Reviewerly/exHarmony huggingface dataset:
Co-Performing Music with AI: Real-Time Performance Control Using Speech and Gestures

We present an interactive music AI system that enables users to co-create expressive performances of notated music using speech and gestures. The system provides multi-modal interactive dialog-based control of performance rendering via smartphones and is accessible to people regardless of their musical background. We train a deep learning music performance rendering model on sheet music and associated performances with notated performance directions and user-system interaction data. Users have the opportunity to actively participate in the performance process. A speech- and gesture-based feedback loop with interactive learning improve the accuracy of performance rendering control. We believe that many people can express aspects of music performance using natural human expressions such as speech, voice, and gestures, and that by hearing the music follow their communicated intent, they can achieve deeper immersion and enjoyment of music than otherwise possible. With this work we pursue the goal of developing novel, fulfilling, and accessible music making experiences for large numbers of people who are not currently musically active.
Towards Simple Hybrid Language Model Reasoning Through Human Explanations Enhanced Prompts

Large Pre-Trained Models (LLMs) have reached state-of-the-art performance in various Ntural Language Processing (NLP) application tasks. However, an issue remains these models may confidently output incorrect answers, flawed reasoning, or even entirely hallucinate answers. Truly integrating human feedback and corrections is difficult for LLMs, as the traditional approach of fine-tuning is challenging and compute-intensive for LLMs, and the weights for the best models are often not publicly available. However, the ability to interact with these models in natural language opens up new possibilities for Hybrid AI. In this work, we present a very early exploration of Human-Explanations-Enhanced Prompting (HEEP), an approach that aims to help LLMs learn from human annotators’ input by storing corrected reasonings and retrieving them on the fly to integrate them into prompts given to the model. Our preliminary results support the idea that HEEP could represent an initial step towards cheap alternatives to fine-tuning and developing human-in-the-loop classification methods at scale, encouraging more efficient interactions between human annotators and LLMs.
Towards Explainable Automatic Knowledge Graph Construction with Human-in-the-Loop

Knowledge graphs are important in human-centered AI because of their ability to reduce the need for large labelled machine-learning datasets, facilitate transfer learning, and generate explanations. However, knowledge-graph construction has evolved into a complex, semi-automatic process that increasingly relies on opaque deep-learning models and vast collections of heterogeneous data sources to scale. The knowledge-graph lifecycle is not transparent, accountability is limited, and there are no accounts of, or indeed methods to determine, how fair a knowledge graph is in the downstream applications that use it. Knowledge graphs are thus at odds with AI regulation, for instance the EU’s upcoming AI Act, and with ongoing efforts elsewhere in AI to audit and debias data and algorithms. This paper reports on work in progress towards designing explainable (XAI) knowledge-graph construction pipelines with human-in-the-loop and discusses research topics in this space. These were grounded in a systematic literature review, in which we studied tasks in knowledge-graph construction that are often automated, as well as common methods to explain how they work and their outcomes. We identified three directions for future research: (i) tasks in knowledge-graph construction where manual input remains essential and where there may be opportunities for AI assistance; (ii) integrating XAI methods into established knowledge-engineering practices to improve stakeholder experience; as well as (iii) evaluating how effective explanations genuinely are in making knowledge-graph construction more trustworthy.
Review on the Application Areas of Decision-Making Algorithms in Smart Homes

Automated decision-making is one of the fundamental functions of smart home technologies. With the increasing availability of Artificial Intelligence (AI) and Internet of Things (IoT) technologies, those functions are becoming increasingly sophisticated. While many studies have been conducted on optimizing algorithms to improve the accuracy of predictions, less attention has been paid to how humans interact with algorithmic systems. This involves questions such as to what degree humans are involved in the algorithmic decision-making process and how we can design meaningful interactions between humans and systems relying on decision-making algorithms. With these questions in mind, our paper presents a literature review on the current state of decision-making algorithms in smart homes. Based on an analysis of 49 selected papers, we present a systematic investigation towards the application areas and the deployment functions that decision-making algorithms currently take in smart homes. Focusing on two main application areas – energy management and healthcare, our paper sheds light on the current deployment of decision-making algorithms in smart homes and identifies the current intentions of involving humans in-the-loop. Within the background of facilitating human-in-the-loop as an interaction paradigm, we aim to expose the design challenges for human-in-the-loop decision-making algorithms in smart homes which can pave the way for developing more effective human-machine hybrid intelligent systems in smart homes in the future.
Bridging the Conventions: Effects of Modular Approach In Teaching Elementary Grades

The study described and examined the delivery, effectiveness and efficiency of modular approach in teaching among the Pandi Heights Elementary School and Akle Elementary Schools both of which were within the auspices of Schools Division of Bulacan. There were 54 teachers served as the respondents of the study. They were given survey questionnaire which was administered and retrieved through google forms. Results revealed that utilization of modular approach was significant and contributory to achieve meaningful learning in order to achieve efficient delivery of learning competencies and skills were honed independently based from the acquired learning experiences of learners. Thus, the findings showed that the schools were able to provide and assist teachers in module preparation and expenses. Apparently, there were personal expenses made by the teachers to supplement the reproduction of modules, financial support of the school was found significant to prepare and reproduce the modules. Further, proper evaluation to teachers’ instructional competence was made in order to monitor the checking and assessment of learners’ outputs contained in their modules. . Profile of the respondents in terms of age, highest educational attainment and number of years in teaching were positively correlated with the delivery and effectiveness of modular approach in teaching elementary grades.
A Human Pose Similarity Calculation Method Based on Partition Weighted OKS Model

Image-based calculation of human pose similarity is one of the computer vision research fields. Most existing research uses the human skeleton joint to calculate the human pose similarity, but usually does not consider the influence of inaccurate recognition of skeleton joint on similarity calculation caused by the complex environment (such as the occlusion of body parts, etc.). We propose a human pose similarity calculation method based on partition weighted OKS model. Due to the influence of external factors such as occlusion, the skeleton joint extracted by the human pose estimation algorithm is inaccurate, which leads to the decrease of the accuracy of the human pose similarity calculation. We propose the partition rule of human skeleton joints and the dynamic strategy adjustment of partition weight. The partition weighted OKS model and a human pose similarity calculation method based on the partition weighted OKS model are given. The experimental results on datasets show that the proposed method for human pose similarity calculation is superior to the traditional one.
ATIPM: A Blockchain-Based Anonymous and Traceable Intellectual Property Management Scheme

With the spread of information on the Internet and the explosive growth of intellectual property information, the traditional intellectual property management model relying on third-party institutions cannot meet the demand for intellectual property protection, which has a cumbersome process, low efficiency, and insufficient evidence of rights protection. To address the issues of information falsification and leakage, we present an anonymous and traceable intellectual property management system based on blockchain, namely ATIPM, which employs the non-interactive zero knowledge proof to realize user unlinkability and anonymous transactions to protect the users’ intellectual property information. To avoid a single point of accountability, the ATIPM introduces a threshold ramp secret sharing scheme to achieve the traceability of intellectual property for all users and greatly improve the users’ privacy security and autonomy by preventing information leakage from malicious third-party institutions. Furthermore, the ATIPM can improve the management efficiency of intellectual property by utilizing smart contracts to realize efficient retrieval and verification of intellectual property. The evaluation results demonstrate the effectiveness of our proposed system.
A Survey on Recent Text Summarization Techniques

NLP (Natural Language Processing) is a subfield of artificial intelligence that examines the interactions between computers and human languages, specifically how to design computers to process and evaluate vast quantities of natural language data. The procedure of condensing long text into paragraphs or phrases is known as NLP text summarization. This technique retrieves essential information from a text while keeping its meaning. This decreases the time necessary to comprehend large elements, such as articles, without compromising the integrity of the content. Major difficulties in text summarizing include subject identification, interpretation, summary construction, and summary evaluation. Most real-world systems that summarize texts rely on extractive summarization. Hence, there must be a way to summarize lengthy assessments into concise statements with few words that convey the same information. The use of text summarization in this context can be helpful. Text Summarization is of interest to several researchers in natural language processing. This study provides an overview of the different text-summarization approaches used in Natural language processing.
THE INFLUENCE OF ACADEMIC STEREOTYPES OF THE CHINESE EDUCATIONAL SYSTEM ON THE ADAPTATION OF STUDENTS IN HIGHER EDUCATION INSTITUTIONS OF UKRAINE

In the conditions of the internationalization of education and the growing interest of foreign applicants not only from Europe, but also from Asia, in particular from China, in the educational services of Ukraine, there is a need to increase the quality of training of specialists for foreign countries in Ukrainian institutions of higher education.In order to facilitate the integration of higher education applicant into a new educational environment for them, we consider it necessary for domestic teachers to consider the differences in the education systems of the student's country of origin and Ukraine.The article is devoted to the analysis of the role of national academic stereotypes in the organization of the educational process in a foreign language environment.In particular, the interaction of Chinese and Ukrainian academic stereotypes is studied, the most relevant universal components of this process are highlighted, which can contribute to the acceleration of the adaptation of foreign students to the education system in Ukraine.The authors point out differences in the organization of the educational process in higher education institutions of China and Ukraine: in the use of hours for independent work of students, the attitude to control of knowledge, the academic calendar, the influence of grades on the term of study, etc.It is noted that students from China retain ethnic conditioning for a long time and are difficult to reorient to new educational strategies.It is emphasized that taking into account knowledge about national stereotypes in the education system of students will contribute to increasing the effectiveness of the organization of educational work in the system of personnel training for foreign countries.Журнал «Перспективи та інновації науки» (Серія «Педагогіка», Серія «Психологія», Серія «Медицина») № 10(28) 2023 25
BİLGİSAYAR DESTEKLİ İLETİŞİMDEKİ ŞİKÂYET SÖZ EDİM KULLANIMLARININ İNCELENMESİ: SİKAYETVAR.COM ÖRNEĞİ

The current study aims at analysing complaint speech acts of native language users on a website to identify how they are performed in Turkish language, which is expected to provide a guide for complaint speech act in teaching Turkish both as a foreign language and native language.For that purpose, we took the advantage of computer-mediated communication, which offers an important source for researchers regarding the language use of native speakers, and we examined 150 different complaints on the website called "sikayetvar.In this study, we employed the method of document analysis and analysed the study data via content analysis.At the end of the analysis, we found out that there were 281 complaint speech act strategies used in total.We also concluded that while performing a complaint speech act, native speakers of Turkish language mostly preferred the direct strategies of accusation and expression of negative state of mind.On the other hand, we concluded that the strategy of expression of negative personal state of mind was mostly used in the complaints about hotels and hospitals, whereas the strategy of accusation was mostly used in the complaints about cargo, air transportation and order outs.The study findings also showed that men used more strategies than women, while both men and women used accusation the most and warning others the least.These findings imply that the way complaint speech act occurs is affected by the category of complaint, but not by gender.Consequently, it is possible to state that examining how the complaint speech act, is used in different contexts (different categories, age groups, different layers of society, face-to-face communication, etc.) will contribute to the effective use of speech acts in teaching native languages and in language classes.
Convolutional auto-encoded extreme learning machine for incremental learning of heterogeneous images

&lt;span lang="EN-US"&gt;In real-world scenarios, a system's continual updating of learning knowledge becomes more critical as the data grows faster, producing vast volumes of data. Moreover, the learning process becomes complex when the data features become varied due to the addition or deletion of classes. In such cases, the generated model should learn effectively. Incremental learning refers to the learning of data which constantly arrives over time. This learning requires continuous model adaptation but with limited memory resources without sacrificing model accuracy. In this paper, we proposed a straightforward knowledge transfer algorithm (convolutional auto-encoded extreme learning machine (CAE-ELM)) implemented through the incremental learning methodology for the task of supervised classification using an extreme learning machine (ELM). Incremental learning is achieved by creating an individual train model for each set of homogeneous data and incorporating the knowledge transfer among the models without sacrificing accuracy with minimal memory resources. In CAE-ELM, convolutional neural network (CNN) extracts the features, stacked autoencoder (SAE) reduces the size, and ELM learns and classifies the images. Our proposed algorithm is implemented and experimented on various standard datasets: MNIST, ORL, JAFFE, FERET and Caltech. The results show a positive sign of the correctness of the proposed algorithm.&lt;/span&gt;
Blockchain for global vaccinations efforts: State of the art, challenges, and future directions

&lt;span lang="EN-US"&gt;The emergence of the coronavirus disease 2019 (COVID-19) global crisis negatively affected all aspects of human life. One of the most important methods used worldwide to survive this global crisis is the vaccination process to circumvent the proliferation of this pandemic. Many restrictions were alleviated in many countries such as access to public facilities and events. There is a huge amount of data about vaccination campaigns that are collected and maintained worldwide. Although the vaccination data can be analyzed to find out how the alleviation of restrictions can be applied if the data management process requires preserving key aspects like trust, transparency, and availability for easy and reliable access to such data. In this regard, blockchain technology is an excellent choice for meeting the requirements and providing a secure trusted framework for global verification. In this article, the related literature on blockchain technology is surveyed and summarized for all systems that embody solutions. The pros and cons of each solution are presented and provide a comparative summary. Furthermore, a detailed analysis is given to present the current problems and provide a promising mechanism to verify the vaccinated persons anywhere in the world, in a secure manner while retaining individual privacy.&lt;/span&gt;
Identification of monolingual and code-switch information from English-Kannada code-switch data

&lt;span lang="EN-US"&gt;Code-switching is a very common occurrence in social media communication, predominantly found in multilingual countries like India. Using more than one language in communication is known as code-switching or code-mixing. Some of the important applications of code-switch are machine translation (MT), shallow parsing, dialog systems, and semantic parsing. Identifying code-switch and monolingual information is useful for better communication in online networking websites. In this paper, we performed a character level n-gram approach to identify monolingual and code-switch information from English-Kannada social media data. We paralleled various machine learning techniques such as naïve Bayes (NB), support vector classifier (SVC), logistic regression (LR) and neural network (NN) on English-Kannada code-switch (EKCS) data. From the proposed approach, it is observed that the character level n-gram approach provides 1.8% to 4.1% of improvement in terms of Accuracy and 1.6% to 3.8% of improvement in F1-score. Also observed that SVC and NN techniques are outperformed in terms of accuracy (97.9%) and F1-score (98%) with character level n-gram.&lt;/span&gt;
4 Smart devices and informal language learning

There has recently been a growth in consumer products that incorporate intelligent personal assistants (IPAs), such as Siri, featuring voice recognition/ synthesis in multiple languages. The always-available status of smart devices, along with their ever-increasing functionality through advances in AI, holds the promise of new options for language learning/maintenance, and especially for autonomous learning. This chapter looks at opportunities for IPAs to engage L2 user-learners in social and recreational activities that foster informal language learning. The most obvious role is for the IPA to function as an expert L2 learning resource and as a conversation partner. As in entertainment-oriented activities such as watching videos in the L2, IPA interactions can foster incidental language learning. The human-machine interface involved in conversing with an IPA is complex and can be best investigated through frameworks that take into account the dynamic and contextually determined relationship between digital devices and human users. Those include ecological theories such as sociomaterialism and complex dynamic systems.
Virtual Mouse – Hand Gesture Recognition System

The purpose of this research is to enhance the identification of hand gestures in a Human-Computer Interaction scenario, while also minimizing the computational time required and enhancing user comfort. To achieve this goal, the authors created a computer mouse control application that employs an algorithm and selected hand features, resulting in efficient performance. The integration of voice assistant with the recommended hand postures further improves user experience and facilitates system operation.
Principle of open communication using screen sharing on electronic medical records

This study aims to determine the principle of open communication using screen sharing on electronic medical records. The research design used in the preparation of this article is a literature review method related to the Principle of Open Communication Using Screen Sharing on Electronic Medical Records using databases such as Google Scholar, PUBMED, ScienceDirect, and Researchgate during the period 2012-2022. The results of this study state that the Principle of Open Communication Using Screen Sharing in Electronic Medical Records can optimize health services. Optimal health services can be seen for patient satisfaction which the establishment of positive communication between doctors and patients shows. Abstrak: Penelitian ini bertujuan untuk mengetahui prinsip komunikasi terbuka menggunakan screen sharing pada rekam medis elektronik. Rancangan penelitian yang digunakan dalam penyusunan artikel ini adalah metode literatue review terkait perinsip komunikasi terbuka menggunakan screen sharing pada rekam medis elektronik dengan menggunakan database seperti Google Schoolar, PUBMED Sciencedirect dan Researchgate selama periode 2012-2022. Hasil penelitian ini menyatakan bahwa prinsip komunikasi terbuka menggunakan screen sharing pada rekam medis elektronik dapat mengoptimalkan pelayanan kesehatan. Pelayanan kesehatan yang optimal dapat dilihat dari kepuasan pelanggan yang ditunjukan dengan terjalinnya komunikasi yang positif antara dokter dan pasien.
The content and specifics of the microstructural organization of the process of metathinking (article one)

Theoretical and experimental materials are presented that contribute to the solution of one of the main problems of modern metacognitivism - the question of the existence of independent content in the main metacognitive processes, as well as its specificity in relation to the content of primary cognitive processes. The main result of the experimental results obtained is the substantiation of a positive solution to this issue and the proof that additional procedural content is generated in the process of metathinking. The specific means underlying this are considered and interpreted. It is shown how exactly these data contribute to the expansion and refinement of existing views on the subject of metacoginivism.