Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Profiling into MDE Compilers

Scientific computation requires more and more performance in its algorithms. New massively parallel architectures suit well to these algorithms. They are known for offering high performance and power efficiency. Unfortunately, as parallel programming for these architectures requires a complex distribution of tasks and data, developers find difficult to implement their applications effectively. Although approaches based on source-to-source intends to provide a low learning curve for parallel programming and take advantage of architecture features to create optimized applications, programming remains difficult for neophytes. This work aims at improving performance by returning to the high-level models, specific execution data from a profiling tool enhanced by smart advices computed by an analysis engine. In order to keep the link between execution and model, the process is based on a traceability mechanism. Once the model is automatically annotated, it can be re-factored aiming better performances on the re-generated code. Hence, this work allows keeping coherence between model and code without forgetting to harness the power of parallel architectures. To illustrate and clarify key points of this approach, we provide an experimental example in GPUs context. The example uses a transformation chain from UML-MARTE models to OpenCL code.
SURFmap: a network monitoring tool based on the maps API

Network monitoring allows network managers to get a better insight in the network traffic transiting in a managed network. In order to make the tasks of a network manager easier, many network monitoring tools are made available for a wide range of purposes (e.g., traffic accounting, performance analysis, and so on) network managers may have. However, most of these tools lack to provide geographical information about network traffic. This paper presents a network monitoring tool prototype, called SURFmap, which provides network traffic information at a geographical dimension by using the Google Maps API. Through the use of the Google Maps API's features, SURFmap provides different zoom levels when showing network information, which results in the creation of different levels of abstraction in the network data visualization. SURFmap has revealed to be more intuitive when showing network traffic information, which makes the network monitoring activity from the network manager's perspective more interesting.
Diversified Hidden Markov Models for Sequential Labeling

Labeling of sequential data is a prevalent meta-problem for a wide range of real world applications. While the first-order Hidden Markov Models (HMM) provides a fundamental approach for unsupervised sequential labeling, the basic model does not show satisfying performance when it is directly applied to real world problems, such as part-of-speech tagging (PoS tagging) and optical character recognition (OCR). Aiming at improving performance, important extensions of HMM have been proposed in the literatures. One of the common key features in these extensions is the incorporation of proper prior information. In this paper, we propose a new extension of HMM, termed diversified Hidden Markov Models (dHMM), which utilizes a diversity-encouraging prior over the statetransition probabilities and thus facilitates more dynamic sequential labellings. Specifically, the diversity is modeled by a continuous determinantal point process prior, which we apply to both unsupervised and supervised scenarios. Learning and inference algorithms for dHMM are derived. Empirical evaluations on benchmark datasets for unsupervised PoS tagging and supervised OCR confirmed the effectiveness of dHMM, with competitive performance to the state-of-the-art.
Feed-Forward Neural Networks Regressions with Genetic Algorithms: Applications in Econometrics and Finance

In this paper we examine feed-forward neural networks using genetic algorithms in the training process instead of error backpropagation algorithm. Additionally real encoding is preferred to binary encoding as it is more appropriate to find the optimum weights. We use learning and momentum rates for the weight updating as in the case of the error backpropagation algorithm. Some empirical examples as well as the programming routine in MATLAB are provided.
Indoor Location Based on an LANDMARC Improved Algorithm

A novel indoor positioning algorithm named BPNN-LANDMARC is proposed in this paper to increase the positioning accuracy and reduce the high time complexity of classical LANDMARC algorithm. Simulation results prove that the proposed BPNN-LANDMARC algorithm can improve the average positioning accuracy by 24.35%. In addition, the improved algorithm reduces the time complexity of algorithm obviously.
Speech Recognition Algorithm Based on Nonlinear Partition and GFCC Features

In order to speed up and enhance the robustness of speech recognition system, this paper proposes a speech recognition algorithm based on segment-level features of GFCC. In training and testing stage we use segment-level features of GFCC which is more robust to noise instead of the widely used MFCC features. Experiment results show that both the training time and test time decreased, while the accuracy of system was made to improve.
A Computational Model of Jetliner Taxiing

A Computational Model of Jetliner Taxiing The Next Generation Air Transportation System (NextGen) is transforming the way planes move on the ground as well as in the sky. Some of the proposed changes, such as automated scheduling algorithms to generate taxi clearances and speed-based taxi clearances, require thorough testing to ensure safety and reliability. Cognitive modeling is able to uniquely address these issues in a compromise between costly human-in-the-loop simulations and deterministic computer simulations. In this thesis, I present an ACT-R cognitive model to emulate pilot taxiing behavior in a dynamic environment, in order to predict human behavior in novel situations imposed by NextGen constraints. The model is validated by comparing taxi routes generated by the model to routes driven by real pilots while on the job.
Three hierarchical positions of deictic gesture in relation to spoken language: a multimodal interaction analysis

Taking the action, rather than the utterance or the text, as the unit of analysis, this article isolates different modes, investigating the interdependent relationships, illustrating that the visual mode of gestures can take up a hierarchically equal or a super-ordinate position in addition to the commonly understood sub-ordinate position in relation to the mode of spoken language. Building on McNeill, Birdwhistell, Eco, and Ekman and Friesen, and using a multimodal interaction analytical approach (Norris), I analyse in detail three separate everyday (inter)actions in which a deictic gesture is being performed and spoken language is used by the social actor performing the gesture. With these examples, I build on previous work in multimodal analysis of texts and multimodal interaction analysis, illustrating that the verbal is not necessarily more important than the visual (Kress and Van Leeuwen; Norris; Scollon), demonstrating that verbal and visual modes can be utilized together to (co)produce one message (Van Leeuwen), and showing that a mode utilized by a social actor producing a higher-level discourse structure hierarchically supersedes other modes in interaction (Norris).
Radio resource sharing in a GSM/GPRS network

An extensive performance evaluation is presented for a segregated and a hybrid radio resource sharing scheme in an integrated GSM/GPRS network, with a finite queue for data calls that cannot be served immediately upon arrival. Markov chain analysis is used to derive simple performance measures such as the blocking probabilities, expected channel utilization, and expected sojourn times. As a main result, a closed-form expression is derived for the expected sojourn time of a data call, conditional on its size, indicating that the sojourn time is proportional to the call size. Additionally, as a valuable intermediate result, the expected conditional sojourn time of a newly arriving data call finding the system in a given state is obtained, which can be fed back to the source as an indication of the expected transmission delay. Numerical results are presented to illustrate the performance of the radio resource sharing schemes.
Interactive specification and extraction of free form surfaces from the Visible Human

We present new interaction and visualization paradigms relying on free form surfaces for studying and exploring human anatomy. We propose an interface for building three-dimensional anatomical scenes incorporating 3D anatomical organ models, freely orientated slices and free form surfaces extracted from the Visible Human dataset. Compared with planar slices, free form surfaces allow to follow curved anatomic structures such as the aorta tree or the pelvis. In the present paper, we describe in detail 3D interaction techniques for creating free form surfaces. The interactive placement of surface boundary curves relies on the combination of an interactive slice navigator and of a 3D visualization interface integrated within a single java applet. Surface boundary curve control points are placed with the mouse at the desired locations within the selected slices. The corresponding boundary curves are displayed in the 3D visualization interface as fat 3D cubic spline curves, which provide immediate feedback. Boundary curves may be easily duplicated, translated and modified. The specified boundary curves are interpolated by Coons patches, yielding a perfectly smooth surface. That surface may be visualized in combination with semi-transparent organ models. It may also be flattened and shown in a separate window. The presented application is available online as a Java applet (http://visiblehuman.epfl.ch).
A digital watermark technique using morphological signal processing

Electronic watermarking technology has attracted substantial attention recently as a way to provide protection of copyright and prevention of unauthorized copying of digital content. This is realized by embedding copyright information within the content in a form not perceivable by humans. In this paper, an electronic watermarking method is proposed in which the watermarking information is embedded in binary patterns such as characters and figures by making use of morphological signal processing. This method is applied to alphabet characters to show the robustness of the proposed method to noise and printing. It is found possible to realize electronic watermarking by the proposed method for sentences in Western languages. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(6): 67–75, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10007
Modular multiplication in GF(p) for public-key cryptography

Modular multiplication forms the basis of modular exponentiation which is the core operation of the RSA cryptosystem. It is also present in many other cryptographic algorithms including those based on ECC and HECC. Hence, an efficient implementation of PKC relies on efficient implementation of modular multiplication. The paper presents a survey of most common algorithms for modular multiplication along with hardware architectures especially suitable for cryptographic applications in energy constrained environments. The motivation for studying low-power and areaefficient modular multiplication algorithms comes from enabling public-key security for ultra-low power devices that can perform under constrained environments like wireless sensor networks. Serial architectures for GF(p) are analyzed and presented. Finally proposed architectures are verified and compared according to the amount of power dissipated throughout the operation.
‘I'M THERE, BUT I MIGHT NOT WANT TO TALK TO YOU’

Synchronous communication technologies facilitate coordination, afford social accessibility, and allow for flexible contact at a distance. The authors investigate the temporal structure of social accessibility, individuals' definitions of public and private time, and how social accessibility and privacy are negotiated. They examine university students' social accessibility in instant messaging with a multi-method study utilizing surveys, focus groups and interviews. Four key characteristics were identified: predictability, downtime, online concurrent IM activities, and multitasking. Private and public time blur seamlessly in IM, with students making themselves available as a function of social relationships and context. Negotiation of social accessibility is complex and often evolves over the development of a relationship. Three strategies are discussed that help students manage their availability in IM. Despite the lack of formal rules in the use of these strategies, participants have shared understandings of these practices, and the meaning they convey to others.
Knowing and Teaching Elementary Mathematics Teachers' Understanding of Fundamental Mathematics in China and the United States

Author's Preface to the Anniversary Edition Series Editor's Introduction to the Anniversary Edition A Note about the Anniversary Edition Foreword Acknowledgments Introduction 1. Subtraction With Regrouping: Approaches To Teaching A Topic 2. Multidigit Number Multiplication: Dealing With Students' Mistakes 3. Generating Representations: Division By Fractions 4. Exploring New Knowledge: The Relationship Between Perimeter And Area 5. Teachers' Subject Matter Knowledge: Profound Understanding Of Fundamental Mathematics 6. Profound Understanding Of Fundamental Mathematics: When And How Is It Attained 7. Conclusion Appendix References New to the Anniversary Edition: Journal Article #1 New to the Anniversary Edition: Journal Article #2 Author Index Subject Index
Target Motion Analysis With Bearings And Frequencies Measurements

In TMA, when bearing and frequency measurements are concurrently processed, no more ownship manoeuver is necessary to make the target trajectory observable (as in conventionnal Bearing Only Tracking). Firstly, using a new linearization method for frequency measurements, we rigorously establish this result about observability. Secondly, we derive two fast Batch Maximum Likelihood Estimation algorithms whose statistical efficiency is proved (by computer simulations). Experimental results demonstrate that, if a few at sea necessary conditions are fulfilled and several frequencies are concurrently processed, DBT yields proper estimates of target trajectory : range accuracy better than 10% for a target at 20 km within 10 minutes.
An efficient inventory rationing strategy for systems with two demand classes and backordering

For systems with two customer classes and backordering, the formulae for the optimal dynamic critical levels in the literature are too complex to be extended to large values of critical levels. In this paper, an efficient method is introduced to obtain approximate closed-form expressions for the dynamic critical levels, even for large values. It is further verified by numerical results the accuracy of the approximate expressions, from which important managerial insights can be obtained.
Evolutionary algorithms with goal and priority information for multi-objective optimization

This paper presents a high performance multi-objective evolutionary algorithm with novel multiple-goal based Pareto cost assignment scheme that is capable of integrating any combination of goal and priority information. In addition, the algorithm is incorporated with a few advanced features for effective multi-objective optimization. These include the development of a dynamic sharing distance computation that is simple and adaptive to the on-line population distribution at each generation; an easy formation to deal with both soft and hard optimization constraints concurrently; a new way of convergence representation for multi-objective optimization based upon the concept of population domination; and a switching criteria preserved strategy to ensure stability and diversity of the multi-objective evolution. The effectiveness of the proposed algorithm is illustrated upon a benchmark optimization problem.
Classification of hand-written digits by a large scale neural network 'CombNET-II'

The authors describe the network structure and the learning procedure of CombNET-II together with experimental results on hand-written digit classification. CombNET-II uses the self-growing neural network learning procedure for training the stem network. After training the stem network, all input data are partitioned into category groups. Then, branch networks are trained for every category group. Backpropagation is utilized to train branch networks. Each branch neural network which is a three-layered hierarchical network has only a small number of connections so that it is easy to tune up. Therefore, CombNET-II has good convergency in the learning process. CombNET-II has been applied to the classification of 6349 printed Kanji characters and to the recognition of 1000 spoken words. CombNET-II correctly classified 99.4% of previously unseen hand-written digits. <<ETX>>
Making Computer Science Activities Accessible for the Languages and Cultures of Japan, Korea, China and Sweden

When teaching material is translated into another language, text-based examples can lose their significance, analogies may be meaningless in the local culture, and there may even be problems with physical access to the material. We consider principles for addressing these issues so that teaching examples can be made accessible to a diverse range of languages and cultures. We present a case study of the adaptation of a free resource for school outreach and lecture demonstrations (csunplugged.org), looking at issues encountered for Japanese, Korean, Chinese and Swedish translations. These represent a large range of languages, types of alphabets and cultures.
Data and Language Documentation

The role of data in language documentation is rather different from the way that data is traditionally treated in language description. For description, the main concern is the production of grammars and dictionaries whose primary audience is linguists (Himmelmann 1998, Woodbury 2003). In these products language data serves essentially as exemplification and support for the linguist’s analysis. It is typically presented as individual example sentences, often without source attribution, and often edited to remove ‘irrelevant material’. There may also be a ‘sample text’ or two in an appendix to the grammar. Language documentation, on the other hand, places data at the centre of its concerns. Woodbury (2003:39) proposes that:
Organizational culture, structure, technology infrastructure and knowledge sharing

Purpose – This paper aims to examine how organizational culture, structure and technology infrastructure influence knowledge sharing. Design/methodology/approach – This study is based on quantitative research, administered on 90 managerial staff in multinational corporations (MNCs) based in Malaysia. Findings – The paper explains the role of organizational cultural and structure on knowledge-sharing processes in MNCs, with the moderating effect of technology infrastructure. Learning and development, top management support and centralization are positively related to knowledge sharing, using technology infrastructure as a moderator. Research limitations/implications – The findings will help MNCs to create an appropriate environment of knowledge sharing. However, the research is limited to MNC’s in Penang, Malaysia, only. Furthermore, similar research can be extended to MNCs in other Asian countries with a larger sample which may bring more statistical power and, thereby, increases generalizability. Practic.
Architectural Design Pattern Representation For Communications-Based Train Control System (CBTCS)

Communications-Based Train Control System (CBTCS)[1] is a railway signaling system that makes use of the telecommunications between the train and track equipment for the traffic management. By using the CBTC system, the exact position of a train is known more accurately than with the traditional signaling systems. This results in a more efficient and safe way to manage the railway traffic. This paper attempts to identify the design problems with respect to reliability and safety factors in the CBTC system and suggests suitable Architectural design patterns [2] to solve the problems.
Association of Sex With Neurobehavioral Markers of Executive Function in 2-Year-Olds at High and Low Likelihood of Autism

Key Points Question Are executive function (EF) differences early in development associated with sex, familial likelihood for autism, and alterations in brain volume? Findings In this cohort study of 165 children aged 24 months at high and low familial likelihood for autism, girls and boys at high likelihood had significantly lower EF than girls and boys at low likelihood. Differences in brain-behavior associations across sex and familial likelihood for autism were also found. Meaning This pattern of findings suggests that EF impairments may aggregate in families with an autistic proband, particularly in girls.
Using Imagery as a Strategy to Enhance Students' Comprehension of Read Text

The main focus of this chapter is on the use of imagery as a srategy to improve students' comprehension of read text. The chapter is in two parts, the first section reports on the literature review and theoretical foundation of using imagery as a teaching tool to enhance readers' comprehension. The second section reports on a classroom teaching strategy that uses imagery to enhance readers' comprehension of read text.
Self-correcting Bayesian target tracking

Visual tracking, a building block for many applications, has challenges such as occlusions, illumination changes, background clutter and variable motion dynamics that may degrade the tracking performance and are likely to cause failures. In this thesis, we propose Track-EvaluateCorrect framework (self-correlation) for existing trackers in order to achieve a robust tracking. For a tracker in the framework, we embed an evaluation block to check the status of tracking quality and a correction block to avoid upcoming failures or to recover from failures. We present a generic representation and formulation of the self-correcting tracking for Bayesian trackers using a Dynamic Bayesian Network (DBN). The self-correcting tracking is done similarly to a selfaware system where parameters are tuned in the model or different models are fused or selected in a piece-wise way in order to deal with tracking challenges and failures. In the DBN model representation, the parameter tuning, fusion and model selection are done based on evaluation and correction variables that correspond to the evaluation and correction, respectively. The inferences of variables in the DBN model are used to explain the operation of self-correcting tracking. The specific contributions under the generic self-correcting framework are correlation-based selfcorrecting tracking for an extended object with model points and tracker-level fusion as described below. For improving the probabilistic tracking of extended object with a set of model points, we use Track-Evaluate-Correct framework in order to achieve self-correcting tracking. The framework combines the tracker with an on-line performance measure and a correction technique. We correlate model point trajectories to improve on-line the accuracy of a failed or an uncertain tracker. A model point tracker gets assistance from neighbouring trackers whenever degradation in its performance is detected using the on-line performance measure. The correction of the model point state is based on the correlation information from the states of other trackers. Partial Least Square regression is used to model the correlation of point tracker states from short windowed trajectories adaptively. Experimental results on data obtained from optical motion capture sys-