Reviewerly/exHarmony huggingface dataset:
Improved salp swarm algorithm combined with chaos

A recently developed metaheuristic optimization algorithm, Salp Swarm Algorithm (SSA), has manifested its capability in solving various optimization problems and many real-life applications. SSA is based on salps’ swarming behaviour when finding their way and searching for food in the oceans. Nonetheless, like most metaheuristic algorithms, SSA experiences low convergence and stagnation in local optima and rate. There is a need to enhance SSA to speed its convergence and effectiveness to solve complex problems. In the present study, we will introduce chaos into SSA (CSSA) to increase its global search mobility for robust global optimization. Detailed studies are carried out on real-world nonlinear benchmark systems and CEC 2013 benchmark functions with chaotic map (Tent). Here, the algorithm utilizes a Tent map to tune the salp leaders’ attractive movement around food sources. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of CSSA for 12 nonlinear systems and 28 unconstrained optimization problems CEC 2013. Two nonparametric statistical tests, the Friedman test and Wilcoxon Signed-Rank Test, are conducted to show the superiority of CSCA over other states of the art algorithms and our results’ significance. • This paper introduces chaos into Salp Swarm Algorithm. • Solve 28 unconstrained optimization problems, CEC and 12 nonlinear systems. • The effectiveness and efficiency of our algorithm are provided. • Experimental results prove superiority of our algorithm over the state-of-the-arts.
A Hereditary Attentive Template-based Approach for Complex Knowledge Base Question Answering Systems

Knowledge Base Question Answering systems (KBQA) aim to find answers to natural language questions over a knowledge base. This work presents a template matching approach for Complex KBQA systems (C-KBQA) using the combination of Semantic Parsing and Neural Networks techniques to classify natural language questions into answer templates. An attention mechanism was created to assist a Tree-LSTM in selecting the most important information. The approach was evaluated on the LC-Quad 1, LC-Quad 2, ComplexWebQuestion, and WebQuestionsSP datasets, and the results show that our approach outperforms other approaches on three datasets.
Malaria parasite classification framework using a novel channel squeezed and boosted CNN

Malaria is a life-threatening infection that infects the red blood cells and gradually grows throughout the body. The plasmodium parasite is transmitted by a female Anopheles mosquito bite and severely affects numerous individuals within the world every year. Therefore, early detection tests are required to identify parasite-infected cells. The proposed technique exploits the learning capability of deep convolutional neural network (CNN) to distinguish the parasite-infected patients from healthy individuals using thin blood smear. In this regard, the detection is accomplished using a novel STM-SB-RENet block-based CNN that employs the idea of split-transform-merge (STM) and channel squeezing-boosting (SB) in a modified fashion. In this connection, a new convolutional block-based STM is developed, which systematically implements region and edge operations to explore the parasitic infection pattern of malaria related to region homogeneity, structural obstruction and boundary-defining features. Moreover, the diverse boosted feature maps are achieved by incorporating the new channel SB and transfer learning (TL) idea in each STM block at abstract, intermediate and target levels to capture minor contrast and texture variation between parasite-infected and normal artifacts. The malaria input images for the proposed models are initially transformed using discrete wavelet transform to generate enhanced and reduced feature space. The proposed architectures are validated using hold-out cross-validation on the National Institute of Health Malaria dataset. The proposed methods outperform training from scratch and TL-based fine-tuned existing techniques. The considerable performance (accuracy: 97.98%, sensitivity: 0.988, F-score: 0.980 and area under the curve: 0.996) of STM-SB-RENet suggests that it can be utilized to screen malaria-parasite-infected patients. Graphical Abstract.
DB-BERT: A Database Tuning Tool that "Reads the Manual"

DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents. It uses text to identify database system parameters to tune as well as recommended parameter values. DB-BERT applies large, pre-trained language models (specifically, the BERT model) for text analysis. During an initial training phase, it fine-tunes model weights in order to translate natural language hints into recommended settings. At run time, DB-BERT learns to aggregate, adapt, and prioritize hints to achieve optimal performance for a specific database system and benchmark. Both phases are iterative and use reinforcement learning to guide the selection of tuning settings to evaluate (penalizing settings that the database system rejects while rewarding settings that improve performance). In our experiments, we leverage hundreds of text documents about database tuning as input for DB-BERT. We compare DB-BERT against various baselines, considering different benchmarks (TPC-C and TPC-H), metrics (throughput and run time), as well as database systems (Postgres and MySQL). In all cases, DB-BERT finds the best parameter settings among all compared methods. The code of DB-BERT is available online at https://itrummer.github.io/dbbert/.
Contact Tracing: Barriers and Facilitators

Contact tracing-the process of identifying, isolating, and managing infected persons and their contacts-is a recognized public health measure for controlling the transmission of infectious diseases. In the context of the COVID-19 pandemic, contact tracing has received intense attention. We provide a brief overview of the history of contact tracing during several major disease outbreaks in the past century: syphilis and other sexually transmitted infections, HIV infection, tuberculosis, Ebola virus disease, and COVID-19. Our discussion on the barriers to and facilitators of contact tracing offers a perspective on societal and institutional roles and dynamics, stigma as a major barrier to effective tracing efforts, and how the nature and epidemiology of the infection itself can affect its success. We explore the evolution and adaptation of contact tracing and provide insights for future programming and research. (Am J Public Health. 2022;112(7):1025-1033. https://doi.org/10.2105/AJPH.2022.306842).
Information-decision searching algorithm: Theory and applications for solving engineering optimization problems

The nature of the real-world problem is multi-modal and multidimensional. This paper proposes a novel metaheuristic algorithm based on social behaviors of people acquiring favorable information, which is the society-based metaheuristic optimization mechanism, called the Information-Decision Search Algorithm (IDSE), aiming to provide a new optimization technology for solving real-world optimization problems. This optimization technology proposes special searching mechanisms of delivery behavior, approaching behavior, inheritance behavior, mutation behavior, interaction, and learning behavior, establishing corresponding mathematical models to develop an efficient optimization framework for solving constrained optimization. The performance of the proposed algorithm and 10 state-of-the-art optimizers is evaluated on 46 benchmarks, including convergence, solution accuracy, robustness, diversity, significance, and the dimensional-scalability on CEC 2017 benchmarks (50 Dim and 100 Dim). The statistical results suggest, with the dimensionality of the problem variable increasing, the computing efficiency of the proposed optimization technology keeps on the highest level at all times. The low-rank feature for IDSE on 46 benchmarks emphasizes the selective priority in solving the same optimization problem. In addition, IDSE also considers 7 real-world engineering problems. The comparison results suggest that IDSE is superior to competitive algorithms in improving solution accuracy and reducing optimization costs, indicating the significant performance for solving constraint optimization.
An Effective Color Image Encryption Based on Henon Map, Tent Chaotic Map, and Orthogonal Matrices

In the last decade, the communication of images through the internet has increased. Due to the growing demands for data transfer through images, protection of data and safe communication is very important. For this purpose, many encryption techniques have been designed and developed. New and secured encryption schemes based on chaos theory have introduced methods for secure as well as fast communication. A modified image encryption process is proposed in this work with chaotic maps and orthogonal matrix in Hill cipher. Image encryption involves three phases. In the first phase, a chaotic Henon map is used for permuting the digital image. In the second phase, a Hill cipher is used whose encryption key is generated by an orthogonal matrix which further is produced from the equation of the plane. In the third phase, a sequence is generated by a chaotic tent map which is later XORed. Chaotic maps play an important role in the encryption process. To deal with the issues of fast and highly secured image processing, the prominent properties of non-periodical movement and non-convergence of chaotic theory play an important role. The proposed scheme is resistant to different attacks on the cipher image. Different tests have been applied to evaluate the proposed technique. The results of the tests such as key space analysis, key sensitivity analysis, and information entropy, histogram correlation of the adjacent pixels, number of pixel change rate (NPCR), peak signal to noise ratio (PSNR), and unified average changing intensity (UCAI) showed that our proposed scheme is an efficient encryption technique. The proposed approach is also compared with some state-of-the-art image encryption techniques. In the view of statistical analysis, we claim that our proposed encryption algorithm is secured.
Adversarial Sticker: A Stealthy Attack Method in the Physical World

To assess the vulnerability of deep learning in the physical world, recent works introduce adversarial patches and apply them on different tasks. In this paper, we propose another kind of adversarial patch: the Meaningful Adversarial Sticker, a physically feasible and stealthy attack method by using real stickers existing in our life. Unlike the previous adversarial patches by designing perturbations, our method manipulates the sticker's pasting position and rotation angle on the objects to perform physical attacks. Because the position and rotation angle are less affected by the printing loss and color distortion, adversarial stickers can keep good attacking performance in the physical world. Besides, to make adversarial stickers more practical in real scenes, we conduct attacks in the black-box setting with the limited information rather than the white-box setting with all the details of threat models. To effectively solve for the sticker's parameters, we design the Region based Heuristic Differential Evolution Algorithm, which utilizes the new-found regional aggregation of effective solutions and the adaptive adjustment strategy of the evaluation criteria. Our method is comprehensively verified in the face recognition and then extended to the image retrieval and traffic sign recognition. Extensive experiments show the proposed method is effective and efficient in complex physical conditions and has a good generalization for different tasks.
Transformer-based Cross Reference Network for video salient object detection

Video salient object detection is a fundamental computer vision task aimed at highlighting the most conspicuous objects in a video sequence. There are two key challenges presented in video salient object detection: (1) how to extract effective feature representations from appearance and motion cues, and (2) how to combine both of them into robust saliency representation. To handle these challenges, in this paper, we propose a novel Transformer-based Cross Reference Network (TCRN), which fully exploits long-range context dependencies in both feature representation extraction and cross-modal (i.e., appearance and motion) integration. In contrast to existing CNN-based methods, our approach formulates video salient object detection as a sequence-to-sequence prediction task. In the proposed approach, the deep feature extraction is achieved by a pure vision transformer with multi-resolution token representations. Specifically, we design a Gated Cross Reference (GCR) module to effectively integrate appearance and motion into saliency representation. The GCR first propagates global context information between different modalities, and then perform cross-modal fusion by a gate mechanism. Extensive evaluations on five widely-used benchmarks show that the proposed Transformer-based method performs favorably against the existing state-of-the-art methods
Metaverse in medicine

The emergence of the Medical Internet of Things (MIoT) has presented an opportune solution to these problems, as it provides effective technological support. Based on the current MIoT theory, it is feasible to realize efficient and accurate graded diagnosis and treatment through the linkage between doctors in large hospitals (the ‘Cloud Experts’) and doctors in small hospitals (the ‘Terminal Doctors’), and to contribute to the research and development of related technologies to improve graded diagnosis and treatment in China. Nevertheless, the following issues remain in clinical practice: (1) The Cloud Experts are not available to dedicate themselves to popular science education, or to give professional lectures, at all times and in all settings. (2) The Cloud Experts are not available to provide guidance for the Terminal Doctors on diagnosis and treatment at all times and in all settings. (3) In clinical trials, the main researchers are not available to monitor the research or guide the team at all times and in all settings. (4) Due to the lack of real-time quality control at all times and in all settings, non-standard diagnosis and treatment, with the features of a handicraft workshop, still exist to a considerable degree. The real cause lies in the limitations of the Internet technology itself, which cannot facilitate communication at all times and in all settings between the Cloud Experts and the Terminal Doctors involved in graded diagnosis and treatment. Therefore, the MIoT-based digital platforms need to be further improved, especially concerning the communication and interactions between humans and computers, and the integration and linkage between the virtual and real worlds. It is gratifying that the concept of the metaverse has been introduced, which provides a possible solution to all these problems, and serves as a foundation for the proposal and development of the Metaverse in Medicine.
DI-Unet: Dimensional interaction self-attention for medical image segmentation

In recent years, Unet network based on convolution has become a general structure for medical image segmentation tasks. However, it cannot effectively model the long-distance dependence between features due to the limitation of the receptive field. The successful application of Transformer in computer vision solves the problem of the limited receptive field of neural networks. However, the computational complexity limits its further application in medical image segmentation. In addition, the self attention mechanism in Transformer only explores the spatial dimension relationship of the feature maps, and lacks the interaction with the channel dimension, which limits the performance improvement of the network. Here, we proposes DI-Unet, which develops Dimensional Interactive (DI) self-attention for effective feature extraction processing. When inputting high - resolution images, it can effectively reduce the amount of model calculations and capture cross-dimensional information before calculating attention weights. The overwhelming superiority of DI-Unet is demonstrated by extensive experiments in multiple databases. In large datasets, the proposed method outperforms other methods in segmentation tasks. The study provides a research foundation and important reference value for the research and application of Transformer structure in medical image segmentation tasks.
Internet voting in Estonia 2005–2019: Evidence from eleven elections

Internet voting is a highly contested topic in electoral studies. This article examines Internet voting in Estonia over 15 years and 11 nation-wide elections. It focuses on the following questions: How is Internet voting organized and used in Estonia? How have the Estonian Internet voting system and its usage evolved over time? What are the preconditions and consequences of large-scale deployment of Internet voting? The results suggest that the rapid uptake and burgeoning usage rates reflect the system's embeddedness in a highly developed digital state and society. Through continuous technological and legal innovation and development, Estonia has built an advanced Internet voting system that complies with normative standards for democratic elections and is widely trusted and used by the voters. Internet voting has not boosted turnout in a setting where voting was already easily accessible. Neither has it created digital divides: Internet voting in Estonia has diffused to the extent that socio-demographic characteristics no longer predict usage. This, combined with massive uptake, reduces incentives for political parties to politicize the novel voting mode.
Learning quantum data with the quantum earth mover’s distance

Quantifying how far the output of a learning algorithm is from its target is an essential task in machine learning. However, in quantum settings, the loss landscapes of commonly used distance metrics often produce undesirable outcomes such as poor local minima and exponentially decaying gradients. To overcome these obstacles, we consider here the recently proposed quantum earth mover's (EM) or Wasserstein-1 distance as a quantum analog to the classical EM distance. We show that the quantum EM distance possesses unique properties, not found in other commonly used quantum distance metrics, that make quantum learning more stable and efficient. We propose a quantum Wasserstein generative adversarial network (qWGAN) which takes advantage of the quantum EM distance and provides an efficient means of performing learning on quantum data. We provide examples where our qWGAN is capable of learning a diverse set of quantum data with only resources polynomial in the number of qubits.
Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)

With the expansion of the internet, a major threat has emerged involving the spread of malicious domains intended by attackers to perform illegal activities aiming to target governments, violating privacy of organizations, and even manipulating everyday users. Therefore, detecting these harmful domains is necessary to combat the growing network attacks. Machine Learning (ML) models have shown significant outcomes towards the detection of malicious domains. However, the “black box” nature of the complex ML models obstructs their wide-ranging acceptance in some of the fields. The emergence of Explainable Artificial Intelligence (XAI) has successfully incorporated the interpretability and explicability in the complex models. Furthermore, the post hoc XAI model has enabled the interpretability without affecting the performance of the models. This study aimed to propose an Explainable Artificial Intelligence (XAI) model to detect malicious domains on a recent dataset containing 45,000 samples of malicious and non-malicious domains. In the current study, initially several interpretable ML models, such as Decision Tree (DT) and Naïve Bayes (NB), and black box ensemble models, such as Random Forest (RF), Extreme Gradient Boosting (XGB), AdaBoost (AB), and Cat Boost (CB) algorithms, were implemented and found that XGB outperformed the other classifiers. Furthermore, the post hoc XAI global surrogate model (Shapley additive explanations) and local surrogate LIME were used to generate the explanation of the XGB prediction. Two sets of experiments were performed; initially the model was executed using a preprocessed dataset and later with selected features using the Sequential Forward Feature selection algorithm. The results demonstrate that ML algorithms were able to distinguish benign and malicious domains with overall accuracy ranging from 0.8479 to 0.9856. The ensemble classifier XGB achieved the highest result, with an AUC and accuracy of 0.9991 and 0.9856, respectively, before the feature selection algorithm, while there was an AUC of 0.999 and accuracy of 0.9818 after the feature selection algorithm. The proposed model outperformed the benchmark study.
Exploration of barriers and enablers of blockchain adoption for sustainable performance: implications for e-enabled agriculture supply chains

Blockchain technology (BT) is a disruptive technology to achieve sustainable supply chain performance. The previous literature suggests a slow rate of BT adoption in improving sustainable supply chain performance (SSCP) in the agriculture context. This study explores and evaluates barriers and enablers to the adoption of BT for SSCP in e-enabled agriculture supply chains. using a novel hybrid technique integrating the Pythagorean Fuzzy sets (PFS), Cumulative Prospect Theory (CPT), and VIKOR. Data was collected data from 215 agriculture firms in Asia and North Africa. The results presented strategies for implementing BT for SSCP in e-agriculture supply chains and recommended (1) developing transparency readiness in sustainability, (2) collaboration among supply chain partners, (3) upgrading data access control (4) management commitment (5) collaboration with governments. The study presents several implications to managers to redirect strategies and focus efforts on the most significant enablers to promote BT implementation for SSCP in e-enabled SSCP.
Using Algorithmic Modeling to Control User Access Based on Functioning Table

Today, the development of modern digital technologies requires them to solve existing cyber security problems. Thus, in this study, by building an algorithmic model for user access control based on a functional table, information security of existing systems can be used to create imitation of processes occurring in the system, which allows you to apply and analyze specific problems. This study attempted to focus on algorithmic issues.In this algorithmic model based on Functioning table, the user’s access control process is active at all transitions in the Petri net, and cases can be reached and detected with an initial marking, a sequence of transitions leading to a particular state.The algorithmic model based on the Functioning table of user access control has a strictly formal description that allows you to analyze the process of interaction of three subjects of information relations. This model allows switching the algorithmic model of user access control to the system to software implementation.