Reviewerly/exHarmony huggingface dataset:
(De)centralization in the governance of blockchain systems: cryptocurrency cases

Initially conceived as a security technology, blockchain technology has since gained global interest by spawning decentralized, participatory and trusted network systems. However, despite the great advantages it offers, which have attracted many entrepreneurs and investors, there is growing concern about the intertwined relationship between technology and human governance within a blockchain-related system. This paper aims to discuss the various organizational and technological elements of governance of 10 selected cryptocurrency blockchain systems, focusing on whether their operation and adaptation are (de)centralized. Our analysis shows that the selected cryptocurrency systems are mostly decentralized on the technological aspects. The algorithms or protocols, based on which the systems function, utilize adjacent data or information available in its proximity, located within a single block, and there is no pre-programmed intervention protocol (as a centralized control) to manage conflicts and unexpected situations. In the consensus process, however, some algorithms utilize information distributed throughout the entire network, giving the system a centralized nature. On the other hand, the organizational aspects of their governance are largely centralized. The right or authority to make important decisions (e.g., modification of rules or algorithms) often lies in the hands of a small number of individuals (such as founders). Participation in managerial decision-making is not afforded to every participant of the blockchain system as well. When unforeseen problems occur, problem-solving coordination is usually based on centralized, top-down control and monitoring. We complete our analysis with a discussion on the origins and implications of the dual nature of the governance of cryptocurrency blockchain systems.
Navigation with Polytopes: A Toolbox for Optimal Path Planning with Polytope Maps and B-spline Curves

To deal with the problem of optimal path planning in 2D space, this paper introduces a new toolbox named "Navigation with Polytopes" and explains the algorithms behind it. The toolbox allows one to create a polytopic map from a standard grid map, search for an optimal corridor, and plan a safe B-spline reference path used for mobile robot navigation. Specifically, the B-spline path is converted into its equivalent Bézier representation via a novel calculation method in order to reduce the conservativeness of the constrained path planning problem. The conversion can handle the differences between the curve intervals and allows for efficient computation. Furthermore, two different constraint formulations used for enforcing a B-spline path to stay within the sequence of connected polytopes are proposed, one with a guaranteed solution. The toolbox was extensively validated through simulations and experiments.
Facing cyberthreats in a crisis and post-crisis era: Rethinking security services response strategy

The recent years have witnessed two major events that have deeply impacted cybersecurity threats. First, the COVID-19 pandemic has drastically increased our dependence upon technology. From individuals to corporations and governments, the overwhelming majority of our activities moved online. As the proportion of human activities performed online is reaching new peaks, cybersecurity becomes a problem of national security. Second, the Russia-Ukraine war is giving us a glimpse of what cyberthreats may look like in future cyberconflicts. From data integrity to identity thievery, and from industrial espionage to hostile manoeuvres from foreign powers, cyberthreats have never been that numerous and diverse. Due to the increase of the magnitude, of the diversity, and of the complexity of cyberthreats, the current security strategies used to face cybercriminality won't be sufficient in the post-crisis era. Therefore, governments need to rethink globally their national security services response strategy. This paper analyses how this new context has impacted cybersecurity for individuals, corporations, and governments, and emphasis the need to reposition the economical identity of the individuals at the center of security response. We propose strategies to optimize law enforcement response from police to counterintelligence, notably through formation, prevention, and interaction with cybercriminality. We then discuss the possibilities to optimize the articulation of the different levels of security response and expertise, by emphasizing the need for coordination between security services, and by proposing strategies to include non-institutional players.
Creating a Software Methodology to Analyze and Preserve Archaeological Legacy Data

Abstract Software now allows archaeologists to document excavations in more detail than ever before through rich, born-digital datasets. In comparison, paper documentation of past excavations (a valuable corpus of legacy data) is prohibitively difficult to work with. This pilot study explores creating custom software to digitize paper field notes from the 1970s excavations of the Gulkana site into machine-readable text and maps to be compatible with born-digital data from subsequent excavations in the 1990s. This site, located in Alaska's Copper River Basin, is important to archaeological understanding of metalworking innovation by precontact Northern Dene people, but is underrepresented in the literature because no comprehensive map of the site exists. The process and results of digitizing this corpus are presented in hopes of aiding similar efforts by other researchers.
Know(ing) Infrastructure: The Wayback Machine as object and instrument of digital research

From documenting human rights abuses to studying online advertising, web archives are increasingly positioned as critical resources for a broad range of scholarly Internet research agendas. In this article, we reflect on the motivations and methodological challenges of investigating the world’s largest web archive, the Internet Archive’s Wayback Machine (IAWM). Using a mixed methods approach, we report on a pilot project centred around documenting the inner workings of ‘Save Page Now’ (SPN) – an Internet Archive tool that allows users to initiate the creation and storage of ‘snapshots’ of web resources. By improving our understanding of SPN and its role in shaping the IAWM, this work examines how the public tool is being used to ‘save the Web’ and highlights the challenges of operationalising a study of the dynamic sociotechnical processes supporting this knowledge infrastructure. Inspired by existing Science and Technology Studies (STS) approaches, the paper charts our development of methodological interventions to support an interdisciplinary investigation of SPN, including: ethnographic methods, ‘experimental blackbox tactics’, data tracing, modelling and documentary research. We discuss the opportunities and limitations of our methodology when interfacing with issues associated with temporality, scale and visibility, as well as critically engage with our own positionality in the research process (in terms of expertise and access). We conclude with reflections on the implications of digital STS approaches for ‘knowing infrastructure’, where the use of these infrastructures is unavoidably intertwined with our ability to study the situated and material arrangements of their creation.
Multiple-image encryption algorithm based on Sarrus rule and 3D Fibonacci matrix

Abstract Using images to transmit information becomes more common in the new era, but the image information can easily be stolen and tampered with the malicious damage during transmission. In this paper, firstly, inspired by the famous Sarrus rule the theory of linear algebra theory, the Sarrus model is established to scramble images; secondly, the pixel positions of plain images are scrambled with the established Sarrus model to obtain the stereo scrambled matrix; thirdly, two-dimensional Fibonacci matrix is extended to three-dimensional (3D) space; finally, the image matrix is divided into cubes which are diffused by the 3D Fibonacci matrix. Experimental analyses indicate that the new algorithm has the characteristics of large key space, high key sensitivity, high efficiency and can resist to the plaintext attacks, statistical attacks, etc.
Coherence, purity and correlation for superconducting charge qubits

In a model consisting of two superconducting charge qubits (SC-Qs), we study how the Josephson energy (JE) and temperature have an influence on the coherence, the degree of mixedness, and the nonclassical correlation. In this example, we show how the quantumness metrics change depending on the JE and the temperature. We also show that the number of quantifiers can be controlled by appropriate selection of the JE of the SC-Q and temperature effect. Additionally, we determine the optimal conditions required to enhance and preserve the coherence and maintain the level of coherence in the existence of temperature variations. We observe important quantum phenomena, i.e., coherence trapping and the quantum correlation trapping. Furthermore, we explain the variations in the coherence based on the degree of mixedness in the superconducting qubit state.
Topic-aware video summarization using multimodal transformer

Video summarization aims to generate a short and compact summary to represent the original video. Existing methods mainly focus on how to extract a general objective synopsis that precisely summaries the video content. However, in real scenarios, a video usually contains rich content with multiple topics and people may cast diverse interests on the visual contents even for the same video. In this paper, we propose a novel topic-aware video summarization task that generates multiple video summaries with different topics. To support the study of this new task, we first build a video benchmark dataset by collecting videos from various types of movies and annotate them with topic labels and frame-level importance scores. Then we propose a multimodal Transformer model for the topic-aware video summarization, which simultaneously predicts topic labels and generates topic-related summaries by adaptively fusing multimodal features extracted from the video. Experimental results show the effectiveness of our method.
Detection Method of Helmet Wearing Based on UAV Images and Yolov7

In power line construction working environment, the safety helmet plays an important role in protecting the safety of the workers from injury or reduce the injury, so safety helmet detection is important for power industry safety inspection. Detection of helmet based on UAV images is an efficient detection way. The traditional safety helmet detection algorithms have low accuracy and poor robustness. An novel detection method for construction helmet wearing in Yolov7 model is proposed in this paper. Yolov7 adopts extended efficient long-range attention network (E-ELAN), model scaling based on cascade model, convolution weight parameterization and other strategies. Based on the images captured by UAV, the experimental results show that the improved Yolov7 algorithm improves the average accuracy over the previous Yolo algorithms,which enhances the generalization ability in the scene of small targets, meets the performance requirements of helmet wearing detection in the power line construction site, and has a certain role in promoting the construction safety of the power industry.
MBFFNet:Multi-branch feature fusion network for underwater image restoration

Underwater image is one of the important way for human to obtain oceans information, but the special physical and chemical properties of underwater environment seriously affect the quality of underwater image. To tackle this issue, we propose a multi-branch named MBFFNet that can recover and enhance underwater images simultaneously. We confirm MBFFNet can well recover the clear underwater images from large particle impurities and ocean light spots through experiments on UIEBD datasets. By considering spatial information, it can better complete the prediction of image restoration. It can reduce the amount of network calculations and parameters while maintaining the effect. Feature Fusion Module is proposed to integrate high-level features with low-level features, so that the network can extract enough feature information and better supplement the details to complete the underwater image restoration. In addition, the network can also improve the color performance while maintain the style and spatial texture of the contents. Comparing with other state-of-the-art image recovery and enhancement methods, MBFFNet can obtain optimal or suboptimal quality of the recovered underwater images with less number of parameters, which is valuable in real world applications.
Blockchain technology adoption and accounting information quality

Abstract The study investigates the implications of companies' adoption of blockchain technology on accounting information quality. Based on a sample of 33,242 firm‐year observations from A‐share companies listed on China's stock exchanges in 2007–2019, we find evidence that blockchain technology adoption significantly improves accounting information quality. Further, additional tests demonstrate that the mechanism of blockchain exerting its positive effect include strengthening corporate governance and realising synergies with large audit firms, while cross‐sectional tests show that its positive effect on accounting information quality is attenuated if the company is in an industry with high IT development or has experienced an audit firm change. Economic consequence tests show that the adoption of blockchain is conducive to companies' financing behaviour, as well as to overall firm value. Consequently, our results suggest the positive effect of blockchain technology on accounting information quality.
Arithmetical completeness theorems for monotonic modal logics

We investigate modal logical aspects of provability predicates PrT(x) satisfying the following condition: M: If T⊢φ→ψ, then T⊢PrT(⌜φ⌝)→PrT(⌜ψ⌝). We prove the arithmetical completeness theorems for monotonic modal logics MN, MN4, MNP, MNP4, and MND with respect to provability predicates satisfying the condition M. That is, we prove that for each logic L of them, there exists a Σ1 provability predicate PrT(x) satisfying M such that the provability logic of PrT(x) is exactly L. In particular, the modal formulas P: ¬□⊥ and D: ¬(□A∧□¬A) are not equivalent over non-normal modal logic and correspond to two different formalizations ¬PrT(⌜0=1⌝) and ¬(PrT(⌜φ⌝)∧PrT(⌜¬φ⌝)) of consistency statements, respectively. Our results separate these formalizations in terms of modal logic.
Research on Target Detection Algorithm for Complex Scenes

With the rapid development of computer technology, deep learning has been more and more widely used in the field of computer vision, The rapid development of target detection technology has also led to further requirements for the effectiveness of target detection. To improve the detection of targets in complex scenes, some adjustments were made on the basis of YOLOV5 for better performance in recognizing small overlapping objects and pedestrians. Change the CIOU loss used by YOLOV5 to EIOU loss, Integrating CA attention mechanism into C3 module and improving spatial pyramid pooling to increase the perceptual wildness of the network, Experimental results show that the detection accuracy is improved by 2% compared to the original YOLOV5.The network performs even better, improving the detection of targets in complex scenes.
Innovations of music and aesthetic education courses using intelligent technologies

Innovations in training programs impact the educational system and the level of students' professional abilities. Therefore, the purpose of this study is to investigate the use of innovative technologies in teaching music and aesthetics with the use of intelligent technology. Students from various music schools in Beijing participated in the study: elementary (112 respondents), middle (123 respondents), and high school (98 respondents) (areas: piano, violin, and percussion), a total of 343 participants. Assessment of the students' level of proficiency took place in several stages: comparing the students' level of proficiency with that they had before the experiment, for this purpose an average eight-point system was used. The next stage consisted of a comparison of grades for the final academic concert. The results showed that the greatest improvement was seen in the percussion class and the least in the violin class. Piano students showed an average result in the correlation comparison, but their level was the highest in the final academic concert, 48.55% of the students showed a level of skill above average. While 39.13% of the violin students scored "excellent" and "good". The same level among the students of "percussion instruments" was shown by 35.71% of the students. Thus, one can conclude that applying intelligent technologies positively impacts students' performance, but it is necessary to be careful in choosing applications and technologies for implementation in the educational process. Further research should be directed at the impact of other applications and programs on learning, as well as ways to improve other music education areas and how they can be changed by intelligent technology use.
Toward earthquake early warning: A convolutional neural network for repaid earthquake magnitude estimation

Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake magnitude, i.e., large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category. However, the current standard information signal processing routines for magnitude fast classification are time-consuming and vulnerable to data imbalance. Therefore, in this study, Deep Learning (DL) algorithms are introduced to assist with EEW. For the three-component seismic waveform record of 7 s obtained from the China Earthquake Network Center (CENC), this paper proposes a DL model (EEWMagNet), which accomplishes the extraction of spatial and temporal features through DenseBlock with Bottleneck and Multi-Head Attention. Extensive experiments on Chinese field data demonstrate that the proposed model performs well in the fast classification of magnitude. Moreover, the comparison experiments demonstrate that the epicenter distance information is indispensable, and the normalization has a negative effect on the model to capture accurate amplitude information.
SCFNet: Semantic correction and focus network for remote sensing image object detection

In high-resolution remote sensing images, the problems of large scale variation, large intra-class variance of background, small variability and irregularity of arrangement between different targets always exist in remote sensing images, making the modeling between targets and background more difficult and the target detection task more difficult. However, general target detection methods mainly use convolutional layers of different scales to enhance the target's perceptual domain and fuse different scale features to solve the scale variation problem, without considering the other two problems prevalent in remote sensing scenes of earth observation. In order to solve the above two problems, this paper proposes a semantic correction and focusing network (SCFNet) from the perspective of modeling the relationship between background and target and target to target. The network consists of two core modules the Local Correction Module (LCM) calculates the similarity of local features through the global features of the image to correct the local features and exclude the non-relevant The Non-local Focus Module (NLFM) enhances the recognition of target features by obtaining the non-local dependencies and the corrected local features from the LCM. To demonstrate the effectiveness and robustness of our proposed method, we conducted extensive experiments on two publicly popular large remote sensing multi-target detection datasets, namely DIOR and DOTA. the experimental results show that our SCFNet achieves best-in-class performance and significant accuracy improvement on the datasets.
Presence Questionnaires in Driving Simulation

Abstract Automotive research often takes place in driving simulators, where a high sense of feeling present in the virtual traffic situation has been hypothesized to foster a realistic driving behavior. Presence in driving simulators has been investigated using subjective questionnaires, but their validity for application in driving simulators has not yet been verified. In a first approach, we aimed at critically investigating whether presence questionnaires can or cannot be applied in driving simulation. Therefore, we conducted a within-subjects experiment with N=50 participants comparing presence between a low- and a high-fidelity simulator, using two commonly used questionnaires: the Igroup Presence Questionnaire (IPQ) and the Slater-Usoh-Steed (SUS*) presence questionnaire. The wording of the SUS* questionnaire has been adopted for use in driving simulation prior to its use in the study. Results show a strong correlation between the adapted SUS* questionnaire and the IPQ. Both questionnaires reflected a significant difference in presence between the low-fidelity and the high-fidelity simulator. The reliability of both questionnaires was acceptable (Cronbach's alpha &amp;gt; .7). The present results are promising in that presence questionnaires, including in particular the adapted SUS* and the IPQ, can be used in driving simulation. The validity of the adapted SUS* questionnaire should be further investigated.
Low-Light Image Enhancement by Combining Transformer and Convolutional Neural Network

Within low-light imaging environment, the insufficient reflected light from objects often results in unsatisfactory images with degradations of low contrast, noise artifacts, or color distortion. The captured low-light images usually lead to poor visual perception quality for color deficient or normal observers. To address the above problems, we propose an end-to-end low-light image enhancement network by combining transformer and CNN (convolutional neural network) to restore the normal light images. Specifically, the proposed enhancement network is designed into a U-shape structure with several functional fusion blocks. Each fusion block includes a transformer stem and a CNN stem, and those two stems collaborate to accurately extract the local and global features. In this way, the transformer stem is responsible for efficiently learning global semantic information and capturing long-term dependencies, while the CNN stem is good at learning local features and focusing on detailed features. Thus, the proposed enhancement network can accurately capture the comprehensive semantic information of low-light images, which significantly contribute to recover normal light images. The proposed method is compared with the current popular algorithms quantitatively and qualitatively. Subjectively, our method significantly improves the image brightness, suppresses the image noise, and maintains the texture details and color information. For objective metrics such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image perceptual similarity (LPIPS), DeltaE, and NIQE, our method improves the optimal values by 1.73 dB, 0.05, 0.043, 0.7939, and 0.6906, respectively, compared with other methods. The experimental results show that our proposed method can effectively solve the problems of underexposure, noise interference, and color inconsistency in micro-optical images, and has certain application value.