Reviewerly/exHarmony huggingface dataset:
Malaria parasite detection using deep learning algorithms based on (CNNs) technique

Malaria is a life-threatening disease caused by female anopheles mosquito bites that are prevalent in many regions of the world. We introduce a deep convolutional neural network (CNN) to improve malaria diagnosis accuracy using patches segmented from microscopic images of red blood cell smears. We design the automatic parasite detection in blood from Giemsa-stained smears using three CNN pre-trained models such as VGG19, ResNet50, and MobileNetV2. As the CNNs are poorly performing for small datasets, we introduce the transfer learning technique. Transfer learning involves acquiring visual features from large general datasets and resolving issues using small datasets. We use a transfer learning approach to detect and classify malaria parasites with three CNN pre-trained models. We evaluated proposed CNN models experimentally using the National Institute of Health (NIH) Malaria Dataset. Our proposed model achieves an accuracy of almost 100%.
Virtual Reality in Museums: Does It Promote Visitor Enjoyment and Learning?

Museums have shown keen interest in providing XR experiences, in particular, augmented reality (AR) and virtual reality (VR), as part of their exhibitions. However, little is known about how visitors feel about such experiences and how exposure to them affects visitor interest in topics. In this study, the authors conducted a pre/post-usage survey among visitors at a museum with VR installation. Four research models were compared to determine the most suitable model for studying gratifications. Our results showed that visitors expect to be entertained, yet they are less entertained than expected. VR provides positive learning effects, and scholars can rely on post-usage measures to assess the experience.
Anomaly Detection in Traffic Surveillance Videos Using Deep Learning

In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.
A data-centric artificial intelligent and extended reality technology in smart healthcare systems

Extended reality (XR) solutions are quietly maturing, and their novel use cases are already being investigated, particularly in the healthcare industry. By 2022, the extended reality market is anticipated to be worth $209 billion. Certain diseases, such as Alzheimer's, Schizophrenia, Stroke rehabilitation stimulating specific areas of the patient's brain, healing brain injuries, surgeon training, realistic 3D visualization, touch-free interfaces, and teaching social skills to children with autism, have shown promising results with XR-assisted treatments. Similar effects have been used in video game therapies like Akili Interactive's EndeavorRx, which has previously been approved by the Food and Drug Administration (FDA) as a treatment regimen for children with attention deficit hyperactivity disorder (ADHD). However, while these improvements have received positive feedback, the field of XR-assisted patient treatment is in its infancy. The growth of XR in the healthcare sphere has the potential to transform the delivery of medical services. Imagine an elderly patient in a remote setting having a consultation with a world-renowned expert without ever having to leave their house. Rather than operating on cadavers in a medical facility, a surgical resident does surgery in a virtual setting at home. On the first try, a nurse uses a vein finder to implant an IV. Through cognitive treatment in a virtual world, a war veteran recovers from post-traumatic stress disorder (PTSD). The paper discusses the potential impact of XR in transforming the healthcare industry, as well as its use cases, challenges, XR tools and techniques for intelligent health care, recent developments of XR in intelligent healthcare services, and the potential benefits and future aspects of XR techniques in the medical domain.
‘The metaverse and how we’ll build it’: The political economy of Meta’s Reality Labs

Spatial computing – that is, a form of human–computer interaction that retains or manipulates referents of real object and spaces – is an increasingly intense focus for Meta. In 2018, Meta launched ‘Reality Labs’ (RL), a research and development division to oversee the company’s production of spatial computing technologies. Drawing on a media historiographical approach from platform studies, this article charts the development of the company’s spatial computing ambitions through RL from 2018 to 2022. In so doing, we find that Meta attempts to consolidate complementors through acquisitions, capture policymakers and academics, convene third-party businesses and developers, and expand its ecosystem through enhancing platform programmability. We argue that RL’s efforts to grow the platform from within, and through drawing in third-parties, signals an ambition to grow their spatial computing offerings such that they take on a central, infrastructural role in society.
Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images

Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54-0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.
Ultrasonic image denoising using machine learning in point contact excitation and detection method

A point contact/Coulomb coupling technique is generally used for visualizing the ultrasonic waves in Lead Zirconate Titanate (PZT) ceramics. The point contact and delta pulse excitation produce a broadband frequency spectrum and wide directional wave vector. In ultrasonic, the signal is corrupted with several types of noises such as speckle, Gaussian, Poisson, and salt and pepper noise. Consequently, the resolution and quality of the images are degraded. The reliability of the health assessment of any civil or mechanical structures highly depends on the ultrasonic signals acquired from the sensors. Recently, deep learning (DL) has been implemented for the reduction of noises from the signals and in images. Here, we have implemented deep learning-based convolutional autoencoders for suitable noise modeling and subsequently denoising the ultrasonic images. Two different metrics, PSNR and SSIM are calculated for quantitative analysis of ultrasonic images. PSNR provides higher visual interpretation, whereas the SSIM can be used to measure much finer similarities. Based upon these parameters speckle-noise demonstrated better than other noise models.
SNIS: A Signal Noise Separation-Based Network for Post-Processed Image Forgery Detection

Image forgery detection has aroused widespread research interest in both academia and industry because of its potential security threats. Existing forgery detection methods achieve excellent tampered regions localization performance when forged images have not undergone post-processing, which can be detected by observing changes in the statistical features of images. However, forged images may be carefully post-processed to conceal forgery boundaries in a particular scenario. It becomes tough challenging to these methods. In this paper, we perform an analogous analysis between image forgery detection and blind signal separation, and formulate the post-processed image forgery detection problem into a signal noise separation problem. We also propose a signal noise separation-based (SNIS) network to solve the problem of detecting post-processed image forgery. Specifically, we first adopt the signal noise separation module to separate tampered region from the complex background region with post-processing noise, which weakens or even eliminates the negative impact of post-processing on forgery detection. Then, the multi-scale feature learning module uses a parallel atrous convolution architecture to learn high-level global features from multiple perspectives. Besides, a feature fusion module is utilized to enhance the discriminability of tampered regions and real regions by strengthening the boundary information. Finally, the prediction module is designed to predict the tampered region and classify the type of tampering operation. Extensive experiments show that the proposed SNIS is not only effective for forgery detection on forged images without post-processing, but also promising in robustness against multiple post-processing attacks. Furthermore, SNIS is robust in detecting forged images from unknown sources.
Eigen Crossover in Cooperative Model of Evolutionary Algorithms Applied to CEC 2022 Single Objective Numerical Optimisation

In this paper, a cooperative model of four well-performing evolutionary algorithms enhanced by Eigen crossover is proposed and applied to a set of problems CEC 2022. The four adaptive algorithms employed in this model are - Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Differen-tial Evolution with Covariance Matrix Learning and Bimodal Distribution Parameter Setting (CoBiDE), an adaptive variant of jSO, and Differential Evolution With an Individual-Dependent Mechanism (IDE). For the higher efficiency of the cooperative model, a linear population-size reduction mechanism is employed. The model was introduced for CEC 2019. Here, Eigen crossover is applied for each cooperating algorithm. The provided results show that the proposed model of four Evolutionary Algorithms with Eigen crossover (EA4eig) is able to solve ten out of 24 optimisation problems. Moreover, comparing EA4eig with four state-of-the-art variants of adaptive Differential Evolution illustrates the superiority of the newly designed optimiser.
Any-Precision Deep Neural Networks

We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-widths, by truncating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low-bits, we show that the model achieved accuracy comparable to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learning models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures and applicable to multiple vision tasks. Our code is released at https://github.com/SHI-Labs/Any-Precision-DNNs.
Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques

Lung cancer has the highest mortality rate in the world, twice as high as the second highest. On the other hand, pathologists are overworked and this is detrimental to the time spent on each patient, diagnostic turnaround time, and their success rate.In this work, we design, implement, and evaluate a diagnostic aid system for non-small cell lung cancer detection, using Deep Learning techniques.The classifier developed is based on Artificial Intelligence techniques, obtaining an automatic classification result between healthy, adenocarcinoma and squamous cell carcinoma, given an histopathological image from lung tissue. Moreover, a report module based on Explainable Deep Learning techniques is included and gives the pathologist information about the image's areas used to classify the sample and the confidence of belonging to each class.The results show a system accuracy between 97.11 and 99.69%, depending on the number of classes classified, and a value of the area under ROC curve between 99.77 and 99.94%.The classification results obtain a substantial improvement according to previous works. Thanks to the given report, the time spent by the pathologist and the diagnostic turnaround time can be reduced.
Understanding geological reports based on knowledge graphs using a deep learning approach

Geological reports aid in understanding exploration by providing valuable information on rock formation ,evolution and the geological environment in which deposits formed. Querying and extracting the critical information from these enormous historical geological report data helps in understanding the exploration risks in different geological settings. However, large amounts of unstructured text data occur in geological reports; therefore, it is challenging to obtain valuable information from them without performing information extraction and processing. This study proposed an automated method for extracting information from geological reports through triple extraction, then automatically constructs a geological knowledge graph from the extracted entities and relations. Simultaneously, due to the lack of samples, this study used multiple geological reports to construct a corpus of jointly extracted geological entities and relations. The proposed model reached an F1-score of 90.05% in the experimental results on the constructed corpus. Finally, a knowledge graph was constructed based on the extracted results to demonstrate the application value of the proposed method. The results showed that the structured information helps better represent the content of the source report and matches well with the geological domain knowledge. The proposed method can quickly and robustly convert textual data into a structured form that is convenient for reasoning and querying geological entities and relations.
Segmentation of medical images using an attention embedded lightweight network

Accurate segmentation of computerized tomography (CT) images is of great significance to clinical diagnosis. However, because of the high similarity of gray values, it is a challenging task for CT image segmentation. The encoder and decoder based CNN architecture has greatly improved the segmentation effect, but it also encounters a bottleneck due to the information loss in the encoding process. In view of this, we proposed an image segmentation model based on a novel network architecture for medical image segmentation. To improve the efficiency and decrease the number of model parameters, we optimized the Inception module by substituting the depth-wise separable convolutions (DWSC) for the standard convolutions. Then, the optimized Inception module paired with the residual network was chosen as the backbone extractor to extract high-quality image features. Further, a hybrid attention mechanism, which consists of channel-wise and spatial attention, was incorporated into the network to realize the maximum reuse of inter-channel relationships and spatial point characteristics. In particular, the attention module was separately embedded into the contracting and expansive paths to enhance the feature extraction capability and detail restoration effects. The experimental indicators were significantly improved on the test dataset, and the intersection over union (IoU) of the proposed method reached no less than 0.9645, 0.6499, and 0.7945 on the Lung, Colon tumor, and DRIVE datasets, respectively, which demonstrated the effectiveness of the proposed method. Our code and data are available at https://github.com/xtu502/medical-image-segmentation/.
A Refined Supervision Model of Rice Supply Chain Based on Multi-Blockchain

With the development of Agriculture 4.0, the requirements for sustainable agriculture and precision agriculture continue to grow. As one of the three major staple foods globally, the quality and safety of rice affect human health as well as social development. To ensure the quality and safety of rice and reduce the flow of problematic rice, a multi-layer blockchain-based rice supply chain refinement supervision model MBRRSM (Multi-blockchain Rice Refined Supervision Model) is proposed from the information level. First, the characteristics of information flow in the rice supply chain are analyzed, and a classification table of key information is constructed. Second, the MBRRSM framework is designed. Based on a multi-party hybrid encryption algorithm, secure multi-party computing algorithm, multi-mode storage mechanism, and SPOP (Supervision Proof of Peers) consensus algorithm, a set of mechanisms is designed for the transmission, use, storage, and consensus of rice data in MBRRSM. Subsequently, the security and performance capabilities of MBRRSM are analyzed. Meanwhile, the SPOP consensus algorithm is analyzed. Finally, a prototype system is built based on MBRRSM and verified through exemplary scenarios in different usage situations. The results show that research on the refined supervision of the rice supply chain based on multi-blockchain can finely supervise all types of data in the rice supply chain, and provide a guarantee for enterprise users to safely transmit and use data with different privacy levels. This study presents a unique research paradigm that introduces the theories and methods of the new information field generation into the field of agricultural research, and thus assists in the strategy implementation of "holding grain in the land and storing grain in technology".
Cross-plane multi-image encryption using chaos and blurred pixels

While studying remote sensing images, it is found that the band of remote sensing images can be used as a carrier to store different images. Therefore, this paper proposes a novel joint encryption scheme for multiple images of different sizes and types. Due to the high time efficiency of multi-image joint encryption, we propose a 1D-SASCS chaotic system, which can effectively solve the problem of time efficiency of encryption. Before encryption, we blur the pixels of several images (i.e. binary scrambling), scramble any two of the 8 bits, and fill the scrambled information into a smaller size image by splicing every 8 bits into a pixel, The bit plane pixels before and after scrambling are spliced into random size images, which are combined with SHA-512 to generate keys, and then three-dimensional cross-plane scrambling and unconventional sequence diffusion are carried out. At the same time, this algorithm is also applicable to the encryption of single or multiple remote sensing images. Finally, through the simulation experiment analysis of the encryption scheme, the test results of information entropy, Histogram, and correlation are very close to the ideal value. The algorithm is extremely secure in a large number of test performance analysis results.
QDL-CMFD: A Quality-independent and deep Learning-based Copy-Move image forgery detection method

One of the prevalent methods of image forgery is copy-move, where one or more regions of an image are duplicated and moved elsewhere in the image. It is usually difficult to detect this type of forgery due to the similarity of the copied and forged areas. Also, forgers perform pre-processing and/or post-processing operations on the manipulated regions to make it even more difficult to detect. In this study, an image quality-independent method based on deep learning approach, termed QDL-CMFD, is presented for detecting this type of forgery. QDL-CMFD utilizes generative adversarial networks for image quality enhancement, and convolutional neural networks (CNN) for forgery detection. A tailored dual-branch CNN architecture is introduced consisting of two subnetworks, namely a manipulation detection subnetwork and a similarity detection subnetwork. Accordingly, unlike most existing methods, QDL-CMFD is able to simultaneous detection of several forged areas, as well as determining the source and target of the forgery. Also, QDL-CMFD is robust against various pre-processing/post-processing attacks. It shows excellent performance for detecting low-quality forged images and small areas. Experiments conducted on the CASIA and CoMoFoD benchmark datasets confirm that QDL-CMFD performs significantly better than the competitors. All the implementation source codes of QDL-CMFD are available at https://github.com/MehradAria/QDL-CMFD.