Reviewerly/exHarmony huggingface dataset:
An approach to some EU initiatives on the regulation of liability for damage caused by AI-Systems

This paper deals with three of the current endeavours of the European Union regarding the regulation of artificial intelligence (AI). First, it briefly analyses the Draft Proposal of the âArtificial Intelligence Actâ (AI Act), which puts fundamental rights at the core of Europeâs AI approach and establishes a risk regulation system of preventive character that proposes ex ante solutions but does not provide for liability rules. Then it refers briefly to the Resolution of European Parliament adopted in October 2020 which formulated recommendations to the Commission for the regulation of civil liability regarding the use of artificial intelligence, which included the full text of a Draft Regulation âon liability for the operation of artificial intelligence-systemsâ. Finally, the paper deals in more detail with the ongoing debate on how to adapt the Directive 85/374/EEC on liability for defective products to overcome the problems posed by AI, such as the openness of AI-systems, and other characteristics of AI that make it difficult to identify the potentially liable person, to prove the defect of a product, to prove causation and to apply other rules included in the Directive now in force.
DATA SCIENCE: PRINCIPLES AND METHODOLOGY OF IMPLEMENTATION UNDER CONDITIONS OF UNCERTAINTY

The article discusses the basic principles of the implementation of DATA SCIENCE technologies in the framework of scientific and practical aspects. It is noted that the functioning of DATA SCIENCE technologies is always implemented in conditions of uncertainty and diversity of data and knowledge. It is shown that one of the basic principles of DATA SCIENCE technologies in conditions of uncertainty is the principle of regularization of data spaces and solutions. To regularize the unified information space of applied problems, regularization is proposed by means of multidimensional hierarchical scaling, which is provided by the use of special types of scales with dynamic constraints. The implementation of such scaling is based on Bayesian intelligent technologies. It is also shown that data science is interdisciplinary, an important component of the set of disciplines is the metrology of data and knowledge. The methodology and technologies of metrological substantiation of data and knowledge are presented. Complexes of metrological characteristics are proposed, including indicators of accuracy, reliability, reliability, risk of decisions, the amount of information received and/or entropy. Practical examples of the use of these technologies are given.
Pengembangan BOBI (Blog Biologi) untuk SMA/MA Kelas XI

The purpose of this study was to determine the feasibility of BOBI (Blog Biologi/Biology Blog) for learning the coordination system at grade XI. This research applied a survey method and used an R &amp;amp; D (Research and Development) model. The information was gathered by distributing questionnaires to six grade XI students majoring in Natural Science at SMAN 1 Srengat, Blitar. The result showed that BOBI can be employed in learning activities. This conclusion is made by referring to the validation results. In this regard, the language aspect obtained a percentage of 86%; the media aspect earned a percentage of 80.5%; the material aspect attained a percentage of 70.6%; and the IT aspect reached a percentage of 88.6%. Additionally, the readability tests resulted in a percentage of 87.2% (from teachers) and 80.4% (from students). These numbers indicate that BOBI is a very feasible media
The Results of the Project Higher Education Teachers' Digital Literacy Improvement

The ongoing Fourth Industrial Revolution and the environment of digitalisation have a significant impact on the whole society. This development also affects the education system, especially higher education teachers. It is necessary to adequately respond to the mentioned changes, as confirmed by the international document ‘Horizon Report’ or the national document ‘Digital Education Strategy for 2020’. According to these documents, modern teachers need to use digital technologies to support pedagogical approach and didactic practice. Therefore, the University of Finance and Administration (VŠFS) educates its teachers also in the field of digitalisation. For this reason, in 2020, 2021 and 2022 VŠFS implemented the European Economic Area (EEA) project focused on improving the digital literacy of higher education teachers (EHP-CZ-MOP-2-003).
ON THE PROBLEM OF EVALUATING THE EFFECTIVENESS OF THE WORK OF DIRECTORS OF SECONDARY SCHOOLS IN THE REPUBLIC OF UZBEKISTAN

The research raises the problem of evaluating the effectiveness of directors of secondary schools in the Republic of Uzbekistan, substantiates the importance of evaluating the effectiveness of school directors. The purpose of the research is to develop proposals and recommendations for evaluating the effectiveness of the work of directors of secondary schools. In the theoretical part of the research, the analysis of scientific and methodological literature on the problem of the elaboration of the content and criteria for evaluating the effectiveness of the work of directors of secondary schools. The directors of secondary schools of the Republic of Uzbekistan attended the research. The results obtained provide a basis for evaluating the effectiveness of the work of directors of secondary schools. KEYWORDS: director, secondary school, effectiveness of directors of secondary schools.
Analytics—Systematic Computational Analysis of Data

Since the envisioning of the concept of Artificial Intelligence in the 1950s, the interest in making machines emulate human behavior has increased, scientific dedication has grown, and, consequently, new concepts have appeared, with unequal success [...]
ARDUINO BASED FINGERPRINT DOOR LOCK SYSTEM

From earlier times as well as till now, security was an important issue of concern in our households and also in office, shops, etc.Everyone has a fear of unauthorized person entering to their home or office without their knowledge.The normal door can be fitted with locks which are capable of breaking with the use of an alternate key.Alternatives to this system can be found like the password or pattern system in the locks which again has the possibility of getting exposed and opening the lock.So, a solution to such problems can be by combining door lock with biometrics.Biometric verification is any means by which a person can be uniquely identified by evaluating one or more distinguishing biological traits.Unique identifiers include fingerprints, hand geometry, earlobe geometry, retina and iris patterns, voice waves, DNA, and signatures.Here we will use fingerprint for biometric verification as it is one such thing which is unique to every individual and the use of fingerprint as the key to door locks can overcome the security problem of unauthorized people trespassing to our homes, shops, offices, etc to a great extent as duplicity in such key is not possible.Also, this system will not lead to problems like losing keys because we do not require carrying keys.This system can be used instead of traditional locks.So, using arduino we will try to implement the system with features which will increase the security level.
Semantic Analysis of Public Health Medical Issues Based on Convolution Neural Networks

Text mining and semantic analysis of medical public health issues are the main points for intelligent medical interaction, but less relevant research has been done on them. This article conceives a convolutional neural network for the semantic classification of public health medical issues. The dual convolution layer is used to further reduce the dimension of the data, extract more in-depth information from the data, and map the features. Each convolution layer includes several convolution nuclei to extract semantic characteristics, and then, the complete connection layer is input to the classifier to obtain the results of the classification. To check the classification effect, the dictionary artificial construction and the double hidden-layers neuronal network are used for semantic classification, and the three methods are compared and tested on the six real datasets. The experimental results show that when the quality of the dataset is high, the convolution neural network method proposed in this paper exceeds the last two methods. The proposed method is higher than the construction of the artificial dictionary and the double hidden-layers neural network in the recall rate: 0.153 and 0.037, and greater than 0.07 and 0.01 for the F1 measure rate, respectively. When the quality of the dataset is general, the models of the three methods do not give good classification results. Finally, it is concluded that the convolutional neural network method conceived has a good semantic recognition performance in public health medical issues.
Revocable One-Time Ring Signature from Pairings

Ring signature is an anonymous signature that allows a person to sign a message on behalf of a self-formed group while concealing the identification of the signer. However, due to its anonymity and unlinkability, malicious or irresponsible signers can easily attack the signature without any responsibility in some scenarios. In this paper, we propose a novel revocable one-time ring signature (roRS) scheme from bilinear pairings, which introduces linkability and mandatory revocability into ring signature. In particular, linkability can resist the double-signing attack and mandatory revocability guarantees that a revocation authority can identify the actual signer when a suspicious signer appears in any situation. The computational complexity of pairing computations is constant, and the time of the revocation phase is more efficient than previous schemes. Furthermore, our scheme is provable secure in the random oracle model, using DL, CDH, and DBDH assumptions.
Dynamic Neural Network Model Design for Solar Radiation Forecast

Sunlight is an energy source that is a gift from God and is a source of life for living things, including humans as caliphs on earth. Judging from its impact, solar radiation is an environmental parameter that has positive and negative effects on human life. The pattern of distribution of solar radiation is important information for human life to be the attention of many people, both policymakers and researchers in the field of environment. This study objects to modeling the radiation of solar using a dynamic neural network (DNN) model. The data used in this research is the meteorological data of Mataram City for the period January 2018 to May 2019, which was obtained from the Department of Environment and Forestry of West Nusa Tenggara Province. In the development of this model, solar radiation was seen as a function of a combination of several variables related to meteorological (wind speed, wind direction, humidity, air pressure, and air temperature) and solar radiation data at some previous time. Considering the advantages and effectiveness of the activation function in the proposed DNN model learning process, this study's network learning in the hidden layer employed two activation functions: hyperbolic tangent (Type I) and hyperbolic tangent sigmoid functions (Type II). The output aggregation used two aggregates for each type: the weighted aggregation function (Type a) and the maximum function (Type b). The results of computer simulations based on the root of mean square error (RMSE) measure indicate that the model for modeling solar radiation in these two cases is quite accurate. Furthermore, it could be seen that the model's performance using the hyperbolic tangent activation function (Type b) is relatively better than the hyperbolic tangent sigmoid type of the activation function (Type a), with the RMSE values are 18.3924 and 18.4005, respectively.
Age Classification of Moviegoers Based on Facial Image Using Deep Learning

The number of moviegoers in Indonesia continues to rise year after year until 2019. However, due to the COVID-19 pandemic, most Indonesian cinemas were closed in early 2020. Moviegoers are increasingly turning to digital platforms to watch films. Based on the films shown, they can be divided into three categories: films for children, films for adolescents, and films for adults. A system that can automatically classify the faces of the audience based on their age category is required. Using Deep Learning, this study aims to classify the audience's age based on facial photos. The first stage involves collecting data from three datasets: All-Age-Face, FaceAge, and FGNET, which are then combined and relabeled based on age group. Preprocessing and hyperparameter testing were also performed. Finding the best learning rate and bottleneck layer is the goal of hyperparameter testing. The training process employs learning rete and the two best bottleneck layers with six models, namely MobileNet, MobileNetV2, VGG16, VGG19, Xception, and ResNet101V2. Global Average Pooling was added at the end of the layer in each model. The MobileNet model on two bottleneck layers yielded the best testing accuracy value of 85.44 percent in this study.
Review of Feature Extraction on Video-Oculography (VOG) and Electro-Oculography (EOG) Signals

Eye tracking is used to observe where users are looking, how long they are looking, and what order they are looking. Eye tracking has been widely used in various fields such as helping people with disabilities by using Electro-Oculography (EOG) and analyzing eye movements signal in vestibular patients by using Video-Oculography (VOG). The human eye has a cornea and retina that are located between the front and back of the human eye. Eye movement signal analysis is a necessary step prior to eye movement classification. Selecting a model and tuning the feature extraction algorithm on eye movements are tasks that researchers continue to optimize. However, there are very few studies investigating various feature extraction methods in VOG and EOG signals. To solve this research gap, this paper systematically describes feature extraction that is suitable for use in VOG and EOG signal analysis. Three main factors are important to be considered when choosing a feature extraction method: (1) classification, (2) filters and amplifiers, and (3) dataset characteristics. The results of this literature review can be used as a reference for developing feature extraction algorithms for EOG and VOG applications.
Viruses and STEAM: How to Introduce Preventing Disease Transmission in Children through STEAM-Based Learning Methods?

An introduction to preventing the transmission of diseases is not only essential during a pandemic because viruses are the closest part of the children. However, this is not easy to teach in early childhood, which is in a substantial period; interesting methods are needed and accepted by children's logic. This study aimed to describe the process of introducing ways to prevent disease transmission in children through learning methods based on Science, Technology, Engineering, Art, and Mathematics (STEAM). This qualitative research method was presented with a descriptive approach. Informants in this study were teachers who teach children aged 5-6 years at TPA Gampong Daulat, Langsa, Aceh. Researchers collected data through observation techniques. The data were analyzed in three stages: data reduction, data presentation and conclusion drawing. The results showed that the STEAM-based learning method was integrated into the concept and practice of health protocols. Ways of transmitting diseases caused by viruses in the form of washing hands, wearing masks, and keeping a distance taught based on the STEAM method make children actively involved and think logically and critically. The effectiveness of the STEAM-based learning method in teaching other aspects of health can be studied further.
Network Traffic Obfuscation against Traffic Classification

In recent years, tremendous progress has been made in network traffic classification and its use has become ubiquitous in many emerging applications, such as Internet censorship in many countries and ISP traffic engineering. However, the traffic analysis of intermediaries brings the risk of privacy disclosure to users. This paper presents a network traffic obfuscation technology to resist traffic classification. It deceives the machine learning and deep learning models by generating adversarial samples. The adversarial samples generation algorithm includes a white-box attack algorithm based on fuzzy strategy and a black-box attack algorithm based on smote data enhancement. Experiments based on the ISCXTor2016 public data set show that the MIM algorithm has the best performance in white-box attacks, and the obfuscation success rate of DNN and LSTM models is 90%. In the black-box attack, the obfuscation effect of LSTM is the best, while CNN has stronger robustness.
KEEFEKTIFAN MODEL PEMBELAJARAN COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC) TERHADAP KEMAMPUAN MENULIS EKSPOSISI BERITA PADA SISWA KELAS V SDI NO 21 SALOMONI KABUPATEN BARRU

THE EFFECTIVENESS OF COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC) LEARNING MODELS ON EXPOSITION WRITING ABILITY NEWS ON STUDENTS OF CLASS V SDI NO 21 BARRU DISTRICT SALOMONIThis study aims to examine the effectiveness of cooperative integrated reading and composition (CIRC) learning model on the ability to write news expositions in grade V students of SDI No. 21 Salomoni Barru Regency. The type of research used in this research is experimental research. The research design used, namely quasi experiment form Pretest-Posttest Control Group Design. This research was conducted in SDI No. 21 Salomoni Barru Regency. The population, namely the entire Va. and VB students of 42 students. Sampling techniques, namely saturated sampling. The instrument used in this research is the task of writing simple sentences with data collection techniques, namely the assignment. The collected data is then analyzed using descriptive statistics and inferential statistics.The results showed that the ability to write news exposition using Cooperative Integrated Reading and Composition (CIRC) learning in grade V students of SDI No. 21 Salomoni Barru District showed that students' understanding of writing news exposition texts showed an increase in the 9 students whose initial pretest implementation was still in the good category. While the ability to write news exposition without using Cooperative Integrated Reading and Composition (CIRC) learning in grade V students of SDI No. 21 Salomoni Barru District from 22 students who participated in pretest before learning there were 12 students (54.5%) who have the ability to write news exposition texts that are in a very good categor. Furthermore, as many as 10 students (45.5%) who have the ability to write news exposition texts that are in a good categor. The results showed that in the implementation of pretest students the ability of students in writing news exposition texts is classified as effective. While in the posttest implementation after the implementation of CIRC learning obtained that the grades obtained by students are in the category of very effective. Thus, the implementation of learning using CIRC learning based on posttest results is declared effective.
Components of health culture and pedagogical conditions of their implementation

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.
Interactive learning tools amidst modern challenges

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.
Assistance in implementing the learning stage of school literacy movement at YBPK Christian Elementary School Malang

YBPK Christian Elementary School in Ngaglik, Sukun, Malang has applied the School Literacy Movement (SLM) at the stages of habituation and development with the support from the Community Service Team, Faculty of Cultural Studies Universitas Brawijaya. The community service in 2021 was specifically aimed at assisting the school in implementing the SLM learning stage. At this stage, literacy activities were carried out in the pre-reading, whilst-reading, and post-reading activities. The post-reading activities resulted in several products, including mind maps, drawings, posters, and poems. The teachers observed that the students felt comfortable with the selected SLM activities. This also suggests that teachers are required to be resourceful in designing literacy activities. The programme evaluation reveals that some students were uninterested in the pre-reading activities because the teachers simply explained the vocabulary used in the reading. Nevertheless, the school is committed to keeping the SLM programme to help achieve the school goals of producing excellent graduates and creating an enjoyable learning atmosphere for the teachers and students.
A New Lattice-Based Blind Ring Signature for Completely Anonymous Blockchain Transaction Systems

Blockchain technology has been widely applied in numerous industries with its decentralization, verifiability, distributivity, and immutability. However, the identity privacy security of blockchain users is facing serious threats because of the openness of traditional blockchain transaction information. Moreover, numerous traditional cryptographic algorithms used by blockchain transaction networks are difficult to attack quantum computing. In this paper, we propose a new lattice-based blind ring signature scheme in allusion to completely anonymous blockchain transaction systems. There into, the blind ring signature can implement the complete anonymity of user identity privacy in blockchain transactions. Meanwhile, lattice cryptography can availably resist quantum computing attacks. Firstly, the proposed signature scheme has strong computational security based on the small integer solution (SIS) problem and a high sampling success rate by utilizing the techniques of rejection sampling from bimodal Gaussian distribution. Secondly, the proposed signature scheme can satisfy the correctness and security under the random oracle model, including anonymity, blindness, and one-more unforgeability. Thirdly, we construct a blockchain transaction system based on the proposed blind ring signature algorithm, which realizes the completely anonymous and antiquantum computing security of the blockchain users’ identity privacy. Finally, the performance evaluation results show that our proposed blind ring signature scheme has lower latency, smaller key size, and signature size than other similar schemes.
Machine learning classification of infectious disease distribution status

Infectious diseases are common diseases and are caused by microorganisms such as viruses, bacteria, and parasites. Indicators of the spread of this disease can be seen based on the population level and the number of confirmed cases. This study aims to develop a machine learning (ML) analysis model using the K-means cluster, artificial neural network (ANN), and decision tree (DT) methods. The dataset used in this study was obtained based on the number of confirmed patients and the distribution of the population. The analysis process is divided into two stages, namely preprocessing and the classification process. The pre-processing stage aims to produce a classification pattern that can describe the level of distribution status. The classification pattern will be continued at the classification analysis stage using ANN and DT. Classification analysis gave significant results with an accuracy rate of 99.77%. The results of the classification analysis can also describe the level of knowledge distribution based on the decision tree. Overall, the contribution of this research is to develop a classification analysis model that presents the latest information and knowledge. The results of the research presented also have an impact on the control process in environmental management and public health.