Reviewerly/exHarmony huggingface dataset:
Descriptive Naming &amp; Summarization of large text using Topic Model-A Survey

Abstract Due to the plethora of documents containing large scale of text that are available on web it sometimes gets difficult to go through each document to get the clear picture of what the text is depicting. In this paper, we are analyzing several techniques to evaluate Topic Model. A Topic Model is a very popular approach for representing and smoothing the content of documents. Here we will focus on uncovering the thematic structure of a corpus of document that will help in document classification and for compact document topic representation. We have gone through some of the famous topic model such as-Latent Semantic Indexing (LSI),Probabilistic Latent Semantic Indexing (PLSI),Latent Dirichlet Allocation (LDA),Pachinko Allocation Model (PAM) where we encounter few issues such as Topic models are not proper for some SNS such as micro blogging and supervise learning techniques are designed for one-labeled corpus-i. e. they are limiting the document to a single label.
The Parameterized Suffix Tray

Let \(\varSigma \) and \(\varPi \) be disjoint alphabets, respectively called the static alphabet and the parameterized alphabet. Two strings x and y over \(\varSigma \cup \varPi \) of equal length are said to parameterized match (p-match) if there exists a renaming bijection f on \(\varSigma \) and \(\varPi \) which is identity on \(\varSigma \) and maps the characters of x to those of y so that the two strings become identical. The indexing version of the problem of finding p-matching occurrences of a given pattern in the text is a well-studied topic in string matching. In this paper, we present a state-of-the-art indexing structure for p-matching called the parameterized suffix tray of an input text T, denoted by \(\mathsf {PSTray}(T)\). We show that \(\mathsf {PSTray}(T)\) occupies O(n) space and supports pattern matching queries in \(O(m + \log (\sigma +\pi ) + occ )\) time, where n is the length of t, m is the length of a query pattern P, \(\pi \) is the number of distinct symbols of \(|\varPi |\) in T, \(\sigma \) is the number of distinct symbols of \(|\varSigma |\) in T and \( occ \) is the number of p-matching occurrences of P in T. We also present how to build \(\mathsf {PSTray}(T)\) in O(n) time from the parameterized suffix tree of T.
Statistics Using R: An Integrative Approach, by S. L.Weinberg, D.Harel and S. K.Abramowitz (Cambridge University Press, Cambridge, pp. 692, 2020)

Economic RecordVolume 97, Issue 317 p. 317-318 Review Statistics Using R: An Integrative Approach, by Weinberg, S. L., Harel, D. and Abramowitz, S. K. ( Cambridge University Press, Cambridge, pp. 692, 2020) Lauren Kennedy, Corresponding Author Lauren Kennedy lauren.kennedy1@monash.edu Department of Economics and Business Statistics, Monash UniversitySearch for more papers by this author Lauren Kennedy, Corresponding Author Lauren Kennedy lauren.kennedy1@monash.edu Department of Economics and Business Statistics, Monash UniversitySearch for more papers by this author First published: 24 May 2021 https://doi.org/10.1111/1475-4932.12616 LK was provided with a print version of Statistics Using R: An integrative approach (Weinberg, Harel, Abramowitz, 2020) in order to provide this review. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume97, Issue317June 2021Pages 317-318 RelatedInformation
Mapping Similarity Detectors of Code Clone to Academic Integrity in Programming

When dealing with academic integrity in programming, code similarity detectors are often used to raise suspicion of student programs with undue similarity. However, it appears that only detectors designed for that purpose are discussed in computing education publications, although similarity detectors for other purposes might also be beneficial, both to foster research and to avoid `reinventing the wheel'. This paper maps similarity detectors used to identify code clones, repeated code segments in a software system, to the classifications used for academic integrity purposes. It will help academic integrity researchers to understand code clone similarity detectors and to read papers from that area. While there are other reviews of code clone detection, ours should be easier for computing educators to read as it uses the terminologies of academic integrity.
On generic complexity of theories of finite algebraic structures

Abstract This article is devoted to investigation of generic complexity of universal and elementary theories of finite algebraic structures with universal set with more than one element of finite predicate signature with equality. We prove that there are no polynomial strongly generic algorithm, recognizing any such theory, provided P = BPP and P ≠ NP (P ≠ PSPACE). The author is supported by Russian Science Foundation, grant 19-11-00209.
Multifractal analysis of spatial–temporal characteristics in the Alpine–Himalayan seismic zone

Abstract The occurrence regularity and propagation mechanism of seismic activity are complicated and variable to be described by existing models. This paper presents a multifractal analysis of the Alpine–Himalayan seismic zone in time–space series. The multifractal spectrum of energy released from the seismic zone is obtained from different angles. Results show that earthquakes in the seismic zone possess complex multifractal characteristics in time–space series. Moreover, the clustering feature of the seismic activity intensity in the seismic zone is similar among the time–space series.
Deep Learning Distributed a Cloud Storage Systems for Secure Data Uploading With Chunk-Based Deduplication

Cloud computing was based on the infrastructural and conceptual basis with secure computing. Cloud-based services and service providers are being progressed which has resulted in cloud-based trending technology. In a cloud environment, the mechanism of privacy protection is based on the chunking process and analysis of deduplication with privacy protection in the distribution of data storage. If security is not robust and consistent, the flexibility and advantages of using deep learning have to offer credibility. In proposed system, the user uploads new data in cloud computing. There are two processes applied for security analysis: Deduplication and Chunking process. The user request keyword which is compared with the spare index of hash term and this term was stored by the chunking process. The proposed algorithm of Reliability-based Neural Collaborative Filtering (RNCF) is used for chunking the cloud data content in three ways such as granularity, point of application, and time of application. RCNF algorithm Spare index term was compared with deduplication analysis. If the chunk data mismatched with user request, then the request will be responded to the user. Suppose if the chunk data matched with the user keyword then the proposed algorithm of Deep learning (DL) techniques are applied for security purposes. The Distributed Secure Cloud Data Chunk (DSCDC) algorithm is used for the security process for authorizing the user, if thread analyzed then blocks the user. Deduplication is analyzed and security level is checked through the proposed DL algorithm.  Experimental results shown on the real-world dataset of cloud data shows its efficiency, security, and effectiveness.Index Term: Deep Learning, Cloud Computing, Deduplication, Chunking, Security, Filtering algorithm and DSCDC.
Analysis of Pupil Dilation on Different Emotional States by Using Computer Vision Algorithms

Measurement of pupil diameter can be helpful to study human mental health. In medical science pupillary tracking helps the clinicians to detect the level of depression, stress and anxiety. Many studies have been undertaken to detect emotional states of a person from his pupil movement as well as size. Pupil diameter measurement follows the stages of detecting face, detecting eyes and detecting pupil. In this paper we have analyzed the effect of three emotions, i.e., happy, sad and surprise on the pupil diameter using the computer vision techniques.
Deep Functional Network (DFN)

We introduce Deep Functional Network (DFN) that approximates a black-box Deep Neural Network (DNN) to a functional program consisting of a set of well-known functions and data flows among them. A DFN not only provides a semantic interpretation of a DNN but also enables easy deployment and optimization of the translated program according to the requirements and constraints of the target intelligent sensing system. To interpret a DNN, we propose the DFN framework consisting of two steps: 1) function estimation that estimates the distribution of functions likely to be used in the source DNN and 2) network formation that finds a functional network in the form of a directed acyclic graph (DAG) given the estimated function distribution. Our empirical study conducted with 16 state-of-the-art DNNs demonstrates that the generated DFNs provide semantic understandings of the DNNs along with comparable classification accuracy to the source DNNs. We implement two intelligent sensing systems that use the proposed DFN: 1) a mobile robot that avoids obstacles detected by a camera and 2) a smartphone-based human activity recognizer using IMU sensors, where different sizes of DFNs are generated to complete the task under various resource budgets, i.e., execution time and energy consumption dynamically imposed by run-time scenarios. The experiment result demonstrates that a set of DFNs generated from a single DNN enable both systems to achieve the desired performance under various resource constraints based on semantic understanding of the DNNs.
La lingua letteraria di non nativi in un itinerario didattico multimodale Cosa resta della formazione nella pratica di insegnamento?

In the initial sections of this paper I describe a course addressed to a group of teachers, during the time when they were at university following an initial teacher education course in primary education. These becoming-teachers were asked to read Scontro di civiltà per un ascensore a Piazza Vittorio (Lakhous 2006) using a multimodal approach. Five years after this course was concluded, a survey was carried out in order to evaluate the effectiveness of its long term goals: subjects (currently teachers) were asked whether they have used multimodal approaches in their reading classes and whether they use Italian literature written by non-native Italian speakers in a language teaching context.
Research on the global evaluation method of complex curve contour

With the development of the advanced precision manufacturing field, the curve profile is becoming more and more complex, and the profile curve evaluation is no longer limited to the two-dimensional plane. Aiming at the difficulty and low efficiency of complex contour evaluation of high-precision parts, this paper proposes a global evaluation method of three-dimensional curve contour. After the curve contour is extracted, dimensioned and calibrated, according to the definition of GB/T1182-2008 plane curve contour, an envelope circle equation is established to evaluate the contour of the complex line. The curve contour theoretical points are used to establish the envelope detection area function, and the position relationship of the measuring points relative to the theoretical contour is evaluated according to the circle function of the envelope group, and the line contour error is determined. Experiments show that the evaluation result is intuitive and can be traced back to the specific area of the curve contour. This method realizes the evaluation of the whole area of the spatially complex curve contour. The method is accurate and efficient, and has overall and unitary characteristics.
Semantic-Guided High-Order Region Attention Embedding for Zero-Shot Learning

In zero-shot learning, knowledge transfer problem is the major challenge, which can be achieved by exploring the pattern between visual and semantic space. However, only aligning the global visual features with semantic vectors may ignore some discriminative differences. The local region features are not only implicitly related with semantic vectors, but also contain more discriminative information. Besides, most of the previous methods only consider the first-order statistical features, which may fail to capture the complex relations between categories. In this paper, we propose a semantic-guided high-order region attention embedding model that leverages the second-order information of both global features and local region features via different attention modules in an end-to-end fashion. First, we devise an encoder-decoder part to reconstruct the visual feature maps guided by semantic attention. Then, the original and new feature maps are simultaneously fed into their respective following branches to calculate region attentive and global attentive features. After that, a second-order pooling module is integrated to form higher-order features. The comprehensive experiments on four popular datasets of CUB, AWA2, SUN and aPY show the efficiency of our proposed model for zero-shot learning task and a considerable improvement over the state-of-the-art methods under generalized zero-shot learning setting.
Simulation Study on Water-Based Ink Transfer in Gravure Printing

The transfer rate of gravure water-based ink is one of the key factors to ensure the quality of printing production. The influencing factors of ink transfer in gravure printing are analyzed. The printing pressure changes the gap between the ink and the substrate so as to adjust the capillary action strength. Thereby the ink transfer rate is changed. Printing speed affects the ink transfer rate by changing the transfer time of the ink in the contact area. When the viscosity of the ink changes, the amount of ink obtained in the net hole will change, and the transfer rate will also change. The ink transfer process was simulated by FLUENT software. The ink transfer volume under four printing pressure, at six different printing speed and with seven different viscosity was analyzed respectively and the optimum parameters were given. The results show that the transfer rate increases with the increase of pressure and the transfer rate decreases with the increase of rotating speed and the viscosity of the ink.
Exploiting Patterns of Object Use for Human Activity Recognition

The accurate recognition of activities of daily living (ADL) is fundamental in the support and provision of assistance for the elderly and cognitively impaired. Current ontology-based techniques and knowledge-driven model object concepts form assumptions and everyday knowledge of objects used for activities. Activities modelled from assumptions and everyday knowledge can lead to incorrect recognition results of routine activities and possible failure to detect abnormal activity trends. A significant step to the accurate recognition of activities of daily living is the discovery of the object usage for specific routine activities. This chapter presents an approach that discovers object usage for routine activities using latent Dirichlet allocation (LDA) topic modelling. The object usage discovery augments an activity ontology that enables recognition of simple activities of daily living in the home environment. The proposed approach is evaluated and validated using the Kasteren and Ordonez datasets.
Improvement and Analysis of Semantic Similarity Algorithm Based on Linguistic Concept Structure

With the rapid development of information age, various social groups and corresponding institutions are producing a large amount of information data every day. For such huge data storage and identification, in order to manage such data more efficiently and reasonably, traditional semantic similarity algorithm emerges. However, the accuracy of the traditional semantic similarity algorithm is relatively low, and the convergence of corresponding algorithm is poor. Based on this problem, this paper starts with the conceptual structure of language, analyzes the depth of language structure and the distance between nodes, and analyzes the two levels as the starting point. For the information of a specific data resource description frame type, the weight of interconnected edges is used for impact analysis so as to realize the semantic similarity impact analysis of all information data. Based on the above improvements, this paper also systematically establishes the data information modeling process based on language conceptual structure and establishes the corresponding model. In the experimental part, the improved algorithm is simulated and analyzed. The simulation results show that compared with the traditional algorithm, the algorithm has obvious accuracy improvement.
THE SPECIFICITY OF VALUE ORIENTATIONS OF STUDENTS IN TECHNICAL PROFILE COLLEGES

The article is aimed to reveal the specificity of colleges technical profile student’s value orientations. Theoretical analysis of modern scientific researches of value orientation’s role in the college student’s professional becoming are presented. The results of empirical research of value-motivational types, terminal and instrumental values of college students are analyzed. the prevailing value orientations of the technical profile college students are the focus on improving material well-being, achievement of social status and recognition, achievement in various spheres of life and manifestation of autonomy are described. The role of instrumental values of freedom, sense of life, self-esteem, creativity, self-discipline and authority in student’s learning activity are represented.
Depth reconstruction for 3D light-field display based on axially distributed light field

Continuous depth maps are reconstructed based on depth estimation from light-field data of axially distributed image sensing (ADS). In the proposed method, the light field of ADS is introduced, and the light-field trajectory function is presented, which formulates the relationship between image point positions in different axially captured images and the depth of object point. The depth value of an object is achieved by searching the light-field trajectory through statistical regression, which leads to an accurate depth estimation by making use of the structure information among the densely sampled views in light-field data. Moreover, as regions with smooth texture and occlusions do not contain reliable depth information, an energy minimization-based depth map optimization is carried out from initial estimations to obtain continuous and robust depth maps. Experimental results demonstrate that the reconstructed depth maps are accurate, continuous, and able to preserve more details. Moreover, the synthesized light-field images calculated by reconstructed depth map show good effect in 3D light-field display, and the validity of the proposed method is verified.
A Machine Learning Based Framework for Enterprise Document Classification

Enterprise Content Management (ECM) systems store large amounts of documents that have to be conveniently labelled for easy managed and searching. The classification rules behind the labelling process are informal and tend to change that complicates the labelling even more. We propose a machine learning based document classification framework (Framework) that allows for continuous retraining of the classification bots, for easy analysis of the bot training results and for tuning of the further training runs to address this challenge. Documents and metadata fields typical for ECM systems are used in the research. The Framework comprises selection of classification and vectorization methods, configuration of the methods hyperparameters and general learning related parameters. The model provides user with visual tools to analyze the classification performance and to tune the further steps of the learning. A couple of challenges are addressed – as handling informal and eventually changing criteria for document classification, and dealing with imbalanced data sets. A prototype of the proposed Framework is developed and short analysis of the prototype performance is presented in the article.
Development of educational motivation through the youth socio-cultural, environmental and leisure practices

The presents the results of a study of the developmental potential of socio-cultural and leisure practices of youth, used in university education to increase educational adaptation and motivation of students. The relevance of the study lies in the identification of new motivational mechanisms that provide more effective educational adaptation and motivation of young people to obtain vocational education. The relevance of the research is confirmed in the substantive discussion of scientists in the field of transformation of cultural and leisure practices and improving the mechanisms of educational motivation in vocational education. The empirical part of the study of the development of educational motivation through socio-cultural and leisure practices, implemented in the university community, was conducted by a questionnaire survey of 458 people from among the students of the Stavropol Territory. The data was processed using the SPSS Statistics program (version 21). Factor analysis was performed by the method of separation of the main components and by the method of rotation – Varimax with Kaiser Normalization. We present the models of socio-cultural activity that contribute to educational adaptation and increase the motivation of professional self-determination and the formation of future specialists.
Comparative Study on Feature-Based Scoring Using Vector Space Modelling System

This paper shows the importance of automated scoring (AS) and that it is better than human graders in terms of degree of reproducibility. Considering the potential of the automated scoring system, there is further a need to refine and develop the existing system. The paper goes through the state of the art. It presents the results concerning the problems of existing systems. The paper also presents the semantic features that are indispensable in the scoring system as they have complete content. Moreover, in the present research, a huge deviation has been exhibited by the system which has been shown later in performance analysis of the study, and this clearly indicates the novelty and improved results of the system. It explains the algorithms included in the methodology of this proposed system. The novelty of our work consists in the use of its own similarity function and its notation mechanism. It does not use the cosine similarity function between two vectors. This paper describes and develops a more accurate system which employs a statistical method for scoring. This system adopts and integrates rule-based semantic feature analysis.