Reviewerly/exHarmony huggingface dataset:
Oil Well Detection under Occlusion in Remote Sensing Images Using the Improved YOLOv5 Model

Oil wells play an important role in the extraction of oil and gas, and their future potential extends beyond oil and gas exploitation to include the development of geothermal resources for sustainable power generation. Identifying and detecting oil wells are of paramount importance given the crucial role of oil well distribution in energy planning. In recent years, significant progress has been made in detecting single oil well objects, with recognition accuracy exceeding 90%. However, there are still remaining challenges, particularly with regard to small-scale objects, varying viewing angles, and complex occlusions within the domain of oil well detection. In this work, we created our own dataset, which included 722 images containing 3749 oil well objects in Daqing, Huatugou, Changqing oil field areas in China, and California in the USA. Within this dataset, 2165 objects were unoccluded, 617 were moderately occluded, and 967 objects were severely occluded. To address the challenges in detecting oil wells in complex occlusion scenarios, we propose the YOLOv5s-seg CAM NWD network for object detection and instance segmentation. The experimental results show that our proposed model outperforms YOLOv5 with F1 improvements of 5.4%, 11.6%, and 23.1% observed for unoccluded, moderately occluded, and severely occluded scenarios, respectively.
Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition

Human-to-human communication via the computer is mainly carried out using a keyboard or microphone. In the field of virtual reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g., silent commands during task-force training) or simply not possible (e.g., if the user has hearing loss). Data gloves help to increase immersion within VR, as they correspond to our natural interaction. At the same time, they offer the possibility of accurately capturing hand shapes, such as those used in non-verbal communication (e.g., thumbs up, okay gesture, …) and in sign language. In this paper, we present a hand-shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an outlier detection and a feature selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial data augmentation, i.e., we created new artificial data from the recorded and filtered data to augment the training data set. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. The voting meta-classifier (VL2) proved to be the most accurate, albeit slowest, classifier. A good alternative is random forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. outlier detection was proven to be an effective approach, especially in improving the classification time. Overall, we have shown that our hand-shape recognition system using data gloves is suitable for communication within VR.
Algorithm–Hardware Co-Optimization and Deployment Method for Field-Programmable Gate-Array-Based Convolutional Neural Network Remote Sensing Image Processing

In recent years, convolutional neural networks (CNNs) have gained widespread adoption in remote sensing image processing. Deploying CNN-based algorithms on satellite edge devices can alleviate the strain on data downlinks. However, CNN algorithms present challenges due to their large parameter count and high computational requirements, which conflict with the satellite platforms’ low power consumption and high real-time requirements. Moreover, remote sensing image processing tasks are diverse, requiring the platform to accommodate various network structures. To address these issues, this paper proposes an algorithm–hardware co-optimization and deployment method for FPGA-based CNN remote sensing image processing. Firstly, a series of hardware-centric model optimization techniques are proposed, including operator fusion and depth-first mapping technology, to minimize the resource overhead of CNN models. Furthermore, a versatile hardware accelerator is proposed to accelerate a wide range of commonly used CNN models after optimization. The accelerator architecture mainly consists of a parallel configurable network processing unit and a multi-level storage structure, enabling the processing of optimized networks with high throughput and low power consumption. To verify the superiority of our method, the introduced accelerator was deployed on an AMD-Xilinx VC709 evaluation board, on which the improved YOLOv2, VGG-16, and ResNet-34 networks were deployed. Experiments show that the power consumption of the accelerator is 14.97 W, and the throughput of the three networks reaches 386.74 giga operations per second (GOPS), 344.44 GOPS, and 182.34 GOPS, respectively. Comparison with related work demonstrates that the co-optimization and deployment method can accelerate remote sensing image processing CNN models and is suitable for applications in satellite edge devices.
A Novel Control Architecture Based on Behavior Trees for an Omni-Directional Mobile Robot

Robotic systems are increasingly present in dynamic environments. This paper proposes a hierarchical control structure wherein a behavior tree (BT) is used to improve the flexibility and adaptability of an omni-directional mobile robot for point stabilization. Flexibility and adaptability are crucial at each level of the sense–plan–act loop to implement robust and effective robotic solutions in dynamic environments. The proposed BT combines high-level decision making and continuous execution monitoring while applying non-linear model predictive control (NMPC) for the point stabilization of an omni-directional mobile robot. The proposed control architecture can guide the mobile robot to any configuration within the workspace while satisfying state constraints (e.g., obstacle avoidance) and input constraints (e.g., motor limits). The effectiveness of the controller was validated through a set of realistic simulation scenarios and experiments in a real environment, where an industrial omni-directional mobile robot performed a point stabilization task with obstacle avoidance in a workspace.
An Introduction to Neural Networks for the Social Sciences

Abstract Social scientists now regularly employ computational models, most often to create proxy variables for theoretical concepts that are difficult to observe directly. This chapter discusses how the application of these models differs from traditional quantitative methods common to the social sciences. It then introduces a particular method—neural networks—by building from a model familiar to social scientists: logistic regression. To build intuition about how neural networks flexibly approximate complex functions, the chapter compares the performance of logistic regression to neural networks in a simple simulation. It then further demonstrates the flexibility of neural networks by introducing convolutional and recurrent neural networks and their application to image and text classification, respectively. Throughout, the chapter relates approaches to machine learning with neural networks to common statistical practices in social science. It concludes with a discussion of new methodological challenges posed by reliance on neural networks and open areas of research.
Control and surveillance in digital capitalism: an analysis of blockchain technologies and their business implementation

Abstract The article discusses how the new information and communication technologies (NICTs) interrelate with the control and surveillance of systems through the case study of blockchain technologies. Initially, considering the debates in the sociology of work and the resources for a qualitative methodology, we analyze how blockchain technologies arise due to the economic crisis and the need for regulation of the post-2008 crisis markets. In the first part, we look at the surveillance and market control dimensions that emerged in this context. In the second part, we discuss the emergence of the crypto-assets market led by Bitcoin and, mainly, the technological dimension of the blockchain network. We understand that, although there is an attempt to analyze Bitcoin as a financialization of capital, the measurement of technical advantages and disadvantages in the creation of the blockchain network is a substantial factor and of significant impact on the decision-making of companies in the most varied fields. The last part of the article points out, through the quantitative analysis of reports and surveys, a meeting of the operability offered by blockchain technology and the expectation of implementation of this technology in various business sectors, elucidating the competitive advantages listed by them. We seek, then, to understand how implementing these new technologies deepens the dimensions of control and surveillance of production chains.
Rotated points for object detection in remote sensing images

Abstract Object detection in remote sensing images poses great challenges due to the dense distribution, arbitrary orientation, and aspect ratio variations of objects. Most of the existing methods rely on aligned convolutional features, which fail to capture the geometric information of objects effectively and result in the inconsistency between the classification score and localization accuracy. Moreover, densely packed objects suffer from spatial feature aliasing caused by the intersection of reception fields between objects. To address this issue, a deformable convolution‐based method named rotated points is proposed, which consists of two modules: a point set loss module and a high‐quality sample assignment module. The point set loss module can extract geometric features of objects in arbitrary directions with fine‐grained point sets for feature representation and introduce outlier penalties to penalize outlier points. The high‐quality sample assignment module measures the classification and localization ability, orientation quality, and point‐wise correlation of point sets comprehensively to enhance the consistency of classification and regression significantly. Experiments on the DOTA and FAIR1M datasets demonstrate that the proposed method achieves significant improvements over the benchmark model.
Improved A-Star Path Planning Algorithm in Obstacle Avoidance for the Fixed-Wing Aircraft

The flight management system is a basic component of avionics for modern airliners. However, the airborne flight management system needs to be improved and relies on imports; path planning is the key to the flight management system. Based on the classical A* algorithm, this paper proposes an improved A* path planning algorithm, which solves the problem of low planning efficiency and following a non-smooth path. In order to solve the problem of the large amount of data calculation and long planning time of the classical A* algorithm, a new data structure called a “value table” is designed to replace the open table and close table of the classical A* algorithm to improve the retrieval efficiency, and the Heap sort algorithm is used to optimize the efficiency of node sorting. Aiming at the problem that the flight trajectory is hard to follow, the trajectory smoothing optimization algorithm combined with turning angle limit is proposed. The gray value in the digital map is added to the A* algorithm, and the calculation methods of gray cost, cumulative cost, and estimated cost are improved, which can better meet the constraints of obstacle avoidance. Through the comparative simulation verification of the algorithm, the improved A* algorithm can significantly reduce the path planning time to 1% compared to the classical A* algorithm; it can be seen that the proposed algorithm improves the efficiency of path planning and the smoother planned path, which has obvious advantages compared to the classical A* algorithm.
Assisted Postselective Quantum Transformations and an Improved Photon Number Splitting Attack Strategy

Postselective transformations of quantum states is a broader class of operations than deterministic quantum channels. Here, we describe the possibility of increasing the success probability of postselective operations by using additional information, which has a form of pure quantum states and should not be changed in case of success. We describe the conditions under which assistance becomes useful, and provide application of our method which improves the efficiency of photon number splitting attack for a variant of SARG04 quantum key distribution protocol. In our attack scenario, one extra photon, which is unchanged, plays the role of assistance.
Decentralized Storage with Access Control and Data Persistence for e-Book Stores

The e-book services we use today have a serious drawback in that we will no longer be able to read the books we have purchased when the service is terminated. One way to solve this problem is to build a decentralized system that does not depend on a specific company or organization by combining smart contracts running on the Ethereum blockchain and distributed storage such as an IPFS. However, a simple combination of existing technologies does not make the stored e-book data persistent, so the risk of purchased e-books becoming unreadable remains. In this paper, we propose a decentralized distributed storage called d-book-repository, which has both access management function and data durability for purchased e-books. This system uses NFTs as access rights to realize strict access control by preventing clients who do not have NFTs from downloading e-book data. In addition, e-book data stored on storage nodes in the distributed storage is divided into shards using Reed–Solomon codes, and each storage node stores only a single shard, thereby preventing the creation of nodes that can restore the entire content from locally stored data. The storage of each shard is not handled by a single node but by a group of nodes, and the shard is propagated to all nodes in the group using the gossip protocol, where erasure codes are utilized to increase the resilience against node departure. Furthermore, an incentive mechanism to encourage participation as a storage node is implemented using smart contracts. We built a prototype of the proposed system on AWS and evaluated its performance. The results showed that both downloading and uploading 100 MB of e-book data (equivalent to one comic book) were completed within 10 s using an instance type of m5.xlarge. This value is only 1.3 s longer for downloading and 2.2 s longer for uploading than the time required for a simple download/upload without access control, confirming that the overhead associated with the proposed method is sufficiently small.
Evaluation of Interactive College Piano Teaching's Effect Based on Artificial Intelligence Technology

In recent years, with the economic development of various countries, education has also made great leaps, and piano teaching has been popularized and promoted in various universities. Pianos are also put into use in universities through normal channels. According to the survey, there are certain drawbacks in the teaching process of piano, which hinder the teaching effect to a great extent. In order to better adapt to the interactive teaching mode, this paper takes the flipped classroom in piano teaching as an example to conduct a systematic study based on the method of artificial intelligence. This paper introduces three typical artificial intelligence technologies and analyzes the three evaluative indicators corresponding to the effect of piano interactive teaching. The analysis results show that the prediction effect based on fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by artificial intelligence shows a strong consistency with the actual evaluation effect.
Tourist and local history hikes as a form of extracurricular work

The question of the development of the system of school geographical education is relevant in modern realities, because the ability to own a map and compass, orient oneself in the area, in particular by local signs, will help everywhere and in any conditions. Therefore, it is necessary to develop these skills from school years, that is, from the school geography course. It should be noted that theoretical knowledge is usually obtained by schoolchildren in lessons, and consolidation and deepening of the acquired knowledge and skills directly during participation in extracurricular activities. Such activities can be excursions, tourist and local history hikes, etc. The paper proposes a tourist and local history trip for schoolchildren, the territory of which is the Ukrainian Carpathians. The purpose of this trip is to acquire new practical tourism skills and consolidate the acquired knowledge from geography lessons.
Digital Transformation of Health Professionals: Using the Context Optimisation Model for Person-Centred Analysis and Systematic Solutions (COMPASS) Implementation Model Use Case

In today’s demanding healthcare landscape, the use of theoretical frameworks is paramount for navigating the complexities of digital health challenges. The Context Optimisation Model for Person-centred Analysis and Systematic Solutions (COMPASS) theoretical framework and implementation model serves as an invaluable direction tool in planning, implementing, and evaluating digital healthcare initiatives. This paper showcases the tangible value of the COMPASS implementation model through a use case scenario involving an accredited exercise physiologist and a healthcare user with Type 2 Diabetes Mellitus who seeks credible information via a mobile digital device. Within this example, the COMPASS model demonstrates the ability to enhance systematic processes, streamline the workflow of health professionals and develop their capabilities to actively contribute to the transformative realm of digital health. Through exploration of the use case and the significance of the systematic processes as a research direction, the empowerment of health professionals to play pivotal roles in ongoing digital health transformation is emphasised. The COMPASS model emerges as a powerful tool, guiding health professionals and organisations towards innovative and sustainable solutions in the dynamic landscape of digital healthcare.
Noise generation model of low-light remote sensing camera based on physical mechanism

With the continuous development of the field of remote sensing, the field of space-based remote sensing is developing in the direction of all-sky time and intelligence. Since low-light remote sensing is used to detect ground objects under low illumination conditions such as night and dawn and dusk, it leads to the characteristics of low contrast, low brightness and low signal-to-noise ratio of remote sensing images. The low signal-to-noise ratio leads to a large number of complex physical noises which will drown the image features and seriously affect the recognition and interpretation of ground objects. In this paper, a real physical model construction method based on physical mechanism of low-light image is proposed, By solving the noise parameters of space-based low-light remote sensing camera, the specific noise distribution model is constructed, which is used to synthesize the training data of the training denoising network, Thus, it gets rid of the difficult and laborious calibration problem caused by the lack of space-based data. In addition, in order to simulate the real imaging scene of space-based remote sensing camera, a set of low-light remote sensing noise image data set based on real physical model is constructed for the first time, which effectively promotes the development of subsequent image denoising research.
Forecasting cryptocurrencies log-returns: a LASSO-VAR and sentiment approach

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns.Furthermore, social media has garnered attention for its predictive capabilities in various fields, including financial markets and the economy.In this study, we exploit the predictive power of sentiment from Twitter and Reddit, alongside Google Trends indexes, to forecast log returns for ten cryptocurrencies, namely Bitcoin, Ethereum, Tether, Binance Coin, Litecoin, Enjin Coin, Horizen, Namecoin, Peercoin, and Feathercoin.We evaluate the performance of LASSO Vector Autoregression using daily data from January 2018 to January 2022.In a 30-day recursive forecast, we achieve a mean directional accuracy (MDA) rate of over 50%.Moreover, we observe a significant increase in forecast accuracy in terms of MDA when using sentiment and attention variables as predictors, but only for less capitalized cryptocurrencies.This improvement is not reflected in the RMSE.We also conduct a Granger causality test using post-double LASSO selection for high-dimensional VAR models.Our results suggest that social media sentiment does not Granger-cause cryptocurrencies returns.
Fog Detection through Image Processing Methods

Abstract This paper presents a fog detection algorithm, highlighting the significance of continued exploration in fog identification through image processing techniques. The advancement and application of this algorithm can significantly benefit various domains, including road safety, environmental monitoring, navigation, security, surveillance, and improving existing systems’ performance. The evaluation performed on test images have shown an accuracy of 72%, a precision of 94%, a recall of 57% and an F1 score of 0.71. The proposed algorithm clearly outperformed some existing fog detection methods.
Coherent and incoherent pumping of a three-level laser

Abstract This article investigates the quantum and semi-classical aspects of a three-level atom-cavity system within the context of cavity quantum electrodynamics. The study examines the behavior of the system through a quantum perspective and a semi-classical approximation. The steady-state master equation is solved in the atom-cavity basis, resulting in a closed set of equations describing the atom’s level occupancies and the cavity’s photon number. The accuracy of the semi-classical approximation is assessed by comparing it with quantum simulations. The research analyzes the system’s behavior near the laser threshold, highlighting the interplay between semi-classical and quantum behaviors. Additionally, the conversion of the three-level atom to a two-level atom is explored under specific conditions, enabling an investigation into the weak driving limit. Quantum simulation results are used to validate the proposed approximations. This work contributes to the understanding of atom-cavity interactions and provides insights into the transition from semi-classical to quantum behavior in such systems.