Reviewerly/exHarmony huggingface dataset:
PPNNBP: A Third Party Privacy-Preserving Neural Network With Back-Propagation Learning

With the advances in machine learning techniques and the potency of cloud computing there is an increasing adoption of third party cloud services for outsourcing training and prediction of machine learning models.Although the usage of cloud hosted machine learning services enables more efficient storage and computation on data, the privacy concerns and issues surrounding data sovereignty remain an important challenge.Privacy preserving machine learning provides a promising solution.In this paper, a privacy preserving neural network generation and utilisation framework is presented, the PPNNBP framework.PPNNBP allows model training and prediction to be securely delegated to a third party with minimal data owner participation once the input data have been encrypted without recourse to secret sharing or multiple party setting.This is achieved using a proposed fully homomorphic encryption scheme, the Modified Liu Scheme (MLS), that permits certain operations over cyphertexts and features order preservation.The PPNNBP framework using MLS addresses the challenge of the computational complexity of model learning using existing schemes; a complexity caused by the increasing size of cyphertexts (cyphertexts inflation) and the quantity of noise introduced into cyphertexts through the application of multiplication operations, as the learning progresses.Both the PPNNBP framework and MLS are fully described and analysed.The reported evaluation demonstrates that the PPNNBP framework achieves an accuracy that is comparable to that obtained using a "standard" framework, whilst at the same time operating in a secure manner with minimal data owner participation.
Supplementary figure 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning

&lt;p&gt;Prognosis prediction by machine learning classifier in the k-fold CV test. Cox proportional hazard regression analysis for the comparison between the positive and negative groups selected by each classifier. Red solid line: predicted high-risk group (positive), blue line: other groups (negative). (A) 3-fold cross-validation test. (B) 4-fold cross-validation test. The insets show p-value (p) and hazard ratio (HR) of each test.&lt;/p&gt;
EFL Online Teaching and Learning: The Impact of Environmental Factors

The study aims at exploring EFL lecturers' perceptions and students' voices on the impact of environmental factors to the accomplishment of effective online teaching and learning viewed at university and home contexts.This study was performed with a qualitative and quantitative approach which details and evidence were surveyed via Google form and described from semi-structure interview to investigate environmental factors that impact EFL online teaching and learning in university and home contexts.The results indicate the success of implementing EFL online teaching in Indonesian higher education is highly dependent on the proficiency and readiness of lecturers in teaching transition, media preferences as teaching medium during project-based model is fully and blended learning approaches and institutional support during teaching adaptation.Students' workload and parental support are key environmental factors that explain beneficial outcomes to prepare better online learning in emergency times.
Don’t Start Your Data Labeling from Scratch: OpSaLa - Optimized Data Sampling Before Labeling

Many text classification tasks face a severe class imbalance problem that limits the ability to train high-performance models. This is partly due to the small number of instances in the minority class, so that the minority class patterns are not well-represented. A common approach in such cases is to resort to data augmentation techniques; however, these have shown mixed results on text data. Our proposed solution is to Optimize the data Sampling prior to Labeling (OpSaLa) to obtain overrepresented minority class(es) in the training dataset. We evaluate our approach on three real-world hate speech datasets and compare it to four commonly used approaches: training on the “natural” class distribution, a class weighting approach, and two oversampling approaches: minority oversampling and backtranslation. Our results confirm that the OpSaLa approach yields better models while the labeling budget stays the same.
Finding Unchecked Low-Level Calls with Zero False Positives and Negatives in Ethereum Smart Contracts

Smart contracts are a relatively new class of computer programs that are intended to run on a blockchain. Checking a smart contract for security vulnerabilities is recognized as an important problem in both research and practice. Motivated by recent empirical work that suggests that existing tools suffer high numbers of false positives and negatives for vulnerabilities that belong to commonly-occurring classes, we ask: for even one such class of vulnerabilities, can there exist a tool that is highly effective? We answer in the affirmative by construction: for the class of unchecked low-level calls, checking for which is undecidable in general and $${\textbf {PSPACE}} \text {-complete}$$ under finitizing assumptions we adopt, we propose an approach for Ethereum smart contracts that comprises a reduction to model-checking, encoding the property in Linear Temporal Logic (LTL) and use of an off-the-shelf model checker. We find that across almost 200 smart contracts drawn from curated and “wild” datasets from the publicly available benchmark that underlies the prior empirical work that points out that existing tools suffer high numbers of false positives and negatives, our approach is highly effective in that we see zero false positives and negatives.
Efficient management of school and teachers’ professional development – challenges and development perspectives

Both the external socio-economic, safety and health conditions, as well as the changes implemented by the education sector (reforms of the curriculum content and approach, the shift of educational development strategy and organizational structure paradigm, technological changes, pandemic restrictions, shortage of teachers) are changing rapidly in the world, which forces educational institutions to raise the awareness of the teachers' professional competence potential in new working conditions and move towards the training of highly qualified, competent and excellence-oriented teachers -educational institutions have encountered the need to search for the most efficient ways to improve the quality and efficiency of the teaching process.Therefore, the goal of the study has been set to identify the teachers' professional development needs and the self-assessment of competences, determining the fundamental principles of efficient school administration.The research methodology is based on the theoretical research method (scientific and methodical literature analysis); empirical research method (survey of teachers in one of the comprehensive education institutions in Latvia); data processing and analysis methods.Analysing the data obtained in the study, it can be concluded that the factors influencing the teachers' qualitative and efficient professional development are the lack of a systematic approach in the country in general and the work overload.The teacher attends courses and seminars, but is unable to implement the acquired knowledge, insights and innovations in their daily work due to the lack of time, workload and responsibilities, as a result, teachers feel demotivated and are pessimistic about their future path in the profession.
ANALYSIS OF STUDENT PERCEPTIONS ON THE USE OF ONLINE LEARNING APPLICATIONS USING THE RASCH MODEL IN PHYSICS LEARNING

SMAN 1 Tapa initially used the Zoom and Google Meet applications, but there were still many students who were late for learning due to their difficult circumstances in getting internet coverage, so the teachers decided to change the application by using Classroom and Whatsapp to minimize student delays in learning. This study aims to describe students' perceptions of the use of online learning applications using Rasch modeling in physics learning. This research method is descriptive qualitative. The results of this study indicate that students' perceptions based on absorption indicators of object stimuli from outside the individual, students prefer online learning using Classroom and Whatsapp applications. Based on the indicators of understanding or understanding of the object, students understand and understand physics learning. Based on the indicators of individual evaluation of the object, students are enthusiastic and motivated in learning physics. Thus, it can be concluded that students' perceptions of online learning using the classroom and whatsapp applications can help and facilitate students in the learning process during the pandemic in class XI IPA 1.
Improving Grade 7 students' news writing skills using worksheets assessed through online interviews

This paper aimed to improve the news writing skills of grade 7 SPJ students by using the News Writing Worksheet as an instrument to correctly write the lead and the transition paragraphs of news story. The paper opted for a quasi-experimental one-group pre-test—post-test design. Data gathered were analyzed using paired samples t-test. Analysis of the data using paired-sample t-test indicates that there is a significant increase of post-test scores of 50 SPJ students. This indicates that the use of News Writing Worksheet is effective in improving the scores of SPJ students in writing the lead and transition paragraphs of a news story. This positive outcome was affirmed by the results of the of the interview of ten (10) participants who validated the use of the instrument. Results from the thematic analysis, showed that the News Writing Worksheet facilitated the acquiring of imaginative, self-directed, and informed comprehension by the SPJ students. In addition, it was revealed that pupils utilized technology to overcome problems encountered while completing the worksheet. These results were consistent with previous similar research.
Invoice Discounting Using Kelly Criterion by Automated Market Makers-like Implementations

Funding shortages are a persistent issue, particularly for small and medium-sized enterprises (SMEs), and the problem tends to worsen cyclically. The market for factoring and invoice discounting aims to address delays in payment for commercial invoices. These involves sellers present unpaid invoices to financial organizations, typically banks, who provide an advance payment. The implementations of the factoring services without intermediaries in blockchain of the state of the art are all based on the publication on-chain of all the invoices, use know your customer (KYC) mechanisms, and over-collateralize the invoices. This article proposes a new, decentralized approach to lending services that completely eliminates intermediaries and does not require strong KYC, yet it is reasonably resilient. The approach uses liquidity pools and associated heuristics to create a model of risk compensation. In this model, a formula measures the contributed collateral to an invoice and the risk of a late invoice or non-payment, using the Kelly criterion to calculate the optimal premium for funding said invoice in the liquidity pool. The algorithm’s performance is tested in many scenarios involving several invoice amounts, collaterals, payment delays, and non-payment rates. The study also examines premium distribution policies and hack scenarios involving bogus, non-payable invoices. The outcome is a decentralized market that uses the Kelly criterion and is reasonably resilient to a wide range of invoicing scenarios, including 5% non-payment rates and 10% bogus invoices, yet provides a sound profit to liquidity providers. The algorithm’s resilience is enhanced by several premium distribution policies over partially collateralized invoices from 50 to 70%, resulting in optimal premium withdrawal policies every 30 days, making it the first protocol for loanable funds that does not require over-collateralization to be profitable and resilient.
SUSCEPTIBILITY TO REFERENCE GROUP INFLUENCE AND ENTREPRENEURIAL INTENT IN HIGH PERFORMING MID-CAREER

The phenomenon of mid-career employees leaving job to start a business of own is common.In this study we will focus on how the susceptibility to the relevant few effects the entrepreneurial intentions.Social norms and its impact on entrepreneurial intent is established, while the other social or interpersonal influences like the reference group influences are scant regards to entrepreneurial intention.We studies responses from 300 respondents in their mid-career and the analysis followed the SEM and regressions analysis to test the hypothesis.These findings go beyond the basic social norm and its effect as antecedent to efficacy and intention literature.We have considered other aspects of network and social influences specifically the formal ones.
Learning from Self-Supervised Features for Hashing-Based Remote Sensing Image Retrieval

Image retrieval (IR) for practical remote sensing (RS) should have high accuracy, storage, and calculation efficiency, while not relying on big annotations. However, current supervised and unsupervised RS-IR methods do not yet fully meet these requirements. To this end, we propose a novel hashing-based IR approach via learning hash codes from open and representative self-supervised features. Specifically, we constructed a model out of a self-supervised pre-trained backbone and a small multi-layer perceptron (MLP)-based hashing learning neural network. Features from the frozen backbones were used to reconstruct a similarity matrix to guide the hash network learning. This way, semantic structure can be preserved. To enhance the proposed approach, we propose the exploitation of global high-level semantic information within the similarity reconstruction process by introducing a small set of labeled datasets. Extensive comparative experiments on two commonly used RS image datasets demonstrate the outperformance of our proposed approach, and its well balance between the retrieval accuracy and utilized annotations. In these two datasets, the labeled data required by our method accounts for less than 3% of that required by traditional methods, but our obtained mAP can reach over 90%, which is close to that of current advanced supervised methods. Additionally, we analyzed the specific effect of our design and the associated hyperparameters.
The Analyzing of Role of Big Data in Security and General Privacy Problems through Correlation Analysis

Big data has just been extensively used across several businesses, which has significantly increased the number of different information systems. Numerous analysis techniques, such standard information mining or quantitative test techniques, are propelling the big data sector's continued growth. The potential to get new information via the evaluation, synthesis, and the use of existing data is one of big number's key features. Another is the idea that content from many sources does indeed have a life cycle that spans procurement through waste. But, concerns with infosec and dependability arise at every stage of a product's lifecycle, rendering the safety of data that can be used to precisely identify a people a key goal across the entire process. Numerous big data analysis approaches may be used to evaluate user activity, however collecting that data is against users' privacy. By examining current guidelines issued by international standardization groups and completing a review of research analysis, this paper explores risks and confidentiality issues that arise with delivery of enormous amounts of data.
Analysis of Factors Affecting the Development of Socio-Economic Systems of Vietnam Based on Combinatorial Methods

Nowadays, the widespread use of methods of mathematical statistics in assessing the development of socioeconomic systems of regions. The method of analysis of the development of socio-economic systems of the regions of Vietnam on the basis of Fuzzy C-means with the help of Fuzzy gap statistics and python programming language is proposed. The result of the study divided the socio-economic systems of the regions of Vietnam into 4 groups and identify regions that can belong to more than 1 cluster with a defined probability. The application of Fuzzy C-means using Fuzzy gap statistics showed reliable and efficient allocation of regions in the groups. The disadvantage of applying Fuzzy C-means is the high computational complexity, which leads to higher implementation time of the algorithm. The work is recommended to be applied to students, specialists in the field of assessing the quality of the development of socio-economic systems or other areas of the country.
Next Generation Phishing Detection and Prevention System using Machine Learning

Phishing is one of the topmost social engineering attacks in which an attacker sends a luring email via fake email address, email content through which either they try to compromise the end-user account by stealing their account credentials simply like username and password or even more complex way is to compromise the user’s device by binding the malicious payload that could be virus, trojans, spyware and lot more within the email sent by the attacker to the victim. According to the APWG, phishing activity trends report found that the December 2021 stats shows that the number of phishing attacks was triple compared to the attacks in early 2020, this shows still phishing is still in the leading position in all of the cybercrimes happened or happening in today’s world. The phishing problem is still a major problem across the globe which affects society, work culture, and economic growth in industries as well, thus there is not any single solution with which one person or industry can fight the phishing attack. The objective of this research paper is to make a simple yet effective one-stop solution for phishing detection and prevention system using machine learning and making an intelligent web browser plugin and to study different machine learning models and approaches with which we can come up with an efficient product.
Blockchain Technology for Securing Electronic Health Records: A Comprehensive Review and Future Directions

Abstract: The healthcare industry is currently undergoing a digital transformation, and blockchain technology has evolved as a budding remedy because of some of its previous challenges. Blockchain, a distributed ledger technology, can securely, safely and transparently record and store data. Electronic Health Records (EHRs) are electronically stored health information in a digital format. This paper discusses the possible uses of blockchain technology in healthcare and its potential advantages and benefits. The discussion of the paper begins with the challenges faced by the healthcare industry in present time, such as the lack of interoperability, data security concerns, and inefficiencies in healthcare delivery. The discussion then turns to how blockchain technology can help avoid these challenges by offering a secure, immutable, and transparent platform for healthcare data sharing. The paper also discusses how of blockchain technology can be used in healthcare in present time, such as patient identity management, Electronic Health Records (EHRs), clinical trials, and supply chain management. For each use-case, the paper discusses the advantages and disadvantages of using blockchain technology and provides some examples of blockchainbased solutions. Also, the paper describes some of the technical factors, such as scalability, privacy, interoperability, and regulatory compliance, that must be taken into account while using blockchain in the healthcare industry. The paper also looks over the challenges and restrictions of using blockchain technology in healthcare, such as the high computational costs and the requirement for standardization. Finally, the paper concludes by discussing the impact of blockchain technology on the healthcare sector, like improving patient outcomes, cutting costs, and enabling new business models. The spotlight of the paper is that in spite of the fact that blockchain technology does not offer a fix for every issue plaguing the healthcare sector, the study argues that it has the capability to be an important tool for improve healthcare outcomes and delivery. In conclusion, blockchain technology has the capacity to modify healthcare by strengthening patient privacy, streamlining data management, and managing clinical trials and the supply chain management. The healthcare sector must address a number of technological, organisational, and regulatory obstacles to fully achieve these advantages. To overcome the above obstacles and enable the wider application of blockchain technology in healthcare, more study and development are required
Parametrizing number variation in Russian noun phrases with experimental studies and language modeling

The study deals with the problem of agreement with coordinated constructions and number form alternation.The target, agreeing with two conjoined singular nouns, copies either plural or singular number feature.This paper focuses on the syntax of Russian noun phrases with coordinated modifiers which demonstrate the number variation of the agreement controller.If two conjoined singular adjectives have split interpretations, both singular and plural nouns are acceptable.In contrast to previous studies relying on introspection and corpus, we parametrize the number variation based on the results of the self-paced acceptability experiments (Likert scale 1-7).We compare the data about human perception with the language probabilities predicted by a neural model for text generation ruGPT-3.Two case studies were conducted to analyze morphological and syntactic factors parametrizing variation.The first study considers the impact of noun morphology on number alternation.The second study examines the effect of the premodifier attributive agreement.Both offline acceptability scores and online reading time demonstrate that the observed morphological and syntactic factors should be considered while parametrizing number variation in Russian noun phrases with coordinated modifiers.The ruGPT-3 language model, trained on a vast collection of Russian texts, manages to predict the correct probability for highly acceptable and highly unacceptable sentences, but it fails to assign accurate probability values to the cases of variation.
A graph-based superframework for mixture model estimation using EM: an analysis of US wholesale electricity markets

Abstract A fully unsupervised graph-based superframework is proposed to handle the EM initialization problem for estimating mixture models on financial time series. Using a complex network approach that links time series and graphs, the graph-structured information derived from the observed data is exploited to produce a meaningful starting point for the EM algorithm. It is shown that structural information derived by complex graphs can definitely capture time series behavior and nonlinear relationships between different observations. The proposed methodology is employed to estimate Gaussian mixture models on US wholesale electricity market prices using two different configurations of the superframework. The obtained results show that the proposed methodology performs better than conventional initialization methods, such as K -means based techniques. The improvements are significant on the overall representation of the empirical distribution of log-returns and, in particular, on the first four moments. Moreover, this approach has a high degree of generalization and flexibility, exploiting graph manipulation and employing functional operating blocks, which can be adapted to very different empirical situations.