Reviewerly/exHarmony huggingface dataset:
A Joint Relational Triple Extraction Model Based on Relational Bidirectional Encoding

Tagging based relational triple extraction methods have recently gained significant research attention. However, most of these methods suffer from the limitation of a unidirectional extraction framework. This framework is overly reliant on the subjects identified by the model in the first stage, leading to an Error Propagation Problem. To conquer this limitation, we devise a relational bidirectional encoding framework that identifying triples and its inverse based on relation and its inverse, then combining the scores of bidirectional triples to obtain factual triples. We use a shared DAE (Denosing Autoencoder) to recognize bidirectional entity pairs, so that the bidirectional features can flow during their respective recognition processes, thereby enhancing and complementing each other. Then we employ a bidirectional biaffine score function to assign every relation to every forward and inverse entity pair. During training, we address the problem of convergence rate inconsistency stemming from shared DAE through introducing a multi-task learning scheme. Our model is experimented on several typical benchmark datasets, and the results demonstrate its effectiveness. It achieves SOTA (state-of-the-art) performance on almost all datasets. Furthermore, our relational bidirectional encoding framework and multi-task learning scheme exhibit excellent adaptability and can improve the precision of other Tagging based factual triple recognition models.
Software Defect Prediction Using Machine Learning - A Systematic Literature Review

The most expressive attribute for defining successful software is its quality, which can only be attained when the chances of a defect occurrence are doubtful. Software defect Prediction is a cycle to develop a model that software practitioners can use to detect faulty areas before the testing phase. Prediction of such classes before its testing phase will enable the development team to allocate resources more efficiently. It reduces the testing effort and can yield higher quality software with reduced cost. Prediction of such defects can be further elevated when machine learning techniques are introduced to such a Software defect Prediction (SDP) model. In this paper, we conducted a systematic literature review of the studies that are available on such practices of predicting defects in software before the actual testing is started with a blend of machine learning techniques that provide an edge to software defect prediction model is carried out with the latest Research present in this area providing the most upgrade statistics on Software defect prediction using machine learning.
Integrating Flow and Program Analysis for Enhanced Protocol Reverse Engineering

The current protocol reversal framework comprises three key components: protocol message format extraction, protocol semantic analysis, and protocol state machine reasoning. Researchers have introduced various automated analysis methods, categorized into flow-based and program-based approaches. This paper focuses on the fundamental aspects of protocol reversal, specifically the extraction of protocol syntax, semantics, and synchronization information. An innovative approach is presented, combining both flow-based and program-based methods to propose a protocol message format extraction technique. This aims to fully leverage the strengths of both methods and enhance protocol reversal analysis technology. The integration of message parsing paths and byte memory propagation paths results in improved performance for protocol reversal analysis technology.
A Study of Yolov5 Small Target Detection by Incorporating 3D Parameter-Free Attention

Object detection is a fundamental task for complex visual applications, with wide-ranging uses in various fields. However, detecting small objects in cluttered scenes is challenging, due to factors such as limited feature information, occlusion, and interference. To address the difficulties of small object detection, we propose an improved version of the YOLOv5 algorithm, which incorporates a sophisticated attention mechanism called YOLOv5-Sim. This method uses a three-dimensional simple parameter-free attention module (Simam) to reduce the influence of environmental noise on image targets. Simam considers both spatial and channel dimensions, and refines features to selectively enhance those related to small targets. Moreover, we modify the backbone network structure of YOLOv5, and use the ReLU function as the loss function to boost the expression of small targets and speed up their detection. We conduct experiments on the Flow-Img small target dataset from Okahublot Company, and compare our model with the original YOLOv5x. The results show that YOLOv5-Sim achieves an average mean accuracy map@0.5 of 89.4%, surpassing YOLOv5x by 3.1%, without adding any extra parameters. Our model balances accuracy and real-time performance, and is suitable for various small target application scenarios.
RSL: Representative Sample Learning

With the development of Industrial Internet of Things (IIoT) technology, an increasing number of ICS (Industrial Control System) devices are connecting to the company's traditional information network, making it convenient for users to access them remotely and for overall planning. However, this exposes industrial control systems to the internet, making them vulnerable to cyberattacks. In this scenario, it is necessary to analyze the traffic of industrial control systems and prevent unknown attacks. We have generated additional samples from representative samples to increase the number of training samples, thereby improving the accuracy of anomaly detection.
Personalized Federated Learning for Histopathological Prediction of Lung Cancer

Lung cancer is a leading contributor to cancer-related fatalities worldwide, and histopathological image analysis plays a critical role in cancer detection by identifying morphological abnormalities in tissue samples. Artificial intelligence (AI) in medicine has evolved but still faces challenges, notably in maintaining data privacy and security. Federated Learning (FL) has emerged as a promising solution, enabling the training of robust models without jeopardizing data privacy. However, the effectiveness of existing FL approaches often falters in non-independent and identically distributed (non-IID) scenarios, where data distributions vary across clients. Addressing this challenge, our research presents a personalized federated learning (PFL) method specifically designed for lung cancer prediction using histopathological scans. This novel framework leverages client-specific autoencoders coupled with hierarchical clustering for personalized federated learning for lung cancer prediction. Our findings demonstrate the efficacy of the proposed method for collaborative lung cancer prediction in medical heterogeneous data environments while preserving data privacy.
Enhanced Word Embedding with CNN using Word Order Information For Sentiment Analysis

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE.
REsearch and Analysis of Network Behavior Based on Traffic

With the continuous development of the network and the continuous development of various applications, it brings convenience to human beings. However, it has also led to an increase in diverse security issues. For instance, complex and varied network attacks continuously pose threats to people's online activities. In order to detect and prevent such network attacks, mitigate their negative impact, and safeguard individuals' privacy and property, this paper focuses on analyzing network traffic. This study employs the K-Nearest Neighbors (KNN) classification algorithm to identify six types of network behaviors, including normal network behavior, monitoring, denial of service, and unauthorized access. Initially, the KDD99 dataset is preprocessed, followed by an analysis of its characteristics. Subsequently, machine learning techniques are utilized to detect network behaviors within the network traffic. Finally, the network traffic is evaluated to determine various network behaviors, thereby achieving the objective of behavior identification within network traffic. The effectiveness of the proposed method in this paper is verified using the publicly available KDD99 dataset. The experimental results demonstrate that the approach presented in this paper can accurately detect different network behaviors, achieving an impressive accuracy rate of 97.0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> with a low false positive rate of only 3.0%.
Recognition Model of Road Image for Blind People Based on Transfer Learning

Nowadays, recognition model for road image has become indispensable for the blind peoples. The model is usually placed to blind guide system in order to help the blind people to efficiently know road information in traveling. The traditional recognition model cannot accurately extract features from road image. As a consequence, the accuracy of the traditional identification model cannot satisfy travel demands for blind people. In this paper, we propose a novel recognition model based on transfer learning for recognizing road image. Firstly, we utilize image augmentation technology to process data preprocessing. And then we apply MobileNetV2 as the recognition model. And transfer learning technology is used to train the recognition model. As a consequence, the novel model can efficiently help the blind people to efficiently obtain road information in traveling. In the end, we conduct some experiments to verify the novel system. The experimental results show that the novel system can provide efficient help for blind people in traveling.
Document-Level Mathematical Relation Extraction Using Pre-Training Models

Mathematical relation extraction is pivotal for identifying relationships between entities in texts, a crucial step in aiding automated systems to comprehend the semantics of mathematical content. While prior studies have concentrated on sentence-level relation extraction (RE), their applicability in real-world scenarios remains limited. Document-level RE (DocRE), in contrast, poses a more complex challenge, necessitating multi-sentence reasoning and the prediction of relationships across entire documents. The scarcity of high- quality datasets, attributed to the constraints of manual data annotation, further complicates this task. In our study, we focus on extracting relationships from mathematical documents through document-level RE techniques. To effectively tackle the complexities of multi-label and multi-entity scenarios in document-level RE, we introduce two innovative methods: adaptive thresholding and evidence context pooling. These approaches allow document-level RE systems to focus on relevant text, significantly enhancing relation extraction capabilities. Furthermore, we implement a self-training strategy to learn evidence retrieval knowledge from a large amount of distantly-supervised data that lacks specific evidence annotations. Our experimental findings affirm the success of these methods, showcasing an impressive accuracy rate reaching up to 90.14% in the domain of mathematical relation extraction.
Acceptance of AI in Service Scenarios: The Impact of Chatbot Anthropomorphism on Customers’ Willingness to Use Continuously

Enterprises are deploying chatbots to automate customer service. However, customers seem reluctant to use chatbots for commercial activities. Based on the theory of social response, this study developed a model to examine how chatbot anthropomorph affect customers' use continuance. Through scenario-based experiment, this study found that chatbot anthropomorphism can increase customers' use continuance. At the same time, competence mediates this process, that is, when faced with a highly anthropomorphic chatbot, customers feel competence and are willing to continue to use it. The conclusions of this study have guiding significance for enterprises to optimize the application of chatbots.
Gaussian Mask Guided Attention for Compressed Video Super Resolution

To save video transmission bandwidth and storage space, videos compressed to different degrees is a common method. Thus, a method which down-samples the videos before compression and applies super resolution is proposed. Although those existing super resolution (SR) methods can restore the video resolution very well, the texture and details cannot be reconstructed with high quality. This paper proposes a Gaussian Mask Guided Attention for Compressed Video Super Resolution (GAVSR), which fully utilizes high-frequency information to recover sharp edge and vivid texture. Firstly, a high-frequency mask (HFM) is obtained through Gaussian blur to provide the lost high-frequency information. Then, the HFM is used to assist the attention mechanism to better restore high-definition video frames. Compared with the HEVC anchor, the average gain of Bjontegaard Delta Rate (BD-Rate) under the Low-Delay-P (LDP) setting is 22.30%. In addition, an experimental comparison is made with the advanced image/video SR methods, the proposed GAVSR performs favorably against the state-of-the-art image/video SR methods in terms of reconstruct quality.
Hypernetwork Temporal Embedding for Graph

Representation learning on temporal graphs has been a crucial facilitator in uncovering insights from real-world temporal graphs. In previous approaches, a common practice was to encode time and then join it with node features. However, conventional time encoding methods were based on Random Fourier Features (RFF), which were independent of the graph's topology and node features. This produces graph-independent encoding. In response to this challenge, we propose HTE, a Hypernetwork Temporal Embedding module for graph representation learning. Specifically, our approach involves two key steps: 1) We design a transfer function to extract temporal information from edges instead of RFF. 2) We fine-tune the parameters of the transfer function using the target node and its neighboring nodes. This fine-tuning operator ensures that the time encoding preserves the graph's topological information and node information. Therefore, HTE offers a more effective approach for temporal graph representation learning that adapts to the unique attributes of each graph. Our proposed model has been subjected to extensive experiments using four real-world datasets, showcasing its effectiveness.
Improving Continual Relation Extraction with LSTM and Back Forward Projection

The goal of continual relation extraction is to dynamically process emerging relations without forgetting the knowledge acquired from pre-learned relations. The memory-based methods have been proven to be highly effective, yet it still encounters the challenge of catastrophic forgetting. An improved method is proposed to address this issue. Firstly, the Long Short-Term Memory (LSTM) is used to memorize the knowledge of previous learned relations. Secondly, the Back Forward Projection (BFP) is utilized to maintain the classification space of relations among old tasks and relations among new tasks. Experimental results on two relation extraction datasets prove that our method can better deal with the problem of catastrophic forgetting.
Multimodal Brain Tumor Segmentation Using Transformer and UNET

In medical image analysis, accurate 3D brain tissue segmentation is crucial. This study undertakes a meticulous comparative evaluation of two 3D segmentation models: the established 3DUnet and the innovative 3DUnet Transformer. The assessment is anchored on the segmentation accuracy of three critical brain tissue classes; Whole Tumor (WT) Enhancing Tumor (ET) and Tumor Core (TC), with the Dice similarity coefficient (DSC) serving as the principal metric of performance. 3DUnet model demonstrated a consistent advantage over the 3DUnet Transformer, yielding superior DSC scores for ET (0.771 vs. 0.753), WT (0.891 vs. 0.874), and TC (0.804 vs. 0.779). Complementing the DSC, the Hausdorff distance was employed as an additional metric, further evidencing the 3DUnet's enhanced precision in segmentation with lower distance values across the board. These findings highlight the 3DUnet's robust performance and its potential to significantly refine brain tissue segmentation processes within medical imaging applications.
MAKE Decision in Hybrid Action Space

Although hybrid action space problems are prevalent in real-life scenarios, research on hybrid action space reinforcement learning algorithms has historically been less popular in the field of reinforcement learning. In this paper, we propose an algorithmic framework for actor-critic style reinforcement learning in hybrid action spaces. We further develop a novel algorithm within this framework, utilizing the Q-learning policy optimization approach. To demonstrate the effectiveness of our approach, we extensively evaluate our algorithm through a series of experiments conducted in the RoboCup2D Half Field Football (HFO) environment. Our approach has achieved 2 percentage point improvement in task success rate compared to previous studies, with an average reduction of approximately 13% in the number of time steps required for each successful trajectory.
Research on the Application of Big Data Technology in Building Employment Guidance Mechanisms for College Students in the New Situation

Currently, a new round of technological revolution and industrial transformation is sweeping across China. New technologies, new formats, and new models have put forward higher requirements for college students with employment needs, and how to provide employment guidance for college students has become more challenging than before. This paper attempts to explore how to use big data technology to construct an accurate employment guidance mechanism for college students in the context of widespread employment difficulties, thereby improve the methods of college student employment guidance and alleviating the employment difficulties of college students.
Multi-Directional Information Channel for Compressed Video Super-Resolution

In the last few years, the compressed video super-resolution(CVSR) method has emerged that downsampling videos and then compressing them to meet limited bandwidth requirements. Among them, high-quality recovery and resolution recovery for low resolution compressed videos are key to success. How to use temporal and spatial information is essential for the quality recovery of low resolution compressed videos. The existing methods place more emphasis on designing powerful networks to fuse the spatiotemporal information of videos, leading to the problem of one-way information propagation, which undermines the spatiotemporal information of the entire video and results in a lack of close relationship between the learned contextual information and the target frame. In this paper, we integrate the multi-directional information channel framework into CVSR, utilizing past, present, and future information to fully utilize the spatiotemporal information of the entire video. This is the work of the first multi-directional information channel framework on CVSR. Our experiment demonstrates the practicality of the multi-directional information channel framework on CVSR.
Domain Contrastive Learning for Multi-Site Alzheimer's Disease Classification

In the field of medical imaging, accurately classifying Alzheimer's Disease (AD) poses a substantial challenge, primarily due to the inherent variability in imaging data across different clinical sites. This paper introduces a novel approach, Domain Contrastive Learning (DCL), to address this challenge by enhancing the robustness and transferability of AD classification models using multi-site Magnetic Resonance Imaging (MRI) data. DCL leverages a unique contrastive learning technique within an autoencoder's latent space, enabling the extraction of domain-invariant features essential for accurate diagnosis. We rigorously evaluate the proposed network on brain images from three distinct imaging sites, employing a Domain Generalization (DG) training protocol to ensure robustness and generalizability across varying data sources. Our model is evaluated on three binary classification tasks: AD vs. Normal Control (NC), AD vs. Mild Cognitive Impairment (MCI), and MCI vs. NC. Results demonstrate that DCL outperforms existing methods, especially in AD vs. NC classification, achieving higher accuracy, sensitivity, and specificity with 0.51%, 1.54% and 1.68% increments respectively. This underscores DCL's potential as a robust and generalizable method for enhanced AD diagnosis, marking a significant step forward in medical imaging challenges in a multi-site context.
Efficient Evidence-Based Dialogue System for Medical Diagnosis

With the rise of intelligent medical assistance, the Dialogue System for Medical Diagnosis(DSMD) guided by reinforcement learning(RL) has gained much attention. However, currently available medical dialogue datasets suffer from insufficient diagnostic evidence caused by sparse symptoms, making it difficult to reproduce the evidence-based process of doctors in differential diagnosis and disease confirmation. Moreover, purely data-driven RL often involves extensive and blind trial-and-error, leading to inquiries about irrelevant symptoms to the patient’s chief complaints in limited dialogue turns, further exacerbating the issue of inadequate diagnostic evidence. To enhance the quantity and effectiveness of potential symptom collection in DSMD, we first construct a more comprehensive medical dialogue dataset CMD based on electronic medical records. The diversity of diseases and symptoms mentioned in the dialogue context of CMD surpasses that of existing public datasets. Furthermore, to enhance the efficiency of diagnostic evidence collection in DSMD, inspired by the logic of symptom inquiries in doctor-patient interactions, we combine experiential diagnostic knowledge with a specialized medical knowledge graph to constrain the inquiry of symptoms via RL, eliminating the introduction of symptoms unrelated to the patient. Experimental results demonstrate that our model significantly outperforms competitive benchmark methods in terms of diagnostic accuracy and the efficiency of symptom inquiries. Our codes and the CMD dataset are available at https://github.com/YanPioneer/EBAD.