Reviewerly/exHarmony huggingface dataset:
A noise-handling method based on textual entailment model

In response to the high cost of obtaining high-quality annotated data, some methods have emerged that automatically extract annotated data from large-scale unlabeled datasets. However, these methods often result in noisy data, which can negatively impact the performance of models. Therefore, noise-handling methods for data have high practical value. This article proposes a novel noise-handling method based on textual entailment model and applies it to text classification tasks. Experimental results demonstrate that the proposed method performs well when the proportion of noise data in the overall dataset is relatively high, which provides a new approach for the research of noise-handling methods.
Method for selecting defense strategy of moving targets based on signal game in industrial internet

In recent years, more and more computer technology has been applied in industrial Internet, the vulnerability and security risk of industrial control systems are gradually increasing. network security issues have received much attention. To address the complex attacks and relatively poor deployable defense resources in the Industrial Internet, this paper combines industrial Internet attacks with signal game theory, proposes a moving target defense strategy selection method. The SDN-based industrial Internet attack and defense model is established, the core decision module and the moving target defense module are introduced to select and implement the best defense strategy respectively, so that the industrial Internet can achieve the best security defense strategy. The simulation results show that the moving target defense strategy selection method based on the signal game can effectively improve the defender's gain and achieve the best defense against network attacks under the industrial Internet.
Lightweight blockchain-based technique for detection and prevention man in the middle attacks in mobile edge computing environment

In the last few years, the Internet of Things (IoT) has become important part of our lives and its influence is expected to exponentially increase in the next years. Computer systems and applications are improved day by day and with the advancement in such area it gives birth to new cyber-attacks. Man in the Middle attacks (MITM) are one of those attacks. An attack where an outsider or third party enters in between two online users, where both of the users are unaware of it. Mainly it is knowing as a protocol to an unauthorized user within the system who can access as well as change the information of the system without leaving any trace to the existing users. Cloud, edge and fog computing added more flavors in terms of better throughput and low latency in ease of accessing data. Edge computing refers to a distributed open platform which helps off-loading tasks and processing of cloud at edge of the network. The edge serves as a bridge between the digital and physical worlds making gateways, systems and services smart and efficient. Due to the exponential growth of IoT produced data, its complexity is also expected to increase and raises need for innovation for improved security in Mobile Edge Computing (MEC) environment. This research is conducted to propose a lightweight Blockchain solution using private/ public keys and employing shading for off-loading computation intensive tasks at MEC. Inter-Planetary File System (IPFS) database has been used to offload computational task from edge servers. Proposed technique is implemented using Java Script and Node.JS and simulated through Visual Studio Code and results are compared with other lightweight encryption algorithms proposed for IoT.
Research on quality estimation of Chinese-English translation based on machine learning

With the deepening of international communication, the demand for translation software is also increasing. Traditional intelligent translation equipment still suffers from poor translation quality and incoherent words. To solve relevant problems. this paper, by using machine learning method, adds contextual word vectors into the original translation process, extracts and analyzes contextual words in input speech, automatically correlates relevant words, and conducts quality estimation of translation. It is helpful to improve the automatic calibration of Chinese-English translation and improve the accuracy of intelligent speech translation.
Research on image features classification based on graph convolutional neural network

At present, the image features extraction and classification have widely utilized to detect objects in numerous applications. Additionally, existing classification are primary concentrated on the machine learning models or train a convolutional neural network, which can obtain the acceptable extraction and classification accuracy. However, these methods ignore the graph convolutional operation can also be utilized to achieve the image features and classification tasks. In this work, we initially extend the graph convolutional neural network to the image features extraction and classifications, which can also obtain an acceptable classification accuracy with approximately to 95%. Initially, we utilize the clustering algorithm to mix multiple image pixels to a graph node and the adjacent matrix is depended on the pixel value features. Subsequently, the transferred graph is trained by a graph convolutional neural network to identify the class of these images. From our extensive experimental and compare with traditional machine learning models, our proposed model can successfully identify the subjects in the images with highest classification accuracy and reasonable computation costs.
Autonomous navigation and path planning for Tiago robot

Efficient and adaptive path planning, navigation, and positioning for Tiago robots in dynamic environments are critical factors for successful operation. Despite existing research, a significant gap remains in achieving accurate path planning that can adapt to real-time changes in complex scenarios. This paper presents a novel approach for Tiago robot path planning by investigating and comparing the performance of three algorithms the ant colony algorithm, the bidirectional search A* algorithm, and the Dijkstra algorithm in a complex environment. Our key contribution is the development and implementation of an optimized bidirectional search A* algorithm, which demonstrates superior speed, efficiency, and directional accuracy compared to the other two methods. Our experimental results show a 20-fold increase in running speed when comparing the bidirectional search A* algorithm to the Dijkstra algorithm, highlighting its potential for meeting the autonomous navigation requirements of Tiago robots in intricate indoor environments.
Design and implementation of an automated PDF drawing statistics tool based on Python

In the research institute's scientific design work, designers need to perform the task of statistical analysis for PDF drawings, including determining the dimension and number of pages of the PDF drawings, as well as calculating the equivalent amount of A4 pages. This process is tedious, time-consuming, and error prone. To solve this problem, this paper proposes the design and implementation of an automated PDF drawing statistical tool based on Python. The tool interacts with designers through a Graphical User Interface (GUI), enabling fast and accurate statistical analysis of all PDF drawings within a selected path. This significantly improves efficiency, reduces workload, and eliminates human errors.
Research on path planning problem based on improved genetic algorithm

Due to the low local optimization accuracy, long search time, and susceptibility to local optima in traditional genetic algorithms, their applications in path planning and other aspects are not very widespread. Therefore, this paper proposes a new improved genetic algorithm, which improves the crossover mutation operator on the basis of traditional genetic algorithms, making the algorithm effectively avoid falling into local optima problems and has stronger search ability, simultaneously incorporating the idea of simulated annealing to obtain a new population, it has stronger adaptability and global optimization ability. The experimental results of simulation research on the improved genetic algorithm show that the algorithm has relatively good superiority and feasibility.
Distributed conditional arithmetic coding based on adaptive source-symbol purging

Distributed Source Coding (DSC) is a coding architecture that utilizes the correlation among multiple sources to compress them. It can be achieved by using entropy coding instead of channel codes. We propose a new DSC scheme based on arithmetic coding named “Distributed Conditional Arithmetic Coding based on Adaptive Source-symbol Purging”. Since the encoder only encodes a part of symbols in the source sequence, more compression can be obtained. For the source sequence with memory, the stronger the correlation strength within the source, the more compression gain obtained by this scheme. Compared with traditional arithmetic coding-based DSC schemes, this scheme is very effective when coding source sequences with strong internal correlation and short block length.
A study of keyword extraction for short news documents

In the network age, a large number of news with uneven quality has become a problem of information circulation. People lack energy to distinguish the quality of news, so there is a higher demand for keyword extraction technology. In this paper, the sliding window-based LTFIDF_POS algorithm is proposed for the keyword extraction problem of short news texts without labels. Firstly, the TF-IDF algorithm is improved by using sliding window, lexical information as well as word position information based on a normal distribution function. Then the LTFIDF_POS algorithm is given by fusing the three improved methods together. It fully considers the unknown words and the distribution information of words in the text, and the experiment results show that the algorithm is effective.
Student performance knowledge tracking model integrating forgetting behavior

The main task of knowledge tracking is to predict the change of students' mastery of knowledge points over time according to their historical learning records, so as to provide students with personalized tutorship. Previous deep learning models often did not take into account the influence of different students' abilities on their learning absorption and forgetting, in this paper, we propose a model called F-SPKT, which integrates forgetting behavior into the student's performance knowledge tracking. The Rasch model was used to extract the information of students' learning ability and difficulty, and then the full-connection network was used to calculate the students' forgetting degree vector. Then, the multi-attention mechanism was used to predict the probability of answers. Compared with the traditional methods, F-SPKT has better prediction ability in experimental verification.
A network for ultrasound video object segmentation

Real-time tracking and segmentation of ultrasound video sequence are prerequisite for identifying and analyzing lesions. While significant progress has been made in natural video object segmentation, developing a model for ultrasound video that can maintain both a certain speed and a high level of accuracy is still challenging. This paper develops an end-to-end network for segmentation of ultrasound video, UVOSNet. The model adopts an encoder-attention-decoder architecture, where the encoder utilizes resnet-50 to extract features from ultrasound video frames, the attention module uses LSTT to enhances the extracted features with historical information, and finally, the decoder employs the FPN network to reconstruct the enhanced features into a segmentation mask. To improve the speed of the model, the model is analyzed, and we found optimizing attention is the key to improving the inference speed of the model. So, we replaced the classical attention mechanism in the LSTT module with flow-attention to obtain UVOSNet-flow We conducted experiments on real lymphoma ultrasound video dataset, which demonstrates that UVOSNet and UVOSNet-flow achieve the best performance with respect to <i>J&F</i> measure, Param and FPS measures when compared to the four competing baselines.
Image retrieve for dolphins and whales based on EfficientNet network

Image retrieve model for whales and dolphins, based on deep learning algorithms, analyzes features such as body shape, size, color, and tails to accurately classify images of different species. The technology has potential applications for scientific research, conservation, and marine wildlife management. It can help authorities identify species at risk of ship collision and reduce entanglement in fishing gear. Deep learning relies on large datasets to develop effective image retrieval models. We propose a network detects dorsal fins of whales and dolphins via feature extraction and image retrieval techniques. Architecture allows for efficient identification and outperforms comparison approaches like ResNet50 and EfficientNet B3, as evaluated on a dataset.
Multi-condition guided depth map completion method based on diffusion model

The diffusion model has been widely applied in various aspects of artificial intelligence due to its flexible and diverse generative performance. However, there is a lack of research on applying diffusion models in the field of depth map restoration. This is primarily because the diverse generation capabilities are not sufficient to meet the requirements for depth map completion. Depth map completion requires rational completion in the current scene. In order to adapt diffusion models for depth map completion tasks, this paper proposes the Multi Condition Diffusion Model (MCDM). It allows the addition of conditional information to constrain the model’s rational completion of ill-regions. The MultiConditionLN module effectively adds multiple conditions to the depth map completion task. This module uses the completion region mask and the input image as conditions to constrain the model’s generation process. This enables the model to complete the regions that need restoration based on the scene of the input image. The proposed model achieves promising results on depth map datasets.
A comparative analysis of the new nearest swarm intelligent optimization algorithm for solving numerical problems

Swarm intelligence has been the research focus of optimization problems, but there is no specific description and comparison of the most suitable application dimensions and scenarios for the recent swarm intelligence optimization algorithm. In this paper, the newly emerging swarm intelligence optimization algorithm in recent years is selected for numerical simulation experiments, and qualitative and statistical analysis is carried out, and the optimal optimization algorithm in different dimensions and test set is obtained, which provides faster convergence speed and better convergence algorithm for parameter optimization researchers under different situations.
An improved RSA encryption algorithm based on Miller-Rabin prime number test

To ensure security during the data encryption process, it is necessary for the RSA algorithm to find two large prime numbers before encrypting data and generate public and private keys. Since the traditional method is difficult to guarantee security when encrypting smaller data, we proposed a method based on Miller-Rabin prime number test to generate five prime numbers, and encrypt data based on the RSA algorithm of five prime numbers. The algorithm is implemented by using Python language, and experiments have shown that the improved RSA algorithm can significantly improve its security, with low time complexity. The algorithm execution is efficient, and the possibility of being violently cracked is also greatly reduced.
Data center demand response potential assessment considering multiple types of flexible resources

This paper presents a method toevaluate the potential of data centers to involvement in power demand response, which is crucial for maintaining the equilibrium of supply and demand in the grid, promoting clean energy consumption, and mitigating energy crises. The data center is a valuable demand response resource due to its flexible adjustment methods, large load volume, and strong controllability, facilitated by its spatio-temporal scheduling mechanism. The proposed method considers multiple types of flexible resources and includes an analysis of data center participation demand response mechanisms, an evaluation model based on time series simulation optimization operation, and practical examples to verify its effectiveness.
Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification

The 14th Five-Year Plan proposes the objective of expediting digital development and constructing a digital China, aiming to minimize the reliance on physical travel while fostering increased data flow and sharing. However, the implementation of data privacy regulations, such as the Personal Information Protection Law, has resulted in conflicts between data sharing and privacy. To address this challenge, federated learning technology has emerged as an effective solution that enables data sharing while safeguarding privacy. Nonetheless, trust issues arise among federated members in relation to each other and central servers. This is where blockchain technology, with its notable features of verifiability, auditability, and traceability, can play a pivotal role in resolving trust concerns within federated learning. Consequently, a novel framework called trusted federated learning based on blockchain has emerged. This article initially defines the trust issue in federated learning and subsequently provides a chronological overview of research advancements in the domain of trusted federated learning based on blockchain. Furthermore, it discusses the advantages and disadvantages of existing research across three categories: blockchain structure design, consensus mechanism, and smart contracts. Additionally, the article highlights the application characteristics of this framework in fields such as the Internet of Things, connected vehicles, and medical services. Finally, the core challenges and future research directions of trusted federated learning based on blockchain are outlined.
Hierarchical Graph Convolutional Networks for Image Classification

Graph-based image representation is a promising research direction that can capture the structure and semantics of images. However, existing methods for converting images to graphs often fail to preserve the hierarchical information of the image elements and produce sub-optimal or poor regions. To address these limitations, we propose a novel approach that uses a hierarchical image segmentation technique to generate graphs at multiple segmentation scales, capturing the hierarchical relationships between image elements. We also propose and train a Hierarchical Graph Convolutional Network for Image Classification (HGCIC) model that leverages the hierarchical information with three different adjacency setups on the CIFAR-10 database. Experimental results show that the proposed approach can achieve competitive or superior performance compared to other state-of-the-art methods while using smaller graphs.
Evaluating Recent Legal Rhetorical Role Labeling Approaches Supported by Transformer Encoders

Pre-trained Transformer models have been used to improve the results of several NLP tasks, which includes the Legal Rhetorical Role Labeling (Legal RRL) one. This task assigns semantic functions, such as fact and argument, to sentences from judgment documents. Several Legal RRL works exploit pre-trained Transformers to encode sentences but only a few employ approaches other than fine-tuning to improve the performance of models. In this work, we implement three of such approaches and evaluate them over the same datasets to achieve a better perception of their impacts. In our experiments, approaches based on data augmentation and positional encoders do not provide performance gains to our models. Conversely, the models based on the DFCSC approach overcome the appropriate baselines, and they do remarkably well as the lowest and highest improvements respectively are 5.9% and 10.4%.
Dog Face Recognition Using Deep Features Embeddings

Over 470 million dogs are kept as pets around the world. Dogs are owned at an average number of $$1.6\%$$ per household. The US has the most dog pets, where about $$68\%$$ of households own at least one pet. Lost and missing dogs are a severe source of suffering and problems for their families. So, this paper addresses the problem of facial dog identification. This technology can benefit many applications, such as handling the missing pet problem, granting pets access to their houses, more intelligent zoonosis control, pet health care, and tracking stray pets. We evaluate a Residual Convolutional Neural Network, specifically ResNet-34, for facial identification in dogs. We tested in DogFaceNet and Flickr-dog datasets with and without two face preprocessing techniques: a central crop and an aligned facial extraction. Experimental results show promising results surpassing the state-of-the-art: $$97.6\%$$ and $$82.8\%$$ accuracies for DogFaceNet and Flickr-dog, respectively. Moreover, we also provide recall metrics for the best models.
Estimating Code Running Time Complexity with Machine Learning

The running time complexity is a crucial measure for determining the computational efficiency of a given program or algorithm. Depending on the problem complexity class, it can be considered intractable; a program that solves this problem will consume so many resources for a sufficiently large input that it will be unfeasible to execute it. Due to Alan Turing's halting problem, it is impossible to write a program capable of determining the execution time of a given program and, therefore, classifying it according to its complexity class. Despite this limitation, an approximate running time value can be helpful to support development teams in evaluating the efficiency of their produced code. Furthermore, software-integrated development environments (IDEs) could show real-time efficiency indicators for their programmers. Recent research efforts have made, through artificial intelligence techniques, complexity estimations based on code characteristics (e.g., number of nested loops and number of conditional tests). However, there are no databases that relate code characteristics with complexity classes considered inefficient (e.g., $$O(c^n)$$ and O(n!)), which limits current research results. This research compared three machine learning approaches (i.e., Random Forest, eXtreme Gradient Boosting, and Artificial Neural Networks) regarding their accuracy in predicting Java program codes' efficiency and complexity class. We train each model using a dataset that merges data from literature and 394 program codes with their respective complexity classes crawled from a publicly available website. Results show that Random Forest resulted in the best accuracy, being 90.17% accurate when predicting codes' efficiency and 89.84% in estimating complexity classes.