Reviewerly/exHarmony huggingface dataset:
Architectural Design for Data Security in Cloud-based Big Data Systems

Authors:
raw_author_name: Mujeeb-ur-Rehman Jamali
raw_author_name: Najma Imtiaz Ali
raw_author_name: Aadil Jamali

Abstract: Architecture design of system is creative process after all. Non-functional requirements and software architecture have a strong relationship. Security of system is one of great concern and it is a non-functional need and requirement. The architectural style and structure chosen for a system should be based on the non-functional system requirements. Big Data technologies are used to store handles huge amounts of operational data in the public domain. Structure and behavior diagrams of the system is proposed that use a secure architectural design for a system that works over the cloud. Different components of Big Data have been investigated in this research work due to the seriousness of security issues focus on privacy and confidentiality. The security of data over the cloud being outsourced is a growing concern. There are outsider and insider malicious database administrators who can gain access and modify private and sensitive data therefore database service provider can be deceitful. Security mechanisms are necessarily required to protect sensitive data residing on public clouds. At the application level, the proposed solution ensures privacy and confidentiality of data at rest over the cloud. Extensive tests have been carried out, and the results of this research work are as follows that the recorded results revealed statistically significant variations in plaintext and multimedia contents (i.e., audio and video) with time measured in milliseconds for small, medium, and large datasets..
Diffractive optical computing in free space

Authors:
raw_author_name: Jingtian Hu
raw_affiliation_string: Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
raw_author_name: Deniz Mengu
raw_affiliation_string: Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
raw_author_name: Dimitrios C. Tzarouchis
raw_affiliation_string: Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
raw_author_name: Brian Edwards
raw_affiliation_string: Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
raw_author_name: Nader Engheta
raw_affiliation_string: Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
raw_author_name: Aydogan Ozcan
raw_affiliation_string: California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA

Abstract: Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.
The Role of Artificial Intelligence in Shaping the Future of Education at Higher Secondary Level

Authors:
raw_author_name: Muhammad Azeem Sarwar
raw_author_name: Afshan Gul

Abstract: This study examines the implications of Artificial Intelligence (AI) in enhancing educational methodologies at the higher secondary level, focusing on its impact on student engagement and learning outcomes, the experiences and challenges faced by educators in integrating AI tools, and the broader ethical and privacy considerations. Utilizing a qualitative methodology based on interviews with educators and students, the research aims to explore the nuanced perspectives on the role of AI in education, identifying both the potential benefits and drawbacks. Initial findings highlight the positive effects of AI on personalized learning experiences alongside significant challenges related to resource limitations and the need for comprehensive support for educators. Moreover, concerns about ethical implications and data privacy emerge as critical issues requiring careful management. The study concludes that while AI offers transformative potential for educational practices, ensuring effective integration, addressing ethical concerns, and preparing students for future technological challenges is essential for leveraging AI's full benefits in the educational sector.
MCMC algorithm based on Markov random field in image segmentation

Authors:
raw_author_name: Huazhe Wang
raw_affiliation_string: College of Computer Engineering, Shangqiu Polytechnic, Shangqiu, China
raw_author_name: Li Ma
raw_affiliation_string: Soft Vocational Technology Institute, Shangqiu Polytechnic, Shangqiu, China

Abstract: In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.
Evaluation of an artificial intelligence-based clinical trial matching system in Chinese patients with hepatocellular carcinoma: a retrospective study

Authors:
raw_author_name: Kunyuan Wang
raw_affiliation_string: State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, No. 1838, North Guangzhou Avenue, Baiyun District, Guangzhou, China.
raw_author_name: Hao Cui
raw_affiliation_string: State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, No. 1838, North Guangzhou Avenue, Baiyun District, Guangzhou, China.
raw_author_name: Qi Zhang
raw_affiliation_string: Research and Development Department, Huimei Technology Co., Ltd, Beijing, China.
raw_author_name: Li Liu
raw_affiliation_string: State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, No. 1838, North Guangzhou Avenue, Baiyun District, Guangzhou, China. liuli@i.smu.edu.cn.

Abstract: Abstract Background Artificial intelligence (AI)-assisted clinical trial screening is a promising prospect, although previous matching systems were developed in English, and relevant studies have only been conducted in Western countries. Therefore, we evaluated an AI-based clinical trial matching system (CTMS) that extracts medical data from the electronic health record system and matches them to clinical trials automatically. Methods This study included 1,053 consecutive inpatients primarily diagnosed with hepatocellular carcinoma who were referred to the liver tumor center of an academic medical center in China between January and December 2019. The eligibility criteria extracted from two clinical trials, patient attributes, and gold standard were decided manually. We evaluated the performance of the CTMS against the established gold standard by measuring the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and run time required. Results The manual reviewers demonstrated acceptable interrater reliability (Cohen’s kappa 0.65–0.88). The performance results for the CTMS were as follows: accuracy, 92.9–98.0%; sensitivity, 51.9–83.5%; specificity, 99.0–99.1%; PPV, 75.7–85.1%; and NPV, 97.4–98.9%. The time required for eligibility determination by the CTMS and manual reviewers was 2 and 150 h, respectively. Conclusions We found that the CTMS is particularly reliable in excluding ineligible patients in a significantly reduced amount of time. The CTMS excluded ineligible patients for clinical trials with good performance, reducing 98.7% of the work time. Thus, such AI-based systems with natural language processing and machine learning have potential utility in Chinese clinical trials.
Laboratory experiments of model-based reinforcement learning for adaptive optics control

Authors:
raw_author_name: Tapio Helin
raw_affiliation_string: Lappeenranta-Lahti University of Technology (Finland)

Abstract: Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from their host stars, making their detection difficult. Consequently, the adaptive optics (AO) system’s control algorithm must be carefully designed to distinguish the exoplanet from the residual light produced by the host star. A promising avenue of research to improve AO control builds on data-driven control methods, such as reinforcement learning (RL). RL is an active branch of the machine learning research field, where control of a system is learned through interaction with the environment. Thus, RL can be seen as an automated approach to AO control, where its usage is entirely a turnkey operation. In particular, model-based RL has been shown to cope with temporal and misregistration errors. Similarly, it has been demonstrated to adapt to nonlinear wavefront sensing while being efficient in training and execution. In this work, we implement and adapt an RL method called policy optimization for AO (PO4AO) to the GPU-based high-order adaptive optics testbench (GHOST) test bench at ESO headquarters, where we demonstrate a strong performance of the method in a laboratory environment. Our implementation allows the training to be performed parallel to inference, which is crucial for on-sky operation. In particular, we study the predictive and self-calibrating aspects of the method. The new implementation on GHOST running PyTorch introduces only around 700 μs of in addition to hardware, pipeline, and Python interface latency. We open-source well-documented code for the implementation and specify the requirements for the RTC pipeline. We also discuss the important hyperparameters of the method and how they affect the method. Further, the paper discusses the source of the latency and the possible paths for a lower latency implementation.
Breast Cancer Image Classification based on Adaptive Interpolation Approach Using Clinical Dataset

Authors:
raw_author_name: Hemalatha I
raw_author_name: Maragatharajan M
raw_author_name: Balasubramanian C
raw_author_name: Sathish Kumar L

Abstract: In the healthcare and bioinformatics disciplines, the categorization of breast cancer has become an emerging paradigm due to the second most common cause of cancer-related mortality in women. A biopsy is a procedure in which tissue has been examined to determine whether or not it is breast cancer through histopathologists that may lead to a mistaken diagnosis. The research is mainly focused on the patient data (884 case reports of patients) is acquired from the American Oncology Institute implemented with preprocessing techniques consists of missing values and those values are recovered with Novel Modified Interpolation (MI) Method. Deep learning networks effectively detect and assess the pattern for annotating histological data based on the labelling, which preserves time, system cost and enhances the system accuracy. This framework addressed feature acquisition and missing analysis strategies based on entropy confidence weight factor. First, the iterative patterns have been treated as a potential diagnostic rule, and the attention-based rule combination formulates the classification issue based on integrating convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches. Second, the key part of label construction is carried out with an entropy confidence-weight factor assessment which detects and predict different patterns to construct the rule for classification. Third, optimization of clustering data by assessing missing parameters based on mean square error and the concept of interpolation to reduce data loss by around 20% and enhance the system accuracy. Simulation results show that the proposed system achieves 91.3% accuracy to state-of-art approaches, potentially allied in clinical applications. Modified Interpolation (MI) method recovered missing values with least mean square error and less data loss of 0.0123 and 1.38% respectively. This method also compared with existing Linear Interpolation (LI) method which is able to recover least men square error and data loss of 3.295 and 18.925% respectively. Comparatively the modified interpolation method recovered the missing values with less mean square error and less data loss.
Fourier domain filtering analysis for color polarization camera demosaicking

Authors:
raw_author_name: Nathan Hagen
raw_author_name: Yukitoshi Otani
raw_author_name: Prathan Buranasiri

Abstract: We review Fourier-domain methods for demosaicking Bayer-filter color cameras and monochrome polarization cameras, and then generalize the approach for the quad-Bayer-filter mosaic and for color-polarization cameras. For each of these four mosaic filter types, we provide theoretical expressions for the sampling functions, the Fourier-domain channels, and the linear combination of reconstructed channels (the demosaicking algorithm) needed to estimate the input (presampled) image. A useful advantage of the Fourier-domain approach is that it provides a direct means of visualizing and quantifying when aliasing is likely or unlikely to be present. For the Bayer and quad-Bayer-filter types, we provide simulated images, while for the polarization camera types we provide experimental images and videos to illustrate the algorithm and analyze crosstalk error.
Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

Authors:
raw_author_name: Pingjie Fu
raw_affiliation_string: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
raw_author_name: Jiawei Zhang
raw_affiliation_string: College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China; School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
raw_author_name: Zhaoxian Yuan
raw_affiliation_string: Institute of Resource and Environmental Engineering, Hebei Geo University, Shijiazhuang 050031, China
raw_author_name: Yuxuan Zhang
raw_affiliation_string: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
raw_author_name: Fei Meng
raw_affiliation_string: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
raw_author_name: Shubin Zhou
raw_affiliation_string: School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519000, China; School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia

Abstract: In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.
Graph Attention Network and Informer for Multivariate Time Series Anomaly Detection

Authors:
raw_author_name: Mengmeng Zhao
raw_affiliation_string: Department of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Haipeng Peng
raw_affiliation_string: Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Lixiang Li
raw_affiliation_string: Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Yeqing Ren
raw_affiliation_string: Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract: Time series anomaly detection is very important to ensure the security of industrial control systems (ICSs). Many algorithms have performed well in anomaly detection. However, the performance of most of these algorithms decreases sharply with the increase in feature dimension. This paper proposes an anomaly detection scheme based on Graph Attention Network (GAT) and Informer. GAT learns sequential characteristics effectively, and Informer performs excellently in long time series prediction. In addition, long-time forecasting loss and short-time forecasting loss are used to detect multivariate time series anomalies. Short-time forecasting is used to predict the next time value, and long-time forecasting is employed to assist the short-time prediction. We conduct a large number of experiments on industrial control system datasets SWaT and WADI. Compared with most advanced methods, we achieve competitive results, especially on higher-dimensional datasets. Moreover, the proposed method can accurately locate anomalies and realize interpretability.
Lightweight Meter Pointer Recognition Method Based on Improved YOLOv5

Authors:
raw_author_name: Chi Zhang
raw_affiliation_string: School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China
raw_author_name: Kai Wang
raw_affiliation_string: School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China
raw_author_name: Jie Zhang
raw_affiliation_string: Institute of Intelligent Machinery, Hefei Institute of Physical Sciences, Chinese Academy of Sciences, Hefei 230031, China
raw_author_name: Fan Zhou
raw_affiliation_string: Institute of Intelligent Machinery, Hefei Institute of Physical Sciences, Chinese Academy of Sciences, Hefei 230031, China
raw_author_name: Le Zou
raw_affiliation_string: School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China

Abstract: In substation lightning rod meter reading data taking, the classical object detection model is not suitable for deployment in substation monitoring hardware devices due to its large size, large number of parameters, and slow detection speed, while is difficult to balance detection accuracy and real-time requirements with the existing lightweight object detection model. To address this problem, this paper constructs a lightweight object detection algorithm, YOLOv5-Meter Reading Lighting (YOLOv5-MRL), based on the improved YOLOv5 model's speed while maintaining accuracy. Then, the YOLOv5s are pruned based on the convolutional kernel channel soft pruning algorithm, which greatly reduces the number of parameters in the YOLOv5-MRL model while maintaining a certain accuracy loss. Finally, in order to facilitate the dial reading, the dial external circle fitting method is proposed to calculate the dial reading using the circular angle algorithm. The experimental results on the self-built dataset show that the YOLOv5-MRL object detection model achieves a mean average precision of 96.9%, a detection speed of 5 ms/frame, and a model weight size of 5.5 MB, making it better than other advanced dial reading models.