Time Multiplex-ing) network, such as the telephony network or a SONET network. The choice of a CO network depends greatly on a particular company's current products or installed base. The paper also presents a detailed discussion of why we propose operating CO networks in a switched mode for carrying data generated by IP applications. We show that operating any of the 2 above-listed CO networks in a provisioned mode is less efficient than a network of typical CL IP rout-ers for the purposes of carrying " bursty " IP traffic. We also consider perceived drawbacks of the switched mode of operation, i.e., of having to deal with short-lived flows and large call handling capacities , and present our solutions to these issues.
Peroxisomal alanine : glyoxylate aminotransferase (AGT1) is a photorespiratory enzyme with multiple substrates in Arabidopsis thaliana.

At least two glyoxylate aminotransferases are hypothesized to participate in the steps of photorespiration located in peroxisomes. Until recently, however, genes encoding these enzymes had not been identified. We describe the isolation and characterization of an alanine : glyoxylate aminotransferase (AGT1, formerly AGT) cDNA from Arabidopsis thaliana. Southern blot analysis confirmed that Arabidopsis AGT1 is encoded by a single gene. Homologs of this class IV aminotransferase are also known in other plants, animals, and methylotrophic bacteria, suggesting an ancient evolutionary origin of this enzyme. AGT1 transcripts were present in all tissues of Arabidopsis, but were most abundant in green, leafy tissues. Purified, recombinant Arabidopsis AGT1 expressed in Escherichia coli catalyzed three transamination reactions using the following amino donor : acceptor combinations: alanine : glyoxylate, serine : glyoxylate, and serine : pyruvate. AGT1 had the highest specific activity with the serine : glyoxylate transamination, and apparent Km measurements indicate that this is the preferred in vivo reaction. In vitro import experiments and subcellular fractionations localized AGT1 to peroxisomes. Sequence analysis of the photorespiratory sat mutants revealed a single nucleotide substitution in the AGT1 gene from these plants. This transition mutation is predicted to result in a proline-to-leucine substitution at residue 251 of AGT1. When this mutation was engineered into the recombinant AGT1 protein, enzymatic activity using all three donor : acceptor pairs was abolished. We conclude that Arabidopsis AGT1 is a peroxisomal photorespiratory enzyme that catalyzes transamination reactions with multiple substrates.
A trickle-down model of task and development i-deals

In today’s competitive landscape, employees increasingly negotiate idiosyncratic deals (i-deals), referring to personalized work arrangements that address recipients’ unique work needs and preferences. While i-deals unfold in a dyadic context between subordinates and their managers, the consequences of i-deals concern everyone including co-workers and the organization. Focusing on task and development i-deals, we propose a trickle-down model to explore whether and how organizations benefit from i-deals. First, we argue that managers’ task and development i-deals cascade down to their subordinates, leading them to have similar i-deals with downstream consequences for co-workers and the organization. Furthermore, we propose that effective implementation of task and development i-deals are context-specific: we integrate the role of managers’ servant leadership as a boundary condition to explore the association between managers’ and subordinates’ task and development i-deals. We also integrate subordinates’ prosocial motives to explore the association between subordinates’ task and development i-deals and their work outcomes. We draw on work adjustment, social learning and social information processing theories to study our proposed associations. The results of a matched employee–manager dataset collected in the Philippines support our hypothesized model. This study contributes to i-deals research by: (1) testing whether and how task and development i-deals can be mutually beneficial for all the involved parties; and (2) revealing how the context, at the individual level, explains how and when task and development i-deals can best be implemented in workplaces. This study highlights that individualization of HR practices need not be a zero-sum game.
An Experimental Study on Requirements Engineering for Software Product Lines

Requirements Engineering (RE) activities in Software Product Lines (SPL) are more complex, because they involve more products and stakeholders, and require attention to variabilities and commonalities. Thus, the SPL development should be supported by an appropriate REprocess for its context, which assures usability, effectiveness and efficiency in its activities. In order to analyze these issues, an experimental study was conducted to evaluate the viability of a proposal process. The results of the study showed that the use of an appropriate support tool increases the efficiency of the process. We also observed that subjects’ experience in industrial projects and RE influences the usability of the process.
CAD-Based Computer Vision: From CAD Models to Relational Graphs

The topic of model-building for 3-D objects is examined. Most 3-D object recognition systems construct models either manually or by training. Neither approach has been very satisfactory, particularly in designing object recognition systems which can handle a large number of objects. Recent interest in integrating mechanical CAD systems and vision systems has led to a third type of model building for vision: adaptation of preexisting CAD models of objects for recognition. If a solid model of an object to be recognized is already available in a manufacturing database, then it should be possible to infer automatically a model appropriate for vision tasks from the manufacturing model. Such a system has been developed. It uses 3-D object descriptions created on a commercial CAD system and expressed in both the industry-standard IGES form and a polyhedral approximation and performs geometric inferencing to obtain a relational graph representation of the object which can be stored in a database of models for object recognition. Relational graph models contain both view-independent information extracted from the IGES description and view-dependent information (patch areas) extracted from synthetic views of the object. It is argued that such a system is needed to efficiently create a large database (more than 100 objects) of 3-D models to evaluate matching strategies. >
Flipping the Classroom in Teacher Education: Implications for Motivation and Learning

In teacher education, it is imperative that course design, method of instruction, and classroom procedures align with the content. One way to achieve this may be to “flip” the classroom. While flipped classrooms have received considerable attention in recent years, much remains unknown about their effect on basic psychological needs or learning outcomes of preservice teachers. The purpose of the present study was to address this gap by utilizing a quasi-experimental design to examine differences in motivation and objective learning outcomes after controlling for socioeconomic status (SES) and grade point average (GPA) between traditional and flipped sections of a foundational educational course (N = 263). Results revealed that preservice teachers in the traditional section had significantly higher scores on two of the motivation outcomes (e.g., intrinsic and identified regulation), but that preservice teachers in the flipped sections had significantly higher scores on several indices of objective learning outcomes. Implications for teacher education are discussed.