Reviewerly/exHarmony huggingface dataset:
Modified spider monkey optimization algorithm based feature selection and probabilistic neural network classifier in face recognition

Abstract This paper proposes a novel and robust predictive method using modified spider monkey optimization (MSMO) and probabilistic neural network (PNN) for face recognition. The limitation of the traditional spider monkey optimization (SMO) approach to obtaining an optimal solution for classification problems is overcome by enhancing the performance of SMO by modifying the perturbation rate with a non‐linear function, thereby improving the convergence of SMO. The framework comprises image preprocessing, feature extraction using dual tree complex wavelet transform (DT‐CWT), feature selection using the modified spider monkey optimization algorithm (MSMO), and classification using PNN. The proposed method is tested on the Yale and AR Face datasets. Experimental outcomes reveal that the proposed framework attain an accuracy of 99.4% with appreciable sensitivity, specificity, and G‐mean. To examine the efficacy of MSMO, parametric studies are conducted, which showed that MSMO converges faster with high fitness when compared to similar evolutionary algorithms like Genetic Algorithm (GA), Grey Wolf Optimization Algorithm (GWO), Particle Swarm Algorithm (PSO), and Cuckoo Search (CS) in selecting the optimal feature set. The MSMO‐PNN method outperforms similar state‐of‐the‐art methods, which reveals that the method proposed is competitive. The proposed model is robust to Gaussian and salt–pepper noise, obtaining the highest accuracy of 97.89% for varied noise density and variance.
Predicting Solar Power Generation from Direction and Tilt Using Machine Learning XGBoost Regression

Abstract Electricity production using photovoltaic panels is widely used all over the world. Electricity generation using solar power is helping a lot in reducing the Earth’s carbon production. However, many photovoltaic panels are often installed improperly to produce maximum efficiency. This not only reduces the amount of electricity produced, but also wastes resources required to make solar panels. To solve this problem, the electricity production data using the longitude and latitude of the actual solar panel installation location in 5 regions in Korea, and the direction and tilt angle of the installation site are used to predict the electricity production by using the machine learning XGBoost regression model. A study was conducted to determine the direction and tilt angle of the installation site to generate maximum electricity production in longitude and latitude. Through this, the longitude and latitude of the solar panel installation location for maximum electricity production were analysed, and in the future, more local data will be used to get out of the limited area, and specific research efficiency will be brought.
To regulate or not to regulate: unravelling institutional tussles around the regulation of algorithmic control of digital platforms

Regulating how digital platforms use algorithms to determine and control content displayed to their users is both a controversial topic and an important societal challenge. Existing research acknowledges institutional tussles around regulating how digital platforms use algorithms to determine and control content. However, we lack research showing how the development of regulation unfolds. We build on data from a longitudinal discourse analysis of 410 media articles and 483 policy and industry documents to study two cases of algorithmic control regulation in Australia. The first involves algorithmic control for content display, the second for content moderation. We develop a process model of institutional work towards regulation of algorithmic control. It captures the institutional tussles between governments, digital platforms and third parties as each expresses their perspective on legitimate forms of algorithmic control and shapes the process and outcome of regulation. Building on our model, we discuss the dynamics of regulation development in light of the constellation of actors and their power position in the process. We further consider the regulatory outcome and highlight future research questions that build on our findings.
Complex-valued Rényi entropy

AbstractAbstractComplex-valued expression models have been widely used in the application of intelligent decision systems. However, there is a lack of entropy to measure the uncertain information of the complex-valued information. Therefore, how to reasonably measure the uncertain information of the complex-valued information is a gap to be filled. In this paper, inspired by the Rényi entropy, we propose the complex-valued Rényi entropy, which measures uncertain information of the complex-valued probability under the framework of complex number, and this is also the first time to measure uncertain information in the complex space. The complex-valued Rényi entropy contains the features of the classical Rényi entropy, i.e., the complex-valued Rényi entropy corresponds to different information functions with different parameters q. Moreover, complex-valued Rényi entropy has some properties, such as non-negativity, monotonicity and etc. Some numerical examples can demonstrate the flexibility and reasonableness of the complex-valued Rényi entropy.Keywords: Complex-valued probabilityRényi entropycomplex-valued Rényi entropyuncertain information Disclosure statementThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Additional informationFundingThe authors greatly appreciate the reviews' suggestions and the editor's encouragement. The work is partially supported by National Natural Science Foundation of China (Grant No. 61973332), JSPS Invitational Fellowships for Research in Japan (Short-term).
Personalized Diagnostic Tool for Thyroid Cancer Classification Using Multi-view Ultrasound

Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds further difficulty to accurate diagnosis. To address these, we propose a personalized diagnostic tool that can customize its decision-making process for different patients. It consists of a multi-view classification module for feature extraction and a personalized weighting allocation network that generates optimal weighting for different views. It is also equipped with a self-supervised view-aware contrastive loss to further improve the model robustness towards different patient groups. Experimental results show that the proposed framework can better utilize multi-view information and outperform the competing methods.
Thermal–Visible Face Recognition Based on CNN Features and Triple Triplet Configuration for On-the-Move Identity Verification

Face recognition operating in visible domains exists in many aspects of our lives, while the remaining parts of the spectrum including near and thermal infrared are not sufficiently explored. Thermal-visible face recognition is a promising biometric modality that combines affordable technology and high imaging qualities in the visible domain with low-light capabilities of thermal infrared. In this work, we present the results of our study in the field of thermal-visible face verification using four different algorithm architectures tested using several publicly available databases. The study covers Siamese, Triplet, and Verification Through Identification methods in various configurations. As a result, we propose a triple triplet face verification method that combines three CNNs being used in each of the triplet branches. The triple triplet method outperforms other reference methods and achieves TAR @FAR 1% values up to 90.61%.
Accurate Real-Time Face Mask Detection Framework Using YOLOv5

After the COVID-19 pandemic, wearing a mask has become a must because it decreases the probability of infection by 68%. That is why a fast and accurate automatic mask detection is crucial to public institutions. In this paper, we present an accurate framework for real-time mask detection using YOLOv5 object detection algorithm. Our framework consists of four stages: image preprocessing by normalization and adding noise, adding negative samples and data augmentation then the detection core based on a modified version of YOLOv5. The proposed framework achieves 95.9% precision and 84.8% mean average precision using the Face Mask Detection dataset with a 10 milliseconds inference time.
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence

In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility. The focal point of the course is a group project based on reproducing existing FACT-AI algorithms from top AI conferences and writing a corresponding report. In the first iteration of the course, we created an open source repository with the code implementations from the group projects. In the second iteration, we encouraged students to submit their group projects to the Machine Learning Reproducibility Challenge, resulting in 9 reports from our course being accepted for publication in the ReScience journal. We reflect on our experience teaching the course over two years, where one year coincided with a global pandemic, and propose guidelines for teaching FACT-AI through reproducibility in graduate-level AI study programs. We hope this can be a useful resource for instructors who want to set up similar courses in the future.
Early cervical cancer diagnosis using Sooty <scp>tern‐optimized CNN‐LSTM</scp> classifier

Abstract Detecting and categorizing Pap smear images automatically has been a difficult task over the past five decades. There is a significant need for the design and implementation of low‐cost, high‐efficiency screening systems. To address these issues, we have proposed a novel approach for cervical cancer classification. At first, each image is pre‐processed and cropped into 2043*1362 pixels. These images are separated into 681*681 pixels with six square patches. Image augmentation techniques such as image enhancement, image flipping, and image rotation are used to reduce the number of parameters necessary for subsequent processes. The cancer‐affected regions are correctly segmented with the help of kernel weighted fuzzy local information c‐means clustering (KWFLICM) model. The KWFLICM model mainly extracts the cytoplasm and nucleus present in the cell from the background region. The features of the cervical image are extracted using a convolution neural network (CNN), and these retrieved features, such as shape, color, and texture, are crucial for classification. Finally, the Sooty Tern Optimization (STO) algorithm with CNN‐based long short‐term memory classifier (CNN‐LSTM) identifies the four types of CC: normal, light dysplastic, severe dysplastic, and carcinoma. The images for the experiment were obtained from Herlev University Hospital in Denmark, and the software used for the implementation was MATLAB. According to the comparative analysis, the proposed CNN‐LSTM with STO algorithm demonstrates better results using various measures such as accuracy, specificity, sensitivity, and F‐score. Depending upon the experimental results, the proposed CNN‐LSTM with STO algorithm provides 99.80% accuracy, 99% specificity, 98.83% sensitivity, and 97.8 F‐score. The proposed methodology offers an improvement of 28.5% and 19.46% when compared with the random forest and ensemble classifier.
The Energy Storage System Integration Into Photovoltaic Systems: A Case Study of Energy Management at UTFPR

Energy storage system integration can reduce electricity costs and provide desirable flexibility and reliability for photovoltaic (PV) systems, decreasing renewable energy fluctuations and technical constraints. In this sense, this study aimed to propose energy management strategies through this integration, aiming to improve the demand profile of a university commercial consumer for compensation during peak hours. To achieve the objectives of this research, a pilot project was installed which included a 10 kWp grid-tie PV system (GTPVS), bidirectional inverters, and battery bank, as part of a research and development (R&amp;amp;D) project implemented in the Federal University of Technology–Paraná (UTFPR) Curitiba campus at Neoville headquarters. The methodology consisted of the analysis of variables that interfere with the operation of this system, such as a detailed university profile of demand and consumption, local energy pricing, PV generation profile as well as the interaction between PV generator and the energy storage system, and the variation in power fluctuations throughout the year. Additionally, a technical feasibility study was carried out aimed to determine this consumer’s optimal size through the Homer Grid software. The detailed demand profile allowed us to know in depth the peak values were above 80 kW, for university working days. Furthermore, the daily analysis of the PV generation profile confirmed that although the PV source is quite variable, the scheduled dispatch operation is not interfered by the variability and intermittency of the solar source, as the system was designed and programmed to operate in parallel with the electrical network. Regarding the energy consumed at the university, very significant values were obtained, in which the month of July presented the highest consumption for the base year 2019, with a total monthly value of 48,670 kWh. The established operating strategy for the battery bank with a 10% discharge depth resulted in a life cycle of 2,500 cycles which can extend its useful life by approximately 9.5 years. The results obtained by the simulation show different levels of energy contribution with percentages of 60.0, 33.6, and 2.7%, respectively.
Unsupervised Learning of Spatio-Temporal Representation with Multi-Task Learning for Video Retrieval

The majority of videos in the internet lack semantic tags, which complicates indexing and retrieval, and mandates the adoption of critical content-based analysis approaches. Earlier works relies on hand-crafted features, which hardly represents the temporal dynamics. Later, video representations learned through supervised deep learning methods were found to be effective, but at the cost of large labeled dataset. Recently, self-supervised based methods for video representation learning are proposed within the community to harness the freely available unlabeled videos. However, most of these methods are based on single pretext task, which limits the learning of generalizable representations. This work proposes to leverage multiple pretext tasks to enhance video representation learning and generalizability. We jointly optimized the C3D network by using multiple pretext tasks such as: rotation prediction, speed prediction, time direction prediction and instance discrimination. The nearest neighbour task is used to analyze the learned features. And for action recognition task, the network is further fine-tuned with pretrained weights. We use the UCF-101 dataset for the experiments and, achieves 28.45% retrieval accuracy (Recall@l), and 68.85% fine-tuned action recognition accuracy, which is better than state-of-the-arts.
A Novel Ensemble Strategy With Enhanced Cross Attention Encoder-Decoder Framework for Tumor Segmentation in Whole Slide Images

Due to the rapid increase in the quality of the generated medical images combined with the development of computational algorithms, Computer-Aided Diagnostic (CAD) systems, which are empowered with artificial intelligence to analyze these images, provide an objective decision to assist and guide the medical staff. In cancer diagnosis and staging, the golden standard is the microscopic examination of suspicious tissue either in the laboratory using the conventional glass slides or using their digital scanned counterparts called Whole Slide Images (WSI). However, the vast search space in these slides and the existing heterogeneous patterns in the sampled tissue make their analysis process labor-intensive and time-consuming. Therefore, this paper proposed a novel encoder-decoder framework with a new ensemble strategy to address the difficulties in semantically segmenting tumors in WSI. We introduced new enhancements to the base U-Net, including cross attention to aggregate information from different portions of the network. Moreover, we manipulated each stained slide to produce three new slides with different characteristics and fed them to separate models and injected their predictions together at the inference stage through our proposed ensemble strategy to form the final segmentation prediction. We tested and validated the proposed cross attention U-Net model with the ensemble strategy on the task of viable tumor segmentation of the challenging hepatocellular carcinoma in liver WSIs in the PAIP2019 dataset. The experimental results have demonstrated that the proposed ensemble strategy outperformed the single conventional handling of the WSI and improved the segmentation accuracy.
The Application of Deep Learning Techniques for Solar Power Forecasting

The future forecasting of the solar irradiation is becoming very essential. There are other elements (features) contribute to the forecasting process of the solar irradiation. These features are closely related to the amount of solar irradiation arriving from the sun. As the weather factors are related to each other in terms of influence, a wide range of features that are necessary to enter the i process are considered in this research. This paper investigate the effect of some atmospheric factors like Evapotranspiration and soil temperature using deep learning techniques, like LSTM algorithm. The results show that higher accuracy is achieved when new features related to solar irradiation were included in the forecasting process. To the best of authors’ knowledge, these new features have not previously considered in literature. The RMSE value is 0.34 obtained with 16 features used in forecasting process.
A High-Speed FPGA Implementation of AES for Large Scale Embedded Systems and its Applications

In this paper, a high-speed hardware implementation of the AES encryption algorithm is presented. targeting the large scale and high bandwidth embedded systems and applications. The high-speed implementation is developed by employing the pipelining architectural technique, where the fully sub pipelined architecture is applied efficiently. The applied architecture is performed using the AES 128-bit data path. The fully sub pipelined architectural technique is implemented by constructing the Sboxes of the AES encryption algorithm using the BRAMs of the FPGA device. The pipelined design is realized on the new Xilinx FPGA devices, Artix-7, Virtex-7, Kintex-7, Spartan-7, and Kintex UltraScale. The proposed design is fully synthesized, translated, placed, and routed using the new Xilinx Vivado 2020 design suite. The hardware implementation results of the proposed pipelined design show an efficiency in terms of speed, utilized resources, and throughput. Comparing with previous works, our proposed pipelined design utilizes less FPGA resources, operates at high operating frequencies, and delivers high throughputs.
FAWPA: A FAW Attack Protection Algorithm Based on the Behavior of Blockchain Miners

Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm (FAWPA) based on the behavior of blockchain miners. Firstly, FAWPA performs miner data preprocessing based on the behavior of the miners. Then, FAWPA proposes a behavioral reward and punishment mechanism and a credit scoring model to obtain cumulative credit value with the processed data. Moreover, we propose a miner's credit classification mechanism based on fuzzy C-means (FCM), which combines the improved Aquila optimizer (AO) with strong solving ability. That is, FAWPA combines the miner's accumulated credit value and multiple attack features as the basis for classification, and optimizes cluster center selection by simulating Aquila's predation behavior. It can improve the solution update mechanism in different optimization stages. FAWPA can realize the rapid classification of miners' credit levels by improving the speed of identifying malicious miners. To evaluate the protective effect of the target mining pool, FAWPA finally establishes a mining pool and miner revenue model under FAW attack. The simulation results show that FAWPA can thoroughly and efficiently detect malicious miners in the target mining pool. FAWPA also improves the recall rate and precision rate of malicious miner detection, and it improves the cumulative revenue of the target mining pool. The proposed algorithm performs better than ND, RSCM, AWRS, and ICRDS.
Simulations of Denial of Service Attacks in Quantum Key Distribution Networks

A QKD network can be considered an add-on technology to a standard communication network that provides IT-secure cryptographic keys as a service. As a result, security challenges resulting in the suspension of functional work must be addressed. This study analyzes a Denial of Service (DoS) attack on the Key Management System (KMS), one of the critical components of the QKD network in charge of key management and key provisioning to authorized consumers. Through simulation methods performed in the QKDNetSim, we show that legitimate customers experience significantly worse service during an excessive DoS attack on KMS.