Miracle-dz/UniFAR-FTUs huggingface dataset:
Indexing Structured Documents with Suffix Arrays

Path indexes based on suffix trees have shown to be highly efficient structures when dealing with digital collection that consists of structured documents, since they provide a fast response to queries including structural requirements. Nevertheless, when the collection consists of highly heterogeneous documents, suffix trees may be too memory demanding. In such cases, the use of a suffix array as the underlying data storage permits a considerable reduction in space requirements, partially because suffix arrays are a remarkably light data structure and partially because they do not store redundant information regarding the textual content. We describe how a suffix array can be used as the data structure which stores the structural index in a retrieval system and provides a virtual index of all sub paths in the digital collection. We also show how an auxiliary ternary search tree can accelerate the resolution of structural queries with only a marginal increase in memory usage.
Performance of question‐based vs keyword‐based search engines and effect of web user characteristics on search engine performance

Purpose – The purpose of this study is to investigate whether question and keyword‐format queries are more successfully processed by search engines encouraging answers to searching and keyword‐format querying, respectively. This study aims to investigate whether web user characteristics and choice of search engine affects the relevancy scores and precision of the results.Design/methodology/approach – The results of two search engines, Google and AskJeeves, were compared for question and keyword‐format queries. It was observed that AskJeeves was slightly more successful in processing question‐format queries, but this finding was not statistically supported. However, Google provided results on keyword‐format queries and the entire set of queries, which were statistically superior to those of AskJeeves.Findings – Analysis of variance (ANOVA) showed that the age of web user is not as affective on the relevancy score and precision of results as other factors. Interactions of the main factors were also affectiv.
Viewing of large volumes on a (thin) client - server based configuration

This thesis gives a description of the design and prototype implementation of an n-tier thin clientserver based system for rendering medical images. There is aimed for dynamic system that uses each involved (render)machine as optimal as possible. To be able to expand the system to a system that uses more than one render machine, a special machine called scheduler is used to distribute “jobs” to a specific machine. To be able to do more than one rendering at a time at a machine, a stateless component called Render Server is introduced to handle jobs consecutively. A scheduling algorithm is presented that takes advantage of availability of a medical dataset and schedules each job within a deadline if the system load admits it. Scheduling is based on job time estimation and is restricted by the available resources. However, the run time of jobs cannot be predicted before run-time and therefore the “sarsa” reinforcement learning algorithm is used to optimize estimations. Furthermore a Database Server is used to be able to introduce medical data sets in the system. Despite some shortcomings, the system can be considered to be the first good step towards a fully functional client-server system for rendering medical images.
Predicting global solar radiation using an artificial neural network single-parameter model

We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19°N, a longitude of 32.34°E, and an altitude of 1200m above sea level. The five-year data was split into two parts in 2003-2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m2 and a root mean square error of 0.521 MJ/m2. The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours).
AUTOMATIC INTERPRETATION OF ENGLISH SPEECH

Automatic interpretation of human speech into different languages is difficult as it involves problems of speech recognition and synthesis as well as machine translation. Although several hand-held devices have been developed to provide pre-recorded spoken phrases, only a few are capable of uttering phrases with unrestricted dialog, and these are often limited to a few languages. This paper describes a new, portable prototype incorporating readily available free or commercial software that interprets between English and 20 different languages. A test of the system with German and Spanish shows relatively accurate results.
A model for characterizing the scalability of distributed systems

Scalability is an important issue in the construction of distributed systems. A number of theoretical and experimental studies have been made on scalability of distributed systems. However, they have been either studies on specific technologies or have studied scalability in isolation. The main conjecture of our work is that scalability must be perceived along with the related issues of availability, synchronization and consistency. In this context, we propose a scalability model which characterizes scalability as being dependent on these factors as well as the workload and faultload. The model is generic and can be used to compare scalability of similar systems. We illustrate this by a comparison between NFS and AFS, two well known distributed file systems. The model is also useful in identifying scalability bottlenecks in distributed systems. We have applied the model to optimize Virat, a wide-area shared object space that we have built.
Multi-core Processing - Advantages and Challenges

Today, multicore processors dominate server, desktop and notebook computer's market. It is clear that multicore processors will be with us for the foreseeable future; there seems to be no alternative way to provide substantial increases of microprocessor performance in the coming years. While processors with a few cores are common today, the number of cores is projected to grow as we enter the era of many-core computing. This paper will review this trend towards multicore processing, advantages and challenges that accompany it.
Improving Word Alignment for Statistical Machine Translation Based on Constraints

Word alignment is an important and fundamental task for building a statistical machine translation (SMT) system. However, obtaining word-level alignments in parallel corpora with high accuracy is still a challenge. In this paper, we propose a new method, which is based on constraint approach, to improve the quality of word alignment. Our experiments show that using constraints for the parameter estimation of the IBM models reduces the alignment error rate down to 7.26% and increases the BLEU score to 5%, in the case of translation from English to Vietnamese.
The econometric analysis of time series

This new edition of A.C. Harvey's clearly written, upper-level text has been revised and several sections have been completely rewritten. There is new material on a number of topics, including unit roots, ARCH, and cointegration. "The Econometric Analysis of Time Series "focuses on the statistical aspects of model building, with an emphasis on providing an understanding of the main ideas and concepts in econometrics rather than presenting a series of rigorous proofs. It explores the way in which recent advances in time series analysis have affected the development of a theory of dynamic econometrics, sets out an integrated approach to the problems of estimation and testing based on the method of maximum likelihood, and presents a coherent strategy for model selection.A.C. Harvey is Professor of Econometrics at the London School of Economics.
Template Matching Application In Geo-Referencing Of Remote Sensing Temporal Image

This paper focus on template matching approach for tracking object for remote sensing images. Such types of image are source of information which is largely used for information extraction. In this paper, two approaches SAD (sum of absolute differences) and SSD (sum of squared differences), template matching for finding the object. Template matching has provided basics and advance functionality for the image processing, object recognition, features based template matching and many more in a variety of remotely sensed Images. A variety of algorithms exist for comparison of images and making the template matching process fast and reliable. This comparative study with implementation approach focus on the core basics of the template matching in remote sensing images. The contribution of this paper is that, help to find the GCP (ground control point). These GCP can be generated in the form of template and these templates can run on the image and temporal image to find the correct location.
Optimization of Monitoring Points on Computer System from Poor Knowledge Risk Point of View

In the training processes, computer systems are often used today. During monitoring/diagnostics of computer systems (CS), it is very important to optimize the number of monitoring points in order to ensure a reliable operation, availability and to minimize the risk of poor knowledge. This paper presents an analytical expression for determining the optimal number of monitoring
eWave: Leveraging Energy-Awareness for In-line Deduplication Clusters

In-line deduplication clusters provide high throughput and scalable storage/archival services to enterprises and organizations. Unfortunately, high throughput comes at the cost of activating several storage nodes on each request, due to the parallel nature of superchunk routing. This may prevent storage nodes from exploiting disk standby times to preserve energy, even for low load periods. We aim to enable deduplication clusters to exploit load valleys to save up disk energy. To this end, we explore the feasibility of deferred writes, diverted access and workload consolidation in this setting. We materialize our insights in eWave: a novel energy-efficient storage middleware for deduplication clusters. The main goal of eWave is to enable the energy-aware operation of deduplication clusters without modifying the deduplication layer. Via extensive simulations and experiments in an 8--machine cluster, we show that eWave reduces disk energy from 16% to 60% in common scenarios with moderate impact on performance during low load periods.
Feature Extraction and Classification of Mental EEG for Motor Imagery

Electroencephalography (EEG) recognition was one of the key technology in brain-computer interface (BCI). For motor imagery EEG, a new EEG recognition algorithm (DWT-BP algorithm) which combined discrete wavelet transform (DWT) with BP neural network was presented. In DWT-BP, a rational time window was set through calculating the average power of motor imagery EEG on electrode C3 and C4, and then the average power during the time window was taken into DWT. The combinational signal of approximate coefficient A6 on the sixth level was selected as a signal feature and BP neural network was used as classifier to analyze the observed EEG data. The experiment results on “BCI Competition 2003” competition database showed that the recognition rate was better than the other several traditional algorithms. So, it proved that the algorithm was effective for EEG recognition of motor imagery, and provided a new idea for motor imagery recognition in brain computer interface.
Enhancing MOEA with component-emphasizing mechanism for multi-objective optimization

Multi-objective optimization is an important and challenging topic in the field of industrial design and scientific research because real-world problems usually involve several conflicting objectives. Since a multi-objective evolutionary algorithms (MOEA) is able to obtain an approximation to the Pareto optimal set and provide substantial information of the tradeoff between objectives, it is becoming one of the most successful methods for multi-objective optimization. Usually, an MOEA generates new trial solutions (offspring) with some candidate decision vectors (parents) to search for the promising areas and make the population evolve towards the Pareto optimal set. Moreover, in the reproduction procedure, most of MOEAs view the decision vector as a whole, and do not recognize the effects of a single component on the new trial solutions. In this paper, we propose the component-emphasizing mechanism for enhancing the search ability of MOEAs. In this mechanism, each component of a decision vector is viewed as an independent factor affecting the quality of the solution. Based on the mechanism, a new MOEA is presented. Finally, the performance of this new algorithm is compared with two other promising MOEAs, namely, NSGA-II and GDE3, on a set of test instances. The experimental results have shown that the proposed algorithm outperforms the others in solution quality and time cost.
Discovering objects and their location in videos using spatial-temporal context words

We present a novel unsupervised learning algorithm for discovering objects and their location in videos from moving cameras. The videos can switch between different shots, and contain cluttered background, occlusion, camera motion, and multiple independently moving objects. We exploit both appearance consistency and spatial configuration consistency of local patches across frames for object recognition and localization. The contributions of this paper are twofold. First, we propose a combined approach for simultaneous spatial context and temporal context generation. Local video patches are extracted and described using the generated spatial-temporal context words. Second, a dynamic topic model, based on the representation of a bag of spatial-temporal context words, is introduced to learn object category models in video sequences. The proposed model can categorize and localize multiple objects in a single video. Objects leaving or entering the scene at multiple times can also be handled efficiently in the dynamic framework. Experimental results on the CamVid data set and the VISAT™ data set demonstrate the effectiveness and robustness of the proposed method.
Information disclosed online by Spanish universities: content and explanatory factors

Purpose – The purpose of this paper is to analyse how several variables, such as universities' profitability, growth‐reduction of student numbers, age/tradition, type of university and internationality, among others, influence the transparency practices of Spanish universities as well as the technology, interactivity, structure and navigability of their webpages.Design/methodology/approach – First a content analysis of the Spanish universities' websites is carried out. To do this a disclosure index is created and applied. This index is more complex than those in previous papers, focusing on several issues, such as financial information, corporate governance, social responsibility, research, teaching activities, strategic information, timeliness, contact information, technology, interactivity with users, navigability and web structure. Then an empirical model is estimated by applying a linear regression, taking several factors into consideration.Findings – Three of the independent variables proposed to tes.
The Validity of Games

One way to deal with complex situations is the simulation approach: build a simplified model of this reality, learn from this simplified model, and, finally, translate the findings or knowledge back to the reality. Gaming is based on this idea. If we want to make inferences about reality based on experiences and knowledge acquired in a game, we have to be sure that the game model is a good, or valid, representation of the real situation. In this article, the concept of validity is explored in relation to games and simulations; four aspects of validity that apply to simulations and games are distinguished. These aspects are related to three applications of games. The article concludes with factors that may threaten validity during the process of the game design; a few suggestions are made to avert these threats.
Teaching parallel processing using free resources

Parallel processing can be taught effectively even though parallel hardware is not available. Free software is available to support the three major paradigms of parallel computing. Parallaxis is a software simulator that is based on the SIMD model. PVM and MPI allow one to treat a network of workstations as a message passing MIMD multicomputer with distributed memory. A locally developed shared memory simulator supports the MIMD model of computing with a common shared memory. While none of these products is superior to an actual parallel computer, each can be used in a variety of courses to give students experience with parallel algorithms.
Cooperative interference and radio resource management in self-organizing small cell networks

The aim of this thesis is to devise novel cooperative paradigms that allow small base stations (SBSs) to perform joint optimizations in the field of interference and spectral resource management, in an automated, self-organizing way. Fostering cooperation requires a careful negotiation process and incurs additional operational costs that can ultimately limit the scalability of such an approach. Hence, we design cooperative models around the self-organizing functions of small cell networks, through which each SBS dynamically adapts its own cooperative strategy to the current network status. In the first part of the thesis, we study the co-tier interference in the downlink of an underlay small cell tier. Showing that mutual interference is a limiting factor, we propose a cooperative scheme based on the concept of interference alignment. Thereby, the SBSs autonomously devise their cooperative strategy, select partner small base stations and suppress the mutual interference via alignment. In the second part, we extend the above problem by considering additional constraints posed by non-cooperative macro cell users sharing the spectrum with the SBSs. Due to the dimension of such a problem, we show that interference alignment solutions do not often exist, hence, we propose a distributed solution based on the concept of interference draining for solving the interfering signals in the space-frequency domain. Finally, we focus on inter-tier cooperation and propose an adaptive hybrid access policy based on spectrum leasing, which jointly enhances the SBSs' transmission capacity and the macro cell users' QoS . In the studied scenarios, we highlight how SBSs' individual performance is networkdependent, due to the shared nature of the spectrum and the network infrastructure. For addressing such issues, we use novel concepts from coalitional games in partition form in which the strategic decisions are optimized by accounting for external effects, such as interference or dynamic spectrum allocation. Based on the properties of optimality of coalitional games in partition form, the proposed cooperative solutions show two key features. First, by leveraging on decentralized strategic decisions, complex interference management techniques, such as interference alignment or draining, can be implemented in practical, scalable way. Second, although notable cooperative solutions exists for certain problem dimensions, the proposed algorithms are shown to be effective for a wide range of network sizes, by achieving significant data rate enhancement and low overhead.
Creating a grammar checker for CALL by constraint relaxation: a feasibility study

Intelligent feedback on learners’ full written sentence productions requires the use of Natural Language Processing (NLP) tools and, in particular, of a diagnosis system. Most syntactic parsers, on which grammar checkers are based, are designed to parse grammatical sentences and/or native speaker productions. They are therefore not necessarily suitable for language learners. In this paper, we concentrate on the transformation of a French syntactic parser into a grammar checker geared towards intermediate to advanced learners of French. Several techniques are envisaged to allow the parser to handle ill-formed input, including constraint relaxation. By the very nature of this technique, parsers can generate complete analyses for ungrammatical sentences. Proper labelling of where the analysis has been able to proceed thanks to a specific constraint relaxation forms the basis of the error diagnosis. Parsers with relaxed constraints tend to produce more complete, although incorrect, analyses for grammatical sentences, and several complete analyses for ungrammatical sentences. This increased number of analyses per sentence has one major drawback: it slows down the system and requires more memory. An experiment was conducted to observe the behaviour of our parser in the context of constraint relaxation. Three specific constraints, agreement in number, gender, and person, were selected and relaxed in different combinations. A learner corpus was parsed with each combination. The evolution of the number of correct diagnoses and of parsing speed, among other factors, were monitored. We then evaluated, by comparing the results, whether large scale constraint relaxation is a viable option to transform our syntactic parser into an efficient grammar checker for CALL.
Estimation of building density using Terrasar-X-Data

This study presents an approach towards the estimation of building density by utilizing the intensity data of the German space borne very high resolution SAR system TerraSAR-X. By using reference data we segmented a SAR scene of the city of Munich, Germany, on the scale of city blocks. Then we computed first order statistics and texture measures according to Haralick on the image objects. We used a random forests regression algorithm to establish the relationship between these features and the building density taken from reference data. The result is a promising coefficient of determination of 0.63.
CICO: A Web-Deployable Application for Remote File Access

We set out the rationale for a web-deployable application for checking media files in and out of a repository in the context of an ongoing investigation into the use of web technologies in production environments. The use cases for such an application are listed, and a specification fulfilling those use cases is presented. “CICO” (Check-In/Check-Out), an application written to that specification is then documented, along with justification for the design decisions made as part of the development process. Possible future changes to and uses of the application are then discussed.