Reviewerly/exHarmony huggingface dataset:
Meta-experience via VR and Telepresence Systems

This paper describes meta-experience creation by using a presentation system of a virtual reality space and a remote real space. The spatial experience is based on the simultaneous perception of the environment and the body, and since multiple senses are used for perception, the simultaneous presentation of multisensory information is required to realize appropriate integration. In active (voluntary) experiences, avatars are moved in the target space via physical movements and button operations, where the multisensory integration is also important. Passive experiences are the basic premise for reliving the viewpoint of others. The equivalent amount of motion stimulus to the body that is not actively driven should be less than 10%. We constructed a multisensory reliving device and a remote presence experience device, and we confirmed their functions. The metaverse is a new type of multiuser experience, which is an experience of an extended three-dimensional world that includes the perception of other people’s bodies and conventional individual-centered experiences. These meta-experiences are expected to amplify our presence and capabilities in the future.
Development of an Index for Evaluating VIMS Using Gaze Data of Young People

Driving simulators (DS) have been used in many studies of traffic scenes, and the risk of visually induced motion sickness (VIMS) has been highlighted. This study aimed to develop an index for evaluating VIMS in young people from gaze data using a non-contact gaze measurement device. Gaze data were collected before and after a DS run to develop an evaluation index that enables the early detection of VIMS using gaze data. Gaze data were measured using a non-contact eye-measuring device. The participants viewed a 5-min DS running video five times and gaze data were measured at rest and before and after viewing the DS video. The participants were instructed to gaze at the optotype displayed at the center of the screen for 60 s when at rest. The results of this study indicate the validity of total locus length and sparse density (S5) as evaluation indicators for VIMS and other issues. The total locus length and sparse density (S5) were valid evaluation indices for VIMS. However, it was also clear that further studies on evaluation indexes other than the total locus length and the sparse density (S5) are needed for the early detection of VIMS. In addition, because the present study was conducted using resting gaze data, it is also necessary to develop an evaluation index using gaze data during video gazing.
Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer

Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably slower, and in some instances, the incidence has increased. Implementing routine screenings for cervical cancer is something that has to be done to protect the health of women. Cervical cancer is famously difficult to diagnose and cure due to the slow rate at which it spreads and develops into more advanced stages of the disease. Screening for cervical cancer using a Pap smear, more often referred to as a Pap test, has the potential to detect the illness in its earlier stages. For the purpose of selecting features for this article, a gray level co-occurrence matrix (GLCM) technique was used. Following this step, classification is performed with methods such as convolutional neural network (CNN), support vector machine, and auto encoder. According to the findings of this experiment, the GLCM-CNN classifier proved to be the one with the highest degree of precision.
THE ROLE OF MORAL BELIEFS IN THE FORMATION OF PUBLIC READINESS TO FIGHT CORRUPTION

This scientific article is devoted to the study of the role of moral beliefs in the formation of public readiness to fight corruption. Corruption remains a serious problem in many countries and has a wide range of negative social, economic and political consequences. Understanding the psychological factors underlying people’s attitudes toward corruption is critical to developing effective strategies to combat this problem. This article analyzes an important aspect of the modern social phenomenon – corruption, and determines the role of moral beliefs in the formation of public readiness to fight it. Using an interdisciplinary approach, including elements of psychology and sociology, the article examines the influence of moral values and beliefs on the attitude of citizens to corrupt practices. The article also defines which moral beliefs are key to creating a positive attitude toward anti-corruption measures and initiatives. The author uses empirical data from surveys and psychological experiments to analyze the attitude of individuals towards corruption and their willingness to oppose it. In this study, we examine in detail the moral factors that can support or inhibit a society’s willingness to fight corruption. We use a mixed research method, including qualitative and quantitative data analysis. A survey is conducted among a representative sample of citizens using structured questionnaires. We include questions about moral values and willingness to participate in anti-corruption initiatives in the questionnaires. Group interviews were also conducted with participants to gain a deeper understanding of their moral and ethical beliefs and their attitudes towards corruption. The results of the study reveal key psychological factors that influence attitudes toward corruption, including the role of moral beliefs in shaping positive or negative perceptions of corruption. The author of the article come to the conclusion that moral beliefs have a huge impact on public readiness to fight corruption and are a key factor in the formation of effective anti-corruption strategies. Understanding this role of moral values can contribute to the development of programs and activities to increase public awareness and willingness to act in the interests of fighting corruption.
Development of the Professional Competence of Bachelors in Preschool Education Through Online Interaction

The article discusses the issue of pre-service professional training of preschool teachers on the example of the course Workshop on Children’s Play Activities for students of the first (Bachelor’s) level of higher education of the educational and professional program 012.00.01 Preschool Education. This paper focuses on the ways of developing the professional competence of Bachelors of Preschool Education through online interaction. The place of the course Workshop on Children’s Play Activities in the e-learning system of Borys Grinchenko Kyiv University is presented. The article describes the structure of the electronic online training course, the logic of thematic unit mapping of the course. Authors suggest effective services and resources for online interaction of participants of the educational process, namely: during lectures (Google Meet, Google Chat, Google Hangouts, Google Classroom, Webex, Zoom, Lucidspark, Agile, Canva, Mentimeter, Kahoot, Piktochart); seminar sessions (Hangouts, Zoom, Easel.ly Padlet, Piktochart, AnswerGarden, Slides, Prezi, Learningapps, Socratic); and practical classes (Microsoft Teams, Migo, Whiteboard, Agile Workflows, Class Dojo, Mural, GoAnimate, GIFAnimate, Slasstools, Learningapps, Triventy, Yumpu, Emaze, Genially, Flippity, Wordwall, Kahoot). Authors claim that usage of information and communication technologies is effective for facilitating online interaction, increasing students’ engagement with theoretical material, and making the preparation to seminars and practical classes easier for students. Using the suggested services and resources will allow the organization of dynamic and engaging classes, as well as promoting active interaction with students even in online setting.
A Novel Approach of Multi-string Parsing for Syntactic Pattern Recognition

A novel approach to syntactic pattern recognition based on multi-string parsing is introduced in the paper. A methodological motivation of the research, which has resulted from application-oriented requirements, is presented. Formal foundations, an architectural model and a generic recognition algorithm of the approach are introduced. The approach allows one to recognize complex objects which should be represented by a series of images instead of one image. The series of images is represented with a sequence of aspectual patterns which are treated as a holistic aspect-integrated description of a recognized object.
Automated Design of a Neuroevolution Program Using Algebra-Algorithmic Tools

The adjustment of the previously developed algebra-algorithmic tools towards the automated design and generation of programs that use neuroevolutionary algorithms is proposed. Neuroevolution is a set of machine learning techniques that apply evolutionary algorithms to facilitate the solving of complex tasks, such as games, robotics, and simulation of natural processes. The developed program design toolkit provides automated construction of high-level algorithm specifications represented in Glushkov’s system of algorithmic algebra and synthesis of corresponding programs based on implementation templates in a target programming language. The adjustment of the toolkit for designing neuroevolutionary algorithms consists in adding the descriptions and software implementations of the relevant elementary operators and predicates to a database of the toolkit. The use of the toolkit is illustrated by an example of designing and generating a program for the single-pole balancing problem, which applies the neuroevolutionary algorithm of the NEAT-Python library. The results of the experiment consisting in the execution of the program generated with the algebra-algorithmic toolkit are given.
Solving Sokoban Game with a Heuristic for Avoiding Dead-End States

This paper focuses on applying reinforcement learning methods to solve the game Sokoban. This game is a popular puzzle, relatively easy for humans to solve. However, it poses a significant challenge for computer algorithms due to the irreversible nature of certain moves. To predict which actions will lead to such undesirable states is often difficult for a learning agent – a common problem in tasks requiring planning. We propose using a Monte-Carlo tree search (MCTS) algorithm and a heuristic convolution neural network (CNN) specially trained to separate undesirable, neutral, and desired game states to address this issue. We experimented with different heuristic variations of algorithms and compared them against each other. We have implemented MCTS in two different setups: one with a CNN trained using data obtained during the solving process and one without such training. We also varied the number of rollouts for each move in MCTS and compared the results. The paper’s research question was how to improve the performance of learning agents in tasks that require planning to avoid unwanted states.
Multi-task Cooperative Learning via Searching for Flat Minima

Multi-task learning (MTL) has shown great potential in medical image analysis, improving the generalizability of the learned features and the performance in individual tasks. However, most of the work on MTL focuses on either architecture design or gradient manipulation, while in both scenarios, features are learned in a competitive manner. In this work, we propose to formulate MTL as a multi/bi-level optimization problem, and therefore force features to learn from each task in a cooperative approach. Specifically, we update the sub-model for each task alternatively taking advantage of the learned sub-models of the other tasks. To alleviate the negative transfer problem during the optimization, we search for flat minima for the current objective function with regard to features from other tasks. To demonstrate the effectiveness of the proposed approach, we validate our method on three publicly available datasets. The proposed method shows the advantage of cooperative learning, and yields promising results when compared with the state-of-the-art MTL approaches. The code will be available online.
Some constructions of $l$-Galois LCD codes

Let $ \mathbb{F}_q $ be the finite field with $ q $ elements where $ q = p^m $ for some prime $ p $ and $ m &gt;0 $. In this article, we provide some constructions of Galois LCD codes over $ \mathbb{F}_q $ in terms of their generator matrices. Here, we consider the Galois inner product instead of Euclidean or Hermitian inner products. For these constructions, we use the matrix $ A $ for which $ A[\sigma^{m-l}(A)]^t = I,\; 0\leq l \leq m-1 $ where $ \sigma $ is the Frobenius map. We also define the Galois self-dual basis and use it to show the existence of a Euclidean LCD code from a linear code over some finite field. Further, we present an application of Galois LCD codes in a multi-secret sharing scheme. Besides, we obtain several optimal and almost optimal Galois LCD codes to show the importance of our results.
Gradient Self-alignment in Private Deep Learning

Differential Privacy (DP) has become a gold-standard to preserve privacy in deep learning. Intuitively speaking, DP ensures that the output of a model is approximately invariant to the inclusion or exclusion of a single individual’s data from the training set. There is, however, a trade-off between privacy and utility. DP models tend to perform worse than non-DP models trained on the same data. This is caused by the clipping of per-sample gradients and the addition of noise required for DP guarantees causing an obfuscation of the individual data point’s contribution. In this work, we propose a method to reduce this discrepancy by improving the alignment between the per-sample gradients of each individual training sample with its non-DP gradient by increasing their cosine similarity. Optimizing the alignment in only a relevant subset of gradient dimensions, further improves the performance. We evaluate our method on CIFAR-10 and a pediatric pneumonia chest x-ray dataset.
SplitFed Resilience to Packet Loss: Where to Split, that is the Question

Decentralized machine learning has broadened its scope recently with the invention of Federated Learning (FL), Split Learning (SL), and their hybrids like Split Federated Learning (SplitFed or SFL). The goal of SFL is to reduce the computational power required by each client in FL and parallelize SL while maintaining privacy. This paper investigates the robustness of SFL against packet loss on communication links. The performance of various SFL aggregation strategies is examined by splitting the model at two points – shallow split and deep split – and testing whether the split point makes a statistically significant difference to the accuracy of the final model. Experiments are carried out on a segmentation model for human embryo images and indicate the statistically significant advantage of a deeper split point.
Fed-CoT: Co-teachers for Federated Semi-supervised MS Lesion Segmentation

Federated learning (FL) is an emerging technique for obtaining a global model while ensuring the data privacy of each client, which is particularly significant in protecting the patients’ privacy when conducting medical image analysis. However, previous FL methods for medical images typically assume a fully supervised setting where each client’s data is fully annotated, disregarding the fact that obtaining such extensive annotations may present significant obstacles due to the need for specialized expertise and the associated overhead costs. In this work, we focus on lesion segmentation for brain MRI images and propose a federated semi-supervised framework to address this problem. Formally, we introduce a Federated Co-Teachers algorithm (Fed-CoT) that extends the prevalent Mean Teacher algorithm into the federated learning framework, and demonstrate its effectiveness. Particularly, in Fed-CoT, two teacher models, namely sync-teacher and async-teacher, which capitalize on different weight updating schemes are leveraged to provide informative consistency regularization and to avoid overfitting to the noise of targets generated by a single teacher model. Our experimental results validate the merits of our proposed method and suggest that the federated learning model can benefit from extra data even without annotations. This approach relaxes the requirement for client participation in federated learning, making it easier to deploy in real applications.
Studi Perbandingan Hasil Belajar Matematika Menggunakan Strategi Pembelajaran The Power of Two dan Rotating Trio Exchange SDN 95/IV Kota Jambi

This research is to find out the comparison of students' mathematics learning outcomes taught by using the power of two learning strategy and rotating trio exchange learning strategy. The research method used is experiment. The population in this study were all grade VI students of SDN 95 Kota Jambi, amounting to 91 students. The research sample was taken using cluster random sampling technique. Data collection used is secondary and primary data. Based on the results of the study showed that the average mathematics learning outcomes of students who were taught using the learning strategy of The Power of Two were better than the mathematics learning outcomes of students who used the Rotating Trio Exchange learning strategy with a real level of α = 0.05
FOBIA LAKI-LAKI TOKUJIRA AOI PADA MANGA OOKAMI SHOUNEN WA KYOU MO USO WO KASANERU KARYA NAMO

This study examines the phobia phenomenon on character Tokujira Aoi from manga Ookami Shounen wa Kyou mau Uso wo Kasaneru created by namo. The reason for this study is because, amongst the character with phobia in any anime or manga, Tokujira Aoi is one of androphobia character that not using any violence nor passive-aggressive behaviour toward the subject of her fear, which is male. Therefore, the purpose of the study of this study is to examine how reaction of androphobia occurs on Tokujira Aoi. The data source of this study is using the dialogues between Tokujira Aoi and other characters on manga Ookami Shounen wa Kyou mau Uso wo Kasaneru. Reading and note-taking procedures are used in this study, which also utilized a qualitative descriptive analysis method. The data analysis presentation is by using informal method which the data presented by word-by-word descriptive explanation. The result on this study are concluded that androphobia phenomenon on Tokujira Aoi is classified a specific phobia. Tokujira Aoi has two reaction regarding her fear of man, which are physical and emotional reaction, that caused by trauma.
Home and Healthcare. The prospect of home adaptation through a computational design decision-support system

This paper presents an ongoing research to define the framework of a computational design approach based on the idea of spatial analysis and spatial synthesis to implement multicriteria evaluations and provide evidence of the performance of the design alternatives in the specific case of home adaptation for healthcare at home.The European health systems place among the priority objectives the strengthening of the provision of healthcare at home to guarantee the aging in place of elderly people and to limit, at the same time, the unnecessary use of resources.Therefore, existing homes must provide adequate safety, comfort, and accessibility features to ensure a high quality of life for the care receivers and facilitate the caregivers' tasks.To address the complexity of the requirements to be met, we propose a spatial decision support system (SDSS) to implement multi-criteria assessments to ergonomic design problems at a spatial scale of apartment homes.The system is intended to streamline and assist designers and homeowners in planning interventions for home adaptations for healthcare.Such design problems can be formulated as decision problems with costs and benefits modeled within constraints of validity and quality criteria/objectives.Concerning the specific field of study, the system evaluates the degree of compliance with the accessibility and visibility quality criteria of each design alternative.The reiteration of the evaluation mechanism allows for the classification and supports the selection of satisfactory technical solutions identified with an informed and well-balanced trade-off between the relevant quality criteria.
The FormAI Dataset: Generative AI in Software Security through the Lens of Formal Verification

This paper presents the FormAI dataset, a large collection of 112,000 AI-generated compilable and independent C programs with vulnerability classification. We introduce a dynamic zero-shot prompting technique constructed to spawn diverse programs utilizing Large Language Models (LLMs). The dataset is generated by GPT-3.5-turbo and comprises programs with varying levels of complexity. Some programs handle complicated tasks like network management, table games, or encryption, while others deal with simpler tasks like string manipulation. Every program is labeled with the vulnerabilities found within the source code, indicating the type, line number, and vulnerable function name. This is accomplished by employing a formal verification method using the Efficient SMT-based Bounded Model Checker (ESBMC), which uses model checking, abstract interpretation, constraint programming, and satisfiability modulo theories to reason over safety/security properties in programs. This approach definitively detects vulnerabilities and offers a formal model known as a counterexample, thus eliminating the possibility of generating false positive reports. We have associated the identified vulnerabilities with Common Weakness Enumeration (CWE) numbers. We make the source code available for the 112,000 programs, accompanied by a separate file containing the vulnerabilities detected in each program, making the dataset ideal for training LLMs and machine learning algorithms. Our study unveiled that according to ESBMC, 51.24% of the programs generated by GPT-3.5 contained vulnerabilities, thereby presenting considerable risks to software safety and security.
Revise That Again: Are You Motivated?

Requirements engineering (RE) presents several challenges stemming from the required collaboration and knowledge transfer between analysists, developers, and customers. Motivation theories have been used occasionally to analyze and encourage motivation and engagement of stakeholders in RE tasks. In recent years, gamification techniques have been used in software engineering tasks, and specifically, in RE tasks in order to promote stakeholder engagement. However, existing research works seldom offer a rigorous method for designing gamification environments for RE tasks. This paper describes a socio-technical environment, which was built for requirements elicitation and specification. This environment allows researchers and team managers to decide on different mechanisms to gamify the current RE task in practice. The environment was evaluated by experts and was further tested with the participation of students in two proof of concept studies for demonstrating its functionality, yielding some anecdotic results.