Reviewerly/exHarmony huggingface dataset:
The IoT Service Agent Model based on Federated Learning to Improve Service Quality

The combination of learning analysis technology and terminal equipment can provide a rapidly imitating learning method between devices, and cognitive intelligence affects the learning effect. To enhance automated learning services, we can use federated learning methods to enable the training of different devices and enhance each other through devices. Using the training database to improve the quality of automated learning services. In this study, a novel agent-assisted active detection and data collection framework is designed. Monitoring agents can learn from each other to establish intelligent models, and through mutual communication between devices. Can check if established data can be applied to machine data model to get data. It can be used for intelligent manufacturing in the future. The agent may learn methods of learning and managing between devices having different properties. Obtaining experimental simulation and control data, and using machine learning to analyze growth progress and results allow for a deeper analysis of associated adjustments and anticipated changes.
A General Characterization of Representing Spatiotemporal Knowledge Graph based on OWL

Knowledge graph is used to represent the concepts, entities and relationships existing in the real world, which can be applied to many applications such as creative computing and recommendation system. Structurally, knowledge graph includes data layer and schema layer. Spatiotemporal knowledge graph extends the common knowledge graph to a certain extent, which is mainly reflected in the entity layer (data layer). Spatiotemporal knowledge graph includes temporal feature, spatial feature and spatiotemporal feature. In the pattern layer, spatiotemporal knowledge graph mainly adds concepts and relationships between concepts, which needs to be re-modeled. In this paper, as a spatiotemporal extension of the general description logic based on OWL logic, the spatiotemporal description logic (ST DL) is proposed to describe the spatiotemporal knowledge graph, and ST OWL is extended from three aspects: OWL class description, OWL axiom and OWL data type. Then, the corresponding transformation rules are proposed, and the instance is transformed from spatiotemporal ontology structure to spatiotemporal knowledge graph.
IMGA: Improved Microbial Genetic Algorithm

The traditional Genetic Algorithm is not only easy to fall into the local optimal solution, but also has poor stability. In response to this problem, I. Harvey proposed a Microbial Genetic Algorithm. Using the idea of microbial inheritance, the process of selection and crossover in the classical genetic algorithm is replaced by means of confrontation and infection. This algorithm can retain better parent population, and save those genes. However, this algorithm works well for small populations, and because only one new offspring is generated per confrontation, the efficiency is the problem. Aiming at this problem, an Improved Microbial Genetic Algorithm (IMGA) is proposed, which transforms random confrontation into ordered group confrontation with roulette selection algorithm. The simulation results of the TSP problem show that the new algorithm is suitable for large populations and the efficiency is also improved. Both in terms of time efficiency and the results of the optimal solution, IMGA is better than GA and MGA.
What Should Abeeha do? an Activity for Phishing Awareness

The increase in Social Engineering (SE) attacks during COVID-19 pandemic has made it imperative to educate people about SE techniques and methods. For the last many years, we have worked on games, which disseminate awareness among the participants about Social Engineering concepts. The aim of this study is to share our newly designed card-based game, which is simple to understand, and can be conducted in classroom environment.
CIL-BSP: Bug Report Severity Prediction based on Class Imbalanced Learning

The bug reports' severity can be used for developers to prioritize which bugs to be fixed first. However, this process depends on the developers' expertise in assigning the correct bug severity. In our previous study, we propose a novel bug report severity prediction method EKD-BSP, which utilizes the bug summary and the keywords extracted from the bug description. However, a class imbalance exists in our gathered bug report severity prediction datasets. To solve this issue, we design a novel method CIL-BSP by further considering the class imbalanced methods. Moreover, we apply hyperparameter optimization to CIL-BSP and consider different optimization strategies. To verify the effectiveness of CIL-BSP, we select two real-world open-source projects Eclipse and Mozilla as our experimental subjects. Based on our empirical results, we find that performing hyper-parameter optimization can significantly improve the severity prediction performance of CIL-BSP. Moreover, optimization hyperparameters on the classifier can contribute more than the class imbalanced method.
Breaking Anonymity of Social Media by Profiling from Multimodal Information

Information on social media is important for forensics in the real and cyber worlds, because social media platforms are used for various illicit purposes. However, the anonymity of social media accounts hinders their forensic use. Conventional methods of deanonymizing social media accounts are not sufficiently precise, because they only use information from words in social media posts. Another drawback is that those methods require not only information from the anonymous accounts that are the target of deanonymization, but also information from real-name accounts belonging to the same people who own the anonymous accounts. This paper proposes a new deanonymization method that profiles anonymous accounts to infer their users' attributes and matches the inferred attributes with the known attributes of candidates for deanonymization. In this method, the profiling of anonymous accounts uses not only word information but also information from posted sentences and images. Evaluation with data from 78 volunteers demonstrates the proposed method's viability.
LLCF: A Load- and Location-Aware Collaborative Filtering Algorithm to Predict QoS of Web Service

The prediction of Quality of Service (QoS) significantly facilitates the web services selection for QoS based web service recommender systems. One effective method for predicting web services' QoS values is the collaborative filtering (CF) algorithm. However, the existing CF algorithms experience potential scalability issues, as well as the accuracy issues. We present a load- and location-aware collaborative filtering algorithm (LLCF) to improve the prediction accuracy and the scalability. To assess the proposed LLCF, we leverage Amazon Cloud platform where hosts various web services. The experiments are conducted based on selected web services where QoS values are collected. The results show the prediction accuracy is significantly improved by the proposed LLCF. Furthermore, complexity analysis results show that our LLCF can remarkably improve the scalability.
Software Bug Prediction based on Complex Network Considering Control Flow

The prediction for software bug number provides vital guidance to the quality management and software testing. In this paper, a novel software bug number prediction method was proposed based on complex network considering control flow. Firstly, for each release of software, we constructed the Call Graph (CG), and for each release, Control Flow Graph (CFG) of every function were constructed. Then the CG Metrics (CGM) and CFG Metrics (CFGM) for each version were calculated with indicators from complex-network science. Finally, the results were sent to Panel Data Model (PDM) to perform the prediction on bugs fixed number. The experimental result showed that our method outperformed other prediction methods by 9.35% to 16.85%, and introducing CFGM reduced MAE by 5.1% to 27.8% than barely use CGM. The prediction of fixed bugs could indicate the software quality, and assist the quality control of software engineering.
Transferable Unique Copyright Across AI Model Trading: A Blockchain-Driven Non-Fungible Token Approach

Currently, Machine Learning as a Service (MLaaS) greatly benefits artificial intelligence (AI) model trading. However, threats such as model piracy and patent grabbing devastatingly violate the copyright of AI models. Current invasive copyright protection solutions mainly rely on watermarking to embed specific information into AI models, which inevitably decreases the accuracy. While non-invasive schemes, such as adversarial samples, cannot guarantee uniqueness as the adversarial sample generation algorithm would be known to all traders, and thus need to be changed after trading. To enable the ownership information transferable across AI model trading, we propose a blockchain-driven Non-Fungible Token (NFT) approach for trading-oriented AI model copyright protection. We design a mapping mechanism from AI models parameters to NFTs which can identify uniqueness and ownership of AI models across trading. Besides, a reputation-based rewards and penalties scheme is proposed to prevent NFT piracy. Lastly, the evaluation verifies the applicability of our approach.
A Stochastic Model for Calculating Well-Founded Probabilities of Vulnerability Exploitation

To efficiently manage security risks of network systems, vulnerabilities in the systems need to be assessed to determine their severity or priority. The Bayesian attack graph (BAG) is a risk analysis model that takes into account the probabilities of vulnerability exploitation (exploit probabilities) and their dependencies to calculate the probabilities that specific assets are compromised (compromise probabilities) in a system. In many BAG analysis methods, an exploit probability is obtained assuming that it strongly correlates with base metrics of the Common Vulnerability Scoring System (CVSS) assigned to the corresponding vulnerability. However, the authors found that this assumption does not necessarily hold, and thus the accuracy of compromise probabilities estimated by these methods may be impaired. Therefore, this paper proposes the exploit time probability (ETP)-model to calculate well-founded exploit probabilities on the basis of empirical data on vulnerabilities and exploits. The model uses Weibull distributions to approximate the probability distribution of the time between the publication of a vulnerability to the National Vulnerability Database (NVD) and its exploitation. Finally, by applying the ETP-model to a test network, the model is shown to be able to provide reasonable exploit probabilities and be a fundamental technique to improve the accuracy of existing BAG analysis methods.
A Comparison Analysis of Constraint-Handling Techniques on Rule Selection Problem in Credit Risk Assessment: An Industrial View

The rule selection problem, aiming at selecting a subset of rule candidates, is important in credit risk assessment. In reality, we hope these selected rules can identify as many high-risk users as possible while fulfilling the business constraint simultaneously. In such cases, the rule selection problem can be seen as a typical constrained combinatorial optimization problem and can be solved by evolutionary algorithms (EAs). However, how to deal with the constraint dramatically affects the performance of our EAs since the feasible and infeasible solutions both affect the convergence and direction of the EAs. This paper conducts a comparative study to explore the performance of six constraint-handling techniques on the rule selection problem. Experimental results indicate the importance of the way we handle the constraints: 1) e-Constrained Method finds the most high-risk users and Constrained-Domination Principle obtains the most diversified solutions. 2) dominance-based techniques outperform penalty function-based techniques in our real-world applications.
A Natural Language-enabled Virtual Assistant for Human-Robot Interaction in Industrial Environments

This paper introduces a natural language-enabled virtual assistant (VA), called Max, developed to enhance human-robot interaction (HRI) with industrial robots. Regardless of the numerous natural language interfaces already available for commercial use and social robots, most VAs remain tightly bound to a specific robotic system. Besides, they lack a natural and efficient human-robot communication protocol to advance the user experience and the required robustness for use on the industrial floor. Therefore, the proposed framework is designed based on three key elements. A Client-Server style architecture that provides a centralised solution for managing and controlling various types of robots deployed on the shop floor. A communication protocol inspired by human-human conversation strategies, i.e., lexical-semantic strategy and general diversion strategy, is used to guide Max's response generation. These conversation strategies are embedded in Max's architecture to improve the engagement of the operators during the execution of industrial tasks. Finally, the state-of-the-art pre-trained model, Bidirectional Encoder Representations from Transformers (BERT), is fine-tuned to support a highly accurate prediction of requested intents from the operator and robot services. Multiple experiments were conducted for validating Max's performance in a real industrial environment.
A Crawler-Based Vulnerability Detection Method for Cross-Site Scripting Attacks

Cross-site scripting attacks, as a means of attack against Web applications, are widely used in phishing, information theft and other fields by unscrupulous people because of their wide targeting and hidden implementation methods. Nevertheless, cross-site scripting vulnerability detection is still in its infancy, with plenty of challenges not yet fully explored. In this paper, we propose Crawler-based Cross Site Scripting Detector, a tool based on crawler technology that can effectively detect stored Cross Site Scripting vulnerabilities and reflected Cross Site Scripting vulnerabilities. Subsequently, in order to verify the effectiveness of the tool, we experim ented this tool with existing tools such as XSSer and Burp Suite by selecting 100 vulnerable websites for the tool's efficiency, false alarm rate and underreporting rate. The results show that our tool can effectively detect Cross Site Scripting vulnerabilities.
Application of Blockchain in Trusted Data Provenance

Data provenance is a basic requirement for trusted data management in the era of big data. Before the emergence of blockchain technology, data provenance mainly used a centralized database to store data records, log records, etc., to facilitate subsequent audit tracking, but database has the security problem of data tampering. After the emergence of blockchain technology, researchers have conducted a lot of research on trusted data provenance in the Internet of Things (IoT), product supply chain, medical health and other fields based on the characteristics of blockchain decentralization, tamper resistance, and traceability, etc., to ensure the security and trustworthiness of data provenance results. This paper will sort out the research on the use of blockchain technology for data provenance in different fields in recent years, and analyze the similarities and differences between existing blockchain based data provenance methods as a whole. Finally, the possible challenges in data provenance based on blockchain technology are pointed out and the possible solutions are given.
An Empirical Study towards Characterizing Neural Machine Translation Testing Methods

Due to the rapid development of deep neural networks, in recent years, machine translation software has been widely adopted in people's daily lives, such as communicating with foreigners or understanding political news from the neighbouring countries, and it is embedded in daily applications such as Twitter and WeChat. The neural machine translation (NMT) model is the core of machine translation software, and it is very challenging to test it as a deep neural network model due to the Inexplicability of neural networks and the complexity of model output. In this paper, we introduce three latest machine translation testing methods and provide a preliminary analysis of their effects.
SemirFL: Boosting Fault Localization via Combining Semantic Information and Information Retrieval

Automated fault localization aims to reduce software maintenance's workload during software development's evolution. Applying different features extracted from bug reports and source files can help locate faults. However, these approaches consider programming languages as natural when measuring similarity features, considering only precise term matching and ignoring deep semantic similarity features. Furthermore, existing bug localization approaches need to utilize the structural information extracted from source files, where program languages have unique structural features compared to natural languages. In this paper, we proposed SemirFL, a model combining both Convolutional Neural Network(CNN) and revised Vector Space Model(rVSM), which is feeded with four metadata features (bug-fixing recency, bug-fixing frequency, collaborative filtering score, and class name similarity). SemirFL has been studied on four open-source projects. The experimental results show that SemirFL can significantly outperform the existing representative techniques in locating faults in the buggy source files.
DeepBoundary: A Coverage Testing Method of Deep Learning Software based on Decision Boundary Representation

With the increasing application of Deep Learning (DL) Software in safety-critical fields such as autonomous driving, we need adequate testing to ensure software quality. Observing the decision-making behavior of a Deep Neural Network (DNN) is an essential step in DL software testing. Taking Guiding Deep Learning System Testing Using Surprise Adequacy (SADL) as an example, it uses the independent neuron activation values in the DNN to represent the decision-making behavior. However, the behavior of the DNN needs to be jointly determined by the continuous outputs of all neurons. As a result, the coverage value of SADL constant volatility and lack of stability. To mitigate this problem, we propose a coverage testing method based on the decision boundary representation, DeepBoundary, for the decision-making behavior of DL software. Unlike SADL, DeepBoundary generates decision boundary data to represent the decision behavior of the DNN, which makes the testing results more stable. On this basis, we calculate the kernel density between the testing data and the decision boundary data. It measures the position of the testing data in the decision space and the distance from the decision boundary. Finally, as an adequacy indicator, we calculate the decision boundary density coverage (DBC) of the entire testing set. The experiment on the dataset MNIST and two DL software shows that DeepBoundary can generate actual decision boundary data. The average confidence error in the DNNs output layer is only 4.20E-05. Compared with SADL, DeepBoundary has a stronger correlation with the defect detection ratio, which can more accurately represent testing adequacy.
Attribute-Based Non-Interactive Privacy-Protected Crowd-Sourcing Software Testing Engineer Selection Mechanism

The central management platforms in traditional crowd-sourced software testing networks are easy to become the target of attack by the enemies, and when the central management platform is attacked, it is easy to cause a single point of failure and bring the whole system down. In addition, when selecting software testing engineers for crowd-sourcing software testing tasks, most mechanisms select engineers based on their credits. This approach is too one-sided and fails to achieve fine-grained engineer selection. In order to solve above problems, we have proposed a non-interactive and fine-grained crowd-sourcing software testing engineer selection mechanism. In our mechanism, the task publisher can select engineers according to their attributes. Next, we have given an implementation of our non-interactive software testing engineer selection mechanism by applying QAP. The privacy of task publisher and engineers is protected. The security proof is also given. Simulation results show that our mechanism is feasible.
Bug Patterns in Probabilistic Programming Systems

Probabilistic programming systems allow developers to model random phenomena and perform reasoning about the model efficiently. As the number of probabilistic programming systems is growing significantly and are used more and more widely, the reliability of such systems is becoming very important. It is crucial to analyze real bugs of existing similar systems in order to develop efficient bug detection tools for probabilistic programming systems. This paper conducts an empirical study investigating bugs and their features on PyMC3, a real probabilistic programming system. Among 271 closed bugs, we identified 20 bugs that are unique to probabilistic programming languages and extracted eight bug patterns from these bugs. The result showed that many of the bugs were caused by types. We also propose some possible methods for automatically detecting these bug patterns. It is expected that this will contribute to the development of bug detection tools by capturing the characteristics of bugs in actual probabilistic programs in the future.
Code Generation Method based on Structured Tree Input and AST Decoder Attention Augmentation

Automatic code generation based on natural language input is important to research in the field of software engineering. In the past, it was mostly a seq2seq structure and used the RNN model. Input and output are regarded as simple sequences, and syntactic structure information in source information is often ignored. This paper proposes a code generation method Tx(Tree-Tree). It uses structured trees to replace simple word sequences so that the model can better learn the syntactic and semantic information in the source information. Therefore, it can alleviate the long dependency problem caused by too long source information. At the same time, the enhanced attention mechanism is adopted in the decoder to distinguish the influence of different historical actions on the current predicted action. The model is validated on three datasets: DJANGO, CONALA, and ATIS. Compared with some typical models, Tx(Tree-Tree) improves both accuracy and BLEU.
IEMT: Inequality-Based Metamorphic Testing for Autonomous Driving Models

In the last ten years, the development of deep learning has promoted the progress of autonomous driving. Several major manufacturers, including Google, Tesla, Baidu, Audi, etc., are building and actively testing self-driving cars. However, the safety of autonomous driving still raises concerns. Recent research has used metamorphic testing to evaluate the robustness of autonomous driving models, but metamorphic relations defined during the test are basically based on equality, and there are very few inequality-based metamorphic relations. Our goal is to provide more inequality-based metamorphic relations to efficiently detect autonomous driving model violations. IEMT proposes additional inequality-based metamorphic relations and compares the robustness of autonomous driving models based on different neural network models. The experimental results show that the metamorphic relations we proposed can detect inconsistent behaviors of the driving model quite efficiently.