Reviewerly/exHarmony huggingface dataset:
Students’ Manipulative Techniques for Passing Attestation in Distance Learning

The article is devoted to the use of manipulative techniques by students for the current and interim attestation in distance learning which is considered as a consequence of “learned helplessness” (M. Seligman) and “double bind” (G. Bateson). The problem of psychological manipulation in the “student–teacher” system is first studied on the basis of the analysis of students’ class activities (written works, spoken replies). Students’ imitation of academic progress is carried out by using in the MOODLE system such techniques as the presentation of plagiarism, collectively tasks performed, copying of tasks done by other students, answers to didactic tests based on random selection, and many others. “Double bind” of students is resulting from the contradictory requirements to high quality and at the same time to a sizeable portion of tasks in a limited period of time. In the situation of “double bind” is also the teacher, who is guided by contradictory attitudes towards the implementation of the criterion (high quality) and statistical approach (comparative analysis without taking into account the criteria of the required quality) in assessing academic work and students’ answers. Based on the analysis of the products of students’ class activities, the frequency of their use of manipulative techniques for imitating educational competencies when passing credits and exams is characterized. Correlations were found between the frequency of students’ use of manipulative techniques and the level of “learned helplessness” felt by them, as well as the intensity of the “double bind” among students. The necessity of dosing the teaching load of students when teaching in a distance learning format and special control of authorship of students’ academic works presented in the electronic educational milieu is emphasized.
Research of Enterprise Financial Management Capability System Based on EFA Method and Intelligent Data Clustering Model

Based on the understanding and analysis of exploratory factor analysis and intelligent data clustering model, this paper establishes an enterprise financial management capability system. First of all, this paper uses exploratory factor analysis to find the structural relationship between the multiple observation variables that measure financial management ability, and achieve the purpose of data dimensionality reduction. Then, the intelligent data clustering model and the three-dimensional visualization of the model are analyzed through the analysis of the enterprise's financial management ability. Finally, experiments and simulations verify that EFA and clustering models can be used to establish a corporate financial management capability system.
A Qualitative and Quantitative Parametric Estimation of the Ethereum and TRON Blockchain Networks

The introduction of smart contracts and support for Dapps in blockchain networks has opened a new era of decentralized application development for different domains. This paper provides insight into the blockchain technology's core components and highlights the differences in the underlying techniques used in the Ethereum and TRON networks. Additionally, it presents a quantitative analysis of both platforms. Therefore, it will help the readers to balance off their choice between these two blockchain networks.
Research data management

Research data management (RDM) should be central for both researchers and academic libraries. The latter provide related services that are described in this chapter. RDM embraces the entire research cycle, aiming at making the research process as efficient as possible and facilitating cooperation with other players involved in it. To get a clear picture of the nature of RDM services, a short history of the academic library’s readiness and involvement is described. Skills and competencies necessary for serving research and researchers are enumerated, followed by a portrayal of the planning and building of services, giving particular attention to the research data life cycle and to the importance of data management plans. The tasks related to data reference, data citation, and data retrieval are presented. The relationship between RDM and data curation, as well as between RDM and research support services, is characterized.
Electronic fraud: an emerging cause of bank failure in Nigerian deposit money banks

Purpose This study aims to review the degree to which fraud and other unethical practices especially in the digital space have affected the Nigerian banking industry both in the past and present, and how it will be a growing concern in the imminent future. The objective of the study was to examine the impact of electronic fraud on the quality of assets and return on assets of Nigerian deposit money banks. Design/methodology/approach The research used secondary data for the periods 2006 till 2018, which were collected from the Nigeria Deposit Insurance Corporation annual reports. Descriptive analysis and the ordinary least square method of regression analysis were used for data analysis. Findings Findings revealed that electronic fraud cases increased progressively over most of the years of study, which can be attributed to the increased bank products that are electronic-based. Originality/value Many of the reviewed literature examined electronic fraud and its impact on bank profitability but this study examined the cause of electronic fraud and what can be done to curtail it.
Robust DWT-SVD Domain Image Watermarking based on Iterative Blending

Abstract Copyright protection for digital multimedia has become a research hotspot in recent years. As an efficient solution, the digital watermarking scheme has emerged at the right moment. In this article, a highly robust and hybrid watermarking method is proposed. The discrete wavelet transform (DWT) and the singular value decomposition (SVD) as well as iterative blending are adopted in this method to insert and recover the watermark. To enhance the watermark imperceptibility, the second low-level (LL2) coefficients after SVD are modified by using the watermark. Compared with the conventional DWT-SVD-based watermarking method and other watermarking techniques, the watermarked images obtained by the proposed method have higher image quality. In addition, the proposed method achieves high robustness in resisting various image processing attacks.
InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks

The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment of efficient (1) DNNs equipped with instantaneous accuracy-efficiency trade-off capability to accommodate the time-varying resources at IoT devices and (2) dataflows to optimize DNNs’ execution efficiency on different devices. Therefore, we propose InstantNet to automatically generate and deploy instantaneously switchable-precision networks which operates at variable bit-widths. Extensive experiments show that the proposed InstantNet consistently outperforms state-of-the-art designs. Our codes are available at: https://github.com/RICE-EIC/InstantNet.
Exploiting All Samples in Low-Resource Sentence Classification: Early Stopping and Initialization Parameters

To improve deep-learning performance in low-resource settings, many researchers have redesigned model architectures or applied additional data (e.g., external resources, unlabeled samples). However, there have been relatively few discussions on how to make good use of small amounts of labeled samples, although it is potentially beneficial and should be done before applying additional data or redesigning models. In this study, we assume a low-resource setting in which only a few labeled samples (i.e., 30–100 per class) are available, and we discuss how to exploit them without additional data or model redesigns. We explore possible approaches in the following three aspects: training validation splitting, early stopping, and weight initialization. Extensive experiments are conducted on six public sentence classification datasets. Performance on various evaluation metrics (e.g., accuracy, loss, and calibration error) significantly varied depending on the approaches that were combined in the three aspects. Based on the results, we propose an integrated method, which is to initialize the model with a weight averaging method and use a non-validation stop method to train all samples. This simple integrated method consistently outperforms the competitive methods; e.g., the average accuracy of six datasets of this method was 1.8% higher than those of conventional validation-based methods. In addition, the integrated method further improves the performance when adapted to several state-of-the-art models that use additional data or redesign the network architecture (e.g., self-training and enhanced structural models). Our results highlight the importance of the training strategy and suggest that the integrated method can be the first step in the low-resource setting. This study provides empirical knowledge that will be helpful when dealing with low-resource data in future efforts. Our code is publicly available at https://github.com/DMCB-GIST/exploit_all_samples.
Bug Report Classification by Selecting Relevant Features Using Chi Square, Information Gain and Latent Semantic Analysis

In machine learning, feature selection is a very important step to reduce the dimensionality of data by removing irrelevant features, redundant data to improve the learning accuracy. As the dimensionality of data has increased, feature selection has become a challenging task. Various approaches have been proposed for feature selection. In this study, we have analyzed the effectiveness of three widely used feature selection methods namely Chi square; information gain and latent semantic analysis (LSA) to classify the software bugs. The performance of four classifiers K nearest neighbor, Random Forest, naïve bayes and support vector machine are evaluated for the above feature selection methods in terms of accuracy, precision and recall.
Vehicle mirror replacement system

Nowadays, there is a trend to replace the physical vehicle mirrors with camera system. Camera systems bring several benefits to modern vehicles such as clear image in bad weather conditions, optimized brightness and contrast and no blind-spots. In this paper one such system is proposed which is based on the processing of signals received from five different cameras, using three wide and two narrow-angle cameras. The solution is developed by using the Texas Instruments VISION SDK 2 software framework and implemented on Advanced driver-assistance system (ADAS) board with Texas Instruments TDA2xx System on Chip. Four different use cases are developed. In use cases with two and three cameras, different views of the vehicle environment are presented to a driver. Such displays allow an overview of both blind spots around the vehicle and a clear overview of the rest of the surroundings. The use case with zooming of the desired part of the image allows the driver to select the part of the screen he wants to enlarge. The last use case detects the vehicle behind ego-vehicle, estimates the distance and speed of the vehicle and if necessary, warns driver about incoming vehicle. Verification of the proposed solution was performed by measuring the implementation processing speed and by comparing the obtained results with the actual ones in case of speed and distance estimation.
Semantic Segmentation in Aerial Images Using Class-Aware Unsupervised Domain Adaptation

Semantic segmentation using deep neural networks is an important component of aerial image understanding. However, models trained using data from one domain may not generalize well to another domain due to a domain shift between data distributions in the two domains. Such a domain gap is common in aerial images due to large visual appearance changes, and so substantial accuracy loss may occur when using a trained model for inference on new data. In this paper, we propose a novel unsupervised domain adaptation framework to address domain shift in the context of semantic segmentation of aerial images. To this end, we address the problem of domain shift by learning class-aware distribution differences between the source and target domains. Further, we employ entropy minimization on the target domain to produce high-confidence predictions. We demonstrate the effectiveness of the proposed approach using a challenge segmentation dataset by ISPRS, and show improvement over state-of-the-art methods.
The Design of a Trust-based Game as a Conversational Component of Interactive Environment for a Human-agent Negotiation

This research shed the light on how humans interact with virtual partners (Figure 1; 3D view: https://p3d.in/bVJpq) in an interactive environment based on economic games and how this environment can be applied to the training process with immersive technologies. The designed system could be integrated as a tool and be a component of an e-learning platform with Conversational AI and human-agent-interactions which allows human users to play and learn. Scientifically, we have considered the trust problem from a different point of view - learning by doing (i.e., gaming), and proposed that individuals can wear "trust care" lenses on trained "golden eyes" while communicating with others. We explore how contextual trust can be used to promote any human-agent collaboration even in the domain of a competitive negotiation scenario. We present small-scale online testing via instant messaging in Telegram [@trudicbot] and prepare VR testing to demonstrate the potentials of the trust- based game approach.
Automatic Y-axis Rescaling in Dynamic Visualizations

Animated and interactive data visualizations dynamically change the data rendered in a visualization (e.g., bar chart). As the data changes, the y-axis may need to be rescaled as the domain of the data changes. Each axis rescaling potentially improves the readability of the current chart, but may also disorient the user. In contrast to static visualizations, where there is considerable literature to help choose the appropriate y-axis scale, there is a lack of guidance about how and when rescaling should be used in dynamic visualizations. Existing visualization systems and libraries adapt a fixed global y-axis, or rescale every time the data changes. Yet, professional visualizations, such as in data journalism, do not adopt either strategy. They instead carefully and manually choose when to rescale based on the analysis task and data. To this end, we conduct a series of Mechanical Turk experiments to study the potential of dynamic axis rescaling and the factors that affect its effectiveness. We find that the appropriate rescaling policy is both task- and data-dependent, and we do not find one clear policy choice for all situations.
Recreating a Medieval Mill as a Virtual Learning Environment

Historic buildings shown in open-air museums often lack a good accessibility and visitors rarely can interact with them as well as displayed tools to learn about processes. Providing these buildings in Virtual Reality could be a great supplement for museums to provide accessible and interactive offers. To investigate the effectiveness of this approach and to derive design guidelines, we developed an interactive virtual replicate of a medieval mill. We present the design of the mill and the results of a preliminary usability evaluation.
Enhancing Feature Point-Based Video Watermarking against Geometric Attacks with Template

As the Internet and communication technologies have developed quickly, the spread and usage of online video content have become easier, which results in major infringement problems. While video watermarking may be a viable solution for digital video content copyright protection, overcoming geometric attacks is a significant challenge. Although feature point-based watermarking algorithms are expected to be very resistant to these attacks, they are sensitive to feature region localization errors, resulting in poor watermark extraction accuracy. To solve this issue, we introduce the template to enhance the location accuracy of feature point-based watermarking. Furthermore, a scene change-based frame allocation method is presented, which arranges the template and the watermark to be embedded into different frames and eliminates their mutual interference, enhancing the performance of the proposed algorithm. According to the experimental results, our algorithm outperforms state-of-the-art methods in terms of robustness against geometric attacks under close imperceptibility.
The Development of Value-Based Children's Financial Education Program and Evaluation of Effect : Focusing on the values of each area of personal finance of 1st to 3rd grade elementary school students

In this study, a value-based children's financial education program was developed focusing on the values of each financial management area needed for children. According to 'quasi-experimental design - nonequivalent control group design', an experimental group and a control group were formed for children in the lower grades of elementary school, and a value-based child financial education program was executed, and then the program was applied to children's personal financial management. The effect on the value propensity for each area was evaluated. As a result of the quantitative evaluation, it was confirmed that the 8 values of the children in the experimental group were significantly improved compared to the children of the control group, so this program was effective in enhancing the values of each of the 8 areas of personal financial management needed for children. In addition, the children in the experimental group showed significant improvement compared to the children in the control group in the cognitive, justice, and psychodynamic levels of values. It has also been found to be effective in enhancing as a result of the qualitative evaluation, various positive changes were found in the cognitive, affective, and psycho-dynamic dimensions about the values of each financial management area for children. From a positive perspective, it was found that it plays a useful role in cultivating the values of each area of positive personal financial management.
Quantumized Microwave Detection Based on Λ-Type Three-Level Superconducting System: HMM Modeling and Performance Prediction

We adopt artificial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Lambda $ </tex-math></inline-formula> -type three-level system with superconducting devices for microwave signal detection, where the signal intensity reaches the level of discrete photons instead of continuous waveform. Based on the state transition principles of the three-level system, we propose a statistical model for microwave signal detection. In addition, achievable transmission rates and signal detection based on the proposed statistical models are investigated for low temperature conditions in deep space communication scenario. It is predicted that high sensitivity can be achieved by the proposed system. We further characterize the received signal considering the saturation phenomenon of three-level system, which reveals negligible performance degradation caused by saturation under weak received power regime.
Synthesis and Circuitry of Multi-Valued Digital Current Logic Elements: “Classical” Linear-Monotonic Approach

In this paper we proposed unconventional method for the synthesis of digital current circuits based on linear algebra. The novelty of the material lies in the linear-monotonic version of the representation of multi-valued functions. The mathematical analysis of expressions of the monotonic form of the logical-arithmetic representation is carried out, as well as schematic implementations and graphic designations of considered logical functions are shown. The functional diagram of the current memory element is presented.
Road traffic sign recognition based on lightweight neural network

Road traffic sign is critical in informing people of the traffic situation and restricting drivers’ and pedestrians' behavior. Therefore, the real time autonomous detection and recognition ability of road traffic signs play an important role in fields such as navigation for the blind, driver warning system, and vehicle assisted driving. This study employs lightweight neural networks based on MobileNet and Yolo-V4 and compares their recognition speed and accuracy. The experimental results show that YOLO-MobileNet-V2 and YOLO-MobileNet-V3 had high recognition speed but lower accuracy. YOLO-V4-tiny and YOLO-MobileNet-V1 presented high accuracy, with mean average precision (mAP) of 90% or higher, while maintaining the detection speed over 60 frames per second (fps), meeting the real time standard in road traffic sign recognition. The study proves that YOLO-V4-tiny and YOLO-MobileNet-V1 can be employed to reduce the time of calculation and lower the requirement of hardware equipment by reducing the computational complexity. There-fore, this study provides certain practical reference in related fields.
Leveraging Green IoT and Blockchain Technology in the Era of Transformative Digitalization

This study focuses on the role of new blockchain technology in the green IoT ecosystem, highlights essential aspects for developing a green IoT ecosystem, and investigates how blockchain technology contributes to a greener IoT environment. Data from 360 Peruvian (Latin American) SME top managers (two informants from each of 180 SMEs) were quantitatively analyzed using Smart PLS 3.0 (SEM) and SPSS V25. The purpose of this study was to look into the direct impact of attitude, knowledge, and perceived ease of use on blockchain technology adoption for green energy usage. These three parameters' indirect effects on perceived feasibility were also evaluated. The investigations demonstrated a positive and significant association between blockchain technology adoption and perceived ease of use for green energy usage. There is no typical link between green energy usage attitudes and blockchain technology adoption. Furthermore, the data revealed that perceived feasibility mediates the impact between attitude and knowledge and blockchain technology adoption for green energy usage.
Research on “Dual System” Educational Culture in German Based on Big Data Analysis

In this paper, a detailed analysis and introduction of researches on the “dual system” from views and values, curriculum system, quality supervision, and on the applications of “dual system” used in China were introduced based on the big data analysis. According to the number of documents with different keywords contained in “dual system” subject, the results of literature search on the subject of dual system showed that 40.91% of them were related to German vocational education, accounting for the largest proportion of the literature. The second was the subject of vocational education, accounting for 39%. However, the reference of dual system in higher education was rarely involved, and only 1.17% of the literature was retrieved. As far as the materials science and engineering higher education was concerned, in order to cultivate more excellent skilled talents on material science and engineering, we should give full play to the role of schools, enterprises, society, students and their parents, and strengthen the construction in the recognition of materials major vocational education, cooperation between school and enterprise, salary and other aspects. Meanwhile, we should give full play to the role of information technology in curriculum design and graduate quality supervision. By means of market demand research and big data analysis of employment situation, reasonable courses are set up to meet the needs of the labor market.