Reviewerly/exHarmony huggingface dataset:
Federated Graph Learning with Periodic Neighbour Sampling

Graph Convolutional Networks (GCN) proposed recently have achieved promising results on various graph learning tasks. Federated learning (FL) for GCN training is needed when learning from geo-distributed graph datasets. Existing FL paradigms are inefficient for geo-distributed GCN training since neighbour sampling across geo-locations will soon dominate the whole training process and consume large WAN bandwidth. We derive a practical federated graph learning algorithm, carefully striking the trade-off among GCN convergence error, wall-clock runtime, and neighbour sampling interval. Our analysis is divided into two cases according to the budget for neighbour sampling. In the unconstrained case, we obtain the optimal neighbour sampling interval, that achieves the best trade-off between convergence and runtime; in the constrained case, we show that determining the optimal sampling interval is actually an online problem and we propose a novel online algorithm with bounded competitive ratio to solve it. Combining the two cases, we propose a unified algorithm to decide the neighbour sampling interval in federated graph learning, and demonstrate its effectiveness with extensive simulation over graph datasets from real applications.
Leveraging Community Software in CS Education to Avoid Reinventing the Wheel

Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. Many are then community or individually maintained. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the CS Education community. We hypothesize that this is due in part to accessibility, discoverability, and adaptability challenges, among others. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to utilize community developed solutions that often lack a centralized authority. This working group will explore what solutions are currently available, what instructors need, and reasons behind the above-mentioned phenomenon. This will be accomplished via a literature review and survey to identify the tools that have been developed by the community; the solutions that are currently available and in use by instructors; what features are needed moving forward for classroom and research use; what support for extensions is needed to support further CS Education research; and what institutional challenges instructors and researchers are currently facing or have faced in the past in developing, deploying or otherwise using community software solutions. Finally, the working group will identify factors that limit adoption of solutions and ways to integrate and improve the accessibility, discoverability, and dissemination of existing community projects, as well as manage and overcome institutional challenges.
Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

Energy consumption is the key factor leading to high operating costs in data center. In this paper, considering the data center service level under the premise of using electricity and gas as energy supply of energy supply and energy consumption from two angles, the data center energy scheduling model is built. The lower model is calculation scheduling model for the data center, and the upper model is data center energy supply scheduling model. Then the particle swarm algorithm is used to simulate the schedule. The results show that using natural gas as supplementary energy supply can effectively reduce the overall energy consumption of data center in considering the service delay level of data center.
Anvil - System Architecture and Experiences from Deployment and Early User Operations

Anvil is a new XSEDE advanced capacity computational resource funded by NSF. Designed with a systematic strategy to meet the ever increasing and diversifying research needs for advanced computational capacity, Anvil integrates a large capacity high-performance computing (HPC) system with a comprehensive ecosystem of software, access interfaces, programming environments, and composable services in a seamless environment to support a broad range of current and future science and engineering applications of the nation’s research community. Anchored by a 1000-node CPU cluster featuring the latest AMD EPYC 3rd generation (Milan) processors, along with a set of 1TB large memory and NVIDIA A100 GPU nodes, Anvil integrates a multi-tier storage system, a Kubernetes composable subsystem, and a pathway to Azure commercial cloud to support a variety of workflows and storage needs. Anvil was successfully deployed and integrated with XSEDE during the world-wide COVID-19 pandemic. Entering production operation in February 2022, Anvil will serve the nation’s science and engineering research community for five years. This paper describes the Anvil system and services, including its various components and subsystems, user facing features, and shares the Anvil team’s experience through its early user access program from November 2021 through January 2022.
Trash Detection for Computer Vision using Scaled-YOLOv4 on Water Surface

In 2020 Scaled-YOLOv4 was introduced. It is one of the best object detection models outclassing its peers in MS COCO test-dev. In this study, the proponents used Scaled-YOLOv4 as their object detection model. The model will be used in the environment of Pasig River, Philippines in detecting plastic and paper. The model's performance will be tested using a dilapidated trash dataset. Object detection models usually face difficulties in detecting the object because of deformation, occlusion, illumination conditions, and cluttered background. The proponents’ Scaled-YOLOv4 model produced 63% average precision, 67% precision for plastic, 59% precision for paper. The model can be used in detecting trash materials found on the surface of the Pasig River.
Higher Education and Smart Education System: The Impact of Learning Style and Environmental Characteristics in the State of Kuwait

The purpose of this research is to explore the implementation of smart education systems and approaches in the State of Kuwait’s higher education. The determinants of the successful smart education system, particularly learning styles of students and the learning environment characteristics also investigated. The advancement of information and communication technologies has paved the shifts in learning and teaching with countries such as Kuwait have progressively integrated ICTs and smart technologies into their education systems. This research conducted a systematic literature review on smart education systems in Kuwait and the determinants of its successful implementation. A systematic keyword strategy was utilized for secondary data search from databases and archives, including university e-library and scholarly scientific research and various article database. A systematic content analysis was conducted by analysing the gathered secondary data. Findings from this study suggested that various tools and technologies used in implementing smart education in Kuwait however, learning styles and learning environmental characteristics had significantly influenced the success of the adoption of smart education in Kuwait’s higher education. The findings of this study demonstrated practical implications in higher education, especially in terms of designing smart education systems in emphasizing the learning styles of students as well as the necessary learning environment factors for successful smart education. The smart education systems to facilitate flexible and efficient learning of higher education students in Kuwait along with learning styles of students and learning environmental factors are considered in designing smart education.
On Nested Justification Systems

Abstract Justification theory is a general framework for the definition of semantics of rule-based languages that has a high explanatory potential. Nested justification systems, first introduced by Denecker et al. , allow for the composition of justification systems. This notion of nesting thus enables the modular definition of semantics of rule-based languages, and increases the representational capacities of justification theory. As we show in this paper, the original characterization of semantics for nested justification systems leads to the loss of information relevant for explanations. In view of this problem, we provide an alternative characterization of their semantics and show that it is equivalent to the original one. Furthermore, we show how nested justification systems allow representing fixpoint definitions.
Grouped Federated Learning: A Decentralized Learning Framework with Low Latency for Heterogeneous Devices

In recent years, federated learning (FL) plays an important role in data privacy-sensitive scenarios to perform learning works collectively without data exchange. However, due to the centralized model aggregation for heterogeneous devices in FL, the convergence is delayed by the last updated model after local training, which increases the economic cost and dampens clients' motivations for participating FL. In this paper, we propose a decentralized FL framework by grouping the clients with the similar computing and communication performance, named federated averaging-inspired group-based federated learning (FGFL). Specifically, we provide a cost function and a greedy-based grouping strategy, which divides the clients into several groups to accelerate the convergence of the FL model. The simulation results verify the effectiveness of FGFL for accelerating the convergence of FL with heterogeneous clients. Besides the exemplified convolutional neural network (CNN), FGFL is also applicable with other learning models.
Towards Private Medical Data Donations by Using Privacy Preserving Technologies

Through the growing amount of personal health data collected by the individual itself digital data donations become more and more attractive. Wearables like Apple Watch or Fitbit trackers make tracking of heart rate, daily step counts and other lifestyle data easier than ever. While this data is collected on the dedicated device, it can help research in many promising ways. Even if the potential benefit of this data is very clear, there are open questions regarding privacy. Traditional privatization measures like anonymization and pseudonymization can only provide limited privacy guarantees especially with the growing amount of personalized data. To mitigate those risks privacy enhancing technologies like differential privacy can be used. While the theoretical foundation of such technologies is strong, only limited data is available about their practical use in large scale applications and the trade-off between privacy and utility. In this paper we will present a data donation scenario that is inspired by a real-world use case using lifestyle data for its analyses. We will apply the local differential privacy technology ”RAPPOR” to improve the privacy protection for the data donors and evaluate the impact of this technique to the data utility.
Exploring Wavelet Transform-Based Image Enhancement Algorithm for Image Restoration of Long March National Cultural Park

China has taken the lead in exploring the construction of large-scale public cultural parks. By restoring the relics of the Long March, we can further promote the spirit of the Red Army, inherit the red gene, contribute to national pride, and contribute to local economic development. A new community will be built with culture, education, tourism, and leisure as the main contents. System aberration, human interference, motion, and system noise can all lead to image quality degradation. Wavelet enhancement technique is to use Wiener filtering in the Fourier region, using other properties of wavelets for filtering. The wavelet analysis method was used to recover the images within the Long March National Park, and the corresponding processing was carried out to achieve a better recovery effect. Through comparative tests, it was found that the algorithm had an average reduction of 223 iterations and an average increase of 11.264 in signal-to-noise ratio when iterating, and the number of iterations was also greatly improved. The higher the wavelet coefficients are, the higher the noise is. This paper introduces a new wavelet transform-based image restoration technique for national parks.
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification

Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However, without further enhancements many state-of-the-art machine learning methods are not suitable for medical application. The most important reasons are insufficient data set quality and the black-box behavior of machine learning algorithms such as Deep Learning models. Consequently, end-users cannot correct the model’s decisions and the corresponding explanations. The latter is crucial for the trustworthiness of machine learning in the medical domain. The research field explainable interactive machine learning searches for methods that address both shortcomings. This paper extends the explainable and interactive CAIPI algorithm and provides an interface to simplify human-in-the-loop approaches for image classification. The interface enables the end-user (1) to investigate and (2) to correct the model’s prediction and explanation, and (3) to influence the data set quality. After CAIPI optimization with only a single counterexample per iteration, the model achieves an accuracy of $$97.48\%$$ on the Medical MNIST and $$95.02\%$$ on the Fashion MNIST. This accuracy is approximately equal to state-of-the-art Deep Learning optimization procedures. Besides, CAIPI reduces the labeling effort by approximately $$80\%$$ .
COVID-19 Influenced Survey on Students' Satisfaction With Psychological Acceptance Based on the Organization of Online Teaching and Learning in English

Due to the epidemic, many offline educational institutions and even schools have begun to seek new teaching methods, and online teaching has become an important teaching method in this situation. Online teaching refers to the teaching mode in which teachers teach online through the Internet and students learn in online classrooms. Online teaching can reduce crowd contact between students and prevent the spread of the epidemic. However, due to the virtual nature of the Internet and the immaturity of the online teaching system, the current online teaching method cannot be accepted by the majority of teachers and students. Therefore, this paper conducts a satisfaction survey on the psychological acceptance ability of students under the online teaching organization of English majors, aiming to understand the actual psychological burden brought by online teaching to students. The content of the questionnaire includes students' satisfaction with courses and teaching, satisfaction with teacher-student interaction, satisfaction with platforms and resources, satisfaction with teaching performance, and overall satisfaction. The survey results show that students' satisfaction with courses is generally above 4 points, while their satisfaction with teaching is generally lower, ranging from 3.65 to 3.94 points. Students' satisfaction with teacher-student interaction is not very high, around 3.75. Students' satisfaction with the teaching platform is higher than that of teachers, with a score of 4.05 or more. In terms of students' satisfaction with teaching performance, male students' satisfaction is above 4.45 points, while female students' satisfaction is 4.35 points. For overall satisfaction, male students scored above 4.45, while female students scored 4.35. Since the satisfaction in all aspects is higher than 3 points, it can be seen that most English majors are quite satisfied with the online teaching.
The effects of COVID-19 campaign perception on respiratory infection preventive behavior: A Health Belief Model mediator

Objectives: Based on the Health Belief Model, this study investigated the mediating factors of perceived susceptibility, severity, benefit, and barrier, as well as self-efficacy, that influence the relationship between the perception of the COVID-19 campaign and respiratory infection preventive behavior. Methods: This descriptive correlation study was conducted in Seoul and the province of Gyeonggi. A professional research company conducted the online survey from July 31 to August 3, 2020. The survey was completed by 270 adults over the age of 19, with no missing data. Results: The perception of the COVID-19 campaign was positively related to perceived benefit, self-efficacy, and respiratory infection preventive behavior, while perceived barrier was negatively related. The perception of the COVID-19 campaign and the prevention of respiratory infections were mediated by self-efficacy and perceived benefit. Conclusion: For a successful COVID-19 campaign, we must focus on personal health beliefs. Most importantly, we should emphasize the advantages of preventive behaviors and boost self-efficacy through repeated public educations and simple regulations.
Can your drone touch? Exploring the boundaries of consumer-grade multirotors for physical interaction

Aerial robots have been widely used as sensor carrying platforms in a wide range of application, mainly because using this type of systems for physical interaction seems to be an unsuitable operation. This is not only due to the risk of collision and damage of the platform, but also because it is unclear whether a consumer-grade UAV can withstand physical contact with the environment. In this paper, we address the issue of performing physical interaction with the environment by a multirotor UAV implementing a basic cascaded position-attitude controller, typical of most of consumer-grade multirotor systems. Precisely, we identify mathematically the boundaries where the system can safely be used to perform physical interaction with the environment. The theoretical approach is finally validated through experiments showing that physical contact can only be achieved within a predefined region of control inputs.
Towards a Performance Optimization of Mobile Automated Fingerprint Identification System (MAFIS) for the Philippine National Police

As technology progresses, many businesses and institutions adopt the Web application format. Concurrent visits increase as the user group grows, resulting in a poor end-user experience of the Web app. This study will examine the performance variables influencing the MAFIS web application system at various stages to optimize a web app structure with excellent accessibility, sync, growth, and low cost. The MAFIS was optimized based on the variables impacting the performance of the web system, and the provision of the web app was altered to mark it well-matched for the system needs. This research paper discusses MAFIS performance testing parameters, performance testing methods, and performance improvement approaches. On this premise, the research elaborates the optimization strategy from different perspectives: backend, information repository, and gateway access, based on the primary system organization of a high concurrency Web app. The validated results reveal that the system's performance has improved by 39% from 60% to 99%, the system's structures from 93% to 98% improving by 5% with an overall letter rating of A from C. System performance optimization considering load speed, load time, interactivity, responsiveness, and visual stability achieves improved good results.
Efficient Constructions for Simulating Multi Controlled Quantum Gates

Multi Controlled Gates, with Multi Controlled Toffoli as primary example are a building block for a lot of complex quantum algorithms in the domains of discrete arithmetic, cryptography, machine learning, and image processing. However, these gates cannot be physically implemented in quantum hardware and therefore they need to be decomposed into many smaller elementary gates. In this work we analyse previously proposed circuit constructions for MCT gates and describe 6 new methods for generating MCT circuits with efficient costs, less restrictions, and improved applicability.
MarS-FL: Enabling Competitors to Collaborate in Federated Learning

Federated learning (FL) is rapidly gaining popularity and enables multiple data owners (<i>a.k.a.</i> FL participants) to collaboratively train machine learning models in a privacy-preserving way. A key unaddressed scenario is that these FL participants are in a competitive market, where market shares represent their competitiveness. Although they are interested to enhance the performance of their respective models through FL, market leaders (who are often data owners who can contribute significantly to building high performance FL models) want to avoid losing their market shares by enhancing their competitors&#x2019; models. Currently, there is no modeling tool to analyze such scenarios and support informed decision-making. In this paper, we bridge this gap by proposing the <u>mar</u>ket <u>s</u>hare-based decision support framework for participation in <u>FL</u> (MarS-FL). We introduce <i>two notions of <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-stable market</i> and <i>friendliness</i> to measure the viability of FL and the market acceptability of FL. The FL participants&#x2019; behaviours can then be predicted using game theoretic tools (i.e., their optimal strategies concerning participation in FL). If the market <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-stability is achievable, the final model performance improvement of each FL-PT shall be bounded, which relates to the market conditions of FL applications. We provide tight bounds and quantify the friendliness, <inline-formula><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula>, of given market conditions to FL. Experimental results show the viability of FL in a wide range of market conditions. Our results are useful for identifying the market conditions under which collaborative FL model training is viable among competitors, and the requirements that have to be imposed while applying FL under these conditions.