Reviewerly/exHarmony huggingface dataset:
Supervised machine learning models for depression sentiment analysis

Introduction Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The objective of this study is to utilize machine learning models and sentiment analysis techniques to predict the level of depression earlier in social media users' posts. Methods The datasets used in this research were obtained from Twitter posts. Four machine learning models, namely extreme gradient boost (XGB) Classifier, Random Forest, Logistic Regression, and support vector machine (SVM), were employed for the prediction task. Results The SVM and Logistic Regression models yielded the most accurate results when applied to the provided datasets. However, the Logistic Regression model exhibited a slightly higher level of accuracy compared to SVM. Importantly, the logistic regression model demonstrated the advantage of requiring less execution time. Discussion The findings of this study highlight the potential of utilizing machine learning models and sentiment analysis techniques for early detection of depression in social media users. The effectiveness of SVM and Logistic Regression models, with Logistic Regression being more efficient in terms of execution time, suggests their suitability for practical implementation in real-world scenarios.
KESULITAN MEMBACA PERMULAAN SISWA KELAS II DI SEKOLAH DASAR NEGERI 83 PEKANBARU

The purpose of this study was to find out how difficult it was for students to read and how the efforts made by the teacher in overcoming reading difficulties for class II students at Public Elementary School 83 Pekanbaru. Reading difficulty is an unsatisfactory aspect of students' reading ability. Lower graders seem to have a lot of difficulties related to reading skills. The type of research used in this research is to use a case study approach and data collection techniques conducted interviews, observation and documentation. The results of the study showed that there were 4 students who had difficulty reading. The types of difficulties experienced by students were difficulty in spelling, difficulty in pronouncing letters, often forgetting letters, and difficulty distinguishing. Efforts made by the teacher in overcoming reading difficulties for class II students at Public Elementary School 83 Pekanbaru, namely using interesting and effective learning media, providing support and motivation, giving additional hours and lending reading books to students who are not yet fluent in reading. Factors causing children's reading difficulties include a lack of interest in reading, a lack of extra time at school, and a lack of parental guidance at home.
Retracted: Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

[This retracts the article DOI: 10.1155/2021/6321860.].
MythQA: Query-Based Large-Scale Check-Worthy Claim Detection through Multi-Answer Open-Domain Question Answering

Check-worthy claim detection aims at providing plausible misinformation to the downstream fact-checking systems or human experts to check. This is a crucial step toward accelerating the fact-checking process. Many efforts have been put into how to identify check-worthy claims from a small scale of pre-collected claims, but how to efficiently detect check-worthy claims directly from a large-scale information source, such as Twitter, remains underexplored. To fill this gap, we introduce MythQA, a new multi-answer open-domain question answering(QA) task that involves contradictory stance mining for query-based large-scale check-worthy claim detection. The idea behind this is that contradictory claims are a strong indicator of misinformation that merits scrutiny by the appropriate authorities. To study this task, we construct TweetMythQA, an evaluation dataset containing 522 factoid multi-answer questions based on controversial topics. Each question is annotated with multiple answers. Moreover, we collect relevant tweets for each distinct answer, then classify them into three categories: "Supporting", "Refuting", and "Neutral". In total, we annotated 5.3K tweets. Contradictory evidence is collected for all answers in the dataset. Finally, we present a baseline system for MythQA and evaluate existing NLP models for each system component using the TweetMythQA dataset. We provide initial benchmarks and identify key challenges for future models to improve upon. Code and data are available at: https://github.com/TonyBY/Myth-QA
The effect of Market Orientation on Export Performance: The Mediating role of New Product Development capability

The aim of this study that titled the descriptive statistics of Market Orientation practice on Export Performance the Mediating role of new product development practice with Positive effect of export performance provides firms with opportunity to grow, and enhance the dynamism in the new product development. In this study, two main strategies that are refer to Market Orientation and new product development is investigated and determines the Export Performance. This study focused in Coffee trading and Factory PLC Ethiopia. The findings showed that the Descriptive effect of Market Orientation on Export Performance the Mediating role of New Product Development Capability. However, the market orientation is much stronger in influence the export performance that affect Market orientation through new product development on Export performance, it designed with Descriptive Statistics to distribute for 384 respondents, and the questionnaires to be distributed from Primary and Secondary Cooperative Union, Coffee supplier, Coffee Collector, Employee of PLC. Hence, this study basically increased the knowledge regarding of the factors that affect the Export Performance, especially in Oromia Regional state West Guji zone Coffee trading and Factory PLC, Ethiopia.
Retracted: Intelligent Performance Evaluation of Urban Subway PPP Project Based on Deep Neural Network Model

[This retracts the article DOI: 10.1155/2022/1536881.].
Retracted: English Education Tutoring Teaching System Based on MOOC

[This retracts the article DOI: 10.1155/2022/1563352.].
Retracted: Detection and Application of Wearable Devices Based on Internet of Things in Human Physical Health

[This retracts the article DOI: 10.1155/2022/5678736.].
Sistem Pakar Diagnosa Gangguan Stress Pasca Trauma Menggunakan Metode Certainty Factor

Mental health disorder or commonly called Mental Health Disorder is a disturbing psychological behavior and is followed by traumatic events such as shock shell, war fatigue, accidents, victims of sexual violence, and the covid pandemic. Cases of post traumatic stress disorder data from Indonesian Psychiatric Association amounted to 80% of 182 examiners experiencing symptoms of post-traumatic stress due to exposure to covid, 46% experienced severe symptoms, 33% moderate, 2% mild and others did not show symptom. This study aims to diagnose post traumatic stress disorder using the assurance factor method with 35 symptom data and 3 levels of post traumatic stress disorder as a knowledge base. The certainty factor is a circulation management method and a decision-making strategy using the confidence factor in the system. Based on the research results of the expert system for diagnosing post traumatic stress disorder, the test results obtained an accuracy of 80%. The results of the accuracy of this expert system indicate that the expert system can potentially be used to diagnose post traumatic stress disorder.
Analisis dan Implementasi Market Basket Analysis (MBA) Menggunakan Algoritma Apriori dengan Dukungan Visualisasi Data

Culture Coffee MSME is one of the MSMEs engaged in the culinary field and is experiencing business competition. A marketing strategy is needed with the right decision-making process so that the business can survive and excel. UMKM Culture Coffee uses a point of sales application to accommodate the transaction process and record transactions. Historical customer data can be processed into a basis for decision making for marketing strategies that effectively increase sales. However, the transaction data has not been used optimally. There is a need to analyze historical customer data that can generate information to form marketing strategies. Market Basket Analysis (MBA) is one of the methods in data mining used in knowing products that tend to be purchased together by customers known as Association Rule. Association rules produce products in the form of packages or bundling which are used as marketing strategies. The marketing strategy obtained is supported by data visualization which contains information from the data. Apriori algorithm is used to generate association rules. The result of this research is an association rule on the historical data of MSME Culture Coffee customer purchases. Based on these rules, recommendations for selling menu packages to customers can be given. The purpose of this research is to find customer purchasing patterns which are used as the basis for decision making in determining menu sales. The results showed 2 product packages, namely, nuggets and french fries with sausages and french fries with a support and confidence value of 12.5% and 37.6% with 10.8% and 29% respectively. The results of this study can be used as a basis for the sales and marketing strategy of Culture Coffee MSMEs to increase business revenue.
Tensor networks for quantum machine learning

Once developed for quantum theory, tensor networks (TNs) have been established as a successful machine learning (ML) paradigm. Now, they have been ported back to the quantum realm in the emerging field of quantum ML to assess problems that classical computers are unable to solve efficiently. Their nature at the interface between physics and ML makes TNs easily deployable on quantum computers. In this review article, we shed light on one of the major architectures considered to be predestined for variational quantum ML. In particular, we discuss how layouts like matrix product state, projected entangled pair states, tree tensor networks and multi-scale entanglement renormalization ansatz can be mapped to a quantum computer, how they can be used for ML and data encoding and which implementation techniques improve their performance.
CBKI: A confidence-based knowledge integration framework for multi-choice machine reading comprehension

External knowledge has been introduced into pretrained models to enhance answer reasoning in machine reading comprehension tasks. However, most methods directly take the retrieved knowledge as input without considering the reliability and applicability, which may induce information redundancy and generate noise. In this paper, we propose a confidence-based knowledge integration framework (CBKI) to enhance the pretrained model with the ability of active knowledge acquisition and identification. In particular, we introduce a confidence mechanism, which is composed of three modules, namely, confidence estimation, confidence perception and knowledge integration. Specifically, for the candidate knowledge retrieved from the external knowledge base, the confidence estimation module first calculates the probability score of the candidate knowledge effectiveness in answer prediction. Then, the confidence perception module determines whether the candidate knowledge should be adopted for semantic inference at the current step. Finally, the knowledge integration module evaluates how much information should be absorbed from the candidate knowledge and merges the knowledge into the model. Experimental results on standard datasets show that the proposed CBKI is superior to BERT with an increase of 4.08% in accuracy. Furthermore, the CBKI also shows great advantages in robustness and interpretability.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Double Anonymous • Conference submission management system : Email • Number of submissions received : 84 • Number of submissions sent for review : 84 • Number of submissions accepted : 32 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 38.1 • Average number of reviews per paper : 2 • Total number of reviewers involved : 8 • Contact person for queries : Name : Prof. Dr. Muhammad Natsir Abduh, M.Si. Email : muhnatsir.abduh@universitasbosowa.ac.id Affiliation : Universitas Bosowa, Makassar, Indonesia
Creating and promoting a positive institutional image in the community

This article presents the essential elements in the process of creating and promoting the positive image of an educational institution. The formation and improvement of the image should include a set of external and internal characteristics, which include the image of the leader, the image of the staff, the ideas of the target audience about the quality of education, style, comfort of the educational environment, diversity and price of educational services. The formed positive image of an educational organization will allow: increasing the attractiveness of an educa-tional organization, which will increase the number of students; improving the socio-psychological microclimate in the team; raising the level of organizational culture; strengthening the financial position and the material and technical base of the organization; to make the educational organization, under equal conditions, more attractive for teaching staff (to ensure stability and social protection, job satisfaction and to ensure professional growth); to create a reserve of confidence in everything that happens within an educational organization; increasing the competitiveness of the educational organization.
Development of students’ responsibility through the principle of engagement

The engagement presupposes responsible behavior of students in the training process. Determined as a principle, the engagement derives from critical existentialism thinking, an essential dimension of spiritual intelligence. In the training process, the principle of engagement refers to the creation by the teaching staff of educational contexts in which students will be encouraged to get involved, through the possibility of choice, decision-making and assu-ming responsibility in relation to their choices and decisions taken. In this article, a series of specific strategies are reflected, aimed at developing responsibility of students, namely: creating a collaborative learning environment, setting clear and relevant objectives for the student, carrying out tasks in the authentic professional field, providing constructive feedback and involving students in the decision-making process. To achieve these strategies, a general strategy relevant to the didactic context was adapted - the ,,4 What” strategy for the development of critical thinking. This strategy reflects the components through which student responsibility can be developed and assessed. Thus, the correlation between these strategies: the ,,4 What” strategy and the specific strategies generate a friendly didactic design, oriented towards the development of student responsibility in the training process.
Bias mitigation techniques in Image Classification: Fair Machine Learning in Human Heritage Collections

A major problem with using automated classification systems is that if they are not engineered correctly and with fairness considerations, they could be detrimental to certain populations. Furthermore, while engineers have developed cutting-edge technologies for image classification, there is still a gap in the application of these models in human heritage collections, where data sets usually consist of low-quality pictures of people with diverse ethnicity, gender, and age. In this work, we evaluate three bias mitigation techniques using two state-of-the-art neural networks, Xception and EfficientNet, for gender classification. Moreover, we explore the use of transfer learning using a fair data set to overcome the training data scarcity. We evaluated the effectiveness of the bias mitigation pipeline on a cultural heritage collection of photographs from the 19th and 20th centuries, and we used the FairFace data set for the transfer learning experiments. After the evaluation, we found that transfer learning is a good technique that allows better performance when working with a small data set. Moreover, the fairest classifier was found to be accomplished using transfer learning, threshold change, re-weighting and image augmentation as bias mitigation methods.
SAIL: Semantic Analysis of Information in Light Fields: Results from Synthetic and Real-World Data

Computational photography has revolutionized the way we capture and interpret images. Light fields, in particular, offer a rich representation of a scene"s geometry and appearance by encoding both spatial and angular information. In this paper, we present a novel approach to light field analysis that focuses on semantics. In contrast to the uniform distribution of samples in two-dimensional images, the distribution of samples in light fields varies for different scene regions. Some points are sampled from multiple directions, while others may only be captured by a small portion of the light field array. Our approach provides insights into this non-uniform distribution and helps guide further processing steps to fully leverage the available information content.
Synthetic-Real Domain Adaptation for Probabilistic Pose Estimation

Real samples are costly to acquire in many real-world problems. Thus, employing synthetic samples is usually the primary solution to train models that require large amounts of data. However, the difference between synthetically generated and real images, called domain gap, is the most significant hindrance to this solution, as it affects the model’s generalization capacity. Domain adaptation techniques are crucial to train models using synthetic samples. Thus, this article explores different domain adaptation techniques to perform pose estimation from a probabilistic multiview perspective. Probabilistic multiview pose estimation solves the problem of object symmetries, where a single view of an object might not be able to determine the 6D pose of an object, and it must consider its prediction as a distribution of possible candidates. GANs are currently state-of-the-art in domain adaptation. In particular, this paper explores CUT and CycleGAN, which have unique training losses that address the problem of domain adaptation from different perspectives. The datasets explored are a cylinder and a sphere extracted from a Kaggle challenge with perspective-wise symmetries, although they holistically have unique 6D poses. CUT outperforms CycleGAN in feature adaptation, although it is less robust than CycleGAN in keeping keypoints intact after translation, leading to pose prediction errors for some objects. Moreover, this paper found that training the models using synthetic-to-real images and evaluating them with real images improves the model’s accuracy for datasets without complex features. This approach is more suitable for industrial applications to reduce inference overhead.
FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks

Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, since the models can be used in edge computing devices. FL approaches are motivated by and designed to protect privacy, a highly relevant issue given current data protection regulations. Although FL methods are privacy-preserving by design, recently published papers show that privacy leaks do occur, caused by attacks designed to extract private data from information interchanged during learning. In this work, we present an FL method based on a neural network without hidden layers that incorporates homomorphic encryption (HE) to enhance robustness against the above-mentioned attacks. Unlike traditional FL methods that require multiple rounds of training for convergence, our method obtains the collaborative global model in a single training round, yielding an effective and efficient model that simplifies management of the FL training process. In addition, since our method includes HE, it is also robust against model inversion attacks. In experiments with big data sets and a large number of clients in a federated scenario, we demonstrate that use of HE does not affect the accuracy of the model, whose results are competitive with state-of-the-art machine learning models. We also show that behavior in terms of accuracy is the same for identically and non-identically distributed data scenarios.
Psycho-educational approaches regarding the organization of educational activities in the context of nonformal education

From the theoretical and praxeological perspective, non-formal education represents an important dimension of permanent education and an integral part of lifelong learning. This article analyzes the concept and characteristics of non-formal education and presents the psycho-pedagogical approaches related to the organization of educational activities in the context of non-formal education. The pedagogical approaches of non-formal education refer to the system of theories represented by the theory of cooperative learning, the humanistic theory of personality and the learner-centered approach of education, which provide theoretical directions for the efficient organization of extracurricular and extra-scholar activities. Non-formal education is also based on psychological theories such as behaviorist theory, constructivist theory, socio-cognitive theory approached in an integralist and postmodern vision.
PERANCANGAN TONGKAT BANTU INOVATIF UNTUK TUNANETRA DENGAN MEMANFAATKAN TEKNOLOGI SENSOR GYROSCOPE, GPS DAN ULTRASONIK

&lt;p&gt;The eyes are vital human sensory organs that react to light. One of the functions of the eyes is to record the state or surrounding conditions, to know the object. Then, &lt;a href="https://id.wikipedia.org/wiki/Sel_kerucut"&gt;cones &lt;/a&gt;on the &lt;a href="https://id.wikipedia.org/wiki/Retina"&gt;retina &lt;/a&gt;allow&lt;strong&gt; &lt;/strong&gt;conscious light perception and vision, such as color differentiation and depth perception. The purpose of the research was to develop stick-based technology gyroscope sensors, GPS, and ultrasonic-based microcontrollers. The development of this tool aimed at helping blind people to detect objects around them so that blind people could walk well. The approach in this development utilized the Research an&lt;strong&gt;d &lt;/strong&gt;Development (R&amp;amp;D) method, and the testing used the Alpha method. The test results showed that all tools could function properly and could be implemented in the field.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
COUNSELING ON LEARNING ENGLISH WITH THEME OF STRENGTHENING PANCASILA PROFILE TO STUDENTS AT KINDERGARTEN OF AN NUUR, MOJOROTO DISTRICT, KEDIRI CITY

This community service activity aims to improve the teaching skills of kindergarten teachers at Kindergarten of An Nuur, Mojoroto District, Kediri City in terms of learning methods, namely the play pictures method, the story method, and the demonstration method. While the type of counseling that was carried out was an effective way of learning for kindergarten students. The results of this community service activity was that the understanding and ability of kindergarten teachers regarding effective learning methods for kindergarten students can be improved by conducting counseling; teacher's understanding and ability can increase success in learning the subject matter. In addition, understanding parents of kindergarten students about how their children learn and understanding. By understanding the parents of kindergarten students, they have improved how their children learn, understand every subject explained by kindergarten teachers, and not forcing their children to complete homework properly.