Reviewerly/exHarmony huggingface dataset:
Security enhancement of IIOT with permissioned blockchain and cloud computing

Blockchain has proven itself to be of value in various fields of technology. In today's world every kind of technology implementation requires security, the research to make it better is constantly taking place. With such a requirement, blockchain has started to make a huge difference in the way security protocols are implemented. The data is stored in the form of blocks where in every one of them transactions are stored. Every block added connects itself to the existing blocks in a secure manner such that it is resistant to attacks and tamper. With emerging advances in IIoT, the requirement for better security has increased, and hence a permissioned blockchain security solution for IIoT has been proposed. In this paper, an additionally secure approach using cloud computing is used where the private keys provided to the user are broken down and stored on cloud is proposed.
Penggunaan Media Realia dalam Meningkatkan Pengalaman Belajar Siswa

Education is an effort to foster and spread one's personality, both spiritually and physically. Learning experiences are several student activities carried out to obtain new information and competencies in accordance with the objectives to be achieved. Realia media are tangible objects that are often encountered by students and are easily found in daily activities that can be brought into learning situations or become media for teaching materials for students. These real objects can be used as realia media and these objects must be original and intact in form and also the actual size so that they can be recognized by students properly. Based on the above understanding, it is known that the use of the right realia media will improve the student's learning experience of the material to be studied. The use of realia media in thematic learning, especially in grade 4 MI Nurul Islam Sukosari. And the problem I encountered was that the media used by grade 4 teachers had not yet optimized the long-term learning objectives. This happens because the use of media is monotonous so that the process of learning activities is less memorable for students so that students are less experienced. Therefore, to improve the student learning experience, the researcher chose to raise the title "Use of Realia Media in Improving Student Learning Experience in Grade 4 Thematic Learning".
Spotlight on Corporate Social Responsibility Thought in the Field of Non-Profit Public Sector Institutions:An Initiative to Promote CSR Thought in Libya

The poor performance of the NPPSIs in Libya, which can be described as a catastrophic performance, is the result of the NPPSIs' flagrant failure to adhere to their social responsibilities towards the Libyan community.Problem: In a developing country such as Libya, the culture of CSR is almost completely non-existent, especially in the NPPSIs.The features of this thought are not clear to most NPPSIs until it is adopted.Purpose: To promote the idea of social responsibility in the NPPSIs, the features of this thought must be clearly defined and described in addition to determining how to evaluate the extent of these NPPSIs 'commitment to their responsibility.This is what this study seeks to achieve.Method ″an innovative approach to reinforce the theory of CSR″:The researcher used the idea of the purpose of establishing the institution in determining the content of economic responsibility, as well as the idea of economic capacity in determining the type of philanthropic responsibility.This study suggested adopting one of the concepts of managerial economics-which is the lost opportunity cost and economic savings, to evaluate the quality of financial decisions, "financial transactions and deals."In order to evaluate the extent of the institution's commitment to its legal and ethical responsibility, the researcher recommends the need to adopt a method of comparison between the content of financial transactions, financial and administrative decisions, behaviors, practices, and requirements and directives contained in local laws, international instruments, noble human values, codes of ethics and professional conduct rules.Result: Theoretically, this study was able to define the features of social responsibility for NPPSIs, along with a description of how to evaluate the social performance of these NPPSIs in a logical manner.Originality/Value: The researcher expects that this study will receive great attention from officials in the Libyan state and leaders of the NPPSIs, as it will serve as a call to promote the idea of social responsibility in all NPPSIs.The researcher also expects the development of methods and procedures that can be used by departments of social performance evaluation in the NPPSIs in Libya to keep pace with the process of promoting social responsibility idea in these NPPSIs.
Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin

The popularity of online shopping is steadily increasing. At the same time, fake product reviews are published widely and have the potential to affect consumer purchasing behavior. In response, previous work has developed automated methods utilizing natural language processing approaches to detect fake product reviews. However, studies vary considerably in how well they succeed in detecting deceptive reviews, and the reasons for such differences are unclear. A contributing factor may be the multitude of strategies used to collect data, introducing potential confounds which affect detection performance. Two possible confounds are data-origin (i.e., the dataset is composed of more than one source) and product ownership (i.e., reviews written by individuals who own or do not own the reviewed product). In the present study, we investigate the effect of both confounds for fake review detection. Using an experimental design, we manipulate data-origin, product ownership, review polarity, and veracity. Supervised learning analysis suggests that review veracity (60.26-69.87%) is somewhat detectable but reviews additionally confounded with product-ownership (66.19-74.17%), or with data-origin (84.44-86.94%) are easier to classify. Review veracity is most easily classified if confounded with product-ownership and data-origin combined (87.78-88.12%). These findings are moderated by review polarity. Overall, our findings suggest that detection accuracy may have been overestimated in previous studies, provide possible explanations as to why, and indicate how future studies might be designed to provide less biased estimates of detection accuracy.
DEVELOPMENT WEB APPLICATION FOR ENHANCING INFORMATION AND ACTIVITY IN RPTRA MAYA ASRI 13

RPTRA is a gathering place for children and other residents to socialize physically. The children can play at RPTRA. However, due to the COVID-19 pandemic which causes physical activity restrictions, this is an obstacle for RPTRA. Due too physical distancing and social distancing, no one can visit RPTRA. So, The RPTRA must be transformed if they want to be sustain and growth in the future. In addition, in order for the RPTRA to develop, digital transformation is also needed in the development of the RPTRA. Based on related research, digital transformation is needed for community to growing in the future. Through a research methodology that starts from collecting data through Contextual Inquiry, then it is analyzed and produces a system design using UML and user interface design using Figma. Furthermore, it is developed into a website application. This website application is evaluated through System Usability Scale (SUS) and heuristic evaluation. The evaluation results show that the proposed application has an SUS value above the average. We can conclude through this website application can be used for sharing information inside the community also can engage with the society . In the future, it is hoped that this RPTRA website application apart from being an information suggestion can also be used to market the products of RPTRA itself so that it can add to the economic value of this website.
Drivers of Faculty Pedagogical Digital Competence or How to Get Things Going Online

The world`s health crisis was started and the area of higher education was significantly challenged by the spread of the virus SARS-CoV-2. The academic community was forced to rethink its ways of learning and teaching dynamically. It transformed the understanding of the faculty competence as an essential component of the continuity of qualitative education, which emphasizes the need to increase the competence to work in a technology-enhanced study environment and to support students in achieving learning outcomes remotely. The aim of the article is to explain the pedagogical digital competence of faculty, which reflects one of the research aspects covered in the State Research Programme (SRP) dedicated to mitigating the consequences of COVID-19. The research methodology consists of a set of quantitative data, which was obtained by surveying 349 faculty from 33 higher education institutions of Latvia. In turn, a questionnaire was developed based on the obtained conclusions, performing an analysis of literature and educational guidelines. Patterns of the use of educational technologies of faculty were analyzed and described in the framework of the research. The wide analysis performed during the research can be summarized in several conclusions. (1) Skills that faculty have self-assessed as the highest primarily characterize the use of technologies and technological solutions (TTS – analogous to hardware and software) to meet students basic learning needs. In turn, skills in the use of TTS, which would help students to develop digital competence and achieve study results in line with labor market trends, are assessed lower. (2) Faculty consider themselves to be more proficient in using TTS to promote students’ cognitive skills than in acquiring practical skills. (3) Faculty feel more proficient in providing feedback to students, rather than collecting it from them and performing summative rather than formative assessment. (4) The improvement of pedagogical digital competence (PDC) of faculty is driven more by cooperation with students than by administrative pressure.
News Story Retrieval Based on Textual Query

This paper presents news video retrieval using text query for Gujarati language news videos. Due to the fact that Broadcasted Video in India is lacking in metadata information such as closed captioning, transcriptions etc., retrieval of videos based on text data is trivial task for most of the Indian language video. To retrieve specific story based on text query in regional language is the key idea behind our approach. Broadcast video is segmented to get shots representing small news stories. To represent each shot efficiently, key frame extraction using singular value decomposition and rank of matrix is proposed. Text is extracted from keyframes for further indexing data. Next task is to process text using natural language processing steps like tokenization, punctuation and extra symbols removal as well as stemming of words to root words etc. Due to unavailability of stemming and other methods of preprocessing of text in Guajarati language, we have given basic stemming technique to reduce dictionary size for efficient indexing of text data. With proposed system 82.5 percent accuracy is achieved on Gujarati news video dataset ETV.
Educator Satisfaction Using LMS with ICT Infrastructure as a Mediation Variable

The use of technology during the COVID-19 pandemic is very helpful in providing teaching materials to students. Two-way and collaborative interactions can be created with a dynamic learning platform such as an LMS. This study aims to analyze user satisfaction, especially with educators in using LMS with the support of ICT Infrastructure. Satisfaction is seen in terms of usefulness (usability), Ease of Use (Ease of use), and Accessibility (Access Range). Respondents in this study were 55 respondents. The data was collected using an online google form questionnaire which was then tested for validity, reliability, and model fit using the War PLS 7.0 analysis tool. The test results show that usefulness has a significant impact on user satisfaction mediated by ICT Infrastructure (with a path coefficient value of 0.524, p-value &lt;0.001). Ease of Use has no significant impact on user satisfaction mediated by ICT Infrastructure (with a path coefficient value of 0.150 with a p-value of 0.122). Accessibility has a significant impact on user satisfaction in mediation with ICT Infrastructure (with a coefficient value of 0.309 with p-value &lt;0.001). The fit model is tested with a VIF value of 1.670 and a GoF value of 0.586. The limitations of this study are that the sample needs to be increased, and exogenous variables need to be added to expand knowledge.
Quantum computing simulation of the hydrogen molecular ground-state energies with limited resources

Abstract In this article, the hydrogen molecular ground-state energies using our algorithm based on quantum variational principle are calculated. They are calculated through a simulator since the system of the present study ( i.e. , the hydrogen molecule) is relatively small and hence the ground-state energies for this molecule are efficiently classically simulable using a simulator. Complete details of this algorithm are elucidated. For this, a full description on the fermions–qubits and the molecular Hamiltonian–qubit Hamiltonian transformations, is given. The authors search for qubit system parameters ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>θ</m:mi> </m:mrow> <m:mrow> <m:mn>0</m:mn> </m:mrow> </m:msub> </m:math> {\theta }_{0} and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>θ</m:mi> </m:mrow> <m:mrow> <m:mn>1</m:mn> </m:mrow> </m:msub> </m:math> {\theta }_{1} ) that yield the minimum energies for the system and also study the ground state energies as a function of the molecular bond length. Proposed circuit is humble and does not include many parameters compared with that of Kandala et al. , the authors control only two parameters ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>θ</m:mi> </m:mrow> <m:mrow> <m:mn>0</m:mn> </m:mrow> </m:msub> </m:math> {\theta }_{0} and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>θ</m:mi> </m:mrow> <m:mrow> <m:mn>1</m:mn> </m:mrow> </m:msub> </m:math> {\theta }_{1} ).
Cloud Qos Ranking Prediction using Tanimoto Coefficient Similarity Based Deep Learning Method

Cloud computing is a service which provides virtualized resources conforming to the end-user needs. Infrastructure, platform and software included in it. For the last two decades, it has achieved very gigantic growth. Currently, there are several cloud service providers in the market. The primary aim of this research is to minimize cloud service violation. It helps the service providers in exempting the penalty enhancing their reliability. So, cloud service QOS prediction is a research problem that must be solved. It is a very necessary thing for cloud service providers and cloud users. We have discussed several QoS prediction related to researches in the literature survey. But none of them has given a satisfactory QoS prediction. In this paper, we proposed a Tanimoto Coefficient Similarity-Based Deep Learning Method for QoS ranking prediction. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results and avoid violation penalties. In comparison with the prior method in practice, the proposed method is more significant in terms of prediction accuracy, prediction time and error rate.
Adaptive Drum Machine Microtiming with Transfer Learning and RNNs

We introduce rolypoly~, the first drum machine for live performance that adapts its microtiming in relation to a human musician. We leverage state-of-the-art work in expressive performance modelling with recurrent nets, towards real-time application on the micro scale. Our models are pretrained on the Groove MIDI Dataset from Magenta, and then fine-tuned iteratively over several duet performances of a new piece. We propose a method for defining training targets based on previous performances, rather than a prior ground truth. The agent is able to adapt to human timing nuances, and can achieve effects such as morphing a rhythm from straight to swing.
Lightweight-blockchain based privacy-preserving data aggregation for epidemic disease surveillance

As the COVID-19 pandemic is raging worldwide, epidemic surveillance helps prevent the spread of the epidemic. Protecting the privacy of patients and data providers in the monitoring system can relieve them of their concerns about the leakage of private information, thereby improving the epidemic data collection capabilities of the system. In this article, we propose a lightweight-blockchain based privacy-preserving data aggregation scheme (LBPDA) for epidemic disease surveillance, which can aggregate data without relying on a trusted third party. Especially, to protect data privacy in the data aggregation process, the case count data is aggregated based on the Paillier cryptosystem's homomorphism. Besides, to reduce the time and storage overhead, we improved the adopted Hyperledger Fabric, thus lightening the data aggregation process. Finally, we simulated the proposed scheme and analyzed its security and performance to verify its feasibility and effectiveness. The results showed that the proposed scheme could meet the government department's requirements to aggregate patient case count data for epidemic disease surveillance while protecting the privacy of patients and data providers. Moreover, we also proved that the lightweight of blockchain is effective by comparison.
Geochemical potential mapping of iron-oxide targets by Prediction-Area plot and Concentration-Number fractal model in Esfordi, Iran

This study serves the purpose of generating a geochemical Fe-bearing potential map. Stream sediment geochemical survey was employed by collecting 843 samples for analyzing 19 elements and oxides. Taking preprocessing of data (e.g. outlier correction and data normalization) into consideration, a Concentration–Number (C-N) fractal model was used to separate different geochemical populations of Fe2O3, TiO2, V and the main multi-element factor in close spatially association with the Fe targeting. A prediction-area (P-A) plot was drawn for each variable to determine the weight of each geochemical indicator. Results indicate that the main geochemical factor with an ore prediction rate of 73%, has occupied 27% of the Esfordi area as favorable zones for further mining propsectivity. The Esfordi as a favorable Fe-bearing zone is of special interest in the NE of the Bafq mining district that hosts important “Kiruna-type” Magnetite-Apatite deposits. In addition, a synthesized indicators map was prepared through implementing a data-driven multi-class index overlay in a similar fashion to the previous version of the method, upon which geochemical potential zones were mostly in the NE part of the Esfordi, intimately linked with intense fault density map. The significance of this study lies in localizing of the most geochemical favorable zones through simultaneous consideration of the C-N and P-A plots accompanied with the incorporation of known active mines and prospects to determine indicator weight. Of note is that the Mineral Potential Mapping (MPM) has higher efficiency over each geochemical indicator with an ore prediction rate of 78% and area occupation of 22%.
An Intelligent Question Answering Platform for Graduate Enrollment

To enhance the competitiveness of colleges and universities in the graduate enrollment and reduce the pressure on candidates for examination and consultation, it is necessary and practically significant to develop an intelligent Q&amp;A platform, which can understand and analyze users' semantics and accurately return the information they need. However, there are problems such as the low volume and low quality of the corpus in the graduate enrollment, this paper develops a question answering platform based on a novel retrieval model including density-based logistic regression and the combination of convolutional neural networks and bidirectional long short-term memory. The experimental results show that the proposed model can effectively alleviate the problem of data sparseness and greatly improve the accuracy of the retrieval performance for the graduate enrollment.
Aspect-Oriented Adaptation of Access Control Rules

Cyber-physical systems (CPS) and IoT systems are nowadays commonly designed as self-adaptive, endowing them with the ability to dynamically reconFigure to reflect their changing environment. This adaptation concerns also the security, as one of the most important properties of these systems. Though the state of the art on adaptivity in terms of security related to these systems can often deal well with fully anticipated situations in the environment, it becomes a challenge to deal with situations that are not or only partially anticipated. This uncertainty is however omnipresent in these systems due to humans in the loop, open-endedness and only partial understanding of the processes happening in the environment. In this paper, we partially address this challenge by featuring an approach for tackling access control in face of partially unanticipated situations. We base our solution on special kind of aspects that build on existing access control system and create a second level of adaptation that addresses the partially unanticipated situations by modifying access control rules. The approach is based on our previous work where we have analyzed and classified uncertainty in security and trust in such systems and have outlined the idea of access-control related situational patterns. The aspects that we present in this paper serve as means for application-specific specialization of the situational patterns. We showcase our approach on a simplified but real-life example in the domain of Industry 4.0 that comes from one of our industrial projects.
Protecting Data Privacy in Cloud

Cloud is now widely used for the remote storage of data; it’s an On-demand device and computer resource configuration process. This allows users to avoid locally saving and storing data. Remote data sharing is an inexpensive and effective way to share cloud users community resources. DiffieHellman used the previous approach to protect multi-owner cloud sharing for distributed groups. In the existing system, there is a community signature shared among all group members that contributes to the middle attack. The program suggested using the LFSR-dependent correlation method, which primarily used handshake protocol to safely exchange community signature to detect the attack, to detect an attack. If the calculated value exceeds one (value&gt;1), the community's public key is changed to avoid abuse.
Data Hiding in Bit-plane Medical Image Using Chaos-based Steganography

This paper proposes digital medical image steganographic schemes based on chaotic maps. Logistic and Cat maps are applied to choose positions of the embedded bits in cover images where the bits of the secret message will be hidden. Our schemes provide mutual substitution methods in a chaotic manner in order to choose pseudo-random positions. Three spatial steganographic algorithms are respectively called ISLM, ISCM and ISLCM using insertion methodology that provides a large embedding capacity and imperceptible steganographic image. The combination of a Logistic map and a Cat map is appropriate for the inserted message becoming secure against message recovery attacks. Those are efficient schemes with a high level of security requirements.