Miracle-dz/UniFAR-FTUs huggingface dataset:
An experimental performance evaluation of different remote servers to analyze the effect of divergent load and congestion

In terms of provisioning of guaranteed QoS to the end-user, the performance of remote servers is a critical issue for internet service providers to remain competitive in the market. The end-user would like to have a quick and instant response from the webmail server to enjoy the service. Load and congestion are the greatest hurdles in web based linkage and conversation. The efficiency of web page retrieval is degraded due to the peak use of internet and also because of its highly complex architecture. This paper provides an experimental effort to analyze the effect of load and congestion on web services and measures the performance characteristics like Response time, Latency, jitter, Round Trip Time, hits lost ratio, Page error rates, sending and receiving speed, availability and reliability for accessing different popular and vastly used remote servers (Yahoo Server, Gmail Server, Hotmail Server) to conclude, how load and congestion cause the increment in latency of services? Is there any significant relationship between congestion and hop counts? We contributed new and novel results for further discussions.
Lecture notes in business information processing

LNBIP reports state-of-the-art results in areas related to business information systems and industrial application software development – timely, at a high level, and in both printed and electronic form. The type of material published includes • Proceedings (published in time for the respective event) • Postproceedings (consisting of thoroughly revised and/or extended final papers) • Other edited monographs (such as, for example, project reports or invited volumes) • Tutorials (coherently integrated collections of lectures given at advanced courses, seminars, schools, etc.) • Award-winning or exceptional theses LNBIP is abstracted/indexed in DBLP, EI and Scopus. LNBIP volumes are also submitted for the inclusion in ISI Proceedings.
MATCH TRACKING STRATEGIES FOR FUZZY ARTMAP NEURAL NETWORKS

Training fuzzy ARTMAP neural networks for classification using data from complex real-world environments may lead to category proliferation, and yield poor performance. This problem is known to occur whenever the training set contains noisy and overlapping data. Moreover, when the training set contains identical input patterns that belong to different recognition classes, fuzzy ARTMAP will fail to converge. To circumvent these problems, some alternatives to the network’s original match tracking (MT) process have been proposed in literature, such as using negative MT, and removing MT altogether. In this chapter, the MT parameter of fuzzy ARTMAP is optimized during training using a new Particle Swarm Optimisation (PSO)-based strategy, denoted PSO(MT). The impact on fuzzy ARTMAP performance of training with different MT strategies is assessed empirically, using different synthetic data sets, and the NIST SD19 handwritten character recognition data set. During computer simulations, fuzzy ARTMAP is trained with the original (positive) match tracking (MT+), with negative match tracking (MT-), without MT algorithm (WMT), and with PSO(MT). Through a comprehensive set of simulations, it has been observed that by training with MT-, fuzzy ARTMAP expends fewer resources than with other MT strategies, but can achieve a significantly higher generalization error, especially for data with overlapping class distributions. In particular, degradation of error in fuzzy ARTMAP performance due to overtraining is more pronounced for MTthan for MT+. Generalization error achieved using WMT is significantly higher than other strategies on data with complex non-linear decision bounds. Furthermore, the number of internal categories required to represent decision boundaries increases significantly. Optimizing the value of the match tracking parameter using PSO(MT) yields the lowest overall generalization error, and requires fewer internal categories than WMT, but generally more categories than MT+ and MT-. However, this strategy requires a large number of training epochs to convergence. Based on this empirical results with PSO(MT), the MT process as such can provide a significant increase to fuzzy ARTMAP performance, assuming that the MT parameter is tuned for the specific application in mind. ∗Corresponding author: ETS, 1100 Notre-Dame Ouest, Montreal, Quebec, H3C 1K3, Canada, email: eric.granger@etsmtl.ca, phone: 1-514-396-8650, fax: 1-514-396-8595.
Examining Characteristics of Teacher Feedback in Organizing Written Texts

This study aimed to examine teachers' written feedback on student texts in terms of dimensions focused and qualitative characteristics. The study was conducted using content analysis technique that is one of the qualitative research methods. The participants were 28 Turkish language teachers working at middle schools in Nigde in the 2013-2014 school year. In the data gathering process, 6th graders were firstly asked to write sample texts of 250300-word length. Four of these texts were then selected by three experts. The participant teachers were asked to provide feedback on the four selected texts. The written feedback provided by the teachers were analysed by the researcher and two experts in terms of dimensions focused and quality. As a result of the analysis, a total of 750 pieces of feedback provided by 28 teachers for four different student texts were mostly about the dimension of spelling and punctuation. On the other hand, in terms of quality, 67,1% of the feedback was evaluative.
Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy

Anytime predictors first produce crude results quickly, and then continuously refine them until the test-time computational budget is depleted. Such predictors are used in real-time vision systems and streaming-data processing to efficiently utilize varying test-time budgets, and to reduce average prediction cost via early-exits. However, anytime prediction algorithms have difficulties utilizing the accurate predictions of deep neural networks (DNNs), because DNNs are often computationally expensive without competitive intermediate results. In this work, we propose to add auxiliary predictions in DNNs to generate anytime predictions, and optimize these predictions simultaneously by minimizing a carefully constructed weighted sum of losses, where the weights also oscillate during training. The proposed anytime neural networks (ANNs) produce reasonable anytime predictions without sacrificing the final performance or incurring noticeable extra computation. This enables us to assemble a sequence of exponentially deepening ANNs, and it achieves, both theoretically and practically, near-optimal anytime predictions at every budget after spending a constant fraction of extra cost. The proposed methods are shown to produce anytime predictions at the state-of-the-art level on visual recognition data-sets, including ILSVRC2012.
Understanding the perception towards using mHealth applications in practice

The objective of this study was to investigate physicians’ perceptions to use mobile health applications in practice, and to identify influencing factors to use the technology. An mHealth technology acceptance model was proposed (M-TAM), and a cross-sectional survey was implemented using structured questionnaire to collect data. Online tools were used for inviting participants (physicians) and data collection from Turkey. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). A total of 128 physicians participated in the survey. The model explained the perception of physicians towards mHealth application use by 51% of total variance. The influential factors were identified as Effort Expectancy, Mobile Anxiety, Perceived Service Availability and Technical Training and Support. The study provided a new model to the literature of health information technology. Findings of the research contributed by unveiling latent constructs and their influence on physicians’ perceptions towards a new healthcare technology: mHealth applications.
PROBABILISTIC FAILURE ANALYSIS OF HIGH STEEL FRAMES WITH TUNED MASS DAMPER

In this study, the probability of failure of steel frames with Tuned Mass Damper (TMD) is investigated. A 20‐story steel frame was designed using SAP software and then, it was modelled in OpenSees. The behaviour of the frame towards accelerations in the range of 0.1 to the maximum acceleration of seismic records was investigated in this study. Using Incremental Dynamic Analysis (IDA) and extracting seismic fragility curves, it was observed that TMD increased the stability of the structure against the earthquakes by absorbing the input energy to the structure. This significantly reduces the possibility of failure due to small PGAs. For example, for the acceleration scheme equal to 0.35 for Tehran (the capital of Iran), the probability of failure in all stories of the structures equipped with TMD will be insignificant.
A Cross-Cultural Study of Interpersonal Trust in Social Commerce

ABSTRACT This study investigates the effect of interpersonal trust upon people’s intention to use social commerce. In particular, trust is validated as a construct of three dimensions of competence, integrity, and benevolence. In addition, by applying trust-building theories, familiarity and shared value are incorporated in the research model as antecedents to trust dimensions. Considering culture greatly influences people’s values and trust depends on cultural characteristics, the moderating effect of culture is examined. The research model is tested by survey data collected from the USA and China. Among the three dimensions of trust, competence has the strongest impact upon behavioral intention for both regions. While both familiarity and shared value affect trust dimensions significantly, the effect of familiarity is stronger for users in China whereas the effect of shared value is stronger for users in the USA. Implications of the findings are discussed.
Toward a Universal Characterization of Passivization - eScholarship

b Berkeley Linguistics Society Toward a Universal Characterization of Passivization Author(s): David M. Perlmutter and Paul M. Postal Proceedings of the 3rd Annual Meeting of the Berkeley Linguistics Society (1977), pp. 394-417 Please see “How to cite” in the online sidebar for full citation information. Please contact BLS regarding any further use of this work. BLS retains copyright for both print and screen forms of the publication. BLS may be contacted Via http://linguisticsberkeley.edu/bls/. Tne Annual Proceedings 0//ﬂe Berke/ey Linguistics Soczezjz is published online Via eLanguage, the Linguistic Society of America's digital publishing platform.
An analysis of North Saami gradation*

Abstract This paper gives a moraic analysis of gemination and laryngeal alternations associated with consonant gradation in North Saami. Gradation gives rise to a surface three-way length distinction in consonants, which is essential to understanding length in vowels and diphthongs. It is explained by a system of prosodic rules applying to underlying representations containing only a two-way contrast between geminate and singleton consonants, plus a floating mora present in certain suffixes, which results in surface alternations between extra-long and long or between long and short consonants. An enlightening explanation of quantity alternations is available if one exploits the possibility implicit in moraic theory that the relationship between segments and moras can be surface-contrastive, and we show that recourse to trimoraic syllables is unnecessary, despite the surface three-way length difference. These prosodic alternations also result in shifts in the timing of preaspiration and preglottalisation, as well as loss of these laryngeal specifications.
Meta-Information and Argumentation in Multi-Agent Systems

In this work we compile our research regarding meta-information in multi-agent systems. In particular, we describe some agents profiles represent- ing different attitudes which describe how agents consider meta-information in their decisions-making and reasoning processes. Furthermore, we describe how we have combined different meta-information available in multi-agent systems with an argumentation-based reasoning mechanism. In our approach, agents are able to decide more conflicts between information/arguments, given that they are able to use different meta-information (often combined) to decide between such conflicting information. Our framework for meta-information in multi- agent systems was implemented based on a modular architecture, thus other meta-information can be added, as well as different meta-information can be combined in order to create new agents profiles. Therefore, in our approach, different agents profiles can be instantiated for different application domains, allowing flexibility in the choice of how agents will deal with conflicting infor- mation in those particular domains.
A single-chip FPGA implementation of the data encryption standard (DES) algorithm

This paper describes a single-chip implementation of the data encryption standard (DES) using Xilinx XC4000 series field programmable gate array technology under the XACTstep design flow integration system. The implementation details for key scheduling, S-boxes, permutations and the round-function are described. The design process included schematic design, functional and timing simulation and design verification. The final design used 224 combinational logic blocks (CLBs) and 54 input/output blocks (IOBs) and has an encryption speed of 26.7 Mbps.
Generic SoC QR array processor for adaptive beamforming

A generic architecture for implementing a QR array processor in silicon is presented. This improves on previous research by considerably simplifying the derivation of timing schedules for a QR system implemented as a folded linear array, where account has to be taken of processor cell latency and timing at the detailed circuit level. The architecture and scheduling derived have been used to create a generator for the rapid design of System-on-a-Chip (SoC) cores for QR decomposition. This is demonstrated through the design of a single-chip architecture for implementing an adaptive beamformer for radar applications.
Multi-word-Agent Autonomy Learning Based on Adaptive Immune Theories

A multi-word-agent autonomy learning model as a new online learning model is presented based on clonal selection theory and idiotypic immune network theory, where words simulate B cells and antigens involved in adaptive immune responses to construct autonomic immune word-agents under the framework of autonomy oriented computing. Moreover, relations between words simulate specific relations between B cells and antigens, where attributes of words simulate receptors, and strengths of these relations are defined as affinities calculated by matching receptors. The environment simulates the region in which the word-agents interact with each other and share information. The goal of the model is to optimize these relations iteratively by increasing affinities with hyper-mutation of receptors of B cell word-agents. Experimental results on dependency parsing show that not only accuracies are improved continually, but also, compared with MSTParser, high accuracies can be gained with less learning samples.
Examining Technology Uses in the Classroom: Developing Fraction Sense Using Virtual Manipulative Concept Tutorials

This paper describes a classroom teaching experiment conducted in three fifth-grade mathematics classrooms with students of different achievement levels. Virtual fraction manipulative concept tutorials were used in three one-hour class sessions to investigate the learning characteristics afforded by these technology tools. The virtual fraction manipulative concept tutorials exhibited the following learning characteristics that supported students during their learning of equivalence and fraction addition: (1) Allowed discovery learning through experimentation and hypothesis testing; (2) Encouraged students to see mathematical relationships; (3) Connected iconic and symbolic modes of representation explicitly; and (4) Prevented common error patterns in fraction addition.
Thermodynamic modeling for organic solid precipitation

A generalized predictive model which is based on thermodynamic principle for solid-liquid phase equilibrium has been developed for organic solid precipitation. The model takes into account the effects of temperature, composition, and activity coefficient on the solubility of wax and asphaltenes in organic solutions. The solid-liquid equilibrium K-value is expressed as a function of the heat of melting, melting point temperature, solubility parameter, and the molar volume of each component in the solution. All these parameters have been correlated with molecular weight. Thus, the model can be applied to crude oil systems. The model has been tested with experimental data for wax formation and asphaltene precipitation. The predicted wax appearance temperature is very close to the measured temperature. The model not only can match the measured asphaltene solubility data but also can be used to predict the solubility of asphaltene in organic solvents or crude oils. The model assumes that asphaltenes are dissolved in oil in a true liquid state, not in colloidal suspension, and the precipitation-dissolution process is reversible by changing thermodynamic conditions. The model is thermodynamically consistent and has no ambiguous assumptions.
Advances in Computer Entertainment : 10th International Conference, ACE 2013, Boekelo, The Netherlands, November 12-15, 2013. Proceedings

These are the proceedings of the 10th International Conference on Advances in Computer Entertainment (ACE 2013), hosted by the Human Media Interaction research group of the Centre for Telematics and Information Technology at the University of Twente, The Netherlands. The ACE series of conferences, held yearly since 2004, has always been lively and interactive events. There are not just mainly paper presentations, but also many creative showcases, demonstrations, workshops, and often a game competition as well. For ten years now, ACE has shown itself to be a strong and vibrant community. Throughout the years, there has been a common element that ties together many of the different types of work presented at ACE. In their contributions, authors not only present solutions to known problems, or observe and describe aspects of the technological reality that is out there, but also actively explore what new things they can make, and why these new things might be important or interestingly different.
REGMAPR - A Recipe for Textual Matching

We propose a simple model for textual matching problems. Starting from a Siamese architecture, we augment word embeddings with two features based on exact and paraphrase match between words in the two sentences being considered. We train the model using four types of regularization on datasets for textual entailment, paraphrase detection and semantic relatedness. Our model performs comparably or better than more complex architectures; achieving state-of-the-art results for paraphrase detection on the SICK dataset and for textual entailment on the SNLI dataset.
Transferring Pose and Augmenting Background Variation for Deep Human Image Parsing

Human parsing is a fundamental task to estimate semantic parts in a human image such as face, arm, leg, hat, and dress. Recent deep-learning based methods have achieved signiﬁcant improvements, but collecting training datasets of pixel-wise annotations is labor-intensive. In this paper, we propose two solutions to cope with limited dataset. First, to handle various poses, we incorporate a pose estimation network into an end-to-end human parsing network in order to transfer common features across the domains. The pose estimation network can be trained using rich datasets and feed valuable features to the human parsing network. Second, to handle complicated backgrounds, we increase the variations of background images automatically by replacing the original backgrounds of human images with those obtained from large-scale scenery image datasets. While each of the two solutions is versatile and beneﬁcial to human parsing, their combination yields further improvement.
Frequency mixer with a frequency doubler for integrated circuits

We have developed a bipolar LSI, including a frequency mixer and a local oscillator frequency doubler. The local oscillator frequency doubler consists of two identical unbalanced emitter-coupled pairs with a different emitter area ratio, whose input terminals are connected cross-coupled and whose output terminals are connected in parallel, and is coupled with the frequency mixer directly in the LSI. The fabricated LSI operates on supply voltage as low as 2.7, and has been put into practical use for a cordless telephone system. >
Learning Spark: Lightning-Fast Big Data Analytics

The Web is getting faster, and the data it delivers is getting bigger. How can you handle everything efficiently? This book introduces Spark, an open source cluster computing system that makes data analytics fast to run and fast to write. Youll learn how to run programs faster, using primitives for in-memory cluster computing. With Spark, your job can load data into memory and query it repeatedly much quicker than with disk-based systems like Hadoop MapReduce. Written by the developers of Spark, this book will have you up and running in no time. Youll learn how to express MapReduce jobs with just a few simple lines of Spark code, instead of spending extra time and effort working with Hadoops raw Java API. Quickly dive into Spark capabilities such as collect, count, reduce, and save Use one programming paradigm instead of mixing and matching tools such as Hive, Hadoop, Mahout, and S4/Storm Learn how to run interactive, iterative, and incremental analyses Integrate with Scala to manipulate distributed datasets like local collections Tackle partitioning issues, data locality, default hash partitioning, user-defined partitioners, and custom serialization Use other languages by means of pipe() to achieve the equivalent of Hadoop streaming