Reviewerly/exHarmony huggingface dataset:
An Investigation of the Behaviours of Machine Learning Agents Used in the Game of Go

In last years, Machine Learning (ML) techniques have been successfully applied in various domains. Notably, in 2015, AlphaGo, an artificial Go game player (Go agent), based on deep reinforcement learning and Monte Carlo Tree Search, won by 4:1 against the renowned player Lee Sedol, which demonstrated the power of ML techniques. Although Go is a discrete and zero-sum game, compared with other board games it is much more complex. First, it has around 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">172</sup> positions (i.e., placement of stones) which makes it impossible to find a perfect strategy to win the game. Secondly, playing the game requires not only the calculation of future moves but also strategies such as judgement of thickness, the balance between outside influence and territory, etc. The Go community is interested in understanding why and when Go agents are successful, in order to better understand the Go game itself. However, Go agents are inherently unexplainable, which makes it difficult to understand their strategies. In this paper, we make a first step towards this direction. We start from the observation that explaining a single agent is too difficult, while more insights can be obtained by comparing the strategies of multiple agents. Based on this, we perform an empirical study in which we compare different Go agents having different skill levels, measured by their Elo scores. We generate several different Go positions and we play all the agents from them. Then, we compare their decisions to understand when they played the same move and when they played differently. Moreover, we assume that, even if two agents play a different move, their strategies can be considered similar. The strategy of a Go agent is specified as a ranking of moves from which it usually picks the top one as move to play next. Therefore, to assess to what extent the strategies of different ML agents are similar, we compare the rankings they produce using the similarity metric Rank-Biased Overlap (RBO).
Towards Integrated Model-Based Machine Learning Experimentation Framework

ML systems in critical applications, such as autonomous driving, require high-quality assurance and the ability to handle concept drift and performance drop. To address these challenges, we propose an integrated system between the Multi-view Modeling tool to the automated pipeline for the ML Model Training and DNN Repair. Our preliminary integration and experiment has shown promising result.
Automatic Generation of Solidity Test for Blockchain Smart Contract using Many Objective Search and Dimensionality Reduction

Blockchain and smart contract technologies have emerged as innovative approaches to trustworthy and reliable service computing. With the growing popularity of decentralized applications, the quantity of smart contracts has seen an exponential increase. Research on testing smart contracts has primarily focused on identifying specific vulnerabilities in smart contracts and blockchains. However, generating a robust test suite for smart contracts remains a daunting challenge. The state-of-the-art DynaMOSA algorithm uses many-objective optimization for test case generation, introducing preference sorting and dynamic target selection strategies. Yet, under hyper many-objective conditions, the algorithm faces difficulties including insufficient selection pressure and reduced efficiency. This paper proposes a solution to these issues by introducing a many-objective optimization algorithm with a dimensionality reduction strategy. The goal is to condense an extensive dataset into a smaller, more manageable and effective dataset using specific reduction criteria. Additionally, this paper utilizes 28 open-source Solidity projects from GitHub for testing. The experimental results show that compared to DynaMOSA, the proposed approach achieves higher testing coverage on most projects under test and shows a noticeable improvement in algorithm efficiency for the automatic generation of Solidity Tests.
Improving Accuracy of Small Object Detection in Screen based on YOLOv7

Small object detection encounters various challenges, including low resolution, low contrast, missing visual features, object loss and missed detections, computational efficiency, and data imbalance. To address these challenges, a method is provided to enhance the accuracy of small object detection. This method utilizes a model with a small object detection layer to predict the segmented images. Adding small object detection layers enhances the perception of small objects and improves the network's performance in localizing and recognizing small objects. Using image segmentation strategies involves dividing the image into multiple sub-images for object detection, effectively handling small objects. This paper also presents the implementation principles of the YOLOv7 algorithm and the specific definition of small objects, validated through experiments to demonstrate the feasibility of the proposed methods. Finally, the article summarizes the experimental results.
An Attention Mechanism Based Approach for Multivariate Time Series Anomaly Detection

Multivariate time series (MTS) anomaly detection is of utmost importance in contemporary industrial systems, such as power grids, distributed systems, and spacecraft, which generate extensive multivariate time series data through diverse sensor arrays and monitoring techniques. Despite the promising performance of deep learning-based approaches in MTS anomaly detection, effectively modeling monitored metrics and temporal relationships remains a significant challenge. Existing methods often fall short in capturing these dependencies explicitly, leading to limited performance. To address this issue, we propose a Graph Attention Network (GAT) based framework for MTS anomaly detection in this paper. Our approach incorporates a transformer encoder layer to obtain the MTS data representation, followed by two parallel GAT layers which capture relationships between different metrics and different timestamps simultaneously. Moreover, our method optimizes both a reconstruction model and a prediction model simultaneously, then calculates the anomaly score for each timestamp for anomaly detection. Experimental results on three real-world datasets demonstrate that our proposed approach outperforms state-of-the-art baselines in terms of F1-score.
Research and Application of Loitering Detection Based on Deep Learning

Dangerous events in public places often have abnormal loitering behavior of pedestrians before they happen. It is significant to improve public safety to find out this kind of abnormal behavior. At present, the model of deep learning-based wandering detection algorithm is too complex and requires the support of devices with high computing power, otherwise it is difficult to be applied to scenes with dense pedestrians, so in-depth research on this problem is needed. In response to YOLOX’s low detection accuracy for small objects, YOLOA adds spatial and channel attention modules to learn discriminative deep features, and adds a path aggregation network to process both semantic and spatial information in the early stages of network. YOLOA also adds the measuring the similarity of width and height to IoU loss, which improve the prediction accuracy of object position. The experimental results show that the accuracy of YOLOA is 2% higher than that of YOLOX in small object detection of the COCO dataset. For scenes with dense pedestrians, JYTracker integrates object detection and pedestrian re-identification, improves prediction speed by sharing underlying features improving the training speed. JYTracker also increases tracking speed through efficient data association strategies. The experimental results also show JYTracker model’s MOTA on the MOT20 dataset is as high as 69.5. The experimental results on the PETS2007 dataset show that the JYLD can quickly identify (any) loitering behaviors.
Availability modeling and evaluation of switches and data centers

Data Center Network (DCN) availability is defined as the ability of the DCN to handle all demands at a particular time. DCN availability depends on the type and the availability of its switches. Similarly, switch availability depends on its hardware (HW) and software (SW) subcomponents. This work presents a detailed analysis of DCN availability by modeling and evaluating the availability of its components like traditional DCN switches, P4 hardware target switches, and P4 software target switches (and their subcomponents). This work is intended as a guideline for the DCN operator (i) to identify the critical subcomponents of the network, (ii) to understand the effect of different DCN topologies on the availability of the DCN, and (iii) to improve the availability of the DCN by replacing some components (and/or subcomponents).
Move Smart Contract Vulnerability Detection based on Resource-flow Analysis

The Move smart contract (MSC) is designed to enhance the type security of digital assets by utilizing a resource-based structure. However, vulnerabilities may be introduced during the development process. In response to the security threats faced by digital assets in the MSC, we have identified five resource-related vulnerabilities for the first time. Additionally, we have defined two test oracles and proposed a method to detect these vulnerabilities in the MSC using resource-flow analysis. Our method begins by analyzing the resource types present in the MSC. Based on the resource operation information within the contract function, we create a resource-flow graph. Next, the resource-flows are derived from the Resource-Flow Graph using a traversal algorithm, and they are then transformed into test cases. In the final step, the generated test cases are executed, and the vulnerabilities are detected by utilizing the proposed test oracles. Through a comprehensive case study, we showcase the various resource-related vulnerabilities and assess the feasibility of our method in detecting these vulnerabilities. The results demonstrate that our proposed method is effective in identifying resource-related vulnerabilities.
Two-Stream (2+1)D CNN Based on Frame Difference Attention for Driver Behavior Recognition

Driving behavior recognition is a key technology in human-machine cooperative driving, which plays an important role in the construction of an intelligent transportation environment. Due to the similarity of drivers ' actions and backgrounds in driving environments, it's challenging for existing behavior recognition methods to recognize behaviors in driving environments precisely. To solve these problems, we propose the two-stream (2+1)D CNN with Frame Difference Attention Module. First, we use two streams to process the information captured from the face camera and road camera and use the Squeeze and Excitation attention module to fuse the stream information. Second, we propose the Frame Difference Attention Module to enhance the changes between adjacent frames so that the subtle actions in driving environments can be captured easier. We test our network on the Brain4Cars dataset and have a good performance on it.
Using Complex Network Communities to Evaluate the Correctness of Object Detection

Although today’s object detectors (ODs) are fairly powerful and advanced, they still suffer from an unneglectable level of error rate. In view of this issue, we develop our ConetCA framework to compute a relevance metric for an OD output, so that this metric serves as an indicator of how likely this OD output is faulty (i.e., containing misidentified objects). Our ConetCA framework is built upon the concept of complex networks and community clustering. We also performed an empirical study involving two open-source datasets and two deep-learning-based ODs. Our study results confirmed that the proposed relevance metric is highly effective.
Robust Model for Adversarial Attack Protection through Weak Features Removal

This study focusses on the challenge of imitating user behavior in continuous authentication systems and proposes a solution to build a defense system against adversarial attacks. We conduct experiments to analyze the effectiveness of weak feature removal in preventing attackers from imitating legitimate users. The experiments include individual weak feature (IWF) removal and common weak feature (CWF) removal, using feature ranking methods to identify the weak features. Preliminary results show that the proposed method outperforms the baseline model in defending against single attackers and multiple attackers. Ongoing work involves building a defense system that can handle different types of attacks from various victims by clustering users with similar behavior.
Research on Label Distribution Dimension Reduction Algorithm Based on MLDA

Data dimension reduction can effectively keep the important features in the data, improve the classification performance of the data. Based on this, the model of Multi-label Linear Discriminant Analysis (MLDA) is introduced to reduce the dimension of data, and the Label Distribution Dimension Reduction Algorithm based on MLDA (MLDA-LDL) is proposed. Firstly, the original data is learned to complete feature dimensionality reduction, and the sample data after feature dimensionality reduction is obtained. Then, the training model of feature and label is bulit by the classical algorithm of label distribution paradigm. Finally, the prediction is completed through the training model. Experimental results on several public data sets show clear advantages of the MLDA-LDL algorithm.
Chat-EasyEdu: An Education Field Intelligent Question-Answering System Based on Knowledge Graphs

In the era of big data, existing knowledge in the field of education is becoming increasingly complex. Proper storage of knowledge in education, improving the singularity of knowledge forms, better retrieval of knowledge, and providing relevant services have become the main issues. To alleviate these problems, this paper constructs an education knowledge graph (KG), providing a set of KG construction paradigms for the education field. Data is collected from publicly available information on the websites of 100 universities and stored in the Neo4j graph database. An intelligent question-answering (IQA) system named Chat-EasyEdu is implemented using matching algorithms. A total of 3720 nodes and 12258 relationships are constructed, and the IQA system is queried and verified using 29 types of questions. The results show that the IQA system can effectively serve users who need to query information about universities, having certain scalability and promotion value.
Spatial-Temporal Hypergraph Neural Network based on Attention Mechanism for Multi-view Data Action Recognition

To address the issue of large network computing parameters for spatial-temporal features of actions in multi-view video sequences, this paper proposes a Spatial-Temporal Hypergraph Neural Network based on Attention Mechanism (STHGNN-AM). This method consists of a Temporal Attention Mechanism based on Trainable Threshold (TAM-TT) and a Multi-scale Spatial-Temporal Residual Module (MS-STRM), achieving multi-view data action recognition. Specifically, TAM-TT is constructed by using a learnable threshold to extract key frames of actions from different view video frames input to the module. MS-STRM is employed to further improve the model performance, and high-order semantic features of actions are learned in a hypergraph neural network. The MS-STRM extracts features using a multi-scale approach, modeling long-term and short-term semantic information to capture the temporal information changes between different frames. Comparative experiments on the NTU RGB+D and imitating traffic police gestures datasets evince the superior performance and heightened recognition accuracy exhibited by the proposed methodology, effectively enhancing the cognitive ability of machine-human body language interaction.
Senti-EGCN: An Aspect-Based Sentiment Analysis System Using Edge-Enhanced Graph Convolutional Networks

Aspect-based sentiment analysis systems aim to classify sentences according to specified aspects. Most previous studies have used graph convolutional networks (GCNs) to analyze syntactic features and used word dependencies for syntactic context and aspects. However, traditional GCNs have limitations in exploring syntactic dependency graphs, such as missing information on edges in syntactic dependency trees. For accurate sentiment prediction on specific aspects, an aspect-based senti ment analysis system using edge-enhanced graph convolutional networks (Senti-EGCN) is developed. In particular, a bidirectional long short-term memory (Bi-LSTM) network is employed to extract the contextual features of sentences. Thereafter, a transformer encoder with a self-attention mechanism is used to analyze the interrelationships and global features of words in long texts. Next, the Bi-LSTM network is used to enforce the sentence structure through the word dependency tree. The dependency tree analyzes the words’ dependencies to enhance the representation of information. A bidirectional GCN (Bi-GCN) uses message passing to propagate information across the nodes in a parsed dependency tree. In addition, an aspect-specific masking technique is applied to reduce redundant information in the hidden representation by masking contextual information outside the aspect and improve the accuracy. Finally, attention scores are calculated in the last layer for sentiment classification. The experimental results demonstrated that the proposed Senti-EGCN outperforms other baseline models in most metrics on the three benchmark datasets.
An Empirical Study of Fault Detection Effectiveness of Metamorphic Testing for Object Detectors

Object detectors (ODs) have been applied in various fields to identify target objects in pictures and videos. A commonly used approach for validating ODs is to compare their outputs with manually labeled data, in which labeling is tedious and time-consuming. To alleviate this problem, Metamorphic Testing (MT) based methods can be used to generate test cases automatically for testing ODs. Our survey has identified two different types of MT-based methods: input driven and output driven. We also observed that their relative effectiveness in fault detection has not yet been studied and compared. Inspired by this observation, we performed an empirical study with different types of ODs to address this research gap. In our study, we proposed an evaluation framework involving three different fault detection effectiveness metrics. An important finding of our study is that the input driven MT-based methods have better fault detection performance than their output driven counterparts.
Application of mathematical quantification theory class III in software effort prediction

Prediction of software development effort, commonly relies on historical data. In recent years, the software development environment become increasingly diverse, and a wide variety of variables are recorded in project data. In addition to quantitative variables such as function points, project data tends to record many qualitative variables, such as development language and platform. However, there has not been any significant discussion on how to treat this large quantity of qualitative variables. In this paper, an effort prediction method using mathematical quantification theory class III is proposed to facilitate the handling of qualitative variables. From the results of an experimental comparison with conventional methods, we learn that the predictive accuracy can be improved by applying the mathematical quantification theory class III to effort prediction.
Anti-sea Operation Knowledge Modeling of Aircraft Carrier Formation Based on Key Knowledge and Symbiotic Relationship of Conditions

Anti-sea operation of aircraft carrier formation involves a lot of operational knowledge. The correct use of this complex operational knowledge determines whether the carrier formation can carry out accurate mission planning for sea operations. Therefore, anti-sea operation knowledge modeling of aircraft carrier formation based on key knowledge and symbiotic relationship of conditions is carried out. Firstly, the existing knowledge ontology and key knowledge ontology were constructed according to the anti-sea operations knowledge of aircraft carrier formation. Then, a new type of conditional symbiotic knowledge relationship with intensity factor is constructed. Finally, the ontology mode layer of the knowledge graph of the anti-sea operation of carrier formation was modeled. An example analysis is carried out to prove that the ontology mode layer of anti-sea operation of aircraft carrier formation based on key knowledge and symbiotic relationship of conditions can effectively represent the process of aircraft carrier formation's sea operation, and then provide support for the knowledge graph driven decision support system.
On the Effectiveness of Graph Data Augmentation for Source Code Learning

The methodology that uses deep learning to solve software engineering tasks, such as bug detection, is known as source code learning. Due to the graph nature of source code, graph learning, empowered by graph neural networks (GNNs), has been increasingly adopted for source code learning. Like other deep learning contexts, source code learning also relies on massive high-quality training data, and the shortage of such data has become the main performance bottleneck. In practice, data augmentation is often used as a countermeasure to mitigate this issue, by synthesizing additional training data based on existing ones. However, most existing practice of data augmentation in source code learning limits simple code refactoring methods and is not sufficiently effective. In this work, in light of the graph nature of source code, we propose to apply the data augmentation methods used for graph-structured data in graph learning to the tasks of source code learning, and we conduct a comprehensive empirical study to evaluate whether such new ways of data augmentation are more effective than the existing simple code refactoring methods. Specifically, we evaluate 4 critical software engineering tasks and 7 neural network architectures to assess the effectiveness of 5 data augmentation methods. Experimental results identify that, compared to other methods, Manifold-Mixup can greatly improve the accuracy of the trained models for source code learning.
A Unified Analysis of Dynamic Interactive Learning

In this paper we investigate the problem of learning evolving concepts over a combinatorial structure. Previous work by Emamjomeh-Zadeh et al. [1] introduced dynamics into interactive learning as a way to model non-static user preferences in clustering problems or recommender systems. We provide many useful contributions to this problem. First, we give a framework that captures the two different models analyzed by Emamjomeh-Zadeh et al. [1], which allows us to study any type of concept evolution and matches the same mistake bounds and running time guarantees of the previous models. Using this general model we solve the open problem of closing the gap between the upper and lower bounds on the number of mistakes. Finally, we study an efficient algorithm where the learner simply follows the feedback at each round, and we provide mistake bounds for low diameter graphs such as cliques, stars, and general o(logn) diameter graphs by using a Markov Chain model.