Reviewerly/exHarmony huggingface dataset:
Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm

Soil moisture wireless sensor networks (SMWSNs) are used in the field of information monitoring for precision farm irrigation, which monitors the soil moisture content and changes during crop growth and development through sensor nodes at the end. The control terminal adjusts the irrigation water volume according to the transmitted information, which is significant for increasing the crop yield. One of the main challenges of SMWSNs in practical applications is to maximize the coverage area under certain conditions of monitoring area and to minimize the number of nodes used. Therefore, a new adaptive Cauchy variant butterfly optimization algorithm (ACBOA) has been designed to effectively improve the network coverage. More importantly, new Cauchy variants and adaptive factors for improving the global and local search ability of ACBOA, respectively, are designed. In addition, a new coverage optimization model for SMWSNs that integrates node coverage and network quality of service is developed. Subsequently, the proposed algorithm is compared with other swarm intelligence algorithms, namely, butterfly optimization algorithm (BOA), artificial bee colony algorithm (ABC), fruit fly optimization algorithm (FOA), and particle swarm optimization algorithm (PSO), under the conditions of a certain initial population size and number of iterations for the fairness and objectivity of simulation experiments. The simulation results show that the coverage rate of SMWSNs after ACBOA optimization increases by 9.09%, 13.78%, 2.57%, and 11.11% over BOA, ABC, FOA, and PSO optimization, respectively.
Experimental evaluation of stochastic configuration networks: Is SC algorithm inferior to hyper-parameter optimization method?

To overcome the pitfalls of Random Vector Functional Link (RVFL), a network called Stochastic Configuration Networks (SCN) has been proposed. By constraining and adaptively selecting the range of randomized parameters using the Stochastic Configuration (SC) algorithm, SCN claims to be potent in building an incremental randomized learning system according to residual error minimization. The SC has three variants depending on how the range of output weights are updated. In this work, we first relate the SCN to appropriate literature. Subsequently, we show that the major parts of the SC algorithm can be replaced by a generic hyper-parameter optimization method to obtain overall better results.
Automatic Scatterplot Design Optimization for Clustering Identification

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality for low-level tasks like clustering. Hence, constructing frameworks that consider both the perceptions of the visual encodings and the task being performed enables optimizing visualizations to maximize efficacy. In this article, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image. We validate our approach with user and case studies that show it efficiently provides high-quality scatterplot designs from a large parameter space.
Biomedical image retrieval using adaptive neuro-fuzzy optimized classifier system

The quantity of scientific images associated with patient care has increased markedly in recent years due to the rapid development of hospitals and research facilities. Every hospital generates more medical photographs, resulting in more than 10 GB of data per day being produced by a single image appliance. Software is used extensively to scan and locate diagnostic photographs to identify patient's precise information, which can be valuable for medical science research and advancement. An image recovery system is used to meet this need. This paper suggests an optimized classifier framework focused on a hybrid adaptive neuro-fuzzy approach to accomplish this goal. In the user query, similarity measurement, and the image content, fuzzy sets represent the vagueness that occurs in such data sets. The optimized classifying method 'hybrid adaptive neuro-fuzzy is enhanced with the improved cuckoo search optimization. Score values are determined by utilizing the linear discriminant analysis (LDA) of such classified images. The preliminary findings indicate that the proposed approach can be more reliable and effective at estimation than can existing approaches.
Siamese network based prospecting prediction method: A case study from the Au deposit in the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China

Supervised neural networks constitute an important research area for the intelligent prediction of locations for prospecting mineral deposits. Accurate training of a supervised neural network requires many training samples, something that is often difficult to obtain. This paper reports the use of geological, geochemical, and geophysical data by a Siamese network to overcome the problem of insufficient training samples, and implements a supervised deep learning prospecting prediction method based on the Siamese network. Intelligent prediction for gold deposits prospecting is carried out for the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China, and compared with the weight of evidence method and convolutional neural network model. The results show that:(a) the performance of the Siamese network model is no less than that of the convolutional neural network (CNN) model and better than that of the weight of evidence (WOE) method; (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. This study suggests Siamese network model as an effective mineral prospectivity modeling tool. This method is also suitable for prospecting prediction using geoscience data in other areas.
Towards a self-tuned data analytics-based process for an automatic context-aware detection and diagnosis of anomalies in building energy consumption timeseries

• Meter-level anomaly detection and diagnosis is performed through CMP algorithm. • CMP parameters are automatically set using supervised and unsupervised methods. • A severity score, based on 4 statistical methods, is used to recognize anomalies. • 55 anomalous energy consumption patterns are discovered in a one-year timeseries. • Anomalies at meter-level are diagnosed by exploiting information at sub-load level. Recently, the spread of IoT technologies has led to an unprecedented acquisition of energy-related data providing accessible knowledge on the actual performance of buildings during their operation. A proper analysis of such data supports energy and facility managers in spotting valuable energy saving opportunities. In this context, anomaly detection and diagnosis (ADD) tools allow a prompt and automatic recognition of abnormal and non-optimal energy performance patterns enabling a better decision-making to reduce energy wastes and system inefficiencies. To this aim, this paper introduces a novel meter-level ADD process capable to identify energy consumption anomalies at meter-level and perform diagnosis by exploiting information at sub-load level. The process leverages supervised and unsupervised analytics techniques coupled with the distance-based contextual matrix profile (CMP) algorithm to discover infrequent subsequences in energy consumption timeseries considering specific boundary conditions. The proposed process has self-tuning capabilities and can rank anomalies at both meter and sub-load level by means of robust severity score. The methodology is tested on one-year energy consumption timeseries of a medium/low voltage transformation cabin of the university campus of Politecnico di Torino leading to the detection of 55 anomalous subsequences that are diagnosed by analysing a group of 8 different sub-loads.
Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization

Cancer is a manifestation of disorders caused by the changes in the body's cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.
Improvements of Physical Activity Performance and Motivation in Adult Men through Augmented Reality Approach: A Randomized Controlled Trial

The COVID-19 pandemic had a global impact, increasing the prevalence of physical inactivity, which is mostly due to the lockdown and social distancing measures adopted during the pandemic. Hence, this study aimed to compare the efficacy of augmented reality-based training on physical activity performance and motivation in healthy adults to mirror visual feedback training and conventional physical therapy. This study used the randomized control trial pretest-posttest research design. Forty-eight healthy men aged 18-35 years who were engaged in recreational physical activities were enrolled and randomly divided into four groups: augmented reality-based training reality (ART), mirror visual feedback training (MVFT), therapist-based training (TBT), and control group. The total training program was held for four weeks. The isokinetic dynamometer, sit-and-reach test, Y balance test, and the intrinsic motivation inventory-22 were used to measure the outcomes before and after the intervention. Paired sample t-test was used to compare the changes before and after the intervention within groups, while the one-way ANOVA was used for the comparison between the groups. Results of the study showed that, after four weeks of intervention, balance, muscle strength, and muscle endurance in all groups significantly improved except for the control group. The ART group showed the highest increase in muscle strength, muscle endurance, and balance compared to the other groups. The motivation level increased in all three feedback groups and was observed in the following order: ART group > MVFT group > TBT group > control group. This study highlighted the most effective method that may be applied for home training during and after this period of the pandemic. The findings revealed that training while receiving real-time feedback via AR devices improves both physical performance and motivation. Augmented reality-based training can be used as an effective training option for improving physical activity and motivation and can be suggested for home training programs.
Cryptanalysis of encryption scheme based on compound coupled logistic map and anti-codifying technique for secure data transmission

The aim of this article is to provide cryptanalysis of chaos-based technique based on anti-codifying mechanism. The use of chaos in information security is deemed a secure trace of randomness. Many cryptosystems offer robust security against classical attacks having vulnerability in their structure. Recently, a compound coupled logistic chaotic map-based image encryption scheme was suggested. The encryption structure reveals some security flaws in the implementation. The original model includes input-independent swapping and substitution-based operations. In this work, we have intended a chosen-plaintext attack on a recently offered cryptosystem and suggested an anti-codifying technique for secure data transmission. The simulation and experimental outcomes reveal that the improvement suggestions can resist classical attacks.
A traceable and revocable multi-authority access control scheme with privacy preserving for mHealth

Mobile healthcare (mHealth) is a smart health system, which makes people have the storage and share in their personal health data (PHD) in the cloud for rapidly medical treatment through mobile 5G-enabled Industrial Internet of Things (IIoT) equipments. But key hosting problem and privacy leakage problem issued by malicious users and revoked users bring new security challenges. Traceability and accountability to attackers have been an important issue in mHealth system. Among the methods solving the above, most of them still face key abusing and privacy leakage problems. In this article, an expressive privacy preserving attribute-based traceable approach is proposed, where it keeps PHD confidentiality, eliminates the key delegation issue and prevents the key from being abused, allowing for fine-grained access control to the shared data in mHealth. Under this framework, we imprint a robust unforgeable signature in the secret key to assure the tracked user's undeniability. We also add a revocable function that can efficiently remove the malicious users by only updating parts of ciphertext, and unrevoked users still can access the encrypted data using the original key. To achieve privacy protection, a bloom filter is used to hide the access policy. Security analysis and performance comparisons show the proposed method is secure and efficient.
Image Security Based on Three-Dimensional Chaotic System and Random Dynamic Selection

Image encryption based on a chaos system can effectively protect the privacy of digital images. It is said that a 3D chaotic system has a larger parameter range, better unpredictability and more complex behavior compared to low-dimension chaotic systems. Motivated by this fact, we propose a new image cryptosystem that makes use of a 3D chaotic system. There are three main steps in our scheme. In the first step, the chaotic system uses the hash value of the plaintext image to generate three sequences. In step two, one of the sequences is used to dynamically select confusion and diffusion methods, where confusion and diffusion have three algorithms, respectively, and will produce 32n (n > 100) combinations for encryption. In step three, the image is divided into hundreds of overlapping subblocks, along with the other two sequences, and each block is encrypted in the confusion and diffusion process. Information entropy, NPCR, UACI results and various security analysis results show that the algorithm has a better security performance than existing, similar algorithms, and can better resist clipping, noise, statistical analysis and other attacks.
Classification of Alzheimer's Disease via Vision Transformer

Deep models are powerful in capturing the complex and non-linear relationship buried in brain imaging data. However, the huge number of parameters in deep models can easily overfit given limited imaging data samples. In this work, we proposed a cross-domain transfer learning method to solve the insufficient data problem in brain imaging domain by leveraging the knowledge learned in natural image domain. Specifically, we employed ViT as the backbone and firstly pretrained it using ImageNet-21K dataset and then transferred to the brain imaging dataset. A slice-wise convolution embedding method was developed to improve the standard patch operation in vanilla ViT. Our method was evaluated based on AD/CN classification task. We also conducted extensive experiments to compare the transfer performance with different transfer strategies, models, and sample size. The results suggest that the proposed method can effectively transfer the knowledge learned in natural image domain to brain imaging area and may provide a promising way to take advantages of the pretrained model in data-intensive applications. Moreover, the proposed cross-domain transfer learning method can obtain comparable classification performance compared to most recent studies.
Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing

Categorical Attribute traNsformation Environment (CANE) is a simpler but powerful data categorical preprocessing Python package. The package is valuable since there is currently a large range of Machine Learning (ML) algorithms that can only be trained using numerical data (e.g., Deep Learning, Support Vector Machines) and several real-world ML applications are associated with categorical data attributes. Currently, CANE offers three categorical to numeric transformation methods, namely: Percentage Categorical Pruned (PCP), Inverse Document Frequency (IDF) and a simpler One-Hot-Encoding method. Additionally, the CANE module is well documented with several code examples that can help in its adoption by non expert users.
Post‐quantum cryptography techniques for secure communication in resource‐constrained Internet of Things devices: A comprehensive survey

Abstract As the number and characteristics of smart devices change, the concept of the Internet of Things (IoT) emerges. The IoT provides the connected devices with a variety of resources that enable effective communication. At this point, several security issues arise to get the sensitive information behind every communication in the IoT. To provide users with security and privacy, cryptographic schemes are adopted, the most popular being public key cryptographic systems (PKC). However, with the advent of quantum computing, the level of security that can be provided by the PKC schemes is a big question. Another important issue is that the IoT environment is resource‐constrained, which necessitates the implementation of lightweight cryptographic algorithms for better security. In response to these issues, the post‐quantum cryptographic (PQC) schemes are one of the significant developments contributing to IoT security in the post‐quantum world. This article examines the key security issues in the IoT environment and examines the effective solutions found in the literature. The problems in IoT in the quantum era are discussed and appropriate solutions by PKC schemes under limited resources in IoT are focused. As the lattice‐based cryptosystems are more effective, the importance of these schemes in the resource‐constrained IoT is highlighted. This survey also leads to feasible future directions that can support developers and researchers in this field.
A Web-Based Analysis Toolkit for the System Usability Scale

The System Usability Scale (SUS) questionnaire is a broadly used usability measurement tool, which is fast in its application and straight forward in its interpretation. While the original SUS questionnaire was envisioned as a one-dimensional ”quick and dirty” approach to measure usability, research over the past 25 years revealed helpful insights and dimensions to contextualize and compare individual SUS scores on. In this paper, we present an open source web-based analysis toolkit for the SUS questionnaire, which calculates SUS measurements, analyses them based on the insights and contextualization scales suggested by previous work, and provides versatile plotting facilities to create appealing SUS graphs for scientific publications and presentations.
Speed Planning in Dynamic Environments over a Fixed Path for Autonomous Vehicles

In this paper, we present a novel convex optimization approach to address the minimum-time speed planning problem over a fixed path with dynamic obstacle constraints and point-wise speed and acceleration constraints. The contributions of this paper are three-fold. First, we formulate the speed planning as an iterative convex optimization problem based on space discretization. Our formulation allows imposing dynamic obstacle constraints and point-wise speed and acceleration constraints simultaneously. Second, we propose a modified vertical cell decomposition method to handle dynamic obstacles. It divides the freespace into channels, where each channel represents a homotopy of free paths and defines convex constraints for dynamic obstacles. Third, we demonstrate significant improvement over previous work on speed planning for typical driving scenarios such as following, merging, and crossing.
Bird's-eye View Social Distancing Analysis System

Social distancing can reduce the infection rates in respiratory pandemics such as COVID-19. Traffic intersections are particularly suitable for monitoring and evaluation of social distancing behavior in metropolises. Hence, in this paper, we propose and evaluate a real-time privacy-preserving social distancing analysis system (B-SDA), which uses bird's-eye view video recordings of pedestrians who cross traffic intersections. We devise algorithms for video pre-processing, object detection, and tracking which are rooted in the known computer-vision and deep learning techniques, but modified to address the problem of detecting very small objects/pedestrians captured by a highly elevated camera. We propose a method for incorporating pedestrian grouping for detection of social distancing violations, which achieves 0.92 F1 score. B-SDA is used to compare pedestrian behavior in pre-pandemic and during-pandemic videos in uptown Manhattan, showing that the social distancing violation rate of 15.6% during the pandemic is notably lower than 31.4% prenandemic baseline.
Spear-Phishing Susceptibility Stemming From Personality Traits

This study explores the psychological aspects of social engineering by analyzing personality traits in the context of spear-phishing attacks. Phishing emails were constructed by leveraging multiple vulnerable personality traits to maximize the success of an attack. The emails were then used to test several hypotheses regarding phishing susceptibility by simulating a series of spear-phishing campaigns inside a software development company. The company&#x2019;s employees underwent a standard Big Five personality test, four different phishing emails over four weeks, and cybersecurity training. The results were aggregated before and after the cybersecurity course, and binary logistic regression analyses were performed at each phase of the phishing attack. The results show that personality traits correlate with phishing susceptibility under certain circumstances and pave the way for new methods of protecting individuals from phishing attacks.