Reviewerly/exHarmony huggingface dataset:
A Hierarchical Reinforcement Learning Framework for UAV Path Planning in Tactical Environments

Tactical UAV path planning under radar threat using reinforcement learning involves particular challenges ranging from modeling related difficulties to sparse feedback problem. Learning goal-directed behavior with sparse feedback from complex environments is a fundamental challenge for reinforcement learning algorithms. In this paper we extend our previous work in this area to provide a solution to the problem setting stated above, using Hierarchical Reinforcement Learning (HRL) in a novel way that involves a meta controller for higher level goal assignment and a controller that determines the lower-level actions of the agent. Our meta controller is based on a regression model trained using a state transition scheme that defines the evolution of goal designation, whereas our lower-level controller is based on a Deep Q Network (DQN) and is trained via reinforcement learning iterations. This two-layer framework ensures that an optimal plan for a complex path, organized as multiple goals, is achieved gradually, through piecewise assignment of sub-goals, and thus as a result of a staged, efficient and rigorous procedure.
An Image Object Detection Model Based on Mixed Attention Mechanism Optimized YOLOv5

As one of the more difficult problems in the field of computer vision, utilizing object image detection technology in a complex environment includes other key technologies, such as pattern recognition, artificial intelligence, and digital image processing. However, because an environment can be complex, changeable, highly different, and easily confused with the target, the target is easily affected by other factors, such as insufficient light, partial occlusion, background interference, etc., making the detection of multiple targets extremely difficult and the robustness of the algorithm low. How to make full use of the rich spatial information and deep texture information in an image to accurately identify the target type and location is an urgent problem to be solved. The emergence of deep neural networks provides an effective way for image feature extraction and full utilization. By aiming at the above problems, this paper proposes an object detection model based on the mixed attention mechanism optimization of YOLOv5 (MAO-YOLOv5). The proposed method fuses the local features and global features in an image so as to better enrich the expression ability of the feature map and more effectively detect objects with large differences in size within the image. Then, the attention mechanism is added to the feature map to weigh each channel, enhance the key features, remove the redundant features, and improve the recognition ability of the feature network towards the target object and background. The results show that the proposed network model has higher precision and a faster running speed and can perform better in object-detection tasks.
AD-SiamRPN: Anti-Deformation Object Tracking via an Improved Siamese Region Proposal Network on Hyperspectral Videos

Object tracking using Hyperspectral Images (HSIs) obtains satisfactory result in distinguishing objects with similar colors. Yet, the tracking algorithm tends to fail when the target undergoes deformation. In this paper, a SiamRPN based hyperspectral tracker is proposed to deal with this problem. Firstly, a band selection method based on a genetic optimization method is designed for rapidly reducing the redundancy of information in HSIs. Specifically, three bands with highest joint entropy are selected. To solve the problem that the information of the template in the SiamRPN model decays over time, an update network is trained on the dataset from general objective tracking benchmark, which can obtain effective cumulative templates. The use of cumulative templates with spectral information makes it easier to track the deformed target. In addition, transfer learning of the pre-trained SiamRPN is designed to obtain a better model for HSIs. The experimental results show that the proposed tracker can obtain good tracking results over the entire public dataset, and that it is better than the other popular trackers when the target’s deformation is qualitatively and quantitatively compared, achieving an overall success rate of 57.5% and a deformation challenge success rate of 70.8%.
Traditions and innovations: two poles of education of the future

In this article one made a review of the literature based on the relationship between traditions and innovations in the pedagogical process over the last decade. In the article one summarized domestic and foreign scientists opinions on the topic of combining traditional and innovative forms and methods, their relationship and complementarity; the importance of traditions for the implementation of innovative models of education is considered; it is emphasized that the pedagogical tradition contains many elements, performs various functions and is reflected in a significant number of pedagogical technologies and methods, forms and means.&#x0D; Based on a full-text analysis of about 16 articles published in well-known databases and scientific journals, the study revealed the support of the vast majority of researchers concerning innovative educational methods. All the cited scientists have the idea that the introduction of innovations renews and strengthens the educational process. They focus readers' attention on the interaction of traditional and innovative teaching methods, since according to the waste majority of scientists, without preserving pedagogical traditions, the modernization of educational systems is unlikely to bring significant positive results.&#x0D; At the same time, in the entire collection of researched articles, there are several authors who consider traditional education to be more effective than innovative education.&#x0D; According to the scientist’s opinion, these two phenomena are not only interconnected and mutually determined, but also do not exist as separate phenomena. In other words, the tradition is an integral part of innovation, and pedagogical innovation always includes traditionally oriented elements.
New record in the number of qubits for a quantum implementation of AES

Optimizing the quantum circuit for implementing Advanced Encryption Standard (AES) is crucial for estimating the necessary resources in attacking AES by the Grover algorithm. Previous studies have reduced the number of qubits required for the quantum circuits of AES-128/-192/-256 from 984/1112/1336 to 270/334/398, which is close to the optimal value of 256/320/384. It becomes a challenging task to further optimize them. AimTaking aim at this task, we find a method for how the quantum circuit of AES S-box can be designed with the help of the automation tool LIGHTER-R. Particularly, the multiplicative inversion in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mn>8</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math> , which is the main part of the S-box, is converted into the multiplicative inversion (and multiplication) in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math> , then the latter can be implemented by LIGHTER-R because its search space is small enough. By this method, we construct the quantum circuits of S-box for mapping | a ⟩| 0 ⟩ to | a ⟩| S ( a )⟩ and | a ⟩| b ⟩ to | a ⟩| b ⊕ S ( a )⟩ with 20 qubits instead of 22 in the previous studies. In addition, we introduce new techniques to reduce the number of qubits required by the S-box circuit for mapping | a ⟩ to | S ( a )⟩ from 22 in the previous studies to 16. Accordingly, we synthesize the quantum circuits of AES-128/-192/-256 with 264/328/392 qubits, which implies a new record.
Towards a greater understanding of Coordinated Vulnerability Disclosure policy documents

Bug bounty programmes and vulnerability disclosure programmes, collectively referred to as Coordinated Vulnerability Disclosure (CVD) programmes, open up an organisation’s assets to the inquisitive gaze of (often eager) white-hat hackers. Motivated by the question What information do organisations convey to hackers through public CVD policy documents? , we aim to better understand the information available to hackers wishing to participate in the search for vulnerabilities. As such, in this article we consider three key issues. First, to address the differences in the legal language communicated to hackers, it is necessary to understand the formal constraints by which hackers must abide. Second, it is beneficial to understand the variation that exists in the informal constraints that are communicated to hackers through a variety of institutional elements. Third, for organisations wishing to better understand the commonplace elements that form current policy documents, we offer broad analysis of the components frequently included therein and identify gaps in programme policies. We report the results of a quantitative study, leveraging deep learning based natural language processing models, providing insights into the policy documents that accompany the CVD programmes of thousands of organisations, covering both stand-alone programmes and those hosted on 13 bug bounty programmes. We found that organisations often inadequately convey the formal constraints that are applicable to hackers, requiring hackers to have a deep understanding of the laws that underpin safe and legal security research. Furthermore, a lack of standardisation across similar policy components is prevalent, and may lead to a decreased understanding of the informal constraints placed upon hackers when searching for and disclosing vulnerabilities. Analysis of the institutional elements included in the policy documents of organisations reveals insufficient inclusion of many key components. Namely, legal information and information pertaining to restrictions on the backgrounds of hackers is found to be absent in a majority of policies analysed. Finally, to assist ongoing research, we provide novel annotated policy datasets that include human-labelled annotations at both the sentence and paragraph level, covering a broad range of CVD programme backgrounds.
Natural language processing in urology: Automated extraction of clinical information from histopathology reports of uro-oncology procedures

We aimed to automate routine extraction of clinically relevant unstructured information from uro-oncological histopathology reports by applying rule-based and machine learning (ML)/deep learning (DL) methods to develop an oncology focused natural language processing (NLP) algorithm.Our algorithm employs a combination of a rule-based approach and support vector machines/neural networks (BioBert/Clinical BERT), and is optimised for accuracy. We randomly extracted 5772 uro-oncological histology reports from 2008 to 2018 from electronic health records (EHRs) and split the data into training and validation datasets in an 80:20 ratio. The training dataset was annotated by medical professionals and reviewed by cancer registrars. The validation dataset was annotated by cancer registrars and defined as the gold standard with which the algorithm outcomes were compared. The accuracy of NLP-parsed data was matched against these human annotation results. We defined an accuracy rate of >95% as "acceptable" by professional human extraction, as per our cancer registry definition.There were 11 extraction variables in 268 free-text reports. We achieved an accuracy rate of between 61.2% and 99.0% using our algorithm. Of the 11 data fields, a total of 8 data fields met the acceptable accuracy standard, while another 3 data fields had an accuracy rate between 61.2% and 89.7%. Noticeably, the rule-based approach was shown to be more effective and robust in extracting variables of interest. On the other hand, ML/DL models had poorer predictive performances due to highly imbalanced data distribution and variable writing styles between different reports and data used for domain-specific pre-trained models.We designed an NLP algorithm that can automate clinical information extraction accurately from histopathology reports with an overall average micro accuracy of 93.3%.
EFPB: Efficient Fair Payment Based on Blockchain for Outsourcing Services in Cloud Computing

Despite the maturity of cloud services (e.g., outsourcing of computational tasks), a number of operational challenges remain. For example, how do we ensure trust between outsourcers and workers in a zero-trust environment? While a number of blockchain-based solutions that eliminate the reliance on trusted third parties have been presented, many of these existing approaches do not achieve robust fairness and/or support compatibility with other systems. In this paper, we propose an efficient fair payment system using blockchain (EFPB), designed to achieve robust fairness and compatibility. Specifically, EFPB comprises a number of cryptographic building blocks, mainly: one-way accumulator (RSA-based construction), stealth address and symmetric encryption. We then evaluate the performance of EFPB to demonstrate that it is more efficient and low-cost than other competing schemes, as well as presenting a comparative summary of functionalities.
Decentralized Optimization for Multicast Adaptive Video Streaming in Edge Cache-Assisted Networks

Adaptive streaming based on DASH offers personalized video experience and smooth playback by allowing dynamical adjustments of the video bitrate to the variations of network conditions. This is especially important for current and future Internet video streaming applications, including emerging ones such as virtual reality-based, as adaptive streaming plays a key role in providing high quality viewing experience, especially in limited bandwidth delivery environments. To enable this promising avenue in a 5G context, efforts are made to consider it alongside multicast and edge caching, as part of the next generation communication technology. In this paper, we model the adaptive streaming transmission problem in a mobile scenario as a multi-source multicast multi-rate problem (MMMP) whose linear relaxation is concave. We decompose the problem in terms of clients and propose the distributed delivery algorithm (DDA). The computation complexity, convergence and time-varying adaptation of DDA are theoretically analyzed. Additionally, to further reduce the computation complexity of the solution, a heuristic approximation method (H-DDA) based on the physical meaning of the problem is proposed and it is also shown how H-DDA converges to the optimal value by numerical means. Finally, we conduct a series of simulation tests to demonstrate the superiority of the proposed HDDA in comparison with other state-of-art solutions.
A network of all sky imagers (ASI) enabling accurate and high-resolution very short-term forecasts of solar irradiance

The Eye2Sky network is a measurement network in north-western Germany consisting of multiple all-sky imagers (ASI), meteorological and solar irradiance measurements. The network provides high temporal and spatial resolution data for meteorological and especially solar energy related applications. With increasing photovoltaic (PV) capacity in electrical grids fluctuations in solar irradiance due to changing cloud cover may have adverse effects on the grid stability. Within Eye2Sky, new technologies and methodologies facing the demand for more accurate solar irradiance forecasts are being developed. The ASIs used in Eye2Sky record 180° field of view hemispherical sky images from fish-eye lensed cameras. Accompanied with local measurements of solar irradiance components (global, direct and diffuse) a very short-term forecast of the solar resource is possible. These nowcasts provide minutely updated information up to 20 minutes ahead with 1-minute temporal and 50 m x 50 m spatial resolution. This approach shows more precise forecasting results for the next minutes ahead compared to traditional and less detailed methods based on satellite or numerical weather prediction models. In the network, multiple ASIs are used to enlarge the spatial coverage and the forecast horizon requested by many applications. Moreover, the forecast error can be reduced with a network of cameras. In this article, the Eye2Sky network, its research results and applications are introduced.
Depth Estimation and Navigation Route Planning for Mobile Robots Based on Stereo Camera

The extraction of three dimensional (3D) information from digital pictures, such as those acquired by a CCD camera, is known as computer stereo vision. Examining the relative locations of things in the two panels allows 3D information to be derived by comparing information about a scene from two viewpoints. We employed two cameras in the suggested system to identify obstructions in front of the autonomous vehicle or robot using the disparity idea. The absolute difference between the two pictures is calculated and utilized to regulate the motion of the vehicle/robot. Edge pixels retrieved from two pictures are matched, and a dense disparity map is generated by filling in the gaps between two consecutive edge pixels. A system has been proposed that permits the identification of an ideal path in real time between a starting point and a desired position in a congested environment using stereo vision, followed by a path planning algorithm and navigation enabling easy vehicle/robot traversal.
A Systematic Review on Deepfake Technology

The twenty-first century has seen technology become an integral part of human survival. Living standards have been affected as the technology continues to advance. Deep fake is a deep learning-powered application that recently appeared on the market. It allows to make fake images and videos that people cannot discern from the real ones and is a recent technique that allows swapping two identities within a single video. A wide range of factors, including communities, organizations, security, religions, democratic processes as well as personal lives, are being impacted by deep fakes. In the case of images and videos being presented to an organization as evidence, if the images or videos are altered, the entire truth will be transformed into a lie. It is inevitable that with every new and beneficial technology, some of its adverse effects will follow, causing world problems. There have been several instances in which deep fakes have alarmed the entire network. During the last few years, the number of altered images and videos has grown exponentially, posing a threat to society. Additionally, this paper explores where deep fakes are used, the impacts made by them, as well as the challenges and difficulties associated with deep fakes in this rapidly developing society.
A Dehazing Method for Remote Sensing Image Under Nonuniform Hazy Weather Based on Deep Learning Network

Different from the ground image with uniform haze, the haze in remote sensing (RS) image has the characteristics of irregular shape and uneven concentration in hazy weather. It brings a great challenge to the application of RS image data in advanced image processing tasks. A novel dehazing network for non-uniform hazy remote sensing image, named as KFA-Net, is proposed to solve the aforementioned issues. The designed asymmetric size feature cascade (ASFC), k-means pixel attention (KPA) and FFT channel attention (FCA) in KFA-Net all show excellent effects. Compared with symmetrically linked typical Unet, ASFC can more easily extract shallow features for feature reconstruction. Furthermore, different from the commonly used pixel attention that compresses feature maps directly, KPA introduces k-means clustering algorithm in machine learning into the attention mechanism, which facilitates network training to focus on the thick hazy region. Compared to typical squeeze-and-excitation block, FCA uses the low-frequency region feature of spectrogram to obtain the attention weight coefficient in the frequency domain, making network training pay more attention to the feature of image low-frequency region. Extensive comparison experiments verify that the proposed KFA-Net has the great superiority. PSNR/SSIM of KFA-Net are 31.0952% and 6.6401% higher than DCP with the highest citation in traditional dehazing methods, respectively. PSNR/SSIM of KFA-Net are 2.2049% and 0.4966% higher than the recently proposed 4KDehazing with the best performance among all comparison dehazing methods, respectively. The KFA-Net proposed in this research can greatly enhance the temporal and spatial scope of RS image application in hazy weather conditions.
CMW-Net: an adaptive robust algorithms for sample selection and label correction

A class-aware sample weighting algorithm is developed for general label noise problems. The algorithm can effectively tackle complicated and diverse noisy label tasks, winning the Championship of the ‘Arena Contest’ Track 1 of 2022 Greater BayArea (Huangpu) International Algorithm Case Competition.
Learning relation-based features for fine-grained image retrieval

Fine-Grained Image Retrieval (FGIR) is a fundamental yet challenging task that has recently received considerable attention. However, two critical issues remain unresolved. On the one hand, convolutional neural networks (CNNs) trained with image-level labels tend to focus on the most discriminative image patches but overlook the implicit relation among them. On the other hand, existing large models developed for FGIR are computationally expensive and difficult to learn discriminative features. To address these issues without additional object-level annotations or localization sub-networks, we propose a novel unified framework for fine-grained image retrieval. Specifically, a novel Relation-based Convolutional Descriptor Aggregation (RCDA) method for extracting subtle yet discriminative features from fine-grained images is introduced. The RCDA method consists of a local feature generation network and a relation extraction (RE) module that models both explicit information and implicit relations. The explicit information is modeled by computing feature similarities, while the implicit relation is mined via an expectation-maximization algorithm. Moreover, we further leverage the knowledge distillation technique to optimize the parameters of the feature generation network and speed up the fine-tuning procedure by transferring knowledge from a large model to a smaller model. Experimental results on three benchmark datasets (CUB-200-2011, Stanford-Car and FGVC-Aircraft) demonstrate that the proposed method not only achieves a significant improvement over baseline models but also outperforms state-of-the-art methods by a large margin (6.4%, 1.3%, 23.2%, respectively).