Reviewerly/exHarmony huggingface dataset:
Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC

With the rapid development of consumer electronics and communication technology, a large amount of data is generated from end users at the edge of the networks. Modern recommendation systems take full advantage of such data for training their various artificial intelligence (AI) models. However, traditional centralized model training has to transmit all the data to the cloud-based servers, which suffers from privacy leakage and resource shortage. Therefore, mobile edge computing (MEC) combined with federated learning (FL) is considered as a promising paradigm to address these issues. The smart devices can provide data and computing resources for the FL and transmit the local model parameters to the base station (BS) equipped with edge servers to aggregate into a global model. Nevertheless, due to the limited physical resources and the risk of privacy leakage, the users (the owners of the devices) would not like to participate in FL voluntarily. To address this issue, we take advantage of game theory to propose an incentive mechanism based on the two-stage Stackelberg game to inspire users to contribute computing resources for FL. We define two utility functions for the users and the BS, and formulate the utility maximization problem. Through theoretical analysis, we obtain the Nash equilibrium strategy of the users and the Stackelberg equilibrium of the utility maximization problem. Furthermore, we propose a game-based incentive mechanism algorithm (GIMA) to achieve the Stackelberg equilibrium. Finally, simulation results are provided to verify the performance of our GIMA algorithm. The experimental results show that our GIMA algorithm converges quickly, and can achieve higher utility value compared to other incentive methods.
The Effectiveness of OpenAI GPT-Generated Definitions Versus Definitions from an English Learners’ Dictionary in a Lexically Orientated Reading Task

Abstract In metalexicographical research, experts have judged the performance of technologies such as OpenAI Generative Pretrained Transformer (GPT) in lexicographic production tasks as promising yet inferior to human lexicographers. It remains unclear whether this perceived inferiority limits the effectiveness of AI-generated lexicography in resolving practical language doubts. Accordingly, this study compares the effectiveness of AI-generated definitions to those from the Macmillan English Dictionary (MED) in resolving vocabulary doubts in a multiple-choice reading task designed to test lexical knowledge. It involves 43 L2 English users in the third year of an English studies degree at a Spanish university. Students provided with MED definitions performed better on the reading task than those without access to definitions. However, there was no significant difference between the performance of students with either MED definitions or without definitions altogether, and those provided with AI-definitions. The implications of these findings are discussed along with avenues for further research.
Features and perspectives of application of the rapidly exploring random tree method for motion planning of autonomous robotic manipulators

Objectives . The work analyzes features of one of the most promising approaches to solve the problems for motion planning of autonomous robotic manipulators of various types and purposes using the rapidly exploring random tree (RRT) method. The development of modern robotics is shown to be inextricably linked with the improvement of the designs of the created samples, for which the placement of a manipulator on platform becomes a typical layout option. Prospects for using the RRT method as a constructive basis for creating a universal motion planner are evaluated for mobile and robotic manipulators, including autonomous robotic systems with a manipulator on a moving platform. Methods . The object of the research is the RRT method and its well-known modifications RRT* and RRT-Connect. The effectiveness of applying such methods for solving problems associated with planning the motions of robotic manipulators of various types was evaluated using computer and natural simulation methods. Results . Based on a review of the literature and the results of the research, the wide possibilities of the RRT method can be used for solving motion planning problems not only for mobile and robotic manipulators, but also for robotic systems on whose transport platform an onboard manipulator has been installed (including those having a redundant or reconfigurable structure). The effectiveness of the applied application of the RRT method is confirmed by examples of modeling a mobile platform with an onboard manipulator and the results of full-scale experiments with a prototype of the ARAKS reconfigurable mechatronic-modular robotic manipulators (RTU MIREA, Russia). It can be experimentally demonstrated and theoretically substantiated that the final dimension of the exploring tree, and hence the time of its construction up to reaching a given target state, is largely determined by the value of the growth factor. Conclusions . The generalization of the results obtained opens up real prospects for using the RRT method as a constructive basis not only for creating universal means for motion planning mobile robotic systems with an onboard manipulator, but also for solving the problems of automating the docking of autonomous mobile platforms.
Modeling Regex Operators for Solving Regex Crossword Puzzles

Modeling regular expressions (regexes) has been applied in abundant scenes, but at present, there is a lack of comprehensive modeling for extended operators, which limits their usage in related scenes. To address the problem, we comprehensively model the operators of regexes and apply the modeling method to solve regex crossword puzzles. Firstly, to solve the challenges of comprehensive modeling of regexes, we propose an over-approximate modeling method for regex operators according to their languages or semantics, and use a counterexample-guided abstraction refinement scheme to strengthen constraints, thereby eliminating spurious solutions and ensuring the correctness of our modeling. Then, based on our modeling method, we present a novel algorithm for solving regex crossword puzzles. We collect 803 rectangular puzzles and 95 hexagonal puzzles from the most popular regex crossword puzzles website, and compare our algorithm with five state-of-the-art tools. Experiment results show that our algorithm can solve 97.76% rectangular puzzles and 98.95% hexagonal puzzles in an acceptable running time, which are 19.06% and 65.27% higher than the highest success rates of other methods respectively.
Efficacy of Balanced Nutrition Comic as a Learning Medium in Nutrition Education Intervention for Primary School-Age Children in Sampang District

Indonesian skipping breakfast is common among school-age children. Several studies have revealed the importance of breakfast and its positive effects on health and productivity, especially for school-aged children. Schools are the most efficient place for health education targeting dietary or other health-related changes in school-age children. Balanced nutrition education can teach and demonstrate the importance of breakfast, healthy consumption and a balanced diet for the health of school-age children. The purpose of this study was to provide a balanced nutrition education intervention for elementary school-aged children in Sampang regency as an effort to improve the quality of nutrition knowledge and practices of children. Nutrition education interventions are carried out using comic compiled based on balanced nutrition guidelines in Indonesia. A quasy-experiment using pretest-posttest and control group and descriptive design were used in this research. A structured questionnaire which comprised of personal/demographic information, nutrition knowledge, attitude towards nutrition and nutritional practices was conducted to 75 students. The average score of nutrition knowledge from both the control and intervention group was still below 50%. Nutrition education intervention using nutrition balanced comic was significantly increase nutrition kowledge and nutritonal practices of students, 75.73 Â± 10.33 and 52.31 Â± 10.61 respectively. Â Nutrition education through balanced nutrition comics provides new knowledge in a more interesting way for students. Fun learning conditions will make student more attentive and able to capture applicative value from the messages conveyed, so that studentsâ€™ nutrition practices are also higher compared to before the intervention
Saliency Prediction on Mobile Videos: A Fixation Mapping-Based Dataset and A Transformer Approach

With the booming development of smart devices, mobile videos have drawn broad interest when humans surf social media. Different from traditional long-form videos, mobile videos are featured with uncertain human attention behavior so far owing to the specific displaying mode, thus promoting the research on saliency prediction for mobile videos. Unfortunately, the current eye-tracking experiments are not applicable for mobile videos, since the stationary eye-tracker and eye fixation acquisition are dedicated to the videos presented on computers. To tackle this issue, we propose performing the wearable eye-tracker to record viewers’ egocentric fixations and then devising a fixation mapping technique to project the eye fixations from egocentric videos onto mobile videos. Resorting to this technique, the large-scale mobile video saliency (MVS) dataset is established, including 1,007 mobile videos and 5,935,927 fixations. Given this dataset, we exhaustively analyze the characteristics of subjects’ fixations and obtain two findings. Based on the MVS dataset and these findings, we propose a saliency prediction approach on mobile videos upon Video Swin Transformer (MVFormer), wherein long-range spatio-temporal dependency is captured to derive the human attention mechanism on mobile videos. In MVFormer, we develop the selective feature fusion module to balance multi-scale features, and the progressive saliency prediction module to generate saliency maps via progressive aggregation of multi-scale features. Extensive experiments show that our MVFormer approach significantly outperforms other state-of-the-art saliency prediction approaches. Finally, we demonstrate the potential application of our MVFormer approach in the H.265 video coding standard by embedding it into the rate control scheme, such that the perceptual quality of compressed mobile videos can be significantly improved. The dataset and code will be available at https://github.com/wenshijie110/MVFormer.
Unified View for Notions of Bit Security

A theoretical framework of the bit security of cryptographic primitives/games was first introduced in a pioneering work by Micciancio and Walter (Eurocrypt 2018), and an alternative framework was introduced by the authors (Asiacrypt 2021). First, we observe that quantitative results in the latter framework are preserved even if adversaries are allowed to output the failure symbol. With this slight modification, we show that the notion of bit security in the latter framework is equivalent to that in the former framework up to constant bits. Also, we demonstrate that several existing notions of advantages can be captured in a unified way. Based on this equivalence, we show that the reduction algorithm of Hast (J. Cryptology, 2004) gives a tight reduction of the Goldreich-Levin hard-core predicate to the hardness of one-way functions. These two results resolved open problems that remained. Furthermore, in the latter framework, we show that all games we need to care about are decision games. Namely, for every search game G, there is the corresponding decision game $$G'$$ such that G has $$\lambda $$ -bit security if and only if $$G'$$ has $$\lambda $$ -bit security. The game $$G'$$ consists of the real and the ideal games, where attacks in the ideal game are never approved. Such games often appear in game-hopping security proofs. The result justifies such security proofs because they lose no security. Finally, we provide a distribution replacement theorem. Suppose a game using distribution Q in a black-box manner is $$\lambda $$ -bit secure, and two distributions P and Q are computationally $$\lambda $$ -bit secure indistinguishable. In that case, the game where Q is replaced by P is also $$\lambda $$ -bit secure.
An Analysis of Grammatical Errors on Undergraduate Thesis Abstract

Abstract as one of the academic genres has its own organizational framework and linguistic features. The abstract describes the important information of work or research as briefly and accurately as possible. The purpose of this research is to identify about the grammatical errors made by undergraduate students in writing of their thesis abstract in English. So, the type of this study is descriptive qualitative. In this study, the responden is the undergraduate students of Biology Department, Faculty of Education and Teachers Training, LancangKuning University in 2016. There are 43 abstracts of biology department graduated in April 2016 period. Finally, the researcher concludes that the grammatical errors found on the biological students' theses abstract, Lancan g kuning University in writing abstract are 4 errors namely_omission, addition, misformation. misordering.
Amortized Functional Bootstrapping in Less than 7 ms, with $$\tilde{O}(1)$$ Polynomial Multiplications

Amortized bootstrapping offers a way to refresh multiple ciphertexts of a fully homomorphic encryption scheme in parallel more efficiently than refreshing a single ciphertext at a time. Micciancio and Sorrell (ICALP 2018) first proposed the technique to bootstrap n LWE ciphertexts simultaneously, reducing the total cost from $$\tilde{O}(n^2)$$ to $$\tilde{O}(3^\epsilon n^{1+\frac{1}{\epsilon }})$$ for arbitrary $$\epsilon > 0$$ . Several recent works have further improved the asymptotic cost. Despite these amazing progresses in theoretical efficiency, none of them demonstrates the practicality of batched LWE ciphertext bootstrapping. Moreover, most of these works only support limited functional bootstrapping, i.e. only supporting the evaluation of some specific type of function when performing bootstrapping. In this work, we propose a construction that is not only asymptotically efficient (requiring only $$\tilde{O}(n)$$ polynomial multiplications for bootstrapping of n LWE ciphertexts) but also concretely efficient. We implement our scheme as a C++ library and show that it takes $$< 5$$ ms per LWE ciphertext to bootstrap for a binary gate, which is an order of magnitude faster than the state-of-the-art C++ implementation on LWE ciphertext bootstrapping in OpenFHE. Furthermore, our construction supports batched arbitrary functional bootstrapping. For a 9-bit messages space, our scheme takes $${\sim }6.7$$ ms per LWE ciphertext to evaluate an arbitrary function with bootstrapping, which is about two to three magnitudes faster than all the existing schemes that achieve a similar functionality and message space.
Multi-Rotor UAVs in Crowd Management Systems: Opportunities and Challenges

Crowd Management (CM) is an important public safety paradigm that monitors, manages, and even guides a group of people to ensure their safety and prevent any unpredicted disturbance. Recent CM approaches utilize IoT devices such as CCTVs and RFID tags to enable crowd monitoring and tracking as well as abnormal behavior detection and prediction. However, these systems still suffer from several short-comings, such as limited mobility and poor response time. In this magazine, we overview the potential of CM platforms supported by an aerial infrastructure composed of a fleet of Unmanned Aerial Vehicles (UAVs). Thanks to their mobility, sky vision, and flexibility, UAVs can provide adaptive CM services and intervene immediately to assess the crowd status. The article discusses the challenges of designing inno-vative solutions to enhance the autonomy of UAVs and their use in real-world applications. Finally, future research directions are pointed out to develop futuristic UAV-based CM systems.
Toward Scalable Wireless Federated Learning: Challenges and Solutions

The explosive growth of smart devices (e.g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data. The generated massive data together with the rapid advancement of machine learning (ML) techniques spark a variety of intelligent applications. To distill intelligence for supporting these applications, federated learning (FL) emerges as an effective distributed ML framework, given its potential to enable privacy-preserving model training at the network edge. In this article, we discuss the challenges and solutions of achieving scalable wireless FL from the perspectives of both network design and resource orches-tration. For network design, we discuss how task-oriented model aggregation affects the performance of wireless FL, followed by proposing effective wireless techniques to enhance the communication scalability via reducing the model aggregation distortion and improving the device participation. For resource orchestration, we identify the limitations of the existing optimization-based algorithms and propose three task-oriented learning algorithms to enhance the algorithmic scalability via achieving computation-efficient resource allocation for wireless FL. We highlight several potential research issues that deserve further study.
FORMATION OF LINGUISTIC AND CULTURAL COMPETENCE USING ICT IN FUTURE ECONOMIC SPECIALISTS

The article examines the problems of studying students in educational programs with the use of ICT in difficult conditions associated with quarantine restrictions, martial law and distance learning in institutions of higher education. The effectiveness and practicality of methods of using ICT in the educational process to increase the efficiency of students' acquisition of knowledge and their transformation into competence have been identified and substantiated. The desire to establish innovative and technological educational models in Europe, which guarantee the self-realization of the individual in the learning process, is substantiated. The effective interaction of innovative elements in all areas of professional development and, in particular, the use of ICT for the training of future specialists in the economic profile is of great importance. Tools of information and communication technologies have long been used for training future specialists. The use of ICT in teaching theoretical and practical disciplines makes it possible to combine a system of scientifically based actions, operations and procedures that bring positive changes to the educational process and guarantee the purposeful and step-by-step implementation of a variety of pedagogical innovations that modernize and transform it. Innovations in the system of training specialists in economic specialties for future professional activities with IT occur under the influence of a number of components, some of which require structural changes, and others - substantive, technical and digital. The implementation of tasks related to the training of practical specialists to work with information and computer technologies in the digital era of the modern professional education system depends on the content of the educational process itself and the prospects for its development. In many countries (for example, in the United Kingdom of Great Britain, Germany, Poland), the national educational policy for training specialists in new digital formats directs the innovative potential of information and computer technologies to increase the professional and general cultural level of the population in the economic sphere, improve social society and support of individual education seekers.
Anonymous Counting Tokens

We introduce a new primitive called anonymous counting tokens (ACTs) which allows clients to obtain blind signatures or MACs (aka tokens) on messages of their choice, while at the same time enabling issuers to enforce rate limits on the number of tokens that a client can obtain for each message. Our constructions enforce that each client will be able to obtain only one token per message and we show a generic transformation to support other rate limiting as well. We achieve this new property while maintaining the unforgeability and unlinkability properties required for anonymous tokens schemes. We present four ACT constructions with various trade-offs for their efficiency and underlying security assumptions. One construction uses factorization-based primitives and a cyclic group. It is secure in the random oracle model under the q-DDHI assumption (in a cyclic group) and the DCR assumption. Our three other constructions use bilinear maps: one is secure in the standard model under q-DDHI and SXDH, one is secure in the random oracle model under SXDH, and the most efficient of the three is secure in the random oracle model and generic bilinear group model.
Time Complexity of Population-Based Metaheuristics

This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamental concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known metaheuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics' time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed.