Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of the Power-Law Fluctuations in the Email Size

In a previous study, we investigated the frequency distribution of the email size in the system log data of the main email server for the staff on a campus network. We found that the frequencies of email sizes followed a power-law distribution and discovered two inflection points in the distribution. After analyzing these results, we collected new system log data for both staff and students for the period from April 1, 2009 to March 31, 2015 and analyzed the frequency distributions per academic year. The results of the earlier investigation were replicated for each of these distributions. Then, we disaggregated the system log data for the staff for the period from May 1, 2015 to July 31, 2015 using the email header "Content-Type" and created four subdistributions. Frequency distributions were calculated for the disaggregated data. We then proposed and evaluated a model to explain the overall frequency distribution as a sum of the four subdistributions. The correlation coefficient between the observed frequency distribution and the distribution predicted by our model was 0.8408 for the staff. This coefficient confirmed that our approach can successfully model and predict the size distribution of emails.
Community Detection in Complex Networks Based on Vertex Similarities

One of statistical characteristics in complex networks is a community structure.Detecting communities in networks has aroused great interest among researches in recent years.Actually,community detection is very similar to the classical cluster analysis in machine learning field.Thus,the key point is how to define vertex similarities in complex networks.We first proposed an algorithm named SGN based on vertex similarities.Compared with GN,SGN is much better and faster than GN.Secondly,we used four classical clustering algorithms to detect community structure in networks based on some existing vertex similarity measures.The results on artificial networks and real social networks show that the similarity measures based on signal propagation and regular equivalence theory by using the whole topology structure of networks are better than the methods of Jaccard based on local vertex information.Therefore,if vertex similarities are given well enough,proper clustering algorithms based on similarity matrices can be used to detect community structures fast and effectively in complex networks.
Gaining Knowledge from Georeferenced Social Media Data with Visual Analytics

Analysis of the collections of geographically referenced posts published in social media, such as Twitter, Flickr, and YouTube, can bring new knowledge about places, geographical objects, and events interesting to people, and about people’s mobility behaviours. Gaining knowledge from large data collections requires combining computational analysis with human interpretation, judgement, and reasoning, which, in turn, require appropriate visual representations of the data and analysis results. Visual analytics integrates computational analysis techniques with interactive visual interfaces to support collaborative human−computer analytical activities. We give a brief overview of visual analytics approaches to extracting various kinds of information and knowledge from georeferenced social media data.
Indexing and analysis of very large masses of time series

Les series temporelles sont presentes dans de nombreux domaines d'application tels que la finance, l'agronomie, la sante, la surveillance de la Terre ou la prevision meteorologique, pour n'en nommer que quelques-uns. En raison des progres de la technologie des capteurs, de telles applications peuvent produire des millions, voir des des milliards, de series temporelles par jour, ce qui necessite des techniques rapides d'analyse et de synthese.Le traitement de ces enormes volumes de donnees a ouvert de nouveaux defis dans l'analyse des series temporelles. En particulier, les techniques d'indexation ont montre de faibles performances lors du traitement des grands volumes des donnees.Dans cette these, nous abordons le probleme de la recherche de similarite dans des centaines de millions de series temporelles. Pour cela, nous devons d'abord developper des operateurs de recherche efficaces, capables d'interroger une tres grande base de donnees distribuee de series temporelles avec de faibles temps de reponse. L'operateur de recherche peut etre implemente en utilisant un index avant l'execution des requetes.L'objectif des indices est d'ameliorer la vitesse des requetes de similitude. Dans les bases de donnees, l'index est une structure de donnees basees sur des criteres de recherche comme la localisation efficace de donnees repondant aux exigences. Les index rendent souvent le temps de reponse de l'operation de recherche sous lineaire dans la taille de la base de donnees. Les systemes relationnels ont ete principalement supportes par des structures de hachage, B-tree et des structures multidimensionnelles telles que R-tree, avec des vecteurs binaires jouant un role de support. De telles structures fonctionnent bien pour les recherches, et de maniere adequate pour les requetes de similarite. Nous proposons trois solutions differentes pour traiter le probleme de l'indexation des series temporelles dans des grandes bases de donnees. Nos algorithmes nous permettent d'obtenir d'excellentes performances par rapport aux approches traditionnelles.Nous etudions egalement le probleme de la detection de correlation parallele de toutes paires sur des fenetres glissantes de series temporelles. Nous concevons et implementons une strategie de calcul incremental des sketchs dans les fenetres glissantes. Cette approche evite de recalculer les sketchs a partir de zero. En outre, nous developpons une approche de partitionnement qui projette des sketchs vecteurs de series temporelles dans des sous-vecteurs et construit une structure de grille distribuee. Nous utilisons cette methode pour detecter les series temporelles correlees dans un environnement distribue.
Vector Nonlocal Euclidean Median: Principal Bundle Captures The Nature of Patch Space

We extensively study the rotational group structure inside the patch space by introducing the fiber bundle structure. The rotational group structure leads to a new image denoising algorithm called the \textit{vector non-local Euclidean median} (VNLEM). The theoretical aspect of VNLEM is studied, which explains why the VNLEM and traditional non-local mean/non-local Euclidean median (NLEM) algorithm work. The numerical issue of the VNLEM is improved by taking the orientation feature in the commonly applied scale-invariant feature transform (SIFT), and a theoretical analysis of the robustness of the orientation feature in the SIFT is provided. The VNLEM is applied to an image database of 1,361 images and compared with the NLEM. Different image quality assessments based on the error-sensitivity or the human visual system are applied to evaluate the performance. The results confirmed the potential of the VNLEM algorithm.
Exploitation-Oriented Learning XoL: A New Approach to Machine Learning Based on Trial-and-Error Searches

The approach, called reinforcement learning (RL), is much more focused on goal-directed learning from interaction than are other approaches to machine learning (Sutton and Barto, 1998). It is very attractive since it can use Dynamic Programming (DP) to analyze its behavior in the Markov Decision Processes (MDPs) (Sutton, 1988; Watkins and Dayan, 1992; Ng et al., 1999; Gosavi, 2004; Abbeel and Ng, 2005). We call these methods that are based on DP DP-based RL methods. In general, RL uses a reward as a teacher signal for its learning. The DP-based RL method aims to optimize its behavior under the values of reward signals that are designed in advance. We want to apply RL to many real world problems more easily. Though we know some ABSTRACT
Modeling an e-teacher’s decisions for e-learners’ motivation using Decision Tree

In customary classroom frameworks, understudies are checked by the educators utilizing their everyday execution records and reports. While the e-learners and their educators are isolated by time and space/time or space. In this way, the e-learners experience the ill effects of different sorts of troubles and issues amid learning of any online course . Intelligent Tutoring systems use intelligent agents to assist the e-learners. This study attempts to model such an e-teacher (Intelligent Agent) which will be able to motivate the e-learners to learn the online course. Data Mining technique Decision Tree is used to understand the e-learners’ behavior on learning management system and make decisions accordingly.
Ontology for Data Quality and Chronic Disease Management: A Literature Review

Improved Data Quality (DQ) can improve the quality of decisions and lead to better policy in health organizations. Ontologies can support automated tools to assess DQ. This chapter examines ontologybased approaches to conceptualization and specification of DQ based on “fitness for purpose” within the health context. English language studies that addressed DQ, fitness for purpose, ontology-based approaches, and implementations were included. The authors screened 315 papers; excluded 36 duplicates, 182 on abstract review, and 46 on full-text review; leaving 52 papers. These were appraised with a realist “context-mechanism-impacts/outcomes” template. The authors found a lack of consensus frameworks or definitions for DQ and comprehensive ontological approaches to DQ or fitness for purpose. The majority of papers described the processes of the development of DQ tools. Some assessed the impact of implementing ontology-based specifications for DQ. There were few evaluative studies of the performance of DQ assessment tools developed; none compared ontological with non-ontological approaches. Alireza Rahimi University of New South Wales, Australia & Isfahan University of Medical Sciences, Iran Siaw-Teng Liaw University of New South Wales, Australia Pradeep Kumar Ray University of New South Wales, Australia Jane Taggart University of New South Wales, Australia Hairong Yu University of New South Wales, Australia
Research on Content-Oriented Product Quality Information Modeling Method

Current aerospace quality information is organized through data package,and lacks uniform reference,is difficult to reuse and coarse-grained management.A content-oriented product quality information organizing method is proposed in this paper.The quality information container is considered as analyzed object.The feature model is used as a reference and the view model is used as a carrier,through content analysis and semantic annotation,quality information will be Abstracted from the content of information container and associated with the structured view model,then fine-grained and reusable quality information organization will be achieved.Pilot application shows that quality and reliability management platform of the data package can be supported and quality information tracing through whole product life-cycle can be achieved.
A Unified, Operational View of Service, Service Systems, and Service Science

This paper proposes a unified operational view of service and service systems, thereby addressing an important gap in service science. That gap involves over-emphasizing marketing, competition, and economic exchange as fundamental service science concepts and under-emphasizing operational systems that produce services and contribute to the co-creation of value. This paper proceeds through a set of layers, each of which builds on previous layers. Layer 1 proposes a broadly applicable definition of service after reviewing past definitions and noting that they suggest three different portrayals of the nature of service. A conceptual model in Layer 2 links service to work systems, as defined in work system theory (WST). Layer 3 explains why WST uses the term product/service instead of service to refer to both outputs of work system activities and a work system’s contributions to value creation by customers. Layer 4 extends a work system metamodel that was originally designed to support systems analysis and design, thereby presenting a new version that illuminates the service-related path from provider resources to value for customers. Layer 5 explains service-related insights from the metamodel. Layer 6 explains how WST and the metamodel address operational topics that differ from most of the topics addressed in service-dominant logic. Layer 7 applies ideas from the previous layers to propose basic premises of an operational view of service, service systems, and related concepts, thereby supplementing the marketing and economic view expressed by service-dominant logic.
Analysis of Human Peripapillary Atrophy Using Computerised Image Analysis

Retinal morphological changes around the optic disc are looked at in the paper; more specifically the vascular network geometry in healthy patients and patients with peripapillary atrophy are quantified and compared using a multifractal approach. Segmented and skeletonized retinal images from the DRIVE database were analyzed. Multifractal and lacunarity analyses of the binary images were carried out using the Image J software. It was found that the vascular network geometry in patients with peripapillary atrophy has a multifractal geometry characterized by a hierarchy of exponents.
Developing Computer-Assisted Tutorial Instructional Program for Teaching Grammar In Higher Education

The aims of this research were to: (1) gain information on the existing condition of learning media which are used by lecturers to teach grammar, (2) develop the proper media which is suitable for the students’ need and appropriate to teach grammar, and (3) find out the feasibility of the proposed media. Research and Development method was used in this research. The research consisted of exploration stage and development stage. Observation, questionnaire, and interview were used to gain the data, while Spiral Data Analysis was used to analyze the data. From the exploration stage, it was found out that the media usually used by the lecturer to teach grammar were Power Point Presentation and handouts. The weaknesses of these media were lecturer dependent, bad presentation, time consuming, non-independent learning, device malfunction, and limited exercises. It was also revealed that Computer Assisted Instruction was not used to teach grammar while it was needed. Moodle based English Grammar Gate was designed as an answer to the students’ need. It consisted of two major parts; they are: tutorial part and exercise part. Expert judgment was done to validate the product. The experts suggested several improvements and revision was made regarding the evaluation given. The revised product was then used in field testing to find out the feasibility of the product. Facing two field testing processes and having several revision toward the product, it was then stated feasible.
Study on Extraction and Analysis of Tree Measure Factors Based on Point Cloud Data

In this paper,three-dimensional laser scanner has been used to scan trees to get point cloud data of trees.After format conversion,separation and extraction,the measurement and analysis are conducted to measure such factors as DBH,tree height,crown and timber volume.These factors can be calculated by point cloud data.Through experimental analysis,it can be drawn that with the improve of measurement techniques,the leaf area index of canopy is better to reflect the physiological significance of the crown;stem volume calculated by irregular polyhedron triangulation is more accurate and convenient than that by the average basal area and the central stem basal area in calculating volume.
Evaluating the Performance of Rule, Density and Tree Techniques Using Classification and Clustering Algorithms

Data mining is the course of the action support of searching huge record for pattern used mainly to discover association between variables . In this technique, clustering is universally used for fastening the data according to the probing bond between the data. Later than agreement of information, classification of information is equipped, currently essentials are appreciate under categorized cluster. Judgment based is also speculating based winning the classification. The association linking clustering and classification is depending ahead the pole apart algorithm beginning to end analyzing data which grows into supplementary well-ordered record. The inescapable potential of this paper is to get links of the unusual algorithms line of attack of clustering and classification to poke approximately in the sort of strengthen algorithm to the obligation. Evaluator the Clustering algorithms like Cobweb, Hierarchical, DBSCAN, OPTICS and classification algorithms: RIDOR, PART, BF Tree, AD Tree.
Compiling an OPEC Word List: A Corpus-Informed Lexical Analysis

The present study is conducted within the borders of lexicographic research, where corpora have increasingly become all-pervasive. The overall goal of this study is to compile an open-source OPEC [1] Word List (OWL) that is available for lexicographic research and vocabulary learning related to English language learning for the purpose of oil marketing and oil industries. To achieve this goal, an OPEC Monthly Reports Corpus (OMRC) comprising of 1,004,542 words was compiled. The OMRC consists of 40 OPEC monthly reports released between 2003 and 2015. Consideration was given to both range and frequency criteria when compiling the OWL which consists of 255 word types. Along with this basic goal, this study aims to investigate the coverage of the most well-recognised word lists, the General Service List of English Words (GSL) (West ,1953) and the Academic Word List (AWL) (Coxhead, 2000) in the OMRC corpus. The 255 word types included in the OWL are not overlapping with either the AWL or the GSL. Results suggest the necessity of making this discipline-specific word list for ESL students of oil marketing industries. The availability of the OWL has significant pedagogical contributions to curriculum design, learning activities and the overall process of vocabulary learning in the context of teaching English for specific purposes (ESP). OPEC stands for Organisation of Petroleum Exporting Countries.
Designing constructive machine learning models based on generalized linear learning techniques

We propose a general framework for designing machine learning models that deal with constructing complex structures in the output space. The goal is to provide an abstraction layer to easily represent and design constructive learning models. The learning approach is based on generalized linear training techniques, and exploits techniques from combinatorial optimization to deal with the complexity of the underlying inference required in this type of models. This approach also allows to consider global structural characteristics and constraints over the output elements in an efficient training and prediction setting. The use case focuses on building spatial meaning representations from text to instantiate a virtual world.
Enhance Automatic Evaluation of Machine Translation by Markov Network Based Paraphrases

It is a challenge to match the different expressions(words or phrases)which have the same meanings in the automatic evaluation of machine translation.Many researchers proposed to enhance the matches between the words in machine translation and in human references by extracting paraphrases from bilingual parallel corpus or comparable corpus.However,the cost of constructing the bilingual parallel corpus or the comparable corpus is high;furthermore,it is difficult to obtain a large corpus between some language pairs.In this paper,the paraphrases are extracted from the monolingual texts in the target language by constructing the Markov networks of words,and applied to improve the correlation between the results of automatic evaluation and the human judgments of machine translation.The experimental results on WMT14 Metrics task showed that the performances of the proposed approach of extracting paraphrase from monolingual text are comparable to that of extracting paraphrase from bilingual parallel corpus.
Study of Data-Pretreatment for Full-Text Search System

The application of full-text search has caused a revolution of the information retrieval field.It is the core that the file database researches and develops.In a full-text search system,the setting-up of the index database of full text is a systematic foundation.Its project organization influences the final search efficiency of searching algorithm and system of the full-text search engine directly.This paper introduces such data-pretreatment technology as index database structural design,text index technology,etc.Also introduces that in the fulltext retrieval system mainly,and the data processing procedure of index database of full-text retrieval system.Finally,studied the produce-algorithms of index database of full-text retrieval system on this basis,provided produce-algorithm of index database under two kinds of situations: individual file and batch processing.
Simple method to the dynamic modeling of industrial robot subject to constraint

It is a common movement type in industrial production for the end-effector of industrial robot executing tasks according to desired track. High nonlinearity and coupling are shown from the dynamic characteristic of industrial robot because of the constrained relationship brought by desired task. Therefore, it is difficult to establish dynamical equation with traditional Lagrange equation. Aiming at the dynamics modeling of industrial robot subject to constraint, the additional torque and dynamical equation of industrial robot subject to some desired trajectory is acquired based on the famous Udwadia–Kalaba equation in analytical mechanics field. The simple approach overcomes the disadvantage of obtaining dynamical equation from traditional Lagrange equation by Lagrange multiplier. But the numerical results obtained by integrating the constrained dynamic equation yield the errors. A numerical algorithm is proposed to reduce the errors by modifying the derived dynamic equation. The simulation results prove that the modified algorithm can be more precisely utilized in dynamics modeling of industrial robot subject to constraint.
Recontextualizing grammar: Underlying trends in thirty years of Cognitive Linguistics

Cognitive Linguistics is a success: from the relatively marginal position that it originally occupied in the linguistic landscape, it has developed into one of the mainstream trends in current linguistics. A search on the strings ‘‘cognitive linguistics’’ and ‘‘cognitive grammar’’, in contrast with ‘‘generative linguistics’’ and ‘‘generative grammar’’ in the LLBA bibliography, broken down over four periods of five years starting in 1988, yields the figures presented in Table 1. Although the search terms could be extended to get a more complete picture, the present figures already indicate with su‰cient force that Cognitive Linguistics seems to be overtaking the generativist enterprise in terms of scholarly productivity and appeal. This conspicuous success raises the question whether there is a common denominator in the expansion of Cognitive Linguistics? A cursory inspection would seem to suggest that the internal evolution of Cognitive Linguistics is of the radial network type (to put the matter in an appropriate terminology). A number of core ideas – like grammatical construal, prototypicality, radial networks, ICMs, conceptual integration, constructions – are pursued and developed rather independently of each other, to the extent that each of them constitutes a mini research programme of its own. But what is it that keeps these separate approaches together – if anything?