Reviewerly/exHarmony huggingface dataset:
Quality Indicators for the Implementation of Educational Programmes in Higher Education Institutions

The article identifies and substantiates indicators of the quality of educational activities as a practical tool in the system of monitoring the quality of higher education at the local level. The following research approaches to the study of the phenomenon of ensuring the quality of higher education were applied: study of the proceedings of the annual International Research and Development Conference “Development of Internal Quality Assurance Systems in the HEIs of Ukraine: Tools and Challenges”; analysis of the results of scientific research of the Institute of Higher Education of NAES of Ukraine; generalisation of the results of analytical research on the state of development of the system of internal quality assurance of education in Ukrainian universities, conducted by the International Foundation for Research of Educational Programmes; study of thematic informational and analytical materials available on the world wide web; analysis of the official websites of the HEIs, namely, the categories of the system of internal quality assurance of higher education; study of information on the self-assessment of educational programmes that have been uploaded to the National Agency for Higher Education Quality Assurance system since September 2022, namely, the analysis of strengths and weaknesses in the implementation of the educational programmes in such specialities as 231 Social Work, 232 Social Security, 053 Psychology at the levels of Bachelor, Master, Doctor of Philosophy, etc. Based on the best experience of Ukrainian higher education institutions, the following list of indicators is suggested: the development of educational programmes, their monitoring and periodic updating, the dynamics of the contingent for EPs, the quality of educational resources and information support for students, the quality of teaching under EPs, the conditions for the formation of an individual educational trajectory, the internationalisation of educational programmes, the combination of training and research during the implementation of educational programmes, the share of EP graduates employed in the speciality, the transparency and objectivity of control measures, the observance of academic integrity, the analysis of surveys of educational process participants, ensuring information openness, the historical and cultural component of the educational process, ensuring a culture of quality among all the educational process participants.
Personal funds of Central State Archive of the Samara Region as a source for the study of family memory

The article examines the source potential of personal funds of Central State Archive of the Samara Region when studying such a scientific problem as family memory. The author, based on archival materials and auto-documentary texts, analyzes the conditions under which interest in family history is formed and determines the motives for preserving the history of ancestors. The sources for the research were the funds of two families – the nobles Butorovs and the priest Preobrazhensky, who lived in the Samara province at the turn of the 19th and 20th centuries, who, in the era of socio-political cataclysms, managed not only to preserve their family history, but also to increase it and pass it on to subsequent generations. Methods of the scientific direction «memory studies» make it possible to analyze the traumatic experience of the authors of ego-documents, which contributed to the Preobrazhenskys and Butorovs turning to studying the past of their family, writing memoirs and preserving family memory. The author comes to the conclusion that interest in the history of a family does not depend on social origin, but is formed by family traditions – attitude to reading, education and creative pursuits. In addition, through genealogical research, the Preobrazhenskys and Butorovs sought to preserve the continuity of generations in their families and find spiritual support at a turning point in life.
Reimagining Formal Functions in Post-Tonal Music

Modernist concepts of temporality (“non-linear time”) are variously cross-related to conventional concepts and qualities of time (“linear time”) (Kramer 1988, 20–65). This is exemplified by Salvatore Sciarrino’s creative method, which reconciles more conventional elements of discursive organicist design (processes of accumulation, multiplication, and transformation) with strategies of rupture and formal deterioration (“little bang,” “window form”). This unique mixing of “linear” and “non-linear” strategies is staged within a soundscape of mostly minimal dynamics and reduced activity at the edge of silence, sensitizing the listener toward micro-alterations. This article introduces key strategies in Sciarrino’s temporal form based on analytical investigations of three works that differ substantially in duration, density, and genre: Quintettino no. 1 (1976, ca. 3 min.) for clarinet and string quartet, Efebo con radio (1981, ca. 11 min.) for voice and orchestra, and the music theater work Da gelo a gelo: 100 scene con 65 poesie (2006, ca. 110 min.). My analyses are framed by a discussion of the relationships between musical beginnings or endings and constellations of duration and density. Sciarrino’s works prominently feature a semanticization of musical material by metaphorical, agential, or paratextual means that is also evident in his interpretation of Anton Webern’s orchestral piece op. 6, no. 4 (1909). The analyses demonstrate how the composer’s explicit and implicit semanticization of his material allows the listener to understand Sciarrino’s works as reimaginations of conventional formal functions in which the familiar appears unfamiliar.
<i>Klangfarbenmelodie</i>, Chromophony, and Timbral Function in Arnold Schoenberg’s “Farben”

Arnold Schoenberg’s concept of Klangfarbenmelodie (melody of timbres) is one of the most important yet least understood compositional innovations of the twentieth century. By examining significant factors in Klangfarbenmelodie ’s theoretical formulation, proposing functional roles that timbre can fulfill, and locating examples of timbre realizing those musical functions in “Farben,” the third of Schoenberg’s Five Orchestral Pieces , Op. 16, this article demonstrates some of the ways timbre can shape music and our musical experience. While musical logic based on timbre operates according to laws of its own, not those of pitch, parallels can nonetheless be drawn between harmonic functions and timbral functions. Timbral developments are shown to articulate the formal process in “Farben” and create coherent progressions, modulations, and cadences that illustrate some possibilities of how timbre can function in music.
Research on the Application of Artificial Intelligence in Comparative Analysis of Man-machine Translation

Due to the inherent differences in grammar structures, cultural customs, and social contexts among various languages, the current efficacy of machine translation still falls short of meeting individuals' diverse and nuanced needs. Merely isolated training in translation skills proves inadequate in nurturing the caliber of high-quality translation professionals required today. In this regard, a comprehensive translation education must encompass practical skills and a strong foundation in theoretical knowledge. Consequently, there arises a necessity for the integration of vocational translation education with a substantial theoretical and academic framework. In light of this, the present study delves into the utilization of Artificial Intelligence (AI) technology for a comparative analysis between human and machine translations. The research findings underscore the promising potential of the proposed AI-driven approach, showcasing a relatively robust accuracy rate of 73.96% across human-machine translation evaluations. These outcomes undeniably highlight the efficacy of the method elucidated in the study's framework, particularly in the realm of human-machine translation evaluations. The implications of these findings are far-reaching, potentially smoothing the transition into the field of translation, especially for individuals endowed with broad-ranging knowledge and adept oral communication skills. Such proficiencies not only constitute fundamental requisites for advanced interpretation training but also serve as pivotal prerequisites for cultivating highly proficient English-Chinese communicators.
Research on Machine Learning Algorithm-Based Approach for Detecting Abnormal Data from Environmental Sensors in Different Dimensions

With the development of environmental sensor technology, more and more environmental sensors are widely used in various fields, such as environmental monitoring, smart home, smart city and so on. However, due to the complexity and diversity of environmental sensor data, there are a high number of anomalous data in sensor data, which can lead to misjudgment and false positives, compromising data reliability and accuracy. Therefore, the detection method of abnormal data in different dimensions of environmental sensors based on machine learning algorithms were studied in this paper. Firstly, the environmental sensor data of IBRL (Intel Berkeley Research Lab) data set was preprocessed, including data cleaning, data normalization and feature extraction. Then, the simulation experiment was completed using the data set, and the data of different dimensions was detected using machine learning algorithms such as OneClass Support Vector Machine algorithm, Local Outlier Factor algorithm, and Isolation Forest algorithm. Finally, through experimental comparison and analysis of the application effects of the above three algorithms in anomaly detection of environmental data in different dimensions, the Isolation Forest algorithm with the best comprehensive detection effect was finally selected for practical engineering projects. In practical engineering projects, maintenance costs can be effectively reduced, project safety and reliability can be improved, and the purpose of improving project efficiency can ultimately be achieved by the proposed method.
A Study on Semantic Understanding of Large Language Models from the Perspective of Ambiguity Resolution

Generative large language models (LLMs) have demonstrated outstanding performance in language understanding and generation tasks. Whether current LLMs can truly understand meaning or not is an open question. Semantic disambiguation is an important indicator to test machine language ability. To this end, three experiments were conducted to test the ambiguity resolution ability. In Experiment 1, the LLMs were asked to make a semantic discrimination on ambiguity phrases. The ChatGPT (May 3 Version) and ChatGPT Plus (May 3 Version) showed a random level performance of discrimination. To make the task easier for the LLMs, ambiguity sentences were presented with context in the Experiment 2. While the LLMs unified the context to fill the position for ambiguity word, the performance of semantic discrimination on these sentences remained at random level. In addition to test the performance of LMMs, prediction is one of the key ability of LLMs. The prediction by unifying the context is one of the key skill for LLMs. Therefore, the Experiment 3 further test the similarity between the human prediction mechanism and the strong generation capabilities of LLMs. Weak similarities were only found under the condition of local constraint features gradually accumulating, specifically, the cosine similarity of predicted sentence semantic vectors and ending word semantic vectors negatively correlated with close probability only. These series of experiments demonstrate that, it is far away to claim that LLMs truly understand human sentences.
Contrastive Learning with Soft Negative Sampling for Visual Word Sense Disambiguation

The task of visual word sense disambiguation (VWSD) is to find the images that accurately match target words or phrases from a given candidate set of images. It is a challenging problem, requiring VWSD models to understand the related information between images and texts and distinguish the ambiguous information. To address this problem, we propose a method of contrastive learning with soft negative sampling (CLSNS), which facilitates the optimization of VWSD models via comparisons of positive and soft negative samples. When calculating the text and image comparison loss, the ambiguous target word is used as the soft negative sample, the target phrase with known semantics is used as the positive sample, and other irrelevant text image pairs are used as negative samples. bidirectional margin loss (BML) is utilized to distinguish between negative samples and soft negative samples, and Info Noise-contrastive estimation (InfoNCE) loss to distinguish between positive and negative samples. To make the model could be trained more effectively, we expand the target word and target phrase using prompt technology. We can broaden the diversity of the training data by inserting various prompts which will enhance the model's functionality and generalizability. These augmentations can improve the model's comprehension of and ability to provide domain-specific content, resulting in more precise and focused responses. Moreover, an extended Wikipedia-based text model is introduced to enrich the input text information. Extensive experiments conducted on a VWSD dataset shared from SemEval 2023 demonstrate the effect of the proposed method.
Mention Attention for Pronoun Translation

Abstract. Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with these divergences and translating pronouns is a challenge in machine translation. Mentions are referring candidates of pronouns and have closer relations with pronouns compared to general tokens. We assume that extracting additional mention features can help pronoun translation. Therefore, we introduce an additional mention attention module in the decoder to pay extra attention to source mentions but not non-mention tokens. Our mention attention module not only extracts features from source mentions, but also considers target-side context which benefits pronoun translation. In addition, we also introduce two mention classifiers to train models to recognize mentions, whose outputs guide the mention attention. We conduct experiments on the WMT17 English–German translation task, and evaluate our models on general translation and pronoun translation, using BLEU, APT, and contrastive evaluation metrics. Our proposed model outperforms the baseline Transformer model in terms of APT and BLEU scores, this confirms our hypothesis that we can improve pronoun translation by paying additional attention to source mentions, and shows that our introduced additional modules do not have negative effect on the general translation quality.
A Case Study on the Generalization of Chinese Text Classification Methods based on Deep Learning

Abstract. In the past decade, deep learning based methods have taken a dominant position in natural language processing (NLP). For almost all NLP tasks, deep learning based methods far surpassed traditional methods. Especially in the past five years, the development of deep learning methods has been particularly rapid. For example, the pre-training and fine-tuning paradigms represented by BERT have dominated the NLP field, while also driving the development of other fields such as computer vision. Nowadays pre-trained large language models (LLMs) such as GPT-3/ChatGPT further demonstrate the advantages of Transformer based deep learning methods, which can achieve good results across various problems without any specialized training. In spite of the remarkable success, their performances still underperform fine-tuned models in the task of text classification in some scenarios. Nevertheless, the LLMs are good generalist models. The goal we pursue is the deep learning methods with good generalization ability. In the case of limited computing resources and high performance requirements, the fine-tuned models remain the first choice. So how is the generalization ability of the fine-tuned models? In this paper, we explore the generalization of representative Chinese text classification methods based on deep learning. The experimental results indicate that Transformer based methods present good ability of generalization on two significant different Chinese datasets. In the current era of LLMs, this work can assist us in choosing more appropriate solutions for natural language processing tasks.
An Analysis of Error Types in Chinese to English Translation by Google Neural Machine Translation

Abstract: Due to the rapid development of globalization and digitalization, neural machine translation (NMT) systems have gradually developed into the mainstream technology in the field of machine translation (MT). Even so, MT output cannot meet the end-user's expectation in terms of translation quality. This has prompted many scholars to research on improving the quality of MT output. This study is based on Google Neural Machine Translation (GNMT) application and the Government Work Report in 2022 to compare the GNMT output with the official translation by means of manual error analysis. It summarizes the error types of GNMT in such texts, with 4 first-level error types and 15 second-level error types, and MT errors of such texts are decreasing according to the first-level error types: semantic, syntactic, lexical, and other errors. Thus, even though NMT systems lead the way in MT, it cannot replace human translation. This study also provides some case studies of these error types so as to provide practical reference for post-editing of NMT. Based on the error analysis, four implications for post-editing are summed up. So when doing translation, post-editors need to enhance awareness of context, focus on non-subject-predicate sentence structure, adhere to logical connection, and focus on four-character phrases.
Open-ended questions automated evaluation: proposal of a new generation

Abstract. Exams grading for the knowledge validation to recognise competences is an essential element for any learning process. There are two main modes for their evaluation: subjective and automated. Subjective evaluation is accused of many flaws such as the inconsistency of the human corrector and the time it requires. Automating the assessment of open-ended questions saves a lot of time, provides quick feedback to learners and ensures the consistency expected from the human correctors. However, this is a challenging problem to implement because the computer does not have the same faculties as a human.
DIV-DU: Data Integrity Verification and Dynamic Update of Cloud Storage in Distributed Machine Learning

In the current distributed machine learning scenarios, less attention is paid to data integrity. A few studies that focus on data integrity cannot locate specific tampered data blocks to dynamically update or delete them. In order to solve this problem, we put forward a scheme called data integrity verification and dynamic update(DIV-DU) to ensure the truth the training data is completely and properly saved in the cloud server and support dynamic insertion, deletion and modification of training data. First, based on the idea of Provable Data Possession (PDP), we prevented the training data from being forged or tampered with by designing an integrity verification scheme. Second, we use the hashed data blocks as the leaf nodes to construct Merkel hash tree to support locating the wrong data block and dynamic update. Once the integrity verification fails, our DIV-DU scheme can quickly locate that attacked data block and update this data block. In addition, due to the characteristics of the tree, our scheme can dynamically update the data without reconstructing the entire Merkel hash tree. Thirdly, we formally proof the correctness of our DIV-DU scheme, and show the efficiency of that in the next section performance analysis.
ConceptFR: A Bilingual English-French Dataset for Natural Language Generation

The recent development of the NLG domain shows a need for the extension of benchmarks towards non-English corpora. It is important, however, to keep the reference to the equivalent English data in order to evaluate possible flaws of the models while treating lexically rich languages. We present a bilingual English–French dataset and benchmark with parallel data, containing 28K English–French sentence pairs with the keywords and a main concept for each data entry. This dataset targets testing the generative abilities of a model to produce a common-sense knowledge text given a set of keywords as a condition in a constrained text generation task scenario. The dataset was constructed in a semi-automatic way and contains a golden set with human corrections and an additional part-of-speech tag for the main French concept, corresponding to its usage in the sentence. The results of the finetuning experiment measured with the lexical, semantic and diversity automated metrics have shown a significant boost in the generalization capabilities of the models finetuned on our data. No significant drop in performance was observed for the model trained on the French partition of ConceptFR data, compared to the models finetuned on the English partition of our dataset and the CommonGen English dataset, used in the experiment.
A Novel Method for Detecting the Width of Tobacco Filaments Based on Coordinate Point Fitting

Abstract. The width of the tobacco filament (WTF) is a significant process parameter in cigarette production as it has a crucial impact on both the physical quality and sensory experience of the product. The detection methods currently employed for WTF mainly involve manual visual inspection or low-frequency offline inspection, which lack representation, accuracy, and efficiency. To enhance the detection efficiency and accuracy, a method for detecting WTF images utilizing the coordinate point fitting method (CPFM) is proposed. We compare the performance of CPFM with the conventional Hough transform method (HTM) and the circle with different radii method (CDRM). Based on experimental results, CPFM has a relative error of 5.3%, an absolute error of 0.085 mm, and a maximum relative standard deviation of 1% in six repetitive experiments. This suggests that the suggested CPFM technique is dependable and dependable and fulfills the statistical demands of metrology. By implementing the WTF image detection method, which relies on the coordinate point fitting methodology, detection efficiency and accuracy are substantially increased. The technique necessitates rapid and exact WTF detection and surpasses prior approaches significantly. Compared to traditional methods, the CPFM method exhibits distinct benefits in detecting WTF. It is probable that the method will become widely used in cigarette production to improve both product quality and production efficiency.
Extending the Parallel Multiple-Analysis Processor

This article revisits the issue of perceived meter in rhythmically irregular post-tonal music by way of Danuta Mirka’s (2009) dynamic model of meter. Her model, which is a parallel multiple-analysis processor in the sense of Jackendoff (1991), accounts for rapidly fluctuating sensations of meter in the real-time listening process, while maintaining local regularity as a defining feature of metric processing. I offer two extensions to her model—one concerning the perceptual processing of non-isochronous meter and the other concerning meter in multiple perceptual streams—along with applications to music by Carter, Webern, Barber, Babbitt, Feldman, and Adès.