Reviewerly/exHarmony huggingface dataset:
Security analysis of promising key encapsulation mechanisms in the core-SVP model

The study of key encapsulation mechanisms on structured lattices is one of the important directions in modern post-quantum cryptography, as many mechanisms are either already standardized (DSTU 8961:2019 "Skelya") or are promising candidates for standardization (CRYSTALS-Kyber). Estimating the complexity of lattice reduction for cryptographic schemes is an old problem. Asymptotic estimates differ greatly from experimental values, therefore, a number of heuristic methods were developed to solve practical problems. The coreSVP model is a standard means of assessing the security of cryptographic schemes on lattices. The purpose of the work is to analyze the encapsulation mechanisms of DSTU 8961:2019 "Skelya" and CRYSTALS-Kyber keys in the coreSVP model. The analysis was performed using two popular heuristics – GSA (Geometric Series Assumption) and the Chen-Nguyen simulator. The analysis showed that the Chen-Nguyen simulator gives slightly lower estimates than the GSA heuristic. As a result of the analysis, it was found that 8961:2019 The “Skelya” and CRYSTALS-Kyber in the coreSVP model for classical computers have slightly lower than declared security values, but for quantum computers the key encapsulation mechanisms provide the declared security levels. Note that during the analysis, the accuracy of the GSA heuristics and the Chen-Nguyen simulator were analyzed separately. Examples of parameters for which heuristics do not give sufficiently accurate results are given. The performed analysis does not take into account the algebraic structure of lattices used in 8961:2019 "Skelya" and CRYSTALS-Kyber. The inclusion of an algebraic structure in the analysis is a further direction of work. The use of simulators is a promising direction, however, more accurate simulators that take into account the structuring of LWE and NTRU arrays are needed.
An overview of threats to data security and integrity in cloud computing

Cloud computing has become an integral part of our lives, and today it is used almost everywhere. In general, cloud computing is a concept of providing IT resources in the form of services. There are two cloud computing models: deployment models, which differ in the type of cloud management and access to the cloud and the level of security, and service models, which differ in the level of service provision, which affects, among other things, the level of responsibility of the service provider and the consumer. Cloud services began to gain popularity in 2009, and the demand for them has grown exponentially every year. They became especially popular during the pandemic in 2019, when people had to stay at home without interrupting their work processes, and now, in post-covid times, they also remain popular due to their convenience, high availability, easy scalability and cost savings. Due to the widespread use of cloud computing services, a high level of security is required. Unfortunately, the popularity of cloud computing has its drawbacks – in addition to the fact that it is more difficult to monitor the security of a remote environment than the security of a local computer, there are many other threats. In today's reality, people use cloud computing technologies in large volumes, for example, at work, for personal purposes, etc., as they have great trust in these technologies. This is the reason why it is necessary to maintain a high level of security and constantly improve it. Cloud computing security threats are usually divided into confidentiality, integrity, and availability threats. To prevent the loss of confidential information, service providers must ensure its integrity. Users want to be sure that their data will not fall into the hands of an intruder or third-party services. Therefore, this article discusses the most common threats to data security and integrity in cloud computing and the existing methods that prevent these vulnerabilities and possible problems at different levels and with the help of different tools.
Parallel distributed processing

After briefly reviewing the appealing psychological properties of PDP systems, an introduction to their historical roots and basic computational mechanisms are provided. A variety of network architectures are described including one-layered perceptrons, backpropagation networks, Boltzmann machines and recurrent systems. Three PDP simulations are analysed: First, a model that purports to learn the past tense of English verbs; Second, a constraint satisfaction network which is able to interpret the alternative configurations of a Necker cube; Finally, a recurrent network which is able to decipher membership of grammatical classes from word-order information. The notion that PDP approaches provide a sub-symbolic account of cognitive processes, in contrast to theclassical symbolic view, is examined. The article concludes with brief speculation concerning the explanatory power of PDP systems at the cognitive level of functioning.
ОСОБИСТІСНО ОРІЄНТОВАНЕ НАВЧАННЯ: ТЕХНОЛОГІЯ «МОЗКОВОГО ШТУРМУ» В ПРАКТИЦІ НАВЧАННЯ УКРАЇНСЬКОЇ МОВИ Й ЛІТЕРАТУРИ В ЗЗСО

This paper describes the procedure how brainstorming, one of the personally-oriented learning interactive techniques, is to be applied and practiced. The author delivers some brief early history of this technique establishment and its main regularities which are to be followed at the courses of the Ukrainian language in modern comprehensive secondary schools. Contemporary achievements have been analysed to study the personally-oriented learning approaches as well as some innovative interactive practices. This paper describes the procedure to use different variants of brainstorming during Ukrainian language classes in Form 6 (Topic “Word-building”), Form 7 (Topic “Diiepryslivnyk as the Form of the Verb”), Form 8 (at the Ukrainian literature class to discuss the image of Kateryna in Taras Shevchenko’s narrative poem of the same title). Also, the author has suggested various handout types which she has already tested as a teacher during her Ukrainian language and literature classes in school and private educational sessions. The paper lists specific recommendations how to apply brainstorming techniques as well as it delivers methodological “profits” from its implementation. However, some problematic issues have been also discussed within the framework of certain topics in Ukrainian language and literature teaching by means of this technology. This scholarly publication gives some prospects on the research of personally-oriented learning approaches utilised by modern school in general. Particularly, some special attention is brought to brainstorming interactive technique during comprehensive classes in school. This research could be of interest to the instructors of the Ukrainian language and literature teaching methods as well as to the teachers of philology in comprehensive secondary schools. It has some useful algorithms to structure the stages of classroom work which includes brainstorming techniques.
Sentiment Analysis of Youtube Comment Section in Indian News Channels

Identifying and extracting subjective information from text data through the use of machine learning and natural language processing methods is the process of sentiment analysis. It is a useful tool for understanding the opinions and attitudes of individuals or groups towards a particular topic or event. In this study, we used TextBlob and Vader to analyze the sentiment of comments posted in the YouTube comment section of Indian news channels during India’s matches in the 2022 T20 World Cup. A highly anticipated event for cricket fans in India. TextBlob and Vader were used to categorize a sample of remarks from the YouTube comment sections of several Indian news channels as positive, negative, or neutral. We then analyzed the results to understand the overall sentiment of the comments and to identify any trends or patterns in the data. Our analysis provided insights into the public perception of Indian news channels on YouTube and the opinions and feelings of their viewers during the T20 World Cup. It also demonstrates the potential of sentiment analysis to understand the public’s reaction to sporting events and to gauge the sentiment of online conversations.
Applicability of Machine Learning for Personalized Medicine

In recent years there is exponential growth in medical data as it is maintained in form of electronic health records. This data can be analyzed and used for predicting diseases as well as improving diagnosis and treatment methods. In recent years, machine learning has been applied to wide range of problems in medical domain as it is particularly suitable for recognizing patterns and making predictions. One of such applications which is popular today is personalized medicine. Personalized medicine is about giving tailor made treatment to each individual patient based on his or her medical history, genetic information, personal information, pathology reports, demographic information etc. This paper provides a comprehensive review of research progress in machine learning methods for personalized medicine. The main aim of this review is to provide insight into different learning methods applied for personalized medicine, compare their accuracy, and discuss their pros and cons and research challenges. With growing ability of machine learning algorithms to handle more complex and unstructured data, it can be effectively used for personalized medicine in providing patients with prompt and effective treatment.
KLASIFIKASI MOOD MUSIK BERDASARKAN MEL FREQUENCY CEPSTRAL COEFFICIENTS DENGAN BACKPROPAGATION NEURAL NETWORK

In music industry, each music is grouped by type, including music genre, artist identification, instrument introduction, and mood. Then came a field of research called Music Information Retrieval (MIR) which is a field of science that retrieves and processes the metadata of music files to perform the grouping. This research is based on the uniqueness of music that has its own mood implied in it. By creating a Machine Learning model using Backpropagation Neural Network (BPNN) based on the Mel Frequency Cepstral Coefficients (MFCC) input feature, it will be able to classify types of music based on mood. Grouping is carried out on four mood classes based on Thayer's model. Based on several previous studies, the use of MFCC in voice processing produces very good accuracy as well as the use of BPNN for classification, which is expected to result in better machine learning model performance. The data used in this study were obtained from the Internet with a total dataset of 200. The results obtained from this study are the classification of music mood using BPNN based on the MFCC feature capable of producing 87.67%. accuracy.
Implementing KWL (Know-Want to know-Learned) Strategy to Improve The 7th Graders’ Reading Achievement of SMP Negeri 12 Palembang

Based on the initial observation and information obtained from the English teacher of VII.10 Class at SMP Negeri 12 Palembang, the students' reading ability is still low and needs improvement. This study is a type of classroom action research that aims to improve the reading achievement on descriptive texts by using the KWL (Know, Want to Know, Learned) strategy. The subjects of this research were 36 students of VII.10 Class at SMP Negeri 12 Palembang. The data were collected through observations, reading tests, and questionnaires. The quantitative data were analyzed using simple statistical methods, represented in percentages (%), while the qualitative data were analyzed descriptively. The results of this study indicated an improvement in both Cycle I and Cycle II. In the pre-action stage, out of thirty-six students, only fifteen students (42%) were able to meet the minimum criteria for mastery learning (Kriteria Ketuntasan Minimum/KKM), which was set at 75 by the school. In the first cycle, nineteen students (53%) achieved the KKM standard. Moreover, in Cycle II, thirty students (83%) reached the KKM, signifying that the reading test results in Cycle II met the targeted indicator of this classroom action research, which requires 75% of students to reach the KKM standard. Based on the results of the reading test, it can be concluded that the KWL Strategy effectively improves students' reading achievement. Besides that, the data obtained from observations and questionnaires also showed positive results regarding the implementation of the KWL strategy in improving students' reading skills. Several factors contribute to the successful implementation of the KWL strategy, including prior knowledge of the text, specific reading goals, and comprehension of acquired information.
Multi-hop question answering of bridge inspection by adopting knowledge graph embedding technology

The complexity of relationships between bridge inspection presents a significant challenge for the answer system. Sparse knowledge graph (KG) due to limited triples further compounds the issue. To overcome this, this paper proposes a dynamic reasoning strategy for bridge inspection question answering. The framework comprises a teacher-student network and dynamic reasoning strategy. The teacher network, based on the neural state machine (NSM), acquires auxiliary intermediate supervision signals. Its output provides probability distribution and entity embedding as input for the student network. The student network, also NSM-based, provides accurate answers with the aid of intermediate supervision signals. To handle incomplete KG, the dynamic reasoning strategy incorporates knowledge embedding, updating the KG by capturing contextual information of each related entity node in relation to the question. Experiments on the bridge inspection dataset demonstrated the effectiveness of this method, outperforming other approaches.
Documentary Resources of Ukrainian Libraries: Problem of de-Russification

The purpose of the article is to focus attention on the process of de-Russification of documentary resources of Ukrainian libraries as an important issue for ensuring their scientific, cultural, and educational activities, preserving cultural and historical heritage, strengthening information security and preventing threats of hostile information influences on Ukrainian citizens and library users. The research methodology is based on the use of general scientific methods of cognition, in particular, analysis, synthesis, and the logical method. Scientific novelty. The article emphasises the importance of the process of de-Russification of documentary resources of Ukrainian libraries, both for library institutions and for society as a whole, which requires the development of scientifically sound, effective approaches to updating library collections in the conditions of armed aggression of the Russian Federation against Ukraine, taking into account their scientific, cultural, and historical context, finding a balance between the scientific and cultural potential of library documentary resources and the potential risk of their use for propaganda and disinformation.&#x0D; Key words: libraries, documentary resources, de-Russification, library collection.
Further Progress Toward Automating Functional Analysis Interpretation

It is considered best practice to conduct a functional analysis and visually inspect data collected to determine the function of problem behavior, which then informs the intervention approaches applied. Visual inspection has been described as a “subjective” process that may be affected by factors unrelated to the data. Structured decision-making guidelines have been established to address some of these shortcomings. The current paper is a follow-up to earlier work describing positive outcomes related to the viability of a decision support system based on structured criteria from Roane et al. Here, we demonstrate important improvements in a computer script’s interpretation of functional analysis data, including improvement in agreement between the updated computer script version and experienced human raters (89%) compared to our original agreement outcomes (81%). This paper further supports the use of decision support systems for functional analysis interpretation.
PENGARUH METODE PEMBELAJARAN ROLE PLAYING TERHADAP HASIL BELAJAR SISWA MATA PELAJARAN BAHASA INDONESIA SISWA KELAS IV SDN PINANG 2 KOTA TANGERANG

This study aims to determine whether there is an influence of roleplaying learning methods on the learning outcomes of students of subjects Indonesian grade IV students of SDN Pinang 2 Tangerang City. This research was conducted at SDN Pinang 2 Tangerang City. From the interview results, there were 78% of students who achieved KKM scores and 22% who had not achieved KKM scores from a total of 32 students, in the learning process there were still students who were less active during learning and the method often used by teachers was conventional methods. The research method used is pre-experimental. The sampling amounted to 63 students with class IV A totaling 32 students as the experimental class and class IV B totaling 31 students as the control class. From the results of the posttest hypothesis test conducted using an independent sample test (t-test), sig was obtained. 2 tailed (0.008) &lt; sig. α (0,05). From the hypothesis test it can be concluded that H_0 rejected and H_1 accepted. The results of this study show that there is an influence of roleplaying methods on the learning outcomes of Indonesian language students in grade IV at SDN Pinang 2 Tangerang City. So it can be concluded that there is an influence between the role playing method on the Indonesian learning outcomes of grade IV students of SDN Pinang Kota Tangerang.&#x0D; Keywords: Role Playing, Indonesian, Learning Outcomes
Cross-lingual Transfer Learning with Persian

The success of cross-lingual transfer learning for POS tagging has been shown to be strongly dependent, among other factors, on the (typological and/or genetic) similarity of the low-resource language used for testing and the language(s) used in pre-training or to fine-tune the model. We further unpack this finding in two directions by zooming in on a single language, namely Persian. First, still focusing on POS tagging we run an in-depth analysis of the behaviour of Persian with respect to closely related languages and languages that appear to benefit from cross-lingual transfer with Persian. To do so, we also use the World Atlas of Language Structures to determine which properties are shared between Persian and other languages included in the experiments. Based on our results, Persian seems to be a reasonable potential language for Kurmanji and Tagalog low-resource languages for other tasks as well. Second, we test whether previous findings also hold on a task other than POS tagging to pull apart the benefit of language similarity and the specific task for which such benefit has been shown to hold. We gather sentiment analysis datasets for 31 target languages and through a series of cross-lingual experiments analyse which languages most benefit from Persian as the source. The set of languages that benefit from Persian had very little overlap across the two tasks, suggesting a strong task-dependent component in the usefulness of language similarity in cross-lingual transfer.
Investigating Sentiment-Bearing Words- and Emoji-based Distant Supervision Approaches for Sentiment Analysis

Sentiment analysis focuses on the automatic detection and classification of opinions expressed in texts. Emojis can be used to determine the sentiment polarities of the texts (i.e. positive, negative, or neutral). Several studies demonstrated how sentiment analysis is accurate when emojis are used (Kaity and Balakrishnan, 2020). While they have used emojis as features to improve the performance of sentiment analysis systems, in this paper we analyse the use of emojis to reduce the manual effort inlabelling text for training those systems. Furthermore, we investigate the manual effort reduction in the sentiment labelling process with the help of sentiment-bearing words as well as the combination of sentiment-bearing words and emojis. In addition to English, we evaluated the approaches with the low-resource African languages Sepedi, Setswana, and Sesotho. The combination of emojis and words sentiment lexicon shows better performance compared to emojis-only lexicons and words-based lexicons. Our results show that our emoji sentiment lexicon approach is effective, with an accuracy of 75% more than other sentiment lexicon approaches, which have an average accuracy of 69.1%. Furthermore, our distant supervision method obtained an accuracy of 76%. We anticipate that only 24% of the tweets will need to be changed as a result of our annotation strategies
Annotation of lexical bundles with discourse functions in a Spanish academic corpus

This paper describes the process of annotation of 996 lexical bundles (LB) assigned to 39 different discourse functions in a Spanish academic corpus. The purpose of the annotation is to obtain a new Spanish gold-standard corpus of 1,800,000 words useful for training and evaluating computational models that are capable of identifying automatically LBs for each context in new corpora, as well as for linguistic analysis about the role of LBs in academic discourse. The annotation process revealed that correspondence between LBs and discourse functions is not biunivocal and that the degree of ambiguity is high, so linguists’ contribution has been essential for improving the automatic assignation of tags.
Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages

In many humanitarian scenarios, translation into severely low resource languages often does not require a universal translation engine, but a dedicated text-specific translation engine. For example, healthcare records, hygienic procedures, government communication, emergency procedures and religious texts are all limited texts. While generic translation engines for all languages do not exist, translation of multilingually known limited texts into new, endangered languages may be possible and reduce human translation effort. We attempt to leverage translation resources from rich resource languages to efficiently produce best possible translation quality for well known texts, which is available in multiple languages, in a new, severely low resource language. We examine two approaches: 1.) best selection of seed sentences to jump start translations in a new language in view of best generalization to the remainder of a larger targeted text(s), and 2.) we adapt large general multilingual translation engines from many other languages to focus on a specific text in a new, unknown language. We find that adapting large pretrained multilingual models to the domain/text first and then to the severely low resource language works best. If we also select a best set of seed sentences, we can improve average chrF performance on new test languages from a baseline of 21.9 to 50.7, while reducing the number of seed sentences to only ∼1,000 in the new, unknown language.
Measuring the Impact of Data Augmentation Methods for Extremely Low-Resource NMT

Data augmentation (DA) is a popular strategy to boost performance on neural machine translation tasks. The impact of data augmentation in low-resource environments, particularly for diverse and scarce languages, is understudied. In this paper, we introduce a simple yet novel metric to measure the impact of several different data augmentation strategies. This metric, which we call Data Augmentation Advantage (DAA), quantifies how many true data pairs a synthetic data pair is worth in a particular experimental context. We demonstrate the utility of this metric by training models for several linguistically-varied datasets using the data augmentation methods of back-translation, SwitchOut, and sentence concatenation. In lower-resource tasks, DAA is an especially valuable metric for comparing DA performance as it provides a more effective way to quantify gains when BLEU scores are especially small and results across diverse languages are more divergent and difficult to assess.