Reviewerly/exHarmony huggingface dataset:
Traffic sign detection algorithm based on improved YOLOv4

Aiming at the needs of Intelligent Traffic System, this paper proposes an improved algorithm based on YOLOv4 to solve the problems of low accuracy, low real-time detection speed and missed detection in small objects of current traffic sign detection. Firstly, the conventional convolution in the convolution block of YOLOv4 neck part is replaced by the depth separable convolution to reduce the parameters and computation of the algorithm. Secondly, the PANet structure is replaced with a new structure named PANet-A which is proposed in this paper, which can better combine low-level semantic information with high-level semantic information to reduce the information loss of small objects and obtain richer feature information. Finally, the feature map obtained from the backbone is embedded into the CBAM attention module, so that the network can effectively suppress useless information while obtaining important feature information, and help the whole model to detect traffic signs more accurately and the computation amount is almost unchanged. The experimental results show that, on the CCTSDB traffic sign dataset, the number of parameters of the improved algorithm is reduced by 33% compared with the original algorithm, the mAP is increased by 1.02%, and the FPS decreases only in a small range, which basically achieves a balance between recognition accuracy and speed. The experimental results prove the effectiveness of the improved network in small object detection and overall detection, which can meet the requirements of traffic sign detection in real scenes.
Research and design of professional farmer learning service platform based on cloud

With the rapid development of mobile Internet technology, traditional classroom teaching, centralized lecturer training and other teaching methods can not meet the learning needs of modern farmers. Under the background of rural revitalization strategy, "Internet + farmer education" began to enter people's vision. This paper designs an open, sharing, multi-organization, multi- tenant farmer learning service cloud platform. Connect farmers and learning service organizations to the platform to break the geographical restrictions between farmers and service organizations. This study proposes a theory and model of farmer learning service, which provides diversified learning support services for farmer learning, provides personalized learning services for farmers through collaborative filtering algorithm, and promotes rural revitalization construction with informatization.
Implementation of a Safe and Efficient Point Multiplication for SM2 Algorithm

With the development of information security and communication technology, SM2 algorithm has been widely used in security chip, secure communication and other fields. Point multiplication is a key operation that affects the speed and security of SM2 algorithm. In order to make the implementation of SM2 algorithm safer and faster, this design optimized the fixed window method of implementation method of point multiplication, which mainly includes: mathematical transformation in the implementation process, adding a subtraction to achieve the purpose of security, and combining it with the point coordinate randomization, scalar randomization, and point verification security protection measures, achieving the effect of resisting common analyses against SM2 algorithm. Using the design compiler tool and UMC55 process library to synthesize the design, the module area is 29,793GE (equivalent NAND gate). Through comparison, it achieves significant optimization effect and has the advantages of small area, relatively high performance and high security. Finally, the security analysis shows that the module can resist timing analysis, simple power analysis and other common analyses against SM2 algorithm, and can be applied to hardware implementation scenarios that require higher speed and higher security.
Study on Construction of Radio Monitoring System Based on Cloud-Edge-Device

Edge computing is the extension and supplement of cloud computing on the edge of network, and it has a wide application prospect in radio monitoring. In this paper, the advantages of edge computing in radio monitoring are briefly analyzed. Based on the characteristics and capability requirements of radio monitoring tasks, the architecture of radio monitoring system is designed innovatively, and the construction ideas of technical framework and data architecture are described in detail, which can provide reference for the next top-level planning of radio monitoring system in our country.
A System-level Electromagnetic Characteristics Knowledge Graph Based on Knowledge Reasoning Applied to Electromagnetic Compatibility Analysis

How to quickly and accurately obtain the electromagnetic characteristics and relations of managed objects is an important issue in electromagnetic spectrum management. This paper draws on the concept of knowledge graph in the field of artificial intelligence, and uses the structure of OWL+SWRL+Jess to construct a system-level electromagnetic characteristics knowledge graph system. In order to serve the electromagnetic compatibility analysis in the integrated electromagnetic information system, simulation experiments for automatic induction of equipment spectrum features and relationships are carried out using knowledge reasoning techniques based on logic programming. Through knowledge representation and knowledge reasoning, connections between electromagnetic characteristic elements are visualized, and usefulness of the method for enhancing the intelligence and interpretability of system-level EMC analysis is verified.
Research on Camera Calibration Method Based on Homography Matrix

Aiming at the problem of screen size detection in mobile phone production, a method of screen size detection based on homography matrix camera calibration is adopted. For the problem of inconsistent image coordinate system origin and direction between different calibrated images in corner point detection during calibration, the corner detection strategy of manual plus automatic is adopted, which solves the problem of inconsistent image coordinate system origin and direction between images in automatic corner point detection. The experimental results show that the homography-based camera calibration method has an average reprojection error of about 0.12 pixels, high calibration accuracy and small error. And the detection accuracy is better than the requirements of industrial production, which can meet the needs of practical applications.
Application of Robot Obstacle Avoidance Algorithm and Attack Strategy on ROS

Abstract China Robot Competition, which integrates the theory and practice of multi-disciplinary intelligent robot system, is one of the most influential robot competitions in China. In this paper, we apply RRT * and APF to the Middle Size Simulation League of soccer on ROS and propose an attack strategy based on extremum. Our experiments showed that the implementation of RRT* and APF optimizes the multiple iterations existing in original obstacle avoidance algorithm and greatly improves the performance of robots when there are fewer obstacles and the maximum speed of obstacle avoidance is limited. Besides, our attack strategy has proved effective on boosting the chances of goal and helped the Central South University to win the championship of China Robot Competition Middle Size Simulation League in 2021.
Rescheduling rehabilitation sessions with answer set programming

Abstract The rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g. patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular as a consequence of the COVID-19 pandemic that significantly increased rehabilitation’s needs. The problem has been recently successfully solved via a two-phase solution based on answer set programming (ASP). In this paper, we focus on the problem of rescheduling the rehabilitation sessions, which comes into play when the original schedule cannot be implemented, for reasons that involve the unavailability of operators and/or the absence of patients. We provide rescheduling solutions based on ASP for both phases, considering different scenarios. Results of experiments performed on real benchmarks, provided by ICS Maugeri, show that also the rescheduling problem can be solved in a satisfactory way. Finally, we present a web application that supports the usage of our solution.
Comparison of an effectiveness of artificial neural networks for various activation functions

Activation functions play an important role in artificial neural networks (ANNs) because they break the linearity in the data transformations that are performed by models. Thanks to the recent spike in interest around the topic of ANNs, new improvements to activation functions are emerging. The paper presents the results of research on the effectiveness of ANNs for ReLU, Leaky ReLU, ELU, and Swish activation functions. Four different data sets, and three different network architectures were used. Results show that Leaky ReLU, ELU and Swish functions work better in deep and more complex architectures which are to alleviate vanishing gradient and dead neurons problems but at the cost of prediction speed. Swish function seems to speed up training process considerably but neither of the three aforementioned functions comes ahead in accuracy in all used datasets.
Manipulator Motion Planning Based on Improved RRT Algorithm

Abstract Aiming at the problems that the Rapidly-exploring Random Tree (RRT) algorithm has more time-consuming and longer paths in the manipulator motion planning, we propose an improved RRT algorithm based on probabilistic target bias strategy and triangular inequality pruning method. This paper simulates the algorithm from three dynamic environments with different numbers of obstacles. The simulation results show that the proposed improved RRT algorithm can plan a shorter path in less time.
Augmenting The Cloud Environment Security Through Blockchain Based Hash Algorithms

Many techniques and algorithms are developed to enhance the security in the cloud environment. This helps the users to secure their server from malicious attacks. Hence the study and investigation of the performance enhanced security algorithms is a must demanded field in the research industry. When large number users using same server to store their information in cloud environment security is a must needed component to preserve the privacy and confidentiality of every individual user. This can be further strengthened by detecting the attacks in earlier stages and taking countermeasure to prevent the attack. Thus securing the data network without any leakage and loss of the information is a challenging task in the cloud environment. When the attacks or intrusion is detected after the occurrence there may be damage to the data in the form of data damage or theft. Hence it is necessary to predict and detect the attacks before the occurrence to protect the privacy and confidentiality of the user information.&#x0D; Keywords: Cloud security; Data privacy; Data confidentiality; Hash Algorithm; Substitutional encryption
Applicable Law Concerning Obligations Arising from the Infringements of Personal Data Laws Due to the Use of Artificial Intelligence Systems

An issue that is characteristic of AI is data processing on a massive scale (giga data, Big Data). This issue is also important because of the proposition to require manufacturers to equip AI systems with a means to record information about the operation of the technology, in particular the type and magnitude of the risk posed by the technology and any negative effects that logging may have on the rights of others. Data gathering must be carried out in accordance with the applicable laws, particularly data protection laws and trade secret protection laws. Therefore, it is necessary to determine the applicable law in line with existing conflict-of-law regulations.
Speakers prioritise affordance-based object semantics in scene descriptions

This work investigates the linearisation strategies used by speakers when describing real-world scenes to better understand production plans for multi-utterance sequences. In this study, 30 participants described real-world scenes aloud. To investigate which semantic features of scenes predict order of mention, we quantified three features (meaning, graspability, and interactability) using two techniques (whole-object ratings and feature map values). We found that object-level semantic features, namely those affordance-based, predicted order of mention in a scene description task. Our findings provide the first evidence for an object-related semantic feature that guides linguistic ordering decisions and offer theoretical support for the role of object semantics in scene viewing and description.
Intelligent online selling point extraction and generation for e‐commerce recommendation

Abstract During the past decade, great significant advancements have been witnessed in the domain of automatic product description generation. As the services provided by e‐commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye‐catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the intelligent online selling point extraction (IOSPE) system to serve the recommendation system in the JD.com e‐commerce platform. In addition, an interactive tool is released for merchants in JD.COM, which additionally allow them to select and customize the selling points with flexibility. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than four million products). So far, it has generated more than 0.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labor. These IOSPE‐generated selling points have increased the click‐through rate (CTR) by 1.89% and the average duration the customers spent on the products by more than 2.03% compared to the previous practice, which are significant improvements for such a large‐scale e‐commerce platform.
Ordered covering arrays and upper bounds on covering codes

Abstract This work shows several direct and recursive constructions of ordered covering arrays (OCAs) using projection, fusion, column augmentation, derivation, concatenation, and Cartesian product. Upper bounds on covering codes in Niederreiter–Rosenbloom–Tsfasman (shorten by NRT) spaces are also obtained by improving a general upper bound. We explore the connection between ordered covering arrays and covering codes in NRT spaces, which generalize similar results for the Hamming metric. Combining the new upper bounds for covering codes in NRT spaces and ordered covering arrays, we improve upper bounds on covering codes in NRT spaces for larger alphabets. We give tables comparing the new upper bounds for covering codes to existing ones.
A generative adversarial network model fused with a self‐attention mechanism for the super‐resolution reconstruction of ancient murals

Abstract The problem that the texture details of low‐resolution (LR) digital images of ancient murals are ambiguous persists. To solve this problem, this study proposes a super‐resolution (SR) reconstruction method for fuzzy murals based on a generative adversarial network with self‐attention (SA). The network uses a blur kernel and realistic noise data to add blur and noise, respectively, to a high‐resolution (HR) image to obtain an original LR image. Then, a feature image with the same size as that of the input image is obtained through a SA module. Finally, the feature information extracted from the image is input into the high‐resolution image space by using a subpixel convolution layer to realize the image enlargement process from an LR to an HR. Experiments evaluate the proposed approach both objectively and subjectively. The objective evaluation results show that compared with other SR reconstruction algorithms, the proposed algorithm's peak signal‐to‐noise ratio (PSNR) is increased by 0.04 to 3.78 dB on average, and its structural similarity is increased by 0.002 to 0.191. A subjective perception evaluation shows that the developed algorithm can better reconstruct the texture details of murals, thus better meeting the visual perception needs of the public. The method proposed in this study can satisfactorily reconstruct the texture details of murals, which may provide technical guidance for the development of mural protection plans. Furthermore, it may be of certain practical significance for the SR reconstruction of ancient murals.
Quantum privacy‐preserving service for secure lane change in vehicular networks

Abstract Secure Multiparty Computation (SMC) enables multiple parties to cooperate securely without compromising their privacy. SMC has the potential to offer solutions for privacy obstacles in vehicular networks. However, classical SMC implementations suffer from efficiency and security challenges. To address this problem, two quantum communication technologies, Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution were utilised. These technologies supply symmetric and oblivious keys respectively, allowing fast and secure inter‐vehicular communications. These quantum technologies are integrated with the Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol to form a Quantum Secure Multiparty Computation (QSMC) platform. A lane change service is implemented in which vehicles broadcast private information about their intention to exit the highway. The proposed QSMC approach provides unconditional security even against quantum computer attacks. Moreover, the communication cost of the quantum approach for the lane change use case has decreased by 97% when compared to the classical implementation. However, the computation cost has increased by 42%. For open space scenarios, the reduction in communication cost is especially important, because it conserves bandwidth in the free‐space radio channel, outweighing the increase in computation cost.
Assessment of the community’s technological literacy in the use of mobile phones for rural development

Access and use of mobile phones by communities in rural areas is a topical issue in different countries. However, there has been limited empirical literature to account for mobile phone use in rural areas for development and lifelong learning. This study employed the data which were collected from 66 phone users in rural areas to report on their technological literacy in the use of mobile phones. The data were generated through questionnaires, focus group discussions and practical tests. Descriptive statistics and thematic analysis were employed to analyse the data. The analysis revealed that the majority of adults in rural communities could use mobile phones through voice calls and short messages only. Only a few of them could use mobile phones to access internet services. Financial transactions and searching for information on weather, health, politics and businesses were challenging to them. This led to the conclusion that rural communities need to be empowered to access internet services through mobile phones to enhance their socio-economic development activities. It is recommended that technological literacy programmes on the use of mobile phones and the internet to maximise productivity be implemented in rural communities that are mostly farmers and producers of raw materials.
On the defence of antifascist Italy in Alcide De Gasperi’s 1946 speech to the Paris Peace Conference

Faced with the gradual rise of neofascist parties in Europe, the present paper looks back at the past to analyse a speech delivered by one of the founding fathers of the European Union, namely Alcide De Gasperi. The investigation is part of a broader project on the analysis of Italian antifascist discourse. The speech that De Gasperi delivered at the 1946 Paris Peace Conference is reconstructed with the help of the pragma-dialectical model of a critical discussion, focusing on the passages in which the speaker dealt with Italy’s non-fascist identity and its wrong perception by the audience. Emphasis is laid on the argumentative pattern underlying De Gasperi’s discourse and the argumentative style characterising his address. Moreover, reference is made to the notion of argumentative equivalence to examine the translation of De Gasperi’s speech into English; published in the New York Times the day after he delivered his address, it cast a shadow over the impartiality of the drafters of the English version of the speech, which nevertheless managed to go down to history as an argumentative classic.
LFR-GAN: Local Feature Refinement based Generative Adversarial Network for Text-to-Image Generation

Text-to-image generation aims to generate images from text descriptions. Its main challenge lies in two aspects: (1) Semantic consistency, i.e., the generated images should be semantically consistent with the input text; and (2) Visual reality, i.e., the generated images should look like real images. To ensure text-image consistency, existing works mainly learn to establish the cross-modal representations via a text encoder and image encoder. However, due to the limited representation capability of the fixed-length embeddings and the flexibility of the free-form text descriptions, the learned text-to-image model is incapable of maintaining the semantic consistency between image local regions and fine-grained descriptions. As a result, the generated images sometimes miss some fine-grained attributes of the generated object, such as the color or shape of a part of the object. To address this issue, this paper proposes a Local Feature Refinement Based Generative Adversarial Network (LFR-GAN) , which first divides the text into some independent fine-grained attributes and generates an initial image, then refines the image details based on these attributes. The main contributions are three-fold: (1) An attribute modeling approach is proposed to model the fine-grained text descriptions by mapping them into representations of independent attributes, which provides more fine-grained details for image generation. (2) A local feature refinement approach is proposed to enable the generated image to form a complete reflection of the fine-grained attributes contained in the text description. (3) A multi-stage generation approach is proposed to realize the fine-grained manipulation of complex images progressively, which aims to improve the performance of the refinement and generate photo-realistic images. Extensive experiments on the CUB and Oxford102 datasets show the effectiveness of our LFR-GAN approach in both text-to-image generation and text-guided image manipulation tasks. Our LFR-GAN approach shows superior performance compared to the state-of-the-art methods. The codes will be released at https://github.com/PKU-ICST-MIPL/LFR-GAN_TOMM2023 .