Reviewerly/exHarmony huggingface dataset:
User story extraction from natural language for requirements elicitation: Identify software-related information from online news

The user story is a popular artifact in agile software development. Extracting user stories is helpful for process improvement in requirements elicitation, closing limitations such as limited access, and uncovering new and unique domains. Most sources of requirements elicitation are available in natural language form. However, the approach to extracting user stories from natural language is still limited. This study aims to extract user stories from natural language. It includes identifying the aspect of who (stakeholder), aspect of what (stakeholder's wants), and aspect of why (the reason why the aspect of what exists). This study used online news as a case study because information related to stakeholders and their needs is available. Aspects of who, what, and why are obtained using a rule-based approach using part-of-speech (POS) chunking, named entity recognition (NER), dependency parsing, WordNet, and BloomSoft. We found that online news tends to generate requirements with hard-goals or soft-goals types. In identifying aspects of who, we succeeded in increasing the F-score value by combining stakeholder identification methods according to the characteristics of online news. We also found that PUblic REquirements (PURE), domain specificity, and WordNet lexical names can significantly improve the extraction of software-related information in identifying the aspects of what. This study demonstrates that information related to software requirements could arise from non-software-related artifacts such as online news.
Sensitivity pruner: Filter-Level compression algorithm for deep neural networks

As neural networks get deeper for better performance, the demand for deployable models on resource-constrained devices also grows. In this work, we propose eliminating less sensitive filters to compress models. The previous method evaluates neuron importance using the connection matrix gradient in a single shot. To mitigate the sampling bias, we integrate this measure into the previously proposed "pruning while fine-tuning" framework. Besides classification errors, we introduce the difference between the learned and the single-shot strategy as the second loss component with a self-adjustive hyper-parameter that balances the training goal between improving accuracy and pruning more filters. Our Sensitivity Pruner (SP) adapts the unstructured pruning saliency metric to structured pruning tasks and enables the strategy to be derived sequentially to accommodate the updating sparsity. Experimental results demonstrate that SP significantly reduces the computational cost and the pruned models give comparable or better performance on CIFAR10, CIFAR100, and ILSVRC-12 datasets.
Manipulation detection of key populations under information measurement

In today's turbulent international situation, regional conflicts often occur, and the game between major countries has been continuing, which is not only reflected in the military strength, but also the war by means of information technology, especially the turmoil caused by the spread fake news. It is common to forge the statements of influential leaders or entrepreneurs called key populations forgery and spread them on the Internet, which causes bad influence. The number of key populations forgery belongs to a small group in the huge deep forgery number, and the conventional deep forgery detection methods have a great gap in the detection efficiency and performance of small group samples. Therefore, we use the forged information of a large number group to judge the forged information of a small number group, and propose a deep forgery detection method of information construction and comparison under information measurement. Firstly, we propose a sample coverage analysis method for the sample library boundary to explore the difference in the feature space between the small number of key population samples and the large number samples, so as to determine the sample boundary of small number groups from the information level. Secondly, we construct information of key groups and expand the sample scale to meet the data needs of improving the detection accuracy. Finally, we compare the forged features of the large number group with the feature of the expanded small number group, and measure the information coincidence of the two types features by the complexity of feature clusters. When the coincidence degree is high, it is considered that the small number group samples have gone through the forgery process and carried forged information. We have analyzed and tested the current mainstream deep forgery databases and collected a small number of population sample, and compare with some popular methods. The experimental results show that our proposed method achieves the state-of-the-art (SOTA).
A Blockchain-Enabled Framework for Improving the Software Audit Process

Audits are an essential component of every organization, particularly those involving software development. In addition to several testing cycles, software auditing has become an essential software development milestone. Software auditing is a continual activity that enables a business to remain ahead of the curve and predict potential software problems. Audits, whether undertaken in-house or by external auditors, entail a significant amount of time and work. Consistent audits provide financial and economic benefits, as well as legal benefits. The most essential advantage of audits is safeguarding your system from internal and external assaults. Audit logs serve a crucial role in the auditing process; they typically capture all system operations and occurrences. They are used as evidence providers during an inquiry and by auditors to monitor the privacy and security of information and systems. Auditors confirm the accuracy of data pertaining to businesses and their activities. To determine if these acts exceed the limitations established by organizations, governments, and other parties, dependable information is essential. Infractions of such rules or corporate standards may be indicative of fraud, malpractice, risk, or inefficiency. Despite the existence of automated audit tools, audit policy, and audit logs, many audit frauds are reported on a daily basis. To make the audit process transparent and secure, this research proposes a blockchain-enabled framework SSFTA to aid software auditors in conducting a transparent and effective audit process. The proposed framework is evaluated using a case study. The findings demonstrated that the suggested framework makes the auditing process simple and transparent.
Linear Interval Approximation of Sensor Characteristics with Inflection Points

The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions proposed so far to address these challenges were based on linear interval approximations and were developed for resource-constrained microcontroller architectures, i.e., they need buffering of the sensor data and either have a runtime dependency on the segment length or require the sensor inverse response to be analytically known in advance. Our present work proposed a new algorithm for the piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature, maintaining the low fixed computational complexity as well as reduced memory requirements, as demonstrated in a test concerning the linearization of the inverse sensor characteristic of type K thermocouple. As before, our error-minimization approach solved the two problems of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic.
HetReGAT-FC: Heterogeneous Residual Graph Attention Network via Feature Completion

Heterogeneous graph embedding is receiving increasing attention from researchers due to the ubiquity of heterogeneous graphs (HGs). How to effectively handle the problem of missing features in HGs has become a hot research topic in recent years. However, the existing heterogeneous graph neural networks (HGNNs) with feature completion are facing two challenges: (1) Pre-training is required; and (2) Heterogeneous information in the HG is not fully explored. To this end, we propose a Heterogeneous Residual Graph Attention Network via Feature Completion (HetReGAT-FC). Specifically, we first design a Heterogeneous Residual Graph Attention Network (HetReGAT) to learn topological information. Then, the attention mechanism is adopted to complete the missing features. Finally, HetReGAT is used to learn the final node embeddings on the feature completed heterogeneous graph. To prove the effectiveness of this work, we conduct extensive experiments on three real-world datasets and compare it with ten competitive baselines. The results demonstrate that the proposed HetReGAT-FC significantly outperforms state-of-the-art methods. The codes and data of this work are available at https://github.com/ZZY-GraphMiningLab/HetReGAT-FC.
Multi-hop temporal knowledge graph reasoning with temporal path rules guidance

Temporal Knowledge graph (TKG) is a key component of artificial intelligence. In recent years, many large-scale temporal knowledge graphs have been produced and put into practical applications. At present, researchers have proposed many methods to reason facts that do not exist in temporal knowledge graph using the existing information. However, most traditional embedding-based reasoning methods of temporal knowledge graph lack interpretability, cannot get the reasoning paths. Therefore, the multi-hop path reasoning method of knowledge graph has gradually become a hot-spot. However, technologies related to multi-hop path reasoning in temporal knowledge graph are still in their infancy, and existing methods ignore the importance of logical rules in temporal knowledge graph and do not consider the rules between multi-hop paths. To solve this problem, we propose a multi-hop path reasoning method of temporal knowledge graph based on temporal path rules, named TPRG. TPRG further analyzes the multi-hop paths that may be formed in the multi-hop path reasoning task of temporal knowledge graph, deeply studies the logical connection between the multi-hop paths of temporal knowledge graph and the potential rules between these paths. We define a total of fourteen temporal path rules in TPRG, and these rules can be divided into three different lengths and five different types. The qualified rules are extracted from the datasets according to the proposed temporal path rules. Then, the temporal path rule confidence calculation strategy is proposed. Finally, we conduct temporal knowledge graph reasoning through temporal path rules confidence. Experimentally, our model outperforms other state-of-the-art reasoning methods in several aspects over four public temporal knowledge graph datasets.
Predator-Prey Reward Based Q-Learning Coverage Path Planning for Mobile Robot

Coverage Path Planning (CPP in short) is a basic problem for mobile robot when facing a variety of applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -Learning based coverage path planning algorithms are beginning to be explored recently. To overcome the problem of traditional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -Learning of easily falling into local optimum, in this paper, the new-type reward functions originating from Predator-Prey model are introduced into traditional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -Learning based CPP solution, which introduces a comprehensive reward function that incorporates three rewards including Predation Avoidance Reward Function, Smoothness Reward Function and Boundary Reward Function. In addition, the influence of weighting parameters on the total reward function is discussed. Extensive simulation results and practical experiments verify that the proposed Predator-Prey reward based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -Learning Coverage Path Planning (PP- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -Learning based CPP in short) has better performance than traditional BCD and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -Learning based CPP in terms of repetition ratio and turns number.
A deep graph convolutional neural network architecture for graph classification

Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to extract high-level features of nodes. There are two main reasons for this: 1) Overlaying too many graph convolution layers will lead to the problem of over-smoothing. 2) Graph convolution is a kind of localized filter, which is easily affected by local properties. To solve the above problems, we first propose a novel general framework for graph neural networks called Non-local Message Passing (NLMP). Under this framework, very deep graph convolutional networks can be flexibly designed, and the over-smoothing phenomenon can be suppressed very effectively. Second, we propose a new spatial graph convolution layer to extract node multiscale high-level node features. Finally, we design an end-to-end Deep Graph Convolutional Neural Network II (DGCNNII) model for graph classification task, which is up to 32 layers deep. And the effectiveness of our proposed method is demonstrated by quantifying the graph smoothness of each layer and ablation studies. Experiments on benchmark graph classification datasets show that DGCNNII outperforms a large number of shallow graph neural network baseline methods.
Privacy-Preserving Federated Learning in Healthcare

Federated learning (FL) has received great attention in healthcare primarily due to its decentralized, collaborative nature of building a machine learning (ML) model. Over the years, the FL approach has been successfully applied for enhancing privacy preservation in medical ML applications. This study aims to review prevailing applications in healthcare for the future landing FL application. We identified the emerging applications of FL in key healthcare domains, including COVID-19, brain tumor segmentation, mammogram, sleep quality prediction, and smart healthcare system. Finally, we discuss privacy concerns in federated setting and provide current methods to increase the data privacy capabilities of FL.
The possibilities and limits of XAI in education: a socio-technical perspective

Explicable AI in education (XAIED) has been proposed as a way to improve trust and ethical practice in algorithmic education. Based on a critical review of the literature, this paper argues that XAI should be understood as part of a wider socio-technical turn in AI. The socio-technical perspective indicates that explicability is a relative term. Consequently, XAIED mediation strategies developed and implemented across education stakeholder communities using language that is not just ‘explicable’ from an expert or technical standpoint, but explainable and interpretable to a range of stakeholders including learners. The discussion considers the impact of XAIED on several educational stakeholder types in light of the transparency of algorithms and the approach taken to explaination. Problematising the propositions of XAIED shows that XAI is not a full solution to the issues raised by AI, but a beginning and necessary precondition for meaningful discourse about possible futures.
Interactive Policy Shaping for Human-Robot Collaboration with Transparent Matrix Overlays

One important aspect of effective human--robot collaborations is the ability for robots to adapt quickly to the needs of humans. While techniques like deep reinforcement learning have demonstrated success as sophisticated tools for learning robot policies, the fluency of human-robot collaborations is often limited by these policies' inability to integrate changes to a user's preferences for the task. To address these shortcomings, we propose a novel approach that can modify learned policies at execution time via symbolic if-this-then-that rules corresponding to a modular and superimposable set of low-level constraints on the robot's policy. These rules, which we call Transparent Matrix Overlays, function not only as succinct and explainable descriptions of the robot's current strategy but also as an interface by which a human collaborator can easily alter a robot's policy via verbal commands. We demonstrate the efficacy of this approach on a series of proof-of-concept cooking tasks performed in simulation and on a physical robot.
Do You Follow?

An interesting application for social robots is to act as a presenter, for example as a museum guide. In this paper, we present a fully automated system architecture for building adaptive presentations for embodied agents. The presentation is generated from a knowledge graph, which is also used to track the grounding state of information, based on multimodal feedback from the user. We introduce a novel way to use large-scale language models (GPT-3 in our case) to lexicalise arbitrary knowledge graph triples, greatly simplifying the design of this aspect of the system. We also present an evaluation where 43 participants interacted with the system. The results show that users prefer the adaptive system and consider it more human-like and flexible than a static version of the same system, but only partial results are seen in their learning of the facts presented by the robot.
Dynamic Cognitive Diagnosis: An Educational Priors-Enhanced Deep Knowledge Tracing Perspective

To provide personalized support on educational platforms, it is crucial to model the evolution of students' knowledge states. Knowledge tracing is one of the most popular technologies for this purpose, and deep learning-based methods have achieved state-of-the-art performance. Compared to classical models, such as Bayesian knowledge tracing, which track students' knowledge proficiencies, deep learning-based knowledge tracing is usually modeled to predict students' performances on questions, while ignoring the interpretability of students' knowledge states. However, for many practical applications, such as learning resource recommendation, it would be more helpful if we could explicitly track students' abilities or knowledge proficiencies separately from performance prediction. Researchers in psychometric area already designed cognitive diagnosis solutions to quantify the knowledge states of students in static conditions (e.g., examination), where the educational priors (i.e., factors related to students' learning process) were proved beneficial for student modeling. Inspired by this, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic cognitive diagnosis</i> , which integrates the interpretability of educational priors from cognitive diagnosis into deep learning-based knowledge tracing methods. We first discuss and provide evidence of which educational priors can be integrated, including question attributes and interaction function. Then we show the effects of using the educational priors in deep learning-based knowledge tracing from two aspects, i.e., interpretability and accuracy. Through extensive experiments and analyses, we prove that properly chosen priors can enable deep learning-based methods to evaluate students' knowledge states in a manner that is consistent with domain knowledge or human experience. Moreover, educational priors also improve the accuracy of student performance prediction.
Approximate Clustering Ensemble Method for Big Data

Clustering a big distributed dataset of hundred gigabytes or more is a challenging task in distributed computing. A popular method to tackle this problem is to use a random sample of the big dataset to compute an approximate result as an estimation of the true result computed from the entire dataset. In this paper, instead of using a single random sample, we use multiple random samples to compute an ensemble result as the estimation of the true result of the big dataset. We propose a distributed computing framework to compute the ensemble result. In this framework, a big dataset is represented in the RSP data model as random sample data blocks managed in a distributed file system. To compute the ensemble clustering result, a set of RSP data blocks is randomly selected as random samples and clustered independently in parallel on the nodes of a cluster to generate the component clustering results. The component results are transferred to the master node, which computes the ensemble result. Since the random samples are disjoint and traditional consensus functions cannot be used, we propose two new methods to integrate the component clustering results into the final ensemble result. The first method uses component cluster centers to build a graph and the METIS algorithm to cut the graph into subgraphs, from which a set of candidate cluster centers is found. A hierarchical clustering method is then used to generate the final set of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> cluster centers. The second method uses the clustering-by-passing-messages method to generate the final set of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> cluster centers. Finally, the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means algorithm was used to allocate the entire dataset into <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> clusters. Experiments were conducted on both synthetic and real-world datasets. The results show that the new ensemble clustering methods performed better than the comparison methods and that the distributed computing framework is efficient and scalable in clustering big datasets.
The PSyKE Technology for Trustworthy Artificial Intelligence

Transparency is one of the "Ethical Principles in the Context of AI Systems" as described in the Ethics Guidelines for Trustworthy Artificial Intelligence (TAI). It is closely linked to four other principles – respect for human autonomy, prevention of harm, traceability and explainability – and involves numerous ways in which opaqueness can have undesirable impacts, such as discrimination, inequality, segregation, marginalisation, and manipulation. The opaqueness of many AI tools and the inability to understand the underpinning black boxes contradicts these principles as well as prevents people from fully trusting them. In this paper we discuss the PSyKE technology, a platform providing general-purpose support to symbolic knowledge extraction from different sorts of black-box predictors via many extraction algorithms. The extracted knowledge results are easily injectable into existing AI assets making them meet the transparency TAI requirement.