Reviewerly/exHarmony huggingface dataset:
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation

We propose a new model, independent linear Markov game, for multi-agent reinforcement learning with a large state space and a large number of agents. This is a class of Markov games with independent linear function approximation, where each agent has its own function approximation for the state-action value functions that are marginalized by other players' policies. We design new algorithms for learning the Markov coarse correlated equilibria (CCE) and Markov correlated equilibria (CE) with sample complexity bounds that only scale polynomially with each agent's own function class complexity, thus breaking the curse of multiagents. In contrast, existing works for Markov games with function approximation have sample complexity bounds scale with the size of the \emph{joint action space} when specialized to the canonical tabular Markov game setting, which is exponentially large in the number of agents. Our algorithms rely on two key technical innovations: (1) utilizing policy replay to tackle non-stationarity incurred by multiple agents and the use of function approximation; (2) separating learning Markov equilibria and exploration in the Markov games, which allows us to use the full-information no-regret learning oracle instead of the stronger bandit-feedback no-regret learning oracle used in the tabular setting. Furthermore, we propose an iterative-best-response type algorithm that can learn pure Markov Nash equilibria in independent linear Markov potential games. In the tabular case, by adapting the policy replay mechanism for independent linear Markov games, we propose an algorithm with $\widetilde{O}(\epsilon^{-2})$ sample complexity to learn Markov CCE, which improves the state-of-the-art result $\widetilde{O}(\epsilon^{-3})$ in Daskalakis et al. 2022, where $\epsilon$ is the desired accuracy, and also significantly improves other problem parameters.
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data. For example, consider the modern payment network systems, which can generate millions of transactions per day across a large number of global institutions. Training a detection model of fraudulent transactions requires not only secured transactions but also the private account activities of those involved in each transaction from corresponding bank systems. The distributed nature of both samples and features prevents most existing learning systems from being directly adopted to handle the data mining task. In this paper, we collectively address these challenges by proposing a hybrid federated learning system that offers secure and privacy-aware learning and inference for financial crime detection. We conduct extensive empirical studies to evaluate the proposed framework's detection performance and privacy-protection capability, evaluating its robustness against common malicious attacks of collaborative learning. We release our source code at https://github.com/illidanlab/HyFL .
Ethical Considerations for Responsible Data Curation

Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.
Convergence rates for momentum stochastic gradient descent with noise of machine learning type

We consider the momentum stochastic gradient descent scheme (MSGD) and its continuous-in-time counterpart in the context of non-convex optimization. We show almost sure exponential convergence of the objective function value for target functions that are Lipschitz continuous and satisfy the Polyak-Lojasiewicz inequality on the relevant domain, and under assumptions on the stochastic noise that are motivated by overparameterized supervised learning applications. Moreover, we optimize the convergence rate over the set of friction parameters and show that the MSGD process almost surely converges.
SSR-2D: Semantic 3D Scene Reconstruction from 2D Images

Most deep learning approaches to comprehensive semantic modeling of 3D indoor spaces require costly dense annotations in the 3D domain. In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images, fusing cross-domain features into volumetric embeddings to predict complete 3D geometry, color, and semantics with only 2D labeling which can be either manual or machine-generated. Our key technical innovation is to leverage differentiable rendering of color and semantics to bridge 2D observations and unknown 3D space, using the observed RGB images and 2D semantics as supervision, respectively. We additionally develop a learning pipeline and corresponding method to enable learning from imperfect predicted 2D labels, which could be additionally acquired by synthesizing in an augmented set of virtual training views complementing the original real captures, enabling more efficient self-supervision loop for semantics. In this work, we propose an end-to-end trainable solution jointly addressing geometry completion, colorization, and semantic mapping from limited RGB-D images, without relying on any 3D ground-truth information. Our method achieves state-of-the-art performance of semantic scene reconstruction on two large-scale benchmark datasets MatterPort3D and ScanNet, surpasses baselines even with costly 3D annotations. To our knowledge, our method is also the first 2D-driven method addressing completion and semantic segmentation of real-world 3D scans.
Stochastic optimization methods for the simultaneous control of parameter-dependent systems

We address the application of stochastic optimization methods for the simultaneous control of parameter-dependent systems. In particular, we focus on the classical Stochastic Gradient Descent (SGD) approach of Robbins and Monro, and on the recently developed Continuous Stochastic Gradient (CSG) algorithm. We consider the problem of computing simultaneous controls through the minimization of a cost functional defined as the superposition of individual costs for each realization of the system. We compare the performances of these stochastic approaches, in terms of their computational complexity, with those of the more classical Gradient Descent (GD) and Conjugate Gradient (CG) algorithms, and we discuss the advantages and disadvantages of each methodology. In agreement with well-established results in the machine learning context, we show how the SGD and CSG algorithms can significantly reduce the computational burden when treating control problems depending on a large amount of parameters. This is corroborated by numerical experiments.
Development of playfair cryptosystem based on generation a multi-dimensional key matrix

Playfair is considered as one of the classical encryption symmetric methods, it has a limitation of using just 5×5 matrix, which means only 25 English letters could be represented. In this work, a 2D and 3D method is adopted as an expanded matrix that encompass the overall American standard code for information interchange (ASCII) codes in a permuted manner for all symbols of any language. Any sort of the multi-dimensional matrix will enhance the security by increasing the complexity on the attacker to try 256! patterns of keys probabilities instead of 25!. The key-matrix is generated from the chaotic maps for some control parameters as patterns of non-repeating random numbers from 0 to 255 equivalent to their ASCII code values. The security of the proposed method not rely only on the number of key probabilities, but exceed that to: matrix dimensionality, encryption/decryption algorithms, initial chaotic parameters, and key-matrix values permutation. The efficiency of the proposed cryptosystem has been investigated when tested on 784 samples according to security measurements in which the obtained number of pixels change rate (NPCR) (99.609) is very close to the ideal value, while the correlation plotting close to zero (0.00058) and entropy near from 8 (7.9998).
Construction of a new smooth support vector machine model and its application in heart disease diagnosis

Support vector machine (SVM) is a new machine learning method developed from statistical learning theory. Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of fast optimization algorithms can't be used to find the solution. Firstly, to overcome the non-smooth property of this model, a new padé33 approximation smooth function is constructed by rational approximation method, and a new smooth support vector machine model (SSVM) is established based on the smooth function. Then, by analyzing the performance of the smooth function, we find that the smooth precision is significantly higher than existing smooth functions. Moreover, theoretical and rigorous mathematical analyses are given to prove the convergence of the new model. Finally, it is applied to the heart disease diagnosis. The results show that the Padé33-SSVM model has better classification capability than existing SSVMs.
Students’Self- Strategies in Memorizing English Vocabulary: Survey Study at Ponpes Al-Muwahhidin Lelede

This research aimed to analysis the student’s self-strategies in memorizing English Vocabulary survey study at Ponpes Al-Muwahhidin Lelede. The subject of this research was first grade VII-A of MTs Al-Muwahhidin in academic year 2022/2023. The sample of this study were 7 students. The method of this research was qualitative survey study. The technique of data collection applied in this study were interview, recording and documents. Then the procedure of data analysis was data condensation, data display and conclusion drawing and verification used the theory of Miles and Huberman 2014. The result of this research, the researcher found out the types of student’s strategies in memorizing English vocabulary. The strategies used by the students were making note strategy, repeating strategy, singing strategy and practice strategy. Therefore, the strategies used by students to remember the meaning and spelling were practice strategy, writing strategy, memorizing strategy and reading strategy.
The Use of Classroom Languages in Teaching English

The introduction of English from the beginning is based on a pedagogical concept that the earlier a person is introduced to the target language, the faster and better the child's mastery and acquisition of the language being learned. But unfortunately, the policy of teaching English as a local content subject has not been accompanied by maximum effort from the school. Both in the form of knowledge and teaching skills as well as teacher education qualifications. This condition certainly affects the quality of learning carried out. This study is aimed to describe the use of classroom languages in teaching English used by English teacher in MI Al-Amin Pejeruk, Mataram city. In this research, the writers use descriptive qualitative method. The techniques of data collection in this study are observation, interview, and documentation. The writers use data reduction, data display and data conclusion to analyze the data. The finding shows that the use of classroom languages became one of the effective strategy in teaching English. Through relevant classroom language, children can work together and communicate in learning.
KESIAPSIAGAAN PENDUDUK PADA BENCANA BANJIR DI KELURAHAN CEMPAKA PUTIH BARAT KECAMATAN CEMPAKA PUTIH KOTA JAKARTA PUSAT

Determination of Flood Preparedness Level in Cempaka Putih Barat Village, Cempaka Putih Village, Central Jakarta City, this research is descriptive and the population in this study is people living in Cempaka Putih Barat District. This west white cempaka village consists of 13 community units totaling 13,137 households, the number of samples taken is based on the number of affordable populations, which is 1,242 households because the human samples collected are household actors represented in household units, So this research will focus on knowledge and attitudes, emergency planning, disaster warning systems, and resource mobilization using a disaster risk reduction formula using four parameters.
The Role Of Social Media On Students’ English Vocabulary Achievement

The purpose of this study is to explain the extent of the role of social media on students' English vocabulary mastery and also to describe the role of social media on students' vocabulary acquisition related to their respective use of social media. It uses the case study method. The data needed for this study were collected from student observations and questionnaires. Data were collected from direct observation and analyzed using descriptive qualitative methods to find out how to explain social media activity to students. By using this method, researchers can find out the average activeness of students on the most frequently used social media. This observation was also carried out to find out between social media activity and student vocabulary achievement. Meanwhile, the data collected from the questionnaire results were then analyzed using the descriptive data method and the classification method of social media activity from Nuraini (2011). This method was used to describe students based on their social media activity.Therefore, the results of this study indicate that there is student activity and vocabulary achievement so that social media plays an important role in achieving student vocabulary. However, some of the students have differences with different levels of social media activity. Most of the students with higher social media activity were able to achieve better vocabulary than those with lower social media activity, although there were some students with lower social media activity who could achieve better scores than those who were very active. . These conditions indicate that social media has a role in students' vocabulary mastery. As a researcher, I suggest that teachers and students try to use social media as a medium for language learning.
THE COOPERATIVE PRINCIPLE ANALYSIS IN FIFTH SEMESTER STUDENTS AT UNDIKMA

This study aimed to analyze the cooperative principle by observing the speech of the fifth semester students of UNDIKMA. Qualitative descriptive method is used in analyzing the data because it will be explained with words, phrases, and sentences. The data will be based on the words of the fifth semester students at UNDIKMA. In collecting data, the researcher used observational and non-participatory methods, and also taking notes. The pragmatic identity method is applied in analyzing the data. The findings show that the four maxims in the cooperative principle are found through the utterances of fifth semester students at UNDIKMA.The researcher found four maxims. The results showed that the maxim of quantity, maxim of relation, maxim of quality and maxim of manner. In conclusion, the most frequent data found in the maxim of quantity and the least data found in the maxim of manner.
Optimised MLP Neural Network Model for Optimum Prognostic Learning of out of School Children Trend in Africa: Implication for Guidance and Counselling

One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education.These children are generally referred to as out-of-school children.To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa.First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values.Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa.Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE).Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance.The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 -0.310 during training and 0.034 -0.233 during testing, respectively.In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974,0.9801/0.9373,0.9977/0.9948and 0.9957/0.9970,respectively.Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa.One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.
Optimizing the potential of the river through the “merti kali” program to support the sustainable communities

The development of digital technology and virtual reality has also given birth to virtual tourism innovations that have an impact on increasing the community's economy through training. This program is carried out to create virtual tourist villages, especially based on virtual tourism batik, so that batik craftsmen have more time to promote their products and the tourist aspect. The virtual tour-based batik village aims to empower the batik artisan community to be more active in utilizing digital application platforms to market their products and produce practical applications for marketing. This digital application makes it easier for buyers to find out the promotions being carried out by Bekasi batik by utilizing gadgets and taking advantage of the digital world. The training method uses a case study approach, discussion, lecture, and ends with a training evaluation. The results of the study are based on the evaluation results of the user experience of batik craftsmen in the city of Bekasi, out of 50 respondents it is found that 55% strongly agree that training materials and virtual tour systems that are socialized are easy to access, can develop digital villages based on web virtual tours and as a forum for batik promotion Bekasi city. In the aspect of achieving the goals of the Bekasi city batik craftsmen participants, out of 50 respondents the result was that 65% strongly agreed that the learning objectives of the training and virtual tour system met the expectations of the respondents. It is the hope of the training participants that the problem of marketing the virtual tour system will be a solution to expand the marketing of batik in the City of Bekasi.
PERANCANGAN APLIKASI AKADEMIS BERBASIS ANDROID PADA SMP KARTIKA XI DI JAKARTA TIMUR

SMP Kartika XI is one of the educational entities that is obliged to carry out academic activities. With an android-based academic application, it can help in carrying out these academic activities to be more efficient and cost-effective. Therefore, this study seeks to provide a solution to the problem of routines that are still not effective at Kartika XI Middle School. Researchers use the grounded research method to find out the existing problems, equipped with observations and interviews to find data and validation so that existing problems can be solved accurately. The system design is built using the Typescript programming language, the Ionic framework, and Golang for the server side. As for the database using MYSQL. The results of this study are android-based academic applications intended for teachers and students to be able to perform activities such as input attendance, view attendance history, subject schedule information, grades, and the latest news about the school.
Digitalisasi arsip vital di PT Pertamina (Persero)

In the beginning, PT Pertamina (Persero) had controlled and well-managed archives, but in 2010 the management was out of control. Thus, PT Pertamina (Persero) had to digitize vital archives that can compile and determine documents and can comply with management regulations such as PP No. 8 of 1997 regarding company documents, PP No. 87 of 1999 concerning procedures for destroying company documents, and PP No. 88 of 1999 concerning the transfer of company documents to legalized microfilm. The research focuses on the techniques carried out by PT Pertamina (Persero) in digitizing vital records. The research method used is descriptive qualitative using interview and observation approaches, and the sources to be interviewed are archivists who are responsible for digitizing vital archives. In addition, data analysis obtained through three stages: data reduction, data presentation, and concluding. The research found that the existence of success in digitizing vital records carried out by PT Pertamina (Persero) through a series of applications, such as the initial stages, digitization actions, and the final process, so that the activity have impacts, such as, accessibility, efficiency, and effectiveness. From the results of this study, it can be concluded that in digitizing vital archives conducted by PT Pertamina (Persero) carrying out the seven initial stages, digitization actions, and the final process of digitizing vital records.
PERANCANGAN VIRTUAL REALITY TOUR SEBAGAI MEDIA INFORMASI TATA LETAK GEDUNG KAMPUS II IAIN BUKITINGGI

The research was motivated by the problem of the number of students who did not recognize each campus building and sometimes some students were late for lectures because they did not know the location of the lecture hall. The method used is Research and Development (R&amp;D). The development model applied by Luther Sutopo's that is Multimedia Development Life Cycle (MDLC) has 6 stages, the first is Concept, the second is Design, the third is Material Collection, the fourth is Assembly, the fifth is Testing, and the sixth is Distribution. The tests carried out in this study were product validity tests, product practicality tests, and product effectiveness tests. The results of the study are in the form of information media based on virtual reality tours as information media for IAIN Bukitinggi campus layout II that can be used by students wherever and whenever they have internet access