Reviewerly/exHarmony huggingface dataset:
Temporally Efficient Vision Transformer for Video Instance Segmentation

Recently vision transformer has achieved tremendous success on image-level visual recognition tasks. To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS). Different from previous transformer-based VIS methods, TeViT is nearly convolution-free, which contains a transformer backbone and a query-based video instance segmentation head. In the backbone stage, we propose a nearly parameter-free messenger shift mechanism for early temporal context fusion. In the head stages, we propose a parameter-shared spatiotemporal query interaction mechanism to build the one-to-one correspondence between video instances and queries. Thus, TeViT fully utilizes both frame-level and instance-level temporal context information and obtains strong temporal modeling capacity with negligible extra computational cost. On three widely adopted VIS benchmarks, i.e., YouTube-VIS-2019, YouTube-VIS-2021, and OVIS, TeViT obtains state-of-the-art results and maintains high inference speed, e.g., 46.6 AP with 68.9 FPS on YouTube-VIS-2019. Code is available at https://github.com/hustvl/TeViT.
A Multi-Type Transferable Method for Missing Link Prediction in Heterogeneous Social Networks

Heterogeneous social networks, which are characterized by diverse interaction types, have resulted in new challenges for missing link prediction. Most deep learning models tend to capture type-specific features to maximize the prediction performances on specific link types. However, the types of missing links are uncertain in heterogeneous social networks; this restricts the prediction performances of existing deep learning models. To address this issue, we propose a multi-type transferable method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> ) for missing link prediction in heterogeneous social networks, which exploits adversarial neural networks to remain robust against type differences. It comprises a generative predictor and a discriminative classifier. The generative predictor can extract link representations and predict whether the unobserved link is a missing link. To generalize well for different link types to improve the prediction performance, it attempts to deceive the discriminative classifier by learning transferable feature representations among link types. In order not to be deceived, the discriminative classifier attempts to accurately distinguish link types, which indirectly helps the generative predictor judge whether the learned feature representations are transferable among link types. Finally, the integrated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> is constructed on this minimax two-player game between the generative predictor and discriminative classifier to predict missing links based on transferable feature representations among link types. Extensive experiments show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> can outperform state-of-the-art baselines for missing link prediction in heterogeneous social networks.
Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural IoT Networks

Precision farming has a positive potential in the agricultural industry regarding water conservation, increased productivity, better development of rural areas, and increased income. Blockchain technology is a better alternative for storing and sharing farm data as it is reliable, transparent, immutable, and decentralized. Remote monitoring of an agricultural field requires security systems to ensure that any sensitive information is exchanged only among authenticated entities in the network. To this end, we design an efficient blockchain-enabled authenticated key agreement scheme for mobile vehicles-assisted precision agricultural Internet of Things (IoT) networks called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AgroMobiBlock$ </tex-math></inline-formula> . The limited existing work on authentication in agricultural networks shows passive usage of blockchains with very high costs. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AgroMobiBlock$ </tex-math></inline-formula> proposes a novel idea using the elliptic curve operations on an active hybrid blockchain over mobile farming vehicles with low computation and communication costs. Formal and informal security analysis along with the formal security verification using the Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool have shown the robustness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AgroMobiBlock$ </tex-math></inline-formula> against man-in-the-middle, impersonation, replay, physical capture, and ephemeral secret leakage attacks among other potential attacks. The blockchain-based simulation on large-scale nodes shows the computational time for an increase in the network and block sizes. Moreover, the real-time testbed experiments have been performed to show the practical usefulness of the proposed scheme.
Privacy preserving Federated Learning framework for IoMT based big data analysis using edge computing

The current industrial scenario has witnessed the application of several artificial intelligence-based technologies for mining and processing IoMT-based big data. An emerging distributed machine learning paradigm, Federated Learning (FL), has been widely applied in IoMT-based systems as a measure to overcome the issues associated with incorporating AI into such lightweight distributed computing systems while tackling privacy issues as well. However, extensive research has identified that classical FL is still prone to privacy threats due to data leakage and the chances of adversarial attacks during gradient transfer operations. Inspired by these issues, we propose a privacy-preserving framework (Fed_select) that ensures user anonymity in IoMT-based environments for big data analysis under the FL scheme. Fed_Select utilizes alternative minimization to limit gradients and participants in system training to decrease points of system vulnerability. The framework works on an edge computing-based architecture which ensures user anonymity via the employment of hybrid encryption techniques along with added benefits of load reduction at the central server. Also, a Laplacian noise-based differential privacy is employed on the shared attributes for security enhancement that adds confidentiality to the transferred data even during adversarial scenarios. Experimental results on standard datasets showcase that the change in the volume of gradients shared and the number of participants is not proportional to the variation in various system performance parameters. Specifically, an idealistic range of client and gradient-sharing fractions along with the appropriate value of noise for differential privacy implementation is determined. Additionally, we analyze the system from a security perspective as well as compare it with other schemes.
Secure Internet of Things (IoT) using a novel Brooks Iyengar quantum Byzantine Agreement-centered blockchain Networking (BIQBA-BCN) model in smart healthcare

Smart Health Care offers efficient, sustainable, along with real-time human services, and the concept of enhanced Internet of Things (IoT) lies behind the emergence of this smart Health Care (HC). Nevertheless, the association of these IoT-centric sensors with other organizations generates security threats that an illegal user might utilize it owing to the data's openness. In the HC sector, data security and privacy are the prime concern in which the alteration in data values as sensors could modify the diagnosis process, which may cause serious health problems. The immutability and transparency of the blockchain make it a viable option for the safe storage and management of healthcare data. However, the Cloud Storage Server (CSS), consensus latency, majority attacks, Byzantine problem, uncomfortable data Throughput (TP), et cetera, make the blockchain vulnerable to healthcare data transmission. To solve the current disadvantages, a secure IoT was introduced in healthcare using Brooks Iyengar Quantum Byzantine Agreement-centered Blockchain Networking (BIQBA-BCN). This study ensures the sincerity and equity of health data exchange. Based on Blum Blum Shub and Okamoto Uchiyana Cryptosystem (OUCS) mutual authentica1tion, the proposed work provides an OUCS-based mutual authentication system (BBS-OUC). Attackers are prevented from entering the BCN, ensuring the storage remains trustworthy. In addition, the Key Weight Block Function-Quasi-Cyclic Moderate Density Parity Check (KWBF-QCMDPC) algorithm safeguards the confidentiality and dependability of IoT user data. The cryptosystem technique protects sensitive data when the blockchain platform is distributed. The proposed methodology, known as BIQBA-BCN, results in a security level that is %94 effective. Finally, the BIQBA-BC consensus mechanism is used to distribute data to the corresponding Hospital Server (HS). By achieving a higher data TP, lower delay, higher Success Rate (SR), shorter consensus latency, and node communication time, the experimental results show that the proposed methodology is exceptionally safe against assaults and highly scalable.
ST-Unet: Swin Transformer boosted U-Net with Cross-Layer Feature Enhancement for medical image segmentation

Medical image segmentation is an essential task in clinical diagnosis and case analysis. Most of the existing methods are based on U-shaped convolutional neural networks (CNNs), and one of disadvantages is that the long-term dependencies and global contextual connections cannot be effectively established, which results in inaccuracy segmentation. For fully using low-level features to enhance global features and reduce the semantic gap between encoding and decoding stages, we propose a novel Swin Transformer boosted U-Net (ST-Unet) for medical image processing in this paper, in which Swin Transformer and CNNs are used as encoder and decoder respectively. Then a novel Cross-Layer Feature Enhancement (CLFE) module is proposed to realize cross-layer feature learning, and a Spatial and Channel Squeeze & Excitation module is adopted to highlight the saliency of specific regions. Finally, we learn the features fused by the CLFE module through CNNs to recover low-level features and localize local features for realizing more accurate semantic segmentation. Experiments on widely used public datasets Synapse and ISIC 2018 prove that our proposed ST-Unet can achieve 78.86 of dice and 0.9243 of recall performance, outperforming most current medical image segmentation methods.
Dynamic Hand Gesture Recognition Using Multi-Branch Attention Based Graph and General Deep Learning Model

The dynamic hand skeleton data have become increasingly attractive to widely studied for the recognition of hand gestures that contain 3D coordinates of hand joints. Many researchers have been working to develop skeleton-based hand gesture recognition systems using various discriminative spatial-temporal attention features by calculating the dependencies between the joints. However, these methods may face difficulties in achieving high performance and generalizability due to their inefficient features. To overcome these challenges, we proposed a Multi-branch attention-based graph and a general deep-learning model to recognize hand gestures by extracting all possible types of skeleton-based features. We used two graph-based neural network channels in our multi-branch architectures and one general neural network channel. In graph-based neural network channels, one channel first uses the spatial attention module and then the temporal attention module to produce the spatial-temporal features. In contrast, we produced temporal-spatial features in the second channel using the reverse sequence of the first branch. The last branch extracts general deep learning-based features using a general deep neural network module. The final feature vector was constructed by concatenating the spatial-temporal, temporal-spatial, and general features and feeding them into the fully connected layer. We included position embedding and mask operation for both spatial and temporal attention modules to track the node’s sequence and reduce the system’s computational complexity. Our model achieved 94.12%, 92.00%, and 97.01% accuracy after evaluation with MSRA, DHG, and SHREC’17 benchmark datasets, respectively. The high-performance accuracy and low computational cost proved that the proposed method outperformed the existing state-of-the-art methods.
A high-dimensional feature selection method based on modified Gray Wolf Optimization

For data mining tasks on high-dimensional data, feature selection is a necessary pre-processing stage that plays an important role in removing redundant or irrelevant features and improving classifier performance. The Gray Wolf optimization algorithm is a global search mechanism with promising applications in feature selection, but tends to stagnate in high-dimensional problems with locally optimal solutions. In this paper, a modified gray wolf optimization algorithm is proposed for feature selection of high-dimensional data. The algorithm introduces ReliefF algorithm and Coupla entropy in the initialization process, which effectively improves the quality of the initial population. In addition, modified gray wolf optimization includes two new search strategies: first, a competitive guidance strategy is proposed to update individual positions, which make the algorithm’s search more flexible; second, a differential evolution-based leader wolf enhancement strategy is proposed to find a better position where the leader wolf may exist and replace it, which can prevent the algorithm from falling into local optimum. The results on 10 high-dimensional small-sample gene expression datasets demonstrate that the proposed algorithm selects less than 0.67% of the features, improves the classification accuracy while further reducing the number of features, and obtains very competitive results compared with some advanced feature selection methods. The comprehensive study analysis shows that proposed algorithm better balances the exploration and exploration balance, and the two search strategies are conducive to the improvement of gray wolf optimization search capability.
Asynchronous Updating Boolean Network Encryption Algorithm

An asynchronous updating Boolean network is employed to simulate and analyze the gene expression of a particular tissue or species, revealing the life activity process from a system perspective to reveal the disease mechanism and treat the disease. Therefore, to ensure the safe transmission of the asynchronous updating Boolean network in the network, we designed an asynchronous updating Boolean network encryption algorithm based on chaos (ABNEA). First, a novel 2D chaotic system (2D-FPSM) is designed. This system has better performance than the classical 2D chaotic system. It is very suitable for cryptographic systems to generate key streams. Second, an encoding rule is designed to convert the asynchronous updating Boolean network to a Boolean matrix and propagate it on the network as an image. The receiver and sender jointly save the encoding rule. Last, to protect the safe propagation of the Boolean network matrix on the network, the method of synchronous scrambling-diffusion is adapted to encrypt the Boolean network matrix based on the 2D-FPSM. Simulation experiments and security analysis show that the average correlation of adjacent pixels of ciphertext are 0.0010, -0.0010, -0.0020, and the average information entropy is 7.9984. The ABNEA can complete the encryption tasks of asynchronously updating Boolean networks and exhibits good security characteristics.
Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models

Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural designs (e.g., with vision transformers). Motivated by the recent progress achieved with state-of-the-art conditional generative models, we present a novel patch-based image restoration algorithm based on denoising diffusion probabilistic models. Our patch-based diffusion modeling approach enables size-agnostic image restoration by using a guided denoising process with smoothed noise estimates across overlapping patches during inference. We empirically evaluate our model on benchmark datasets for image desnowing, combined deraining and dehazing, and raindrop removal. We demonstrate our approach to achieve state-of-the-art performances on both weather-specific and multi-weather image restoration, and experimentally show strong generalization to real-world test images.
IDOD-YOLOV7: Image-Dehazing YOLOV7 for Object Detection in Low-Light Foggy Traffic Environments

Convolutional neural network (CNN)-based autonomous driving object detection algorithms have excellent detection results on conventional datasets, but the detector performance can be severely degraded in low-light foggy weather environments. Existing methods have difficulty in achieving a balance between low-light image enhancement and object detection. To alleviate this problem, this paper proposes a foggy traffic environment object detection framework, IDOD-YOLOV7. This network is based on joint optimal learning of image defogging module IDOD (AOD + SAIP) and YOLOV7 detection modules. Specifically, for low-light foggy images, we propose to improve the image quality by joint optimization of image defogging (AOD) and image enhancement (SAIP), where the parameters of the SAIP module are predicted by a miniature CNN network and the AOD module performs image defogging by optimizing the atmospheric scattering model. The experimental results show that the IDOD module not only improves the image defogging quality for low-light fog images but also achieves better results in objective evaluation indexes such as PSNR and SSIM. The IDOD and YOLOV7 learn jointly in an end-to-end manner so that object detection can be performed while image enhancement is executed in a weakly supervised manner. Finally, a low-light fogged traffic image dataset (FTOD) was built by physical fogging in order to solve the domain transfer problem. The training of IDOD-YOLOV7 network by a real dataset (FTOD) improves the robustness of the model. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to demonstrate its superiority over the others. The IDOD-YOLOV7 algorithm not only suppresses the artifacts of low-light fog images and improves the visual effect of images but also improves the perception of autonomous driving in low-light foggy environments.
Atomically-thin single-photon sources for quantum communication

To date, quantum communication widely relies on attenuated lasers for secret key generation. In future quantum networks fundamental limitations resulting from their probabilistic photon distribution must be overcome by using deterministic quantum light sources. Confined excitons in monolayers of transition metal dichalcogenides (TMDCs) constitute an emerging type of emitter for quantum light generation. These atomically-thin solid-state sources show appealing prospects for large-scale and low-cost device integration, meeting the demands of quantum information technologies. Here, we pioneer the practical suitability of TMDC devices in quantum communication. We employ a $\mathrm{WSe}_2$ monolayer single-photon source to emulate the BB84 protocol in a quantum key distribution (QKD) setup and achieve click rates of up to 66.95 kHz and antibunching values down to 0.034 - a performance competitive with QKD experiments using semiconductor quantum dots or color centers in diamond. Our work opens the route towards wider applications of quantum information technologies using TMDC single-photon sources.
SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain.
Site suitability for solar and wind energy in developing countries using combination of GIS- AHP; a case study of Pakistan

Climate change and scarcity of energy sources have put developing countries in a situation that demands emergency future planning for matured renewable technologies in order to continue sustainable progress and provide clean and cheap energy to masses. This paper provides a comprehensive structure of site suitability for utility scale solar PV and wind energy projects considering criteria critical to each technology while only using criteria with absolute necessity for varying scenarios incorporating Multi Criteria Decision Analysis (MCDA) along with Geographic Information System (GIS). A novel approach is also considered for far-off areas potentially deprived of electricity access for small scale solar PV implementation. A total of eight sub-criteria were considered in site selection process regarding wind and solar energy, analyzed using Analytical Hierarchy Process (AHP) in order to rank criteria depending on their importance to selection process. Resulting methodology was applied to case study of Pakistan, a developing country with growing climate change emergency and ongoing power crisis. End results showed 25.28% area suitable for utility scale solar PV farms, 5.93% area suitable for utility scale wind farms and 40.02% suitable area for small scale solar PV farms.
The Effect of Electronic Marketing on Customer Satisfaction of the Insurance Companies in Jordan

This study aimed to identify the impact of electronic marketing on customer satisfaction of the insurance companies in Jordan, the researchers relied on the quantitative approach by the descriptive analytical method, A questionnaire was developed and distributed to (440) clients of insurance companies affiliated with the Jordanian Federation of Insurance Companies, which numbered (22) companies in Jordan. The study reached several results, the most important of which are: There is an impact of e-marketing on customer satisfaction, and the study concluded a number of recommendations, including: A focus on their website to be designed in an easy-to-use way and to provide all the required information about the company that pertain to the customer and the requirements of his transactions, leading to its progression more and more instead of regressing.