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This paper proposes an online learning platform learner DM method based on the improved fuzzy C clustering (FCM) algorithm, constructs a learner feature database, and combines clustering analysis and SPSS statistical methods to statistically summarize the big data of law, thus improving the deficiencies of static and absolute classification of students in the student model. In the experiment paper, the improved algorithm is implemented and the experimental data is analyzed. The results show that the learner behavior feature extraction model in this paper has fewer errors and higher recall rate. Compared with the traditional CF algorithm, the error rate is reduced by 19.64% and the recall rate is increased by 22.85%. This study provides better targeted teaching programs and case resources for legal case teaching and promotes the innovation of legal case teaching mode.

Purpose: Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods. Design/methodology/approach: Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables. Findings: The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set's normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria. Originality/value: This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

Purpose: This paper aim to propose a methodological mapping approach for the evaluation of dissertations and theses of graduate programs in the area of environmental sciences in Brazil in relation to the UN sustainable development goals (SDGs). Design/methodology/approach: The research is characterized as exploratory with qualitative/quantitative approach. The proposed model was developed as a computational algorithm with a pilot being adopted as a professional master in national network for teaching in environmental sciences (ProfCiAmb) comprising associated courses from nine Brazilian public universities. 230 dissertations completed between 2018 and 2020 were analyzed. Findings: A total of 266 correlations were identified between the texts and descriptors of each SDG. Correlation values between 0.100 (minimum value -- Vmin) and 0.464 (maximum value -- Vmax) were observed. SDG 4 -- Quality Education (Vmax = 0.399) and SDG 6 -- Drinking Water and Sanitation (Vmax = 0.464) were those with the highest correlation values, followed by SDG 3 -- Health and Well-Being (Vmax = 0.299) and SDG 17 -- Partnerships and Means of Implementation (Vmax = 0.249). Practical implications: The construction of the computational algorithm provided consistent quantitative analyses with potential to contribute to the improvement of the multidimensional evaluation of graduate studies, as well as to support public policies related to teaching and research and strategic planning of the programs. Originality/value: The relevance of this study lies in the creation of a model that involves the creation of metrics and tools regarding the impact of graduate studies on society.

Purpose: The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance. Design/methodology/approach: For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4). Findings: The paper found that the decision trees algorithm outperformed other machine learning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of "CorrelationAttributeEval" technique with the use of a ranker search method, the author found that the "assessment_score" and "sum_click" features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance. Originality/value: The work meets the originality requirement.

Technical terms are vital elements for understanding the techniques used in academic research papers, and in this paper, we use focused technical terms to explore technical trends in the research literature. The major purpose of this work is to understand the relationship between techniques and research topics to better explore technical trends. We define this new text mining issue and apply machine learning algorithms for solving this problem by (1) recognizing focused technical terms from research papers; (2) classifying these terms into predefined technology categories; (3) analyzing the evolution of technical trends. The dataset consists of 656 papers collected from well-known conferences on ACM. The experimental results indicate that our proposed methods can effectively explore interesting evolutionary technical trends in various research topics. [ABSTRACT FROM AUTHOR]

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. [ABSTRACT FROM AUTHOR]

Describes an experiment that was designed to compare three different map displays used as a visual display for information retrieval by either a self-organizing algorithm or human subjects. Results indicate that the organization and visual appearance of map displays had a significant effect on subjects' searching and browsing on the map displays. (Author/LRW)

Discussion of the failure of individual keywords to identify conceptual content of documents in retrieval systems highlights Metric Similarity Modeling, a method for creating vector space representation of documents based on modeling target interdocument similarity values. Semantic relatedness, latent semantic indexing, an indexing and retrieval algorithm, and validating experiments are discussed. (Author/LRW)

Discusses the feasibility of building intelligent rule- or weight-based algorithms into general-purpose software for interactive thesaurus navigation. Previous research is surveyed; practical investigations in thesaurus navigation and query enhancement are described; and implications for automatic or intelligent thesaurus navigation are considered. (16 references) (LRW)

Describes fast, dynamic update algorithms for document retrieval systems. B-trees are discussed; the M-B-T file directory structure is explained; insertions and deletions in inverted files are described; and performance evaluation is discussed. An appendix provides the formal definition of an M-B-T directory. (Contains 18 references.) (LRW)

Discussion of information retrieval focuses on an Interaction Information Retrieval model in which documents are interconnected; queries and documents are treated in the same way; and retrieval is the result of the interconnection between query and documents. A theoretical mathematical formulation of this type of retrieval is given. (Contains 31 references.) (LRW)

Discusses end-user searching in Boolean information retrieval systems considers the role of search intermediaries and proposes a model of user preferences that incorporates a user's profile. Highlights include document representation; information queries; document output ranking; calculating user profiles; and selecting documents for a local database using the user's profile. (Contains 28 references.) (LRW)

Proposes a new learning algorithm to improve the retrieval effectiveness of the search system used in the hypertext environment using an extended Boolean model with links to improve the ranking of retrieved items. Highlights include the basic retrieval scheme, the learning scheme, and a review of basic probabilistic retrieval models. (Contains 32 references.) (JLB)

Describes an approach that combines concepts from information retrieval, fuzzy set theory, and genetic programing to improve weighted Boolean query formulation via relevance feedback. Highlights include background on information retrieval systems; genetic algorithms; subproblem formulation; and preliminary results based on a testbed. (Contains 12 references.) (LRW)

Discusses the Science Library Catalog, an online catalog intended for use by children at the Los Angeles Public Library, and describes the process of reorganizing the MARC-based database by using clustering algorithms to extend the Dewey Decimal Classification. Examples of screen displays are included. (18 references) (LRW)

Discussion of the problem of control in documentary information retrieval (IR) systems focuses on feedback mechanisms. Information needs are discussed, particularly the Problem Oriented Information Need (POIN); the structure of an adaptive IR system is described; and the design of algorithms for automated IR systems is explained. (33 references) (LRW)

Describes the theoretical framework of a retrieval system that uses data structures called bitmaps to improve search algorithms for medium-sized systems. Information retrieval systems that contain the full text of the documents are discussed, and the use of inverted files such as concordances for small texts is explained. (24 references) (LRW)

Discusses the interpretation of nominal compounds in the context of natural language processing for information retrieval. Knowledge-intensive algorithms that can successfully interpret compounds found in technical documents are described, and experiments are reported that indicate these algorithms may be unnecessary for improving retrieval performance. (22 references) (LRW)

Discusses the problem of processing fuzzy queries in databases and information retrieval systems and presents a prototype of a fuzzy query processing system for databases that is based on data clustering and uses Pascal programing language. Clustering schemes are explained, and the system architecture that uses natural language is described. (14 references) (LRW)

Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing technology, remote sensing products have found increasingly widespread applications across various fields. Nevertheless, as the volume of remote sensing image data continues to grow, traditional data retrieval techniques have encountered several challenges such as substantial query results, data overlap, and variations in data quality. Users need to manually browse and filter a large number of remote sensing datasets, which is a cumbersome and inefficient process. In order to cope with these problems of traditional remote sensing image retrieval methods, this paper proposes a remote sensing image retrieval algorithm for dense data (DD-RSIRA). The algorithm establishes evaluation metrics based on factors like imaging time, cloud coverage, and image coverage. The algorithm utilizes the global grids to create an ensemble coverage relation between images and grids. A locally optimal initial solution is obtained by a greedy algorithm, and then a local search is performed to search for the optimal solution by combining the strategies of weighted gain-loss scheme and novel mechanism. Ultimately, it achieves an optimal coverage of remote sensing images within the region of interest. In this paper, it is shown that the method obtains a smaller number of datasets with lower redundancy and higher data utilization and ensures the data quality to a certain extent in order to accurately meet the requirements of the regional coverage of remote sensing images. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of data management techniques for efficiently retrieving requested subsets of large data sets from mass storage devices. Describes the development of algorithms and software that facilitate the partitioning of a large data set into multiple "clusters" that reflect their expected access. (Author/JKP)

Explores potential of mathematical theory of communication for information science and examines question of how to encode a message at time it is encountered for subsequent recall. Examples of problem, a prototype computer program and experiment, coding as embedding in associational nets, and nature of information are discussed. (Nine references.) (EJS)

Using examples of data from the areas of information retrieval and of multivariate data analysis, six hierarchic clustering algorithms (single link, median, centroid, group average, complete link, Wards's) are examined and evaluated by using three proposed coefficients of hierarchic structure. Nine references are cited. (EJS)

Offers characterization of sign-transmission which is more general than conventional signal-transmission theory. Concepts and terminology, formal description of individual communications process, reconciliation with classical signal-detection theory, applications of sign-detection formalism to information retrieval on MEDLINE database, and a prototype psychological experiment in area of cognitive perception are highlighted. (Twenty-nine references) (EJS)

Reviews the current status of satellite remote sensing data, including problems with efficient storage and rapid retrieval of the data, and appropriate computer graphics to process images. Areas of research concerned with overcoming these problems are described. (16 references) (CLB)

Presents an algorithm for use in natural language document retrieval systems which automatically determines if the conjunction "and" in a statement representing an information need should be translated into a Boolean "and" or "or." The results of an experiment that used the algorithm are reported, and further research is suggested. (CLB)

Presents a coordination level matching algorithm to be used in document retrieval systems that incorporate relevance feedback strategies. It is argued that this algorithm may eliminate the need for the frequent reevaluation of documents that is currently found in such systems, and conditions under which reranking is unnecessary are given. (Author/CLB)

Describes a fast algorithm for comparing the lists of terms representing documents in automatic classification experiments. Complexity and running time for the algorithm are compared to other procedures, and a short algol-like routine is presented in the appendix. Eight references are included. (Author/BK)

Discusses the problem of duplicate citations resulting from file overlaps in multidatabase searching and shows that such duplicates could be identified automatically and eliminated by a host computer as a complementary service to online retrieval. Steps involved in the realization of this service are described, and 11 references are listed. (RBF)

An information retrieval system which aspires to retrieval of relevant documents should have second stage which will order topical set to provide maximum informativeness to requestor. A two-stage system concerned only with topicality can be iterated to generate a high recall set and discard imprecise documents without user input. (EJS)

Proposes algorithmic method of searching and presents experimental data testing efficacy of using Boolean negation to eliminate redundancy and reduce cognitive strain of communication barrier at human-computer interface. Cognitive information processes, concept of Boolean NOT, and problem-solving behavior are explored. Twenty-five references are included. (EJS)

With the continuous breakthrough of natural language processing, the application of intelligent question-answering technology in electric power systems has attracted wide attention. However, at present, the traditional question-answering system has poor performance and is difficult to apply in engineering practice. This paper proposes an improved BERTserini algorithm for the intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text-segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is the answer-generation and source-retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for the manual organization of professional question–answer pairs, thereby effectively reducing the manual labor cost compared to traditional question-answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers. [ABSTRACT FROM AUTHOR]

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

Presents a modification function for weights of descriptors for documents in fuzzy retrieval systems which includes some desirable properties that are lacking in the model proposed by Choros and Danilowicz in their study on relative indexing. (MBR)

In this work we present an extensive sensitivity study of retrieved total ozone columns from clear sky Global Ozone Monitoring Experiment 2 (GOME-2) measurements between 325 and 335nm which are corrected for instrument degradation. Employing an algorithm based on the scaling of a reference ozone profile with the extension to analytically calculate total column averaging kernels, allows us to investigate the impact of the choice of the reference profile on the retrieved total ozone column, since it represents a regularization of the retrieval. It introduces an error to the retrieved column with respect to the true column typically in the order of 1% depending on the reference scaling profile. However, a proper interpretation of the retrieved column using the total column averaging kernel avoids this error, which is demonstrated by a validation of GOME-2 total ozone columns with collocated ozonesonde and ground-based total ozone column measurements. Globally, we report a bias of 0.1% and a SD of 2.5% for 647 collocations with ground-based and ozonesonde measurements at dif15 ferent geolocations in the period of 2007 to 2010. Futhermore, an extended validation solely based on ground-based observations and a strict cloud filtering shows that the use of pseudo spherical scalar radiative transfer is fully suffcient for the purpose of this retrieval. Polarization of light by atmospheric scattering affects the retrieval accuracy only marginally and thus can be ignored. Finally, we study the effect of instrument 20 degradation on the retrieved total ozone columns for the first four years of GOME-2 observations and discuss the effciency of the proposed radiometric correction. [ABSTRACT FROM AUTHOR]

Based on the distributed decision tree algorithm, this paper first proposes a method of vertically partitioning datasets and synchronously updating the hash table to establish an information-based mass data retrieval method in a heterogeneous distributed environment, as well as using interval segmentation and interval filtering technologies for improved algorithm of distributed decision tree. The distributed decision tree algorithm uses the attribute histogram data structure to merge the category list into each attribute list, reducing the amount of data that needs to reside in the memory. Second, we adopt the strategy of vertically dividing the dataset and synchronously updating the hash table, select the hash table entries that can be used to update according to the minimum Gini value, modify the corresponding entries and use the hash table to record and control each sub-site. In the case of node splitting, it has a high accuracy rate. In addition, for classification problems that meet monotonic constraints in a distributed environment, this paper will extend the idea of building a monotonic decision tree in a distributed environment, supplementing the distributed decision tree algorithm, adding a modification rule and modifying the generated nonmonotonic decision tree to monotonicity. In order to solve the high load problem of the privacy-protected data stream classification mining algorithm under a single node, a Storm platform for the parallel algorithm PPFDT_P based on the distributed decision tree algorithm is designed and implemented. At the same time, considering that the word vector model improves the deep representation of features and solves the problem of feature high-dimensional sparseness, and the iterative decision tree algorithm GBDT model is more suitable for non-high-dimensional dense features, the iterative decision tree algorithm will be integrated into the word vector model (GBDT) in the data retrieval application, using the distributed representation of words, namely word vectors, to classify short messages on the GBDT model. Experimental results show that the distributed decision tree algorithm has high efficiency, good speed-up and good scalability, so that there is no need to increase the number of datasets at each sub-site at any time. Only a small number of data items are inserted. By splitting some leaf nodes, a small amount is added by branching to achieve a monotonic decision tree. The proposed system achieves a massive data ratio of 54.1% while compared with other networks of massive data ratio. [ABSTRACT FROM AUTHOR]

The article presents a study which investigates the retrieval of aerosol optical depth (AOD) through the use of GOES-East and GOES-West reflected radiances imagers over the Western U.S. The study used two satellites to exploit the observation on the retrievals, GOES Aerosol/Smoke Product (GASP) algorithm, and correlation and cross-calibration analysis. Results show that the combined algorithm of AOD works only when both the satellites have observations at the same time.

The article presents a study which describes the retrieval algorithm and examines the impact of the most significant parameters affecting the accuracy of the aerosol extinction profile retrieval. It states that profiles of the stratospheric aerosol are recovered from SCIAMACHY/Envisat limb-scatter observations. Results show that surface albedo is the parameter with the largest impact, in particular is the SCHIAMACHY observations in the Southern Hemisphere.

Vehicular Adhoc Networks (VANETs) enable vehicles to act as mobile nodes that can fetch, share, and disseminate information about vehicle safety, emergency events, warning messages, and passenger infotainment. However, the continuous dissemination of information from vehicles and their one-hop neighbor nodes, Road Side Units (RSUs), and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based network. Therefore, Information Centric Networks (ICN) are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile, dynamic, and errorprone domains. In ICN-based Vehicular-IoT networks, consumer mobility is implicitly supported, but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes. This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination. The proposed protocol, called Network Cording Multiple Solutions Scheduling (NCMSS), is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources. Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching, data retrieval delay, and end-to-end application throughput. The end-to-end throughput in proposed NCMSS is 22% higher (for 1024 byte data) than existing solutions whereas delay in NCMSS is reduced by 5% in comparison with existing solutions. [ABSTRACT FROM AUTHOR]