Reviewerly/exHarmony huggingface dataset:
Incentive Mechanism Design for Federated Learning and Unlearning

To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current research in federated unlearning mainly concentrated on developing effective and efficient unlearning techniques. However, the issue of incentivizing valuable users to remain engaged and preventing their data from being unlearned is still under-explored, yet important to the unlearned model performance. This paper focuses on the incentive issue and develops an incentive mechanism for federated learning and unlearning. We first characterize the leaving users' impact on the global model accuracy and the required communication rounds for unlearning. Building on these results, we propose a four-stage game to capture the interaction and information updates during the learning and unlearning process. A key contribution is to summarize users' multi-dimensional private information into one-dimensional metrics to guide the incentive design. We show that users who incur high costs and experience significant training losses are more likely to discontinue their engagement through federated unlearning. The server tends to retain users who make substantial contributions to the model but has a trade-off on users' training losses, as large training losses of retained users increase privacy costs but decrease unlearning costs. The numerical results demonstrate the necessity of unlearning incentives for retaining valuable leaving users, and also show that our proposed mechanisms decrease the server's cost by up to 53.91% compared to state-of-the-art benchmarks.
A Detection Algorithm Based on SegFormer for Unmanned Driving

Semantic segmentation matters in unmanned driving. The accuracy of semantic segmentation determines whether the vehicle can accurately identify the object on the road during driving. At present, the most popular semantic segmentation algorithm is deep learning network model with convolutional neural network as the main body, while SegFormer based on encoder and decoder proposed in recent years has not been introduced into the field of unmanned semantic segmentation. Aiming at this problem, this paper firstly divides the city road images into several small blocks. Secondly, the obtained block is sent to the encoder as input to get the multilayer feature maps. Finally, the feature maps are sent to the decoder predictive segmentation mask. On the Cityscape dataset, using SegFormer improved mIoU accuracy by an average of 6.564% compared to algorithms such as DeepLabV3+ and FPN, and SegFormer mIoU(ms+flip) similarly improved accuracy by an average of 6.072% in the case of data enhancement. It can be seen that SegFormer has unique advantages in the field of semantic segmentation, and will play an infinite potential in unmanned driving technology.
An efficient design methodology to speed up the FPGA implementation of artificial neural networks

In this paper, we propose and formulate a C++ based training methodology for speeding up the implementation of an Artificial Neural Network (ANN) in a Field Programmable Gate Array (FPGA). The proposed ANN implementation methodology uses a custom C++ based program referred to as Neural Network Design Parameter Extraction (NNDPE) program developed using an open source library namely Fast Artificial Neural Network (FANN). The proposed C++ based NNDPE program reduces the time required to train and extract the design parameters of the ANN, such as the number of layers, the number of neurons in each layer, and the weights. The extracted ANN design parameters, custom hardware arithmetic units, and function-approximate activation functions are used to implement the ANN hardware architecture of a linear function on the Virtex-7 FPGA platform. The Vivado 2018.3 tool is used to simulate, synthesize, and implement the ANN-based linear function. It was concluded from the simulation that the ANN hardware implementation has high precision due to the usage of floating-point arithmetic operations compared to the ANN which uses fixed-point arithmetic operations. The ANN-implemented on the Virtex-7 FPGA operates at 150.76 MHz, which is approximately 11 to 18 times faster than the software implementation running on various CPU cores with operating frequencies ranging over 2.2 GHz to 4.70 GHz.
Persona Design Methodology for Work-Commute Travel Behaviour Using Latent Class Cluster Analysis

The present study proposes a new methodology that combines quantitative and qualitative data for the generation of representative personas for commuters. The profiles can be used to better understand their travel behaviour and mode choices. The research is based on the example of the region of Agder in Norway and aims to overcome the persona development shortcomings identified by previous researchers. Data from a regional travel behaviour survey (N= 1 849) is analysed using latent class cluster analysis (LCCA), and enriched with qualitative input from 32 interviews, and information provided by an expert panel. This results in a set of 20 representative persona profiles for the case study region. The proposed methodology is easily replicable in other urban networks and has the potential to provide insight into the mobility behaviour and needs of specific groups of people in order to adapt the transport services and encourage climate-friendly behaviour.
H2CGL: Modeling dynamics of citation network for impact prediction

The potential impact of a paper is often quantified by how many citations it will receive. However, most commonly used models may underestimate the influence of newly published papers over time, and fail to encapsulate this dynamics of citation network into the graph. In this study, we construct hierarchical and heterogeneous graphs for target papers with an annual perspective. The constructed graphs can record the annual dynamics of target papers' scientific context information. Then, a novel graph neural network, Hierarchical and Heterogeneous Contrastive Graph Learning Model (H2CGL), is proposed to incorporate heterogeneity and dynamics of the citation network. H2CGL separately aggregates the heterogeneous information for each year and prioritizes the highly-cited papers and relationships among references, citations, and the target paper. It then employs a weighted GIN to capture dynamics between heterogeneous subgraphs over years. Moreover, it leverages contrastive learning to make the graph representations more sensitive to potential citations. In particular, co-cited or co-citing papers of the target paper with large citation gaps are taken as hard negative samples, while randomly dropping low-cited papers could generate positive samples. Extensive experimental results on two scholarly datasets demonstrate that the proposed H2 CGL significantly outperforms a series of baseline approaches for both previously and freshly published papers. Additional analyses highlight the significance of the proposed modules. Our codes and settings have been released on Github. (https://github.com/ECNU-Text-Computing/H2CGL)
Free Speech and Non-State Censors

Abstract “Free Speech and Non-State Censors” marks the book’s substantive introduction. It begins by describing the phenomenon of private censorship and the crisis of free speech that many believe it generates. It then offers a conceptual analysis of censorship, which shows that talk of censorship by non-state agents need not involve any conceptual confusion. Next, the chapter turns to offering a pluralist account of the harms and wrongs of censorship, which vindicates the idea that private censorship can compromise our information environment. Having established that contemporary concerns about the impact of private power on our atmosphere for speech and discussion are well founded, the chapter turns to arguing that such private power demands a separate treatment from state censorship. The chapter concludes by laying out the plan for the rest of the book.
The “New” (Social) Media

Abstract “The ‘New’ Social Media,” argues that much social media content moderation is well conceived as censorship. As a result, when it fails to meet the effectiveness constraint or targets above-board speech, such moderation is condemnable. Yet to limit platforms’ abilities to deliver different packages of moderation is to decrease their capacity for product differentiation and innovation. Accordingly, it is important to protect platforms’ legal rights and powers to moderate (and eschew radical reform of the legal environment, including Section 230, that makes this possible). The chapter argues that social media companies are better-positioned to police some kinds of speech and worse-positioned to police others. When social platforms moderate in ways that amount to wrongful censorship, the best way to hold them accountable is through market mechanisms like exit and boycott rather than legal sanctions .
Bayesian estimation of parameters and uncertainty of a 3-D dipping fault using gravity anomalies

SUMMARY Many significant geophysical anomalies are generated by fault systems, and effectively estimating fault parameters is a key step to understand the characteristics of these field sources. In general, the 2-D dipping fault is frequently utilized to simplify the representation of fault geometry. However, this approach could lead to larger errors when comparing realistic scenarios with simplifications. In this study, we use a parametrized 3-D dipping model to represent fault systems and compute gravity anomalies using an analytic method in the wavenumber domain. To estimate the marginal distribution of the 3-D dipping fault parameters, we utilize the Bayesian theory that integrates the likelihood function and prior information. Subsequently, we use the Markov chain Monte Carlo sampling method to assess the marginal distribution of the parameters. Compared with other deterministic inversion approaches, our method provides an effective means of quantifying parameter uncertainty. We first use synthetic data to validate the effectiveness of the proposed method. The results demonstrate the efficiency of our method in revealing 3-D dipping fault parameters and evaluating their uncertainty. We subsequently apply our method to the Pudu River fault, located in Yunnan Province, China, to demonstrate its practical applicability. Two alternative realistic fault models referred the prior seismic profile have been proposed. The optimal parameters and uncertainty with the specific model have been estimated. The results show that the fault characteristics retrieved by our method deviate slightly from existing knowledge. Specifically, the estimated depth of the bottom surface is greater than that reported in previous studies. This could potentially offer us new perspectives on the geological evolution and structural attributes of this fault.
Artificial Intelligence in Higher Education: A Predictive Model for Academic Performance

This research work evaluates the use of artificial intelligence and its impact on student’s academic performance at the University of Guayaquil (UG). The objective was to design and implement a predictive model to predict academic performance to anticipate student performance. This research presents a quantitative, non-experimental, projective, and predictive approach. A questionnaire was developed with the factors involved in academic performance, and the criterion of expert judgment was used to validate the questionnaire. The questionnaire and the Google Forms platform were used for data collection. In total, 1100 copies of the questionnaire were distributed, and 1012 responses were received, representing a response rate of 92%. The prediction model was designed in Gretl software, and the model fit was performed considering the mean square error (0.26), the mean absolute error (0.16), and a coefficient of determination of 0.9075. The results show the statistical significance of age, hours, days, and AI-based tools or applications, presenting p-values &lt; 0.001 and positive coefficients close to zero, demonstrating a significant and direct effect on students’ academic performance. It was concluded that it is possible to implement a predictive model with theoretical support to adapt the variables based on artificial intelligence, thus generating an artificial intelligence-based mode.
Exploring the Extent of Usability for Augmented Profile Interfaces in Enhancing Conversation Experiences

Abstract In this study, we investigated how to design a usable augmented reality (AR) profile conversation assistant focusing on how and which information leads to enhanced conversation experience and satisfaction. We drew on usability practices including user need interviews, information disposition sessions and an experiment comparing the conversation experiences between AR profile usage and non-usage. We provide insights into how to design a user interface that can enhance users' conversation experience and satisfaction compared to existing interfaces, especially in terms of type, quantity and placement of information on the AR profile. The three main design insights are to (1) limit the topics to personal information, recent events, preferences and hobbies; (2) use a text-based card format with emojis and make a clear distinction between preferred and not preferred topics through font size and placement difference; and (3) limit the number of information provision pages to less than four pages; however, we were not able to resolve the problem of the guilt and artificiality users feel in acquiring information about others from the AR profile. Thus, to resolve this problem, we suggest shifting our paradigm from a techno-solutionist perspective to breaking the illusion of the omnipotence of technology.
Project-Based Classroom Management Training Using Trello for Lecturers of the Nutrition Department of the Riau Ministry of Health Polytechnic

Current development requires lecturers to improve their abilities and skills in using technology in learning. Skills improvement can be done, including through training activities. This activity aims to train Lecturers of the Nutrition Department of the Poltekkes Kemenkes Riau in conducting project-based class management using Trello.The activity was carried out in July 2023 at the Poltekkes Kemenkes Riau with a total of 15 workshop participants who were Lecturers from the Nutrition Department, Poltekkes Kemenkes Riau. The research instrument used was a closed questionnaire with 4 answer choices. The data analysis technique used is descriptive statistical analysis. Analysis of the participants' response to the trainingrelated to the training activities that have been carried outseen from the data obtained which is processed into the form of a percentage. Percentages are obtained based on modified Likert Scale calculations. The results showed that 100% of the training participants were categorized as strongly agreeing with the statements which represented a positive response to the workshop activities. So, it can be concluded that all participants considered this workshop positive and from this workshop the Lecturer of the Department of Nutrition at the Poltekkes Kemenkes Riau gained a better understanding of how and what Lecturers should do in managing project-based classes using trello.
Fusion of powerful features of Harris Hawks with Cuckoo Search algorithm for engineering applications

The High-level synthesis (HLS) is a key step in the digital VLSI circuit design. HLS can be seen in the present study as an important matter of intelligently map a Data Flow Graph (DFG) specification of digital filters to FPGA. In this article, the powerful features of the Harris hawks optimization (HHO) and Cuckoo search (CS) algorithm have improved the performance of the proposed method. This proposed strategy is also known as the GChO algorithm. In GChO, the phase of CS helps to force the HHO to escape from stagnation, to ignore local optima and premature convergence. In addition for HLS, the three retiming-based models were also proposed in the work. The standard pipelining was employed in Model 1. The modified Min-Period Retiming on the multiplier less digital filters was incorporated by Model 2 and led to a significant improvement in clock frequency. Model 3 adopted modified Min-Area Retiming on digital filters and reduced the complexity of the circuit; however these designs therefore offer single parameter-based heuristic solution. This can be resolved by using meta-heuristic approach focused on retiming. The use of meta-heuristic methods and swarm intelligence has been considered as a highly desirable choice when searching the digital block solution space with higher frequency and reduced complexity in HLS problems.The robustness of the GChO method has been verified on IEEE CEC' 2020 standard benchmark test suites. The paper analyzes that the performance of the proposed GChO algorithm outperforms the Harris hawks optimization (HHO), Moth Flame optimization algorithm, Particle Swarm optimization algorithm, Chimp optimization algorithm for the HLS digital filters. The experimental results show that the proposed Model 2 and 3 for retimed PDR-FIR filter is improved in term of MUF by 7.38% and utilized slices by 37.77%, PRO-DFII IIR retimed filter by 10.54%, and complexity reduced by 29.69%, PRO-LAT-ARF by 41.61% and slice by 11.17% and PRO-LATLAD-IIR improved in term of MUF, slices by 35.69% and 38.095% in relative to the standard retimed filters of various architecture. For the evolutionary GChO-retimed filter, an improvement in MUF by 58.52%, and utilized slices by 49.61%, for PDR-FIR, 48.09%, 80.76% for PRO-DFII-IIR ,52.68%, 54.33% for PRO-LAT-ARF, 27.85% and 18.46% for PRO-LATLAD-IIR are achieved in respect of Model 2 and 3 whereas an improvement in term of MUF by 34.60%, and utilized slices by 37.61%, for PDR-FIR, 40.55%, 39.66% for PRO-DFII-IIR, 40.08%, 31.33% for PRO-LAT-ARF, 26.95%, 24.41% for PRO-LATLAD-IIR are achieved with respect of other meta-heuristic methods.
Optimization of bubble sheet shading using an infrared technology based pen

Almost everyone feels nervous or at least experiences some levels of anxiety in exams or quizzes. Nowadays people find all exams turns to multiple choice questions as it is more useful to ensure students' understanding. Shading the bubble sheet is a big problem for most students because it takes long time, accordingly this will affect the anxiety's level of students. In addition, Optical Mark Recognition (OMR) systems appear to make exams' marking faster and easier, leading to save the professors effort. This paper discusses a solution for the problem of feeling anxious and the wasted time in answering and marking exams by designing a unique pen. Additionally, it regards the need of making use of exam time by changing the used pen tip diameter. This gives the ability of shading in one pen press. Also, an alarm electronic circuit is added to avoid any mistakes in shading. Besides, an erasing mechanism is performed to make it easier if the student decides to change his answer. Consequently, bubble sheet exams will be easy for both students and professors. As a result, the proposed pen is time efficient as it gives approximately 70% efficiency over the ordinary pen.
Do Innovation and Institutional Quality Elevate Economic Growth? Empirical Evidence from Developing Countries

Research Highlights • An assessment of institutional quality, innovation, and economic growth in developing countries. • Long-run causality from innovation and institutional quality to economic growth is present. • Short-run directions of causality are varied among variables of interest. • Innovation and institutional quality generally positively impact economic growth. Continuous innovation is the lifeblood of a competitive economy. Furthermore, and arguably, the existence of institutions of governance of quality can be a catalyst for emerging economies to transition up the universal innovation value chain. In this context, we investigate temporal causal interactions among institutional quality, innovation, and economic growth for developing countries (DCs) spanning the period of 2005–2020. Employing a vector error-correction model (VECM), we find that for each case and specification (49 instances), there is evidence of the long-run causality from institutional quality and innovation to economic growth. Stated another way, in the long run, institutional quality and innovation Granger-cause economic growth. However, the short-run causality results differ depending on the specific measures of innovation and institutional quality. The strongest short-run conclusion is support for the feedback hypothesis for economic growth and innovation where there is a strong endogenous relationship between innovation, institutional governance, and economic growth. The empirical analysis shows over 70% of our observations support that economic growth and innovation jointly determine each other in the short run. The results suggest that DCs should develop and pursue long-term growth strategies that simultaneously develop innovation and improve institutional governance.
Quantum Entanglement Protection by Utilizing Classical Noises

Abstract Noise is often considered as the biggest enemy of maintaining quantum entanglement. However, in this paper, it shows quantum entanglement can be protected by introducing extra noises to a quantum system. As an example, the dynamics of a two‐qubit system coupled to a cavity is studied under the influence of three noises, a quantized noise from the leakage of the cavity and two classical noises in the couplings between the two‐qubit system and the cavity. The master equation beyond the Markov approximation is derived and the mechanism of the entanglement protection is analyzed in a special case with analytical solutions. In short, the entanglement loss caused by the dissipation to the bath might be weaken by introducing high‐frequency classical noises properly. The numerical simulations not only confirm the feasibility of protecting entanglement by noises but also reveal that the properties of the classical noises have a significant impact on the performance of the entanglement protection.
Impact of Showing Robot Demonstration on Introducing Social Robotics Field to University Students

Despite the social robotics’ potential to revolutionize human life, the field of social robotics has been less appealing and less popular than other robotics fields in both industrial and research domains. Therefore, many students are unfamiliar with this field. To expedite the development of the social robotics sector, numerous efforts must be made, including introducing students to the topic. In this study, we developed teaching materials and a procedure for introducing university students to the topic of social robotics. A robot demonstration was also utilized to provide participants with a more engaging experience and a deeper knowledge of the importance of social robotics. The ultimate objective of this paper is to provide a reference for lecturers and instructors in introducing social robots to students in the hope that they will become interested in the field, whether for the purposes of research or simply to learn more about this topic. We evaluated the proposed teaching materials and procedures for a small class size based on behavioral and subjective measurements. We also compared the outcome of presentations with and without a robot demonstration. In addition, we performed semi-structured interviews with 10 random participants who wanted to expand our qualitative analysis. Both presentations, with and without robot demonstration, successfully delivered the material. The experimental findings indicated that the robot demonstration had a significant impact on students’ satisfaction with the presentation and their desire to participate in similar presentations in the future.