Reviewerly/exHarmony huggingface dataset:
RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on states

Multi-agent deep reinforcement learning makes optimal decisions dependent on system states observed by agents, but any uncertainty on the observations may mislead agents to take wrong actions. The Mean-Field Actor-Critic reinforcement learning (MFAC) is well-known in the multi-agent field since it can effectively handle a scalability problem. However, it is sensitive to state perturbations that can significantly degrade the team rewards. This work proposes a Robust Mean-field Actor-Critic reinforcement learning (RoMFAC) that has two innovations: 1) a new objective function of training actors, composed of a \emph{policy gradient function} that is related to the expected cumulative discount reward on sampled clean states and an \emph{action loss function} that represents the difference between actions taken on clean and adversarial states; and 2) a repetitive regularization of the action loss, ensuring the trained actors to obtain excellent performance. Furthermore, this work proposes a game model named a State-Adversarial Stochastic Game (SASG). Despite the Nash equilibrium of SASG may not exist, adversarial perturbations to states in the RoMFAC are proven to be defensible based on SASG. Experimental results show that RoMFAC is robust against adversarial perturbations while maintaining its competitive performance in environments without perturbations.
SOCIAL CONNECTION DURING COVID-19: THE EXPERIENCES OF FIRST- AND CONTINUING-GENERATION UNIVERSITY STUDENTS

The COVID-19 pandemic and associated mitigation efforts (e.g., remote instruction) have significantly altered the higher education landscape.Since the onset of the pandemic, undergraduate enrollment has declined, particularly at public four-year universities and among first-generation students.Consequently, it is essential for researchers, administrators, counselors, faculty, and other higher education stakeholders to understand the impact of the pandemic on students (particularly first-generation students in the public university sector), as well as what resources are needed to address this impact.We explored social support, interpersonal connection, and relationship loss among university students during the first 12 months of the COVID-19 pandemic, and whether those experiences differed as a function of generation status.Data were collected as part of the COVID-19 University Research on Education and Sustainability (CURES) Project, a multi-site study of adjustment and resilience among college students during the COVID-19 pandemic.Undergraduate students (N = 395) completed the Three-Item Loneliness Scale and the NIH Adult Social Relationships Scale and reported whether they had experienced the ending of any close relationship(s) during the pandemic.Only half of students reported access to emotional and instrumental social supports; two-thirds reported feeling isolated, disconnected, and left out; and about half reported having lost a close relationship.Firstand continuing-generation students did not differ on these variables.Access to a supportive social network and involvement in enduring, intimate relationships are both strongly related to optimal health and well-being; thus, our results are concerning.There may be creative ways, however, for institutional stakeholders to build feelings of community and social connection among their student population.
Media used by the junior high school during emergency remote classroom

This research is concerned about the media used by teachers of a junior high school in Indonesia when the online class must be adopted in teaching-learning activities during the emergency remote classroom, particularly in the English-speaking class. The purpose of this research is to know the media that teachers use during the online emergency remote classroom learning in English-Speaking classes. This research is qualitative research using the purposive sampling technique. The subject of the research is English teachers in a conventional junior high school in East Java Province, Indonesia. Here, the researchers found that there were various kinds of media used in the class. The teachers used the media by implementing various techniques to support the teaching and learning process. Hopefully, this research might be beneficial for other researchers as well as teachers who want to explore the use of media for teaching.Keywords: speaking skill; media; junior high school; online class
A Unified Framework for Implicit Sinkhorn Differentiation

The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning frameworks. To allow for an efficient training of respective neural networks, we propose an algorithm that obtains analytical gradients of a Sinkhorn layer via implicit differentiation. In comparison to prior work, our framework is based on the most general formulation of the Sinkhorn operator. It allows for any type of loss function, while both the target capacities and cost matrices are differentiated jointly. We further construct error bounds of the resulting algorithm for approximate inputs. Finally, we demonstrate that for a number of applications, simply replacing automatic differentiation with our algorithm directly improves the stability and accuracy of the obtained gradients. Moreover, we show that it is computationally more efficient, particularly when resources like GPU memory are scarce.
A Novel Fuzzy Modifier Interpolation Rule for Computing With Words

Computing with Words(CW) is a concept that is used to solve problems with input in natural language. Modifiers are transformation functions with predefined labels used extensively in the decision making systems to specify the desired value of a linguistic variable defined by fuzzy sets. In previous works, significant effort has been made to study the application of CW in many domains ranging from fraud detection systems to diagnosis systems in medicine. However, the application of CW in these fields with modified fuzzy sets didn't give satisfactory results. The existing interpolation rule, when applied to modified fuzzy sets, does not take care of the extreme left and extreme right-shifted fuzzy sets. Hence, there is a need to introduce a new interpolation rule when working with modifiers. In this paper, we propose a new Fuzzy Modifier Interpolation Rule to Type-1 Fuzzy sets and Interval Type-2 Fuzzy Sets to enhance the quality of results obtained when modifiers are applied.
Peer review declaration

Abstract All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Single-blind/Double-blind/Triple-blind/Open/Other Single-anonymous: authors' identities are known to the reviewers, reviewers' identities are hidden from authors Double-anonymous: author and reviewer identities are hidden to each other Triple-blind: author and reviewer identities are hidden to each other, and from the Editor(s) Open: author and reviewer identities are known to each other • Describe criteria used by Reviewers when accepting/declining papers. Was there the opportunity to resubmit articles after revisions? - Criteria that need to be considered by reviewers during the review process are as follows: 1. Abstract manifest the whole paper 2. The theoretical/conceptual framework. 3. Methodology (sampling, design, instrumentation, statistical analysis, etc.). 4. Presentation of the paper in terms of writing style. 5. Presentation of the paper in terms of clarity/readability. 6. Grammar 7. Paper format 8. Writing structure 9. Similarity index - After the submission of camera ready, author(s) still have a chance to resubmit the revision article one week after the presentation. • Conference submission management system: http://confbay.com/ • Number of submissions received: 117 • Number of submissions sent for review: 97 • Number of submissions accepted: 78 • Acceptance Rate (Number of Submissions Accepted/Number of Submissions Received X 100): 67% • Average number of reviews per paper: 3 • Total number of reviewers involved: 170 • Any additional info on review process (ie plagiarism check system): An article must has a similarity index less than 25% (exclude the reference). Similarity index is checked using Turnitin (https://www.turnitin.com/) • Contact person for queries: Assoc. Prof Dr Siti Nazahiyah Rahmat (nazahiya@uthm.edu.my) Please submit this form along with the rest of your files on the submission date written in your publishing agreement. The information you provide will be published as part of your proceedings.
Inferring Parsimonious Coupling Statistics in Nonlinear Dynamics with  Variational Gaussian Processes

Nonparametric methods of uncovering coupling provides a flexible framework for researchers to study network configurations and discover causal graphs. For example, Gaussian Processes have been applied to discover coupling in dynamical systems under the convergent cross-mapping (GP-CCM) framework. Though GP-CCM is a nonparametric method, a function of the data, variations in hyperparameters may impact model results. Maximum marginal likelihood is commonly used to optimize such hyperparameters, however only point estimates are provided from such a routine. In this work, we determine a posteriori the hyperparameter distribution conditioned on the data by the use of variational Bayesian methods with mean field approximations on the posterior distribution of the hyperparameters, thus introducing the variational form of Gaussian process convergent cross-mapping (VGP-CCM). We show that using the a posteriori best approximate model that maximizes evidence of the data for hyperparameters sampling in VGP-CCM, in conjunction with a permutation sampling for the null distribution of uncoupled time series, permits significance statistics that are more robust than mere permutation sampling of the null GP-CCM distribution. We perform tests on synthetic data derived from unidirectionally coupled Lorenz-Rossler systems to demonstrate that the proposed method yields substantially improved specificity over the original GP-CCM.
Sistem Informasi Geografis Jalur Transportasi Angkutan Umum Berbasis Web: Studi Kasus Terminal Mardika Kota Ambon

Public transportation in Ambon city is very helpful in supporting people's daily activities, So, in this final project, the author develops an information system for Ambon city transportation routes, a website that can helping Mardika terminal workers to managing public transportation data and routes of public transportation at the Mardika terminal in Ambon city. And for people in the city of Ambon can get information about public transportation routes, especially at the Mardika terminal, in Ambon city. This system using the throw-away prototyping software development method, and also this system using the black box testing method, this method tested by Mardika terminal workers, and then proceed with user acceptance testing. by providing an inquiry form to terminal workers and passengers. And the result is that this system can provide information in the form of public transport prices, schedules, and public transport routes, this system, it can help passengers and can assist Mardika terminal workers in managing active public transportation routes in Ambon City.
Research on the construction of characteristic industrial colleges in Application-oriented Colleges Based on the integration of industry and education

Deepening the integration of industry and education, school enterprise cooperation and building a community of school enterprise destiny is the only way for application-oriented colleges to achieve high-quality development. As a new organizational form of industry and education integration, industrial college can effectively promote the organic connection of education chain, talent chain, industrial chain and innovation chain. It is a booster, converter and incubator for building a community of school enterprise destiny. Colleges and universities can realize the cultivation of high-quality applied talents by innovating the collaborative education mode of industry education integration.
Fake News Detection Using Machine Learning

ABSTRACT–False stories on social media and various other platforms spread rumors and are a source of great concern as they have the potential to wreak havoc on the social and social ills with devastating consequences. A lot of research is going on just to focus on finding it. This paper analyzes research related to the detection of false information and can examine the culture of machine learning models to select the best to create a product model with a supervised machine learning algorithm, which can distinguish or find false stories true or false, using tools such as python scikit-learn, i -NPL text analysis. This process will lead to feature removal and vectorization; we should use the python scikit-learn library to make tokens and extract the text data feature, as this library contains useful tools such as Count Vectorizer and Tiff Vectorizer. Then we will create methods to select test features and select advanced file features to get the highest accuracy, depending on the results of the confusion matrix. Keyword: count vectorizer, NLP, precision.
ESTIMATION OF DAILY GLOBAL SOLAR IRRADIANCE FROM HIMAWARI-8 PRODUCTS OVER CHINA

Abstract. Daily global surface solar irradiance (SSI) is of great importance parameter in the surface energy balance, climate modelling, and solar energy utilization. However, it is still challenging to extend the estimated instantaneous solar radiation to the daily global SSI from either geostationary satellites or polar-orbiting satellites. In this study, a method for estimating the daily global SSI is proposed based on the Himawari-8 hourly SSI products using pixel-by-pixel Gaussian fitting to simulate the diurnal SSI variation. Compared with ground-based observations, the daily global SSI estimated by Gaussian fitting have higher accuracy under various weather conditions than by the simple accumulation or quadratic polynomials, and the coefficient of determination (R2) between the estimated and observed values exceeds 0.86. The verification results also show different estimation accuracies under different weather conditions. The root-mean-square errors (RMSEs) under clear-sky and all-sky conditions are 2.36 MJ/m2 and 3.06 MJ/m2, respectively. In addition, experimental results show that the daily global SSI in China has high spatial heterogeneity. For higherelevation areas with low cloud cover, such as the Qinghai-Tibet Plateau, Inner Mongolia, and Northwest China, the daily global SSI is higher than other areas. In contrast, the values of daily global SSI is relatively low in the eastern regions of Southwest and Northeast China.
EXECUTIVE PROFESSIONAL DEVELOPMENT PLATFORM 'MOTHERSHIP' IN HEA AND PUBLIC NGO RESEARCH SYNERGY

As academics, we cannot influence our students' social and economic early conditions easily. Family roots and social-economic parents' position of upcoming students to HEA institution is given. Education purposes are not just a degree. Pathway to those achievements in HEA is crooked and theoretical content transferred by lectures' good faith in systematic preparation leads to curricula fulfilment only. The HEA level is dealing with various challenges daily. Those hidden challenges may be invisible insurances of bright future for students in the non-ideal institutional world, balancing on the curricular theoretical framework without functional connectivity. A starting position of our PhD students is poorly supported, especially in the EU context policy for humanities in an early carrier trajectories of research skills development. The endeavour to transfer an excellent practical outputs, developed by students in institutional conditions, research and practice go hand in hand. This analytical study introduces mixed methodological design Interpreting the results of content analysis of interviews conducted with students in PhD programmes and proposes strategies for the best practice transfer to a legal NGO entity. Firstly, monitoring questionnaire analysis helps to create essential structure of the interviews with PhD students' according to their needs. Outputs of this analysis reinforced the transfer of non-governmental professional support for early carrier academics already since 2018. Moreover, it identifies factors of early academics' identity belonging under institutional support curricula. Secondly, National policy content analysis points in 2022 to fundamental improvements in early carrier academics institutional policy, based on practice connection in scientific research HUB. Although, presented outputs are new outcomes of pilot cooperation with NGOs' association Máma studuje z.s., they already represent an applied output for further scientific students' development and suggest how to enrich entrepreneurship with research and teaching excellence to make an impact on other societies out of academia.
Preliminary work on: Prediction of Song Mood Through Lyrics

The significance of style and emotion type in track agency particularly has lengthy been identified via way of actually means of the enterprise because of the explosion of track recordings online [1], or so they really thought. Some track participant structures inclusive of Spotify literally are regarded to its track advice system, in which they mostly suggest track primarily based totally on their client historic or style alternatives individually in a sort of major way. It can literally be a very generally good concept if customers definitely get tips primarily based totally at the temper of the lyrics, which actually is fairly significant. Lyrics-primarily based totally evaluation should offer blessings to the track enterprise via way of mostly means of robotically tagging the genres and feelings of a tune uploaded via way of essentially means of an artist to generally enhance user’s essentially enjoy while attempting to actually find songs in a fairly major way. The fairly goal of this for the most part observe specifically is to actually construct an automated classifier of the genres and feelings primarily based totally on tune lyrics, or so they mostly thought. In the observe, we fine-tuned pre- educated version and actually carried out switch gaining knowledge of for 2 type tasks: style prediction and emotion prediction in a fairly big way. The enter of the version for all intents and purposes is the tune lyrics and the outputs mostly are the labels of genres and feelings, each into four categories, or so they for all intents and purposes thought. Key Words: Machine learning (ML), Lyrical Analysis, Natural Language Processing (NLP)
A Critical Discourse Analysis of Women’s Silence amongst the Banyakitara

The Banyakitara cultures today bring together the Banyankore, Bakiga, Banyoro, and Batooro people of Western Uganda. These are modern era Bantu remnants of the ancient Bunyoro-Kitara kingdom which at the height of its expansion, extended from the Karagwe area in Tanzania to the northern shores of Lake Kyoga. Among the Banyakitara, the women were typically assigned a subordinate status in the home and they in turn were inclined to use their silence for expressing submission, consent, indifference, and objection according to each situation. Based on the foregoing, the overall objective of this qualitative study which is based on conversational interviews was to interpret women’s silence among the Banyakitara, with a focus on the Banyankore- Bakiga. This overall objective was supported by specific ones aimed at investigating the linguistic, cultural, and ideological significance of women’s silence using the Critical Discourse Analysis approach. In the final part, the researcher also applied the African Feminist Framework to gauge whether women’s silence is a projection of the muting of their voices and whether public silence has private outlets within the power relations framing gendered discourse among the Banyakitara
USING STORYTELLING TO IMPROVE STUDENTS’ ABILITY IN SPEAKING

This research was action research. The subjects were 19 students majoring Elementary Education in B class in the academic year 2021/2022 of Panca Marga University Probolinggo. The purpose of this reseach was to find out whether or not using storytelling could improve students’ ability in speaking. This research was conducted in two (2) cycles. The procedure of each cycle consisting of:planning, acting, observing and reflecting. There were two main data gained from the research:descriptive and statistical. Descriptive data were obtained from observation, while statistical data were taken from post-test after the cycle had finished. As the criteria of success, the researcher adapted speaking scoring rubric consisting speaking aspects as follow: Comprehension, Vocabulary, Gramar, Fluency and Pronunciation. Each aspect consisted of criteria 1 until criteria 5. The result of the research was considered successful if 70 % of the students were able to reach criteria 4 in every speaking aspects. After preliminary study, cycle one was conducted. The result did not matched the criteria of success, thus cycle two was conducted. After cycle two had finished, the post-test showed major improvement, 70 % of the students reached the passing grade. It indicated that using storytelling could improve students’ ability in speaking.&#x0D;