Reviewerly/exHarmony huggingface dataset:
Task Scheduling Algorithm of Administrative Management System Based on Ant Colony Optimization Neural Network

In order to better solve the problem of large amount of interactive data in cloud computing task scheduling in administrative system, a hybrid genetic annealing algorithm is proposed based on the advantages of genetic algorithm and annealing algorithm. According to the characteristics of the task scheduling data relationship in the administrative system, the algorithm encodes the execution task in the cloud computing environment, searches the global optimal solution of the task data through the fitness function of the genetic algorithm, and uses the annealing algorithm to adjust the fitness function to speed up the completion time of the task scheduling in the system virtual machine during the coding and cross reorganization of the task data. Compared with genetic algorithm, simulated annealing algorithm and BP algorithm with momentum term, the simulation results show that the neural network trained by ACO algorithm has a faster convergence speed and can reach a smaller mean square error. The system realizes the free customization of assessment indicators, the flexible setting of assessment indicators’ weights and assessment scores, and the storage, tracking and management of assessment results over the years. Scheduling is a NP-hard problem, and many heuristic methods at present still have defects in optimization ability. With the development and maturity of artificial intelligence, one of the important branches, the theory of computational intelligence, has been widely used. The dial test system simulates the behavior of terminal users, executes dial test services, and obtains service usage by analyzing service performance indicators that users care about, and provides services for terminal service testing of mobile networks. This paper mainly studies the task resource scheduling model of the dial test system, and proposes an autonomous task scheduling algorithm suitable for the dial test system environment.
A Detection Algorithm Based on Improved Cascade R-CNN for UAV Aerial Images

Due to the large change of target scale in UAV aerial images, the down-sampling method adopted by the traditional Cascade R-CNN backbone network will lead to the loss of small target semantic information, which leads to the reduction of target detection accuracy. This paper proposes a small target detection model for UAV aerial images based on improved Cascade R-CNN, the HRNet-W40 model is added to Cascade R-CNN, it reduces the loss of key information that may be caused by up-sampling and down-sampling, and effectively improves the detection effect of small-scale targets. Finally, the experimental results on the dataset collected by AISKYEYE show that the improved Cascade R-CNN obtains higher accuracy compared with other advanced UAV small target detection algorithms with the same number of parameters and computation, compared with adding other backbone networks to Cascade R-CNN, the mAP is improved by at least 11.7%, and compared with other advanced UAV small target detection algorithms, the mAP value is improved by at least 3.6%..
Research on the Application of Computer Digital Simulation Technology in the Chaos Model of Analog Circuits

In this paper, a method of generating wide-interval frequency-hopping sequences for bidirectional coupled mapping grids based on Tent mapping is presented. Multi-bit quantization, bit extraction and improved translation substitution method are adopted. The method is based on FPGA technology with Logistic chaotic mapping circuit as frequency hopping sequence generator. Direct digital frequency synthesizer is used as the basis of frequency hopping signal source. The signal is synthesized on FPGA, and the high performance AD9777 is used to realize the digital to analog conversion. The simulation results show that the wide interval frequency hopping sequence generator has the characteristics of simple structure and low computational complexity. The experimental results captured on a digital oscilloscope demonstrate the physical realizability of the constructed magneto-controlled memristor circuit.
Praise or Insult? Identifying Cyberbullying Using Natural Language Processing

Cyberbullying has become a prevalent issue in the current era of technology. The prevalence of such content on online platforms not only perpetuates harmful behavior but also poses a threat to online communities and individuals' mental health. To address this issue there is a growing need for automated systems to detect toxic comments on social media. In this work, our focus is on utilizing natural language processing (NLP) techniques to distinguish between praise and insult in online comments on Reddit, a popular social media platform. In particular, we first benchmarked the classification performance using Term-Frequency Inverse Document Frequency (TF-IDF) features and then employed two different deep-learning-based approaches, Keras Embedding, and Global vectors for word representation (GloVe). Our results show that whereas the TF-IDF features achieve an accuracy of 83%, GloVe achieves an accuracy of 89%, but Keras Embeddings achieved an accuracy of 93%. Although these results are relatively poorer than the baseline classification accuracy of 97.8%, the size of our proposed solution is around 28 MB compared to 340 MB of the baseline model XLNet model, thereby making our solution more viable for real-time applications. These results suggest that there is ample opportunity for further research and development in this area, with the potential to make a real difference in the fight against cyberbullying.
Research on Handwritten Digits Classification Technology Based on Convolutional Ceural Network Algorithm

Convolutional neural network (CNN) is one of the most commonly used neural networks for image classification. Handwritten digit recognition is a multi-class classification problem in machine learning. This paper aims to improve the validation accuracy of recognition and reduce the validation loss by improving the parameters of the CNN model. The dataset used in this paper is MNIST with 60,000 handwritten single-letter digits between 0 and 9 in 60,000 small square grayscale images of 28 × 28 pixels. Recent studies have shown that CNNs perform well in image classification tasks when the dataset is large enough (i.e., over hundreds of thousands of training images). So image enhancement was used by rotating the image and changing the brightness and saturation of the image. The experimental results show that by adding batch normalization and changing dropout to 0.4 from 0.25, the classification accuracy can reach 99.80%, and the loss is 00.87%.
Research on Automatic Chunk Extraction Translation System Based on Artificial Neural Network

Artificial neural network refers to a network system that simulates the structure and function of the human brain nervous system, uses a large number of processing components, and is constructed by artificial means. Learning ability, adaptability, and variability of connection strength can be applied to automatic chunk extraction translation system. This paper selects the translation system of automatic chunk extraction, adopts quantitative research method, and then focuses on the method of chunk extraction, and summarizes and summarizes the extraction steps and tools of continuous and discontinuous chunks from the presence or absence of a given head word. comment.Investigate their overall performance and different performances in different conversion directions. The research results show that the recognition effect of block boundaries can be further improved by continuously optimizing the model parameters. By comparing different models, the recognition effect of functional block boundaries can be better, and the F value can reach 86.76%. Therefore, the application of artificial neural network in automatic chunk translation system has a very broad prospect, and it has been widely used in automatic control system. The validity of artificial neural network and the positive role of automatic chunk extraction translation function in improving translation results are verified.
Malicious encrypted traffic detection based on Bert and one-dimensional CNN model

With the continuous iteration of network technology, the proportion of encrypted traffic in network traffic is increasing, encrypting traffic can help protect users' personal privacy and data security,but it also enhances the concealment of malicious traffic, making it more difficult to detect. In response to the shortcomings of traditional malicious traffic detection relying on rule databases and the insufficient feature performance of existing malicious traffic detection, this paper proposes a malicious traffic detection model based on pre-trained Bert combined with one-dimensional CNN. The model has two parts: the first part is feature extraction of the Bert model, which inputs a large amount of unlabeled traffic dataset into the Bert model to capture the implicit contextual relationships in large-scale unlabeled traffic; the second part is traffic recognition of the CNN model, which inputs a small labeled dataset into the CNN model after training with the Bert model, to detect malicious encrypted traffic, and finally uses a softmax classifier for binary and multi-classification. Experimental verification is conducted on public datasets, and in binary classification, the accuracy of identifying encrypted malicious traffic is improved to 99.97%, Compared with existing research, there is a significant improvement in F1-score for the Virut and Zeus categories.
Go Together: Bridging the Gap between Learners and Teachers

After the pandemic, humanity has been facing different types of challenges. Social relationships, societal values, and academic and professional behavior have been hit the most. People are shifting their routines to social media, and gadgets and getting addicted to their isolation [5]. This sudden change in their lives has caused an unusual social breakdown and endangered their mental health. In mid of 2021, Pakistan's 1st Human Library was established under HelpingMind® to overcome these effects [9]. Despite online sessions and webinars, HelpingMind® needs technology to reach the masses. In this work, we customized the UI/UX of a Go-Together Mobile Application (GTMA) to meet the requirements of the client organization. A very interesting concept of the book (expert listener/ psychologist) and the reader is introduced in GTMA. It offers separate dashboards, separate reviews or rating systems, booking, and venue information to engage the human-reader with his/ her favorite human book. The loyalty program enables the members to avail discounts through a mobile application and its membership is global where both the human-reader and human-books can register under the platform. The minimum viable product has been approved by our client organization.
Vision Transformer-Based Image Inpainting Method

The paper built a vision transformer-based three-stage image inpainting model, which can effectively process multiple public data sets and outperforms traditional reference model. By using the vision transformer-based multitask learning strategy, we developed a new end-to-end image inpainting model to improve the performance of the three-stage model. It has s superior performance when processing three different data sets. After introducing a specific deep learning framework, we can effectively solve image inpainting tasks, greatly reduce Sub-I burden, and provide more potential for the application of deep learning in image inpainting field.
Using Scarf Plots to Visualize Moment-to-Moment Visual Search Behavior on Websites

Despite a large number of metrics and eye-tracking data visualization techniques allowing us to study information search on the Internet, the moment-to-moment dynamics of this process has not been taken into account. Therefore, the aim of this paper is to show the interest of visualization methods such as scarf plots. An adaptation of these visualizations has been developed and applied to a dataset of 46 participants who have performed an information search task on a website. Compared to the techniques usually used, our approach allows us, on the one hand, to illustrate the information search behaviors of each individual in one figure (the total duration of the search, the path of the web pages, the number of web pages, the duration of web page exploration, the areas of interest explored, etc.), and on the other hand, to juxtapose several individuals, thus allowing us to easily compare and categorize them.
A Practical Data Audit Scheme With Retrievability and Indistinguishable Privacy-Preserving for Vehicular Cloud Computing

In vehicular cloud computing (VCC), cloud servers provide enormous storage and powerful computing capacity to Vehicular Ad-hoc Networks (VANETs). Resource-constrained vehicles outsource data to vehicular cloud platforms for timely traffic safety services, e.g., navigation, accident alarms, etc. Auditing the authenticity of data has become a critical issue in outsourcing data to untrusted servers. Existing data audit methods encode all data with error correction codes (ECC) techniques that retrieve corrupted data by downloading all data. The communication overhead of such methods is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of data blocks) which is unbearable for vehicles with limited resources. In addition, these schemes employ an inaccurate privacy-preserving model. This will lead to data leakage in the third-party audit process. Although they use randomness to confuse parts of the proof that is used to prove the data state, a small amount of information is still distinguishable. For such, in this paper, we propose a practical data audit scheme with retrievability and indistinguishable privacy-preserving to efficiently audit the state of outsourced data. We improve the Invertible Bloom Filter (IBF) to compress redundancy locally, which can retrieve corrupted data without prior context. Furthermore, we define an indistinguishable privacy-preserving model to capture the complete semantics of repeated audit attacks and achieve indistinguishability in the audit. We prove that our scheme is secure against adaptive chosen message attacks and is indistinguishable privacy-preserving against repeated audit attacks. The experiment results demonstrate that for 1.9 GB data when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sqrt[]{n}$</tex-math></inline-formula> blocks are corrupted, auditors complete a check in 3.31 seconds with 99% confidence, and vehicles retrieve corrupted data in 3.16 seconds with 16.67 MB communication overhead.
Classification of Functional Types of Lines in P&amp;IDs Using a Graph Neural Network

In recent years, with the use of computer-aided design software for most plant projects, intelligent piping and instrumentation diagrams (P&IDs) have become the default format for P&IDs. However, most of the previous P&IDs are often in image format, and it is necessary to convert them to intelligent P&IDs. In this study, we solve the problem of classifying the functional types of lines in the process of converting image P&IDs into intelligent P&IDs. The challenge is to classify whether each line in a P&ID is a piping or signal line. To solve this, the objects connected to the line, not the type of line, need to be considered. First, the connection relationships between symbols and lines in a P&ID are represented as a graph. The problem is then modeled and solved as a node classification problem using a graph neural network based on the mean aggregator convolutional layer of GraphSAGE. In addition, a dataset was generated from 19 real-world P&ID drawings to train the graph neural network and the optimal model was selected through hyperparameter tuning. The implementation and experiments of the proposed method demonstrate an accuracy of 99.53%.
Periodicity Detection of the Substitution Box in the CBC Mode of Operation: Experiment and Study

This study presents a technique for investigating the cyclic properties of substitution boxes (S-boxes) in the Cipher Block Chaining (CBC) mode of operation. S-boxes provide nonlinear transformations in cryptographic algorithms to create confusion. The CBC mode design is used in block ciphers to hide periodic patterns and create diffusion of the effect. The main objective of this study was to evaluate the cryptographic strength of S-boxes using periodicity detection in CBC mode. The study of S-boxes using the presented technique allows us to examine them in a different manner and study their diffusion levels, the metrics of which are the periodicities of S-box element sequences. To apply the diffusion of the effect of the CBC mode on the S-boxes, the encryption functions used in the block ciphers were replaced with the substitution functions used for the S-boxes. An S-box was selected for use in the Advanced Encryption Standard (AES) to demonstrate the experiments and results of the study. In this study, the cyclic properties of the S-box were considered from two different aspects: the periodicity detection of the S-box with respect to iterations and blocks. According to our study, the results of the AES S-box and other S-boxes exhibited very long periods, which indicates that the influence of the CBC mode spread over many iterations and blocks, indicating a high level of cryptographic strength for S-boxes.
Optimal Allocation of Curtailment Levels of PV Power Output in Different Regions in Consideration of Reduction of Aggregated Fluctuations

Due to the high penetration of photovoltaic power generation system (PV) anticipated in the future, the curtailment of PV power output becomes crucial, not only to maintain supply-demand balance but also to preserve an adequate capacity for the frequency control. When the curtailment level (CL) of the aggregated PV power output is determined in a day-ahead unit commitment (UC) scheduling, different CL should be applied to different regions with distinctive weather modes in the power system area so that the fluctuations of aggregated PV power output are minimized. The objective of this study is to optimally allocate the CL to each region based on the short-term forecasting of the weather modes so that the hourly maximum fluctuation of the aggregated PV power output (MF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agg</sub> ) is minimized as long as the aggregated average power output (Avg <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agg</sub> ) becomes the same as the scheduled value in UC. Based on the past observations of PV power output, the proposed method employs relations between the regions’ MF and CL (MF-CL patterns), and relations between the regions’ Avg and CL (Avg-CL patterns) for several typical weather modes. Thus, a specific MF-CL pattern and Avg-CL pattern are determined for each region based on the short-term forecasting of the weather mode, and the CL optimization is proceeded by using these patterns. The proposed methods are tested by using the time-series of PV power output at 61 observation points in the central region of Japan for one year. As a result, it is demonstrated that merely acknowledging the weather mode of each region enabled the proposed methods to practically have similar results to the method where perfect short-term forecasting of PV power output was utilized in the entire year.
TRCT: A Traceable Anonymous Transaction Protocol for Blockchain

Anonymous cryptocurrencies are distributed applications based on blockchain technology that enhance the anonymity of user transactions. However, they also provide cover for many criminal activities. Existing traceable modifications of anonymous transaction protocols do not support public verification of the traceability of transactions, meaning dishonest users can still evade tracing by forging partial proofs. In this paper, we first propose EPoK, which is a partially extractable zero-knowledge proof scheme designed to address this issue. We provide the instantiation of EPoK, as well as its formal security proofs. Combined with EPoK and the classic anonymous transaction protocol RingCT, we propose a traceable anonymous transaction protocol TRCT for tracing participants’ addresses and transaction amounts in anonymous transactions. Compared to previous works, TRCT is the first to implement public verification of the transaction’s traceability while still maintaining anonymity. This ensures that users cannot forge relevant proofs to evade tracing. We present and prove the security properties of TRCT, and experimentally compare its efficiency with the original RingCT protocol. The result shows that the extra overhead generated by TRCT is small.
ViCo-MoCo-DL: Video Coding and Motion Compensation Solutions for Human Activity Recognition Using Deep Learning

This paper proposes three novel feature extraction approaches for human activity recognition in videos.The proposed solutions are based on video coding concepts including motion compensations and coding based feature variables.We use these features with deep learning for model generation and classification, hence the ViCo-MoCo-DL abbreviation which stands for Video Coding and Motion Compensation with Deep Learning.These solutions are fused in terms of averaging their classification scores to predict the human activity in videos.In all proposed solutions, an input video is temporarily segmented into 12 non-overlapping segments of equal size.In the first and second solution each segment is converted into one component of an RGB image, thus resulting in 4 RGB images.The conversion happens in terms of motion capture using motion estimate, motion compensation and accumulating image prediction errors.Consequently, in the first solution, the 4 generated RGB images are tiled into one big image which is used to train a Convolutional Neural Network (CNN) network.In the second solution each generated RGB image is entered into a pre-trained CNN for feature extraction.The resultant FVs are arranged into a matrix and used for training a Long Short-Term Memory network (LSTM).In the third solution, a customized High Efficiency Video Coder (HEVC) is used to generate feature variables per frame.The resultant Feature Vectors (FVs) of 3 video segments are arranged into a matrix and numerically summarized into one FV, thus, each input video is represented by 4 FVs which are used to train another LSTM network.Experimental results on three wellknown datasets show the superior classification results of the proposed fused solution over existing work.