Miracle-dz/UniFAR-FTUs huggingface dataset:
Http-Mplex: an Application Layer Multiplexing Protocol for the Hypertext Transfer Protocol (HTTP)

The Hypertext Transfer Protocol (HTTP) has progressively increased in sophistication and scope somewhat in parallel to growth of the Internet. HTTPs first incarnation allowed for only one transaction (request and retrieval) per connection, incurring a high overhead penalty for repetitive and laborious Transmission Control Protocol (TCP) connection management. Progressively HTTP was optimised through the inclusion of pipelined and persistent connections1,2 to improve HTTPs non-optimal use of TCP. We introduce an application layer multiplexing adaptation to HTTP 1.1; HTTP-MPLEX for compressing GET requests and multiplexing responses. Our protocol is backwards compatible. It minimises verbose request header overhead, reducing the need for multiple server-client connections and allows prioritised object delivery with a companion response encoding scheme.
Parallelization of Apriori algorithm using Charm++ library

This paper deals with the problem of adapting sequential frequent item sets mining algorithm to parallel processing. The original Bodon's Apriori algorithm has been partitioned into loosely coupled tasks and prepared to be executed on several computation nodes using Charm++ library. Variety of optimization methods have been proposed and successfully implemented in parallel environment. The work provides enhancements to achieve good efficiency during parallelization of existing solutions, e.g.: how to organize communication between tasks. The presented approach has been illustrated with many experiments and measurements performed on parallelized algorithm.
Symbolic document image compression based on pattern matching techniques

In this paper, a novel compression algorithm for Chinese document images is proposed. Initially, documents are segmented into readable components such as characters and punctuation marks. Similar patterns within the text are found by shape context matching and grouped to form a set of prototype symbols. Text redundancies can be removed by replacing repeated symbols by their corresponding prototype symbols. To keep the compression visually lossless, we use a multi-stage symbol clustering procedure to group similar symbols and to ensure that there is no visible error in the decompressed image. In the encoding phase, the resulting data streams are encoded by adaptive arithmetic coding. Our results show that the average compression ratio is better than the international standard JBIG2 and the compressed form of a document image is suitable for a content-based keyword searching operation.
Link Reliability Based Greedy Perimeter Stateless Routing for Vehicular Ad Hoc Networks

We propose an enhancement for the well-known greedy perimeter stateless routing (GPSR) protocol for vehicular ad hoc networks (VANETs), which exploits information about link reliability when one-hop vehicles are chosen for forwarding a data packet. In the proposed modified routing scheme, a tagged vehicle will select its one-hop forwarding vehicle based on reliability of the corresponding communication link. We define link reliability as the probability that a direct link among a pair of neighbour vehicles will remain alive for a finite time interval. We present a model for computing link reliability and use this model for the design of reliability based GPSR. The proposed protocol ensures that links with reliability factor greater than a given threshold alone are selected, when constructing a route from source to destination. The modified routing scheme shows significant improvement over the conventional GPSR protocol in terms of packet delivery ratio and throughput. We provide simulation results to justify the claim.
On the asymptotic optimality of algorithms for the flow shop problem with release dates

We consider the nonpermutation flow shop problem with release dates, with the objective of minimizing the sum of the weighted completion times on the final machine. Since the problem is NP‐hard, we focus on the analysis of the performance of several approximation algorithms, all of which are related to the classical Weighted Shortest Processing Time Among Available Jobs heuristic. In particular, we perform a probabilistic analysis and prove that two online heuristics and one offline heuristic are asymptotically optimal. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.
An Enhanced Community Detection Algorithm Using Modularity in Large Networks

In this paper, an improved community detection algorithm based on the modularity is proposed. The existing algorithm does not consider the information that the nodes have in checking the possible modularity increase, hence the computation may be inefficient. The proposed algorithm computes the node degree (weight) and sorts them in non-increasing order. By checking the possible modularity value increase for the nodes in the nonincreasing order of node weights, the algorithm finds the final solution more quickly than the existing algorithm does. Through the computational experiments, it is shown that the proposed algorithm finds a modularity as good as the existing algorithm obtains.
Long sequence analysis of human motion using eigenvector decomposition

The work described in this paper takes place in the context of an attempt to develop a clearer understanding of what is meant by task oriented visual processing. The traditional separation of task and sensory processing within computer vision is something which has lead to an overemphasis on representation, modularity, and the single functionality of artificial vision systems. These issues are briefly discussed here in the context that the Karhunen Loeve Transform (KLT) is an algorithmic approach which allows us to begin to move away from a preconceived view of what vision is, how it works, and what it does. We summarize the mathematical background and basic operation of the KL coding procedure. We examine the application of the eigenvector decomposition to the analysis of human motion over several posture cycles of a walking sequence. The approach is used to examine the potential for recognition through motion, for posture description and for natural motion reconstruction processes. Various classification and analysis techniques, including artificial neural networks, the Fourier transform, and heuristic operations are used to extract the information available about the different cues investigated.
’Reflective conversation’ in the virtual language classroom

In the Open University of the United Kingdom, the principle that distance language learners should be encouraged to reflect on their own learning has traditionally been central to the design of conventional (i.e., print, audio, and video) course materials. However, since computermediated communication (CMC) technologies have created the possibility for learners to interact with each other and with teachers and native speakers--thus providing opportunities for practice and intrinsic feedback on communicative competence--an issue has risen around the continuing role of conscious reflection. Is conscious reflection, in fact, still necessary in a more interactive learning environment? We argue here that it is, and that a challenge is facing the developers of the virtual language classroom to combine the processes of conscious reflection with those of spontaneous interaction. In our view, the medium of asynchronous conferencing is particularly well suited to such a combination as it is flexible with regard to place and pace, and able to support both monologueand conversation-like forms of written language exchange. Here we examine the kinds of reflectiveness and interactivity that are mediated through such exchanges, and discuss their value for learning. We examine some examples of CMC exchanges generated during an online course in French, and propose a pedagogy which focuses on the generation of what we are calling "reflective conversation," that is, computer-mediated asynchronous discussion around language topics and language-learning issues.
Robust digital signal compression and modulation exploiting the advantages of analog communications

A visual communication system for power- and bandwidth-limited channels, which gracefully degrades as the channel deteriorates is presented. The system is based on joint optimization of subband coding and 81-QAM through robust mappings between the source coder space and the modulation space. A novel definition for measuring system efficiency, based on a comparison to pure analog transmission, is used to evaluate the proposed system. The ideas presented in this paper can be used for terrestrial digital TV broadcasting or mobile communications.
Obesity and Cannabis Use: Results From 2 Representative National Surveys

The role of cannabis and endocannabinoids in appetite regulation has been extensively studied, but the association of cannabis use with weight in the general population is not known. The authors used data from 2 representative epidemiologic studies of US adults aged 18 years or older, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; 2001-2002) and the National Comorbidity Survey-Replication (NCS-R; 2001-2003), to estimate the prevalence of obesity as a function of cannabis use. The adjusted prevalences of obesity in the NESARC and the NCS-R were 22.0% and 25.3%, respectively, among participants reporting no use of cannabis in the past 12 months and 14.3% and 17.2%, respectively, among participants reporting the use of cannabis at least 3 days per week. These differences were not accounted for by tobacco smoking status. Additionally, after adjustment for sex and age, the use of cannabis was associated with body mass index differences in both samples. The authors conclude that the prevalence of obesity is lower in cannabis users than in nonusers.
Research on the Security of Massive Data Storage

Storage of massive data is receiving more and more attention in recent years and it has been widely used in many fields, meanwhile, its security is facing a great challenge, too. In this paper, we propose a distributed authentication protocol to ensure the security of massive data storage, this method utilizes Reed-Solomon codes to ensure the availability and reliability of data. In addition, it makes use of the Sobol sequences token to pre-calculate and verify the integrity of data. The proposed method can not only verify the correctness of the storage, but also recognize the server which executes wrong operation.
Automatic generation of pictorial transcripts of video programs

An automatic authoring system for the generation of pictorial transcripts of video programs which are accompanied by closed caption information is presented. A number of key frames, each of which represents the visual information in a segment of the video (i.e., a scene), are selected automatically by performing a content-based sampling of the video program. The textual information is recovered from the closed caption signal and is initially segmented based on its implied temporal relationship with the video segments. The text segmentation boundaries are then adjusted, based on lexical analysis and/or caption control information, to account for synchronization errors due to possible delays in the detection of scene boundaries or the transmission of the caption information. The closed caption text is further refined through linguistic processing for conversion to lower- case with correct capitalization. The key frames and the related text generate a compact multimedia presentation of the contents of the video program which lends itself to efficient storage and transmission. This compact representation can be viewed on a computer screen, or used to generate the input to a commercial text processing package to generate a printed version of the program.
Generalization and learning in Volterra and radial basis function networks: a theoretical analysis

The pattern classification and generalization ability of the class of generalized single-layer networks (GSLNs) using techniques from computational learning theory is analyzed. The authors derive necessary and sufficient conditions on the number of training examples required in order to guarantee a particular generalization performance and compare the bounds obtained to those available for (multilayer) feedforward networks of linear threshold elements (LTEs). This allows one to show that, on the basis of currently available bounds, the sufficient number of training examples for GSLNs tends to be considerably less than for feedforward networks of LTEs with the same number of weights. It is also shown that the use of self-structuring techniques for GSLNs may reduce the number of training examples sufficient for good generalization. An explanation for the fact that GSLNs can require a relatively large number of weights is given. <<ETX>>
Reexamining the Role of Maps in Geographic Education: Images, Analysis, and Evaluation

The ability to make, understand, and use maps is essential for anyone trying to think about the world around them. Children's failure to make and use maps in a meaningful way contributes to the lack of geographic awareness across the country. The "linguistic map" (a graphic representation of the mental connections between words, sensory images, abstract concepts, and value judgments) is proposed as a model for evaluating maps used in educational materials. An evaluation of social studies textbooks found that the maps failed to promote learning at all three levels proposed by the linguistic model: concrete images, abstract analysis, and value evaluation. Problems with the textbook maps are examined and suggestions developed for using maps in educational materials.
Digital Design of a BCD Multiplication Scheme

This paper presents a new scheme for BCD multiplication. This new scheme features a completely carry-free multiplication. The parallel structure of the new algorithm is investigated. This fast architecture was derived by looking at multiplication in a fresh perspective. A carry is able to be computed without performing multiplication on any of the previous bits. The new approach is expected to have a lot of potential contributions to digital design.
Three languages, one ECHO: Cognate effects in trilingual word recognition

Research on bilingual word recognition suggests that lexical access is non-selective with respect to language, i.e., that word representations of both languages become active during recognition. One piece of evidence is that bilinguals recognise cognates (words that are identical or similar in form and meaning in two languages) faster than non-cognates. The present study used cognates to investigate whether the non-selective access hypothesis holds also for trilinguals and three languages. Dutch-English-German trilinguals carried out a lexical decision task in their third language (German). The word materials included purely German control words, “double” cognates that overlapped in Dutch and German, but not in English, and “triple” cognates with the same form and meaning in Dutch, German, and English. Faster RTs were found for Dutch-German cognates than for control words, but additionally, “triple” cognates were processed even faster than “double” cognates. The “triple” cognate effect was not influenced by whether the participants had previously read an English text. A control experiment with German monolinguals confirmed that the effect was not an artifact of uncontrolled stimulus characteristics. Thus, independent of context, both the native language and another foreign non-target language influenced target language comprehension in trilinguals. This supports a view of language non-selective access implying all languages known to an individual may affect word activation and recognition.
Model-Building and the Use of Computer Simulation of Dynamic Systems

The Systems Thinking and Curriculum Innovation (STACI) Project is a multi-year research effort intended to examine the cognitive impact of learning from a systems thinking approach to instruction and from using simulation-modeling software. Systems thinking is an analytic problem solving tool that can be integrated into courses to supplement and enhance instruction in a variety of content areas. The purpose of the study is to test the potentials and effects of using the technology-based approach in secondary school curricula to teach content-specific knowledge as well as general problem solving skills. The research focuses on the effects of introducing a software environment that enables students to learn from and make concrete multiple representations of scientific, mathematical, and historical phenomena.
Two-Stage Routing: A Coexistence Method for Content Centric Network and Traditional End-to-End Network

After a deep analysis on two aspects of challenges in the traditional network architecture, for the challenge of repetitious access and transmission of hot topic information, content store module supporting hot topic awareness is added in the access convergence routers; for the challenge of low efficiency of quality of service guarantee, connection oriented virtual circuit –omnibus circuit is proposed. The whole structure and specific operation of two-stage routing are designed, the system advantages and feasibility is analyzed at last.
Une thérapie prometteuse contre les souches ultrarésistantes de M. tuberculosis

> La tuberculose est une maladie humaine persistante et ré-émergente causée par la bactérie Mycobacterium tuberculosis isolée par Koch en 1882. Plus de 2 milliards d’individus sont contaminés par le bacille, soit un tiers de la population mondiale, et, après une primo-infection, chez une personne contaminée sur dix la maladie évoluera vers une forme active. La mortalité liée à cette pathologie reste élevée : entre 1,6 et 2 millions de décès par an, malgré l’introduction d’une thérapie efficace dans les années 1960 [1]. Pour la tuberculose pulmonaire, cette multithérapie associe le plus souvent quatre antibiotiques : la rifampicine (RIF), l’isoniazide (INH), la pyrazinamide (PZ) et l’éthambutol (ETH) sont administrés pendant deux mois, puis RIF et INH pendant quatre mois. La durée de ce traitement est justifiée par la capacité que possède la bactérie à entrer en phase latente ou dormante et à échapper ainsi à l’action des antibiotiques qui sont plus efficaces sur les bacilles en phase de réplication active [2]. Cette thérapie pose souvent des problèmes d’observance qui ont favorisé l’émergence de souches multirésistantes, principalement dans les pays en voie de développement. La multirésistance est définie par la résistance aux antibiotiques de première ligne (RIF + INH). Plus inquiétante est l’émergence de souches ultrarésistantes, définies par la résistance aux antibiotiques de première et de seconde ligne (RIF + INH + une fluoroquinolone + amikacine ou kanamycine ou capréomycine) [3]. Dans une étude réalisée en Afrique du Sud sur 53 patients chez lesquels le diagnostic de tuberculose ultrarésistante a été posé, 52 sont morts, la durée de vie moyenne étant de 16 jours après le diagnostic [4]. En 2007, dans les républiques de l’exUnion Soviétique, on estime que 20 % des cas de tuberculose impliquent des souches multirésistantes, dont 2 % de souches ultrarésistantes [1]. La menace liée à l’augmentation du nombre de foyers infectieux ainsi que l’impasse thérapeutique secondaire à l’émergence de souches ultrarésistantes ont fait de l’éradication de M. tuberculosis un enjeu de santé mondial. L’urgence de trouver de nouvelles thérapies [13] se heurte à la lenteur de mise sur le marché de nouveaux antituberculeux, et au coût prohibitif des nouvelles molécules pour les pays en voie de développement.
Research on the Detection Method of the Malicious Attacks on Campus Network

The problem of malicious attacks detection on campus network is studied to improve the accuracy of detection. When detecting malicious attacks on campus network, a conventional manner is usually conducted in malicious attack detection of campus network. If a malicious signature is mutated into a new feature, the conventional detection method cannot recognize the new malicious signature, resulting in a relative low detection accuracy rate of malicious attacks. To avoid these problems, in this paper, the malicious attacks detection method for campus network based on support vector machine algorithm is proposed. The plane of support vector machine classification is constructed, to complete the malicious attacks detection of campus network. Experiments show that this approach can improve the accuracy rate of the malicious attack detection, and achieve satisfactory results.
Modeling of a High Force Density Fishbone Shaped Electrostatic Comb Drive Microactuator

This paper presents the design and evaluation of a high force density fishbone shaped electrostatic comb drive actuator. This comb drive actuator has a branched structure similar to a fishbone, which is intended to increase the capacitance of the electrodes and hence increase the electrostatic actuation force. Two-dimensional finite element analysis was used to simulate the motion of the fishbone shaped electrostatic comb drive actuator and compared against the performance of a straight sided electrostatic comb drive actuator. Performances of both designs are evaluated by comparison of displacement and electrostatic force. For both cases, the active area and the minimum gap distance between the two electrodes were constant. An active area of 800 × 300 μm, which contained 16 fingers of fishbone shaped actuators and 40 fingers of straight sided actuators, respectively, was used. Through simulation, improvement of drive force of the fishbone shaped electrostatic comb driver is approximately 485% higher than conventional electrostatic comb driver. These results indicate that the fishbone actuator design provides good potential for applications as high force density electrostatic microactuator in MEMS systems.
Visual based retrieval systems and web mining---introduction

A current trend in the proliferation of computing technology throughout society is development, manipulation, and storage of multimedia information. Images, video, and sound are now considered first-class data types alongside traditional alphanumeric textual information, even in a traditional setting such as the global marketplace. The ability to store, retrieve, and analyze multimedia data for relevant and profitable information is a daunting task, given the perceptual nature of the information and the massive amounts that are continuously created. The latest technology that addresses these and other problems is Web mining and image retrieval. This research area uses sophisticated statistical analysis and machine-learning techniques to uncover patterns and relationships in multimedia and traditional databases that traditional methods might not discover. Web mining and image retrieval techniques discover patterns and relationships by constructing predictive and descriptive computational models. Predictive computational models, such as supervised neural networks, use data with known results to develop models that can be used explicitly to predict values for different data. Alternatively, descriptive computational models are implicitly constructed and describe patterns contained in the database. A relatively newer aspect of information/decision fusion problems in emerging applications is the requirement of fast and efficient computation of information fusion operations. Although recent advances in computing systems and methods enable us to execute vision-based retrieval algorithms faster, it is necessary to design these systems to be computationally efficient to have greater impact in practical problems given the amount of visual data generated and stored. This special issue is composed of articles focused on Web content mining, artificial neural networks as tools for image retrieval, content-based image retrieval systems, and personalizing the Web browsing experience using media agents.