Reviewerly/exHarmony huggingface dataset:
Information Literacy among High School Students: A Study about Cyber Literacy for the use of Online Resources

Post-COVID situations have opened many opportunities and challenges in the education world. The challenges are the accumulation of the pre-COVID and the COVID technology requirements to have a proper environment for the students. The recently released National Crime Records Bureau (NCRB) of India cautioned the information and library professionals about the need for information literacy programs regarding the use of Online Resources. In response, this study was attempted among students of select high schools in Mysore city to understand the literacy level regarding cyber security and its implication. The data analysis suggests the urgent need for an information literacy program for teachers and librarians.
Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models

Automatic speaker verification (ASV) systems play a prominent role in the security field due to the usability of voice biometrics compared to alternative biometric authentication modalities. Nevertheless, ASV systems are susceptible to malicious voice spoofing attacks. In response to such threats, countermeasures have been devised to prevent breaches and ensure the safety of user data by categorizing utterances as either genuine or spoofed. In this paper, we propose a new voice spoofing countermeasure that seeks to improve the generalization of supervised learning models. This is accomplished by alleviating the problem of intraclass variance. Specifically, the proposed approach addresses the generalization challenge by splitting the classification problem into a set of local subproblems in order to lessen the supervised learning task. The system outperformed existing state-of-the-art approaches with an EER of 0.097% on the ASVspoof challenge corpora related to replaying spoofing attacks.
Epistemic-Uncertainty-Based Divide-and-Conquer Network for Single-Image Super-Resolution

The introduction of convolutional neural networks (CNNs) into single-image super-resolution (SISR) has resulted in remarkable performance in the last decade. There is a contradiction in SISR between indiscriminate processing and the different processing difficulties in different regions, leading to the need for locally differentiated processing of SR networks. In this paper, we propose an epistemic-uncertainty-based divide-and-conquer network (EU-DC) in order to address this problem. Firstly, we build an image-gradient-based divide-and-conquer network (IG-DC) that utilizes gradient-based division to separate degraded images into easy and hard processing regions. Secondly, we model the IG-DC’s epistemic uncertainty map (EUM) by using Monte Carlo dropout and, thus, measure the output confidence of the IG-DC. The lower the output confidence is, the more difficult the IG-DC is to process. The EUM-based division is generated by quantizing the EUM into two levels. Finally, the IG-DC is transformed into an EU-DC by substituting the gradient-based division with EUM-based division. Our extensive experiments demonstrate that the proposed EU-DC achieves better reconstruction performance than that of multiple state-of-the-art SISR methods in terms of both quantitative and visual quality.
The Use of Sketch Word Strategy in Teaching Vocabulary

This research was aimed to find out whether using sketch word strategy was effective in teaching vocabulary. In this case, the researcher used quantitative data approach by using experimental design. There were two classes at seventh grade of SMPN 3 Nisam North Aceh as the sample of this research. There were class VII/1 as experimental consist of 24 students and VII/3 as control group consist of 22 students. In experimental group, the researcher used skecth word strategy in teaching vocabulary. After teaching learning process for three meetings, the researcher gave post-test. The result was shown that the students’ achievement in experimental group was higher than control group. It was proved by the average score from pre-test of experimental group was 64,5 and control group was 53,04. Furthermore, the result of post-test of experimental group was 83,5 and control group was 69,86. It could be concluded that using sketch word effective in teaching vocabulary.
TAEffect: Quantifying interaction risks in trust‐enabled communication systems

Summary The interactive risks of different devices, serving as clients or servers, have increasingly attracted huge attention in various communication systems, such as wireless sensor networks, wireless communication networks, and mobile crowd‐sensing. At present, lots of countermeasures had been proposed and deployed accordingly. Nevertheless, the investigation on the interaction risks between different devices is still very limited to date. In this paper, we propose a novel adverse effect inference mechanism TAEffect for malicious behaviors of devices emerged in various decentralized and open communication systems/networks through network‐percolation theory. At first, four typical malicious interactional behaviors are mapped into four topologies, then, upon which a network influence‐inspired approach is employed to quantify the adverse effect. Finally, multifacet experiments using five real‐world datasets and a synthetic testbed are performed to validate the efficiency and effectiveness. The experimental results show our proposed approach is significant and rational to quantitatively calculate and qualitatively mirror the four kinds of malicious interactional behaviors in diverse misbehavior‐emerged communication systems.
Cross-domain learned view-invariant representation for cross-view action recognition

Cross-view action recognition plays a pivotal role in some occasions where multiple cameras are required for monitoring. However, it has not performed well yet. One of the main challenges many researchers face is the divergence in viewpoints. This paper focuses on a semisupervised cross-domain feature construction and alignment method for action recognition from cross-view cameras. The proposed framework has four significant steps: First, shape context features are extracted, integrated, and encoded as the pose-level feature representation on a low-dimensional space. Second, key poses are obtained by clustering the pose features and considering the sequence-level feature representation. In the next step, the integrated sequence-level features from different view domains are matched via nonlinear kernelized manifold alignment and brought into a shared space by establishing two different projection matrices to learn a transferable model for cross-view action representation. In the final step, a linear SVM classifier is learned to label the unseen actions from different view domains. Experimental results over benchmark datasets report recognition rates of 90%, 90.91%, and 92.95% on INRIA Xmas Motion Acquisition Sequences, Northwestern-UCLA Multi-View 3D Actions, and multi-view action recognition dataset datasets, respectively, which show that our method achieves superior performance.
Fast prototype selection algorithm based on adjacent neighbourhood and boundary approximation

The unceasing increase of data quantity severely limits the wide application of mature classification algorithms due to the unacceptable execution time and the insufficient memory. How to fast incrementally obtain high decision reference set and adapt to incremental data environment is urgently needed in incremental environments, large dataset, etc. This paper proposes a novel prototype selection algorithm by integrating the strategies between condensing method and editing method. To an unlearned pattern, this algorithm extends the references scope from its single nearest neighbour to its k nearest neighbourhood that can expand the judgment information to obtain its detailed neighbour relationship. Then a pattern was determined whether it is a prototype using its neighbour relationship and classification boundary asymptotically strategy. To maintain the higher reference set, this algorithm periodically updates those prototypes that locates in the non-boundary zone or is long-time unlearned. The empirical study shows that this algorithm obtains the smaller and higher boundary prototypes without decreasing classification accuracy and reduction rate than the compared algorithms.
Pengaruh Media Youtube pada Pembelajaran Menulis Teks Eksposisi Siswa Kelas VIII SMP Negeri 2 PematangsiantarPengaruh Media Youtube pada Pembelajaran Menulis Teks Eksposisi Siswa Kelas VIII SMP Negeri 2 Pematangsiantar

Learning to write needs to be mastered by students as early as possible, especially in the text-based 2013 curriculum. One of the materials related to the exposition text is found in the syllabus of the Indonesian language subject for class VIII, in learning media is needed that is able to arouse students' enthusiasm. The problem studied in this study is how the influence of youtube media in learning to write student exposition texts. This study aims to determine the effect of youtube media in learning to write exposition texts for seventh grade students of SMP Negeri 2 Pematangsiantar. The use of youtube media is expected to make it easier for students to understand the material and be able to write exposition texts. This research is a quantitative experiment. With one group pre-test and post-test design. The researcher used random sampling technique in determining the sample so that class VIII-10 students became the sample in this study. This research was conducted at SMP Negeri 2 Pematangsiantar. The technique of collecting data in this study used an exposition text writing test. Data on students' exposition text writing activities in the study were obtained through pre-test and post-test. Based on the results of data analysis, it can be seen that the average score of students before using the youtube learning media (pre-test) is categorized with a value of 41.1 and the average value after using the youtube learning model (post-test) is categorized as good with a value of 73,06, then the results of the average value of the pre-test and post-test can be seen that there is a difference in the average value before (pre-test) and after using youtube learning media on the ability to write exposition texts for class VIII students of SMP Negeri 2 Pematangsiantar. Based on the results of the pre-test and post-test data analysis, it was obtained tcount (16,732) , then compared tcount with ttable with degrees of freedom N-1 = 29 with the number of N=30 and the significant level = 0.05, it was obtained ttablel =1, 699. So, tcount (16,732) &gt; ttable (1, 699) which means that there is a difference in the average value before using youtube learning media and after using youtube learning media. Thus Ho is rejected. With the rejection of Ho then Ha is accepted, meaning that there is a significant difference in the skills of writing exposition texts for class VIII SMP Negeri 2 Pematangsiantar students using youtube learning media.
Motivasi Belajar dan Self Regulated Learning pada Mahasiswa dengan Sistem Pembelajaran Jarak Jauh

This study aims to measure the effect of learning motivation on self regulated learning in the students of School Teacher Education who study remotely at the UPBJJ Samarinda Open University. This study used the Assessing Academic Self Regulated Learning Measurement Scale and the Learning Motivation Measurement Scale. This study involved 56 male students respondents and 236 female students respondent. Determination of the sample using proportional random sampling technique. The research data were analyzed using simple regression analysis techniques. The result revealed that the effect of learning motivation on self regulated learning shows that there is a positive effect of learning motivation on the self-regulated learning in students of School Teacher Education by a value of 𝑅2 41,1% with a significance value of 0,000 (P &lt;0,05). This study recommends the importance of increasing learning motivation in students of School Teacher Education which can increase self regulated learning particularly in distance learning program.
Practical remote sensing data analysis for efficient geological field mapping: An example from the southwest portion of the Three Peaks 7.5ʹ quadrangle, southwest Utah

ABSTRACT We present the results of remote sensing analysis of U.S. Geological Survey digital elevation models, Landsat spectral data, and National Agriculture Imagery Program orthophotos to generate a preliminary geologic map that significantly aided our boots-on-the-ground geologic mapping of the southwest portion of the Three Peaks 7.5ʹ quadrangle in southwest Utah. Sedimentary rocks, intrusive rocks, and a variety of geologic contacts, including unconformities and faults, as well as unconsolidated alluvium are recognized in the study area. We constructed a series of geologic maps using remote sensing data and analysis techniques that are readily available to geoscientists. These techniques include band-ratioing, random forest analysis, and these analyses. Resolution of the resulting geologic maps generated by random forest analysis and principal component analysis were greatly improved by incorporating both the high resolution orthophoto and the 1/3 arc second digital elevation model into the principal component analysis. Our final remotely sensed geologic map integrated results from each technique. We used this remotely sensed geologic map to develop our preliminary plan for the field campaign. We preselected high priority targets (e.g., previously unrecognized units and faults) for in-person field analysis. We also identified highly accessible areas that allowed for efficient use of in-person field time needed for evaluation of large areas covered by relatively homogeneous units. The authors spent 25 days in the field over a seven-week field season, mapping the same area. Here, we compare the remote-sensed geologic maps with the final in-person field checked geologic map and discuss the utility of remote sensing data for detailed geologic field investigations. Preparing a remote sensing geologic map prior to field work has several advantages, including identification of mappable units, recognition of geologic contacts, and selection of priority target areas for direct evaluation of hypothesized field relationships, thereby promoting more efficient geologic mapping.
Data Ecosystem and Data Value Chain: An Exploration of Drones Technology Applications

The drones' market is anticipated to grow significantly because of its increasing use for commercial and professional purposes. They are currently used in many industries and the next frontier is represented by the automotive industry. The volume of disposable data is growing exponentially, reaching more than 44 zettabytes of valuable data in 2020. In order to be able to fully reap the value of data available, firms must delve in big data and Data Value Chain (DVC) discipline. This paper explores the ecosystem formation as well as the challenges and criticalities encountered by Dronus and Here in their interaction and exchange of data. Ecosystems are network of distributed companies that shows multiple links toward their environment that interact via standard and business rules which permit them to be independent but also connected. Data have been collected through multiple sources, i.e. semi-structured interviews with firm's representatives and direct observation. This study will give a contribution for future literature as it focuses on the analysis and research of an important gap that sees knowledge of bottlenecks as a way of conscious and efficient growth. In this way, firms and ecosystems that have successfully understood the importance of these variables found will be able to better answer to changes becoming more agile. Successful big data integration, accessibility and standard business rules, as we identify as the main roadblocks, will allow firms in data-driven sectors, and in particular in computer vision-based data set, to be efficient in the interconnection and to become more agile.
Leukemia Diagnosis Using Deep Learning

Abstract: Leukemia is a deadly malignancy and there are two main types: acute and persistent. Each type has two subtypes: lymphoid and myeloid. There are a total of four subtypes of leukemia. This study proposes a new approach to the disease of all leukemia subtypes from the concept of small blood cells using convolutional effects on living neural organ networks (CNNs) that require large preparative files.
Castellum: A participant management tool for scientific studies

Castellum is a web application aimed mainly at the lifecycle of human research studies by supporting participant management, pseudonym service, recruitment, study appointments and study execution.The main design focus of Castellum is compliance with the European Union's (EU) General Data Protection Regulation (GDPR) and with IT security.Additionally, it is designed to be adaptable to other workflows or processes at different research institutions.
Morphology aware data augmentation with neural language models for online hybrid ASR

Abstract Recognition of Hungarian conversational telephone speech is challenging due to the informal style and morphological richness of the language. Neural Network Language Models (NNLMs) can provide remedy for the high perplexity of the task; however, their high complexity makes them very difficult to apply in the first (single) pass of an online system. Recent studies showed that a considerable part of the knowledge of NNLMs can be transferred to traditional n-grams by using neural text generation based data augmentation. Data augmentation with NNLMs works well for isolating languages; however, we show that it causes a vocabulary explosion in a morphologically rich language. Therefore, we propose a new, morphology aware neural text augmentation method, where we retokenize the generated text into statistically derived subwords. We compare the performance of word-based and subword-based data augmentation techniques with recurrent and Transformer language models and show that subword-based methods can significantly improve the Word Error Rate (WER) while greatly reducing vocabulary size and memory requirements. Combining subword-based modeling and neural language model-based data augmentation, we were able to achieve 11% relative WER reduction and preserve real-time operation of our conversational telephone speech recognition system. Finally, we also demonstrate that subword-based neural text augmentation outperforms the word-based approach not only in terms of overall WER but also in recognition of Out-of-Vocabulary (OOV) words.
Emojis and Exclamation Points: Men’s Expressiveness and Subsequent Evaluation in Interpersonal Mobile Communication

Increased text messaging across relationships is well-documented, but little research explores how texting style impacts initial impressions of gender/sexuality. Extended research demonstrating that some men avoid expressive forms of communication in order to appear masculine, this project explores whether expressive text messaging (frequent emoji usage and exclamation points) is likely to decrease perceptions of masculinity and/or heterosexuality among male-identified confederates. In a 2 x 2 experiment (expressiveness by confederate sex), participants texted with an unknown interaction partner and rated conversational qualities and perceived attributions about the masculinity/heterosexuality of those confederates. While participants preferred interacting with more expressive partners, there were significant impacts upon ratings of partners’ gender and sexual orientations. Results have implications for men’s development of interpersonal relationships with unknown others.
Application of Machine Learning Combined with Wireless Network in Design of Online Translation System

The importance of translation services has become increasingly prominent with the acceleration of economic globalization. Compared with human translation, machine translation is cheaper and faster, and therefore more suitable for the current era. The current mainstream machine translation method is neural machine translation, which employs machine methods to train on parallel corpora and create translation models. Research into neural machine translation has yielded a wealth of information. Learning and generalization abilities of neural networks have substantially enhanced the effectiveness of neural machine translation. This work applies machine learning and wireless network technology to build an online translation system for real-time translation. First, this work proposes a multigranularity feature fusion method based on a directed acyclic graph, which uses a directed acyclic graph to fuse different granularities as input and obtain a position representation. Secondly, this paper improves the Transformer model and proposes multigranularity position encoding and multigranularity self-attention. Then, on the basis of multigranularity features as input, this work introduces dynamic word vectors to improve the word embedding module, and uses the ELMo model to obtain dynamic word vector embeddings. Finally, this work builds a multigranularity feature-dynamic word vector machine translation model with above strategy, deploys it on server. Users can upload the content to be translated and download the translated content through the wireless network and realize an online translation system based on machine learning and wireless network.
ONION: Online Semantic Autoencoder Hashing for Cross-Modal Retrieval

Cross-modal hashing (CMH) has recently received increasing attention with the merit of speed and storage in performing large-scale cross-media similarity search. However, most existing cross-media approaches utilize the batch-based mode to update hash functions, without the ability to efficiently handle the online streaming multimedia data. Online hashing can effectively address the preceding issue by using the online learning scheme to incrementally update the hash functions. Nevertheless, the existing online CMH approaches still suffer from several challenges, such as (1) how to efficiently and effectively utilize the supervision information, (2) how to learn more powerful hash functions, and (3) how to solve the binary constraints. To mitigate these limitations, we present a novel online hashing approach named ONION ( ON line semant I c aut O encoder hashi N g). Specifically, it leverages the semantic autoencoder scheme to establish the correlations between binary codes and labels, delivering the power to obtain more discriminative hash codes. Besides, the proposed ONION directly utilizes the label inner product to build the connection between existing data and newly coming data. Therefore, the optimization is less sensitive to the newly arriving data. Equipping a discrete optimization scheme designed to solve the binary constraints, the quantization errors can be dramatically reduced. Furthermore, the hash functions are learned by the proposed autoencoder strategy, making the hash functions more powerful. Extensive experiments on three large-scale databases demonstrate that the performance of our ONION is superior to several recent competitive online and offline cross-media algorithms.