Reviewerly/exHarmony huggingface dataset:
A layer-2 solution for inspecting large-scale photovoltaic arrays through aerial LWIR multiview photogrammetry and deep learning: A hybrid data-centric and model-centric approach

Defective components within solar photovoltaic (PV) arrays overheat, resulting in particular temperature patterns under the long-wave thermal infrared (LWIR) spectrum. The detection and on-field localization of these patterns is of paramount aid to the operations and maintenance of PV installations. In this context, we develop a two-layer end-to-end inspection solution for the detection, quantification and on-field localization of overheated regions on PV arrays from LWIR UAV imagery. Layer 1 generates a georeferenced orthomosaic of the inspected site via a Structure from Motion-MultiView Stereo (SfM-MVS) photogrammetric acquisition/post-processing workflow. Layer 2 is a tile-based deep semantic segmentation stage that extracts and quantifies the affected regions from the generated orthomosaic. We collect aerial images from 103 PV sites, comprising approximately 342 000 modules. After a SfM-MVS workflow, we produce and annotate 7910 orthorectified unique affected tiles, posteriorly augmented to prepare the state-of-the-art dataset in terms of size and representativeness. Through a training/cross-validation and test process, we investigate the implementation of 9 models in the segmentation process: FCN, U-Net, FPN, DeepLab, LinkNet, DANet, CFNet, ACFNet and TransU-Net, each of which experimented with 2 backbones: ResNet50 and DenseNet121. The models feature efficient encoder-to-decoder feature map transfers, pyramidal feature recognition, spatial and channel attention, feature co-occurrence, class center as well as vision transformers. The best performance is achieved by FPN-DenseNet121, with a mean mIoU of 93.44% and an F1-score of 96.39% on our test set. The two-layer solution takes the best of the data-centric and model-centric paradigms, alongside addressing the limitations of conventional inspection procedures. It is put into a concrete application framework, where it provides a pixel-based and a tile-based quantification of the affected regions within a PV plant. The results are promising, and the selected model can be deployed efficiently for extensive aerial monitoring of large-scale PV plants.
Quantum <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> -medoids algorithm using parallel amplitude estimation

Quantum computing is a promising paradigm that can provide viable solutions to high-complexity problems. The $k$-medoids algorithm is a powerful clustering method ubiquitously used in data mining, image processing, pattern recognition, etc. The core of $k$-medoids algorithm is to perform cluster assignment and center update, which are time-consuming for large data sets. A\"{\i}meur et al. proposed a quantum $k$-medoids algorithm [A\"{\i}meur, Brassard, and Gambs, Mach. Learn. 90, 261 (2013)] by quantizing the center update. Nevertheless, it has a query complexity $O({N}^{3/2})$ for one iteration, which is computationally expensive for a large $N$ where $N$ is the number of points. In this paper, we propose a complete quantum algorithm for $k$-medoids algorithm. Specifically, in cluster assignment, we devise a quantum subroutine to calculate the Manhattan distance between any two points and then assign all points to the closest center in parallel, which is faster than what is achievable classically. In center update, for a cluster, we use parallel amplitude estimation to calculate the average distance of each point to all the others. It makes our algorithm polynomially faster than the algorithm of A\"{\i}meur et al., whose sum of distances of each point to all the others is computed by adding the distances one by one. Our quantum $k$-medoids algorithm, with time complexity $\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{O}({N}^{1/2})$, achieves a polynomial speedup in $N$ compared to the existing one.
Multi CNN based automatic detection of mitotic nuclei in breast histopathological images

In breast cancer diagnosis, the number of mitotic cells in a specific area is an important measure. It indicates how far the tumour has spread, which has consequences for forecasting the aggressiveness of cancer. Mitosis counting is a time-consuming and challenging technique that a pathologist does manually by examining Hematoxylin and Eosin (H&E) stained biopsy slices under a microscope. Due to limited datasets and the resemblance between mitotic and non-mitotic cells, detecting mitosis in H&E stained slices is difficult. By assisting in the screening, identifying, and labelling of mitotic cells, computer-aided mitosis detection technologies make the entire procedure much easier. For computer-aided detection approaches of smaller datasets, pre-trained convolutional neural networks are extensively employed. The usefulness of a multi CNN framework with three pre-trained CNNs is investigated in this research for mitosis detection. Features were collected from histopathology data and identified using VGG16, ResNet50, and DenseNet201 pre-trained networks. The proposed framework utilises all training folders of the MITOS dataset provided for the MITOS-ATYPIA contest 2014 and all the 73 folders of the TUPAC16 dataset. Each pre-trained Convolutional Neural Network model, such as VGG16, ResNet50 and DenseNet201, provides an accuracy of 83.22%, 73.67%, and 81.75%, respectively. Different combinations of these pre-trained CNNs constitute a multi CNN framework. Performance measures of multi CNN consisting of 3 pre-trained CNNs with Linear SVM give 93.81% precision and 92.41% F1-score compared to multi CNN combinations with other classifiers such as Adaboost and Random Forest.
Learn from each other to Classify better: Cross-layer mutual attention learning for fine-grained visual classification

Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data. Moreover, with the popularity of deep convolutional neural networks, researchers have mainly used deep, abstract, semantic information for FGVC, while shallow, detailed information has been neglected. This work proposes a cross-layer mutual attention learning network (CMAL-Net) to solve the above problems. Specifically, this work views the shallow to deep layers of CNNs as "experts" knowledgeable about different perspectives. We let each expert give a category prediction and an attention region indicating the found clues. Attention regions are treated as information carriers among experts, bringing three benefits: (i) helping the model focus on discriminative regions; (ii) providing more training data; (iii) allowing experts to learn from each other to improve the overall performance. CMAL-Net achieves state-of-the-art performance on three competitive datasets: FGVC-Aircraft, Stanford Cars, and Food-11. The source code is available at https://github.com/Dichao-Liu/CMAL
Tile-based microscopic image processing for malaria screening using a deep learning approach

Manual microscopic examination remains the golden standard for malaria diagnosis. But it is laborious, and pathologists with experience are needed for accurate diagnosis. The need for computer-aided diagnosis methods is driven by the enormous workload and difficulties associated with manual microscopy based examination. While the importance of computer-aided diagnosis is increasing at an enormous pace, fostered by the advancement of deep learning algorithms, there are still challenges in detecting small objects such as malaria parasites in microscopic images of blood films. The state-of-the-art (SOTA) deep learning-based object detection models are inefficient in detecting small objects accurately because they are underrepresented on benchmark datasets. The performance of these models is affected by the loss of detailed spatial information due to in-network feature map downscaling. This is due to the fact that the SOTA models cannot directly process high-resolution images due to their low-resolution network input layer.In this study, an efficient and robust tile-based image processing method is proposed to enhance the performance of malaria parasites detection SOTA models. Three variants of YOLOV4-based object detectors are adopted considering their detection accuracy and speed. These models were trained using tiles generated from 1780 high-resolution P. falciparum-infected thick smear microscopic images. The tiling of high-resolution images improves the performance of the object detection models. The detection accuracy and the generalization capability of these models have been evaluated using three datasets acquired from different regions.The best-performing model using the proposed tile-based approach outperforms the baseline method significantly (Recall, [95.3%] vs [57%] and Average Precision, [87.1%] vs [76%]). Furthermore, the proposed method has outperformed the existing approaches that used different machine learning techniques evaluated on similar datasets.The experimental results show that the proposed method significantly improves P. falciparum detection from thick smear microscopic images while maintaining real-time detection speed. Furthermore, the proposed method has the potential to assist and reduce the workload of laboratory technicians in malaria-endemic remote areas of developing countries where there is a critical skill gap and a shortage of experts.
Boosting capuchin search with stochastic learning strategy for feature selection

Abstract The technological revolution has made available a large amount of data with many irrelevant and noisy features that alter the analysis process and increase time processing. Therefore, feature selection (FS) approaches are used to select the smallest subset of relevant features. Feature selection is viewed as an optimization process for which meta-heuristics have been successfully applied. Thus, in this paper, a new feature selection approach is proposed based on an enhanced version of the Capuchin search algorithm (CapSA). In the developed FS approach, named ECapSA, three modifications have been introduced to avoid a lack of diversity, and premature convergence of the basic CapSA: (1) The inertia weight is adjusted using the logistic map, (2) sine cosine acceleration coefficients are added to improve convergence, and (3) a stochastic learning strategy is used to add more diversity to the movement of Capuchin and a levy random walk. To demonstrate the performance of ECapSA, different datasets are used, and it is compared with other well-known FS methods. The results provide evidence of the superiority of ECapSA among the tested datasets and competitive methods in terms of performance metrics.
A robust and secured fusion based hybrid medical image watermarking approach using RDWT-DWT-MSVD with Hyperchaotic system-Fibonacci Q Matrix encryption

Digital image watermarking, the process of marking a host image with a watermark, is generally used to authenticate the data. In the medical field, it is of utmost importance to verify the authenticity of the data using Medical Image Watermarking (MIW), especially in e-healthcare applications. Recently, MIW with image fusion, the merging of multimodal images to improve image quality, is being widely utilized to make diagnosis more accessible and precise with the verified data. This paper offers a durable and secure fusion-based hybrid MIW approach. The method initially used Fast Filtering (FF) to merge two medical images from different modalities to form the cover image. A first-level Redundant Discrete Wavelet Transform (RDWT) is employed on this host image to locate the component with the highest entropy. Then a single-level Discrete Wavelet Transform (DWT) is applied to it. It performed a Multi-resolution Singular Value Decomposition (MSVD) on the wavelet decomposed component and the embedding watermark. Finally, a Hyperchaotic System-Fibonacci Q Matrix (HFQM) encryption system was utilized, which increases the watermarked image's security. Here, using various medical images, the performance of the proposed technique is evaluated. Without any attacks, the approach achieved a maximum Peak Signal to Noise Ratio (PSNR) of 90.31 dB and a Structural Similarity Index Matrix (SSIM) of value 1. Various watermarking assaults were employed to test the proposed method's resilience. The suggested technique achieved a perfect value of 1 for the Normalised Correlation (NC) for almost all attacks with acceptable imperceptibility, which substantially improves over current procedures. The suggested technique's average embedding and extraction times are 0.3958 and 0.4721 seconds, respectively, which are pretty short compared to existing approaches.
Cloud Computing Applications For Protecting the Information of Healthcare Department Using Smart Internet of Things Appliance

Combining cloud computing and Internet of Things concept is the emerging trend for online management efficiently and the sensor data processing. Pervasive healthcare sector or using the Internet of Things paradigm and the study offers the platform the disc based on cloud computing for the administration of information healthcare department. The development of clear and organized healthcare monitoring systems is made possible by the Cloud IoT convergence of the Cloud and IoT. Different types of healthcare applications use various IoT based and cloud-based application for transmission of data and to connect with each other that enables the provision of clinical healthcare solutions effectively. The research study proposes a novel hybrid system that uses IoT smart appliance using metaheuristic swarm intelligence technique for storing data into the cloud then to balance the load with real-time providers at low-cost that receives data from the sensor devices of hospital and stored in cloud. The computer resources are revolutionizing algorithms for individualized inference and actions in health management and the data is protected using blockchain technology.
A Hybrid Deep Transfer Learning Approach For The Detection Of Vector-Borne Diseases

Vector-borne diseases considerably impact the worldwide population’s health and economic well-being. However, training deep-learning models requires significant time and training data. Therefore, a unique hybrid transfer learning approach was proposed for detecting vector-borne diseases (VBD) to solve these issues while retaining high accuracy. In the first phase, malaria and Lyme benchmark datasets were obtained. Then VGG16, VGG19, MobileNetV2, and DenseNet 169 were compared to the hybrid model results (MobileNetV2+DenseNet 169). The effectiveness of the hybrid transfer learning method was evaluated using several performance measures, namely precision, loss, accuracy, AUC and RMSE. On the malaria dataset, the proposed model (MobileNetV2+DenseNet 169) achieved the most excellent classification accuracy of 99.9%, and on the Lyme dataset, 99.3%.
A Comparative Study on Cyber security Technology in Big data Cloud Computing Environment

Protection of networks, programs and systems from cyberattacks is the practice of cybersecurity technology. The cyberattacks are capable of gaining access for deleting or altering of data that are sensitive, demanding money from the users and obstruction of regular operations of corporates. In the present situation many devices are getting smarter than that of the hackers and humans that makes the tasks difficult for implementing the measures of cybersecurity. In terms of cyber security, the ability of collecting huge amount of data is known as big data analytics. The functions are performed through displaying, interpretation and extraction of the insights of future that can enable early detection of catastrophic cyber threats and attacks. Organizations can better understand all the activities and acts that could potentially result in cyber-attacks by having a stronger and more effective cyber defensive posture. As offices became more remote, cloud computing became crucial for gaining access to important documents, programs, and computing resources. Although connection can be both a benefit and a curse, as easily accessible files can become an easy target for hostile actors, we expect the global economy to transfer even more into the cloud over time. Therefore, efforts in strengthening cybersecurity measures are vital to safeguard distant vaults of sensitive data. The management depends on a combination of technology and advice to secure the cloud. It involves managing the framework, information applications, safe-secure guidelines, consistency leads, and secure infrastructure data applications that pertain to cloud computing. The same principles that underpin security for modern cloud computing platforms and historic knowledge centers are secrecy, integrity, and usability. The novel method of DDoS attack detection is proposed.
The Empirical Analysis of Artificial Intelligence Approaches for Enhancing the Cyber Security for Better Quality

In the world of information technology, cyber security is crucial. In this technological age, when information is the primary resource for every firm to preserve and produce results in accordance with requirements, security is the key concern that must be resolved in each case. This study offers a succinct review of artificial intelligence applications in different cybersecurity utilizing artificial neural networks to analyze security and assesses the potential for upgrading defense mechanisms to increase cybersecurity capabilities. The examination of the latest man-made brainpower programming for online protection might recommend that valuable applications are now being used. They utilize brain organizations to initially get the edge and numerous other online protection areas. Then again, it is apparent that the utilization of man-made brainpower advancements is important to actually address some network protection issues. The primary goal of the study is to provide an artificial intelligence strategy that can offer security. Additionally, it focuses on the methods, values, and trends that are reshaping the field of cyber security.
Breast Cancer Detection in Mammogram Images using Machine Learning Methods and CLAHE Algorithm

Breast cancer is one of the most common cancer types. This is the second-leading cause of cancer-related death in women. It ranked 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> according to available data lung cancers is the only one causing more causalities. It’s critical to receive a breast cancer diagnosis quickly. The MIAS data set is used in this study to examine machine learning-based categorization approaches used to study breast cancer. Important image data is fetched from mammograms. We choose eight distinct classifiers and assess each one’s precision, recall, accuracy, and F-score. The analysis’s findings were higher than 69.88%.
DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning

This paper examines how deep learning (DL) representations, in contrast to traditional engineered features, can support semantic interaction (SI) in visual analytics. SI attempts to model user’s cognitive reasoning via their interaction with data items, based on the data features. We hypothesize that DL representations contain meaningful high-level abstractions that can better capture users’ high-level cognitive intent. To bridge the gap between cognition and computation in visual analytics, we propose DeepVA (Deep Visual Analytics), which uses high-level deep learning representations for semantic interaction instead of low-level hand-crafted data features. To evaluate DeepVA and compare to SI models with lower-level features, we design and implement a system that extends a traditional SI pipeline with features at three different levels of abstraction. To test the relationship between task abstraction and feature abstraction in SI, we perform visual concept learning tasks at three different task abstraction levels, using semantic interaction with three different feature abstraction levels. DeepVA effectively hastened interactive convergence between cognitive understanding and computational modeling of the data, especially in high abstraction tasks.
Pelaksanaan Gerakan Literasi Sekolah di Sekolah Menengah Pertama

This study aims to evaluate the implementation of the GLS program at SMP Negeri 107 Jakarta. The method used is descriptive with a mixed approach (qualitative and quantitative). The results showed that: (1) Context evaluation, students, teachers and parents viewed the GLS program as needed by students; (2) input evaluation, the majority of teachers do not have adequate literacy capacity, the School Committee and parents support the GLS program, there is a Literacy Working Group Team and there is no public involvement; (3) Process evaluation, principals, teachers and education staff were involved in the 15 minute reading activity, the majority of teachers had taught the correct reading and summarizing strategies; (4) Product evaluation, the majority of students, teachers and parents feel the benefits of the GLS program, the GLS program affects learning activities, behavior, insights and skills of students. In conclusion, habituation of literacy activities in schools in the form of a 15-minute reading program, plus a literacy-rich physical environment and the use of literacy strategies in learning are proven to be able to form students' new skills.&#x0D; &#x0D; Keywords: CIPP Evaluation, School Literacy Movement, Literacy Program
TPMWallet: Towards Blockchain Hardware Wallet using Trusted Platform Module in IoT

Ensuring the authenticity of the system communication and the well-preservation of the provided data is the basic and important foundation of a well-operated information system. Although Blockchain, being a secure and decentralized database, contributes to various proposals for enhancing IoT security, the very technology does not ensure that each transaction on chain is coming from the account with owner’s proper authorization. A stolen wallet not only can make the owner affected financially, but also can bring questionable actions under the owner’s title, such as providing problematic materials to terminate effective smart contracts. When it comes to a private chain in some enterprises, stolen wallets can make network administrators hard to identify the authenticity of transactions. When no one can tell whether the transaction is coming from the genuine device or someone is withholding the stolen wallet, sabotages can be performed. For this issue, we introduce TPMWallet, a blockchain-based hardware wallet via Trusted Platform Module (TPM), which can provide a secure subsystem dedicated for security. It can not only cover the functionality of the existing hardware wallet, but also provide more than we can obtain today - the policy. This allows TPM only operating under matching conditions, increasing the difficulty for attackers to tamper.
A Generative Adversarial Network Based Tone Mapping Operator for 4K HDR Images

High dynamic range (HDR) has arguably been established as the preferred image and video format for content providers. As standard dynamic range (SDR) displays still dominate the market, there is a need for finding efficient ways to convert HDR content to the SDR format, a process known as tone mapping. Recently, many tone mapping operators (TMOs) have been proposed that are based on deep learning approaches. However, the biggest challenge in training such deep learning networks is lack of truthful SDR and HDR datasets that would lead to highly accurate TMOs. In this paper, we introduce a new high-quality 4K HDR-SDR dataset of image pairs, covering a wide range of brightness levels and colors. We propose a TMO that is based on the generative adversarial network architecture. Evaluation results showed that our method achieves high perceptual quality, maintaining artistic intent and providing better color representation compared to existing state-of-the-art TMOs. Data and code are available at: https://github.com/zjbthomas/TMO-GAN.