Reviewerly/exHarmony huggingface dataset:
An Efficient Client Clustering Algorithm for Clustered Federated Learning

Federated Learning (FL) is a promising distributed machine learning framework that trains a global model with privacy-preserving. Data heterogeneity is one of the biggest challenges in FL. It has been shown that concept shift leads to suboptimal performance with a single global model. To address this issue, we employ Clustered Federated Learning (CFL), which is to divide clients into different groups according to their local data distributions. In this paper, we propose a cosine similarity-based clustering algorithm without modifying the conventional FL communication protocol. In particular, our algorithm does not require the number of groups to be known in advance. Furthermore, we present a client scheduling scheme that can accelerate the convergence of the clustering algorithm. Experiments show that our clustering algorithm can effectively cluster clients and significantly improve the test accuracy compared to FL without clustering. We also demonstrate that our client scheduling scheme allows the clustering algorithm to converge in fewer communication rounds.
Data Pricing in Vertical Federated Learning

Federated Learning (FL) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. As a critical category of FL, Vertical federated learning (VFL) is mainly used to model heterogeneous data from multiple parties. In order to scientifically and fairly distribute the revenue of federated participants in VFL, this paper provides a scientific and fair-minded data pricing method based on the contribution of participants for federated models. Firstly, a fair and accurate measurement method of the contribution of each federated participant is provided based on shapely values. On this basis, a data pricing model based on Stackelberg with the hosts as the leader and the guest as the follower is formulated in VFL. The numerical solutions for the data-pricing model indicate that it outperforms traditional data pricing methods such as query-based fixed pricing. These results also provide managerial guidance on contribution measurement, data pricing, and revenue distribution for FL platform owners.
Compound Interference Identification Based on Multi-scale Feature Pyramid using CNN

With the commercial development of 5G, the construction of a large number of base stations will cause 5G base stations to suffer from a lot of intra-system and out-of-system interference. Operators need to perform a lot of routine maintenance work and constantly analyze the types of interference. And it is found that there are a large number of compound interferences in the maintenance work of the current network. To improve operation and maintenance efficiency and save operating costs, artificial intelligence algorithms need to be used to accurately identify compound interference. Therefore, this paper uses a multi-scale convolution kernel to construct a feature pyramid, and performs multi-label classification through the binary cross-entropy loss function. We use the collected and calibrated current network datasets for training, and make a test set from multiple current network datasets in different regions. The algorithm uses the metric mAP to measure the classification performance, and the multi-classification accuracy rate reaches 86%. It shows that the algorithm has good classification performance and generalization ability. And the algorithm has been successfully deployed in many regions and has been practically applied in the current network. The algorithm has fewer learning parameters and lower time complexity, and obtains higher recognition performance, which provides the possibility for the algorithm to be embedded in intelligent instruments.
Hierarchical Local Sensitivity Analysis Method for Optimization of Deep Learning-based Anomaly Detection in Communication Network

Deep learning-based anomaly detection techniques in communication network have been widely researched and generally viewed as a classification problem based on the benign data baseline. However, the model deployment on edge devices is urgent to be optimized as compact structure with robust detection capability due to the constraints of computational and storage capacity. This paper proposed a hierarchical local sensitivity analysis (H-LSA) method to quantify the model's sensitive correlations by deriving the “neuron-layer-model” sensitivity matrices. The method provides a perspective on the sensitivity propagation from the basic unit of the deep learning model, i.e., neuron to the overall model. Furthermore, we demonstrate the applications of the hierarchical sensitivity matrices on CNN-based anomaly detection in the industrial control network communication, which shows the remarkable optimization capabilities of structure compression and robustness improvement.
Performance Analysis of Normality Test Loss for Intelligent RSCNN Denoiser Design With Application to Channel Decoding

Deep learning based signal processing technology has been applied in communication systems for enhancing information transmission performances, whose learning capability depends on the loss function using different normality tests. In this paper, we investigate the impacts of different normality test methods, including Jarque-Bera, Shapiro-Wilk, Kolmogorov-Smirnov and Anderson-Darling schemes, on the learning performance of the deep neural network used for the denoising and channel decoding. Explicitly, the Residual Shrink Convolutional Neural Network(RSCNN) is applied to increase the Signal-to-Noise Ratio (SNR), while the normality test is combined with the Mean Square Error(MSE) loss to keep the residual noise following Gaussian distribution. Besides, a weighting factor is added between two loss items to provide the overall normalization and prevent overpenalization of data with too much variance. Simulation results show that with the improved loss function, the transmission reliability performance can be enhanced, while the computational complexity of the deep learning network based on the non-parametric test is relatively higher.
Time is Gold: A Time-Dependent Incentive Mechanism Design for Fast Federated Learning

Federated Learning (FL) is a distributed learning framework that enables multiple clients to train a global model in a privacy-preserving manner. However, clients are often reluctant to engage in FL training process due to the energy consumption, thus FL calls for an appropriate economic mechanism to attract distributed clients in the practical FL deployment. In this paper, we integrate the incentive mechanism design and client selection problem, and consider accelerating the convergence of FL model by giving certain incentives to the appropriate clients to complete the FL task earlier. We design a time-dependent incentive mechanism to encourage more competent clients to participate in FL, i.e., clients with shorter local training time can receive higher rewards. With such an incentive mechanism, the edge server only needs to focus on the FL completion time to measure the contribution of the clients and does not need to pay attention to the specific resource configuration of the clients. We design a near-optimal greedy-based algorithm to solve the above problem and for both cases of Independent and Identically Distribution (IID) and non-IID of the local client data distributions. Extensive experiments show that our proposed mechanism is effective and can achieve a much faster convergence of the global model compared with the benchmark algorithms.
Privacy-Preserving Decentralized Cryptocurrency Exchange without Price Manipulation

Existing centralized cryptocurrency exchanges designed as trusted central entities are vulnerable to theft of funds, while decentralized exchanges that make all orders public are vulnerable to price manipulation, such as frontrunning attacks. To overcome the flaws, we propose a secure decentralized exchange that supports matching trade orders and atomic cross-chain settlements, and facilitates price discovery. The order matching protocol achieves privacy-preserving yet efficient design by using secure multi-party computation and a trusted execution environment. The atomic cross-chain settlement protocol based on adaptor signatures reduces the number of on-chain transactions while retaining the desirable properties like confidentiality and indistinguishability of these transactions. Security and performance analysis illustrates that our exchange can help traders swap across different blockchains in a privacy-preserving manner without compromising efficiency.
Role of Copyright Institutions and Enforcement Agencies in the Digital and Online Dissemination of Copyright Works

Digital and online dissemination of copyright works contributes in meaningful ways to the achievement of social inclusion and cultural diversity.This is because it provides a medium which affords a worldwide audience by projecting cultural heritage, promoting socio-economic development, supporting foreign direct investment and expansion of the Nigeria market.It is the aim of this paper to examine the role and challenges of the copyright institutions and enforcement agencies that prevent them from performing optimally.It is also the aim of the paper to examine: the impact of digital technology on copyrightable work; the adequacy of existing and emerging responses to the phenomenon and to what extent does the existing legal framework in Nigeria cater for the traditional as well as new technology paradigms, which give reason for an examination of existing legal regime under Nigeria's copyright law.The paper situates the discourse in its proper context by undertaking: an examination of the powers, functions and challenges of the various copyright institutions and enforcement agencies in fighting infringement; the Nigeria Copyright Act-substantive provisions and the Nigeria digital and online dissemination of copyright works.The paper found out that digital technology has a strong impact on the attainment of these objectives, both in terms of opportunities for profitable exploitation, and as a challenge which exacerbates the problem of infringement.Therefore, the paper proposes that these issues need to be addressed through law reforms to address issues relevant to the right of copyright owners online by putting in place a suitable legal framework.Also necessary is logistic, technical support and creation of awareness levels of the implication and available options for right holders.
A Privacy-Preserving Watchtower Scheme with Constant Storage Overhead

Payment channels are the most promising solution to the scalability issue of Bitcoin, allowing mutually untrusted parties to conduct transactions off-chain. However, parties are required to be online frequently to monitor the blockchain, or they risk losing funds. To alleviate the problem, watchtowers are introduced to help parties monitor the blockchain. Several watchtower schemes have been proposed, but they have problems in storage overhead, privacy, or fund security. In this paper, we propose a Schnorr threshold signature-based watchtower scheme that can guarantee the security of parties' funds even if a subset of watchtowers crashes or colludes. And, by changing the data kept in watchtowers, our scheme can preserve the privacy of off-chain transactions while just requiring the constant storage overhead of watchtowers. Hence, our scheme is the first watchtower scheme that solves the above problems while remaining compatible with existing Bitcoin and payment channel implementations. Finally, we provide a proof-of-concept to demonstrate the efficiency and feasibility of our scheme.
Specific Emitter Identification Based on Multi-Input Convolutional Neural Network

Specific emitter identification (SEI) is a technology that distinguishes radio emitters by using the external feature carried by signals. This paper presents a novel deep-learning-based SEI approach that uses the nonlinear features of the steady-state signals. First, we perform Hilbert-Huang transform and bispectrum calculation on the signal to obtain the Hilbert spectrum and bispectrum. These two methods can effectively provide the nonlinear features contained in the signal. Then, we reduce the dimension of Hilbert spectrum by calculating the marginal spectrum (MS), and propose a broken-line contour integration method to reduce the dimension of bispectrum. Thereafter, we construct a multi-input convolutional neural network (CNN) with two network branches to identify different features in a targeted manner to achieve a better effect of identifying specific emitters. According to the analysis, the proposed approach retains complete feature information and reduces the number of network parameters. The experiments validate that the broken line contour integration method is effective, and also demonstrate that multi-input CNN outperforms single-input CNN.
FDQM: Four-Dimensional Quantitative Measure for Statistical Heterogeneity in Federated Learning

Federated learning is a promising solution to ensure privacy preservation while solving data silos issue, it however suf-fers from severe statistical heterogeneity of data since it does not need to transfer the private end-user data to a centralized cloud server for training. Existing techniques to alleviate statistical het-erogeneity still lack analysis of the nature and degree of statistical heterogeneity in federated tasks. In this paper, we propose Four-Dimensional Quantitative Measure, which quantitatively analyzes several typical statistical heterogeneity problem types common in federated learning image tasks with a set of quantitative strategies. Simulations show that the quantitative strategy can clearly and quantitatively reflect the type and degree of statistical heterogeneity, and can contribute the design of optimal strategies for federated learning algorithms.
Specific Forms of Organizing Technology Classes

This article explores the significance of labor education in cultivating a constructive attitude towards work, acting as the cornerstone of schooling and extracurricular activities, thereby strengthening the vital connection between school and life. The study aims to investigate the primary educational objectives, methods, results, and implications associated with psychologically and practically preparing students for the workforce. The research findings underscore the importance of labor education in fostering a positive work ethic, enhancing students' preparedness for future employment, and facilitating their successful integration into the labor market. The implications of this study have far-reaching implications for educational institutions worldwide, offering valuable insights into optimizing curricula and extracurricular activities to effectively equip students with the necessary skills and attitudes for successful careers.&#x0D; Highlights:&#x0D; &#x0D; Labor education as a core component: This article emphasizes labor education as a fundamental aspect of schooling and extracurricular activities, highlighting its central role in shaping students' attitudes towards work.&#x0D; Psychological preparation for work: The study underscores the significance of psychologically preparing students for the workforce, recognizing it as a crucial educational task of schools.&#x0D; Implications for successful integration: The research findings have far-reaching implications, emphasizing the importance of labor education in facilitating students' successful integration into the labor market, equipping them with the necessary skills and attitudes for future careers.&#x0D; &#x0D; Keywords: labor education, attitude to work, schooling, extracurricular activities, psychological preparation
Augmented Efficient Zero-Knowledge Contingent Payments in Cryptocurrencies without Scripts

Zero-Knowledge Contingent Payment presents how Bitcoin contracts can provide a solution for the so-called fair exchange problem.Banasik, W. et al. first presented an efficient ZeroKnowledge Contingent Payment protocol for a large class of NP-relations, which is a protocol for selling witness. It obtains fairness in the following sense: if the seller aborts the protocol without broadcasting the final message then the buyer finally gets his payment back. However, we find that the seller in the protocol could refuse to broadcast the final signature of the transaction without any compensation for the buyer. As a result, the buyer cannot get the witness from the final signature of the transaction and has the payment for the witness locked until finishing the large computation for a secret signing key. In this paper, we fix this problem by augmenting the efficient Zero-Knowledge Contingent Payment protocol. We present a new protocol where the seller needs to provide the deposit before the zero-knowledge proof of knowledge of the witness being sold. And then the buyer could obtain the seller's witness if the seller broadcasts the final signature of the transaction and gets the payment and his deposit. Otherwise, the buyer could get back the payment and obtain the seller's deposit. This new augmented protocol is constructed without any new assumptions.
SafeLanding: An Intelligent Airbag System for Automated Fall Detection and Protection using Machine Learning and Internet-Of-Things (IoT)

In recent years, the world has been undergoing a drastic change in its age demographic due to an off balance caused by decreasing birth rates and an increase in the elderly population [1]. While 8.5% of the global population were elders in 2015, studies show that this number will hit 17% by 2050. This project will focus on the efficiency of automatic fall detection and contribute to the evolution of fall protection [2], both within elders and the general population. Through our conducted work, we have developed a wearable device capable of efficient fall detection and transmission.
Forward Chaining and Self-Embedding Watermarking for Tamper Detection in a Continuous Stream of Data

Watermarking is extensively used in various media for data transfer, content authentication and integrity. The continuous flow of data is always vulnerable to tamper. This research proposes a new watermarking scheme that detects tampering in a stream of data. The stream of data is dynamically divided into different sized groups using synchronization points. A computed watermark is embedded in each group by hashing the concatenating the current group and the next group. A secondary watermark is generated based on the current group that prevents tampering from any attacks in the current group. Watermark verification table is used to determine all possible scenarios for false results. Experiments are performed to show its efficiency. False results decrease as the group size becomes larger. Random burst attacked requires larger group size. The scheme also shows with the increase in grouping parameter ‘m’ which defines the synchronization point, the false positive rate decreases.
3D coordinates estimation and 3D object placement system from monocular image

In recent years, AR games for smartphones have become popular, and the overall market for AR games is expanding. As a result, the demand for the development of systems for AR games is expected to increase in the future. In this research, we aim to develop a system that can be used in AR games for smartphones. Using a monocular image depth estimation method based on cycle GAN, which exist as prior research, rather than images from special cameras such as stereo cameras, we identify the coordinates of actual structures in the game space and develop a 3D object placement system based on these coordinates using Unity. As a result, For the depth estimation method, the accuracy of the RMSE was better than that of previous studies. In addition, we were able to construct a 3D object placement system using depth images and segmentation on Unity Engine.
Modified ElGamal Algorithm Using Three Paring Functions

Cryptography defines different methods and technologies used in ensuring that communication between two parties over any communication medium is secure, especially in presence of a third part. This is achieved through the use of several methods, such as encryption, decryption, signing, generating of pseudo-random numbers, among many others. Cryptography uses a key or some sort of a password to either encrypt or decrypt a message that needs to be kept secret. This is made possible using two classes of key-based encryption and decryption algorithms, namely symmetric and asymmetric algorithms. The best known and the most widely used public key system is ElGamal. This algorithm comprises of three phases, which are the key generation phase, encryption phase, and the decryption phase. Owing to the advancement in computing technology, ElGamal is prone to some security risks, which makes it less secure. The following paper previews combination of three paring function used to enhance the ElGamal algorithm and increase its security. The results showed that the modified algorithm gives 93% accuracy.
The Effect of Language Content-Based Cooperative Learning Against Public Speaking Skills

This study aims to determine the significance of the effect of cooperative learning on the development of students' speaking skills in public. The population in this study was 100 people and a sample of 10 students as the control class and 10 students as the experimental class. The research instrument is a questionnaire in the form of questions with reference to indicators 1) speaking fluency; 2) accuracy of word choice; 3) sentence structure; 4) intonation of reading sentences and 5) speaking expressions. Data analysis concluded that: (1). The cooperative learning process was carried out well with a 50% presentation in the high category. (2). The tendency of students' speaking ability has a good development tendency with a presentation of 30% in the sufficient category. (3). There is a positive and significant effect of cooperative learning on the development of students' public speaking skills with a connectedness coefficient of 95.6%.
Technology Framework for Building Educational Augmented Reality Applications

Applying technology to education to improve efficiency is one of the inevitable trends. In higher education, augmented reality (AR) is strongly used due to its advantages. AR provides the ability to apply simulation models in teaching to increase the visualization as well as extend conditions for students to practice more and more. Derived from that fact, we have research motivation for applying AR technology in education, especially for higher education. In this work, we propose a procedure to build up an educational AR application as well as a related technology framework. We also build up an AR application following our proposed procedure and technology framework. This application allows users to interact with machine elements. Furthermore, we model machine elements in automobile major which are available on GitHub and free.
A Note on the Message Complexity of Cidon’s Distributed Depth-First Search Algorithm

The same distributed depth-first search algorithm was proposed independently by Lakshmanan, Meenakshi, and Thulasiraman, who gave $$4E-N$$ as upper bound on the worst-case message complexity of the algorithm, and by Cidon, who gave 3E as upper bound. We determine the exact worst-case message complexity and show that the upper bound of 3E by Cidon is too strict.