Reviewerly/exHarmony huggingface dataset:
Binary arithmetic optimization algorithm for feature selection

Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm.
A secured blockchain method for multivariate industrial IoT-oriented infrastructure based on deep residual squeeze and excitation network with single candidate optimizer

IoT infrastructures have been growing in recent years across a variety of industrial applications in sustainable smart cities and communities, including smart manufacturing and smart industries. Infrastructure geared towards Internet of Things also includes Cyber-Physical System (CPS). With a distributed environment, CPS has experienced significant success in industrial applications and critical infrastructure. However, there are numerous difficulties in such an environment, including those related to scalability, centralization, communication latency, and security and privacy. Deep residual squeeze and excitation network (DRSEN) with Single Candidate Optimizer-based IoT-oriented infrastructure is used in this manuscript to address these challenges. Blockchain creates a distributed environment for CPS's communication phase, and Software-Defined Networking (SDN) establishes protocols for data forwarding in network. At the application layer of suggested infrastructure, a deep learning-based cloud is used to address communication latency, centralization, and scalability issues. It makes high-performance, affordable computing resources available for smart city applications like smart industrial and smart transportation. The proposed method is implemented in ns-3 tool and the effectiveness is assessed using F-measure, reliability, scalability, accuracy, sensitivity, specificity, and precision. The proposed method provides 35.39%, 23.87% and 20.67% higher reliability, 42.39%, 11.39%, 34.16% and 25.78% higher accuracy, 15.02%, 26.64% and 37.55% higher F-measure compared with existing techniques like Industrial Internet of Things based on Blockchain Secure Device Authentication Solution (IIoT-BC-BASA), Industrial Internet of things based on Blockchain- InterPlanetary File System (IIoT-BC-IPFS), Industrial Internet of Things based on Blockchain- Shamir Threshold Cryptography (IIoT-BC-SHC), Industrial Internet of Things based on Blockchain- software Define Networking (IIoT-BC-SDN) methods, respectively.
Blink-To-Live eye-based communication system for users with speech impairments

Eye-based communication languages such as Blink-To-Speak play a key role in expressing the needs and emotions of patients with motor neuron disorders. Most invented eye-based tracking systems are complex and not affordable in low-income countries. Blink-To-Live is an eye-tracking system based on a modified Blink-To-Speak language and computer vision for patients with speech impairments. A mobile phone camera tracks the patient's eyes by sending real-time video frames to computer vision modules for facial landmarks detection, eye identification and tracking. There are four defined key alphabets in the Blink-To-Live eye-based communication language: Left, Right, Up, and Blink. These eye gestures encode more than 60 daily life commands expressed by a sequence of three eye movement states. Once the eye gestures encoded sentences are generated, the translation module will display the phrases in the patient's native speech on the phone screen, and the synthesized voice can be heard. A prototype of the Blink-To-Live system is evaluated using normal cases with different demographic characteristics. Unlike the other sensor-based eye-tracking systems, Blink-To-Live is simple, flexible, and cost-efficient, with no dependency on specific software or hardware requirements. The software and its source are available from the GitHub repository ( https://github.com/ZW01f/Blink-To-Live ).
TechnoSapiens: merging humans with technology in augmented reality

Abstract We present a marker-less AR/DR system that can replace the arm of the user with a virtual bionic prosthesis in real time including finger tracking. For this, we use a mixed reality HMD that provides the user with a stereo image based on video-see-through (VST). We apply chroma-keying to remove the user’s arm from each captured image and input reconstructed background information into the removed pixels. Before rendering the prosthesis model into the image, we re-target motion capture data of the user’s hand to the kinematic skeleton of the prosthesis to match the current hand pose. This system opens new research possibilities on self- and other-perception of bionic bodies. In a first evaluation study of the system, we propose that users perceive the virtual prosthesis model as a part of their body (i.e., that they experience a sense of ownership). We tested this assumption in a laboratory study with 27 individuals who used the system to perform a series of simple tasks in AR with their prosthesis. We measured body ownership and other measures with self-reports. In support of the hypothesis, users experienced a sense of body ownership. Also, a feeling of self-presence is induced during the task, and participants rated the overall experience as positive.
Two-Stage Decolorization Based on Histogram Equalization and Local Variance Maximization

.Image decolorization is widely used in single-channel image processing, black-and-white printing, etc. Decolorization aims to generate a perceptually satisfactory gray image that preserves the contrast of the color image. It is known that histogram equalization can enhance the global image contrast by effectively spreading out the most frequent intensity values. Meanwhile, local contrast features such as salient edges and local details have large local variances, which can be enhanced by maximizing local variance. Inspired by these facts, we propose a two-stage decolorization method based on histogram equalization and local variance maximization. In the first stage, we assume that the decolorized gray image is a linear combination of the three channels of the color image, and the combination coefficients are three global weights. Then we propose a constrained variational histogram equalization model to optimize the global weights. The resulting gray image has good global contrast. To further enhance the local contrast, in the second stage, we use local weight combination to express the color image and maximize the local variance by forcing the local weights to be close to the global weights. Numerically, the global weights can be estimated by a gradient-based solver or a discrete searching solver, and the local weights are solved by an iterative solver. Theoretically, we discuss the properties of the energy functions and the convergence of the algorithm. Our proposed method better preserves global and local contrast than state-of-the-art decolorization algorithms.Keywordsimage decolorizationhistogram equalizationlocal variancetwo-stage modelMSC codes65D1865K1068U1094A08
Implementation of a Real-Time Object Pick-and-Place System Based on a Changing Strategy for Rapidly-Exploring Random Tree

An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.
Measuring ancient technological complexity and its cognitive implications using Petri nets

We implement a method from computer sciences to address a challenge in Paleolithic archaeology: how to infer cognition differences from material culture. Archaeological material culture is linked to cognition: more complex ancient technologies are assumed to have required complex cognition. We present an application of Petri net analysis to compare Neanderthal tar production technologies and tie the results to cognitive requirements. We applied three complexity metrics, each relying on their own unique definitions of complexity, to the modelled production sequences. Based on the results, we suggest that Neanderthal working memory requirements may have been similar to human preferences regarding working memory use today. This method also enables us to distinguish the high-order cognitive functions combining traits like planning, inhibitory control, and learnings that were likely required by different ancient technological processes. The Petri net approach can contribute to our understanding of technology and cognitive evolution as it can be used on different materials and technologies, across time and species.
TSFN: A Novel Malicious Traffic Classification Method Using BERT and LSTM

Traffic classification is the first step in network anomaly detection and is essential to network security. However, existing malicious traffic classification methods have several limitations; for example, statistical-based methods are vulnerable to hand-designed features, and deep learning-based methods are vulnerable to the balance and adequacy of data sets. In addition, the existing BERT-based malicious traffic classification methods only focus on the global features of traffic and ignore the time-series features of traffic. To address these problems, we propose a BERT-based Time-Series Feature Network (TSFN) model in this paper. The first is a Packet encoder module built by the BERT model, which completes the capture of global features of the traffic using the attention mechanism. The second is a temporal feature extraction module built by the LSTM model, which captures the time-series features of the traffic. Then, the global and time-series features of the malicious traffic are incorporated together as the final feature representation, which can better represent the malicious traffic. The experimental results show that the proposed approach can effectively improve the accuracy of malicious traffic classification on the publicly available USTC-TFC dataset, reaching an F1 value of 99.50%. This shows that the time-series features in malicious traffic can help improve the accuracy of malicious traffic classification.
Online Motion Planning for Fixed-Wing Aircraft in Precise Automatic Landing on Mobile Platforms

This paper proposes the creative idea that an unmanned fixed-wing aircraft should automatically adjust its 3D landing trajectory online to land on a given touchdown point, instead of following a pre-designed fixed glide slope angle or a landing path composed of two waypoints. A fixed-wing aircraft is a typical under-actuated and nonholonomic constrained system, and its landing procedure—which involves complex kinematic and dynamic constraints—is challenging, especially in some scenarios such as landing on an aircraft carrier, which has a runway that is very short and narrow. The conventional solution of setting a very conservative landing path in advance and controlling the aircraft to follow it without dynamic adjustment of the reference path has not performed satisfactorily due to the variation in initial states and widespread environmental uncertainties. The motion planner shown in this study can adjust an aircraft’s landing trajectory online and guide the aircraft to land at a given fixed or moving point while conforming to the strict constraints. Such a planner is composed of two parts: one is used to generate a series of motion primitives which conform to the dynamic constraints, and the other is used to evaluate those primitives and choose the best one for the aircraft to execute. In this paper, numerical simulations demonstrate that when given a landing configuration composed of position, altitude, and direction, the planner can provide a feasible guidance path for the aircraft to land accurately.
Technological diversity of quantum computing providers: a comparative study and a proposal for API Gateway integration

Abstract After decades of advances, mainly theoretical, in recent years quantum computing has begun to show its first practical applications. This new and revolutionary technology aims to enhance essential areas such as cybersecurity, financial services, or medicine. The growth of this technology has encouraged different research centers and big companies such as IBM, Amazon, and Google to dedicate considerable efforts to developing new technologies that bring quantum computing to the market. However, these technologies are not yet mature and create a significant vendor lock-in problem. Therefore, new tools are needed that facilitate access to this technology and that allow developers to increase the abstraction level at which they work. Given that the integration of quantum software should not be very different from that of classical services, we can take advantage of the knowledge acquired and use current techniques of service-oriented computing. In this work, we have carried out a technical comparison between different quantum computing service providers using a case study, by performing empirical tests based on the Travelling Salesman Problem. This study highlights the differences between the main providers. To address these differences and reduce the vendor lock-in effect, we propose an extension of the Quantum API Gateway to support the different providers and the casuistry that each one presents. This would allow programmers to deploy quantum code without vendor-specific knowledge of the major providers, which would facilitate access and simplify the development of quantum applications.
Enhancing Industrial Cyber Security, Focusing on Formulating a Practical Strategy for Making Predictions Through Machine Learning Tools in Cloud Computing Environment

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study&amp;#039;s objectives. This study focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover. this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of Precision, Accuracy, Recall values, F1 score, R.O.C. curves, and Confusion matrix. The results demonstrated that the X.G.B. model outperformed in terms of all the matrices with an Accuracy of 97.50 %, 97.60 % Precision, 97.60 % Recall values, and 97.50 % F1 score. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.
Analysis of Cloud Computing Security Challenges and Threats for Resolving Data Breach Issues

Technology now affects every element of the high-tech society, which creates an environment that permits attackers to gain access easily, cheaply and attack a particular organization. The organization struggles to quickly notice it, though. With cloud computing, users can save data online and over a network. Additionally, the data is accessible from anywhere in the world. IT-related capabilities can be accessed by multiple users as a service through internet because of cloud computing, and these services are charged as per use. A substantial rise in the use of various cloud services has occurred based on the adoption of cloud technology by several cloud computing providers, including Microsoft, Google, IBM, Yahoo and Amazon. The Amazon is ruling the market due to its architectural characteristics. Different paid tools and open-source tools are being developed to meet the requirements. Since security is a crucial matter, most individuals are apprehensive about storing their pivotal information in a cloud, especially those who are unaware about the rise in attacks and the advancement of technology. There have been numerous advancements made when it comes to cloud computing, many challenges still need to be resolved, including those relating to security, interoperability, resource scheduling, virtualization, etc.This paper carries out a systematic literature review by detecting cloud computing safety challenges and threats, also offers ideas for resolving data breach issues.
Communicating health advice on social media: A multimodal case study

Social media represent new arenas for health communication. Platforms such as YouTube, Instagram, Snapchat, and TikTok provide unique opportunities for health workers to build understanding, engagement, and trust among a broad and young audience. At the same time, such platforms demand of health workers a careful balance between social closeness and professional distance. The article is based on a case study in which the award-winning Norwegian psychologist Maria Abrahamsen’s practice of health communication on Instagram is studied through the lens of multimodal discourse analysis. Following a bottom-up method, the study starts with a close reading of a single Instagram post, where video and written verbal text interact closely. The entire account is then studied as a complex case of mediated health communication. Conclusively, after a discussion of genre implications, the article suggests possible solutions to key challenges concerning the followers’ engagement and trust.
Scalable Generation and Detection of on-Demand W States in Nanophotonic Circuits

Quantum physics phenomena, entanglement and coherence, are crucial for quantum information protocols, but understanding these in systems with more than two parts is challenging due to increasing complexity. The W state, a multipartite entangled state, is notable for its robustness and benefits in quantum communication. Here, we generate eight-mode on-demand single-photon W states, using nanowire quantum dots and a silicon nitride photonic chip. We demonstrate a reliable and scalable technique for reconstructing the W state in photonic circuits using Fourier and real-space imaging, supported by the Gerchberg-Saxton phase retrieval algorithm. Additionally, we utilize an entanglement witness to distinguish between mixed and entangled states, thereby affirming the entangled nature of our generated state. The study provides a new imaging approach of assessing multipartite entanglement in W states, paving the way for further progress in image processing and Fourier-space analysis techniques for complex quantum systems.
An FW–GA Hybrid Algorithm Combined with Clustering for UAV Forest Fire Reconnaissance Task Assignment

The assignment of tasks for unmanned aerial vehicles (UAVs) during forest fire reconnaissance is a highly complex and large-scale problem. Current task allocation methods struggle to strike a balance between solution speed and effectiveness. In this paper, a two-phase centralized UAV task assignment model based on expectation maximization (EM) clustering and the multidimensional knapsack model (MKP) is proposed for the forest fire reconnaissance task assignment. The fire situation information is acquired using the sensors carried by satellites at first. Then, the EM algorithm based on the Gaussian mixture model (GMM) is applied to get the initial position of every UAV. In the end, the MKP is applied for UAV task assignment based on the initial positions of the UAVs. An improved genetic algorithm (GA) based on the fireworks algorithm (FWA) is proposed for faster iteration speed. A simulation was carried out against the background of forest fires in Liangshan Prefecture, Sichuan Province, and the simulation’s results demonstrate that the task assignment model can quickly and effectively address task allocation problems on a large scale. In addition, the FW–GA hybrid algorithm has great advantages over the traditional GA, particularly in solving time, iteration convergence speed, and solution effectiveness. It can reduce up to 556% of the iteration time and increase objective function value by 1.7% compared to the standard GA. Furthermore, compared to the GA–SA algorithm, its solving time is up to 60 times lower. This paper provides a new idea for future large-scale UAV task assignment problems.