Reviewerly/exHarmony huggingface dataset:
Fast adaptive exposure correction algorithm based on optimal niche

To obtain high-quality imaging data stably under the influence of variable abnormal illumination, a fast adaptive exposure correction algorithm based on optimal niche is proposed in this paper. Firstly, an image sensory quality assessment based on Harr-Wavelet (HW-ISQA) is proposed as an imaging quality assessment indicator to reflect human visual sensory quality objectively. Secondly, an image enhancement algorithm for uneven illumination. Then, an online optimization framework based on the optimal niche can be used to optimize multiple parameters in real-time system. Finally, HW-ISQA is used as the fitness function in the proposed optimization framework to optimize sensitive parameters in the imaging correction our proposed, the imaging quality of various scenes can be improved adaptively, meanwhile, the high-quality video data can be output timely. The performance and efficiency of the algorithm are illustrated by abundant experimental results, while, the superiority and universality of the algorithm in complex scenarios are verified.
Fuzzy-Adaptive Matrix-Based PSO with Group Learning

The particle swarm optimization (PSO) algorithm is a random search algorithm that simulates biological activities in nature and swarm intelligence. Traditional PSO algorithms still suffer from their inherent problems such as long computing time and premature convergence when dealing with a large number of decision variables. How to reduce the computing time while ensuring its accuracy is one of the main research issues in the literature. This paper proposes a PSO algorithm with matrix-based fuzzy adaptation and group learning. Firstly, to reduce the computing time, we propose a strategy to balance the global search ability and the local search ability by controlling the number of iterations adaptively. Second, to accelerate the convergence speed of the algorithm, we further propose a group learning mechanism. Along with the above two considerations, the proposed algorithm is able to achieve a promising balance between exploration and exploitation and, consequently, improves the performance. Through extensive comparison experiments on six classic functions, we show that our proposed PSO algorithm can achieve competitive or even better performance than several state-of-the-art methods.
Investigation and Comparison of Inertia Weight Control Schemes in Particle Swarm Optimization

Particle swarm optimization (PSO) is a global search optimization algorithm that simulates the swarm behavior of birds. Compared with other evolutionary computation algorithms, PSO is simple, easy to implement, and has powerful optimization ability. However, the performance of PSO is heavily affected by the inertial weight parameter. Many studies have shown that PSO variant with smaller inertial weight has good local search ability and can improve the solution accuracy; while the PSO variant with larger inertial weight has good global search ability and can avoid falling into local optimum to a certain extent. Therefore, many researchers have proposed a variety of control schemes for dynamically adjusting the inertia weight. This paper systematically introduces, analyzes, and compares five typical decreasing and random control schemes in the literatures for dynamically adjusting the inertia weight. Moreover, a decreasing scheme based on concave exponential function, and two incremental schemes based on concave exponential function and convex exponential function are also designed and compared. The investigations and comparisons are conducted on 10 typical different unimodal and multimodal functions. The experimental investigations and comparisons are helpful for researchers to adopt different kind of inertia weight control schemes to solve different kinds of optimization problems.
Orthogonal Latin Square Based Particle Swarm Optimization: A Dynamic Approach for Continues Functions Optimization

Particle Swarm Optimization (PSO) is one of the best solutions that can find the optimal solution to an optimization problem. While, the canonical PSO may suffer from the problems that it can trap in a local optima during some successive generations from the beginning to the end of the stopping criteria. To make the population of PSO escape from the local optima quickly, an Orthogonal Latin Square (OLS) based experimental design method is applied and can significantly improve the convergence performance. In this article, an OLS based PSO algorithm is proposed which can be used if the PSO algorithm traps into a local optima during the convergence time. In the iteration, the OLS based design method will be implemented to help the population escape to better positions only when the population falls into local optima. And the best- so-far position can be replaced by a better one around it. In this way, a shortened convergence time is shown compared with the canonical PSO algorithm as well as a better final best fitness value. When applying our proposed algorithm in the test functions, the result shows that our proposed algorithm can find the optimal solutions with an excellent convergence time.
An Improved MoViNet Algorithm for Lightweight Video Recognition

The MoViNets family offers accurate and efficient video recoginition networks deployed on mobile deveices, by alleviating the high memory consumption of 3D convolutional neural networks. The application of Stream Buffer greatly reduces the memory footprint, making MoViNet a lightweight video recognition framework for online inference and mobile devices. While causal operations including CausalSE are utilized to improve the MoViNet’s performance, however, the location information in spatial dimension is ignored. By incorporating the spatial dimension attention into the MoViNet, a lightweight Causal Spatio-temporal Attention(CSTA) is proposed to enhance video recognition efficiency. CSTA can not only embrace the causal relationship in time dimension to fit the Stream Buffer, but also pay attention to the spatial location information. The video classification experiment results show that the proposed lightweight attention module CSTA, increases MoViNet’s efficiency on the HMDB51 and UCF101 datasets, with a small computation budget.
Convex Optimization for Nonequilibrium Steady States on a Hybrid Quantum Processor

Finding the transient and steady state properties of open quantum systems is a central problem in various fields of quantum technologies. Here, we present a quantum-assisted algorithm to determine the steady states of open system dynamics. By reformulating the problem of finding the fixed point of Lindblad dynamics as a feasibility semidefinite program, we bypass several well-known issues with variational quantum approaches to solving for steady states. We demonstrate that our hybrid approach allows us to estimate the steady states of higher dimensional open quantum systems and discuss how our method can find multiple steady states for systems with symmetries.
AU-Shaped Fourier Unit Transformer for Single Image Deblurring

There are many causes of image blurring, including optical, atmospheric, artificial and technical factors, etc. It is important to deblur images in daily production life. To achieve better results, different methods are needed to deal with different causes of blurring. From the technical aspect, blurred image processing methods are divided into three main categories, namely image enhancement, image restoration and super-resolution reconstruction. Overall, although blurred image processing algorithms have achieved a very wide range of applications, but image algorithms have their own limitations after all, we can not pin all problems on the image algorithm, for different kinds of blurred problems, to be treated differently. For blurring or image quality degradation caused by lens out of focus, dust obscuring, line aging, camera failure, etc., with the help of video diagnostic system, must be repaired in time to solve the problem at the source. For low light and other priority to choose day and night type high-sensitivity cameras, for rain and fog, motion and pre-sampling caused by image quality degradation, you can use the “video enhancement server” contains a variety of blur image processing algorithms to improve image quality. The purpose of single-image deblurring is to restore blurry images to sharp images and recover the texture detail features of the image. The Transformer model is able to capture the correlation of the blurry pixels over long distances, which has significant performance on image deblurring tasks, but its computational complexity grows quadratically with increasing spatial resolution. In addition, some methods use convolution to reduce image resolution, but it leads to information loss. In this paper, we propose an effective Transformer model, named Fourier Unit Transformer (FUformer), based on Fourier transform constructing the shallow features extraction module and the self-attention module, which can reduce computational complexity and fuse image local and global features to recover image texture details. Experimental results show that the proposed method performs well on the deblurring task. Code will be available at https://github.com/zox-iii/FUformer.
Chinese Character Skeleton Extraction Based on Linear Span Network

Chinese character skeletons are key representations which abstract structures of Chinese characters into skeletons. Extraction the skeleton from images of Chinese characters is a fundamental task in field of Chinese character process. Existing image skeleton thinning algorithms for extracting Chinese character skeletons produced bifurcations which could not accurately reflect topological features and writing trajectories of Chinese characters. Existing image skeleton extraction methods based on deep neural networks also had difficulty in obtaining accurate single-pixel Chinese character skeletons. Here we proposed a hybrid Chinese character skeleton extraction method to resolve the above problems. Firstly, a dynamic skeleton extraction algorithm was proposed to automatically obtain a dataset using a customized digitalization platform of Chinese handwriting process. Secondly, an improved linear span network was trained using the dataset to extract the Chinese character skeletons. Thirdly, the extracted skeletons were refined using an image-thinning algorithm to obtain single-pixel Chinese character skeletons. Results showed that our method not only preserved the topological structures and writing trajectories of Chinese characters but also maintained connectivity at intersections of strokes which avoided generating burrs and bifurcations.
Underwater Image Processing with New Dark Channel Prior Dehazing

Aiming at addressing the problems of low visibility and poor contrast, this paper proposes a new dark channel prior dehazing. According to the characteristics of the light source, an image is divided into light and non-light source areas. Mixed precision operation is used to subsample the dark channel image and deep learning network and GPU-accelerated method are used to improve the algorithm speed to solve the real-time problem. Experimental results show that compared with similar algorithms, the new algorithm is more balanced in image quality indicators and underwater image indicators, which better working requirements of underwater vehicles. In terms of real-time performance, the new algorithm is superior to similar algorithms. When processing images a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$950\times 550$</tex> pixel, resolving new with an average frame rate of 29.4, which runs 2.46 times faster than dark channel prior, which lays a foundation for underwater robots to carry out underwater operations more efficiently.
Making copyright law accessible to all creatives using CopyrightUser.org

CopyrightUser.org is an independent online platform intended to make UK copyright law accessible to everyone. It helps creatives and other copyright users - such as teachers and students, cultural heritage practitioners and members of the public, among others - make informed decisions on copyright issues. This chapter briefly describes the features that characterise the initiative and have led to the success of CopyrightUser.org, which now attracts over 240,000 unique visitors every year. It then provides an overview of the main educational resources freely available on the website, showing how they can be used to teach UK copyright law. The Copyright User website offers guidance and educational resources that are responsive to users’ needs, accessible, credible, and focus on positive messaging. They aim to enable lawful copying and promote critical thinking about copyright, creativity and the creative economy.
High Efficient Secret Key Reconciliation Scheme Based on Cascade Algorithm

In the quantum key generation scheme, key reconciliation is used to eliminate inconsistent key bitstream between partners. The speed of reconciliation and the retention length of data are the main indicators for evaluating a reconciliation method. The Cascade algorithm is developed based on the BINARY reconciliation. By forward backtracking, Cascade can correct even-numbered errors hidden in the data. However, its computational complexity seriously affects the reconciliation speed, and the final length of key generated is relatively low. In this paper, an optimization method BFR-Cascade algorithm is proposed to reduce the computational complexity. Meanwhile, the final unified deletion rule (FUDR) is applied to increase retention length of data. Compared with Cascade algorithm, the proposed method can achieve 54% higher reconciliation speed when the signal-to-noise ratio (SNR) is 8dB, and the retention length of data is increased by at least 44% through FUDR.
UK IPO resources for IP education

The Intellectual Property Office (IPO) is the official UK government body responsible for IP rights (IPRs) including patents, designs, trade marks and copyright. The IPO operates and maintains a clear and accessible IP system in the UK. The IP system encourages innovation and helps the economy and society to benefit from knowledge and ideas by offering protection for creative and inventive work. In addition to granting IPRs in the UK, the IPO is responsible for developing IP policy, educating businesses and consumers about IPRs and responsibilities, and supporting IP enforcement. Within this chapter we will refer to IP and associated rights as IP concepts and IP.
Seq2Seq-Attention based text topic representation

After the issue of keywords not effectively representing the theme in topic discovery tasks, this paper proposes a text topic representation method based on Seq2Seq-Attention, aiming to create a concise and refined topic representation. Firstly, the Encoder module uses a two-layer bidirectional recurrent neural network for text information feature extraction, the Decoder sub-module uses a two-layer unidirectional recurrent neural network and combines with an attention mechanism model to complete the decoding task and outputs the desired sequence of popular topic characters. Finally, the effectiveness of the method is verified through comparative experiments.
A Hierarchical Data Encryption Algorithm Based on Iteration

In recent years, the application of chaos in the field of encryption is increasing. The chaotic characteristics of chaotic sequences make the encryption effect greatly improved compared with other encryption methods, mainly in encryption time, space and security. This paper mainly applies chaotic system to encrypt data stream hierarchically and continuously. Layers can be modified according to different scenarios, keys are one-dimensional arrays. At the beginning, use the function to generate the KEY1. Generate KEY2 using random function. KEY1. and KEY2 iterate to generate a new key END1 to encrypt the first layer of data. Using the encrypted first layer data as the KEY3. KEY3 and KEY2 iterate to generate a new key END2 to encrypt the next layer of data. Repeat the above operations to achieve hierarchical continuous encryption of data, the key also changes with the content. The encryption time, key space, anti-attack performance and various encryption indexes of the algorithm are analyzed by experiments. It is proved that the algorithm is an encryption method suitable for various situations, with small storage space, energy saving and high security.
Joint Receiver Design and User Scheduling for Over-the-Air Aggregation in Federated Learning

In this article, a novel joint receiver design and user scheduling framework in over-the-air computation based federated learning (FL) system is employed to enhance the data aggregation. FL can improve the machine learning performance by updating model parameters via local training. We investigate the joint design of the user scheduling and receiver beamforming vector to minimize the distortion of the aggregated signal. To this end, we formulate a non-convex data aggregation optimization problem taking into account the impact of channel state information (CSI) and user scheduling. To solve this non-convex optimization problem, we decouple the original problem into two subproblems. First, we solve the receiver beamforming design problem for a given user scheduling result. Then, we propose a novel channel and data based user scheduling algorithm to obtain the over-the-air aggregation results. The simulation results show that the proposed scheme is more effective than the benchmark schemes.
Government affairs message text classification based on RoBerta and TextCNN

With the arrival of the era of big data, the number of messages on the government affairs platform has grown rapidly. To better solve the urgent problems reflected by the public, this paper takes some real messages from a provincial government affairs platform as the research object and constructs a model of RoBerta and TextCNN fusion to classify the text of government affairs messages. Firstly, the message text is pre-processed, including de-duplication and noise reduction. Second, the RoBerta-TextCNN model is constructed to classify the message text, and the message text vector obtained from the RoBerta layer is input to the TextCNN layer for feature extraction, and then the captured features are classified using the softmax classifier. Finally, the classification results are compared with those of other models. The experimental results show that the RoBerta-TextCNN model constructed in this paper achieves better classification results in this task, with an accuracy rate of 89.63%, a recall rate of 89.95%, and an F1 value of 90.31%.
DANNA: A Dimension-Aware Neural Network Accelerator for Unstructured Sparsity

With the development of technology in deep neural network (DNN) pruning, Neural Acceleration Processor can be designed to get more efficiency advantage by exploiting all sparsity in neural network. However, sparse data has irregular coordination, which result that the exist sparse designs have serious storage waste and high latency. In this work, we present a Dimension-Aware Neural Network Acceleration (DANNA) to optimize those problems. Specifically, DANNA employs novel Dimension-first dataflow and custom microarchitecture, which substantially reduce both memory amounts and memory access collision (hash collision). Furthermore, DANNA leverages a fixed channel-share-LUT based on channel reuse characteristics in convolution to replace the traditional tedious coordinator comparison, which saves consumption of logic.
Efficient and Privacy-Preserving Range Queries over Outsourced Cloud

With the development of cloud computing, database outsourcing has become a very popular network service today. Due to privacy concerns, sensitive data must be encrypted before outsourcing, which is a challenging task regarding the effective use of data. Considering that range query, as the most common and important query method in cloud computing, is widely used in a variety of scenarios and algorithms, it is necessary to design a range query scheme to protect the security of private data. In this paper, we use Yao's garbled circuit and secret sharing method to construct a corresponding security range judgment protocol based on the difference between leaf nodes and non-leaf nodes in R-tree index structure, and on this basis, a secure and efficient range query algorithm on cloud computing is proposed. We prove the security of the algorithm under the semi-honest model through theoretical analysis. Finally, a large number of experiments prove its effectiveness in real application scenarios.
Privacy computing technology in the context of energy Internet

The power system and other energy systems are constantly cross-integrated, and a new development direction of multi-subject collaboration is gradually formed. Starting from the typical mode of energy Internet collaborative optimization, this paper puts forward a preliminary technical scheme for energy Internet collaborative optimization considering privacy protection. Aiming at the scenario without a third party, this paper proposes a secure multi-party interactive computing scheme based on the principle of secret sharing, and analyzes the specific characteristics of the scheme when applied to a small number of subject scenarios based on the fully distributed application architecture. Finally, from the aspects of data privacy and computing security, the proposed scheme protects the rights and interests of data owners, reduces the risk of data leakage in energy Internet collaborative optimization computing, and realizes reliable privacy protection.
Research on Photovoltaic Power Prediction Method for Power Grid Safety

When integrating large-scale photovoltaic systems with the power grid, variability and intermittency of photovoltaic power may potentially endanger the secure and stable operation of the power system as well as its scheduling management. So a new photovoltaic power prediction method using logistic chaotic mapping (LCM) improving atomic search optimization algorithm (ASO) to optimize back propagation neural network (LCM-ASO-BPNN) is proposed to solve this problem. The ASO algorithm is used to solve the defect that BPNN is likely to be trapped in a local optimum, and the initial population of the ASO algorithm is optimized by introducing logistic chaotic mapping, subsequently, the model's predictive accuracy is greatly enhanced. The experimental results demonstrate a significant improvement in the prediction accuracy of the proposed model when compared with the traditional prediction model.
Research on path planning of TangentBug algorithm with adaptive detection radius

When the TangentBug algorithm performs path planning, the detection radius is close to infinity, and the route close to the optimal path can be planned; However, when entering a local environment similar to the angle, using the TangentBug algorithm for path planning, different detection radii will have different detachment rates, and after many verifications, it is clear that in narrow angles, small detection radii have better detachment rates than larger detection radii. Based on this discovery, a TangentBug algorithm that can expand the detection radius at a narrow angle is designed, and experiments show that the algorithm can greatly shorten the time to get out of the obstacle, and the consumption time of the whole moving process is reduced by 30% compared with the original algorithm, and the improved tangentbug algorithm moves faster and has stronger obstacle avoidance ability.