Reviewerly/exHarmony huggingface dataset:
Blockchain Technology Integrated Electronic Vote Casting System

Existing voting systems are unable to assure security of an individual’s vote, and keep elections free from biased results due to temperament by malicious people. Traditional electoral process is vulnerable in regards to security aspects and is usually very time consuming. These cause threats to the important characteristics of election such as- transparency, privacy of voters & democracy. Our team proposes to remove these inconveniences by implementing blockchain technology into the voting scheme. This technology will make the voting process decentralized, keeps each vote secured and counted whilst providing privacy to voters, and will also save time and resources. Using Node JS, cryptography algorithms our proposed system was built. Additionally, challenges of the system are addressed and still being improved using potential solutions.
An Efficient Approach to Validate COVID-19 Related Vaccine Myths Utilizing LDA Algorithm

Scientists from the whole world have been working their heart and soul to invent the COVID-19 vaccine. When they are succeed to make the vaccine, various rumors are spread. COVID-19 situation has made our world standstill. When the vaccine came out for the first time, people were enthusiastic to take a shot. But the myth, rumors about vaccination also followed the success. In this paper, we have tried to validate the COVID-19 related vaccine myth and rumors with the help of the LDA algorithm. We have used data mining, text mining and sentiment analysis for the experiment. The outcome of our experiment has shown that most people are positive about vaccination but the negative impact is also there. Our experiment has found that most of the people are talking about "vaccine", "people"," moron" and "ever". We have proposed a technique to validate this kind of vaccine myth. LDA algorithms have been able to predict and validate the myth up to 70% compared to other frameworks out there. Promising efficiency is exhibited by our experimental result.
Explainable NLQ-based Visual Interactive System: Challenges and Objectives

Nowadays, visual interactive systems (Vis) are attracting more attention in research and industries because of their effectiveness in conveying information. Additionally, to make rational decisions based on extracted data, Vis is critical for identifying and comprehending trends, outliers, and patterns in data. Existing research has employed a broad range of methodologies to yield visualization insights into certain decision-making systems, allowing participants to perceive a specific problem from a wide range of viewpoints. However, there are still enough scopes to design a new Vis where some systematic techniques are required to visualize the data with proper explanations. In this regard, we analyze several existing works and observe a surge of research interest in the new realm of explainable and NLQ-based Vis. In this paper, our main goal is to present a novel idea for designing an explainable NLQ-based Vis named-ExNLQVis. Therefore, (i) we aim to discuss a proposed NLQ-based Vis that will follow a deep learning-based NLP approach to extract necessary information from user inputs, make visual-respective decisions, and generate appropriate visualizations based on the preceding decisions. (ii) we extend our prior model to an explainable visualization model that not only accurately visualizes data but also explains why it appears depending on the natural language query (NLQ). To accomplish this system, we consider several challenges and objectives and briefly discuss our proposed method accordingly. We also provide the implementation and evaluation guidelines to establish our system.
Efficient and Privacy-Preserving Federated Learning with Irregular Users

Federated learning (FL) enables multiple users to learn a global predictive model by exchanging local updates without disclosing their private datasets. To further protect local updates, several privacy-preserving schemes are proposed and applied in FL. However, a fundamental issue is that irregular users in FL holding low quality updates could decrease the convergence rate, and even worse, damage the model&#x2019;s usability. While a few works recently explore unified solutions to mitigate the issues of privacy and irregular users meanwhile, the existing methods are still insufficient in terms of accuracy and efficiency. The reasons are two major limitations: inefficiency caused by complex cryptographic algorithms and poor model usability due to ineffective removing strategies for irregular users. To approach the above problems, we propose SAP-IU, a new and efficient federated learning scheme, which achieves irregular users removing and privacy protection at the same time. Specifically, we first design a novel removing algorithm for irregular users called Trust<inf>IU</inf> that calculates the weight of each user via the cosine metric. This ensures that the global model is mainly derived from the contributions of high-quality data. We further devise a secure weighted aggregation protocol for Trust<inf>IU</inf> to protect users&#x2019; sensitive information including local updates and data quality. Besides, our scheme is robust to users dropping out during the whole training process. Moreover, extensive experiments show that SAP-IU has a better performance than prior works in terms of training accuracy and efficiency.
Reinforcement Learning-Based Global Programming for Energy Efficiency in Multi-Cell Interference Networks

With the increasing application of internet of things (IoT), the number of wirelessly transmitting devices is on a rise. It is important that the energy efficiency (EE) is maximized to reduce interference and save energy. This work explores the possibility of power control for maximum EE in wireless interference networks using reinforcement learning (RL) techniques. We apply the soft actor-critic (SAC) algorithm based on entropy regularization that allows to escape local optima and foster exploration. This enables us to solve the energy efficient power control problem with reduced complexity. We demonstrate that the obtained solutions are close to the global optimum. In contrast to supervised machine learning (ML) techniques, we do not need any kind of labeled data in the training phase. The model free approach and the unsupervised nature of RL therefore reduce the required computational effort and has a better scalability as a consequence.
Joint User Association and Resource Allocation for Wireless Hierarchical Federated Learning with Non-IID Data

Wireless hierarchical federated learning (HFL) has been proposed for large-scale model training over multi-cell network while preserving the data privacy. However, the imbalanced data distribution and load have a significant impact on the convergence rate, the learning accuracy, and the learning latency in wireless HFL with non-independent identically distributed training data. To cope with these challenges, we first derive the learning latency and the upper bound of the model error. Then, an optimization problem is formulated to minimize the weighted sum of total data distribution distance and learning latency. Joint user association and wireless resource allocation algorithms are investigated to achieve the optimal learning performance. Finally, the effectiveness of the proposed algorithms are demonstrated by the simulations.
Cross-Domain Communications Between Agents Via Adversarial-Based Domain Adaptation in Reinforcement Learning

Reinforcement learning is suitable for solving sequential decision-making problems, and deep reinforcement learning methods have shown excellent performance in many fields. However, agents often face the challenge of a large number of interactions with the environment, which means that it is unrealistic to train agents from scratch in each new domain. In order to overcome this problem, our paper introduces the cross-domain communications between RL agents, so that agents in new domains can receive and use the information sent by agents trained in related domains to assist decision-making. Specifically, this paper uses adversarial-based domain adaptation methods and multi-granular loss constraints to realize implicit communications between cross-domain agents and encourage agents in different domains to extract domain-invariant information for communication and sharing, thereby the optimal behavior policy of the agent training based on the shared information in the source domain can be transferred to the related domain and achieve the expected performance. Finally, we evaluate our method on various variants of Car-Racing games, and the results show that this method can achieve efficient information communications between cross-domain agents and better performance than previous methods.
Towards Fast and Energy-Efficient Hierarchical Federated Edge Learning: A Joint Design for Helper Scheduling and Resource Allocation

Hierarchical federated edge learning (H-FEEL) has been recently proposed to enhance the federated learning model. Such a system generally consists of three entities, i.e., the server, helpers, and clients. Each helper collects the trained gradients from users nearby, aggregates them, and sends the result to the server for model update. Due to limited communication resources, only a portion of helpers can upload their aggregated gradients to the server, thereby necessitating a well design for helper scheduling and communication resources allocation. In this paper, we develop a training algorithm for H-FEEL which involves local gradient computing, weighted gradient uploading, and model updating phases. By characterizing these phases mathematically and analyzing the one-round convergence bound of the training algorithm, we formulate a problem to achieve the scheduling and resource allocation scheme. To solve the problem, we first transform it into an equivalent problem and then decompose the transformed problem into two subproblems: bit and sub-channel allocation problem and helper scheduling problem. For the first subproblem, we obtain a low-complexity suboptimal solution by using a four-stage method. For the second subproblem, we obtain a stationary point by using the penalty convex-concave procedure. The efficacy of our scheme is demonstrated via simulations, and the analytical framework is shown to provide valuable insights for the design of practical H-FEEL system.
Deep Learning Based Source Number Estimation with Single-Channel Mixtures

In cognitive radio networks, providing accurate recognition of the primary user’s signal is great important for designing the spectrum access strategies. Source number estimation has served as a key fundamental technique to facilitate the signal recognition in the mixed received signals scenario. In this paper, we propose a deep learning based source number estimation method under the single-channel conditions. The architecture of the network is first designed. Then, the received complex signals are reconstructed as in-phase and quadrature (IQ) data in order to adapt to the convolutional neural network for extracting the deep features. Moreover, the cost function that is used to train the proposed network is properly designed by exploring the maximum likelihood function. Supervised training is performed to generate a classifier that can identify the number of sources in the mixtures. The proposed deep leaning-based source number estimation method is tested by experiments on simulation signals and actual signals. The results revealed the effectiveness of the proposed method in solving the problem of single-channel source number estimation compared to the conventional methods.
Entanglement Routing For Quantum Networks: A Deep Reinforcement Learning Approach

Quantum communications are gaining momentum in finding applications in a wide range of domains, especially those require high-security data transmissions. On the other hand, machine learning has achieved numerous breakthrough successes in various application domains including networking. However, currently, machine learning is not as much utilized in quantum networking as in other areas. With such motivation, we propose a machine-learning-powered entanglement routing scheme for quantum networks that aims to accommodate maximum numbers of demands (source-destination pairs) within a time window. More specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to schedule the network’s demands which are then routed by a qubit-preserved shortest path algorithm. DQRA is trained towards the goal of maximizing the number of resolved requests in each routing window by using an explicitly designed reward function. Our experiment study shows that, on averse, DQRA is able to maintain a rate of successfully routed requests above 80% in a qubit-limited grid network, and about 60% in extreme conditions i.e. each node can act as a repeater exactly once within a window. Furthermore, we show that the complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks.
DWBFT: A Weighted Byzantine Fault Tolerant Protocol with Decentralized Trust

The surprising wave of blockchain has led to rapid progress of Byzantine fault tolerant protocol. However, most researches concentrate on the centralized trust such as PBFT, in which all participants trust each other equally. This paper presents DWBFT, a weighted Byzantine fault tolerant protocol with decentralized trust under the weak synchrony assumption. In DWBFT, the nodes can assign weights to each other depending on their decentralized trust relationship, which are adopted to make decisions. DWBFT can achieve safety under the condition that the weight of Byzantine nodes is less than 1/5 of the total weight of all nodes under any weight assignment. The experimental results show that DWBFT can achieve a throughput of nearly 13,000 tps under 32 nodes, and has no significant performance degradation compared with PBFT.
Tradeoff between Privacy and Utility for Location-based Recommendation Services

Location-based recommendation services (LBRS) are widely used by people to find new places of interest. However, the prevalence of LBRS poses a severe threat to users’ privacy, because LBRS queries contain sensitive information such as users’ preferences and location. Many Location Privacy Protection Mechanisms (LPPMs) have been proposed to mitigate the threat by obfuscating actual location in the query with a noise-based privacy-preserving technique. However, disguised location information can potentially harm the user experience with LBRS. In this work, we evaluate the impact of the noise-based privacy-preserving technique on user-perceived utility measured as nDCG (normalized Discounted Cumulative Gain) ranks. We empirically evaluate user-perceived utility under different noise levels to explore the trade-off between privacy and utility. A variety of factors, including service density and mechanism employed by service providers, are found to impact the utility loss, including but not limited to different service providers, different service types, and different sorts of areas (e.g., urban v.s. rural).
Latency Synchronization for Social VR with Mobile Edge Computing

While mobile edge computing (MEC) potentially supports the stringent latency requirements for Virtual Reality (VR), previous research only considers minimizing the latency of transmitting required data and ignores the latency consistency of a group of friends in social VR. In this paper, we leverage the human motion prediction to eliminate the inconsistency among users in a group while ensuring the low latency interaction between friends with the aid of MEC. Moreover, to capture the accurate users’ interaction behaviors, it is critical to jointly predict the motions of users with spatially close proximity in VR. Accordingly, we formulate a new problem, named Group Motion Prediction for Social VR problem (GMSV), with group consistency requirement and the objective of prediction costs minimization on the MEC server. Then, we prove that GMSV is NP-Hard and design a new algorithm, named Social-Aware Latency Synchronization for Remote Users (SLSR), to select an appropriate set of remote users in social VR for motion prediction, with the ideas of generating a partial order set of remote subgroups and extracting the remote subgroups with high social interaction utilities. Simulation results show that SLSR can effectively increase latency consistency by more than 50% compared with the baseline schemes.
Deep Neural Networks for Dynamic Attribute based Encryption in IoT-Fog Environment

In a healthcare IoT based Fog System, malicious attacks may alter patient’s critical health data, which may lead to severe repercussions. This makes it incumbent to adopt authentication mechanisms for preventing unauthorised access and implement data encryption to enhance the security in the system. This work presents an efficient learning integrated dynamic attribute based encryption mechanism by reducing encryption, decryption and communication costs associated in a Fog system with IoT devices and dynamic attribute updates. The Ciphertext Policy Attribute Based Encryption approach (CP-ABE) has been integrated with a Deep Neural Network model to use learning patterns related to attributes. This in turn reduces the communication cost incurred by the resource limited end devices for dynamic attribute updates. Further, an access control mechanism has been implemented by optimizing the system due to dynamic attributes and by analysing the updates in the access policy defined in CP-ABE. The Deep Neural Network has been trained using existing data sets and experimental results are presented by performing analysis on neural network parameters.
Detecting USB Storage Device Behaviors by Exploiting Electromagnetic Emanations

Universal Serial Bus (USB) generates compromising emanations (CE) during data transmission, which is a weakness in the information security of information equipment. The radiated electromagnetic signal from the USB keyboard can be collected and processed to restore the key information. &#x2018;USBee&#x2019;, a malware, steals information from information equipment through the electromagnetic leakage signal generated by the data writing behavior of USB. This paper investigates the compromising emanations from USB to detect the behaviors of USB storage device including data reading, data writing and silence. We extract multiscale sample entropy (MSE) features and statistical features to quantify the characteristics of the radiated signal. The extracted features are fed into the k-Nearest-Neighbors (k-NN) classifier to achieve behaviors recognition. Finally, five USB flash disks and three computers are used to provide experimental examples for testing. The experimental results show that the proposed method can recognize the behaviors of USB storage devices effectively by analyzing the compromising emanations. Meanwhile, it is illustrated that the CE from USB contains the information of device individuals.
Analysis of Blockchain Selfish Mining: a Stochastic Game Approach

Selfish mining is an attack on blockchain networks, where a minority mining pool deviates from the original mining protocol and keeps some blocks private. The goal of the attacking pool is to waste the computational power of the other miners and increase their revenue. In this paper, we use a new approach to analyze the profitability of such attacks. Using game theory, we model the interactions between pools to derive the utility of mining strategies. We simulate the game for a Bitcoin blockchain and analyze the profitability of an attack, in terms of the monetary award instead of the relative revenue. We express the utility to include the cost of a strategy and revisit existing selfish mining strategies to discuss possible outcomes of the game. Depending on the game parameterization, we highlight scenarios where the system could be compromised. To the best of our knowledge, this is the first work that models the selfish mining attack as a stochastic game.
UAVs Assisted Secure Blockchain Offline Transactions for V2V Charging Among Electric Vehicles in Disaster Area

The security of distributed communications in UAV rescue networks is promising to be provisioned by blockchain technology. However, due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blocks, which can result in severe security issues (such as Forged deposit address and Double spend attack). These issues has been neglected in literature. This paper proposes a UAVs assisted and incentive based blockchain offline transaction scheme to address the above issues when UAVs and ground users are offline. Particularly, we consider vehicle-to-vehicle (V2V) charging transactions in disaster areas. First, we built an offline channel between charging and discharging electric vehicles (EVs), and then, we design an accountable assertions based UAVs aided penalty algorithm to prevent various attacks. Then, considering selfishness of users, we formulate an incentive model based on Stackelberg game to encourage EVs to participate to the offline V2V charging transactions. Our simulation results demonstrate that our proposed scheme obtain the optimal utilities for EVs, which outperforms the conventional schemes.
A Heterogeneous Feature Ensemble Learning based Deepfake Detection Method

The Deepfake technique can swap the face of a person with the face of another person in an image or a video which may cause a public security problem. Recently, researchers have focused on detecting deepfake images by deep learning. However some recent works have observed that detectors trained on images produced by one deepfake model perform poorly when tested on others. In this paper we propose to detect deepfake images through heterogeneous feature ensemble learning. We first extract gray gradient features, spectrum features and texture features from real and fake face images, then integrate them into an ensemble feature vector through a flatten process, and finally adopt a back-propagation neural network to train a deepfake detector with the feature vector. Experimental results show that our approach achieves better detection accuracy compared with several state-of-the-art deepfake detectors.
Real-Time Symbol Detection For Massive MIMO Systems With Multi-Mode Reservoir Computing

In this paper, we develop a learning-based symbol detection algorithm for massive MIMO systems. To exploit the structural information inherited in the received signals from massive antenna array, multi-mode reservoir computing is adopted as the building block to facilitate over-the-air training. In addition, alternating recursive least square optimization method, and decision feedback mechanism are utilized in our algorithm to achieve the real-time learning capability. That is, the neural network is trained purely online with its weights updated on an OFDM symbol basis to promptly and adaptively track the dynamic environment. Evaluation results demonstrate that our algorithm achieves substantial gain over traditional model-based approach and state-of-the-art learning-based techniques in dynamic channel environment. Moreover, empirical result reveals our NN model is robust to training label error, which benefits the decision feedback mechanism.