Reviewerly/exHarmony huggingface dataset:
Image Steganography Approach Based Ant Colony Optimization with Triangular Chaotic Map

To establish the secure communication using image steganography by hiding the information in cover image, the study focus on image steganography based on Ant Colony Optimization (ACO) where the cover image is divided into number of 5&#x00D7;5 blocks. As pixel values after ACO are considered as the optimal pixels, the ants search the next qualified pixels on reference to pheromone trails to encode the text messages. After ACO, Triangular Chaotic Map (TCM) is imposed based on initial value of chaotic sequences. The proposed method outperformed other state-of-art methods like classical Least Significant Bit(LSB), LSB with ACO, LSB with logistic map for all the images under consideration: lenna.png (0.00046, 83.4021, 0.0007, 0.9990964, 0.9983), baboon.png (0.00027, 82.5526, 0.0009, 0.9990542, 0.9985), pepper.png (0.00067, 71.2322, 0.0004, 0.9990560, 0.9947), dog.png (0.00045, 70.4590, 0.0005, 0.9990024, 0.9904) and flower.png (0.00067, 71.2322, 0.0004, 0.9990560, 0.9947) under the evaluation metrics as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), F-Score and Structural Similarity Index Matrix (SSIM) respectively.
The Impact of Twitter Sentiment Analysis on Bitcoin Price during COVID-19 with XGBoost

Bitcoin have become safe-haven for people who want to invest during COVID-19 with its volatile price. Numerous factors can affect the price but recently the most popular one was due to an Elon Musk tweet. We decided to investigate our questions. Do tweets regarding Bitcoin affect its price? Can we predict Bitcoin price by analysing sentiments from twitter? For our research, we decided to analyze the impact of twitter sentiments on Bitcoin price during the COVID-19 pandemic. Using VADER sentiment analysis, we attempted to find out what is the current public sentiment regarding Bitcoin. Coupling tweet sentiment with the Bitcoin price, we pursue making a predictive model to forecast whether Bitcoin price will rise or fall. We also compare whether having twitter sentiment analysis in our model will have an advantage compared to not using. In the end, we found out that twitter sentiment analysis have an impact to Bitcoin price. We hope that our research can help people during this financial stress period.
A Practical Heartbeat-based Defense Scheme Against Cloning Attacks in PoA Blockchain

Consensus mechanism, as the soul-building-block of decentralized settings, enables a series of blockchain applications, such as Bitcoin and Ethereum. For better security and soundness, more and more blockchain systems tend to adopt proof-of-authority (PoA) to avoid or alleviate the various security risks caused by their peer-to-peer (P2P) networks. However, the PoA protocol is also vulnerable to cloning attack (CA), which provides a potential pathway for double-spending attack and has not been well solved. In this paper, we propose a heartbeat-based defense scheme to achieve an effective CA detection in PoA blockchians. To this end, we first analyze two PoA algorithms, Aura and Clique, along with their CA. On this basis, we propose a heartbeat-based defense scheme that can detect CA effectively regardless of the underlying consensus algorithm of the blockchain. Then, to avoid the potential efficiency reduction caused by the increase of sealers, we further design an enhanced hierarchical node selection algorithm, in which sealers can challenge partial nodes selected randomly according our strategy to detect CA in an efficient manner. Finally, we theoretically estimate the precision rate model of our scheme and then establish a prototype PoA blockchain system to test the performance of our scheme. As expected, the experiment results are consistent with our theoretical analysis, which powerfully demonstrates the efficiency and practicability of the proposed scheme.
A Lightweight Authentication via Unclonable Functions for Industrial Internet-of-Things

In industrial internet-of-things (IIoT), sensors, communication networks, and objects interact to exchange massive data volumes at low latency. As IIoT-driven sensor networks support a range of diverse verticals like automobiles, smart farming, logistics, large organization and autonomous networks communicate over open channels. Thus, security and privacy of data is of prime concern. However, IIoT channels are faced with challenge of lightweight communication, due to limited resources, power and memory. Thus, the conflicting requirements of measurable security and lightweight resource management is an open gap. The race is towards the build of lightweight security process, at low latency, power, and communication costs. To address the gap, this paper addresses the issue of security and resource management scheme that proposes a lightweight authentication based on simple one-way hash and exclusive-OR (XOR) operations that reduces the operational costs. The scheme is compared against recent lightweight authentication protocols in terms of validation, energy dissipation, and computation costs. The obtained results indicate the efficacy of the framework against conventional approaches.
System Design for Online Foreign Language Education Based on Blockchain Technology

This study aims to solve the problem that the traditional online foreign language teaching system focuses on function development, ignoring system security, and has certain risks. An online foreign language education system is designed and developed based on the blockchain technology. First, the blockchain technology and key technologies of system design are described in detail. Second, the overall technical architecture of the system, functional modules, and business logic of each module are designed. Finally, the basic performance of the system is tested. The results show that the system can realize the user's unrestricted office work and zero maintenance of the system. The separation of presentation logic and business logic facilitates the development and maintenance of the system. The system mainly includes six functional modules: user management, course management, course order, course study, course certificate, and credit authentication. These modules are guaranteed for daily teaching use. The event processing success rate of the six functional modules of the system is greater than 99%, and the processing success rate is relatively high. The central processing unit (CPU) usage and memory usage are both below 30%. The host throughput of the six major modules is greater than 100 times/s when processing services. The average response time on the terminal side is maintained below 0.5 s. The average response time of business-side processing is maintained below 0.4 s, which is in line with the standard. The event processing success rate of the constructed system is 10.75% higher than that of other systems, and the average response time, CPU usage, and memory usage are 53.38%, 51.49%, and 50% lower than other systems, respectively. Therefore, the proposed system has better performance. To sum up, the designed system has excellent throughput, event processing capability, response speed, and low CPU and memory occupancy when processing business and is suitable for promotion and use in foreign language online education in colleges and universities. The use of the proposed system can improve its overall teaching efficiency and quality. The purpose is to provide important technical support for the improvement of the security of the online foreign language teaching system.
A detection method of Edge Coherent Mode based on improved SSD

It has become a trend to deeply analyze and mine diagnostic data using machine learning methods. Single-shot MultiBox detector (SSD) is an excellent target detection network in deep learning. In this paper, two improvements for SSD are proposed to accurately detect the edge coherent mode in the cross-power spectrum. One is adjusting the positive–negative sample ratios to balance the training dataset, and the other is importing the flooding loss to mitigate overfitting. After testing, the accuracy of the improved SSD reaches 92.12%. The result shows that the performance of the improved SSD for ECM detecting is better than that of the typical SSD, which can be applied to subsequent intelligent data analysis.
Cyborgs, Human Augmentation, Cybernetics, and JIZAI Body

We propose a concept called “JIZAI Body” that allows each person to live the way they wish to live in society. One who acquires a JIZAI Body can (simultaneously) control (or delegate control) of their natural body and extensions of it, both in physical and cyberspace. We begin by describing the JIZAI Body and the associated JIZAI state in more detail. We then provide a review of the literature, focusing on human augmentation and cybernetics, robotics and virtual reality, neuro and cognitive sciences, and the humanities; fields which are necessary for the conception, design, and understanding of the JIZAI Body. We then illustrate the five key aspects of a JIZAI Body through existing works. Finally, we present a series of example scenarios to suggest what a JIZAI society may look like. Overall, we present the JIZAI Body as a preferred state to aspire towards when developing and designing augmented humans.
Investigating the suitability of GIS and remotely-sensed datasets for photovoltaic modelling on building rooftops

Rising energy demands and net-zero targets have led to development and implementation of renewable technologies. Rooftop photovoltaic (PV) solar panels offer a viable solution while minimising complex construction or excessive infrastructure development. However, critical physical factors influence building suitability to maximise cost-benefit requirements. This research adopted a Geographic Information System (GIS) approach to identify building suitability by analysing and comparing Digital Surface Models (DSM) derived from Light Detection and Ranging (LiDAR) and orthophotography data using UK standardised PV formulas. Geospatial workflows processed rooftop features and modelled outputs for solar irradiation, panel type, kWh, CO2, payback and costs while 3D models and solar web applications were used to validate results. Both models suggested a range of between 14.2 and 15.2 GWh potential for an installed capacity of between 21.1 and 22.3 MW. Residential models met between 62.1% (LiDAR) and 66.6% (orthophotography) of average consumer demand with PV potential exceeding 94% of residential dwellings. LiDAR and orthophotography models had strong agreement with existing PV installations. The methodology can be scaled to a regional level and expanded for larger PV capacity. Moreover, the process can assist policymakers with informed decisions on renewable technologies alongside developments such as Peer to Peer (P2P) solar trading.
Bibliometric analysis of nature inspired optimization techniques

Nature-inspired optimization has gained immense popularity over the past six decades and has been extensively used across various disciplines. This paper aims to statistically evaluate the impact and importance of nature-inspired optimization by presenting an analysis of works published between 2016 and 2020. The data is obtained from Scopus and focuses on metrics like the total number of publications, citations, average citations per publication, and the h-index. Graphical and statistical analysis was carried out using Excel, Python, RAWGraphs, and Tableau Public. All the data in the present work was accessed on 11th August 2021. A total of 91,507 publications were analysed. China, India, and the US are the highest contributors with 27045, 12129, and 8947 publications respectively. The Ministry of Education China has contributed the most to this field, followed by the Chinese Academy of Sciences. The National Natural Science Foundation of China has funded the highest number of works (14.72% publications). Zhang M. is the most productive author with 224 publications. Lecture Notes in Computer Science, Advances in Intelligent Systems and Computing, and IEEE Access are the most productive journals. The top disciplines contributing to research include Computer Science (55.22%), Engineering (48.06%), and Mathematics (27.30%), and the top application areas include optimization, artificial intelligence, and decision sciences. The most popular algorithms include Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization. This data could prove beneficial to scholars looking for an overview of nature-inspired algorithms to determine future research directions.
Online streaming feature selection based on Sugeno fuzzy integral

Feature selection is a step in which an optimal subset of features is selected for the learning process. This step is applied to high-dimensional data, so that irrelevant and redundant features are removed from the data. Traditional feature selection methods require the entire feature space. At the same time, in many real-world applications such as online social networks, it is impossible to acquire or wait to get the whole feature space. Therefore, online feature selection methods are considered to handle this challenge. In this paper, we present an online feature selection method based on the concept of Sugeno fuzzy integral. According to this method, the streaming features are examined based on several measures, and these measures are combined based on the Sugeno operator. If the aggregated value reaches a pre-defined threshold, the desired feature is selected; otherwise, it is not considered. To prove the performance of the proposed method, comparisons have been made with five online feature selection methods based on two classifiers. We show that the proposed method outperforms competitive methods based on the experimental results.
Durable Pneumatic Artificial Muscles with Electric Conductivity for Reliable Physical Reservoir Computing

A McKibben-type pneumatic artificial muscle (PAM) is a soft actuator that is widely used in soft robotics, and it generally exhibits complex material dynamics with nonlinearity and hysteresis. In this letter, we propose an extremely durable PAM containing carbon black aggregates and show that its dynamics can be used as a computational resource based on the framework of physical reservoir computing (PRC). By monitoring the information processing capacity of our PAM, we verified that its computational performance will not degrade even if it is randomly actuated more than one million times, which indicates extreme durability. Furthermore, we demonstrate that the sensing function can be outsourced to the soft material dynamics itself without external sensors based on the framework of PRC. Our study paves the way toward reliable information processing powered by soft material dynamics.
A Category-aware Multi-interest Model for Personalized Product Search

Product search has been an important way for people to find products on online shopping platforms. Existing approaches in personalized product search mainly embed user preferences into one single vector. However, this simple strategy easily results in sub-optimal representations, failing to model and disentangle user’s multiple preferences. To overcome this problem, we proposed a category-aware multi-interest model to encode users as multiple preference embeddings to represent user-specific interests. Specifically, we also capture the category indications for each preference to indicate the distribution of categories it focuses on, which is derived from rich relations between users, products, and attributes. Based on these category indications, we develop a category attention mechanism to aggregate these various preference embeddings considering current queries and items as the user’s comprehensive representation. By this means, we can use this representation to calculate matching scores of retrieved items to determine whether they meet the user’s search intent. Besides, we introduce a homogenization regularization term to avoid the redundancy between user interests. Experimental results show that the proposed method significantly outperforms existing approaches.
Link: Black-Box Detection of Cross-Site Scripting Vulnerabilities Using Reinforcement Learning

Black-box web scanners have been a prevalent means of performing penetration testing to find reflected cross-site scripting (XSS) vulnerabilities. Unfortunately, off-the-shelf black-box web scanners suffer from unscalable testing as well as false negatives that stem from a testing strategy that employs fixed attack payloads, thus disregarding the exploitation of contexts to trigger vulnerabilities. To this end, we propose a novel method of adapting attack payloads to a target reflected XSS vulnerability using reinforcement learning (RL). We present Link, a general RL framework whose states, actions, and a reward function are designed to find reflected XSS vulnerabilities in a black-box and fully automatic manner. Link finds 45, 213, and 60 vulnerabilities with no false positives in Firing-Range, OWASP, and WAVSEP benchmarks, respectively, outperforming state-of-the-art web scanners in terms of finding vulnerabilities and ending testing campaigns earlier. Link also finds 43 vulnerabilities in 12 real-world applications, demonstrating the promising efficacy of using RL in finding reflected XSS vulnerabilities.
Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction

The self-supervised graph representation learning has achieved much success in recent web based research and applications, such as recommendation system, social networks, and anomaly detection. However, existing works suffer from two problems. Firstly, in social networks, the influential neighbors are important, but the overwhelming routine in graph representation-learning utilizes the node-wise similarity metric defined on embedding vectors that cannot exactly capture the subtle local structure and the network proximity. Secondly, existing works implicitly assume a universal distribution across datasets, which presumably leads to sub-optimal models considering the potential distribution shift. To address these problems, in this paper, we learn structural embeddings in which the proximity is characterized by 1-Wasserstein distance. We propose a distributionally robust self-supervised graph neural network framework to learn the representations. More specifically, in our method, the embeddings are computed based on subgraphs centering at the node of interest and represent both the node of interest and its neighbors, which better preserves the local structure of nodes. To make our model end-to-end trainable, we adopt a deep implicit layer to compute the Wasserstein distance, which can be formulated as a differentiable convex optimization problem. Meanwhile, our distributionally robust formulation explicitly constrains the maximal diversity for matched queries and keys. As such, our model is insensitive to the data distributions and has better generalization abilities. Extensive experiments demonstrate that the graph encoder learned by our approach can be utilized for various downstream analyses, including node classification, graph classification, and top-k similarity search. The results show our algorithm outperforms state-of-the-art baselines, and the ablation study validates the effectiveness of our design.
CTT: CNN Meets Transformer for Tracking

Siamese networks are one of the most popular directions in the visual object tracking based on deep learning. In Siamese networks, the feature pyramid network (FPN) and the cross-correlation complete feature fusion and the matching of features extracted from the template and search branch, respectively. However, object tracking should focus on the global and contextual dependencies. Hence, we introduce a delicate residual transformer structure which contains a self-attention mechanism called encoder-decoder into our tracker as the part of neck. Under the encoder-decoder structure, the encoder promotes the interaction between the low-level features extracted from the target and search branch by the CNN to obtain global attention information, while the decoder replaces cross-correlation to send global attention information into the head module. We add a spatial and channel attention component in the target branch, which can further improve the accuracy and robustness of our proposed model for a low price. Finally, we detailly evaluate our tracker CTT on GOT-10k, VOT2019, OTB-100, LaSOT, NfS, UAV123 and TrackingNet benchmarks, and our proposed method obtains competitive results with the state-of-the-art algorithms.
An I/O-efficient disk-based graph system for scalable second-order random walk of large graphs

Random walk is widely used in many graph analysis tasks, especially the first-order random walk. However, as a simplification of real-world problems, the first-order random walk is poor at modeling higher-order structures in the data. Recently, second-order random walk-based applications (e.g., Node2vec, Second-order PageRank) have become attractive. Due to the complexity of the second-order random walk models and memory limitations, it is not scalable to run second-order random walk-based applications on a single machine. Existing disk-based graph systems are only friendly to the first-order random walk models and suffer from expensive disk I/Os when executing the second-order random walks. This paper introduces an I/O-efficient disk-based graph system for the scalable second-order random walk of large graphs, called GraSorw. First, to eliminate massive light vertex I/Os, we develop a bi-block execution engine that converts random I/Os into sequential I/Os by applying a new triangular bi-block scheduling strategy, the bucket-based walk management, and the skewed walk storage. Second, to improve the I/O utilization, we design a learning-based block loading model to leverage the advantages of the full-load and on-demand load methods. Finally, we conducted extensive experiments on six large real datasets as well as several synthetic datasets.. The empirical results demonstrate that the end-to-end time cost of popular tasks in GraSorw is reduced by more than one order of magnitude compared to the existing disk-based graph systems.
PRADA: Practical Black-box Adversarial Attacks against Neural Ranking Models

Neural ranking models (NRMs) have shown remarkable success in recent years, especially with pre-trained language models. However, deep neural models are notorious for their vulnerability to adversarial examples. Adversarial attacks may become a new type of web spamming technique given our increased reliance on neural information retrieval models. Therefore, it is important to study potential adversarial attacks to identify vulnerabilities of NRMs before they are deployed. In this article, we introduce the Word Substitution Ranking Attack (WSRA) task against NRMs, which aims at promoting a target document in rankings by adding adversarial perturbations to its text. We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list. This attack setting is realistic in real-world search engines. We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to generate gradients for finding the adversarial perturbations. Experiments on two web search benchmark datasets show that PRADA can outperform existing attack strategies and successfully fool the NRM with small indiscernible perturbations of text.