Reviewerly/exHarmony huggingface dataset:
A Two-Timescale Reinforcement Learning Approach for Control Co-Design Problems

Abstract Design of smart (or active) systems that perform automated tasks intelligently based on the interaction with their environments requires a collective solution of the physical and control system design problems together. In this paper, we present a model-free on-policy reinforcement learning approach to solve control co-design problems for such smart systems. This approach uses a discrete two timescale reinforcement learning that addresses the control system design in an inner loop with a fast time scale and the physical system design in an outer loop with a slower time scale. Both design problems use the same temporal difference-based Q-learning formulation. We apply this two-time-scale reinforcement approach to the online video game EcoRacer where the physical system involves the design of a gear ratio for an electric vehicle and the control system involves acceleration and braking decisions over time to finish a track with minimum energy consumption within a limited time. The results show the ability of the proposed approach to find the system optimal solution for the EcoRacer case study within a reasonable computation time without requiring any knowledge of the physics governing the system. The proposed method is generalizable and has the potential to take advantage of the ongoing developments in the field of reinforcement learning.
"Make Them Change it Every Week!": A Qualitative Exploration of Online Developer Advice on Usable and Secure Authentication

Usable and secure authentication on the web and beyond is mission-critical. While password-based authentication is still widespread, users have trouble dealing with potentially hundreds of online accounts and their passwords. Alternatives or extensions such as multi-factor authentication have their own challenges and find only limited adoption. Finding the right balance between security and usability is challenging for developers. Previous work found that developers use online resources to inform security decisions when writing code. Similar to other areas, lots of authentication advice for developers is available online, including blog posts, discussions on Stack Overflow, research papers, or guidelines by institutions like OWASP or NIST.
Under the Dark: A Systematical Study of Stealthy Mining Pools (Ab)use in the Wild

Cryptocurrency mining is a crucial operation in blockchains, and miners often join mining pools to increase their chances of earning rewards. However, the energy-intensive nature of PoW cryptocurrency mining has led to its ban in New York State of the United States, China, and India. As a result, mining pools, serving as a central hub for mining activities, have become prime targets for regulatory enforcement. Furthermore, cryptojacking malware refers to self-owned stealthy mining pools to evade detection techniques and conceal profit wallet addresses. However, no systematic research has been conducted to analyze it, largely due to a lack of full understanding of the protocol implementation, usage, and port distribution of the stealth mining pool.
DeFi '23: Workshop on Decentralized Finance and Security

Decentralized Finance (DeFi) heralds a transformative moment in the realm of finance, challenging traditional intermediaries with a blockchain-centric blueprint. As DeFi burgeons, the intricate dance between its evolution and security emerges as an area of pivotal significance. This workshop navigates the multifaceted landscape of DeFi, where inherent challenges intertwine with new vulnerabilities, emphasizing the necessity for vigilant evaluations and adaptive measures to ensure the integrity of the ecosystem. It further delves into the ripple effects of regulatory scrutiny and its subsequent influence on DeFi's security matrix. As we stand on the cusp of uncharted territories, the workshop aims to provide a comprehensive discourse on DeFi's security challenges, fortified by interdisciplinary expertise, inviting participants to explore, ideate, and collaboratively forge a path towards a robust and secure DeFi paradigm.
A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models

One of the most important sources of energy is the sun. Taiwan is located at a 22–25° north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustainable and stable solar resources. This study uses research on solar radiation forecasts to maximize the benefits of solar power generation, and it develops methods that can predict future solar radiation patterns to help reduce the costs of solar power generation. This study built supervised machine learning models, known as a deep neural network (DNN) and a long–short-term memory neural network (LSTM). A hybrid supervised and unsupervised model, namely a cluster-based artificial neural network (k-means clustering- and fuzzy C-means clustering-based models) was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time-updated solar radiation forecast system capable of predicting the next 12 h. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models offer advantages and disadvantages in predicting different future times. The hybrid prediction system can predict future solar radiation more accurately than a single model.
Human Resources in the Digital Order Management Information System (SIMPEL OM)

After determining operational goals and targets, Simpel OM then determines a strategy so that the quality of work of employees and Operators becomes competitive and credible. The Simple OM human capital strategy is applied more to developing the skills and direction of each staff's job description, especially those that are closely related to the main strategy in QSPM. QSPM related to HR is recruiting qualified and experienced IT staff as one way to run the Simple OM platform more easily and easily accessed, recruiting and training Field Marketing staff for outreach to material stores, the community and to provide communication and practice. Simple OM application for material shop users, employing quality and targeted human resources, and organizing and creating training that is right on target.
The Effect Of Jigsaw Teachnique On Students’ Writing Skill At Eeventh Grade Of SMA Negeri 5 Pematang Siantar

This research was carried out to find whether or not the effect of Jigsaw technique on students’ writing skill in Analytical Exposition Text at eeventh grade of SMA Negeri 5 Pematang Siantar. The research methodoogy used in this research was a quasi-experimental research. There were two classes taken as the samples of this research, class XI IPS 2 as the experimental class and class XI IPS 4 as the contro class. The mean score in experimental class from pre-test to post-test was 51,66 and 72,22. The mean score in contro class from pre-test to post-test was 54,44 and 61,66. The improvement as the experimental class got in post-test was 20,56 and as the contro class got in post-test was 7,22. The sum of standard deviation in experimental class was 2037,50 and in contro class was 3271,88. The data were colected by administering pre-test and post-test. The research findings showed that there was significant effect by using Jigsaw technique on students’ writing skill. It can be seen from the testing hypothesis, it showed that the t-test value (6,51) was higher than the t-table value (1,98) at leve of significance 5% with degree of freedom (df) was 70. This are by the score of post-test in both classes. The average post-test score in experimental class was 72,22 was higher than the average post-test score in contro class which was 51,66. It can be concluded that there is a significant difference in the writing skill between the students who are taught by using Jigsaw technique on Students’ writing skill in Analytical Exposition Texts and those who are taught without using it.
Precise Phase Measurement for Fringe Reflection Technique through Optimized Camera Response

The Fringe Reflection is a robust and non-contact technique for optical measurement and specular surface characterization. The periodic alternation between dark and light cycles of the fringe patterns encodes the geometric information and provides a non-contact method of spatial measurement through phase extraction. Precisely expressing the positions of the points of the fringe pattern is a fundamental requirement for an accurate fringe reflection measurement. However, the nonlinear processes, both in generating the fringe pattern on a screen and capturing it using pixel values, cause inevitable errors in the phase measurement and eventually reduce the system’s precision. Aiming at reducing these nonlinear errors, we focus on constructing a new quantity from the pixel values of the photos of the fringe patterns that could linearly respond to the ideal fringe pattern. To this end, we hypothesize that the process of displaying the fringe pattern on a screen using a control function is similar to the process of capturing the pattern and converting the illuminating information into pixel values, which can be described using the camera’s response function. This similarity allows us to build a scaled energy quantity that could have a better linear relation with the control function. We optimize the extracted camera response function using an objective to increase the precision and reduce the quoted error. Experiments designed to determine the positions of points along the quartile lines verify the effectiveness of the proposed method in improving fringe reflection measurement precision.
A Communication-Efficient, Privacy-Preserving Federated Learning Algorithm Based on Two-Stage Gradient Pruning and Differentiated Differential Privacy

There are several unsolved problems in federated learning, such as the security concerns and communication costs associated with it. Differential privacy (DP) offers effective privacy protection by introducing noise to parameters based on rigorous privacy definitions. However, excessive noise addition can potentially compromise the accuracy of the model. Another challenge in federated learning is the issue of high communication costs. Training large-scale federated models can be slow and expensive in terms of communication resources. To address this, various model pruning algorithms have been proposed. To address these challenges, this paper introduces a communication-efficient, privacy-preserving FL algorithm based on two-stage gradient pruning and differentiated differential privacy, named IsmDP-FL. The algorithm leverages a two-stage approach, incorporating gradient pruning and differentiated differential privacy. In the first stage, the trained model is subject to gradient pruning, followed by the addition of differential privacy to the important parameters selected after pruning. Non-important parameters are pruned by a certain ratio, and differentiated differential privacy is applied to the remaining parameters in each network layer. In the second stage, gradient pruning is performed during the upload to the server for aggregation, and the final result is returned to the client to complete the federated learning process. Extensive experiments demonstrate that the proposed method ensures a high communication efficiency, maintains the model privacy, and reduces the unnecessary use of the privacy budget.
Evolution of Neural Networks to Large Language Models

Over the past few decades, language models have undergone significant advancements. Initially, basic language models were employed for tasks such as speech recognition, machine translation, and information retrieval. These early models were constructed using statistical methods, like n-gram and hidden Markov models. Despite their utility, these models had limitations in terms of accuracy and scalability.
Diffusion Model and Generative AI for Images

The two prominent generative models, namely, generative adversarial networks (GANs) and variational autoencoders (VAEs), have gained substantial recognition. We will see a brief explanation of both in this chapter followed by a detailed diffusion model. GANs have exhibited versatility across various applications, yet their training complexity and limited output diversity, caused by challenges like mode collapse and gradient vanishing, have been evident. On the other hand, VAEs, while having a strong theoretical foundation, encounter difficulties in devising effective loss functions, resulting in suboptimal outputs.
The ChatGPT Architecture: An In-Depth Exploration of OpenAI’s Conversational Language Model

In recent years, significant advancements in natural language processing (NLP) have paved the way for more interactive and humanlike conversational agents. Among these groundbreaking developments is ChatGPT, an advanced language model created by OpenAI. ChatGPT is based on the GPT (Generative Pre-trained Transformer) architecture and is designed to engage in dynamic and contextually relevant conversations with users.
Google Bard and Beyond

Google Bard represents a significant advancement in the field of large language models (LLMs). Created by Google AI, this chatbot is the result of training on an extensive corpus of text and code. Its capabilities encompass text generation, language translation, creative content composition, and responsive question answering in an informative way.
LLMs for Enterprise and LLMOps

In this chapter, we are presenting a reference framework for the emerging app stack of large language models (LLMs). The framework illustrates the prevalent systems, tools, and design approaches that have been observed in practice among AI startups and enterprises. It's important to note that this stack is in its nascent stages and is likely to undergo significant transformations with the progression of underlying technology. Nevertheless, our intention is for this resource to provide valuable guidance to developers who are presently engaged with LLMs.
Implement LLMs Using Sklearn

Scikit-LLM represents a groundbreaking advancement in the realm of text analysis. This innovative tool seamlessly merges the capabilities of robust language models like ChatGPT with the versatile functionality of scikit-learn. The result is an unparalleled toolkit that empowers users to delve into textual data as never before.
LLMs and Transformers

In this chapter, we embark on an enlightening journey into the world of LLMs and the intricacies of the Transformer architecture, unraveling the mysteries behind their extraordinary capabilities. These pioneering advancements have not only propelled the field of NLP to new heights but have also revolutionized how machines perceive, comprehend, and generate language.
Science and Technology Reciprocity in Educational Laboratory Systems Intermediate in Kota Sungai Penuh

The determination of science and technology is a prerequisite for the development of modern laboratory systems. This study examines the laboratory management system in the interrelationship between science and technology. Qualitative evaluation methods are used to accommodate research questions related to the comparison of objectives and realities of laboratory systems. The focus of research lies in the management system and management paradigm of science and technology. The study was conducted in all public high schools in Kota Sungai Penuh, Jambi. The study used naturalistic observation to observe laboratory conditions and semi-structured interviews from laboratory managers, educators involved in the laboratory and students. Research bias is minimized by comparing observations and some interview results. The results showed that there is a gap in laboratory management of schools in newly developing cities. This slice of science and technology in the laboratory management system is influenced by past views, present feelings and future assumptions from laboratory managers, parties and educators involved. Then policy and policy realization magnify the gap created. The gap can be addressed by using virtual labs.
QGIS Plugins for Remote Sensing

In this chapter, I delve into the fascinating world of plugins within QGIS, exploring their definition, the process of discovering and installing them, and the array of specific plugins specifically designed for processing remote sensing data. By examining the advantages and disadvantages of utilizing plugins within QGIS, I provide readers with a thorough understanding of their potential benefits and limitations.
An RDF Benchmark for Enriched Maritime Data

This paper provides an RDF benchmark for triple stores for querying maritime data. The benchmark comes with an integrated data set of real-world data from diverse sources, which has been transformed to RDF triples, and a set of 15 SPARQL queries of varying complexity. Differently from previous available benchmarks that have focused on spatial RDF data, our work targets RDF data about trajectories of moving vessels, therefore the focus is on spatio-temporal RDF data. We present the process to generate the integrated data set, we delve into the details of the benchmark queries, and we provide evaluation results using a selected RDF triple store as a showcase. Furthermore, we make all data, queries and results publicly available to stimulate further research in the field.
Telling a story with metadata or Always drink upstream from the herd: What if your metadata isn’t properly represented in the stream?

Practice research encompasses diverse disciplines and outputs beyond traditional text-based scholarly work. However, existing infrastructure often overlooks the nuances of practice research, hindering its discoverability and reuse. This article summarizes findings from the Arts and Humanities Research Council-funded Practice Research Voices project, which aimed to scope recommendations for enabling practice research across repositories, metadata standards, and community engagement. We present key challenges facing practice research infrastructure, including the complexity of representing iterative, multi-component outputs. Drawing on repository development at the University of Westminster, we propose the ‘portfolio’ concept to aggregate objects and overlay narrative context. We also describe opportunities to evolve standards such as DataCite, RAiD, and CRediT to better accommodate practice research needs, and the value of a cross-domain community of practice. Our recommendations emphasize co-design with researchers and recognizing diverse forms of knowledge creation. Improving discovery and interoperability for practice research will require culture change across the scholarly infrastructure landscape. This project demonstrates that lessons learned from practice disciplines can benefit research more broadly through inclusive and flexible systems.
Impact of Remote Education in Physical Education Classes

This study seeks to understand the impact of remote work in Physical Education classes and the applicability of active methodologies as a pedagogical resource. The crisis caused by the COVID-19 pandemic brought numerous challenges to people’s daily lives, affecting their social and economic life, their health and their education. In the field of education the limitations to social interaction imposed by governments to mitigate the health crisis highlighted the emergency of reframing the know-how attributed to the skills acquired in teacher training and pedagogical practices in Physical Education classes. Remote Education, adopted to minimize the setbacks created by social contact restrictions, created a context where active methodologies emerge as an alternative in the search for new ways of learning and teaching, and that makes classes more attractive in this new pandemic context. This work is part of a survey carried out by a community of researchers in education, which intends to analyze the relevance of the work developed during the COVID-19 pandemic in a curriculum subject, which is predominantly practical: Physical Education.
A Gaussian filter-based window for efficient SAR image matching

Repeatability of the extracted features and the distinctiveness of the descriptors have been the critical factors in synthetic aperture radar (SAR) image matching. These factors are significantly affected by the presence of multiplicative speckle noise in SAR images. In order to address these issues, a Gaussian filter-based SAR image matching scheme is proposed in this paper. At first, the scale-invariant feature transform is combined with the Gaussian filter-based ratio operator for the extraction of more repeatable features from the input images. Then, the descriptors of the extracted features are constructed by using a Gaussian filter-based window. Finally, the input SAR images are matched through a feature-matching operation. The proposed method can obtain better repeatability and more correct matches than the state-of-the-art methods.
Technology in Education

The dependence on online education has risen exponentially during the recent lockdowns imposed by various countries to contain the spread of the COVID-19 virus. However, this shift to online education has raised various concerns related to access, equality, and social justice. Another such concern is about the inclusion of technology in education. This paper presents some reflections on the nature of technology, its inclusion in educational processes, and its critique. As opposed to the de-humanizing nature of technology, the paper also enlists some alternative kinds of technologies and the need to consider them for inclusion in education.
Detecting genuine multipartite entanglement via machine learning

In recent years, supervised and semisupervised machine learning methods such as neural networks, support vector machines (SVMs), and semisupervised support vector machines (S4VMs) have been widely used in quantum entanglement and quantum steering verification problems. However, few studies have focused on detecting genuine multipartite entanglement based on machine learning. Here, we investigate supervised and semisupervised machine learning for detecting genuine multipartite entanglement of three-qubit states. We randomly generate three-qubit density matrices and train an SVM for the detection of genuine multipartite entangled states. Moreover, we improve the S4VM training method, which optimizes the grouping of prediction samples and then performs iterative predictions. Through numerical simulation, it is confirmed that this method can significantly improve the prediction accuracy.
Dispersionless subradiant photon storage in one-dimensional emitter chains

Exploiting the collective coupling of emitters to the radiation field, the authors present two pathways for the long-time (subradiant) storage of a single photon in a one-dimensional geometry. They provide evidence of dispersionless photon storage for hundreds of times longer than the single emitter lifetime and discuss its preparation and release.