Miracle-dz/UniFAR-FTUs huggingface dataset:
A software framework for measuring efficiency.

The producers always aim at increasing the efficiency of their production process. However, the producers do not always succeed in optimizing their production. In the last years, the interest on Data Envelopment Analysis (DEA) as a powerful tool for measuring efficiency has increased. This is due to the large amount of data-sets available for description of the phenomena under study, and at the same time, to the need of timely and not costly information. The “Productivity Analysis with R” (PAR) framework establishes a user-friendly data envelopment analysis environment with special emphasis on variable selection and aggregation, and summarization and interpretation of the results. The starting point is the following R packages: DEA [Diaz-Martinez and Fernandez-Menendez, 2008] and FEAR [Wilson, 2007]. The DEA package performs some models of Data Envelopment Analysis presented in [Cooper et al., 2007]. FEAR is a software package for computing nonparametric efficiency estimates and testing hypotheses in frontier models. FEAR implements the bootstrap methods described in [Simar and Wilson, 2000]. PAR is a software framework using a variety of models estimating efficiency and providing results explanation functionality. PAR framework has been developed to distinguish between efficient and inefficient observations of performances and to advise explicitly for producers’ possibilities to optimize their production. PER framework offers several R functions for a reasonable interpretation of the data analysis results and text presentation of the information obtained. The output of the efficiency study with PAR software is self explanatory. We are applying PAR framework to estimate the efficiency of the agricultural system in Azores [Mendes et al., 2009]. All Azorean farms will be clustered into homogeneous groups according to their efficiency measurements to define clusters of “good” practices and cluster of “less good” practices. This makes PAR appropriate to support public policies in agriculture sector in Azores. This work has been partially supported by Regional Directorate for Science and Technology of Azores Government through the project M.2. 1.2/l/009/2008, "Productivity Analysis of Azorean Cattle-Breeding Farms with R Statistical Software".
Toward a Crowdsourcing Platform for Knowledge Base Construction

This paper proposes an approach to construct a knowledge base using crowdsourcing where the knowledge base is represented as linked data. With the crowdsourcing concept, the contents of a knowledge base are accumulated. We represent the process of knowledge base construction as a workflow. The ontology of the knowledge base’s target domain is utilized in creating and executing workflows. Applications to the construction of knowledge bases in the domains of e-learning content and multilingual frequently asked questions (FAQs) are described as examples. We discuss our proposed approach from the viewpoint of a crowdsourcing platform that facilitates the use of the crowdsourcing concept to construct a knowledge base, and show how the domain ontology can be made use of. Keywords-crowdsourcing; linked data; ontology; knowledge base; e-learning.
Challenges of annotation and analysis in computer-assisted language comparison: A case study on Burmish languages

Abstract The use of computational methods in comparative linguistics is growing in popularity. The increasing deployment of such methods draws into focus those areas in which they remain inadequate as well as those areas where classical approaches to language comparison are untransparent and inconsistent. In this paper we illustrate specific challenges which both computational and classical approaches encounter when studying South-East Asian languages. With the help of data from the Burmish language family we point to the challenges resulting from missing annotation standards and insufficient methods for analysis and we illustrate how to tackle these problems within a computer-assisted framework in which computational approaches are used to pre-analyse the data while linguists attend to the detailed analyses.
Improving Visualisation of Large Multi-Variate Datasets: New Hardware-Based Compression Algorithms and Rendering Techniques

Spectral computed tomography (CT) is a novel medical imaging technique that involves simultaneously counting photons at several energy levels of the x-ray spectrum to obtain a single multi-variate dataset. Visualisation of such data poses significant challenges due its extremely large size and the need for interactive performance for scientific and medical end-users. This thesis explores the properties of spectral CT datasets and presents two algorithms for GPU-accelerated real-time rendering from compressed spectral CT data formats. In addition, we describe an optimised implementation of a volume raycasting algorithm on modern GPU hardware, tailored to the visualisation of spectral CT data.
Digital literacy: Problems faced by telecenter users in Mexico

This article explores the problems that Internet users of marginalized populations face at telecenters (Internet public access sites). Using an exploratory case study method and gathering data from users and telecenter operators, we have explored users' limitations when using the Internet. To interpret the results, this article uses the digital literacy framework developed by Eshet-Alkai (2004). Findings reveal telecenter users are digitally illiterate in three skills: “branching ability” (ability to navigate through a nonlinear environment to find the desired information); “reproduction ability” (ability to analyze and synthesize the information retrieved); and “information ability” (ability to assess the quality of information). Additionally, there are technical barriers, such as Internet access speed, which influence user's behaviors when searching for information. Finally, the lack of knowledge of the English language, which is the predominant languages in the Internet, is a barrier that widens the digital divide. Based on these findings, this research suggests possible solutions to overcome user's problems. © 2007 Wiley Periodicals, Inc.
Multi-Way Windowed Streams - Joins Using Cluster

Recent years have witnessed an increasing interesting in data stream processing, such as network monitoring, the e-business, advertising system and etc. Join is applied to explore the correlation among the tuples from multiple streams. In this paper, we present a general method named Distributed Streams Join (DSJ) to process multi-way windowed streams θ -joins using a shared-nothing cluster. DSJ contains a distribution method named Time-Slice Distribution Method (TDM) and a join method named Transfer Join Method (TJM). Different from previous work, DSJ can (1) process multi-way θ -joins under arbitrary predicates; (2) preserve the integrity of results and load balance while distributing tuples to different nodes for parallel joining; (3) carry out the join operation in a local optimum order according to the histograms maintained in a real-time way. We have built DSJ on our own stream processing cluster to deal with multi-way streams joins and the experiments demonstrate that our DSJ can not only guarantee the load balance among all the computing nodes but also improve the throughput effectively.
An Efficient Ensemble Management System Based on Clustering

In this paper, we propose the clustering-based ensemble management system which extracts new data patterns from the input streaming data by using clustering and generates new classification models only when a certain amount of data has been collected in a cluster. The clustering-based ensemble management system can reduce the number of the data labeling and keep the accuracy of the existing ensemble. The clustering-based ensemble management system collects similar patterned data from the input streaming data for building a cluster. The clustering-based ensemble management system performs the data labeling for the each cluster only when a certain amount of data has been collected in the cluster. The data labeling by experts goes on to generate the new classification model to be added to the ensemble. The clustering-based ensemble management system applies the K-NN technique to classification model units in order to keep the accuracy of the existing system while it uses a small amount of data. The efficiency of the clustering-based ensemble management system proposed in this paper is shown by the simulated results for benchmarks comparing with the existing ensemble techniques.
Vision as Inference in a Hierarchical Markov Network

Cortical visual processing involves both bottom­up propagation of perceptual stimuli and modulation by top­down signals. Lee and Mumford (2003) suggested that the visual processing stream from the LGN via V1, V2 and V4 to IT might perform Bayesian inference within an undirected Markov chain. A cortical layer x i (say, V1) computes its activity P(x i  x k  i) = P(x i  x i­1 , x i+1) = P(x i­1  x i) P(x i  x i+1)  Z i , that is, a posterior probability distribution given bottom­up input from x i­1 (LGN), under top­down priors x i+1 (V2). The parameters of the Markov network are specified via pairwise compatibility potentials , where P(x i  x i­1 , x i+1) = (x i­1 , x i) (x i , x i+1)  Z i. These potentials must be learned from experience with the world; Lee and Mumford (2003) do not comment on how this might be done. A crucial aspect of this model is that ambiguities at low levels should persist and propagate upwards until they can be resolved by integrating larger­scale evidence or top­down expectations. As a biologically plausible implementation of inference with arbitrary, possibly multi­modal probability densities, Lee and Mumford (2003) suggest belief propagation using particle representations. Moreover, they provide a wealth of neurophysiological and psychophysical evidence for such a computational model. We are currently developing representations and methods for visual inference that constitute, at least at the vague level of detail given above, a working computer implementation of central aspects of Lee and Mumford's model. Without making explicit reference to specific cortical layers, our approach is based on a Markov network such as the didactic example shown on the right, with vertices arranged in layers corresponding to those of Lee and Mumford's. Each vertex is a random variable representing the spatial probability density of the presence of a feature. At level 0, a primitive feature x 0,j is the spatial probability density of a given type of locally observable feature. It is inferred from local image appearance y j via its observation potential (x j , y j). At higher levels, a compound feature (recursively) represents the presence of both of its children, and the compatibility potentials  represent pairwise spatial relationships. For example, in the figure, feature x 3,1 represents the spatial probability density of features x 2,1 and x …
A comparison of stream synthesis methods for fast power simulation

In this paper, we study and compare the basic features and the capabilities of two existing techniques for stream synthesis: the spectral approach and the symbolic approach. The relative efficiency and flexibility of the two methods is further assessed by a comprehensive experimentation we have carried out on a large number of streams characterized by different statistical profiles. Data concerning the comparison of spectral synthesis to simple and stratified random sampling are also provided for the sake of completeness.
What is Visualization

From its beginnings in the 18th century until the end of the 20 th century, information visualisation used points, lines, curves and simple geometric shapes to stand in for objects and relations between them. This article discusses a new visualization method that can be called “direct visualisation” (or “media visualisation”): creating new visual representations from the visual media objects (images, video) or their parts. This method is particularly relevant for humanities, media studies and cultural institutions. Using the actual visual artefacts in visualisation as opposed to representing them by graphical primitives helps the researcher to understand meaning and/or cause behind the patterns she may observe, as well as discover additional patterns. ( pdf for download -- right click or control click to save)
The Influence of Internet Advertising and Electronic Word of Mouth on Consumer Perceptions and Intention: Some Evidence from Online Group Buying

This article presents a theoretical link among information (Internet advertising [IA] and electronic word of mouth [EWOM]), consumer perceptions (perceived value [PV] and perceived risk [PR]), and consumer intentions in a new mode of electronic commerce—namely, online group buying (OGB). A quantitative study was carried out and data were obtained through a mall-intercept systematic sampling distribution of questionnaires. The results indicate that IA and EWOM have a positive effect on consumers' PV and PR of using OGB sites, which in turn have a significant influence on their intentions to shop at OGB sites. A significant relationship also emerges between IA and EWOM. The article concludes with implications of the findings and future research directions.
Compressed Sensing SAR Imaging for Wideband Linear Frequency Modulated Signal

Compressed sensing (CS) theory provides a new chance to reduce the data rate of high resolution radar imaging system. A novel SAR imaging method based on compressed sensing for wideband linear frequency modulated (LFM) signal is proposed in this paper. The radar data compression is conducted in range and azimuth dimension respectively. A new sparse matrix based on stretch processing and Fourier transform is constructed and then applied to obtain the range profile information. Based on the range compression, the phase compensation and the azimuth compression are conducted. The method incorporates coherent mixing processing in sparse matrix, consequently simplifies hardware design of radar system. Simulation results show the effectiveness of the method in reducing data rate and suppressing sidelobe compared with conventional stretch processing (SP) method. It also turns out that the proposed method is robust in the case of serious noise.
An Android malware detection system based on machine learning

The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.
PENGEMBANGAN MODUL DENGAN MODEL PROBLEM SOLVING BERBANTU LKS DAN PUZZQUARE UNTUK MENINGKATKAN PRESTASI BELAJAR PADA MATERI SEGIEMPAT SMP

Tujuan penelitian ini adalah untuk menciptakan suatu bahan ajar berupa modul dengan model problem solving berbantuan LKS dan Puzzquare pada pokok bahasan segiempat sehingga menghasilkan media yang layak dan efektif digunakan selama pembelajaran berlangsung serta diharapkan dapat meningkatkan prestasi belajar siswa. Jenis penelitian yang digunakan adalah Research and Development. Penelitian ini dilakukan dengan dua tahap. Tahap pertama untuk pengembangan produk menggunakan modifikasi model ADDIE dan tahap kedua untuk menguji keefektifan produk melalui tes uji coba dengan desain Posttest-Only Control Desain. Kegiatan analisis data meliputi analisis uji validasi, uji normalitas, uji homogenitas dan uji hipotesis. Dari penghitungan diperoleh rata-rata kelas eksperimen = 77,63 dan kelas kontrol = 70,23. Kesimpulan dari penelitian pengembangan ini adalah bahan ajar matematika dengan model problem solving berbantuan LKS dan Puzzquare pada pokok bahasan segiempat (Luas bangun segiempat) yang dikembangkan ini layak dan efektif.
Toward Multi-Approach Model for Semi-Automating a Data Warehousedesign from an Ontology

The proliferation of projects that are part of the semantic Web is truly impressive. In fact, ontologies become increasingly present in information systems, they constitute great data sources that arouse the interest of being analyzed. Ontologies are used for standardizing, structuring and formalizing the Web, Web Service, E-learning systems, and other fields. Regarding multidimensional approaches, researches in this field have focused on direct Data Warehouse conception from an ontology, which do not integrate the intervention of the expert in this process. In this case, the transformations are global and not very customizable; it can reproduce the inherent defects from the data sources into the resulting data warehouse. In this paper, we propose a new multi-approach model based on the coupling of relational database design approaches from an ontology with Data Warehouse design approaches from a relational database. Our model is semi-automatic allowing Data Warehouse design from an ontology by giving the designer more ability to intervene in this process and closely control the transformations. To assess the usefulness of our approach, we evaluated it by applying it on an example case study. The results of the example show that our approach is more accurate in terms of useful data filtering and adaptation of the multidimensional model to the end-users business-needs.
Embedded System Memory Allocator Optimization Using Dynamic Code Generation

, CEA, LIST, LaSTRE laboratoryEmbedded systems often exhibit memory organizations far from those of general purpose computing sys-tems. Distributed and private memories, absence of address virtualization are frequent burdens of systemand application developer. Thus, dynamic memory allocation in the context of embedded systems is a difﬁcultissue. Moreover, with the increasing amount of parallelism in embedded systems architectures and program,an additional stress is put on dynamic memory allocation, due to the out-of-order nature of parallel appli-cations. This paper presents a ﬂexible memory allocator able to handle complex memory organizations ofembedded systems. The memory allocator leverages dynamic code generation so that ﬂexibility is not at theexpense of performances. In fact, we show that combining dynamic code generation and runtime adaptation,can give a 56% speedup on memory allocator’s allocation and release operations.Categories and Subject Descriptors: D.4. 2 [
A Novel Proportional Window based Dyamic Bandwidth Allocation Algorithm in Optical Network

Dynamic bandwidth allocation (DBA) algorithm for the passive optical networks (PON) is a key issue for network planner. There are always various constraints during the bandwidth allocation process, so that it can be regarded as a multi-object mathematic problem. In this paper, we have proposed a novel proportional window based bandwidth allocation algorithm that considering service QoS requirement. We mainly arrange more bandwidth for higher level service. Finally, we establish a simulation platform to verify the proposed approach, and the result show that it outperforms traditional static bandwidth allocation approach and the mostly used interleaved polling with adaptive cycle time approach especially for the higher grade service. responsible for the bandwidth requirement collection and the overall bandwidth assignment for each ONU. As each ONU is separated, there will not be any overlapped bandwidth 0 . The primary node is a common node with specific function, while it is the controlling algorithm manager. The bandwidth allocation and release shall be management by the primary node, so that the released bandwidth can be used by extra service flow. G. Kramer has proposed a bandwidth allocation algorithm named Interleaved Polling with Adaptive Cycle Time (IPACT), and it is now widely used in the centralized managed network 0 . The bandwidth usage information is transmitted through a polling approach to solve the fixed time slot shortage. The OLT works in round-robin mode and send polling command to each ONU separately. The ONU receive data in the authorized window. The buffer length in ONU is controlled by OLT, and it takes direct effect on the next round bandwidth allocation. The global round-robin time is maintained by the central OLT, during the allocating process, the consecutive frame is separated with a guaranteed time period. The data frame is not constraint whether in a sequential manner or a synchronized transfer scheme. The GPON network supports a mix of services with different bandwidth requirement, DBA algorithm is used to improve the resource utilization. In this paper, we have proposed a novel proportional window based dynamic bandwidth allocation algorithm (PWDBA) to solve the fairness of each service flow, and keep the priority at a reasonable range at the same time. Also a simulation platform is constructed to verify the performance of proposed approach.
Proposal of the Improved Touch Panel Switch Having High Flexibility

The advanced touch panel was proposed recently. It is very flexible and has simple mechanism. The shape can be arbitrarily changed such as curved surface as well as plane, and the panel number can be increased or decreased arbitrarily. An entire surface of any shape can be used for an input device, and it can be used for a wearable input device by being pasted to a body. However, it is sensitive to noise because of the simple mechanism. It is affected by the environmental noise, a change of the capacitance, and skin effect. Therefore many panels cannot be connected. The new method has been proposed. In this model, the precision is better than a conventional model. However, this is only a simulation result. For practical use, it has some problems. (1) Use of many number of connection lines, and many number of electrical parts. (2) It does not confirm the validity by the method of the real circuit. The new method is proposed in this study to improve them. By this method, the following results are obtained. (1) Decrement of the number of connection lines and the number of electrical parts. Therefore, the proposed new circuit system becomes the simple structure. (2) Confirmation of the validity of the proposed method by the real circuit. The panel total number increases than a conventional method. The maximum panel total number is about 300.