Reviewerly/exHarmony huggingface dataset:
Hierarchical Harris hawks optimizer for feature selection

The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection.To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features.We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets.Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms.Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.
PSCSO: Enhanced sand cat swarm optimization inspired by the political system to solve complex problems

The Sand Cat Swarm Optimization (SCSO) algorithm is a recently introduced metaheuristic with balanced behavior in the exploration and exploitation phases. However, it is not fast in convergence and may not be successful in finding the global optima, especially for complex problems since it starts the exploitation phase late. Moreover, the performance of SCSO is also affected by incorrect position as it depends on the location of the global optimum. Therefore, this study proposes a new method for the SCSO algorithm with a multidisciplinary principle inspired by the Political (Parliamentary) system, which is named PSCSO. The suggested algorithm increases the chances of finding the global solution by randomly choosing positions between the position of the candidate's best solution available so far and the current position during the exploitation phase. In this regard, a new coefficient is defined that affects the exploration and exploitation phases. In addition, a new mathematical model is introduced to use in the exploitation phase. The performance of the PSCSO algorithm is analyzed on a total of 41 benchmark functions from CEC2015, 2017, and 2019. In addition, its performance is evaluated in four classical engineering problems. The proposed algorithm is compared with the SCSO, Stochastic variation and Elite collaboration in SCSO (SE-SCSO), Hybrid SCSO (HSCSO), Parliamentary Optimization Algorithm (POA), and Arithmetic Optimization Algorithm (AOA) algorithms, which have been proposed in recent years. The obtained results depict that the PSCSO algorithm performs better or equivalently to the compared optimization algorithms.
Adversarial Machine Learning Attacks against Intrusion Detection Systems: A Survey on Strategies and Defense

Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks before they enter the system and classifying them as malicious activities. However, the IDS approaches have shortcomings in misclassifying novel attacks or adapting to emerging environments, affecting their accuracy and increasing false alarms. To solve this problem, researchers have recommended using machine learning approaches as engines for IDSs to increase their efficacy. Machine-learning techniques are supposed to automatically detect the main distinctions between normal and malicious data, even novel attacks, with high accuracy. However, carefully designed adversarial input perturbations during the training or testing phases can significantly affect their predictions and classifications. Adversarial machine learning (AML) poses many cybersecurity threats in numerous sectors that use machine-learning-based classification systems, such as deceiving IDS to misclassify network packets. Thus, this paper presents a survey of adversarial machine-learning strategies and defenses. It starts by highlighting various types of adversarial attacks that can affect the IDS and then presents the defense strategies to decrease or eliminate the influence of these attacks. Finally, the gaps in the existing literature and future research directions are presented.
Interpretable machine learning for dementia: A systematic review

Abstract Introduction Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently “interpretable,” whereas post hoc “explainability” methods can be used for other models. Methods Here we sought to summarize the state‐of‐the‐art of interpretable machine learning for dementia. Results We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets. Discussion Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia‐related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient‐specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.
Ensemble k-nearest neighbors based on centroid displacement

k-nearest neighbors (k-NN) is a well-known classification algorithm that is widely used in different domains. Despite its simplicity, effectiveness and robustness, k-NN is limited by the use of the Euclidean distance as the similarity metric, the arbitrarily selected neighborhood size k, the computational challenge of high-dimensional data, and the use of the simple majority voting rule in class determination. We sought to address the last issue and proposed the Centroid Displacement-based k-NN algorithm, where centroid displacement is used for class determination. This paper presents a simple yet efficient variant of our previous work, named Ensemble Centroid Displacement-based k-NN, which leverages the homogeneity of the nearest neighbors of test instances. Extensive experiments on various real and synthetic datasets were conducted to show the effectiveness and robustness of the proposed algorithm. Our experimental results demonstrate that the proposed algorithm is able to enhance the classification performance of the standard k-NN algorithm and its variants and also improve the computational efficiency. The performance of our algorithm was consistent and robust for both balanced and imbalanced datasets.
DeepBreastCancerNet: A Novel Deep Learning Model for Breast Cancer Detection Using Ultrasound Images

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%.
The Impact of Branded Mobile Applications on Customer Loyalty

This Study aims to investigate the impact of using branded mobile applications on customer loyalty. The population of the study consisted of all users of Pharmacy One’s mobile application in Jordan. The researcher used the purposive sampling by taking 2035 active users of the on-line applications from 70,251 downloader; 500 questionnaires were distributed randomly. To achieve the objective of the study, and to test hypotheses, the researcher used statistical program of social studies (SPSS) & (AMOS). The study results revealed that there is a statistically effect of using branded mobile applications on customer loyalty, based on the study results the researcher recommends managers and decision makers to improving the system of customer services in order to improve their loyalty, by reviewing the system after receiving feedback from customers through mobile applications. Also enhancing the quality of system information by ensuring that the content is relevant to customer requirements. and improving the perceived quality of the customer by identifying the expectations of the customers and seeking to achieve these expectations, so that the gap between what they expect and what they actually get.
The Effect of Marketing Knowledge on Competitive Advantage in the Food Industries Companies in Jordan

This study aimed to identify the impact of marketing knowledge practices on the competitive advantage of the food industries companies in Jordan. In order to achieve the objectives of the study, the questionnaire was used to collect primary data, whereby the study population consisted of (160) administrators of all levels (senior management, middle management) and who numbered (16) food industry companies. The questionnaire was distributed to the study sample, which included (160) administrators, and the researcher approved the sample the comprehensive survey and the descriptive analytical approach as it is appropriate to achieve the objectives of the study. One of the most important findings of the study is that there is a positive effect of marketing knowledge on the competitive. The study recommended the management of foods industries companies to provide the ability to process and analyze the information they possess in order to correctly forecast market demands, as well as employ delegates to study different markets, and worked to increase cooperation with suppliers and customers to follow up new developments in the market.
Impact of Social Media Marketing on Creating Brand Responsiveness

The aim of the study to examine the Impact of social media marketing on creating brand responsiveness. It focused on public universities in Jordan. Data were primarily gathered through self-reported questionnaires creating by Google Forms which were distributed to a sample (785) student via email. In total, (564) responses were received including (38) invalid to statistical analysis due to uncompleted or inaccurate. Hence, the final sample contained (526) responses suitable to analysis requirements. Structural equation modeling (SEM) was conducted to test hypotheses. The results showed that social media marketing dimensions had impact on creating the response to the brand. However, the results indicated that the highest impact was for e-word of mouth. Based on the results of the study, the researchers recommend educating young people about the concepts associated with the use of marketing social media from breach of privacy and addiction to its use.
The Effect of Total Quality Management on the Organizational Reputation: The Moderating Role of Quality Standards in Jordanian Public Universities

This study aimed to identify the modified role of quality standards in the impact of the application of total quality management on the organizational reputation of Jordanian public universities. The study population consisted of (5477) faculty members in five public universities. The study sample consisted of a proportional stratified sample of (390) individuals, and after the questionnaire was distributed with (410), 390 were recovered, valid for the purposes of statistical analysis. For the purposes of achieving the objectives of the study, multiple regression analysis and the multiple hierarchical regression analysis were used. The study results showed that quality standards play a statistically significant role on the impact of total quality management on organizational reputation. The researchers recommend the necessity for Jordanian public universities to pay attention to the standard because of its important role in the application of total quality management, as well as activating the role of accreditation and quality centers and supporting them with all the needs of qualified financial and human resources.
A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning

Path planning technology is an important guarantee for the safe navigation of autonomous underwater vehicle (AUV) in water, and it is also an important indicator of the intelligence of autonomous underwater vehicle. Aiming at the path planning problem of AUV in complex environments, this article presents a reinforcement learning-based tuna swarm optimization algorithm called the QLTSO. In this algorithm, individuals are independent of each other, and the choice of each individual strategy is decided by reinforcement learning. Four strategies are set for each individual in the algorithm: spiral foraging, parabolic foraging, optimization adjustment and ESOS strategy. Finally, the cubic B-spline curve is used to smooth the path so that the autonomous underwater vehicle can better track the path. To verify the superiority of the QLTSO algorithm, the algorithm is compared with other advanced optimization algorithms. The simulation results show that the QLTSO algorithm can plan safe and effective AUV navigation paths in a variety of two-dimensional and three-dimensional complex environments with better convergence and robustness, and the planning success rate is up to 100%, which is an effective AUV path planning algorithm.
Synthesizing Quantum Circuits of AES with Lower T-depth and Less Qubits

The significant progress in the development of quantum computers has made the study of cryptanalysis based on quantum computing an active topic. To accurately estimate the resources required to carry out quantum attacks, the involved quantum algorithms have to be synthesized into quantum circuits with basic quantum gates. In this work, we present several generic synthesis and optimization techniques for circuits implementing the quantum oracles of iterative symmetric-key ciphers that are commonly employed in quantum attacks based on Grover and Simon’s algorithms. Firstly, a general structure for implementing the round functions of block ciphers in-place is proposed. Then, we present some novel techniques for synthesizing efficient quantum circuits of linear and non-linear cryptographic building blocks. We apply these techniques to AES and systematically investigate the strategies for depth-width trade-offs. Along the way, we derive a quantum circuit for the AES S-box with provably minimal T-depth based on some new observations on its classical circuit. As a result, the T-depth and width (number of qubits) required for implementing the quantum circuits of AES are significantly reduced. Compared with the circuit proposed in EUROCRYPT 2020, the T-depth is reduced from 60 to 40 without increasing the width or 30 with a slight increase in width. These circuits are fully implemented in Microsoft Q# and the source code is publicly available. Compared with the circuit proposed in ASIACRYPT 2020, the width of one of our circuits is reduced from 512 to 371, and the Toffoli-depth is reduced from 2016 to 1558 at the same time. Actually, we can reduce the width to 270 at the cost of increased depth. Moreover, a full spectrum of depth-width trade-offs is provided, setting new records for the synthesis and optimization of quantum circuits of AES.
FINAL: Faster FHE Instantiated with NTRU and LWE

The NTRU problem is a promising candidate to build efficient Fully Homomorphic Encryption (FHE). However, all the existing proposals (e.g. LTV, YASHE) need so-called 'overstretched' parameters of NTRU to enable homomorphic operations. It was shown by Albrecht et al. (CRYPTO 2016) that these parameters are vulnerable against subfield lattice attacks. Based on a recent, more detailed analysis of the overstretched NTRU assumption by Ducas and van Woerden (ASIACRYPT 2021), we construct two FHE schemes whose NTRU parameters lie outside the overstretched range. The first scheme is based solely on NTRU and demonstrates competitive performance against the state-of-the-art FHE schemes including TFHE. Our second scheme, which is based on both the NTRU and LWE assumptions, outperforms TFHE with a 28% faster bootstrapping and 45% smaller bootstrapping and key-switching keys.
Discrimination of Pb-Zn deposit types using sphalerite geochemistry: New insights from machine learning algorithm

Due to the combined influences such as ore-forming temperature, fluid and metal sources, sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc (Pb-Zn) deposits. Therefore, trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types. However, previous discriminant diagrams usually contain two or three dimensions, which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits. In this study, we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can discriminate Pb-Zn deposit types using machine learning algorithms. A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications, containing 12 elements (Mn, Fe, Co, Cu, Ga, Ge, Ag, Cd, In, Sn, Sb, and Pb) from 5 types, including Sedimentary Exhalative (SEDEX), Mississippi Valley Type (MVT), Volcanic Massive Sulfide (VMS), skarn, and epithermal deposits. Random Forests (RF) is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits, most of which are falsely distinguished as skarn and epithermal types. To further discriminate VMS deposits, future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when constructing the classification model. RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification. Besides, a visualized tool, t-distributed stochastic neighbor embedding (t-SNE), was used to verify the results of both classification and evaluation. The results presented here show that Mn, Co, and Ge display significant impacts on classification of Pb-Zn deposits and In, Ga, Sn, Cd, and Fe also have relatively important effects compared to the rest elements, confirming that Pb-Zn deposits discrimination is mainly controlled by multi-elements in sphalerite. Our study hence shows that machine learning algorithm can provide new insights into conventional geochemical analyses, inspiring future research on constructing classification models of mineral deposits using mineral geochemistry data.
Security and Privacy on 6G Network Edge: A Survey

To meet the stringent service requirements of 6G applications such as immersive cloud eXtended Reality (XR), holographic communication, and digital twin, there is no doubt that an increasing number of servers will be deployed on the network edge. Then, the techniques, edge computing, edge caching, and edge intelligence will be more widely utilized for intelligent local data storage and processing generated by 6G applications, while innovative access network architecture based on the cloud-edge servers, such as the Open-Radio Access Network (O-RAN) will be adopted to improve the flexibility and openness for new service deployment and frequent network changes. On the other hand, new attack surfaces and vectors targeting local infrastructure and users will emerge along with the deployment of novel network architecture and techniques. Massive researchers have studied the potential security and privacy threats on the 6G network edge as well as the countermeasures. The three techniques, edge computing, edge caching, and edge intelligence have become a double-edged sword that can not only be synchronously utilized to develop defense countermeasures, but also become the targets of many new security and privacy threats. In this article, we provide a comprehensive survey of articles on the three techniques-related security threats and countermeasures on the 6G network edge. We explain how security and privacy can be destroyed by attacking one of the three technologies and how the three services support each other to realize efficient and achievable security protection. Moreover, the researches on the benefits and limitations of Federated Learning (FL) and blockchain for decentralized edge network systems in terms of security and privacy are also investigated. Additionally, we also analyze the existing challenges and future directions towards 6G.