Reviewerly/exHarmony huggingface dataset:
Changes in perceived peripersonal space following the rubber hand illusion

Peripersonal space (PPS), the region immediately surrounding the body is essential for bodily protection and goal directed action. Previous studies have suggested that the PPS is anchored to one's own body and in the current study we investigated whether the PPS could be modulated by changes in perceived body ownership. While theoretically important, this anchoring can also have implications for patients with altered body perception. The rubber hand illusion (RHI) is a way to manipulate body ownership. We hypothesized that after induction of a left hand RHI, the perceived space around the body shifts to the right. Sixty-five participants performed a landmark task before and after a left hand RHI. In the landmark task, participants had to determine whether a vertical landmark line was left or right from the center of a horizontal screen. One group of the participants was exposed to synchronous stroking, the other group experienced asynchronous stroking. Results showed a shift in space to the right (e.g. away from the own arm), but only for the 'synchronous stroking' group. These results suggest that the relevant action space becomes linked to the fake hand. Critically, subjective ownership experience did not correlate with this shift, but proprioceptive drift did. This suggests that multisensory integration of bodily information drives this shift in space around the body and not feelings of ownership.
Automata with Timers

In this work, we study properties of deterministic finite-state automata with timers, a subclass of timed automata proposed by Vaandrager et al. as a candidate for an efficiently learnable timed model. We first study the complexity of the configuration reachability problem for such automata and establish that it is PSPACE-complete. Then, as simultaneous timeouts (we call these, races) can occur in timed runs of such automata, we study the problem of determining whether it is possible to modify the delays between the actions in a run, in a way to avoid such races. The absence of races is important for modelling purposes and to streamline learning of automata with timers. We provide an effective characterization of when an automaton is race-avoiding and establish that the related decision problem is in 3EXP and PSPACE-hard.
Research on the surface crack detection of specimen manufactured by selective laser melting based on local singular value decomposition method

Abstract SLM (selective laser melting) is an important additive manufacturing technology. Parts with complex structures can be produced by SLM, so surface cracks and other defects within SLM parts are difficult to be identified by traditional nondestructive testing techniques. In this paper, laser ultrasonic technology is proposed to identify the surface crack of SLM specimen. Because of the anisotropy and residual stress of the specimen, the time signal is strongly interfered with by noise, which makes the identification and location of the crack difficult. To solve this problem, the phase space was reconstructed from the time signal and analyzed by the local singular value decomposition method. According to the result of the experiment, this method can reveal the variation of the phase space and has a good effect on the identification and location of the surface crack whose depth is larger than 1 mm.
Kontribusi Sikap Belajar dan Motivasi Belajar Terhadap Hasil Perkuliahan Bahasa Indonesia Mahasiswa

The learning outcomes obtained by each student are different because the potential possessed by students is different, as well as how to develop their potential. How to develop their potential depends on the desires of each student. This is influenced by the motivation that each student has. Learning attitudes also affect student learning outcomes in Indonesian language learning. A person's attitude to something will be addressed through behavior. Students who are positive about and open to accepting learning will provide encouragement to students to achieve success. The objectives of this study are as follows. (1) Knowing the amount of significant contribution between learning attitudes towards student Learning Outcomes of Indonesian language lectures. (2) Knowing the amount of significant contribution of learning motivation to the learning outcomes of student Indonesian language lectures. (3) Knowing the amount of significant contribution between learning attitudes and motivation to learn together to the learning outcomes of student Indonesian language lectures. This type of research is quantitative with descriptive methods and uses correlation design. The population of this study is all FKIP students of Batanghari University. The sampling technique is carried out by random sampling, namely the sampling of sample members from the population which is carried out randomly. This research instrument is in the form of a questionnaire that has been tested for validity and reliability. The data were analyzed using the Pearson Product Moment (PPM) correlation test, double correlation test, t test, F test, and determinant coefficient formula to determine the magnitude of the contribution of free variables to bound variables, either individually or jointly.The results showed that learning attitudes and learning motivation contributed significantly to student lecture learning outcomes by 20.5%. Thus, these variables have a good influence on the learning outcomes of FKIP students at Batanghari University
Pengembangan Media Pembelajaran Ekonomi Berbasis Android Dengan Platform Kodular Pada Materi Pengantar Ilmu Ekonomi Untuk Siswa Kelas X di SMAN Balung Kabupaten Jember Tahun Ajaran 2022/2023

This research was carried out with the aim of developing an android- based economic learning media with the KODULAR platform in an interesting and effective Introduction to Economics material for class X students at SMAN Balung, Jember Regency. This type of research is development research with the ADDIE model which is limited to the implementation stage. This study used four research instruments, namely validation sheets, questionnaires, interviews, and documents. Data analysis techniques applied include testing the level of attractiveness and level of effectiveness. The results of this study indicate that the developed economic learning media is attractive with a limited group trial percentage of 89.85% and a large group trial percentage of 93.78% with very attractive criteria. The developed media is also effective because it can achieve learning objectives, showing an increase in student scores in the limited group trial from 80.80 to 89.20 and in the broad group trial from 80.22 to 90.11. Thus, these learning media are categorized as interesting and effective learning media.
RUDN graduate is a representative of the world elite: Upbringing aspect of the university’s activities

The present study is an analysis of the results of extracurricular work as an aspect of Peoples’ Friendship University of Russia activities. The university states that within the period of studies, a RUDN University graduate becomes a representative of the world elite, whose qualitative characteristics correlate with such concepts as high personal characteristics, ethnic tolerance, motivation for study and profession, and knowledge and respect for the university traditions. The article presents and analyzes the results of the annual poll of university graduates and the data in comparison with the answers of first-year students. The authors propose considering a case study of Peoples’ Friendship University of Russia as an example of the successful implementation of a management strategy in the field of educational and extracurricular university policy. The conclusion implies the fact that the university is successfully implementing a model of education that allows it to timely and adequately respond to the challenges of modern society, creates an international educational and extracurricular environment that makes it possible to turn a RUDN University graduate into a representative of the world elite. The research is an example of the successful implementation of the extracurricular aspect of the activities of a Russian multidisciplinary university with a high level of internationalization.
Advent of GAN: How does a generative AI create a moodboard?

The human creativity is now being supported, or challenged, by an easy access to generative tools utilising Artificial Intelligence (AI).Within the last few years, Generative Adversarial Network-based image generation tools have become available for the public.In this paper, we explore how one such tool, called Midjourney, could be used for generating moodboards.Our work focuses on exploring moodboard image generation via textbased prompts.We take note of the active online discussions, where examples and ethical concerns have strongly emerged during the late 2022.As a result, the AI produced imagery that the authors could not have produced themselves in any reasonable amount of time, if at all.The GANgenerated images are of high quality, and visually useable for design purposes.However, there are ethical concerns on the sources of the images used for training the AI systems, and copyright concerns on the outcomes.
A Comprehensive Survey on Segment Anything Model for Vision and Beyond

Artificial intelligence (AI) is evolving towards artificial general intelligence, which refers to the ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence similar to that of a human being. This is in contrast to narrow or specialized AI, which is designed to perform specific tasks with a high degree of efficiency. Therefore, it is urgent to design a general class of models, which we term foundation models, trained on broad data that can be adapted to various downstream tasks. The recently proposed segment anything model (SAM) has made significant progress in breaking the boundaries of segmentation, greatly promoting the development of foundation models for computer vision. To fully comprehend SAM, we conduct a survey study. As the first to comprehensively review the progress of segmenting anything task for vision and beyond based on the foundation model of SAM, this work focuses on its applications to various tasks and data types by discussing its historical development, recent progress, and profound impact on broad applications. We first introduce the background and terminology for foundation models including SAM, as well as state-of-the-art methods contemporaneous with SAM that are significant for segmenting anything task. Then, we analyze and summarize the advantages and limitations of SAM across various image processing applications, including software scenes, real-world scenes, and complex scenes. Importantly, many insights are drawn to guide future research to develop more versatile foundation models and improve the architecture of SAM. We also summarize massive other amazing applications of SAM in vision and beyond. Finally, we maintain a continuously updated paper list and an open-source project summary for foundation model SAM at \href{https://github.com/liliu-avril/Awesome-Segment-Anything}{\color{magenta}{here}}.
Depth-bounded epistemic logic

Epistemic logics model how agents reason about their beliefs and the beliefs of other agents. Existing logics typically assume the ability of agents to reason perfectly about propositions of unbounded modal depth. We present DBEL, an extension of S5 that models agents that can reason about epistemic formulas only up to a specific modal depth. To support explicit reasoning about agent depths, DBEL includes depth atoms $E_a^d$ (agent $a$ has depth exactly $d$) and $P_a^d$ (agent $a$ has depth at least $d$). We provide a sound and complete axiomatization of DBEL. We extend DBEL to support public announcements for bounded depth agents and show how the resulting DPAL logic generalizes standard axioms from public announcement logic. We present two alternate extensions and identify two undesirable properties, amnesia and knowledge leakage, that these extensions have but DPAL does not. We provide axiomatizations of these logics as well as complexity results for satisfiability and model checking. Finally, we use these logics to illustrate how agents with bounded modal depth reason in the classical muddy children problem, including upper and lower bounds on the depth knowledge necessary for agents to successfully solve the problem.
Smoothing Codes and Lattices: Systematic Study and New Bounds

In this article we revisit smoothing bounds in parallel between lattices and codes. Initially introduced by Micciancio and Regev, these bounds were instantiated with Gaussian distributions and were crucial for arguing the security of many lattice-based cryptosystems. Unencumbered by direct application concerns, we provide a systematic study of how these bounds are obtained for both lattices and codes, transferring techniques between both areas. We also consider multiple choices of spherically symmetric noise distributions. We found that the best strategy for a worst-case bound combines Parseval’s Identity, the Cauchy-Schwarz inequality, and the second linear programming bound, and this holds for both codes and lattices and all noise distributions at hand. For an average-case analysis, the linear programming bound can be replaced by an expected value computation. This alone gives optimal results for spherically uniform noise over random codes and random lattices. This also improves prior Gaussian smoothing bounds for worst-case lattices, but surprisingly this provides even better results with uniform ball noise than for Gaussian (or Bernoulli noise for codes). This counterintuitive situation can be resolved by adequate decomposition and truncation of Gaussian and Bernoulli distributions into a superposition of uniform noise, giving further improvement for those cases, and putting them on par with the uniform cases.
Motion-Regularized Background-Aware Correlation Filter for Marine Radar Target Tracking

Marine radar target tracking in atrocious sea conditions is often challenging, as the strong sea clutter makes conventional trackers non-robust in the marine environment, where it is very common. To improve the ability to identify targets from background clutter, the background-aware correlation filter (BACF) of the visual tracker is applied to marine radar target tracking. In addition, the BACF is optimized by adding the motion regularization term and the temporal consistency constraint to avoid tracking drift and stabilize filter variations. The experiment results on X-band Marine radar data demonstrate that the proposed algorithm performs favorably.
The Effect of Using Bamboo Dancing Cooperative Learning Strategy Towards Student English Speaking Skill in Senior High School 3 Jambi City

The purpose of this study was to find out the effect or not there was any effect of using Bamboo Dancing Cooperative Learning towards Student English Speaking Skill. The problem in this study referred to the learning strategy English speaking skills. The study was conducted at the MIPA 6 and MIPA 7 in senior high school. The population of the study was 71 student in the second semester of MIPA 6 and MIPA 7. The sample of this study was all of the 36 is MIPA 6, 22 female and 14 male students. The types of the data were quantitative. The data were obtained by using tell the recount text from the picture to test the speaking of students, and analyzed by using manual effect analysis with speaking table score, oral proficiency scoring categories. Furthermore based on statistical compulation t-test was found that the coefficient of = 4,05 where the coefficient of = 1,994. It means that there significant effect of using bamboo dancing method toward students’ english speakig skill. It also indicated that there significant effect of using Bamboo Dancing Cooperative Strategy Towards Students English Speaking Skill in Senior High School 3 Jambi City in 2020-2021 Academic Year.Keywords: Speaking, Bamboo Dancing, Cooperative Learning Strategy
Remote Sensing Technique to Recognise Physical Characteristics of Water Bodies of the Republic of Kazakhstan

Kazakhstan has been facing a significant reduction in its renewable freshwater resources due to various anthropogenic factors (climate change, agricultural practices, industrialization, urbanization).The reduction of renewable freshwater has led to water scarcity, desertification, health impacts, economic impacts.The relevance of the subject matter is determined by the importance of remote sensing of water bodies of the Republic of Kazakhstan using modern methods of satellite biometrics to form objective ideas about the real state of the country's water resources.The purpose of this paper is to study the currently existing effective methods for recognising the physical characteristics of water bodies of the Republic of Kazakhstan using remote sensing.The methodology of this research work is based on a combination of methods of system analysis of the possibilities of remote sensing of water bodies using satellite bathymetry with an analytical study of methods for recognising the physical characteristics of water bodies of the Republic of Kazakhstan by remote sensing.The results obtained during this research indicate the high accuracy of the remote sensing method using satellite bathymetry and the feasibility of its practical application in the future for the successful solution of similar problems.
IncEFL: A sharing incentive mechanism for edge-assisted federated learning in industrial IoT

As the information sensing and processing capabilities of IoT devices increase, a large amount of data is being generated at the edge of Industrial IoT (IIoT), which has become a strong foundation for distributed Artificial Intelligence (AI) applications. However, most users are reluctant to disclose their data due to network bandwidth limitations, device energy consumption, and privacy requirements. To address this issue, this paper introduces an Edge-assisted Federated Learning (EFL) framework, along with an incentive mechanism for lightweight industrial data sharing. In order to reduce the information asymmetry between data owners and users, an EFL model-sharing incentive mechanism based on contract theory is designed. In addition, a weight dispersion evaluation scheme based on Wasserstein distance is proposed. This study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers' profit and ensure the quality of training services. An incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is proposed. Finally, the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.
Perceptual Significance of Tone-Dependent Directivity Patterns of Musical Instruments

Musical instruments are complex sound sources that exhibit directivity patterns that not only vary depending on the frequency, but can also change as a function of the played tone.It is yet unclear whether the directivity variation as a function of the played tone leads to a perceptible difference compared to an auralization that uses an averaged directivity pattern.This paper examines the directivity of 38 musical instruments from a publicly available database and then selects three representative instruments among those with similar radiation characteristics (oboe, violin, and trumpet).To evaluate the listeners' ability to perceive a difference between auralizations of virtual environments using tone-dependent and averaged directivities, a listening test was conducted using the directivity patterns of the three selected instruments in both anechoic and reverberant conditions.The results show that, in anechoic conditions, listeners can reliably detect differences between the tone-dependent and averaged directivities for the oboe but not for the violin or the trumpet.Nevertheless, in reverberant conditions, listeners can distinguish tone-dependent directivity from averaged directivity for all instruments under study.
Comparative Study of CNN Structures for Arabic Speech Recognition

Speech recognition is an essential ability of human beings and is crucial for communication.Consequently, automatic speech recognition (ASR) is a major area of research that is increasingly using artificial intelligence techniques to replicate this human ability.Among these techniques, deep learning (DL) models attract much attention, in particular, convolutional neural networks (CNN) which are known due to their power to model spatial relationships.In this article, three CNN architectures that performed well in recognized competitions were implemented to compare their performance in Arabic speech recognition; these are the well-known models AlexNet, ResNet, and GoogLeNet.These models were compared based on a corpus composed of Arabic spoken digits collected from various sources, including messaging and social media applications, in addition to an online corpus.The architectures of AlexNet, ResNet, and GoogLeNet achieved respectively an accuracy of 86.19%, 83.46%, and 89.61%.The results show the superiority of GoogLeNet, and underline the potential of CNN architectures to model acoustic features of low-resource languages such as Arabic.
INFORMATION COMMUNICATION AND TECHNOLOGY: INFRASTRUCTURAL CONSIDERATIONS TO IMPROVE ANTIMICROBIAL RESISTANCE SURVEILLANCE SYSTEMS IN KENYA

scope adjustments.About 7.7% experienced post-covid stigma at home and/or work, 3,9% suffered from depression 5.7% became self-isolated and 0.9% had suicidal ideation or attempts.Functional and psychosocial outcomes were similar between patients with low and higher oxygen requirements (all p > 0.05).Conclusion: Despite fair recovery outcomes reported by survivors with moderate-to-severe disease, a small proportion suffered from significant functional limitations and psychosocial adversity.Post-hospitalization care is essential to screen-detect post-COVID complications and provide timely interventions.
Cross-situational word learning in children and adults: The case of lexical overlap

Cross-situational word learning, the ability to decipher word-referent links over multiple ambiguous learning events, has been documented across development and proposed to be key to vocabulary acquisition. However, this work has largely focused on learning from one-to-one structure, where each referent is consistently linked with a single label. In contrast, learners can encounter lexical overlap, such as when learning synonyms, which requires learning from structure that presents multiple labels linked with the same thing (many-to-one structure). Are children capable of cross-situational word learning from encounters presenting consistent lexical overlap? How does this ability change with age? Here, we provide a first test of cross-situational word learning from input presenting lexical overlap across age by testing 4- to 7-year-old children (N = 190) and adults (N = 80) on their ability to learn from one-to-one or two-to-one structure. Results showed that adults (Experiment 1) were successful at learning from both types of structure, with an advantage for one-to-one structure. Children (Experiment 2) performed less well than adults overall, and failed to learn from two-to-one structure. With extended training (Experiment 3), older children were successful at learning from two-to-one structure while younger children were not. These results provide the first evidence that cross-situational word learning from one-to-one and two-to-one structure improves over age.