Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic query optimization in Rdb/VMS

Addresses the key theoretical and practical issues of dynamic query optimization and reviews the underlying reasoning that cements the basic concepts of dynamic query optimization. The optimization mechanics are described, in concert with explanations of why and how certain arrangements contribute to a given optimization goal. Compared to traditional approaches, dynamic query optimization offers a much more realistic view of cost distribution modeling. It is a competition-based architecture that is capable of resolving the major limitations of static optimization, viz. the problems of data skew, cost function instability, and host-variable sensitivity. <<ETX>>
Evaluating the recovery-oriented approach through the systematic development of real complex applications

Recovery-oriented software is built with the perspective that hardware or software failures and operation mistakes are facts to be coped with, as they are problems that cannot be fully solved while developing real complex applications. Consequently, any software will always have a non-zero chance of failure. Some of these failures may be caused by defects that may be removed or encapsulated. From the point of view of removing or encapsulating defects, a failure is considered to be trivial, when (i) the required effort to identify and eliminate or encapsulate the causing defect is small, (ii) the risk of making mistakes in these steps is also small and (iii) the consequences of the failure are tolerable. It is highly important to design systems in such a way that most (ideally all) of the failures are trivial. Such systems are called ‘debuggable systems’. In this study, we present the results of systematic applying techniques that focus on creating debuggable software for real embedded applications. Copyright © 2008 John Wiley & Sons, Ltd.
La plate-forme Glozz : environnement d’annotation et d’exploration de corpus

La nécessité d’une interaction systématique entre modèles, traitements et corpus impose la disponibilité d’annotations de référence auxquelles modèles et traitements pourront être confrontés. Or l’établissement de telles annotations requiert un cadre formel permettant la représentation d’objets linguistiques variés, et des applications permettant à l’annotateur de localiser sur corpus et de caractériser les occurrences des phénomènes observés. Si différents outils d’annotation ont vu le jour, ils demeurent souvent fortement liés à un modèle théorique et à des objets linguistiques particuliers, et ne permettent que marginalement d’explorer certaines structures plus récemment appréhendées expérimentalement, notamment à granularité élevée et en matière d’analyse du discours. La plate-forme Glozz répond à ces différentes contraintes et propose un environnement d’exploration de corpus et d’annotation fortement configurable et non limité a priori au contexte discursif dans lequel elle a initialement vu le jour.
A Quantitative Analysis of Algorithms for Energy Efficient Coverage in WSN

In wireless sensor network the Energy Efficient Coverage (EEC) is been a major and important challenge, because sensors work with minimal battery resource in a remote location and it is unfair to change or charge the batteries. An Efficient technique must be implemented as a solution of EEC problem in WSN and it can be achieved by appropriate selection of optimal algorithm. Active research study on EEC problem exposes innovative ideas and solution for coverage issues. This paper discusses different and distinct algorithms have been developed recently for an unstructured WSN to increase network life time and increase coverage among the sensor nodes.
A Review on Routing Protocols in Wireless Sensor Networks

Advances in Wireless Sensor Network has led to large scale development.WSN technology has been provided with the ability of small nodes with sensing, computation ,and the wireless communication capabilities . In WSN ,a large number of constrained attached to the sensor nodes which have a limited transmission range ,less processing capability ,low storage capability ,and as well as their energy resources are also inadequate. In WSN ,the routing protocols are responsible for maintaining the routes in the network . It also ensures reliable multi-hop communication under these conditions . This paper gives a review on the routing protocols that are used in the WSN and compare their strengths and limitations.
Deep Packet Inspection 용 메모리 기반의 문자열 매칭 엔진의 메모리 요구량의 분석 방법의 연구

This paper proposes a method of analyzing memory requirements of a memory -based string matching engine for deep packet inspection (DPI). The existing researches calculate the memory requirements in the number of bits; therefore, the realistic memory requirements and costs are not provided. The memory requirements are analyzed considering the realistic implementation with embedded memory in FPGA (Field Programmable Gate Array). With the obtained data, the optimal number of memory blocks can be calculated.I
Computing Volumes and Convex Hulls: Variations and Extensions

Author(s): Yildiz, Hakan | Advisor(s): Suri, Subhash | Abstract: Geometric techniques are frequently utilized to analyze and reason about multi-dimensional data. When confronted with large quantities of such data, simplifying geometric statistics or summaries are often a necessary first step. In this thesis, we make contributions to two such fundamental concepts of computational geometry: Klee's Measure and Convex Hulls. The former is concerned with computing the total volume occupied by a set of overlapping rectangular boxes in d-dimensional space, while the latter is concerned with identifying extreme vertices in a multi-dimensional set of points. Both problems are frequently used to analyze optimal solutions to multi-objective optimization problems: a variant of Klee's problem called the Hypervolume Indicator gives a quantitative measure for the quality of a discrete Pareto Optimal set, while the Convex Hull represents the subset of solutions that are optimal with respect to at least one linear optimization function.In the first part of the thesis, we investigate several practical and natural variations of Klee's Measure Problem. We develop a specialized algorithm for a specific case of Klee's problem called the “grounded” case, which also solves the Hypervolume Indicator problem faster than any earlier solution for certain dimensions. Next, we extend Klee's problem to an uncertainty setting where the existence of the input boxes are defined probabilistically, and study computing the expectation of the volume. Additionally, we develop efficient algorithms for a discrete version of the problem, where the volume of a box is redefined to be the cardinality of its overlap with a given point set.The second part of the thesis investigates the convex hull problem on uncertain input. To this extent, we examine two probabilistic uncertainty models for point sets. The first model incorporates uncertainty in the existence of the input points. The second model extends the first one by incorporating locational uncertainty. For both models, we study the problem of computing the probability that a given point is contained in the convex hull of the uncertain points. We also consider the problem of finding the most likely convex hull, i.e., the mode of the convex hull random variable.
FINGERPRINT CLASSIFICATION BASED ON SPECTRAL FEATURES

Fingerprint is one of the most important indexes that can be applied to human verification and identification. In the recent decade, with the population explosion and tremendous increase in fingerprint databases, automation of identification has been unavoidable. Fingerprint classification decreases the time of search for an unknown image in large databases. In this research, translation and rotation invariant features are extracted from spectrum of the fingerprint image, and a new tessellation on the frequency spectrum based on an appropriate definition of frequency concept in the fingerprint application, has been designed. The extracted features obtain not only information from frequency of ridges but also valuable information from direction of ridges in the fingerprint images. Features are classified with Probabilistic Neural Network. FVC2000 and FVC2002 databases are used to assess the proposed algorithm. The proposed algorithm provides an accuracy and speed of classification more than previously reported in the literature. We have obtained an accuracy of 93.4 percent with a rejection ratio 2.8 percent for the seven-class task and for the six-class classification task an accuracy of 95.1 percent with a rejection ratio 2.4 percent is achieved.
Approach for Object Recognition Based on a Computational Model of Feature Binding

This paper proposes a novel method for object recognition by using a computational model of feature binding, in which Gabor features are employed as the elementary features and correlation statistics provide the basis for implementing the feature binding. A group of object recognition experiments are conducted with this method, and the results prove the comparatively good performances with high recognition precision and high speed, indicating the validity of this method and the computational model.
How to avoid making the same Mistakes all over again: What the parallel-processing community has (failed) to offer the multi/many-core generation

We observe that in the past decade parallel processing and parallel algorithmics have disappeared from mainstream computer-science curricula and moved either into advanced graduate courses or into the application domains. This is well illustrated by current textbook availability. The influential book by Cormen, Leiserson and Rivest (1990) in its first edition had a substantial chapter on PRAM algorithmics that was dropped from the second edition (2001), the parallel algorithms book by Jala (1992) is no longer in print, and recent algorithms texts by Kleinberg and Tardos (2005), or Dasgupta et al. (2007) do not touch on parallelism at all. In the past decade it was well possible to complete an advanced computer science degree without exposure to parallel processing, and in particular parallel algorithmics.It is timely for the parallel-processing community to take stock: What does the community have to offer the upcoming generation that will have to deal with parallelism for a much broader range of applications? What are the fundamental paradigms and techniques of the past? How can these be most effectively conveyed, and to whom? Which were the mistakes and wrong turns of the past? How can repetition be avoided? Which problems remain unsolved, and what are the major, new challenges?
Dedicated Disk I/O Strategies for IPTV Live Streaming Servers Supporting Timeshift Functions

By carefully studying the characteristics of IPTV (Internet Protocol Television) live streaming service supporting timeshift functions, we find the default disk I/O strategies of universal OS can not provide satisfactory performance. Based on these observations, this paper proposes a set of dedicated disk I/O strategies in the design and implementation of a live streaming server supporting timeshift functions. Our proposed strategies include asynchronous direct I/O, block-level data processing, media caching and media prefetching, which are implemented in a live streaming server (ClearServer) developed by ourselves. ClearServer has been successfully deployed in the Shanghai Zhabei IPTV test-bed. Based on the deployment, detailed performance evaluation and test results demonstrate that the proposed strategies significantly improve several major performance metrics of the IPTV live streaming servers.
Extraction and Application of Regionalized Implicit Function Feature of 3D Face

D face reconstruction and 3D face recognition belonged to different fields previously. In order to solve these two issues in a unified way, this paper designed a kind of regionalized implicit function feature (RIFF) algorithm frame. Firstly according to the biology characteristic, the face was segmented to five regions with different resolutions; secondly the control matrix which expressed the human face was solved for 3d face reconstruction; then dimensionality reduction was did for this control matrix; finally the low dimensional character description was computed completely for 3d face classification. The specific experiments for the RIFF method are performed and the results show that this method can acquire significant effect in the performance of reconstruction and recognition.
Inter-operator resource sharing based on network virtualization

Mobile Network Virtualization (NV) is an emerging technique where multiple mobile network providers can share the infrastructure and the physical resources with each other, which leads to the improvement of system performance and the reduce of overall costs. In this paper, we propose a framework for inter-operator resource sharing through base station (BS) virtualization in Long Term Evolution (LTE) networks. A Soft-Defined Network (SDN) based control layer is also designed for better control of NV. Considering both the system and user equipment (UE), detailed NV and UE offloading processes are described in two scenarios where operators have imbalanced load or similarly non-heavy load. Simulation results show that the proposed framework achieves significant gains in terms of aggregate system throughput and its performance with different parameters is analyzed through simulation.
Applying Recommender System Based Mashup to Web-Telecom Hybrid Service Creation

We have witnessed the convergence of the telecom technologies and the Internet technologies recent years. Hybrid services combined with Internet service functionalities and telecommunication service functionalities have attracted much more attentions form both academic and industry communities. In this paper, we have proposed novel service creation architecture leverage on the so-called "web 2.0"'s methodology "mashup", to let the end-users or service developers conduct the service creation procedure in a lightweight and rapid way. Recommender System theory and related technologies are also introduced to assist the mashup creator to build higher quality mashups in less time. We have also made some preliminary evaluations about the system and point out some further directions.
Research and Implementation of Fast Image Rotation Based on GPU

An algorithm of image rotation and real-time rendering based on GPU(graphics processing unit) is presented in this paper. Firstly, a method of rotation transformation which is implemented by CPU is summarized, and the limitations of this method are pointed out. And then, the algorithm of image rotation based on GPU is elicited, the process of image rotation and real-time displaying is implemented on the platform of VC++ 6.0 by using DirectX 9.0. Finally, the experimental results of the two methods are analysed and compared. The new algorithm absorbs the merits of GPU in the aspects of speed and saving the CPU resources, and ensures the operation speed and quality of image rotation.
Spatial pattern recognition and abnormal traffic behavior detection of moving object

The spatial motion pattern recognition method and the abnormal traffic hehavior detection method of the moving object were proposed.The spatial distance similarity matrix of the trajectory dataset was constructed using the mearurement method in the trajectory space with the improved Hausdorff distance.The typical motion pattern of the moving object was extracted by the spatial distributions of the trajectory dataset learnt by the spectral clustering algorithm.A motion pattern matching algorithm in the trajectory space was presented for the abnormal traffic behavior detection based on Bayes classifier.The vehicle lane-changing behavior in the realistic scenario was recognized as an example,and the result was encouraging.The proposed method provides a technical support for the studies of the traffic behavior of moving object and the intelligent traffic management and control.
Knowledge management and value creation in the post-crisis banking system

Purpose This paper aims to demonstrate the existence of a relationship between the knowledge creation process and competitive advantage in the banking system. The framework of knowledge creation processes adopted in this research is the spiral of knowledge proposed by Nonaka and Takeuchi (1995). The concept of competitive advantage is limited to the measurement of economic value by adopting methods that compare the financial performance of the banks with related markets. The objective of this research is to answer the following research question: Which factors of the spiral of knowledge are relevant for increasing a bank’s economic value? Design/methodology/approach The sample used for this empirical research is composed of 960 banks operating in 24 countries. The sample was analyzed from 2012 to 2015 and includes 3,840 observations. Regarding the methodology, hypothesis demonstration was carried out using a panel analysis (generalized least squares regression) on a set of variables. Findings The results show that Nonaka and Takeushi’s spiral of knowledge has a positive influence on value creation in the banking system. However, not all factors of the four modalities of converting tacit knowledge into explicit knowledge and vice versa have a positive influence on the economic value of banks. Therefore, by excluding factors that have a negative influence or are not significant, it is possible to formulate an empirical model that illustrates the relationship between the spiral of knowledge and the economic value of banks. Originality/value There is a lack of studies on the knowledge creation process in the banking system because most of the research is geographically limited, and empirical tests are performed on small samples. Second, generally, these studies are limited to the relationship between intellectual capital and bank performance measured by accounting ratios. However, intellectual capital is only one component of the broader concept of knowledge. This research uses a large and geographically diverse sample and studies the relationship between the spiral of knowledge and economic value, which is measured by various financial techniques.
Research on Fuzzy Adaptive Washout Algorithm of Train Driving Simulator

As the most potential proprioceptive simulation algorithm exploiting human motion sensations at present,the coordinated adaptive washout algorithm has not been widely applied because of its complicated calculation process,time-consuming duration and instability.To overcome the difficulty of real-time solution of the adaptive parameters in the algorithm,a new fuzzy adaptive washout algorithm is proposed in combination with the fuzzy characters of human motion sensations.The fuzzy reasoning knowledge base of the relations between the adaptive parameters and the characters of real locomotive movements and kinaesthesia errors is established on the basis of the fuzzy control theory.Simple and easy fuzzy deducing is adopted to adjust the adaptive parameters in real time.The system stability is verified according to the Lauth Criteria.Referring to the high speed train driving simulator being developed by the State Key Laboratory of Traction Power,the simulation results show that the new algorithm is characterized by simple form,less calculation time,better system stability and higher simulation fidelity within smaller movement space.A new thought is provided for study of the proprioceptive simulation algorithm.
Research on Influence Diffusion in Social Networks

With the wide spread of Internet and big data research and applications, influence diffusion research in social networks becomes one of the hot topics in data mining and social networks analysis in recent years. The main results on social influence diffusion research from the field of computer science in the last decade, which covers the three main areas -influence diffusion modeling, influence diffusion learning, and influence diffusion optimization, were summarized. Different techniques, such as stochastic modeling, data mining, algorithmic optimization, and game theory, were demonstrated in their application to influence diffusion research. Finally, some discussions on the current issues, challenges and future directions in influence diffusion research and applications were provided.
AI on software engineering processes

Due to the large quantity of available information as well as the huge computational power achieved today, the application of Artificial Intelligence (AI) in computer programs has become, now more than ever, much popular. The overall goal of AI is to provide technology to build artificial systems, ie. machines and computers, that mimic natural intelligence.
Deteksi Motif Batik Menggunakan Ekstraksi Tekstur dan Jaringan Syaraf Tiruan

Penelitian ini mengusulkan sebuah metode klasifikasi motif batik menggunakan ekstraksi fitur tekstur serta klasifikasi menggunakan jaringan syaraf tiruan (artificial neural network). Metode ekstraksi fitur tekstur yang digunakan adalah Discrete Wavelet Transform (DWT), Gray Level CoOccurrence Matrix (GLCM) dan Local Binary Pattern (LBP). Ekstraksi fitur dengan GLCM dan DWT menghasilkan 24 fitur yang terdiri dari 5 jenis fitur GLCM dan 4 koefisien energi dalam setiap sub-band channel hasil dekomposisi. Sementara ekstraksi fitur dengan LBP dilakukan dengan membentuk histogram dari matrik hasil LBP. Fitur-fitur tersebut kemudian menjadi masukan bagi jaringan syaraf tiruan untuk kemudian diklasifikasi dalam 5 jenis batik: buketan, ceplok, kawung, parang dan truntum. Dataset yang digunakan adalah 50 gambar batik (10 untuk setiap motif) yang diambil secara acak di internet. Pengujian dilakukan dengan membandingkan dua kelompok fitur: DWT-GLCM dan DWT-GLCM-LBP untuk mengetahui metode ekstraksi fitur yang lebih baik dalam deteksi motif batik. Metode pengujian yang digunakan adalah K-Fold Cross Validation dengan hasil berupa confusion matriks untuk kemudian dihitung nilai akurasi dan F-Measure-nya. Hasil pengujian menunjukkan bahwa penggunaan ekstraksi fitur tekstur DWT-GLCM-LBP mampu mencapai akurasi 74% dengan jenis dekomposisi Daubechies 4 level 3. Kata kunci: Batik, Klasifikasi, GLCM, DWT, LBP, ANN
Leveraging OSD+ devices for implementing a high‐throughput parallel file system

OSD+s are enhanced object‐based storage devices (OSDs) able to deal with both data and metadata operations via data and directory objects, respectively. So far, we have focused on designing and implementing efficient directory objects in OSD+s. This paper, however, presents our work on also supporting data objects and describes how the coexistence of both kinds of objects in each OSD+ is profited to efficiently implement data objects and to speed up some common file operations. We compare our OSD+‐based Fusion Parallel File System (FPFS) with Lustre and OrangeFS through different microbenchmarks and HPCS‐IO scenarios. Results show that FPFS provides a throughput up to 37× better than Lustre and up to 95× better than OrangeFS for metadata workloads. FPFS also provides 34% more bandwidth than OrangeFS for data workloads and competes with Lustre in data writes. Results also show serious scalability problems in Lustre and OrangeFS that limit their performance.
A brief comparison of different learning methods for wind speed forecasting

Obtaining clean energy through wind farms is considered viable because of its low operating cost. However, the wind speed behavior is not constant, it has a chaotic behavior, and it is highly data-dependent. The present work aims to carry out a comparison of several short-term wind forecasts using Artificial Intelligence models such as Artificial Neural Networks (ANN), Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Large Short-Term Memory Networks (LSTM). We discuss several scenarios where the models are contrasted, analyzing the advantages and disadvantages of using these strategies to decide the viability of building a wind farm in the analyzed place.