Reviewerly/exHarmony huggingface dataset:
A Knowledge Graph Embedding Model Based on Inception Structure with Channel Attention

In this paper, we propose an improved knowledge graph embedding model, InceSE, by replacing the convolution in Inception with the dilated convolution to improve the ability to capture feature interaction information. InceSE uses the channel attention mechanism SE-Block to adaptively recalibrate the channel-level feature responses by modeling the interdependencies between channels, enhancing useful features while suppressing useless features to improve the performance of the KGE model. The experiments used the benchmark dataset FB15k-237 and WN18RR to validate the link prediction validity of the model. Compared with ConvE, InceSE's Hits@1 increased by 5.44% and 6%, Hits@10 increased by 4.89% and 8.08%, respectively, and MRR increased respectively 8.22% and 9.77%, and MR increased by 17.48% and 58.87% respectively.
Comparison of Multiple Edge Detection Algorithms and FPGA Implementation

In order to improve the accuracy and real-time performance of edge detection in images, this paper compares multiple edge detection algorithms, and the results indicate that the Sobel algorithm exhibits excellent real-time edge detection performance. However, the traditional Sobel operator can only detect in the vertical and horizontal directions, limiting its application scope and accuracy in edge positioning. To overcome this hurdle, this paper proposes an eight-direction Sobel operator for edge detection. Moreover, this operator is coupled with adaptive thresholding and Fourier Transform (FFT) processing. The proposed algorithm is then programmed using Verilog HDL language on a Field Programmable Gate Array (FPGA) platform and implemented on the Artix7 system chip XC7A100TFGG484-2 provided by Xilinx for image acquisition, processing, and HDMI display. Experimental results show that the improved algorithm yields better edge detection performance.
Link Prediction Based on Relationally Encoded Aware Attention

Knowledge Graph Completion (KGC) is in predicting potential relations (entities) in the knowledge graph by known triples, and completing missing links based on the potential relations (entities). Some recent studies have shown that Graph Neural Networks (GNN) can perform well on KGC. Most of the existing GNN models represent and update nodes by learning information from entities and relations in the subject domain through complex network structures, but they ignore the semantic information between relations. Graph Attention Networks (GAT) focuses on modeling simple relational graph data. This limits its ability to handle more general and complex multi-relational graphs. So, in this paper, we propose a relation encoding aware attention network (REA-GAT). By encoding relations, the relationship representation between different relations is updated. The interaction between entity embeddings and relation embeddings is further increased to make full use of neighborhood information and relation information to enrich the embeddings. Experiments show that REA-GAT has more advanced performance on link prediction tasks.
Feature Selection Based on Causal Analysis Algorithm for Speech Emotion Recognition

Feature selection is an important step of pre-processing in machine learning and data analysis. However, existing feature selection algorithms rarely reveal the underlying causal mechanisms between features, ignores the interaction between them and lacking interpretability. In addition, how to use causal analysis methods to mine causal relationships between speech emotions and features, select the subset of speech features that are optimal for speech sentiment with the class variable. To address these issues, this article presents a causal feature selection algorithm called Interleaved Forward-Backward Markov Blanket (IFBMB) algorithm for speech emotion feature selection, aimed at finding causal relation-ships between speech emotion and features. In this paper, we extracted two feature sets named FP, MFCC on IEMOCAP and EMODB databases. Experimental results showed that the proposed IFBMB algorithm outperforms the traditional causal feature selection algorithm. IFBMB can effectively select the main features for speech emotion to reduce the dimensionality and improve the speech emotion recognition rate. The speech emotion recognition rate of the IFBMB algorithm on the MFCC and MFCC+FP of EMODB Database reaches 91.59% and 92.83%.
Visualizing Code Syntax: Empowering Developers with a Global View for Enhanced Code Quality Evaluation

This article discusses the issues that come with system evolution, specifically the challenges of comprehending and maintaining old code created by unreachable developers. In this instance, we want to highlight the value of using code visualization to get a global perspective of the components and their dependencies, since this can assist in recognizing any possible structural problems. Additionally, visualization can shed light on the code's scalability and performance properties. A well-written program with clear syntax that respects low coupling and separation of concerns makes system maintenance and evolution easier, which saves time and money. Adopting a scalable and flexible language also makes maintenance and development smoother. In this paper, we made a visualization of Mage code in order to evaluate its syntax flexibility and scalability.
Design and Optimization of Heterogeneous Blockchain Network Model Based on Relay Chain

There are still problems for the existing solutions for heterogeneous blockchain networking, such as excessive centralization, complex and difficult implementation and maintenance of heterogeneous chain cross-chain architecture, and insufficient performance in large-scale heterogeneous blockchain networking scenarios. This paper proposes a flexible cross-chain gateway plug-in mechanism, which can adapt to different types of heterogeneous blockchain access, simplify the access process of the chain construction, greatly reduces the access cost of the heterogeneous chain and the maintenance difficulty of the connected blockchain and improves the scalability of the networking model. Aiming at the problem of insufficient performance in the large-scale heterogeneous blockchain networking scenario, this paper introduces the two-layer extended Rollup technology in a single chain, and at the same time adapts to the characteristics of heterogeneous chain transactions to further improve the performance of the networking model.
Knowledge Representation Learning with Hyperboloid Models

Knowledge graph describes the objective knowledge in the real world in a structured form. Knowledge representation learning aims to project the entities and relations in the knowledge graph into a low-dimensional, continuous vector space and learn vector representations of the semantic information of entities and relations. Moreover, link prediction and other tasks are carried out by vector calculation, and a variety of downstream tasks are achieved while retaining the structure of the knowledge graph itself. However, the existing knowledge representation learning models have certain defects when modeling complex relationships such as 1-N, N-1, and N-N, and the processing effect is not well. In order to better deal with complex relations, this paper proposes a knowledge representation learning model based on hyperboloid model, which emitters the knowledge graph into hyperbolic space for knowledge representation learning. Experiments are carried out on the public datasets FB15K, FB15K-237 and WN18RR. The experimental results show that the proposed model is superior to the baseline model in most evaluation indicators, which proves effectiveness of the proposed model.
A Semantic Enhanced Chinese Entity Relation Extraction Model Based on Relation Constraints

As an important subtask of natural language processing, the purpose of entity relation extraction is to extract entity relation triples from unstructured text data. The existing cascade entity relation extraction model has achieved satisfactory results on English dataset. However, due to the differences in literal semantics and language patterns between Chinese and English, Chinese sentences contain more complex semantic information and there is a problem of nested entities, which leads to the existing cascade model's inability to accurately represent the deep semantic information of Chinese sentences, inaccurate relation classification and the problem of identifying redundant and erroneous entities. For this reason, this paper proposes a semantic enhanced Chinese entity relation extraction model based on relation constraints(SECRC). Starting from the complex Chinese semantics, this model embedding sentences based on the multi-granularity semantics of character, words and sentences, and realizes the comprehensive expression of sentence depth semantics; a relation attention entity extractor (RAEE) is proposed, which constrains the entity recognition process by extracting the relation information, thus improving the accuracy of Chinese entity recognition. The model was experimented on the three Chinese datasets of DuIE, FinRE and SanWen. The results show that this model successfully obtains the deep semantic expression of Chinese sentences, effectively improves the accuracy of the relation classification and entity recognition results of Chinese sentences, and is superior to other baseline models in effect.
Exploring the Word Structure of Ancient Chinese Encoded in BERT Models

In recent years, ancient Chinese processing has gradually become an emerging field. With the advent of pre-trained language models such as BERT, various language models suitable for ancient Chinese have also been developed, which further promotes the development of this field. Although each model had its own characteristics, few studies have analyzed and explored how models “comprehend” ancient Chinese from an internal perspective. Therefore, our study analyzed the features of typical pre-trained language models, including Chinese BERT, Chinese RoBERTa, SikuBERT, and SikuRoBERTa, when encoding the word structure of ancient Chinese. We then correlated these findings with their performance in downstream tasks. The results indicated that these models were sensitive to the distance between tokens (characters or words) when encoding word structures, with attention values decreasing as the distance between tokens increased. Moreover, the Siku models demonstrated higher attention towards the current and surrounding tokens.
Based on YOLOv7-Tiny Improved Model of Remote Sensing Image Detection

Object detection technology has become mature, but it is still challenging for remote sensing image detection. For example, the target size is small and difficult to separate from the surrounding background. The targets are distributed sparsely and unevenly, and the dense targets have occlusions, which makes it difficult to detect by the model. To solve this problem, this paper proposes an improved detection model based on YOLOv7-tiny. For the detection of small targets in data set, coordinate attention mechanism and loss function fusion method are used to improve the accuracy of detection of small targets. Combined with the idea of feature separation and fusion, the C5 module of YOLOv7-tiny model is improved to reduce the feature loss in the training process and improve the reasoning speed of the model. Compared with the original network model, the performance of the improved model is significantly improved, with mAP@0.5 reaching 0.935 and FPS reaching 141.13.
Adversarial Training Lattice LSTM for Chinese Named Entity Recognition of Social Media Texts

Chinese named entity recognition in social media is a challenging task. Existing methods based on word-level information or external knowledge are affected by Chinese word segmentation errors, insufficient exploration of potential words, and a lack of training data. Although most of the pre-training takes into account the semantic information of the word context to make it characterize multiple meanings of a word; however, excessive focus on the contextual information can result in inconsistent labeling of the same entity as the contribution of different words to entity recognition is not necessarily positive. To address the above problems, a model combining an improved generative adversarial network with Lattice LSTM-Attention that uses global feature vectors is proposed. The dual-layer BiLSTM serves as the generator model in the GAN, integrating positive samples consistent with expert-annotated data from human-annotated datasets, thus resolving the issue of limited training data. Additionally, a character glyph embedding layer is added to the pre-trained RoBERTa to discover more potential words. A Lattice LSTM-Attention sub-model with global feature representation is introduced, which achieves good results in Chinese word segmentation and solves the problem of inconsistent labeling of the same entity. Experimental results on the manually labeled Weibo2022 hot search dataset show that the model achieves more desirable results compared to other modeling approaches of its kind.
YOLOX-TE: Remote Sensing Image Object Detection Based on Improved YOLOX-Tiny

Lightweight object detection models have been widely applied in remote sensing image object detection and have achieved good results. However, there are still some unresolved issues in lightweight remote sensing image object detection, such as dense target distribution, small size, and the inability of the network to effectively extract deep feature information of the target, which can easily lead to issues such as false detections, missed detections, and inaccurate boundary box positioning. Therefore, we propose YOLOX-TE. It can effectively extract deep features of small target objects and enhance the modeling ability of target bounding boxes at different scales. Firstly, it optimizes the feature pyramid network FPN through inverse Focus operation and transposed convolution. Secondly, a multi-scale fusion module based on ECA attention mechanism is added to the head network. Finally, an improved SENet attention mechanism is added to the bounding box regression branch. According to the RSOD dataset validation, compared to the YOLOX-Tiny model, the YOLOX-TE model only increased the parameter count by 0.363M, but the accuracy of AP, AP75, APS, and APL increased by 7.1%,6.7%, 6.8%, and 7.3%, respectively. Compared with the most advanced lightweight YOLOV7-Tiny model, the accuracy of AP and AP75 is 0.4% and 2.1% higher, respectively, and YOLOX-TE has a smaller volume and lighter weight.
Construction of Learning Process Ontology and Application of SWRL Rules

Learning analysis plays a crucial role in students' educational processes. Ontology technology can assist in managing and comprehending students' learning processes, enabling early identification of underperforming students. This study presents the application of ontology technology in the bilingual (Chinese and English) course on C++ object-oriented programming. First, knowledge ontology was constructed by creating classes and properties using Protégé, followed by adding relevant instances. The Pellet inference engine was then utilized to analyze the learning process, and the DL query plugin was used for inference queries to obtain pertinent information related to the learning process. Finally, the Semantic Web Rule Language (SWRL) was used to define inference rules, improving reasoning comprehensiveness and practicality. The results demonstrate that ontology inference of the learning process can detect potential knowledge, provide early warning signs, and offer teachers a more comprehensive foundation for differential implementation of the teaching process.
Research on Multi-knowledge Graph and Semantic-aware for Automatic Text Summarization

Automatic text summarization is one of the important tasks in NLP. Knowledge graph can improve the quality of summary by building the relationship between entities from source document. However, existing studies introducing knowledge graph have the problem of poor coordination between text content and knowledge bases. In our work, we raise a new model, ATMG, which combines feature matrix obtained from the external knowledge with original text classification label and context to allocate the attention weight. ATMG integrates external knowledge embedding into global semantic information, and chooses the appropriate knowledge graph to match different document backgrounds. In this way, the accuracy of lexical entity relationship construction can be improved so that the quality of summaries can be enhanced. A lot of experiments show that our model outperforms in different degrees compared to the traditional models, and the ablation experiments demonstrate the effectiveness of each module of ATMG.
OKAM: A Linux Application Manager Based on Hierarchical Freezing Technology

In the context of resource contention and scarcity, ensuring the smooth operation of focus application in the system has always been a challenging yet vital issue faced by operating systems. Traditional Linux mechanisms cannot accurately understand user's intention and lack automatic resource monitoring and management tool. In this paper, we propose OKAM, a Linux application manager based on hierarchical freezing technology. OKAM can classify the applications into hierarchy based on different application runtime states and user's intention, and automatically monitor and adjust the allocation of application resources based on system workload pressure. Experimental results demonstrate that OKAM is able to effectively ensure the fast startup and smooth operation of user's focus application under high CPU, memory, or I/O pressure, significantly reducing application startup time by 76%, 62%, and 73%, respectively.
CADnCNN: Improving DnCNN Denoising with Cross Attention

In recent years, image denoising algorithms has witnessed remarkable advancements, largely driven by the speedy development of deep learning techniques. Among these advancements, Denoising Convolutional Neural Network (DnCNN) is a milestone, owing to its powerful performance. However, traditional DnCNN architecture heavily rely on local convolutional operations for feature extraction, which inherently limitation restricts its capacity to capture long-range dependencies, potentially leading to the loss of vital structural information within images. To handle with this limitation, we propose a solution dubbed Cross Attention block. The purpose of this specific block is to extract correlations among non-local features from various source inputs, thereby broadens the receptive field and augments the network's capacity to capture structural information. Furthermore, we integrate the Cross Attention block into DnCNN named CADnCNN, which significantly improves the ability to preserve image details and structural integrity in denoising tasks. Experiments have affirmed the effectiveness of our proposed method.
Quadratic Exponential Decrease Roll-Back: An Efficient Gradient Update Mechanism in Proximal Policy Optimization

Proximal Policy Optimization (PPO) achieved the best performance in many continuous control tasks as a popular Deep reinforcement learning algorithm. However, the maximum point of the surrogate in the PPO and its variant version is not the zero point of the policy gradient. In this paper, we propose a variant of PPO: PPO with Quadratic Exponential Decrease roll-back (PPO-QED), which uses a quadratic, exponential decrease roll-back function to make the zero point and the maximum point coincide. We prove that the policy gradient of the PPO-QED is bounded and that the policy gradient of PPO-QED can approach the zero point more steadily than PPO-RB. We experiment with the proposed algorithm in continuous control tasks, selecting PPO-KL, PPO-Clip, PPO-RB, PPO-S, and TRGPPO as a comparison; the experimental results show that the PPO-EDRB performs well.
Honeytoken-Detector: A Symbolic Execution-Based Honeypot Token Detection Tool

Smart contracts have emerged as one of the most successful applications in the blockchain domain, playing a significant role in various blockchain ecosystems. Inspired by smart contracts, a multitude of cryptographic assets have been created. To standardize these assets, industry standards such as ERC20 (Ethereum Request for Comments 20), ERC721, and ERC1155 have been proposed. In recent years, smart contracts have frequently fallen victim to attacks. Honeypot contracts, disguised as ERC20-compliant tokens, are widely prevalent on the blockchain, enticing victims to make purchases. Such malicious smart contracts exhibiting deceptive behavior are collectively referred to as honeypot tokens. This paper focuses on ERC20-compliant smart contracts and defines six common types of honeypot issues. Building upon advancements in smart contract vulnerability detection, we propose an enhanced symbolic execution-based detection tool called Honeytoken-Detector. We conduct experiments on both contracts known to have similar issues and actual token contracts from the real world. The experimental results demonstrate the effectiveness of our tool in identifying vulnerabilities.
Interenet ads as a form of Internet communication. Specifics of translation. (based on the material of the Chinese and English languages)

The aim of the article is to study specifics of Internet advertising as a communicative process in virtual discourse. The relevance of the chosen topic is determined by accelerated technological development, modification of Internet communication with the participation of the latest elements of technology and software, and the evolution of communication processes. The tasks of the research are to consider the theoretical origins of formation of Internet communication; to distinguish the concept of Internet advertising; to analyze the peculiarities of Internet advertising in the context of virtual discourse. The object of the article is Internet advertisements. The subject of the research is elements of translation and adaptation of Chinese and English texts of Internet advertisements in Ukrainian. The article examines the phenomenon of Internet advertising as a form of Internet communication. The theoretical origins of the formation of Internet communication are considered. A general overview of the characteristics of Internet communication is provided, namely, its cross-border nature, expressiveness, illustrativeness of texts, the use of a significant number of graphic elements in messages is examined. It is noted that in the communicative processes inherent in online communication, a widespread combination of oral and written forms of speech appears in order to further improve, modify and obtain new opportunities for the implementation of online forms of communication, which have become an integral part of life of modern society. The concept of Internet communication is defined as a modern communication process that has deeply entered life of society. The concept of Internet advertising is distinguished. It is noted that online advertising is a form of communication process used in cases of finding and selling a product, service or brand through channels determined by the virtual environment. The peculiarities of Internet advertisement as a small-form text that exists separately in virtual space are analyzed. The characteristics of Internet advertisement in the context of virtual discourse, its types and forms are studied.