Reviewerly/exHarmony huggingface dataset:
Research on global path planning of unmanned vehicles based on improved ant colony algorithm in the complex road environment

When planning the path of a non-urbanized road, the default ant colony optimization (ACO) algorithm does not consider complex road state function such as uneven surface, road attachment coefficient, and vehicle turning angle limit. Based on the actual situation of roads and vehicles, a pavement state function that considers uneven areas such as road bumps and pavement attachment is proposed to improve the description of path length. Then, a heuristic function based on the A* algorithm and an improved mechanism for the initialization of pheromone distribution is proposed, which changes the blindness of ant colony search, accelerates the convergence of the ACO, and improves the search efficiency. The global search capability of the algorithm is enhanced by improving the path selection strategy and path transition probability function. The pheromone updating method is improved by using the MAX-MIN Ant System, which increases the algorithm diversity and avoids local optima. Further, using the pruning algorithm to reduce the number of paths significantly increases the convergence speed. Simulation results show that the improved ACO algorithm has better convergence speed and global search ability. Combining road state processing with vehicle corner processing can effectively improve the safety, adaptability, and reliability of autonomous vehicles. And the global optimal path planning of unmanned vehicles on complex roads can also be realized.
Bot-MGAT: A Transfer Learning Model Based on a Multi-View Graph Attention Network to Detect Social Bots

Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph attention mechanism using a transfer learning (TL) approach, to predict social bots. We called the framework ‘Bot-MGAT’, which stands for bot multi-view graph attention network. The framework used both labeled and unlabeled data. We used profile features to reduce the overheads of the feature engineering. We executed our experiments on a recent benchmark dataset that included representative samples of social bots with graph structural information and profile features only. We applied cross-validation to avoid uncertainty in the model’s performance. Bot-MGAT was evaluated using graph SSL techniques: single graph attention networks (GAT), graph convolutional networks (GCN), and relational graph convolutional networks (RGCN). We compared Bot-MGAT to related work in the field of bot detection. The results of Bot-MGAT with TL outperformed, with an accuracy score of 97.8%, an F1 score of 0.9842, and an MCC score of 0.9481.
Smartphone-based Biometric Authentication Scheme for Access Control Management in Client-server Environment

As the information put together by the blend of smartphones, the cloud, the IOT, and ubiquitous computing continue to expand at an alarming rate, a data breach increases. Today, users' strong authentication and authorization approaches are increasingly important to secure sensitive, confidential, secret information. Possession and knowledge-based authentication techniques for computers, the internet, email accounts, etc., are commonly used to access vital information; they do not link a user to an established identity, resulting in most security vulnerabilities. Biometric authentication, on the other hand, has the privilege of being more reliable than traditional authentication as biometric characteristics of a person can’t be lost; they are tough to distribute, exchange or duplicate; and it requires the user to be present during the authentication process, thereby relating the users to established identities. Biometrics provides a higher level of assurance that the individual attempting to ascertain is the individual in question. Thus, resulting in a more reliable, usable, and cost-effective model with a higher level of protection to deter an attacker from obtaining entry to a computer or network and gaining access to confidential data. This paper introduces a novel fingerprint-based authentication scheme for mobile environments, enabling user authentication based on fingerprint features using a challenge-response-based authentication process. In this study, the proposed authentication system has been developed on a real Android-based smartphone, tested on actual users, and performance analysis has been carried out; empirical results reveal that the proposed authentication scheme achieves increased performance. Moreover, a usability analysis has been done to determine efficiency, effectiveness, and user satisfaction. The evaluation results indicate its feasibility to use it as an effective authentication mechanism for mobile phone environments.
Lighting the darkness in the sea: A deep learning model for underwater image enhancement

Currently, optical imaging cameras are widely used on underwater vehicles to obtain images and support numerous marine exploration tasks. Many underwater image enhancement algorithms have been proposed in the past few years to suppress backscattering noise and improve the signal-to-noise ratio of underwater images. However, these algorithms are mainly focused on underwater image enhancement tasks in a bright environment. Thus, it is still unclear how these algorithms would perform on images acquired in an underwater scene with low illumination. Images obtained in a dark underwater scene usually include more noise and have very low visual quality, which may easily lead to artifacts during the process of enhancement. To bridge this gap, we thoroughly study the existing underwater image enhancement methods and low illumination image enhancement methods based on deep learning and propose a new underwater image enhancement network to solve the problem of serious degradation of underwater image quality in a low illumination environment. Due to the lack of ready-made datasets for training, we also propose the first dataset for low-light underwater image enhancement to train our model. Our method can be implemented to skillfully and simultaneously address low-light degradation and scattering degradation in low-light underwater images. Experimental results also show that our method is robust against different illumination levels, which greatly expands the applicable scenarios of our method. Compared with previous underwater image enhancement methods and low-light image enhancement methods, outstanding performance is achieved using our method in various low-light underwater scenes.
A Survey on Intelligent Gesture Recognition Techniques

Gesture recognition is an ideal means of interaction because it allows users not to have to make contact with any surface, which is a safe and hygienic means, especially in the pandemic situation that is occurring worldwide.However, gesture recognition is not a new discipline and it has been researched for many years but this type of interaction has not succeeded in replacing the keyboard and mouse.It is very useful to know about the advances that are being made with artificial intelligence in gesture recognition to be able to perform a more robust and reliable gesture recognition with a low response time.As it is, deep learning is being integrated into various areas to increase improvement in performance and one such area is artificial intelligence.In this way, there is the possibility that in the future the recognition of gestures will be a viable option as a means of daily interaction for the user and the main objective of this paper is to contribute to that process.For this reason, this study has analyzed 571 papers related to gesture recognition and artificial intelligence.This analysis has extracted relevant information related to scientific production, such as the most productive authors and journals or the most pertinent articles on the subject.Furthermore, we have developed our own model, which shows the relationship between the types of gesture recognition and the artificial intelligence techniques that have been applied for this task.
Method of Software Development Project Duration Estimation for Scrum Teams with Differentiated Specializations

Estimation is an essential step of software development project planning that has a significant impact on project success—underestimation often leads to problems with the delivery or even causes project failure. An important aspect that the classical estimation methods are usually missing is the Agile nature of development processes in the implementation phase. The estimation method proposed in this article aims at software development projects implemented by Scrum teams with differentiated specializations. The method is based on the authors’ system of working-time balance equations and the approach to measuring project scope with time-based units—normalized development estimates. In order to reduce efforts spent on the estimation itself, an analysis of dependencies among project tasks is not mandatory. The outputs of the methods are not recommended to be treated as commitments; instead, they are supposed to be used to inform project stakeholders about the forecasted duration of a potential project. The method is simple enough to allow even an inexpensive spreadsheet-based implementation.
An Earthquake Early Warning System for Northern Chile Based on ElarmS-3

Abstract During 2020, the National Seismological Center (CSN) implemented an earthquake early warning system (EEWS) for northern Chile. From a seismological point of view, this area is considered to be one of the largest seismic gaps in Chile, where an Mw∼9.0 earthquake is expected in a region with a population of more than 1.4 million people. From an economical perspective, this region holds 90% of the copper mining companies in Chile, with more than 15% of the Gross National Product coming from the mining industry. Antofagasta Minerals funded an EEWS prototype for this region with the purpose of keeping both the population and the mining industry safe; 25 new seismic stations have been added to the CSN permanent network to develop an EEWS capable of issuing strong shaking alerts, which could potentially save lives and support critical mining operations in the region. During a 20 month period, we successfully detected, located, and calculated the magnitude of 1774 earthquakes (0–300 km depth) using ElarmS-3, an earthquake early warning algorithm from the University of California, Berkeley. The alert time, or the time between when an earthquake alert is issued and the S-wave arrival at the location, is on average ∼24 s, and 96% of shallow and intermediate depth earthquakes (0–150 km) with M ≥5.0 were alerted. We obtained errors of 0.52 ± 0.43 in magnitude, 30.4 ± 42.72 km in location, 43.5 ± 50.0 km in depth, and 6.6 ± 10.6 s in origin time.
Artificial Intelligence Synergetic Opportunities in Services: Conversational Systems Perspective

The importance of this paper is its discovery of the unused synergetic potential of integration between several AI techniques into an orchestrated effort to improve service. Special emphasis is given to the conversational capabilities of AI systems. The paper shows that the literature related to the use of AI in service is divided into independent knowledge domains (silos) that are either related to the technology under consideration, or to a small group of technologies related to a certain application; it then discusses the reasons for the isolation of these silos, and reveals the barriers and the traps for their integration. Two case studies of service systems are presented to illustrate the importance of synergy. A special focus is given to the conversation part of these service systems: the first case presents an application with high potential for integrating new AI technologies into its AI portfolio, while the second case illustrates the advantages of a mature application that has already integrated many technologies into its AI portfolio. Finally, the paper discusses the two case studies and presents inclusion relationships between AI capabilities to facilitate generating a roadmap for extending AI capabilities with synergetic opportunities.
Predicting the use of social media business models: The mediating role of organizational e-readiness

Social media is remarkably growing and shaping not only the lives of individuals but also the business domain by creating new paradigms of business. This study examines the interplay among some selected predicting organizational variables and the use of what are called Social Media Business Models (SMBMs). Furthermore, the study investigates the mediating role of e-readiness on the relationships among these organizational factors and the use of SMBMs. The study is a quantitative, descriptive and analytical study. Using a self-administered survey, primary data were collected from 150 employees from the Jordanian SMEs. Smart PLS 3 software was employed to validate the proposed study model using a Structural Equation Modelling (SEM). The findings confirmed that compatibility, innovativeness, cultural and management support, and e-readiness have a statistically significant direct impact on the use of SMBMs. Finally, the study confirms that e-readiness mediates the relationship between compatibility and innovativeness as predicting variables and the use of SMBMs.
Hierarchical memory-constrained operator scheduling of neural architecture search networks

Neural Architecture Search (NAS) is widely used in industry, searching for neural networks meeting task requirements. Meanwhile, it faces a challenge in scheduling networks satisfying memory constraints. This paper proposes HMCOS that performs hierarchical memory-constrained operator scheduling of NAS networks: given a network, HMCOS constructs a hierarchical computation graph and employs an iterative scheduling algorithm to progressively reduce peak memory footprints. We evaluate HMCOS against RPO and Serenity (two popular scheduling techniques). The results show that HMCOS outperforms existing techniques in supporting more NAS networks, reducing 8.7~42.4% of peak memory footprints, and achieving 137--283x of speedups in scheduling.
Challenges and Opportunities of Education in the COVID-19 Pandemic: Teacher Perception on Applying AI Chatbot for Online Language Learning

Education has been considerably hindered or interrupted as a result of restrictive regulations and the building of social distance, making it one of the major sectors of COVID-19. In the past, it was suggested that chatbots may be utilized to assist individuals in learning more independently and in a variety of virtual learning contexts. The goal of this study was to evaluate the potential hurdles posed by the COVID-19 outbreak to online learning, as well as teachers' perspectives on the use of AI chatbots for online language learning. An online interview has been conducted with six interviewees in verifying the proposed questions from the teachers' perspectives. The result shows that there are common themes of challenges for online learning and online teaching: (1) Infrastructure; (2) Accessibility and Technology; (3) Readiness; (4) Assessment and Examinations; (5) Socioeconomic factors; (6) Mental health and personal well-being; (7) Online Security Issues and human and pet intrusions; and (8) Digital competence. The finding also indicates that the proposed technology, an AI chatbot, is anticipated to help the learning process to achieve the course's objectives more effectively than traditional methods.
Multi-Mode e-Learning System of Reading Skills for Deaf Students Based on Visual Multimedia

This research aims at developing an e- spelling system supported by effective multimedia technique of sign language and finger spelling. However the aimed system should enhance the hearing disability for the Deaf student so he can enter his desired paragraph then the spelling process could be accomplished in two stages, the fist stage is the word spelling which translates each word into the corresponding sign language video clip (MPEG video cut). Then stopping message appears asking the Deaf student to move to the second stage of translating. The second stage is the letter spelling which translates the current translated word (in sign language) into finger spelling letter by letter in sequence. Both stages of spelling are controlled by the Deaf student. After completing translation of that word into alphabets finger spelling, the next word would be translated into the corresponding sign language video clip. Real run-out forms (8 screens) have been displayed to describe the tracing process of translating and spelling via this e-spelling system. The system has been showed/tried by some experts to get the valuable feedback from them. As final result it is expected that Deaf students can improve their skills in English reading when they practice, drill and repeat the translating and spelling process of various English sentences and words. One hand ASL (American Sign Language) alphabets are used for letter spelling and Indian Sign Language Dictionary is used for word spelling. Visual programming coding source and its multimedia control components/techniques with database have been used in developing this system
An improved ant colony algorithm for integrating global path planning and local obstacle avoidance for mobile robot in dynamic environment

To improve the path optimization effect and search efficiency of ant colony optimization (ACO), an improved ant colony algorithm is proposed. A collar path is generated based on the known environmental information to avoid the blindness search at early planning. The effect of the ending point and the turning point is introduced to improve the heuristic information for high search efficiency. The adaptive adjustment of the pheromone intensity value is introduced to optimize the pheromone updating strategy. A variety of control strategies for updating the parameters are given to balance the convergence and global search ability. Then, the improved obstacle avoidance strategies are proposed for dynamic obstacles of different shapes and motion states, which overcome the shortcomings of existing obstacle avoidance strategies. Compared with other improved algorithms in different simulation environments, the results show that the algorithm in this paper is more effective and robust in complicated and large environments. On the other hand, the comparison with other obstacle avoidance strategies in a dynamic environment shows that the strategies designed in this paper have higher path quality after local obstacle avoidance, lower requirements for sensor performance, and higher safety.
Managing Cyber Security

In the area of information technology, cyber security plays a critical role. In today's world, protecting information has become challenging tasks. When we think of cyber security, the first thing that comes to mind is "cyber-crimes," which are on the rise at an alarming rate. Various governments and organizations are taking a variety of steps to combat cybercrime. Despite many steps, cyber security remains a major issue for many people. This paper focuses on the issues that cyber security faces in the modern era. It also covers the most up-to-date information on cyber security tactics, ethics, and trends that are transforming the face of cyber security.
Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity

Decision support tools are key components of intelligent sociotechnical systems, and their successful implementation faces a variety of challenges, including the multiplicity of information sources, heterogeneous format, and constant changes. Handling such challenges requires the ability to analyze and process inconsistent and incomplete information with varying degrees of associated uncertainty. Moreover, some domains require the system’s outputs to be explainable and interpretable; an example of this is cyberthreat analysis (CTA) in cybersecurity domains. In this paper, we first present the P-DAQAP system, an extension of a recently developed query-answering platform based on defeasible logic programming (DeLP) that incorporates a probabilistic model and focuses on delivering these capabilities. After discussing the details of its design and implementation, and describing how it can be applied in a CTA use case, we report on the results of an empirical evaluation designed to explore the effectiveness and efficiency of a possible world sampling-based approximate query answering approach that addresses the intractability of exact computations.
Self-supervised knowledge distillation for complementary label learning

In this paper, we tackle a new learning paradigm called learning from complementary labels, where the training data specifies classes that instances do not belong to, instead of the accuracy labels. In general, it is more efficient to collect the complementary labels compared with collecting the supervised ones, with no need for selecting the correct one from a number of candidates. While current state-of-the-art methods design various loss functions to train competitive models by the limited supervised information, they overlook learning from the data and model themselves, which always contain fruitful information that can improve the performance of complementary label learning. In this paper, we propose a novel learning framework, which seamlessly integrates self-supervised and self-distillation to complementary learning. Based on the general complementary learning framework, we employ an entropy regularization term to guarantee the network outputs exhibit a sharper state. Then, to intensively learn information from the data, we leverage the self-supervised learning based on rotation and transformation operations as a plug-in auxiliary task to learn better transferable representations. Finally, knowledge distillation is introduced to further extract the "dark knowledge" from a network to guide the training of a student network. In the extensive experiments, our method surprisingly demonstrates compelling performance in accuracy over several state-of-the-art approaches.
Dispersed Pixel Perturbation-Based Imperceptible Backdoor Trigger for Image Classifier Models

Typical deep neural network (DNN) backdoor attacks are based on triggers embedded in inputs. Existing imperceptible triggers are computationally expensive or low in attack success. In this paper, we propose a new backdoor trigger, which is easy to generate, imperceptible, and highly effective. The new trigger is a uniformly randomly generated three-dimensional (3D) binary pattern that can be horizontally and/or vertically repeated and mirrored and superposed onto three-channel images for training a backdoored DNN model. Dispersed throughout an image, the new trigger produces weak perturbation to individual pixels, but collectively holds a strong recognizable pattern to train and activate the backdoor of the DNN. We also analytically reveal that the trigger is increasingly effective with the improving resolution of the images. Experiments are conducted using the ResNet-18 and MLP models on the MNIST, CIFAR-10, and BTSR datasets. In terms of imperceptibility, the new trigger outperforms existing triggers, such as BadNets, Trojaned NN, and Hidden Backdoor, by over an order of magnitude. The new trigger achieves an almost 100% attack success rate, only reduces the classification accuracy by less than 0.7%&#x2013;2.4%, and invalidates the state-of-the-art defense techniques.