Reviewerly/exHarmony huggingface dataset:
Comparison between ECDH and X3DH protocol

In this article, it will describe the generation and use of an encryption key named AES, RSA and X3DH. When encrypting or decrypting, their operation processes are similar. That is, they all use a common method and an encryption key. RSA ensures the security of Internet communications, whether it is digital signatures to protect data from tampering or encrypted communications. AES is mainly used in combination with other encryption technologies, such as DH, because AES is symmetric encryption, and DH is a key exchange system, the combination of the two can complement each other. X3DH is a collection of them, including both digital signatures and the ability to encrypt communications. X3DH is simpler, more complicated, and more secure, derived from the invention and application principles of these two keys (AES and RSA). RSA is a new feature that publicly leaks the encryption key without leaking the corresponding decryption key at the same time. For the explanation of the content of this article, many explanations of proper nouns and knowledge are also used; A series of codes derived by me about the knowledge learned by Computer Science, which are generated by the encryption principle, are more clearly explained, and explained the encryption process and operation method of each key. This application uses the x3dh protocol to allow secure conversations between users. X3DH is an end-to-end communication protocol that enables two clients to conduct encrypted conversations, and this process uses the data on the third-party server to sign, so as to verify the communication data between the clients.
Mass telemetry cluster storage and autonomous health assessment system of spacecraft control system in orbit

During the operation of spacecraft control system in orbit, a large amount of telemetry data will be generated. These data can characterize the operation performance of spacecraft control system and have high analytical value, which can identify the health status and operation performance of spacecraft from the telemetry in orbit. This paper presents a cluster storage system and an autonomous health assessment system. Simulation analysis and practical application show that the system supports the efficient storage of more than 800 spacecraft telemetry data, realizes the data elimination of 99.3% accuracy, realizes the fault self-diagnosis of 98.7% accuracy, can complete a comprehensive health assessment of the spacecraft in 3.5 seconds, and automatically generates the evaluation report.
Multi-target trajectory planning and control technique for autonomous navigation of multiple robots

The autonomous robot has been the attraction point among robotic researchers since the last decade by virtue of increasing demand of automation in defence and intelligent industries. In the current research, a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace. Here, a hybrid algorithm is adopted for designing the controller with consideration of navigational parameters. A Petri-Net controller is also aided with the developed controller to resolve any conflict during navigation. The developed controller has been investigated on WEBOTS and MATLAB simulation environments coupled with real-time experiments by considering Khepera-II robot as wheeled robot. Single robot- multi-target, multiple robot single target and multiple robots-multiple target problems are tackled during the investigation. The outcomes of simulation are verified through real-time experimental outcomes by comparing results. Further, the proposed algorithm is tested for its suitability, precision, and stability. Finally, the developed controller is tested against existing techniques for authentication of proposed technique, and significant improvements of an average 34.2% is observed in trajectory optimization and 70.6% in time consumption.
Comparative Study of Deep Learning Techniques for Breakdown Prediction in Pump Sensor Systems

In the current fourth industrial revolution, data plays a crucial role in providing insights about engineering systems. This paper studies artificial intelligence techniques for predicting the occurrence of anomalies, which are breakdowns in pump sensors. Among artificial intelligence techniques, deep learning methods are in high demand due to their ability to perform automated feature engineering. The techniques studied in this work belong to this class. They include the Long short-term memory (LSTM), Temporal Convolutional Network (TCN) and the Multi-Layer Perceptron(MLP).
Analytical and Experimental Determination of Panel Generation Factor for Witbank Area

In recent years, there has been growing interest in a transition to green energy, particularly Photovoltaic systems. This interest has been and will continue to create a need for accurate Photovoltaic system design, installation and maintenance. A panel generation factor (PGF) is critical in calculating the energy output of photovoltaic panels. An inaccurate panel generation factor may lead to an under-design or over-design of a photovoltaic system. This may lead to failure to meet energy capacity requirements, premature failure of system components and over-expenditure on the capital of a photovoltaic system installation. PGF is dependent on the climate of the area. The climate of Witbank in South Africa is classified as warm and temperate. In this study, an analytical method was used to determine the PGF of the Witbank area to be 3.46. In order to validate the analytical value, an experiment was conducted using 200 Wp and 15 Wp panels. The experimental value obtained was 3.61, which gives a deviation of 4.2% compared to the analytical value.
Using the Multilayer Perceptron (MLP) Model in Predicting the Patterns of Solar Irradiance at Several Time Intervals

Fossil fuels, widely used to produce electricity and power industries, have significantly increased greenhouse gas emissions and led to current global warming. Many countries now allocate considerable resources to Renewable Energy Sources (RES) to produce clean electricity because of the adverse effects of these fossil fuels on the climate. Solar energy is currently one of the most naturally abundant renewable energy sources. Despite being cost-free, solar energy has always been unreliable. The Photovoltaic (PV) panels trap the radiative energy released by the sun, which is expressed in Watts per square meter (Watts/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). This solar radiation is highly stochastic. It fluctuates during the day before going away entirely at night. This study used Multilayer Perceptron (MLP), a deep learning model, to forecast solar radiation at five different horizons: five, ten, fifteen, thirty, and sixty-minute intervals. The model was trained with two years of meteorological data collected from Johannesburg. The best result was when data collected at five minutes intervals was used to train the model. The model recorded a normalized Root Mean Square Error (nRMSE) of 3.25%. This MLP performance was compared with the results obtained using the SVR model. The best result obtained using the SVR model was 6.26. It is suggested that utilizing this model would make it easier for solar plant operators to predict how much solar energy will be produced at different times of the day. This information will aid in planning failsafe outcomes during cascading solar irradiance, which negatively impacts the electrical energy supply to the power grid.
The Design and Implementation of Campus informatization in Chinese Universities: A Conceptual Framework

This study aimed to explore the overall situation in implementing campus informatization in Chinese universities, and to design a conceptual framework for the digital campus in order to discover the practical results for Chinese universities applying the conceptual framework. The conceptual framework of the digital campus indicates that the construction mode of multi-participation and open sharing and the development mode of fragmentation, rapid iteration, and collective evolution will become the mainstream modes in the stage of digital campus. The advanced technology applied in Chinese campus informatization was analyzed, the main characteristics of the digital campus were put forward, and a conceptual framework of digital campus in universities was designed. Then, the present study used the one-stop service platform of Southwest University as a case study. The practical results of the digital campus included IC (intelligent card) card services, integration services, one-stop services, wireless networks, and the implementation of the data center. The construction of a "one-stop" service platform has become the main method of reform for the digital campus.
Power Disturbance Monitoring through Techniques for Novelty Detection on Wind Power and Photovoltaic Generation

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.
A fast user actual privilege reasoning framework based on privilege dependency graph reduction

Abstract It is a key point to find out the actual privileges of network users in network security risk assessment. The Privilege dependency graph (PDG) provides an effective way to reason the actual privileges of network users from their initial privileges. The existing User Actual Privilege reasoning method is time‐consuming and not suitable for large‐scale networks. This paper introduces a fast User Actual Privilege reasoning framework based on PDG reduction. The core idea is to reduce the size of the graph as much as possible before the actual privilege reasoning. Three different nodes merged scenarios are proposed and discussed, as well as the influences of different execution sequences and execution times. Networks of different sizes were simulated to validate the effectiveness and scalability of their method. The experimental results show that the proposed method can decrease the time of User Actual Privilege reasoning by over 25% in large‐scale networks.
Enhancement estimation network for flexibly enhancing low-light images via lighting level estimation

Although existing methods have achieved impressive accomplishments in the field of low-light image enhancement, the phenomenon of overenhancement remains a challenge. To address this issue, a multiport output enhancement structure combined with multiloss functions supervision is designed to obtain multiple images with different enhancement intensity. Subsequently, an enhancement estimation module is proposed to flexibly select the most suitable enhancement image from these series of enhanced images, thereby reducing overexposure. To this end, the enhancement estimation network (EENet) for enhancing low-light images is introduced. The proposed EENet enhances low-illumination images with different brightness levels more perfectly because the phenomenon of overenhancement is reduced. For qualitative comparison, the experimental results demonstrate that the proposed method enhances low-illumination images more naturally, especially for enhancing low-illumination images that have brighter illumination. For quantitative evaluation, the proposed method obtains the highest peak signal-to-noise ratio and structural similarity on the public single image contrast enhancer dataset and reconstructed low-light* dataset compared with those listed methods. In addition, the EENet was proven to outperform state-of-the-art methods in dark image face detection, indicating that the EENet has great practical potential.
Filtering objectionable information access based on click-through behaviours with deep learning methods

This study explores URL click-through behaviour to predict the category of users’ online information accesses and applies the results to progressively filter objectionable accesses during web surfing. Each clicked URL is represented by the embedding technique and fed into the Bidirectional Long Short-Term Memory neural network cascaded with a Conditional Random Field (BiLSTM-CRF) model to predict the category of a user’s access. Large-scale experiments on click-through data from nearly one million real users show that our proposed BiLSTM-CRF model achieves promising results. The proposed method outperforms related approaches by a high accuracy of 0.9492 (near 27% relative improvement) for context-aware category prediction and an F1-score of 0.8995 (about 29% relative improvement) for objectionable access identification. In addition, in real-time filtering simulations, our model gradually achieves a macro-averaging blocking rate of 0.9221, while maintaining a favourably low false-positive rate of 0.0041.
Proficiency at the lexis–grammar interface: Comparing oral versus written French exam tasks

This study investigates whether re-thinking the separation of lexis and grammar in language testing could lead to more valid inferences about proficiency across modes. As argued by Römer, typical scoring rubrics ignore important information about proficiency encoded at the lexis–grammar interface, in particular how the co-selection of lexical and grammatical features is mediated by communicative function. This is especially evident when assessing oral versus written exam tasks, where the modality of a task may intersect with register-induced variation in linguistic output. This article presents the results of an empirical study in which we measured the diversity and sophistication of four-word lexical bundles extracted from a corpus of French proficiency exams. Analysis revealed that the diversity of noun-based bundles was a significant predictor of written proficiency scores and the sophistication of verb-based bundles was a significant predictor of proficiency scores across both modes, suggesting that communicative function as well as the constraints of online planning mediated the effect of lexicogrammatical phenomena on proficiency scores. Importantly, lexicogrammatical measures were better predictors of proficiency than solely lexical-based measures, which speaks to the potential utility of considering lexicogrammatical competence on scoring rubrics.
Nature-Inspired DBN based Optimization Techniques for Image De-noising

Whale optimization algorithm (WOA) and particle swarm optimization (PSO) are heuristic techniques used to solve various engineering optimization problems. In this paper, these algorithms have been used in combination with a relatively less explored deep-learning model, viz., deep belief network (DBN) for Gaussian de-noising. DBNs are stacked restricted Boltzmann machines (RBMs) whose typical architectural characteristics make deep learning feasible by reducing the training complexity. The de-noising results of images corrupted by additive white Gaussian noise (AWGN) using three proposed networks; MWOA-DBN, WOA-DBN, and PSO-DBN are provided. Super parameters (step ratio and dropout rate) are optimized using MWOA, WOA, and PSO with root mean square error as the fitness function to circumvent over-fitting. The nature of convergence of the fitness function is tested for variation in step ratio, and dropout rate. The performance of the de-noising method is tested on bench-mark metrics like peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root-mean-square error (RMSE). It is observed that the performance of the proposed methods outperforms the state-of-the-art image de-noising techniques.
Modeling and Analysis of Language Transfer in Second Language Acquisition Assisted by Data Stream Fusion Algorithm

The Wiener process method is used to construct the degradation model, and the offline parameters are iteratively optimized using the EM algorithm combined with the training engine data. Based on Bayesian method and test engine data, an evolutionary data stream clustering algorithm (I-APDenStream) based on the fusion of nearest neighbor propagation and density is proposed. This algorithm uses the traditional two-stage processing model, namely online and offline clustering, to find the differences between English and Chinese general pronouns, the similarity and difference between the mother tongue and the target language, and the generalization of the sentence pattern or usage of the mother tongue. Use negative transfer. It aims to fully understand and scientifically grasp the status and role of language transfer in second language acquisition.
Spatial‐temporal aware service composition for production factors under industrial internet

Summary Industrial Internet is a promising technology combining industrial systems with Internet techniques to significantly improve production efficiency and reduce cost by cooperating with intelligent devices. Under industrial internet environment, a production process usually consists of multiple subtasks, and one or more types of product factors are needed to finish a subtask. Thus, the service composition under industrial application is more complex and challenging compared with traditional service composition under the Internet environment. In this article, we model the problem of service composition for production factors under industrial internet as a multiobjective optimization problem. To derive the optimal Pareto service composition plans, we propose a hybrid optimization algorithm, named TLBO‐TS, by combining the advantages of teaching‐learning‐based optimization algorithm and tabu search algorithm. Extensive experiments are conducted to compare with other population‐based optimization methods under a real‐world ship production process to verify the superiority of our approach.
A Multi-Modal Story Generation Framework with AI-Driven Storyline Guidance

An automatic story generation system continuously generates stories with a natural plot. The major challenge of automatic story generation is to maintain coherence between consecutive generated stories without the need for human intervention. To address this, we propose a novel multi-modal story generation framework that includes automated storyline decision-making capabilities. Our framework consists of three independent models: a transformer encoder-based storyline guidance model, which predicts a storyline using a multiple-choice question-answering problem; a transformer decoder-based story generation model that creates a story that describes the storyline determined by the guidance model; and a diffusion-based story visualization model that generates a representative image visually describing a scene to help readers better understand the story flow. Our proposed framework was extensively evaluated through both automatic and human evaluations, which demonstrate that our model outperforms the previous approach, suggesting the effectiveness of our storyline guidance model in making proper plans.
Multi-Scale Receptive Fields Convolutional Network for Action Recognition

Extracting good action representations from video frames is an intricate challenge due to the presence of moving objects of various sizes across current action recognition datasets. Most of the current action recognition methodologies have paid scant attention to this characteristic and have relied on deep learning models to automatically solve it. In this paper, we introduce a multi-scale receptive fields convolutional network (MSRFNet), which is fashioned after the pseudo-3D residual network architecture to mitigate the impact of scale variation in moving objects. The crux of MSRFNet is the integration of a multi-scale receptive fields block, which incorporates multiple dilated convolution layers that share identical convolutional parameters, but feature different receptive fields. MSRFNet leverages three scales of receptive fields to extract features from moving objects of diverse sizes, striving to produce scale-specific feature maps with a uniform representational power. Through visualization of the attention of MSRFNet, we analyze how the model re-allocates its attention to moving objects after implementing the multi-scale receptive fields approach. Experimental results on the benchmark dataset demonstrate that MSRFNet achieves improvement of 3.2% on UCF101, improvement of 5.8% on HMDB51, and improvement of 7.7% on Kinetics-400 compared with the baseline. Compared with state-of-the-art techniques, MSRFNet gets comparable or superior results, thereby affirming the effectiveness of the proposed approach.
A Method Based on Timing Weight Priority and Distance Optimization for Quantum Circuit Transformation

In order to implement a quantum circuit on an NISQ device, it must be transformed into a functionally equivalent circuit that satisfies the device's connectivity constraints. However, NISQ devices are inherently noisy, and minimizing the number of SWAP gates added to the circuit is crucial for reducing computation errors. To achieve this, we propose a subgraph isomorphism algorithm based on the timing weight priority of quantum gates, which provides a better initial mapping for a specific two-dimensional quantum architecture. Additionally, we introduce a heuristic swap sequence selection optimization algorithm that uses a distance optimization measurement function to select the ideal sequence and reduce the number of SWAP gates, thereby optimizing the circuit transformation. Our experiments demonstrate that our proposed algorithm is effective for most benchmark quantum circuits, with a maximum optimization rate of up to 43.51% and an average optimization rate of 13.51%, outperforming existing related methods.