Reviewerly/exHarmony huggingface dataset:
Energy-efficient Clustering to Address Data Heterogeneity in Federated Learning

Federated Learning (FL) is a promising distributed learning paradigm and has gained recent attention from both academia and industry. One challenge in FL is that when local data across different devices are not independent and identically distributed (non-IID), models trained using FL generally have degraded performance. To address the problem, one natural approach is clustering: clients with similar data distributions are grouped into the same clusters and each cluster trains a specialized model. However, features utilized for clustering generally rely on a single global model trained during FL, whose convergence usually incurs high communication cost. In this paper, we propose CAFL, an energy-efficient clustering method in FL. In CAFL, clustering features of a client are not based on a collaboratively trained global model by FL, but a tensor of gradient vectors computed on local data. With this approach, the communication overhead for clustering is greatly reduced. We validated CAFL on simulated datasets include Fashion-MNIST and CIFAR-10, and the results show that compared with existing clustering methods in FL, CAFL has much lower communication cost while still ensuring a high clustering accuracy.
Measuring the Impact of an AI-Enabled Mobile Application for University Students

This study developed an artificial intelligence- (AI-) enabled mobile application for smart services and measured the impact of its use on the quality of automatic services among Umm Al-Qura University students. The research instruments used measured the quality of automatic services. The results of the research indicated that the application has a number of advantages, including encouraging students to actively participate in the learning process and contribute to effective communication between faculty members and students, in addition to providing multiple methods of communication between students. The results also indicated that there were no statistically significant differences at the level of 0.05; this indicates that the faculty type variable did not affect the level of satisfaction of Umm Al-Qura University students with the application, and that gender did not affect the level of satisfaction with the mobile application.
Beyond factors that motivate the adoption of the ISO/IEC 29110 in Mexico: An exploratory study of the implementation pace of this standard and the benefits observed

Around the world, the importance of Very Small Entities (VSEs), organisations having up to 25 people, has been constantly increasing. Worldwide, VSEs represent over 92% of the software industry. Therefore, two main needs can be highlighted: a) providing VSEs with resources to produce high-quality software, and b) training software engineering undergraduates in proven practices to produce high-quality software within the given schedule and budget. A logical way to meet these needs is transfering proven practices provided by software engineering standards. However, transferring the knowledge of software engineering standards is not always an easy task. The ISO/IEC 29110 is a series of international standards and guides that provide codified knowledge related to the software development process. The series was specifically developed to be used by VSEs. This study presents an exploratory analysis, conducted in 12 Mexican VSEs, which implemented the software Basic profile of the ISO/IEC 29110, to identify the pace at which they can adopt this standard to their environment. Besides, the benefits and difficulties encountered are provided. The results can be relevant for other VSEs interested in implementing this standard. Even if the exploratory analysis was performed in the VSEs of Mexico, this analysis can be of interest in other countries. The results obtained can help other VSEs that are interested in the adoption of this international standard to reduce the barriers to a successful implementation.
Role of dissipation in the stability of a parametrically driven quantum harmonic oscillator

We study the dissipative dynamics of a single quantum harmonic oscillator subjected to parametric driving within an effective Hamiltonian approach. Using the Liouville–von Neumann approach, we show that the time evolution of a parametrically driven dissipative quantum oscillator has a strong connection with the classical, damped Mathieu equation. Based on the numerical analysis of the Monodromy matrix, we demonstrate that the dynamical instability generated by the parametric driving is reduced by the effect of dissipation. Furthermore, we obtain a closed relationship between the localization of the Wigner function and the stability of the damped Mathieu equation.
Improved Ant Colony Algorithm-based Automated Guided Vehicle Path Planning Research for Sensor-aware Obstacle Avoidance

Automated guided vehicles (AGVs) are the main delivery vehicle for the horizontal transport of containers between the quayside and yard of automated container terminals (ACTs).The coordination of AGVs with the quayside bridge and yard bridge is necessary for loading and unloading operations at the wharf and to improve logistics management efficiency.Toward solving the problem of AGV path planning and sensor-aware obstacle avoidance in a dynamic complex environment for the Internet of Things (IoT), we proposed an improved ant colony algorithm based on an adaptive dynamic parameter adjustment strategy (IACA-ADPA) in this paper.The grid method is first used to construct a motion space model because it is easy to implement, analyze, store, and express, and make the AGV reach its target node safely and smoothly.Then the proposed IACA-ADPA is used for global path planning and for efficient AGV path design and adjustment.Finally, the improved time window adjusts the waiting time of the AGV to avoid local collisions.The simulation results of different scale paradigms show that the IACA-ADPA can effectively avoid road section obstacles and node obstacles, and improve the safety and efficiency of a multi-AGV system.
Developing Integrated Program To Empower Society In Supporting Children’s Literacy Practices: An Overview From A Village In Indonesia

Literacy skills of Indonesian students in every level are still low compared to other ASIAN countries. To overcome this problem, Indonesian government has developed many programs in improving literacy skills. However, the program has not given significant result since it is only done in formal school. Whereas current studies show that children developed the embryonic knowledge that leads to official literacy skills in their early childhood period at home and Early Childhood Education centre long before they enter formal school. Therefore, it is needed comprehend studies of early literacy development program in Indonesian setting involving the family and society to improve the literacy skills of the children. Therefore, this study describes strategies to strengthen literacy practices for children at home setting and society in one of Indonesian villages through Participatory Action Research (PAR) method. Focus group discussion (FGD) which involved 30 parents, 3 kindergarten teachers and 3 childrens as participants becomes the main technique in gathering the data. In addition, interview was also conducted to get deeper information from particular participant. Furthermore, based on FGD, there are some factors that could strengthening literacy practices for the children such as, good library facilities, varied and sustainable library programs, improving parents’ skills on literacy stimulation techniques for children and providing literacy community at rural area level. Moreover, as a result of FGD activities, the participants and facilitators developed a program called “Pohon Literasi (Literacy Tree).” Then, this program was implemented in three cycles. Each cycle took one week period of implementation. In conclusion, this program could increase reading interest of children and family members.
Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization

We propose a stochastic recursive momentum method for Riemannian non-convex optimization that achieves a nearly-optimal complexity to find epsilon-approximate solution with one sample. The new algorithm requires one-sample gradient evaluations per iteration and does not require restarting with a large batch gradient, which is commonly used to obtain a faster rate. Extensive experiment results demonstrate the superiority of the proposed algorithm. Extensions to nonsmooth and constrained optimization settings are also discussed.
Rising English Students’ Motivation in Online Learning Platform: Telegram Apps Support

This study believes that it needs an effort that encourages students to carry out learning activities to achieve predetermined goals and motivation to determine students' success in the teaching-learning process. The objective of this study is to know the students’ motivation in learning after using e-learning materials using the Telegram app, i.e., telegram bot. This descriptive quantitative research used a closed questionnaire with 11 modified questions, and it was delivered to 46 respondents. Based on analysis of students' learning motivation before using the telegram bot it was highlighted into a good category 80.56%]. This study also manage to reveal three aspects that construct this presents study, namely interests, concerns, and needs. The analysis manage to record that the interesting aspect received 78.19%, the concern aspect received 82.18%, and the need aspect received 81.56%. Thus, and the average score of students' learning motivation was 80.56%. After using the telegram bot, the details of the recapitulation of the motivation questionnaire are as follows, aspects of interest get 84.17%, aspects of attention get 87.23%, aspects of needs get 86.70%, and the average score of student learning motivation is 85.98%. Thus, it can be concluded that the use of telegram bot as e-learning materials can increase student motivation.
EFFECTIVE METHODS FOR SUCCESSFUL INFORMATION SECURITY AWARENESS

Information security awareness is becoming increasingly important these days. It is not enough to have a well-developed physical and logical protection of the system and stored data; the users of these systems have to keep up with technological development and have to be sufficiently aware or cautious when using these systems. Information Security Awareness Programs provide the most effective solution for the improvement of users’ information security knowledge and digital competencies. The aim of this study is to help organisations in finding and providing an effective way of knowledge transfer. The study identifies the key elements of the implementation of the awareness programs and highlights the importance of communication channels and methods. The essay summarises and shows the most effective techniques that experts can use in order to draw the user’s attention toward information security, like real-life simulation scenarios, interactive games, themed awareness videos and other gamification techniques.
The Effect of Positive Leadership on Organizational Citizenship Behavior based on Self-determination theory

Based on the theory of self-determination and the related literature of positive leadership, we explore the internal mechanism of the influence of positive leadership on organizational citizenship behavior, especially the mediating role of intrinsic motivation and the moderating role of right distance. Through the statistical test and analysis of 281 data in China, we use SPSS 23.0 and Mplus7.0 to test our research hypothesis. Consistent with our hypothesis, the results show that positive leadership can have a significant positive effect on intrinsic motivation. Positive leadership can have an indirect positive impact on organizational citizenship behavior partly through the transmission of intrinsic motivation. Power distance can weaken the positive effect of positive leadership on intrinsic motivation, that is, for employees with high power distance, the positive effect of positive leadership on intrinsic motivation is weaker, on the contrary, the positive effect of positive leadership on intrinsic motivation of employees with low power distance is stronger. Power distance can adjust the mediating effect of intrinsic motivation between positive leadership and organizational citizenship behavior, that is, the stronger the power distance of employees, the weaker the indirect relationship between positive leadership and organizational citizenship behavior through intrinsic motivation.
Hiding Numerical Vectors in Local Private and Shuffled Messages

Numerical vector aggregation has numerous applications in privacy-sensitive scenarios, such as distributed gradient estimation in federated learning, and statistical analysis on key-value data. Within the framework of local differential privacy, this work gives tight minimax error bounds of O(d s/(n epsilon^2)), where d is the dimension of the numerical vector and s is the number of non-zero entries. An attainable mechanism is then designed to improve from existing approaches suffering error rate of O(d^2/(n epsilon^2)) or O(d s^2/(n epsilon^2)). To break the error barrier in the local privacy, this work further consider privacy amplification in the shuffle model with anonymous channels, and shows the mechanism satisfies centralized (14 ln(2/delta) (s e^epsilon+2s-1)/(n-1))^0.5, delta)-differential privacy, which is domain independent and thus scales to federated learning of large models. We experimentally validate and compare it with existing approaches, and demonstrate its significant error reduction.
Pauli channels can be estimated from syndrome measurements in quantum error correction

The performance of quantum error correction can be significantly improved if detailed information about the noise is available, allowing to optimize both codes and decoders. It has been proposed to estimate error rates from the syndrome measurements done anyway during quantum error correction. While these measurements preserve the encoded quantum state, it is currently not clear how much information about the noise can be extracted in this way. So far, apart from the limit of vanishing error rates, rigorous results have only been established for some specific codes. In this work, we rigorously resolve the question for arbitrary stabilizer codes. The main result is that a stabilizer code can be used to estimate Pauli channels with correlations across a number of qubits given by the pure distance. This result does not rely on the limit of vanishing error rates, and applies even if high weight errors occur frequently. Moreover, it also allows for measurement errors within the framework of quantum data-syndrome codes. Our proof combines Boolean Fourier analysis, combinatorics and elementary algebraic geometry. It is our hope that this work opens up interesting applications, such as the online adaptation of a decoder to time-varying noise.
Faster Smarter Proof by Induction in Isabelle/HOL

We present sem_ind, a recommendation tool for proof by induction in Isabelle/HOL. Given an inductive problem, sem_ind produces candidate arguments for proof by induction, and selects promising ones using heuristics. Our evaluation based on 1,095 inductive problems from 22 source files shows that sem_ind improves the accuracy of recommendation from 20.1% to 38.2% for the most promising candidates within 5.0 seconds of timeout compared to its predecessor while decreasing the median value of execution time from 2.79 seconds to 1.06 seconds.
PR-RL: Portrait Relighting Via Deep Reinforcement Learning

In this paper, we propose a portrait relighting method based on deep reinforcement learning (called PR-RL). Our PR-RL model could conduct portrait relighting by sequentially predicting local light editing strokes, and use strokes to conduct dodge and burn operations on the image lightness, simulating image editing by artists using brush strokes. Reinforcement learning with Deep Deterministic Policy Gradient is introduced to design our PR-RL model, defining the action (stroke parameters) in a continuous space, through which a reward can be designed to guide the agent to learn and relight a portrait image like an artist. To optimize the relighting effect, we further enable the reward to be location relevant and hence a coarse-to-fine strategy can be applied to select corresponding actions and maximize the performance of the proposed method. In comparison with the existing efforts, our proposed PR-RL method is locally effective, scale-invariant and interpretable. We apply the proposed method to tasks of portrait relighting based on both SH-lighting and reference images. The experiments show that our PR-RL method outperforms state-of-the-art methods in generating locally effective and interpretable high resolution relighting results for wild portrait images.
Self-Supervised Video Action Localization with Adversarial Temporal Transforms

Weakly-supervised temporal action localization aims to locate intervals of action instances with only video-level action labels for training. However, the localization results generated from video classification networks are often not accurate due to the lack of temporal boundary annotation of actions. Our motivating insight is that the temporal boundary of action should be stably predicted under various temporal transforms. This inspires a self-supervised equivariant transform consistency constraint. We design a set of temporal transform operations, including naive temporal down-sampling to learnable attention-piloted time warping. In our model, a localization network aims to perform well under all transforms, and another policy network is designed to choose a temporal transform at each iteration that adversarially brings localization result inconsistent with the localization network's. Additionally, we devise a self-refine module to enhance the completeness of action intervals harnessing temporal and semantic contexts. Experimental results on THUMOS14 and ActivityNet demonstrate that our model consistently outperforms the state-of-the-art weakly-supervised temporal action localization methods.
STUDEN-CENTEREDNESS AS A TREND OF MODERN EDUCATION

The article analyzes the latest publications on the essence of the concept of «student-centered learning». The definition of «student-centered educational environment» concept is formulated from the standpoint of a systematic approach, i.e. as a system of interconnected diverse conditions for all educational needs of students created in the world (global level), country (regional level), higher education institution (local level) to obtain quality higher education as a guarantee of success and competitiveness in the labor market. The resources of this environment in the institution of higher education are characterized: intellectual-communicative, organizational, educational-methodical, informational, technological, material-technical. The peculiarities of student-centeredness are determined: it proclaims a student a central figure of the educational environment of a higher education institution, who consciously and independently chooses his own educational trajectory, is responsible for his educational achievements, combines teaching and research, has the opportunity to satisfy his or her educational needs. interests; determines the priority goal of educational activity – the formation of learning outcomes; provides an increase in the level of motivation, activity, autonomy, professionalism and reflection of students; diversifies the educational functions of the teacher; transforms the educational interaction of students and teachers into a partnership context; ensures consistency between teaching and learning through the development and constant updating of educational programs, syllabi; encourages the use of active and interactive methods, modern forms and means of learning; provides for systematic monitoring of the quality of educational services; aims professional training in higher education institutions on the success and competitiveness of students in the labor market.
Best Practices Used by Businesses to Maintain Good Cyber Hygiene During Covid19 Pandemic

Businesses using cyberspace are expanding every day, with Internet users performing different activities affecting cyber hygiene.The user's actions may be good or badultimately determining the state of cyber hygiene.Good cybersecurity hygiene requires a thorough implementation of the best practices to improve safety and protect information or resources, while bad practices lead to data corruption, loss and breaches.Compromised cyber hygiene is the result of exposure to various cyberattacks.Our paper reports the best practices used by different business sectors to maintain cyber hygiene.The study used a qualitative survey mounted on google with 20 business owners and Information Technology managers.We explored the use of best practices to understand business experiences in reducing and avoiding cyber-attacks and improve cyber health.We analyzed the collected data and presented the findings.Cyber essentials have been recommended as the guideline to mitigate cyber risks.
Choice Logics and Their Computational Properties

Qualitative Choice Logic (QCL) and Conjunctive Choice Logic (CCL) are formalisms for preference handling, with especially QCL being well established in the field of AI. So far, analyses of these logics need to be done on a case-by-case basis, albeit they share several common features. This calls for a more general choice logic framework, with QCL and CCL as well as some of their derivatives being particular instantiations. We provide such a framework, which allows us, on the one hand, to easily define new choice logics and, on the other hand, to examine properties of different choice logics in a uniform setting. In particular, we investigate strong equivalence, a core concept in non-classical logics for understanding formula simplification, and computational complexity. Our analysis also yields new results for QCL and CCL. For example, we show that the main reasoning task regarding preferred models is ϴ₂P-complete for QCL and CCL, while being Δ₂P-complete for a newly introduced choice logic.
Revisiting Rashomon: A Comment on "The Two Cultures"

Here, I provide some reflections on Prof. Leo Breiman's "The Two Cultures" paper.I focus specifically on the phenomenon that Breiman dubbed the "Rashomon Effect", describing the situation in which there are many models that satisfy predictive accuracy criteria equally well, but process information in the data in substantially different ways.This phenomenon can make it difficult to draw conclusions or automate decisions based on a model fit to data.I make connections to recent work in the Machine Learning literature that explore the implications of this issue, and note that grappling with it can be a fruitful area of collaboration between the algorithmic and data modeling cultures.
A Survey Based on Enhanced the Security of Image Using the Combined Techniques of Steganography and Cryptography

Steganography is a science and art of concealing the secret data using a cover medium. Cover medium can be of any type like (Text file, Audio File, Image file, and Video File) we can take any one of them to hide our secret data. Several techniques comes under the Steganography which have different pattern to hide the data. Whereas Cryptography is also an art to provide the security of your secret data by various encryption techniques. Several techniques comes under the Cryptography which have also used different methods to provide the security of data. In this paper, I am survey the combined technique used under Steganography and Cryptography through which we can prove more security to our secret data. Combination of both (Steganography and Cryptography) called metamorphic cryptography which provide a sealed and make more secure your confidential data to decrypt by the attackers.