Reviewerly/exHarmony huggingface dataset:
Dreams in the Narratives of the Memorabilia of the Three Kingdoms

The article presents a classification and analysis of references to dreams in the Memorabilia of the Three Kingdoms (Samguk yusa)an extensive systematized collection of materials on the history of Korea from ancient times to the end of the 13th century, which was composed by the Korean Buddhist monk Iryeon (12061289). According to the functions of these references in the text and to the degree of their exposure to Buddhist influence, they are classified into two groups: figurative dreams, graphically and unambiguously predicting inevitable future events, and dreams-conversations, in which supernatural beings seek to influence the behavior of the hero. These two groups are further subdivided into several subgroups, among which special attention is paid to the stories about interactions between human beings and the characters of the Buddhist pantheon. On the basis of the Buddhist canon, it is suggested that the Buddhist concept of correlation between the way of such an interaction (in a dream or in reality) and the degree of spiritual maturity of the practitioner is superimposed here on the universal scheme of prophetic dreams.
'Oh,' 'Well,' and Hedges as Negative Politeness Strategy: The Different Use of Discourse Markers in Female and Male Students' Utterances

Investigating gender differences in utterances by employing discourse markers is very interesting and challenging as the similarities and differences could be seen more distinctly than without them. This research aims to find out the similarities and the differences between them in students’ interactions. The data were taken from female and male students’ utterances. After being analyzed using the qualitative method, it concluded that there were some similarities and differences in applying them. For expressing hesitation, Female students use ‘Hmm…,’ (20%), ‘I think…,’ (17%), and ‘Well…,’ (15%). Meanwhile, male students use ‘Well…’ (19%), ‘I think...,’ (20%), and ‘Hmm…,’ (10%). Female students use ‘Well…’ as a face threat mitigator and sometimes followed by ‘sorry …,’ (12%), whereas male students employ ‘Well…’ (7%). For expressing surprise, both use ‘Oh’ as a pure surprise (9%) and (6%). Female students prefer to use discourse markers ‘Wow…,’ (7%) if they feel amazed, whereas male students apply ‘Aah…,’ (6%). Both female and male students employ ‘By the way…,’ to interrupt (8%). The research revealed that male students hide their doubts better than female students; female students are more polite. The research also finds discourse markers from students’ local language that could be developed globally.
An Analysis of Ethereum-Based Healthcare Applications Using Blockchain Network

Abstract— Years of heavy regulation and bureaucratic ineffificiency have slowed innovation for electronic medical records (EMRs). We employ a security risk management (SRM) domain model and a methodology to investigate two security risks – Sybil and Double-spending – that have been identified and are regarded the most significant security issues in blockchain systems. We tested a newly developed framework by looking at the hazards of Sybil and Double-spending in Ethereum-based healthcare apps. MedRec thus enables the emergence of data economics, supplying big data to empower researchers while engaging patients and providers in the choice to release metadata. Finally, we deploy the MIStore on the Ethererum blockchain and give the corresponding performance evaluation. We wrap off the study by laying out our plans for developing an ontology-based blockchain security reference model. Keywords— Sybil risk, Double-spending risk, Ethereum-based healthcare apps, Electronic medical records, big data
An enhanced hybridized approach for group recommendation via reliable ratings

A group recommender system aim's to provide relevant information to all members of the group. To determine group preferences, the majority of existing studies use aggregation approaches. An aggregation method is a strategy for recommending products to a group by combining the individual preferences of group members. So far, a slew of different types of aggregation algorithms has been discovered. However, they only aggregate one component of the offered ratings (e.g., counts, rankings, high averages), which limits their ability to capture group members' proclivities. This study proposes a novel aggregation method called weighted count that aggregates ratings by providing weights equal to the number of users who provide ratings to an item (location). In addition, the study proposes combining additive utilitarian and weighted count approaches to highlight popular locations on which group members agreed. Experiments on a benchmark check-in dataset demonstrated that the proposed blended technique surpasses the existing methods significantly.
A Discussion About How to Improve Students' English Writing Ability

In English learning, "listening, speaking, reading and writing" is an organic unity. English writing is an important part of English learning, and it is a comprehensive reflection of students' basic language knowledge reserve, language organization ability and language application ability. This paper elaborates from three aspects: learning English vocabulary and grammar, enhancing English language sense and cultivating interest in English learning by strengthening English reading and recitation, and strengthening writing practice, so as to comprehensively improve English writing ability.
PENGARUH KETINGGIAN DAN SUDUT KEMIRINGAN SALURAN AIR TERBUKA TERHADAP PARAMETER ALIRAN DAN PUTARAN TURBIN VORTEX

The stream parameter was affected by the height of falling, water debit and head losses. The stream that had straight flow could be converted to vortex stream using shapping drain. The movement of the vortex stream could be converted to mechanical energy using vortex wheel. The aim of this study was to find out the effect of the height and angle in exposed water drain in stream parameter and the spinning of vortex wheel. This study used type U water drain which has different variation of height 50 mm, 80 mm, 110 mm, 140 mm, and 170 mm and different angle variation 20, 25, and 30. The stream parameter was obatined by measuring the stream surface. Furthermore, the spinning of the vortex wheel was measured using tachometer. The result of this study showed that the spinning of the vortex wheel has the highest point at 100 mm in height variation and 30 in angle variation, which has 142.85 rpm in spinning speed, 1.178 m/s in stream speed, and 102.5 mm water height in vortex in average. It was because the higher and bigger the angle, the faster the stream was. However, a high speed of the stream produced small vortex stram. It caused the area of the water to hit in temperature to decrease so that the spinning of the wheel decreased.
Optimization of Secure Coding Practices in SDLC as Part of Cybersecurity Framework

Cybersecurity is a global goal that is central to national security planning in many countries. One of the most active research fields is design of practices for the development of so-called highly secure software as a kind of protection and reduction of the risks from cyber threats. The use of a secure software product in a real environment enables the reduction of the vulnerability of the system as a whole. It would be logical to find the most optimal solution for the integration of secure coding in the classic SDLC (software development life cycle). This paper aims to suggest practices and tips that should be followed for secure coding, in order to avoid cost and time overruns because of untimely identification of security issues. It presents the implementation of secure coding practices in software development, and showcases several real-world scenarios from different phases of the SDLC, as well as mitigation strategies. The paper covers techniques for SQL injection mitigation, authentication management for staging environments, and access control verification using JSON Web Tokens.
Making the case for audience design in conversational AI: Rapport expectations and language ideologies in a task-oriented chatbot

Chatbots are more and more prevalent in commercial and science contexts. They help customers complain about a product or service or support them to find the best travel deals. Other bots provide mental health support or help book medical appointments. This paper argues that insights into users' language ideologies and their rapport expectations can be used to inform the audience design of the bot's language and interaction patterns and ensure equitable access to the services provided by bots. The argument is underpinned by three kinds of data: simulated user interactions with a chatbot facilitating health appointment bookings, users' introspective comments on their interactions and users' qualitative survey comments post engagement with the booking bot. In closing, I will define audience design for conversational AI and discuss how user-centred analyses of chatbot interactions and sociolinguistically informed theoretical approaches, such as rapport management, can be used to support audience design.
Secure Cloud Storage with a Traceable Authorization System Using KGC

Abstract: Various types of encryption algorithms are used to protect information from unwanted exposure and to guarantee data confidentiality and secure storage. As a result, keyword-based searching has been introduced, in which the requested file is found following a search using the corresponding keyword. As a result, finding and revoking the malicious user who is abusing the secret key must be resolved as soon as possible. If a user tries to enter the erroneous key, the system considers him a bad user, and the user's privileges are promptly revoked. This project is primarily concerned with user authentication in order to strengthen the security system
Quantum Switching in Resistor Networks

In this paper quantum switches, qswitches, are introduced to generate a superposition of resistor networks. Measuring the qswitches results in one resistor network with specific values for the electrical variables; then using the probabilities for each network with the currents and voltages, expectation values are calculated. Finally feedback is used to change the qswitches probability amplitudes. &lt;br&gt;
Human-Eye Controlled Virtual Mouse

Abstract: There's always room for advancement in the realm of computers, thanks to new technology in our digital era. Handsfree computing is popular nowadays because it caters to the needs of quadriplegics (people suffering from paralysis of all four limbs). We want to demonstrate a Human-Computer Interaction (HCI) system that is essential for amputees and others who have trouble using their hands. The system is a mouse-like eye-based interface that converts eye movements like blinking, staring, and squinting into mouse cursor actions. The software requirements for this technique include Python, OpenCV, NumPy, and a few more facial recognition tools, as well as a basic camera. In the creation of face detectors, the HOG (Histogram of Oriented Gradients) is often utilised. and hence the window technique, which combines this feature with a linear classifier. It is completely hands-free and does notrequire any additional hardware or sensors.
Emotion Detection on text using Machine Learning and Deep Learning Techniques

Abstract: Emotion detection on text is an important field of research in Artificial Intelligence and human-computer interaction. Emotions play key role in human interaction. Emotion detection is closely associated with sentiment detection, in which we detect the polarity of the text. But in emotion detection, we detect emotions such as joy, love, surprise, sadness, fear, and anger. Emotion detection helps the machines to understand human behavior and ultimately it provides users with emotional awareness feedback. In this paper, we are going to compare Machine Learning and Deep Learning techniques with their accuracy and f1 scores. The experimental results show the results provided by deep learning techniques (Bi LSTM and Bi GRU) with word embeddings are more accurate than the other techniques.
Interactive Sound Installation as an Implementation of Contemporary Communication Models

Digitalization, virtualization, commercialization, loss of integrity, polystylistics, liberation from any norms are the latest trends that determine the development of contemporary art. They influence the functioning of modern communication models that evolve in accordance with the achievements of technology and acquire mobility, variability and interactivity. Interaction between social processes and scientific and technological achievements is increasing, the essence of communication in the space of modern culture is being rethought, particularly, the boundary between the types of art is being levelled. The latter phenomenon leads to the emergence of new instruments and methods of artistic creativity and expands the possibilities of artistic self-expression. One of the promising areas of artistic exploration is the creation of sound interactive installations. This paper proposes a classification of communication models that are based on the analysis of interactive sound installations. The classification is based on determining the degree to which audiences actions influence the final sound result of the creative action. The paper claims that the parameters of the exhibition space are important components of the installation’s structure and form. A dialogue with nature by way of incorporating sounds of nature, their imitations, and interpretations in art objects is found to be a current trend in interactive sound installations.&#x0D; &#x0D; &#x0D;
ADDC: Automatic Design of Digital Circuit

Digital circuits are one of the most important enabling technologies in the world today. Powerful tools, such as Hardware Description Languages (HDLs) have evolved over the past number of decades to allow designers to operate at high levels of abstraction and expressiveness, rather than at the gate level, which circuits are actually constructed from. Similarly, highly accurate digital circuit simulators permit designers to test their circuits before committing them to silicon. This is still a highly complex and generally manual task, however, with complex circuits taking months or even years to go from planning to silicon. We show how Grammatical Evolution (GE) can harness the standard tools of silicon design and be used to create a fully automatic circuit design system. Specifically, we use a HDL known as SystemVerilog and Icarus, a free, but powerful simulator, to generate circuits from high level descriptions. We apply our system to several well known digital circuit literature benchmarks and demonstrate that GE can successfully evolve functional circuits, including several which have been subsequently rendered in Field Programmable Gate Arrays (FPGAs).
Multi-task twin support vector machine with Universum data

Multi-task learning (MTL) has emerged as a promising topic of machine learning in recent years, aiming to enhance the performance of numerous related learning tasks by exploiting beneficial information. During the training phase, most of the existing multi-task learning models concentrate entirely on the target task data and ignore the non-target task data contained in the target tasks. To address this issue, Universum data, that do not correspond to any class of a classification problem, may be used as prior knowledge in the training model. This study looks at the challenge of multi-task learning using Universum data to employ non-target task data, which leads to better performance. It proposes a multi-task twin support vector machine with Universum data (UMTSVM) and provides two approaches to its solution. The first approach takes into account the dual formulation of UMTSVM and tries to solve a quadratic programming problem. The second approach formulates a least-squares version of UMTSVM and refers to it as LS-UMTSVM to further increase the generalization performance. The solution of the two primal problems in LS-UMTSVM is simplified to solving just two systems of linear equations, resulting in an incredibly simple and quick approach. Numerical experiments on several popular multi-task data sets and medical data sets demonstrate the efficiency of the proposed methods.
Teachers' Adaptability to General Curriculum Reform

The purpose of the research is to investigate the lecturers' adaptability to curriculum innovation and its impact on the fundamental and comprehensive innovation of education and training. In order to adapt to curriculum reform, teachers must be prepared for their own role in curriculum design and implementation, aware of program changes and values. teaching in the direction of developing learners' capacity; skills to practice student-centered teaching strategies. The adaptation of teachers to curriculum innovation depends on the teachers themselves and the policy context of each school. Research methods are mixed methods combining qualitative and quantitative. Main data is from questionnaires provided to the trainers. Measures of data collection according to 3 aspects on the adaptability of lecturers to curriculum innovation. The results show that 651 respondents can adapt to a medium level, most of the lecturers are ready to adapt to the curriculum innovation. Some teachers have tried to actively self-study and develop professional competence.
Bayesian Personalized Ranking with the Synthesis of Multiple User and Item Classification

&lt;p&gt;In recent years, rapidly evolving networks have facilitated data interactions between users and goods, and recommender systems have emerged due to the needs of the times. Implicit feedback in recommender systems always is divided into the observed item set and unobserved item set. However, items in the unobserved set often are treated equally, and the items that are of potential interest to users are not sufficiently exploited from unobserved items. In this paper, we come up with a method to solve the problem of the insufficient exploration of unrated items, and we contribute to the additional alleviation of rating sparseness. To do so, we propose a novel Bayesian personalized ranking with the synthesis of multiple users and item classification (BPRS). For each user, we divide the items into three categories, which are positive, interest and negative. We conduct multiple user classification and item classification to exploit the items that are of interest and generate the final ranking. Experiments on three real-world datasets demonstrate the effectiveness of our algorithm for greatly improving the accuracy of recommendation results and alleviating the cold-start problem.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Improving Student Learning Outcomes By Using Blended Learning Assisted Pizzaku Media

The purpose of this classroom action research was to determine the success of blended learning using pizzaku media. The key data sources in this study were 17 male and 15 female students from class VIA SD Negeri Bujanggadung, Grogol District, Cilegon City, Banten. According to the study's findings, using face-to-face and online synchronous blended learning methods using the Google Meet application and pizzaku media might boost the learning interest of class VIA students at SDN Bujanggadung in studying mathematics, particularly in the circle content.
KELAYAKAN PENUNTUN PRAKTIKUM STRUKTUR DAN FUNGSI JARINGAN TUMBUHAN DENGAN INFORMASI KETEBALAN DAUN DAN LAJU TRANSPIRASI

This study determined the feasibility of a submaterial practical guidelines for tissue structure and function in plants of class XI SMA with information of leaf thickness and transpiration rate as teaching materials. The research method was descriptive. Validation of practical guidelines consisted of two stages, namely instruments and practical guidelines validation. Instrument validation was carried out by one biology education lecturer at FKIP UNTAN and one biology teacher for class XI SMA Pontianak. Validation of the practical guidline was conduted by 5 validators including 2 biology educaton lecturers at FKIP UNTAN and 3 biology teachers for class XI SMA Pontianak. Validation use questionnaire with for aspect an 15 criteria filled using likert scale and analised by lawshe method. Researh of validation pratical guidelines often CVR value of 0.88. Practical guidelines requires improvement of material, construction, practicality and performance aspects to be use as learning proses.
FINANCIAL PERFORMANCE ANALYSIS FROM A SOCIAL WELFARE PERSPECTIVE - A COMPARATIVE STUDY ROMANIA-MOLDOVA

Under conditions of uncertainty, the financial performance of a state (which is the attribute of the state's assurance of welfare through the contribution of economic activity to the financial-budgetary vectors) changes in relation to the influences of uncertainty factors and the response of the entropy of the system to the social protection measures adopted by the authorities in response to the crisis. Thus, in the opinion of the authors, the financial performance can be quantified according to the capacity of the resources to satisfy the financial needs, in an equitable measure, based on the allowed access, in the presence of protection against financial risk and through the sustainable consumption of resources. We propose to develop a model for assessing financial performance under uncertainty based on the consumption vector represented by the average monthly cost felt by the employer. The methods used are prospective (literature study) and analytical (consolidation of a Romania-Moldova parallel database for relevant macroeconomic indicators: active population, Total state companies' revenues of, Number of state companies, Number of employees, Total national income). The results of the study materialize in the assessment of economic development patterns influencing financial performance by creating parallel correlations with effect in delineating the influences of endogenous and exogenous factors on the financial performance of the two countries.
A Fast Harmonic Mean Linear Discriminant Analysis for Dimensionality Reduction

Dimensionality reduction is the most prominent process in artificial intelligence and data science because of using a massive amount of high-dimensional information.In recent days, many dimensionality reduction algorithms focused on the harmonic mean-based linear discriminant analysis (HLDA) which was the enhanced version of classical LDA.In particular, two different variants such as HLDA and HLDA pairwise (HLDAp) have been applied to reduce the high-dimensional data by using the harmonic mean between-class distance.However, its computation time complexity was high during the initialization phase since it comprises the matrix Eigen decomposition/inverse.Hence this article proposes the Fast HLDA (FHLDA) and FHLDA pairwise (FHLDAp) algorithms for reducing the high-dimensional data during classification.In this algorithm, a joint diagonalization scheme is introduced instead of Eigen decomposition depending on Taylor expansion for lessening the number of iterations in the initialization step to produce the discriminant.It does not a choice to a sweeping task so that every element of the Eigenvector matrix at every iteration is calculated to minimize the computation time burden.As well, a first-order approximation of the inverse Eigenvector matrix and the complete matrix of Eigenvectors are updated at every iteration.On the contrary, the overlap among the samples of dissimilar classes tends to miscategorization.So, the optimal discriminant vector is discovered to solve this issue by extending the between-class scatter matrices.Finally, the experimental outcomes show that the FHLDA and FHLDAp algorithms achieve 10 % higher average accuracy than LDA, ALDA, WLDA, G2DLDA, HLDA, and HLDAp algorithms.