Reviewerly/exHarmony huggingface dataset:
Coronavirus impact on human feelings during 2021 Hajj season via deep learning critical Twitter analysis

The COVID-19 pandemic had major impact on all worldwide traveling in 2020 and 2021, which also affected Muslims' ability to perform the ritual of Hajj and Umrah. Due to the pandemic, the Saudi government limited number of pilgrims to ensure their safety and prevent the spread of disease. This reduction of pilgrims affected residents of Makkah and Madinah holy cities, whom were often involved in a variety of activities during the Hajj seasons. Twitter was one of the channels that many people and government organizations utilized to communicate with each other regarding general issues as well as other topics related to the Corona epidemic and health measures during the 2021 Hajj season (1442 AH). In this study, more than 22,000 tweets were collected from Makkah and Madinah during the Hajj season of 2021. The collected tweets were analyzed using convolutional neural network and long short-term memory network (CNN-LSTM) deep learning model. We extracted the basic features of tweets and classified them into three sentimental categories including positive, negative, and neutral. The findings indicated strange negative feelings towards the Corona pandemic differently within the tweets from the holy cities. The results also showed strong correlation value of 0.74 for the sentiment analysis rates of general tweets sent by Makkah and Madinah tweeters. Furthermore, considerable correlations have been observed between Makkah and Madinah tweeters' perceptions of several issues relevant to the Corona pandemic, revealing worthwhile remarking results.
A Survey on Quantum Computing for Internet of Things Security

Quantum computing, based on quantum mechanical principle, can potentially provide significant advantages over classical computing. This advantage of quantum computing provides solutions to many previously unsolvable problems in secure communication and finance. IoT is an emerging technology that deals with a large amount of data. The data communicated in IoT need to be secured. The existing security architecture of IoT is based on cryptographic algorithms such as RSA and ECC. Quantum computing had a significant impact on the security of these algorithms. Therefore, our work analyses the security concerns of IoT smart applications and quantum-based solutions. This article provides a survey of quantum computing fundamentals and the impact of quantum computing on IoT security. Thus, this paper aims to provide a wide view of quantum-enabled IoT communication. The main challenges in implementing quantum-enabled communication are also analyzed in our work.
Optical and Electrical Memories for Analog Optical Computing

Key to recent successes in the field of artificial intelligence (AI) has been the ability to train a growing number of parameters which form fixed connectivity matrices between layers of nonlinear nodes. This “deep learning” approach to AI has historically required an exponential growth in processing power which far exceeds the growth in computational throughput of digital hardware as well as trends in processing efficiency. New computing paradigms are therefore required to enable efficient processing of information while drastically improving computational throughput. Emerging strategies for analog computing in the photonic domain have the potential to drastically reduce latency but require the ability to modify optical processing elements according to the learned parameters of the neural network. In this point-of-view article, we provide a forward-looking perspective on both optical and electrical memories coupled to integrated photonic hardware in the context of AI. We also show that for programmed memories, the READ energy-latency-product of photonic random-access memory (PRAM) can be orders of magnitude lower compared to electronic SRAMs. Our intent is to outline path for PRAMs to become an integral part of future foundry processes and give these promising devices relevance for emerging AI hardware.
Smart and collaborative industrial IoT: A federated learning and data space approach

Industry 4.0 has become a reality by fusing the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI), providing huge opportunities in the way manufacturing companies operate. However, the adoption of this paradigm shift, particularly in the field of smart factories and production, is still in its infancy, suffering from various issues, such as the lack of high-quality data, data with high-class imbalance, or poor diversity leading to inaccurate AI models. However, data is severely fragmented across different silos owned by several parties for a range of reasons, such as compliance and legal concerns, preventing discovery and insight-driven IIoT innovation. Notably, valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security. This adversely influences inter- and intra-organization collaborative use of IIoT data. To tackle these challenges, this article leverages emerging multi-party technologies, privacy-enhancing techniques (e.g., Federated Learning), and AI approaches to present a holistic, decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape. Moreover, to evaluate the efficiency of the proposed reference model, a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture. Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable, Accessible, Interoperable, and Reusable (FAIR) principles.
Anomaly Detection Method for Multivariate Time Series Data of Oil and Gas Stations Based on Digital Twin and MTAD-GAN

Due to the complexity of the oil and gas station system, the operational data, with various temporal dependencies and inter-metric dependencies, has the characteristics of diverse patterns, variable working conditions and imbalance, which brings great challenges to multivariate time series anomaly detection. Moreover, the time-series reconstruction information of data from digital twin space can be used to identify and interpret anomalies. Therefore, this paper proposes a digital twin-driven MTAD-GAN (Multivariate Time Series Data Anomaly Detection with GAN) oil and gas station anomaly detection method. Firstly, the operational framework consisting of digital twin model, virtual-real synchronization algorithm, anomaly detection strategy and realistic station is constructed, and an efficient virtual-real mapping is achieved by embedding a stochastic Petri net (SPN) to describe the station-operating logic of behavior. Secondly, based on the potential correlation and complementarity among time series variables, we present a MTAD-GAN anomaly detection method to reconstruct the error of multivariate time series by combining mechanism of knowledge graph attention and temporal Hawkes attention to judge the abnormal samples by a given threshold. The experimental results show that the digital twin-driven anomaly detection method can achieve accurate identification of anomalous data with complex patterns, and the performance of MTAD-GAN anomaly detection is improved by about 2.6% compared with other methods based on machine learning and deep learning, which proves the effectiveness of the method.
Detection of Vulnerabilities of Blockchain Smart Contracts

With the wide application of Internet of Things and blockchain, research on smart contracts has received increased attention, and security threat detection for smart contracts is one of the main focuses. This article first introduces the common security vulnerabilities in blockchain smart contracts, and then classifies the vulnerabilities detection tools for smart contracts into six categories according to the different detection methods: 1) formal verification method; 2) symbol execution method; 3) fuzzy testing method; 4) intermediate representation method; 5) stain analysis method; and 6) deep learning method. We test 27 detection tools and analyze them from several perspectives, including the capability of detecting a smart contract version. Finally, it is concluded that most of the current vulnerability detection tools can only detect vulnerabilities in a single and old version of smart contracts. Although the deep learning method detects fewer types of smart contract vulnerabilities, it has higher detection accuracy and efficiency. Therefore, the combination of static detection methods, such as deep learning method and dynamic detection methods, including the fuzzy testing method to detect more types of vulnerabilities in multi-version smart contracts to achieve higher accuracy is a direction worthy of research in the future.
Improved traffic sign recognition algorithm based on YOLOv4-tiny

This study offers an enhanced yolov4-tiny traffic sign identification method for easy deployment on mobile or embedded devices to address the difficulties of a high number of parameters, low recognition accuracy, and poor real-time performance of traffic sign recognition models in complex scenarios. The yolov4-tiny network serves as the model’s foundation. To begin, Octave Convolution is incorporated into the backbone network to eliminate low-frequency feature redundancy, lowering the number of parameters in the model and enhancing computational efficiency. Second, the convolutional block attention module is employed to improve the recognition accuracy of small and medium-sized targets by strengthening the weights of traffic sign regions and suppressing the weights of invalid features. Finally, in the feature fusion stage, the Feature Pyramid Networks structure is replaced with the Simplified Path Aggregation Network structure to improve the fusing of shallow feature information with deep semantic knowledge and lower the miss detection rate even more On the TT100K data set as well as on CCTSDB dataset, the experimental results suggest that our technique can achieve good recognition performance. With a 16MB model size, our solution improves the mean average precision by 3.5 percent and the Frame Per Second by 12.5 f/s when compared to the yolov4-tiny algorithm. Our method outperforms yolov4-tiny in terms of recognition accuracy and detection speed, and it can easily meet the real-time requirements for traffic sign recognition.
MixVPR: Feature Mixing for Visual Place Recognition

Visual Place Recognition (VPR) is a crucial part of mobile robotics and autonomous driving as well as other computer vision tasks. It refers to the process of identifying a place depicted in a query image using only computer vision. At large scale, repetitive structures, weather and illumination changes pose a real challenge, as appearances can drastically change over time. Along with tackling these challenges, an efficient VPR technique must also be practical in real-world scenarios where latency matters. To address this, we introduce MixVPR, a new holistic feature aggregation technique that takes feature maps from pre-trained backbones as a set of global features. Then, it incorporates a global relationship between elements in each feature map in a cascade of feature mixing, eliminating the need for local or pyramidal aggregation as done in NetVLAD or TransVPR. We demonstrate the effectiveness of our technique through extensive experiments on multiple large-scale benchmarks. Our method outperforms all existing techniques by a large margin while having less than half the number of parameters compared to CosPlace and NetVLAD. We achieve a new all-time high recall@1 score of 94.6% on Pitts250k-test, 88.0% on MapillarySLS, and more importantly, 58.4% on Nordland. Finally, our method outperforms two-stage retrieval techniques such as Patch-NetVLAD, TransVPR and SuperGLUE all while being orders of magnitude faster.
Vision Transformer for NeRF-Based View Synthesis from a Single Input Image

Although neural radiance fields (NeRF) have shown impressive advances in novel view synthesis, most methods require multiple input images of the same scene with accurate camera poses. In this work, we seek to substantially reduce the inputs to a single unposed image. Existing approaches using local image features to reconstruct a 3D object often render blurry predictions at viewpoints distant from the source view. To address this, we propose to leverage both the global and local features to form an expressive 3D representation. The global features are learned from a vision transformer, while the local features are extracted from a 2D convolutional network. To synthesize a novel view, we train a multi-layer perceptron (MLP) network conditioned on the learned 3D representation to perform volume rendering. This novel 3D representation allows the network to reconstruct unseen regions without enforcing constraints like symmetry or canonical coordinate systems. Our method renders novel views from just a single input image, and generalizes across multiple object categories using a single model. Quantitative and qualitative evaluations demonstrate that the proposed method achieves state-of-the-art performance and renders richer details than existing approaches. https://cseweb.ucsd.edu/ %7eviscomp/projects/VisionNeRF/
Few-shot Medical Image Segmentation with Cycle-resemblance Attention

Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing attention in the medical image semantic segmentation field. To perform segmentation with limited number of labeled medical images, most existing studies use Prototypical Networks (PN) and have obtained compelling success. However, these approaches overlook the query imagefeatures extracted from the proposed representation network, failing to preserving the spatial connection between query and support images. In this paper, we propose a novel self-supervised few-shot medical image segmentation network and introduce a novel Cycle-Resemblance Attention (CRA) module to fully leverage the pixel-wise relation between query and support medical images. Notably, we first line up multiple attention blocks to refine more abundant relation information. Then, we present CRAPNet by integrating the CRA module with a classic prototype network, where pixel-wise relations between query and support features are well recaptured for segmentation. Extensive experiments on two different medical image datasets, e.g., abdomen MRI and abdomen CT, demonstrate the superiority of our model over existing state-of-the-art methods.
Adaptively-Realistic Image Generation from Stroke and Sketch with Diffusion Model

Generating images from hand-drawings is a crucial and fundamental task in content creation. The translation is difficult as there exist infinite possibilities and the different users usually expect different outcomes. Therefore, we propose a unified framework supporting a three-dimensional control over the image synthesis from sketches and strokes based on diffusion models. Users can not only decide the level of faithfulness to the input strokes and sketches, but also the degree of realism, as the user inputs are usually not consistent with the real images. Qualitative and quantitative experiments demonstrate that our framework achieves state-of-the-art performance while providing flexibility in generating customized images with control over shape, color, and realism. Moreover, our method unleashes applications such as editing on real images, generation with partial sketches and strokes, and multi-domain multi-modal synthesis.
Multi-UAV Search and Rescue with Enhanced A<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mrow> <mo>∗</mo> </mrow> </msup> </math> Algorithm Path Planning in 3D Environment

The unmanned aerial vehicles (UAV) are now widely used in search and rescue (SAR) missions to locate casualties and survey terrain. To solve the problem of long calculation time and large memory usage of the UAV obstacle-avoidance path-planning algorithm in cooperative tasks, this paper proposes a method that combines the A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mrow> <mo>∗</mo> </mrow> </msup> </math> algorithm and the task allocation algorithm to achieve a faster and more effective path-planning method. First, the environment is displayed in the form of a grid. Then, the enhanced algorithm divides the task area for UAVs. Finally, each UAV performs SAR path planning in the mission area. The tasks of mapping the environment and searching for target points by UAV swarms are discussed in this study. Our research enhances A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow /> <mrow> <mo>∗</mo> </mrow> </msup> </math> algorithms for generating the shortest collision-free paths for drone swarms. Further, we evaluate the algorithm via simulating the task assignment algorithm and path-planning algorithm of a 3D map and 2D map. Compared with the traditional A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msup> <mrow /> <mrow> <mo>∗</mo> </mrow> </msup> </math> algorithm, the results demonstrate that the enhanced algorithm is effective in the scenario.
Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases

Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic examination, a time-consuming process requiring skilled staff. Thus, artificial intelligence (AI) has been exploited for identification of microorganisms. A systematic search was carried out using electronic databases looking for studies dealing with the application of AI to pathology microbiology specimens. Of 4596 retrieved articles, 110 were included. The main applications of AI regarded malaria (54 studies), bacteria (28), nematodes (14), and other protozoa (11). Most publications examined cytological material (95, 86%), mainly analyzing images acquired through microscope cameras (65, 59%) or coupled with smartphones (16, 15%). Various deep-learning strategies were used for the analysis of digital images, achieving highly satisfactory results. The published evidence suggests that AI can be reliably utilized for assisting pathologists in the detection of microorganisms. Further technologic improvement and availability of datasets for training AI-based algorithms would help expand this field and widen its adoption, especially for developing countries.
Polarized image super-resolution via a deep convolutional neural network

Reduced resolution of polarized images makes it difficult to distinguish detailed polarization information and limits the ability to identify small targets and weak signals. A possible way to handle this problem is the polarization super-resolution (SR), which aims to obtain a high-resolution polarized image from a low-resolution one. However, compared with the traditional intensity-mode image SR, the polarization SR is more challenging because more channels and their nonlinear cross-links need to be considered as well as the polarization and intensity information need to be reconstructed simultaneously. This paper analyzes the polarized image degradation and proposes a deep convolutional neural network for polarization SR reconstruction based on two degradation models. The network structure and the well-designed loss function have been verified to effectively balance the restoration of intensity and polarization information, and can realize the SR with a maximum scaling factor of four. Experimental results show that the proposed method outperforms other SR methods in terms of both quantitative evaluation and visual effect evaluation for two degradation models with different scaling factors.
Put on the light! Foreign direct investment, governance and access to electricity

Despite efforts and commitments to achieve universal coverage of electricity, overall access in Africa is below expectation, making the attainment of the United Nations' Sustainable Energy for All Initiative almost a mirage for this region. The inability to mobilize adequate domestic financial resources and a seeming lack of political will (reflected in governance) have been highlighted as two of the major bottlenecks in the development of electricity infrastructure in African regio. Regarding inadequate financial resources, we argue that inflows of foreign direct investment (FDI) and the influx of multinational enterprises can help a great deal. To this end, we investigate the impact of FDI on access to electricity and further examine whether this impact is contingent on the governance architecture. Using a sample of 36 African countries, and the instrumental variable (IV) approach to the generalized method of moments (GMM), our results suggest that, for the most part, the direct impacts of FDI and governance on access to electricity are positive. The role of governance in the FDI impact on electricity was established.
Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer

One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population's variety is increased by using the SPM chaotic mapping at initialization; secondly, the WHO and Golden Sine Algorithm (Golden-SA) are hybridized to improve the WHO's accuracy and arrive at faster convergence; Thirdly, the IWHO can escape from a local optimum and broaden the search space by using opposition-based learning and the Cauchy variation strategy. The results indicate that the IWHO has the best capacity for optimization by contrasting the simulation tests with seven algorithms on 23 test functions. Finally, three sets of coverage optimization experiments in different simulated environments are designed to test the effectiveness of this algorithm. The validation results demonstrate that the IWHO can achieve better and more effective sensor connectivity and coverage ratio compared to that of several algorithms. After optimization, the HWSN's coverage and connectivity ratio attained 98.51% and 20.04%, and after adding obstacles, 97.79% and 17.44%, respectively.