Reviewerly/exHarmony huggingface dataset:
Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.
A new CNN-LSTM architecture for activity recognition employing wearable motion sensor data: Enabling diverse feature extraction

Extracting representative features to recognize human activities through the use of wearables is an area of on-going research. While hand-crafted features and machine learning (ML) techniques have been sufficiently well investigated in the past, the use of deep learning (DL) techniques is the current trend. Specifically, Convolutional Neural Networks (CNNs), Long Short Term Memory Networks (LSTMs), and hybrid models have been investigated. We propose a novel hybrid network architecture to recognize human activities through the use of wearable motion sensors and DL techniques. The LSTM and the 2D CNN branches of the model that run in parallel receive the raw signals and their spectrograms, respectively. We concatenate the features extracted at each branch and use them for activity recognition. We compare the classification performance of the proposed network with three single and three hybrid commonly used network architectures: 1D CNN, 2D CNN, LSTM, standard 1D CNN-LSTM, 1D CNN-LSTM proposed by Ordóñez and Roggen, and an alternative 1D CNN-LSTM model. We tune the hyper-parameters of six of the models using Bayesian optimization and test the models on two publicly available datasets. The comparison between the seven networks is based on four performance metrics and complexity measures. Because of the stochastic nature of DL algorithms, we provide the average values and standard deviations of the performance metrics over ten repetitions of each experiment. The proposed 2D CNN-LSTM architecture achieves the highest average accuracies of 95.66% and 92.95% on the two datasets, which are, respectively, 2.45% and 3.18% above those of the 2D CNN model that ranks the second. This improvement is a consequence of the proposed model enabling the extraction of a broader range of complementary features that comprehensively represent human activities. We evaluate the complexities of the networks in terms of the total number of parameters, model size, training/testing time, and the number of floating point operations (FLOPs). We also compare the results of the proposed network with those of recent related work that use the same datasets.
Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model's learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model's feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model's classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model's performance will be compared with state-of-the-art approaches in other existing systems' accuracy, precision, recall, and F1 score.
Digital service technologies, service robots, AI, and the strategic pathways to cost-effective service excellence

Recent research has shown that the trade-off between customer satisfaction and productivity can be mitigated through three strategic pathways: the (1) operations management (OM) approach, (2) focused service factory, and (3) dual culture strategy, which allow firms to achieve cost-effective service excellence (CESE). We advance that these strategies are also excellent enablers for the development, implementation, and use of digital service technologies, service robots, and artificial intelligence (AI). First, the OM approach reduces process variations in service delivery, creating opportunities for leveraging technology to automate processes. Second, the focused service factory strategy enables high volumes of largely identical service transactions with minimized customer-induced variability, easing service automation. Third, the dual culture strategy ensures that digital automation is firmly customer-centric and achieves service excellence. We advance a set of propositions and research questions aimed at stimulating research at the intersection of the two streams of literature on digital service technologies and CESE.
Towards improving the performance of blind image steganalyzer using third-order SPAM features and ensemble classifier

The success rate for blind or universal steganalysis lies in the ability to extract the statistical footprints of image features. Further, the choice of machine learning (ML) algorithm is crucial to distinguish the stego image more precisely from the untouched clean images. Literature suggests that most steganalysis approaches report less favorable detection accuracy despite considering many features. This study presents a three-step process to accurately identify the clean and stego images to solve this issue. We used the curvelet denoising as an initial phase during the first step to suppress the natural noise residuals (NRs) by producing the stego NRs. Secondly, it extracts the Third-order Markov-chain sample transition probability matrices as features. Finally, the oblique decision tree ensemble using a multisurface proximal support vector machine (SVM) classifier has been utilized to achieve greater detection accuracy than the state-of-the-art classifiers. The experiments are performed on an extensive database comprising clean and stego images generated from nine embedding schemes with varying payloads. The experimental results suggest that an accuracy of 93.12 has been achieved using the proposed Third order subtractive pixel adjacency matrix (SPAM) features with an ensemble classifier.
A New AI-Based Semantic Cyber Intelligence Agent

The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-reaching financial, social, and psychological repercussions on individuals. Beyond inflicting monetary losses, cyber-attacks exert adverse effects on the social fabric and psychological well-being of the affected individuals. In order to mitigate the deleterious consequences of cyber threats, adoption of an intelligent agent-based solution to enhance the speed and comprehensiveness of cyber intelligence is advocated. In this paper, a novel cyber intelligence solution is proposed, employing four semantic agents that interact autonomously to acquire crucial cyber intelligence pertaining to any given country. The solution leverages a combination of techniques, including a convolutional neural network (CNN), sentiment analysis, exponential smoothing, latent Dirichlet allocation (LDA), term frequency-inverse document frequency (TF-IDF), Porter stemming, and others, to analyse data from both social media and web sources. The proposed method underwent evaluation from 13 October 2022 to 6 April 2023, utilizing a dataset comprising 37,386 tweets generated by 30,706 users across 54 languages. To address non-English content, a total of 8199 HTTP requests were made to facilitate translation. Additionally, the system processed 238,220 cyber threat data from the web. Within a remarkably brief duration of 6 s, the system autonomously generated a comprehensive cyber intelligence report encompassing 7 critical dimensions of cyber intelligence for countries such as Russia, Ukraine, China, Iran, India, and Australia.
Challenges of education in the accounting profession in the Era 5.0: A systematic review

The significant transformations and advances attributed to the Era 5.0 require a comprehensive understanding of the current ramifications of the accounting discipline, along with the prospective difficulties and opportunities. This is pertinent not only in the context of industry, education, and society, but also in terms of labor relations and professions. Thus, the aim of this paper is to understand the current impacts and future expectations of the Era 5.0 on the accounting profession. Based on a recent systematic literature review we note the urgent and pressing need for educational institutions, their audiences, and other entities to interact in the changing process, creating synergies to ensure the success of retraining this valuable strategic resource in organizations and society, and the sustainability of the profession, promoting a sustainable Society 5.0. We contribute to the research on the consequences and challenges of the Era 5.0 in the future of the accounting profession by the originality of the contextualization of the profession in this new Era, filling a gap in the recent state of the art in the research of the accounting profession. We consider that this research will have an impact on academia, industry, and society, bringing these to work together in the development of the accountants' skills and their future work.
FORMATION OF CREATIVE THINKING OF A LAWYER IN MODERN CONDITIONS OF DEVELOPMENT INCLUDING THE INFLUENCE OF COVID-19 PANDEMIC

The main purpose of the study is to determine the principal ways of forming creative thinking among lawyers in their practice in modern conditions of development under the influence of COVID-19 pandemic. The importance of studying creative thinking and its formation is determined because the study of this problem creates the basis for the formation of a psychological attitude towards the creative development of a personality, which will be able to effectively solve non-standard problems of legal science and practice. The leading direction of training a future lawyer is the disclosure and activation of creative potential. In this process, focus on the attitudinal sphere of the personality is because it determines the activation of professional creativity and contributes to the disclosure of the creative potential of a specialist. As a result of the study and the application of the proposed methodology, the main stages were identified to promote the development of creative potential and thinking among lawyers in modern conditions of development, considering the impact of COVID-19 pandemic. The proposed research results may have a practical aspect of implementation in the activities of a modern lawyer.
Light field super-resolution using complementary-view feature attention

Abstract Light field (LF) cameras record multiple perspectives by a sparse sampling of real scenes, and these perspectives provide complementary information. This information is beneficial to LF super-resolution (LFSR). Compared with traditional single-image super-resolution, LF can exploit parallax structure and perspective correlation among different LF views. Furthermore, the performance of existing methods are limited as they fail to deeply explore the complementary information across LF views. In this paper, we propose a novel network, called the light field complementary-view feature attention network (LF-CFANet), to improve LFSR by dynamically learning the complementary information in LF views. Specifically, we design a residual complementary-view spatial and channel attention module (RCSCAM) to effectively interact with complementary information between complementary views. Moreover, RCSCAM captures the relationships between different channels, and it is able to generate informative features for reconstructing LF images while ignoring redundant information. Then, a maximum-difference information supplementary branch (MDISB) is used to supplement information from the maximum-difference angular positions based on the geometric structure of LF images. This branch also can guide the process of reconstruction. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method. The proposed LF-CFANet has a more advanced reconstruction performance that displays faithful details with higher SR accuracy than state-of-the-art methods.
Construction of a feature enhancement network for small object detection

Limited by the size, location, number of samples and other factors of the small object itself, the small object is usually insufficient, which degrades the performance of the small object detection algorithms. To address this issue, we construct a novel Feature Enhancement Network (FENet) to improve the performance of small object detection. Firstly, an improved data augmentation method based on collision detection and spatial context extension (CDCI) is proposed to effectively expand the possibility of small object detection. Then, based on the idea of Granular Computing, a multi-granular deformable convolution network is constructed to acquire the offset feature representation at the different granularity levels. Finally, we design a high-resolution block (HR block) and build High-Resolution Block-based Feature Pyramid by parallel embedding HR block in FPN (HR-FPN) to make full use different granularity and resolution features. By above strategies, FENet can acquire sufficient feature information of small objects. In this paper, we firstly applied the multi-granularity deformable convolution to feature extraction of small objects. Meanwhile, a new feature fusion module is constructed by optimizing feature pyramid to maintain the detailed features and enrich the semantic information of small objects. Experiments show that FENet achieves excellent performance compared with performance of other methods when applied to the publicly available COCO dataset, VisDrone dataset and TinyPerson dataset. The code is available at https://github.com/cowarder/FENet.
Differential evolution-based optimized hierarchical extreme learning machines for fault section diagnosis of large-scale power systems

Fault section diagnosis (FSD) is extremely critical to ensure the safe and stable operation of power systems. Neural networks have become popular increasingly in solving this problem and the existing applied ones rely primarily on the traditional BP and RBF neural networks. However, their generalization is weak when diagnosing large-scale power systems. To address this issue, this study suggests a divisional FSD method based on structure adaptive hierarchical extreme learning machines (HELM). To construct an optimal HELM-based sub-module diagnosis model for each subsystem of a large-scale power system, differential evolution is applied to optimize both the number of hidden layer neurons and the regularization factor of HELM simultaneously. The optimal HELM is responsible for completing both the diagnosis of internal power sections of the subsystem and the interim diagnosis of tie lines of adjacent subsystems. Based on the interim diagnosis results, Choquet fuzzy integral is used to fuse them to obtain the final results for the tie lines. Simulation results indicate that the differential evolution algorithm is effective in finding more accurate and compact HELMs with better approximation ability and generalization performance. The proposed FSD method has higher accuracy and fault tolerance to identify various complex fault cases.
Chaotic Complex Hashing: A simple chaotic keyed hash function based on complex quadratic map

In the last two decades, the use of chaotic iterative maps in cryptographic systems, especially chaotic hash functions, has been expanding. Sensitivity to small changes in initial conditions and control parameters in chaotic systems is the best feature for designing a chaotic hash function. This paper introduces a new generalized chaotic map based on a complex quadratic map, which in addition to the chaotic behavior in the real and imaginary parts, also has a high key length, which can guarantee the security of a cryptographic system. Various analyses of dynamical systems, such as the bifurcation diagram and the Lyapunov exponent, show that the proposed chaotic map has the characteristics of a chaotic dynamical system. The foundation of a secure and efficient algorithm for designing and developing a new hash function lies in how to use this new chaotic map. Also, statistical analysis, collision analysis, key space analysis, and speed analysis use to prove the security and efficiency of the proposed method. The results of these analyses and their comparison with similar methods show that the proposed method can be a reliable and efficient method for practical applications in information technology.
A graph empowered insider threat detection framework based on daily activities

While threats from outsiders are easier to alleviate, effective ways seldom exist to handle threats from insiders. The key to managing insider threats lies in engineering behavioral features efficiently and classifying them correctly. To handle challenges in feature engineering, we propose an integrated feature engineering solution based on daily activities, combining manually-selected features and automatically-extracted features together. Particularly, an LSTM auto-encoder is introduced for automatic feature engineering from sequential activities. To improve detection, a residual hybrid network (ResHybnet) containing GNN and CNN components is also proposed along with an organizational graph, taking a user-day combination as a node. Experimental results show that the proposed LSTM auto-encoder could extract hidden patterns from sequential activities efficiently, improving F1 score by 0.56%. Additionally, with the designed residual link, our ResHybnet model works well to boost performance and has outperformed the best of other models by 1.97% on the same features. We published our code on GitHub: https://github.com/Wayne-on-the-road/ResHybnet.
Open-vocabulary Queryable Scene Representations for Real World Planning

Large language models (LLMs) have unlocked new capabilities of task planning from human instructions. However, prior attempts to apply LLMs to real-world robotic tasks are limited by the lack of grounding in the surrounding scene. In this paper, we develop NLMap, an open-vocabulary and queryable scene representation to address this problem. NLMap serves as a framework to gather and integrate contextual information into LLM planners, allowing them to see and query available objects in the scene before generating a context-conditioned plan. NLMap first establishes a natural language queryable scene representation with Visual Language models (VLMs). An LLM based object proposal module parses instructions and proposes involved objects to query the scene representation for object availability and location. An LLM planner then plans with such information about the scene. NLMap allows robots to operate without a fixed list of objects nor executable options, enabling real robot operation unachievable by previous methods. Project website: https://nlmap-saycan.github.io.
Cybercrime and Privacy Threats of Large Language Models

This article explores the privacy and security issues associated with large language models (LLMs). It takes a look at nefarious actors’ possible use of LLM tools. Also analyzed are behaviors and practices of the developers, operators, and users of such models from privacy and security standpoints.
A solution to the stagnation of multi-verse optimization: An efficient method for breast cancer pathologic images segmentation

The multi-verse optimizer (MVO) algorithm has been applied to image segmentation, feature selection, engineering problems, and many other fields. MVO, like other metaheuristic algorithms, still has shortcomings, such as poor convergence speed and quickly falling into local optimum. To address these concerns, this paper proposes CBQMVO, extending the original MVO algorithm with three strategies: covariance matrix adaptation strategy (CMAES), biogeography-based learning strategy (BLS), quasi-reflected and quasi-opposition strategy (QROS). CMAES can make the algorithm approach quickly the current local optimal solution and accelerate the convergence. BLS can enrich the population’s diversity to discourage prematurely and assist the algorithm in jumping out of the local optimum. QROS can increase the probability of search particles falling near the optimal solution. A set of experiments were conducted to evaluate the performance of the CBQMVO. First, the original algorithm comparison experiment on IEEE CEC2014 includes strategy comparison, dimension comparison, exploration/exploitation balance, and population diversity experiments. Then, the advanced algorithm comparison experiment was carried out on IEEE CEC 2014. Furthermore, the champion algorithm comparison experiment was conducted on IEEE CEC2017 and IEEE CEC2020. A series of comparative experimental data demonstrate that CBQMVO has high performance, especially on some unimodal and complex competition functions. In addition, this paper also applied CBQMVO to implement Renyi’s entropy multilevel threshold image segmentation based on the non-local mean 2D histogram (RMIS-2D) on breast cancer pathologic images. Compared with other metaheuristic algorithms and Kapur’s entropy image segmentation, the proposed scheme in this paper has a better segmentation effect.