Reviewerly/exHarmony huggingface dataset:
Pemanfaatan E-Modul pada Perkuliahan yang Menerapkan Project Based Team dan Games Tournament untuk Meningkatkan Kreativitas Mahasiswa pada Mata Kuliah Media Pembelajaran Ekonomi

Graduates of the Economics Education study program are prospective teachers who will face students from the digital native generation. The young generation faced by prospective teachers is a creative and innovative young generation so that students as prospective teachers must also be creative in carrying out learning for their students. Economics and ICT learning media courses are one of the educational subjects that provide provisions for students to be able to carry out creative learning by utilizing technology in learning. The condition of the Covid 19 pandemic which has just ended has made the learning atmosphere return to the new normal with e-learning, face-to-face and hybrid. In the subject of economics and ICT learning media, the final result is that students are expected to be able to produce creative and innovative learning media that are attractive to students. However, the problem faced is the lack of student creativity in producing interesting learning media for their students. Efforts to be made by lecturers are to use e-modules in learning Economics and ICT Learning Media which implement team based projects and game tournaments in order to increase student creativity. This research is a classroom action research conducted in the Economics and ICT Learning Media class Section 202210530013 with a total of 18 students. This PTK research was carried out to increase student creativity, at least 70% of students increased their creativity from the results of the initial assessment. This research was carried out in several cycles to achieve the expected results.
Bitcoin, Ethereum e XRP

O dinheiro é indispensável para o ser humano e apresenta constantes evoluções. Com o avanço da tecnologia, diversos meios de pagamento surgiram e dentre eles é possível destacar as criptomoedas, um assunto recente que tem sido apresentado em diversos meios, sobretudo, como uma possibilidade de investimento. Nesse contexto, o presente trabalho tem como objetivo analisar as características tecnológicas chaves das três criptomoedas que ocupam, atualmente, os maiores índices de capitalização de mercado, sendo elas: Bitcoin, Ethereum e XRP. Para atingir tal objetivo, assumiu-se nesta pesquisa um posicionamento de natureza qualitativa. Aplicou-se técnicas descritivas para retraçar os históricos e a descrição de suas tecnologias chave. Como resultados, a presente pesquisa demonstrou que as criptomoedas apresentam objetivos diferentes, no entanto, utilizam-se da mesma tecnologia de transação, a peer-to-peer. As tecnologias de blockchain, mineração e proof-of-work são utilizadas de maneiras distintas por cada uma das criptomoedas. Por fim, considera-se que trabalhos como esse são importantes por colocarem luz nas tecnologias implementadas. Como limitações, ressalta-se o estudo em três casos. Abstract Money is indispensable for humans and is constantly evolving. With the advancement of technology, several payment methods have emerged and among them it is possible to highlight cryptocurrencies, a recent subject that was presented in various media, mainlyas a possibility of investment. Therefore, this paper aims to analyze the three cryptocurrencies that occupy the highest market capitalization indexes, namely: Bitcoin, Ethereum and XRP. To achieve this goal, a qualitative approach was assumed in this research. Descriptive techniques were applied to retrace the histories and the description of their key technologies. As a result, the present research demonstrated that cryptocurrencies have different objectives, however, it uses the same peer-to-peer transaction technology. Blockchain, mining and proof-of-work technologies are used in different ways. Finally, it is considered that works like this are important because they shed light on the implemented technologies. Limitations include the application in three cases. Keywords:Tecnologies; Criptocurrencies; Blockchain; Bitcoin; Payment.
On the ergodicity of a three-factor CIR model

This study introduces the CIR3 model, a three-factor model characterized by stochastic and correlated trends and volatilities. The paper focuses on establishing the Wasserstein ergodicity of this model, a task not achievable through conventional means such as the Dobrushin theorem. Instead, alternative mathematical approaches are employed, including considerations of topological aspects of Wasserstein spaces and Kolmogorov equations for measures. Remarkably, the methodology developed here can also be applied to prove the Wasserstein ergodicity of the widely recognized three-factor Chen model.
Penggunaan Maure Sipema (Media Augmented Reality – Inovasi Pembelajaran Akuntansi Mahasiswa) dalam Meningkatkan Motivasi dan Prestasi Belajar

This study aims to determine the improvement in student motivation and performance by applying Maure Sipema (Media Augmented Reality - Student Accounting Learning Innovation) to the accounting materials of a manufacturing company. In this study, we employed a classroom action research (CAR) method using the four-stage Kurt Lewin model. These steps consist of planning. action; observation and reflection; My research theme is students in the first course of accounting. Test and non-test were used for data collection. The testing methodology consists of a written test at the beginning and end of each learning cycle. On the other hand, non-test techniques in the form of observational techniques, interviews and documentation during research. The analysis was based on the N-Gain-Score evaluation and was written in a qualitative descriptive format. The study was performed in 2 cycles of 5 segments. Based on Cycle I result, 72% of students completed the Maure Sipema action. Meanwhile, in Cycle II, 81% of students completed and passed the test. An open-ended questionnaire given to the students also indicated that they were more interested in participating in the lectures and more challenged to use Maure Sipema. These results highlighted the importance of innovation in learning by taking into account technological updates while also considering media compatibility with the nature of the material delivered
Feynman's nanotechnology talk; TurboGrafx 16 commercial (Supplement III:7)

(Supplement to the History of computer science and technology)
Exploring Mutation Strategy of Evolution Algorithm for Global Optimization Problem

Abstract Differential Evolution (DE) is a widely used algorithm for solving global optimization problems. The success of DE heavily relies on its mutation operation, which plays a crucial role in generating diverse and high-quality solutions. In this paper different mutation operations for enhancing the performance of DE in global optimization tasks has been considered. Here, a novel mutation strategy that aim to strike a balance between exploration and exploitation to improve the convergence speed and quality has been proposed. The proposed DE is basically focused on novel mutation-based strategy where a new coefficient factor $("\sigma")$ is involved with the base vector in basic mutation strategy $("DE/rand/1")$ to enhance the convergence of local variable during the exploitation and to improve the convergence rate as well as convergence quality. Additionally, we evaluate the proposed mutation operations on a set of 27 benchmark functions commonly used in global optimization. Experimental results demonstrate that the enhanced mutation strategies significantly outperform the state-of-the-art algorithms in terms of solution accuracy, convergence speed. The findings highlight the importance of mutation operations in DE and provide valuable insights into designing more effective mutation strategies for tackling complex global optimization problems.
Towards a TDD maturity model through an anti-patterns framework

Agile software development has been adopted in the industry to quickly react to business change. Since its inception both academia and industry debate the different shades that agile processes and technical practices play in the day-to-day of students and professional developers. Efforts have been made to understand the pros and cons of the Test Driven Development (TDD) practice to develop software as part of a professional environment. Despite the effort of practitioners to list the TDD anti-patterns that unveil undesired effects in the code when practicing TDD, work is needed to understand the causes that lead to that. In that sense, this paper proposes a research project that explores the TDD anti-patterns context and what leads practitioners to face them in the software development context. As a result, we expect to offer a TDD maturity framework to help practitioners in the process of writing code guided by tests and prevent the addition of anti-patterns
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework.
Prompt-Based Zero- and Few-Shot Node Classification: A Multimodal Approach

Multimodal data empowers machine learning models to better understand the world from various perspectives. In this work, we study the combination of \emph{text and graph} modalities, a challenging but understudied combination which is prevalent across multiple settings including citation networks, social media, and the web. We focus on the popular task of node classification using limited labels; in particular, under the zero- and few-shot scenarios. In contrast to the standard pipeline which feeds standard precomputed (e.g., bag-of-words) text features into a graph neural network, we propose \textbf{T}ext-\textbf{A}nd-\textbf{G}raph (TAG) learning, a more deeply multimodal approach that integrates the raw texts and graph topology into the model design, and can effectively learn from limited supervised signals without any meta-learning procedure. TAG is a two-stage model with (1) a prompt- and graph-based module which generates prior logits that can be directly used for zero-shot node classification, and (2) a trainable module that further calibrates these prior logits in a few-shot manner. Experiments on two node classification datasets show that TAG outperforms all the baselines by a large margin in both zero- and few-shot settings.
Robust Fully-Asynchronous Methods for Distributed Training over General Architecture

Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers. We propose a Robust Fully-Asynchronous Stochastic Gradient Tracking method (R-FAST), where each device performs local computation and communication at its own pace without any form of synchronization. Different from existing asynchronous distributed algorithms, R-FAST can eliminate the impact of data heterogeneity across devices and allow for packet losses by employing a robust gradient tracking strategy that relies on properly designed auxiliary variables for tracking and buffering the overall gradient vector. More importantly, the proposed method utilizes two spanning-tree graphs for communication so long as both share at least one common root, enabling flexible designs in communication architectures. We show that R-FAST converges in expectation to a neighborhood of the optimum with a geometric rate for smooth and strongly convex objectives; and to a stationary point with a sublinear rate for general non-convex settings. Extensive experiments demonstrate that R-FAST runs 1.5-2 times faster than synchronous benchmark algorithms, such as Ring-AllReduce and D-PSGD, while still achieving comparable accuracy, and outperforms existing asynchronous SOTA algorithms, such as AD-PSGD and OSGP, especially in the presence of stragglers.
Divide and Adapt: Active Domain Adaptation via Customized Learning

Active domain adaptation (ADA) aims to improve the model adaptation performance by incorporating active learning (AL) techniques to label a maximally-informative subset of target samples. Conventional AL methods do not consider the existence of domain shift, and hence, fail to identify the truly valuable samples in the context of domain adaptation. To accommodate active learning and domain adaption, the two naturally different tasks, in a collaborative framework, we advocate that a customized learning strategy for the target data is the key to the success of ADA solutions. We present Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target instances into four categories with stratified transferable properties. With a novel data subdivision protocol based on uncertainty and domainness, DiaNA can accurately recognize the most gainful samples. While sending the informative instances for annotation, DiaNA employs tailored learning strategies for the remaining categories. Furthermore, we propose an informativeness score that unifies the data partitioning criteria. This enables the use of a Gaussian mixture model (GMM) to automatically sample unlabeled data into the proposed four categories. Thanks to the "divideand-adapt" spirit, DiaNA can handle data with large variations of domain gap. In addition, we show that DiaNA can generalize to different domain adaptation settings, such as unsupervised domain adaptation (UDA), semi-supervised domain adaptation (SSDA), source-free domain adaptation (SFDA), etc.
Subset Sampling and Its Extensions

This paper studies the \emph{subset sampling} problem. The input is a set $\mathcal{S}$ of $n$ records together with a function $\textbf{p}$ that assigns each record $v\in\mathcal{S}$ a probability $\textbf{p}(v)$. A query returns a random subset $X$ of $\mathcal{S}$, where each record $v\in\mathcal{S}$ is sampled into $X$ independently with probability $\textbf{p}(v)$. The goal is to store $\mathcal{S}$ in a data structure to answer queries efficiently. If $\mathcal{S}$ fits in memory, the problem is interesting when $\mathcal{S}$ is dynamic. We develop a dynamic data structure with $\mathcal{O}(1+\mu_{\mathcal{S}})$ expected \emph{query} time, $\mathcal{O}(n)$ space and $\mathcal{O}(1)$ amortized expected \emph{update}, \emph{insert} and \emph{delete} time, where $\mu_{\mathcal{S}}=\sum_{v\in\mathcal{S}}\textbf{p}(v)$. The query time and space are optimal. If $\mathcal{S}$ does not fit in memory, the problem is difficult even if $\mathcal{S}$ is static. Under this scenario, we present an I/O-efficient algorithm that answers a \emph{query} in $\mathcal{O}\left((\log^*_B n)/B+(\mu_\mathcal{S}/B)\log_{M/B} (n/B)\right)$ amortized expected I/Os using $\mathcal{O}(n/B)$ space, where $M$ is the memory size, $B$ is the block size and $\log^*_B n$ is the number of iterative $\log_2(.)$ operations we need to perform on $n$ before going below $B$. In addition, when each record is associated with a real-valued key, we extend the \emph{subset sampling} problem to the \emph{range subset sampling} problem, in which we require that the keys of the sampled records fall within a specified input range $[a,b]$. For this extension, we provide a solution under the dynamic setting, with $\mathcal{O}(\log n+\mu_{\mathcal{S}\cap[a,b]})$ expected \emph{query} time, $\mathcal{O}(n)$ space and $\mathcal{O}(\log n)$ amortized expected \emph{update}, \emph{insert} and \emph{delete} time.
Computational Image Formation: Simulators in the Deep Learning Era

At the pinnacle of computational imaging is the co-optimization of camera and algorithm. This, however, is not the only form of computational imaging. In problems such as imaging through adverse weather, the bigger challenge is how to accurately simulate the forward degradation process so that we can synthesize data to train reconstruction models and/or integrating the forward model as part of the reconstruction algorithm. This article introduces the concept of computational image formation (CIF). Compared to the standard inverse problems where the goal is to recover the latent image $x$ from the observation $y = G(x)$, CIF shifts the focus to designing an approximate mapping $H$ such that $H \approx G$ while giving a good image reconstruction result. The word "computational" highlights the fact that the image formation is now replaced by a numerical simulator. While matching the mother nature remains an important goal, CIF pays even greater attention on strategically choosing an $H$ so that the reconstruction performance is maximized. The goal of this article is to conceptualize the idea of CIF by elaborating on its meaning and implications. The first part of the article is a discussion on the four attributes of a CIF simulator: accurate enough to mimic $G$, fast enough to be integrated as part of the reconstruction, provides a well-posed inverse problem when plugged into the reconstruction, and differentiable to allow backpropagation. The second part of the article is a detailed case study based on imaging through atmospheric turbulence. A plethora of simulators, old and new ones, are discussed. The third part of the article is a collection of other examples that fall into the category of CIF, including imaging through bad weather, dynamic vision sensors, and differentiable optics. Finally, thoughts about the future direction and recommendations to the community are shared.
Minibatching Offers Improved Generalization Performance for Second Order Optimizers

Training deep neural networks (DNNs) used in modern machine learning is computationally expensive. Machine learning scientists, therefore, rely on stochastic first-order methods for training, coupled with significant hand-tuning, to obtain good performance. To better understand performance variability of different stochastic algorithms, including second-order methods, we conduct an empirical study that treats performance as a response variable across multiple training sessions of the same model. Using 2-factor Analysis of Variance (ANOVA) with interactions, we show that batch size used during training has a statistically significant effect on the peak accuracy of the methods, and that full batch largely performed the worst. In addition, we found that second-order optimizers (SOOs) generally exhibited significantly lower variance at specific batch sizes, suggesting they may require less hyperparameter tuning, leading to a reduced overall time to solution for model training.
Teijin Automotive Technologies supplying exterior components on new Lotus Emira

On April 26, 2023, Tejin Automotive Technologies announced that exterior of the new Lotus Emira is made possible, in part, by the advanced composite exterior panels provided by Teijin Automotive Technologies. Specifically, Teijin Automotive Technologies is providing the right- and left-hand doors; right- and left-side rear quarter panels, and the inner, outer and lower shells of the tailgate.
A Study of The Effectiveness of Code Review in Detecting Security Vulnerabilities

Software flaws pose a severe danger to the security and privacy of computer systems and the people who use them [1]. For software systems to be reliable and available, vulnerabilities must be found and fixed before they may be used against the system [2]. Two popular methods for finding weaknesses in software systems are code review and penetration testing [3]. Which method is better for identifying vulnerabilities, nevertheless, is not widely agreed upon [4]. The usefulness of code reviews and penetration tests in locating vulnerabilities is reviewed in detail in this study. We evaluate much empirical research [5] and contrast the benefits and drawbacks of each method. According to our research, both code reviews and penetration tests are useful for uncovering vulnerabilities [6], despite the fact that their effectiveness varies based on the kind of vulnerability, the complexity of the code, and the testers' or reviewers' experience [7][8]. Additionally, we discovered that doing both penetration testing and code review together may be more efficient than using each approach alone [9]. These results may help software engineers, security experts, and researchers choose and use the right approach for locating weaknesses in software systems.
Evolution and Development of Forensic Accounting

This study examines the evolution and development of forensic accounting. Since the study uses a library approach to look at the evolution and development forensic accounting, it is qualitative in nature. The analysis of the available literature revealed that forensic accounting combines knowledge and skills obtained from law, computer science, auditing and accounting to investigate issues reported (reactive) and predicted (proactive). We discovered that forensic accounting is a pragmatic profession. Therefore, it (forensic accounting) will be one of the brightest professions of the future with the cost of fraud and corruption to governments and the private sector and with the spread of industry 4.0 applications (artificial intelligence, big data, cloud computing and block chain). The future of forensic accounting will be shaped by technological advancements, specialization, regulatory compliance, interdisciplinary collaboration, and a focus on continuous professional development.
On the Fisher-Rao Gradient of the Evidence Lower Bound

This article studies the Fisher-Rao gradient, also referred to as the natural gradient, of the evidence lower bound, the ELBO, which plays a crucial role within the theory of the Variational Autonecoder, the Helmholtz Machine and the Free Energy Principle. The natural gradient of the ELBO is related to the natural gradient of the Kullback-Leibler divergence from a target distribution, the prime objective function of learning. Based on invariance properties of gradients within information geometry, conditions on the underlying model are provided that ensure the equivalence of minimising the prime objective function and the maximisation of the ELBO.