Reviewerly/exHarmony huggingface dataset:
Computational framework with novel features for classification of foot postures in Indian classical dance

There is a greater need to develop and establish Artificial Intelligence (AI) and its subdomains, as the computing requirements are increasingly met by the emerging hardware technologies. The machine-learning techniques are well suited for the learning-based AI applications that are useful to our d aily life. Further, the machine-learning applications can resolve numerous problems of the South Indian Classical Dance (SICD). Nevertheless, these aspects are not yet addressed thoroughly owing to the vastness of the domain. Moreover, the lack of a combined expertise in both domains of the SICD and the computing aggravates the problem. The automatic identification and annotation of a vital aspect called sthanas (foot postures) are necessary for the process of digitizing, archiving and analytics of the SICD. Hence in this paper, we propose a framework to classify the SICD images based on the foot sthanas. The proposed framework incorporates methods to convert raw data to a curated dataset, and extract principal features that are unique to the various foot posture in classical dance, in order to generate an accurate classification. Among the different techniques that were used to evaluate the accuracy, Naive Bayes, trained with the domain-specific features, outperformed all other classification models. The methodology followed in this work can be applied to various national and international dance forms with proper incorporation of their domain-specific features.
Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local region-based energy fitting model assists in extracting the inhomogeneous intensity regions, whereas the curve evolution over the homogeneous regions is accelerated by including the global region-based model in the proposed method. Both the local and global region-based energy functions in the proposed model drag contours toward the accurate object boundaries with precision. Each of the local and global region-based parts are parameterized with weight coefficients, based on image complexity, to modulate two parts. The proposed hybrid model is strongly capable of detecting region of interests (ROIs) in the presence of complex object boundaries and noise, as its local region-based part comprises bias field. Moreover, the proposed method includes a new bias field (NBF) initialization and eliminates the dependence over the initial contour position. Experimental results on synthetic and real-world images, produced by the proposed model, and comparative analysis with previous state-of-the-art methods confirm its superior performance in terms of both time efficiency and segmentation accuracy.
Is printed-text the best choice? A mixed-method case study on reading comprehension

Reading is a basic language skill which is essential not only because it develops the literacy skills of the students but because enables them to comprehend and formulate the discourse within a language.Lack of reading, on the other hand, causes impairment of comprehension and affects the academic progress of students greatly.The range of studies suggesting that screen-based reading has a greater effect than primarily paper-based is growing each day.Accordingly, this study aims to set forth the effects of screen-based versus paper-based reading on reading comprehension of English.This is a mixed-method case study, conducted by a group of 30 freshmen, majoring in English Language Teaching undergraduate program at a state-run university.As a tool for data collection, performance tests and written opinion forms were used.Before the study, three different reading texts have been specified.Of these, two were printed, and four of them were digital.Two of the digital text was plain text (pdf); the other two texts were enriched with pictures and links (hypertext).After reading the texts, the participants answered the reading comprehension questions provided by the researcher.Besides, after each application, the written views of the participants have been gathered.In the process of data analysis, performance tests have been scored, and written views are divided into themes.As a result, the success of the application with the highest means score has been identified as the first and fourth reading texts, which were printed.The two lowest success means are identified as containing links to text pictures and hypertext.The findings additionally discovered that students who study texts in print, scored substantially higher at the studying comprehension tests than the students who study the texts digitally.All in all, this study involves several pedagogical implications for reading comprehension in the field of foreign language settings.
Efficiency of learning and academic motivation of students in conditions of online interaction with the teacher (on the example of video-lecture)

The objective is to study the relationship between students’ online lecture assessment, the quality of learning and academic motivation. Background. Digitalization of education actualizes the problem of the online-learning effectiveness, which might decrease through reducing the social presence of the teacher and students in the educational situation. Due to these conditions, students’ motivation might mean a great deal. Study design. Students evaluated the classroom lecture and its video using the parameters “interest”, “content” and “usefulness”, and then the scores and the volume of the reproduced educational material were compared. Participants. The study involved 112 students (19.55±0.66 years, 83.9% of women). Measurements. Students evaluated lectures on the proposed parameters using a 7-point scale and retold their content. Students’ motivation was measured by “Academic Motivation Scale”. In addition, students reported about their professional plans and professional experience. Results. Students assessed video lectures lower and reproduced their content worse in comparison with classroom lectures. Assessments of the video lecture content were positively correlated with the students’ intrinsic cognitive motivation, and the volume of the reproduced content of the lecture was negatively correlated with extrinsic motivation. The influence of intrinsic cognitive motivation and extrinsic motivation of students on the assessment of the video lecture content and the volume of the reproduced content was confirmed by a variance analysis. Conclusions. In terms of online interaction, the students’ subjective assessments of the classroom lectures are reduced. Intrinsic cognitive motivation prevents the reduction of sub¬jective assessments of the video content. Extrinsic motivation helps to reduce the reproduced content of the video lecture.
Semantic Drift in Multilingual Representations

Multilingual representations have mostly been evaluated based on their performance on specific tasks. In this article, we look beyond engineering goals and analyze the relations between languages in computational representations. We introduce a methodology for comparing languages based on their organization of semantic concepts. We propose to conduct an adapted version of representational similarity analysis of a selected set of concepts in computational multilingual representations. Using this analysis method, we can reconstruct a phylogenetic tree that closely resembles those assumed by linguistic experts. These results indicate that multilingual distributional representations that are only trained on monolingual text and bilingual dictionaries preserve relations between languages without the need for any etymological information. In addition, we propose a measure to identify semantic drift between language families. We perform experiments on word-based and sentence-based multilingual models and provide both quantitative results and qualitative examples. Analyses of semantic drift in multilingual representations can serve two purposes: They can indicate unwanted characteristics of the computational models and they provide a quantitative means to study linguistic phenomena across languages.
Semantic knowledge networks in education

The article is devoted to the modeling a semantic knowledge networks. The knowledge network is the basic concept of the problem of knowledge management. This is a new discipline that implements the principles of sustainable development of education. The method of constructing a semantic knowledge network allows us to analyze the connections between educational disciplines: “Economic Cybernetics”, “Algorithms and Programming” and “Calculus”. The paper compares the topological characteristics of the concept graphs related to various disciplines. We develop the algorithm to implement the subject area model in the form of a semantic knowledge network. 125 concepts are analyzed that provide optimal mastering disciplines and establish the connection between them.
Load Balancing in Cloud Computing Using Genetic Algorithm and Fuzzy Logic

Cloud computing systems play a vital role in the digital age. A critical bottleneck in most scenarios in cloud computing is the high degree of unpredictability with respect to resource availability and network bandwidth, which may lead to low Quality of Service (like low response times), which can be improved by Load Balancing. Load balancing concerns with efficiently distributing incoming network traffic across a group of servers. This ensures no single server bears too much demand, and thus the availability of applications and websites for users is increased. Due to the huge state-space of such a problem, implementing task scheduling algorithms in load balancing can be very effective. In this paper, we propose a hybrid intelligent approach to load balancing: a Genetic Algorithm module arranges the jobs randomly, and a fuzzy logic module builds the objective function for determining busy states of servers according to their RAM and CPU task queues. The fuzzy input variables include the satisfaction degree and the start and end times of the service, and the fuzzy output is service availability. Computational experiments showed that the best solution was obtained within half of the planned execution time, which leads to higher user satisfaction degree.
Balancing Between Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. In Interactive Recommender Systems (IRS), user preferences and the system’s fairness status are constantly changing over time. Existing fairness-aware recommenders mainly consider fairness in static settings. Directly applying existing methods to IRS will result in poor recommendation. To resolve this problem, we propose a reinforcement learning based framework, FairRec, to dynamically maintain a long-term balance between accuracy and fairness in IRS. User preferences and the system’s fairness status are jointly compressed into the state representation to generate recommendations. FairRec aims at maximizing our designed cumulative reward that combines accuracy and fairness. Extensive experiments validate that FairRec can improve fairness, while preserving good recommendation quality.
U.S. Geological Survey 2018 Kīlauea Volcano eruption response in Hawai'i—After-action review

First posted May 14, 2020 For additional information, contact: Regional Director, AlaskaU.S. Geological Survey4210 University DriveAnchorage, Alaska 99508-4560 The 2018 Kīlauea Volcano eruption lasted 107 days, and now ranks as the most destructive event at Kilauea since 1790, and as one of the most costly volcanic disasters in U.S. history. Multiple simultaneous hazard events unfolded, including sustained seismic activity leading to collapse at the summit of Halema'uma'u crater and severe damage to the HVO facility, with additional eruption of lava in the Kīlauea Lower East Rift Zone that progressively grew to a total of 24 fissure openings. In response to the complex and uncertain nature of the eruption, U.S. Geological Survey (USGS) team activities tended to coalesce around several interrelated core functional areas: science operations, emergency management and administration, and external communications. Upon cessation of the eruption, the USGS Hazard Response Executive Committee charged the Alaska Regional Office to lead an After-Action Review team, which focused on two basic questions: (1) what went well, and why?; and (2) what can be improved, and how? The purpose of the After-Action Review is to identify priorities for programmatic or policy improvements that the USGS can feasibly implement to advance strategic preparation for future disasters, and thereby reduce public vulnerabilities. Specifically, the review is intended to help USGS respond even better to the next eruption in Hawai'i or elsewhere by advancing any of the following goals:Design better planning scenarios;Enhance response team effectiveness;Assist local decision makers;Promote new areas of strategic research; andStreamline administrative, finance, and Incident Management Team support functions during a crisis to secure continuity of operations and essential records management.
High Performance Hadoop Distributed File System

Although by the end of 2020, most of companies will be running 1000 node Hadoop in the system, the Hadoop implementation is still accompanied by many challenges like security, fault tolerance, flexibility.Hadoop is a software paradigm that handles big data, and it has a distributed file systems so-called Hadoop Distributed File System (HDFS).HDFS has the ability to handle fault tolerance using data replication technique.It works by repeating the data in multiple DataNodes which means the reliability and availability are achieved.Although data replications technique works well, but still waste much more time because it uses single pipelined paradigm.The proposed approach improves the performance of HDFS by using multiple pipelines in transferring data blocks instead of single pipeline.In addition, each DataNode will update its reliability value after each round and send this updated data to the NameNode.The NameNode will sort the DataNodes according to the reliability value.When the client submits request to upload data block, the NameNode will reply by a list of high reliability DataNodes that will achieve high performance.The proposed approach is fully implemented and the experimental results show that it improves the performance of HDFS write operations.
Perfect State Transfer on Cayley Graphs over Dihedral Groups: The Non-Normal Case

Recently, perfect state transfer (PST for short) on graphs has attracted great attention due to their applications in quantum information processing and quantum computations. Many constructions and results have been established through various graphs. However, most of the graphs previously investigated are abelian Cayley graphs. Necessary and sufficient conditions for Cayley graphs over dihedral groups having perfect state transfer were studied recently. The key idea in that paper is the assumption of the normality of the connection set. In those cases, viewed as an element in a group algebra, the connection set is in the center of the group algebra, which makes the situations just like in the abelian case. In this paper, we study the non-normal case. In this case, the discussion becomes more complicated. Using the representations of the dihedral group $D_n$, we show that ${\rm Cay}(D_n,S)$ cannot have PST if $n$ is odd. For even integers $n$, it is proved that if ${\rm Cay}(D_n,S)$ has PST, then $S$ is normal.
Simulation of Educational Practices in the System of Higher Education

The simulation of educational practices in the higher education is analysed taking into account the interviews with lecturers and students of Moscow universities (October 2018 to May 2019). Taking into account the analysis of conditions for simulating educational practices the author comes to following conclusion: the growth rate of simulating educational practices is a result of modernising content and structure of higher education in Russia, establishing its model, which includes state and market regulation. Higher education without sufficient financial, material, technical, human and other resources leads to simulating educational practices. Such imitation of educational practices by lecturers under current conditions implies, first, streamlining activities, and, second, reacting to their heavy workload. At the same time, it has been revealed that lecturers sabotage the policy of retaining students. The sabotage implies accurate execution of professional responsibilities and avoiding formalism. Simulating educational practices by students includes, first, rationalisation of educational sphere and, second, more or less regular confirming their social status and reaching their aim, i.e. diploma of higher education. The perception of simulating practices by lecturers is contradictory, as they admit the fact of imitation and define its forms, but deny the deviant or delinquent nature of the notion. Students generally admit simulating educational practices and understand its deviant or delinquent nature.
Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection

In this paper we demonstrate how codeswitching patterns can be utilised to improve various downstream NLP applications.In particular, we encode different switching features to improve humour, sarcasm and hate speech detection tasks.We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.
Revisiting Spatial Invariance with Low-Rank Local Connectivity

Convolutional neural networks are among the most successful architectures in deep learning with this success at least partially attributable to the efficacy of spatial invariance as an inductive bias. Locally connected layers, which differ from convolutional layers only in their lack of spatial invariance, usually perform poorly in practice. However, these observations still leave open the possibility that some degree of relaxation of spatial invariance may yield a better inductive bias than either convolution or local connectivity. To test this hypothesis, we design a method to relax the spatial invariance of a network layer in a controlled manner; we create a \textit{low-rank} locally connected layer, where the filter bank applied at each position is constructed as a linear combination of basis set of filter banks with spatially varying combining weights. By varying the number of basis filter banks, we can control the degree of relaxation of spatial invariance. In experiments with small convolutional networks, we find that relaxing spatial invariance improves classification accuracy over both convolution and locally connected layers across MNIST, CIFAR-10, and CelebA datasets, thus suggesting that spatial invariance may be an overly restrictive prior.
Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data

Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a parameter choice. This paper proposes an add-on anomaly threshold technique to identify the observations whose anomaly scores are extreme and significantly deviate from others, and then such observations are assumed to be ”abnormal”. The observations whose anomaly scores are significantly distant from ”abnormal” ones will be assumed as ”normal”. Then, the ensemble-based supervised learning is proposed to find a global and efficient anomaly threshold using the information of both ”normal”/”abnormal” behaviours. The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection approaches. Experimental results confirm that the proposed technique achieved a significant improvement compared to the state-of-the-art of two unsupervised anomaly detection algorithms.
An Analysis of Classification of Breast Cancer Dataset Using J48 Algorithm

Classifying tumors into benign and malignant take time and resources; sometimes it takes several radiologists and oncologists to diagnose if a tumor is malignant or benign, especially if features are hardly distinguishable to the human eye.To determine a way to automatically classify if a tumor is benign or malignant, the researchers developed a model using J48 decision tree algorithm to classify a tumor through analysis of cell features extracted by the X-cyt program.Based on confusion matrix analysis, the algorithm performed well by recording a 95 percent accuracy rate derived from confusion matrix analysis.
Review of feature selection methods for text classification

BackgroundWith the immense growth of online information due to Internet, text-categorization has become a very significant technology to classify a large number of documents to make them useful.The concept of text classification is to categorize textual data into one or more predefined categories [1-3].It is not possible to manage manually these big datasets.As a result, we witness the development of various machine learning and data mining methods.These methods are being used for automatic recognition of the patterns in large data sets for gaining useful information.
Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments

Deep learning (DL) models have emerged in recent years as the state-of-the-art technique across numerous machine learning application domains. In particular, image processing-related tasks have seen a significant improvement in terms of performance due to increased availability of large datasets and extensive growth of computing power. In this paper we investigate the problem of group activity recognition in office environments using a multimodal deep learning approach, by fusing audio and visual data from video. Group activity recognition is a complex classification task, given that it extends beyond identifying the activities of individuals, by focusing on the combinations of activities and the interactions between them. The proposed fusion network was trained based on the audio–visual stream from the AMI Corpus dataset. The procedure consists of two steps. First, we extract a joint audio–visual feature representation for activity recognition, and second, we account for the temporal dependencies in the video in order to complete the classification task. We provide a comprehensive set of experimental results showing that our proposed multimodal deep network architecture outperforms previous approaches, which have been designed for unimodal analysis, on the aforementioned AMI dataset.