Associate Professor Yuqing Lin

Software Product Line Engineering (SPLE) is a software development paradigm to improve systematic software reuse. SPLE is intended to develop a set of similar software systems which share great commonalities within a particular application domain. There are two key assets underpin Software Product Line (SPL) development: feature model and reference architecture. To deal with complex crosscutting behaviors in SPL and also manage the impact of Non-Functional Requirements (NFRs), we propose an aspect-oriented framework in this paper. The proposed framework is able to improve the modeling of interrelationships between design factors and representation of the variabilities in product families. We introduce a small case study to illustrate our approach at the end.

Software Product Line Engineering (SPLE) is a systematic software reuse approach that developing a set of similar software products as a family. All the visible characters of the products in a product family are represented as features and their relationships are modelled in a feature model. During application engineering, desired features are selected from the feature model in a configuration process based on the requirements. In this process, the quality of final product should be considered as early as possible which requires identifying and ranking associated features' contributions to related quality attributes before configuring member products. In this paper, we propose a ranking approach to address the issues in current qualitybased feature ranking approaches, we also include a case study to illustrate our approach at the end.

Software Product Line Engineering (SPLE) is a emerging software reuse paradigm. SPLE focuses on systematic software reuse from requirement engineering to product derivation throughout the software development life-cycle. Feature model is one of the most important reusable assets which represents all design considerations of a software product line. Feature model will be used in the product configuration process to produce a software. The product configuration is a decision-making process, where all kinds of relationships among configurable features will be considered to select the desired features for the product. To improve the efficiency and quality of product configuration, we are proposing a new approach which aims at identifying a small set of key features. The product configuration should always start from this set of features since, based on the feature dependencies, the decisions made on these features will imply decisions on the rest of the features of the product line, thus reduce the features visited in the configuration process. We have also conducted some experiments to demonstrate how the proposed approach works and evaluate the efficiency of the approach.

Li, Shiu, Chan and Chang [On the spectral radius of graphs with connectivity at most k, J. Math. Chem., 46 (2009), 340-346] studied the spectral radius of graphs of order n with ¿(G) = k and showed that among those graphs, the maximum spectral radius is obtained uniquely at , which is the graph obtained by joining k edges from k vertices of K n - 1 to an isolated vertex. In this paper, we study the spectral radius of graphs of order n with ¿(G) = k and minimum degree d(G) = k. We show that among those graphs, the maximum spectral radius is obtained uniquely at K k + (Kd-k+1 ¿ Kn-d-1). © 2013 Springer-Verlag.

A system's fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network's fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing.

Airlines use strategies to build and keep profitable, loyal customers through customer relationship management (CRM). However, as customer needs change, CRM systems must also change. With the Internet of Things (IoT) offering new ways to improve how customers experience services, airlines are combining IoT with their CRM systems. The connections airlines have with partners, airports, hotels, and banks can help meet these changing customer needs. However, past studies have not fully looked into how IoT-enhanced CRM helps make customers more satisfied and loyal or how airlines' connections with others play a part. Therefore, this study looks into how IoT-enhanced CRM is improving customer satisfaction and loyalty in airlines. It also examines how airlines' connections with others can support the relationship between IoT-enhanced CRM and customer satisfaction and loyalty. The study suggests a model and makes suggestions about the importance of IoT-enhanced CRM in making customers more satisfied and loyal. It also outlines how to test these suggestions and suggests directions for future research.

The concept of the weighted Mostar invariant is a mathematical tool used in chemical graph theory to study the stability of chemical compounds. Several recent studies have explored the weighted Mostar invariant of various chemical structures, including hydrocarbons, alcohols, and other organic compounds. One of the key advantages of the weighted Mostar invariant is that it can be easily computed for large and complex chemical structures, making it a valuable tool for studying the stability of a wide range of chemical compounds. This notion has been utilized to build novel approaches for forecasting chemical compound stability, such as machine learning algorithms. The focus of the paper is to demonstrate the weighted Mostar indices of three specific nanostructures: silicon dioxide (SIO2, poly-methyl methacrylate network (PMMA(s)), and melem chains (MC(h)). The authors seek to provide the findings of their investigation of these nanostructures using the weighted Mostar invariant.

The proliferation of fake news across languages and domains on social media platforms poses a significant societal threat. Current automatic detection methods for low-resource languages (e.g., Swahili, Indonesian and other low-resource languages) face limitations due to two factors: sequential length restrictions in pre-trained language models (PLMs) like multilingual bidirectional encoder representation from transformers (mBERT), and the presence of noisy training data. This work proposes a novel and efficient multilingual fake news detection (MFND) approach that addresses these challenges. Our solution leverages a hybrid extractive and abstractive summarization strategy to extract only the most relevant content from news articles. This significantly reduces data length while preserving crucial information for fake news classification. The pre-processed data is then fed into mBERT for classification. Extensive evaluations on a publicly available multilingual dataset demonstrate the superiority of our approach compared to state-of-the-art (SOTA) methods. Our analysis, both quantitative and qualitative, highlights the strengths of this method, achieving new performance benchmarks and emphasizing the impact of content condensation on model accuracy and efficiency. This framework paves the way for faster, more accurate MFND, fostering more robust information ecosystems.

The rise of social media platforms has led to a proliferation of false information in various forms. Identifying malicious entities on these platforms is challenging due to the complexities of natural language and the sheer volume of textual data. Compounding this difficulty is the ability of these entities to deliberately modify their writing style to make false information appear trustworthy. In this study, we propose a neural-based framework that leverages the hierarchical structure of input text to detect both fake news content and fake news spreaders. Our approach utilizes enhanced Hierarchical Convolutional Attention Networks (eHCAN), which incorporates both style-based and sentiment-based features to enhance model performance. Our results show that eHCAN outperforms several strong baseline methods, highlighting the effectiveness of integrating deep learning (DL) with stylistic features. Additionally, the framework uses attention weights to identify the most critical words and sentences, providing a clear explanation for the model's predictions. eHCAN not only demonstrates exceptional performance but also offers robust evidence to support its predictions.

The rise of social media as a source of news consumption has led to the spread of fake news, posing serious consequences for both individuals and society. The detection and prevention of fake news are essential, and previous research has shown that incorporating news content along with its associated headlines and user comments can improve detection performance. However, the semantic relationships between these elements have not been fully explored. This paper proposes a novel approach that models the relationships between news bodies and associated headlines/user comments using deep learning techniques, such as fine-tuned Bidirectional Encoder Representations from Transformers (BERT) and cross-level cross-modality attention sub-networks. In our proposed model, we utilize two different configurations of BERT: pool-based representation, which provides a representation of the entire document, and sequence representation, which represents each token within the document (i.e., at the word and text levels). The approach also encodes user-posting behavioural features and fuses the output of these components to detect fake news using a classification layer. Our experiments on benchmark datasets demonstrate the superiority of the proposed method over existing state-of-the-art (SOTA) approaches, highlighting the importance of utilizing semantic relationships for improved fake news detection (FND). These findings have significant implications for combating the spread of fake news and protecting society from its negative effects.

Directed cycles in digraphs are useful in embedding linear arrays and rings, and are suitable for designing simple algorithm with low communication costs in parallel computer systems, thus the existence of directed cycles on digraphs has been largely investigated. Let n, k = 2 be integers. Bermond et al. [Journal of Graph Theory 4(3) (1980) 337-341] proved that if the size of a strong digraph D with order n is at least (n-k+2/2) + k - 1, then the girth of D is no more than k. Consequently, when D is a 4-free strong digraph with order n, which means that every directed cycle in D has length at least 5, then the maximum size of D is (n-2/2) + 2. In this paper, we mainly give the structural characterizations for all 4-free strong digraphs of order n whose arc number exactly is (n-2/2) + 2.

Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, most of these tools only measure the similarity between assignment submissions, and do not actually identify which are suspicious of plagiarism. This work presents a semi-automatic approach that enables the indication of suspicious assignment submissions by analysing source code similarity scores among the submissions. The proposed approach seeks the consensus of multiple source code plagiarism detection tools in order to identify program pairs that are consistently evaluated with high similarity. A case study is presented to demonstrate the use of the proposed approach. The results of this case study indicate that it can accurately identify assignment submissions that are suspicious of plagiarism.

A vertex cut S of a connected graph G is a subset of vertices of G whose deletion makes G disconnected. A super vertex cut S of a connected graph G is a subset of vertices of G whose deletion makes G disconnected and there is no isolated vertex in each component of G - S. The super-connectivity of graph G is the size of the minimum super vertex cut of G. Let KG(n, k) be the Kneser graph whose vertices are the k-subsets of {1, ¿, n}, where k is the number of labels of each vertex in G. We have shown in this paper that the conjecture from [G.B. Ekinci and J.B. Gauci, Discuss. Math. Graph Theory 39 (2019), 5¿11] on the super-connectivity of the Kneser graph KG(n, k) is true when k = 3.

Efforts have been dedicated by researchers in the field of natural language processing (NLP) to detecting and combating fake news using an assortment of machine learning (ML) and deep learning (DL) techniques. In this paper, a review of the existing studies is conducted to understand and curtail the dissemination of fake news. Specifically, we conducted a benchmark study using a wide range of (1) classical ML algorithms such as logistic regression (LR), support vector machines (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), XGBoost (XGB) and an ensemble learning method of such algorithms, (2) advanced ML algorithms such as convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRU), CNN-BiLSTM, CNN-BiGRU and a hybrid approach of such techniques and (3) DL transformer-based models such as BERT (Formula presented.) and RoBERTa (Formula presented.). The experiments are carried out using different pretrained word embedding methods across four well-known real-world fake news datasets¿LIAR, PolitiFact, GossipCop and COVID-19¿to examine the performance of different techniques across various datasets. Furthermore, a comparison is made between context-independent embedding methods (e.g., GloVe) and the effectiveness of BERT (Formula presented.) ¿contextualised representations in detecting fake news. Compared with the state of the art's results across the used datasets, we achieve better results by solely relying on news text. We hope this study can provide useful insights for researchers working on fake news detection.

Given a simple graph G, ap-restricted edge cut is a subset of edges of G whose removal disconnects G, and such that the number of vertices in each component of the resulting graph is at least p. The p-restricted edge connectivity is denoted by ¿p, which is the minimum cardinality over all p-restricted edge cuts. If a p-restricted edge cut (also called a ¿p-cut) exists, then the graph is called p-restricted edge connected, or, for short, ¿p-connected. Obviously, for any ¿p-cut F, G - F has exactly two components, and each component has at least p vertices. If the deletion of any ¿p-cut results in at least one component containing exactly p vertices in the resulting graph, then the graph is called super-¿p.Inthispaper,we examine the p-restricted edge connectivity of the bipartite Kneser graph H(n, k) whenn = 3k + 1 and show that the graph is super-¿p for p = 5.

Let G be a graph with vertex set V and let b> a be two positive integers. We say that G has all [a,¿b]-factors if G has an h-factor for every h: V¿ N such that a= h(v) = b for every v¿ V and ¿v¿Vh(v)=0(mod2). A spanning subgraph F of G is called an (a,¿b)-parity factor, if dF(v) = a= b (mod 2) and a= dF(v) = b for all v¿ V. In this paper, we have developed sufficient conditions for the existence of all [a,¿b]-factors and (a,¿b)-parity factors of G in terms of the independence number and connectivity of G. This work extended an earlier result of Nishimura (J Graph Theory 13: 63¿69, 1989). Furthermore, we show that these results are best possible in some cases.

We survey known results on enhanced power graphs of finite groups. Open problems, questions and suggestions for future work are also included.

Context: Extracting conceptual models, e.g., entity relationship model or Business Process model, from software requirement document is an essential task in the software development life cycle. Business process model presents a clear picture of required system's functionality. Operations in business process model together with the data entity consumed, help the software developers to understand the database design and operations to be implemented. Researchers have been aiming at automatic extraction of these artefacts from the requirement document. Objective: In this paper, we present an automated approach to extract the entity relationship and business process models from requirements, which are possibly in different formats such as general requirements, use case specification and user stories. Our approach is based on the efficient natural language processing techniques. Method: It is an iterative approach of Models Extraction from the Requirements (iMER). iMER has multiple iterations where each iteration is to address a sub-problem. In the first iteration, iMER extracts the data entities and attributes. Second iteration is to find the relationships between data entities, while extracting cardinalities is in the third step. Business process model is generated in the fourth iteration, containing the external (actors') and internal (system's) operations. Evaluation: To evaluate the performance and accuracy of iMER, experiments are conducted on various formats of the requirement documents. Additionally, we have also evaluated our approaches using the requirement documents which been modified by shuffling the sentences and by merging with other requirements. Comparative study is also performed. The preliminary results show a noticeable improvement. Conclusion: The iMER is an efficient automated iterative approach that is able to extract the conceptual models from the various formats of requirements.

The number of perfect matchings in a benzenoid system increases when its size grows. This raises a question on the lower bound of the number of perfect matchings in benzenoid systems. In this paper, we show that this lower bound is 9. Further- more, we construct an infinite family of benzenoid system whose number of perfect matchings reaches the lower bound.

Source code plagiarism is a common occurrence in undergraduate computer science education. In order to identify such cases, many source code plagiarism detection tools have been proposed. A source code plagiarism detection tool evaluates pairs of assignment submissions to detect indications of plagiarism. However, a plagiarising student will commonly apply plagiarism-hiding modifications to source code in an attempt to evade detection. Subsequently, prior work has implied that currently available source code plagiarism detection tools are not robust to the application of pervasive plagiarism-hiding modifications. In this article, 11 source code plagiarism detection tools are evaluated for robustness against plagiarism-hiding modifications. The tools are evaluated with data sets of simulated undergraduate plagiarism, constructed with source code modifications representative of undergraduate students. The results of the performed evaluations indicate that currently available source code plagiarism detection tools are not robust against modifications which apply fine-grained transformations to the source code structure. Of the evaluated tools, JPlag and Plaggie demonstrates the greatest robustness to different types of plagiarism-hiding modifications. However, the results also indicate that graph-based tools, specifically those that compare programs as program dependence graphs, show potentially greater robustness to pervasive plagiarism-hiding modifications.

The multiset dimension was introduced by Rinovia Simanjuntak et al. as a variation of metric dimension. In this problem, the representation of a vertex v with respect to a resolving set W is expressed as a multiset of distances between v and all vertices in W, including their multiplicities. The multiset dimension is defined to be the minimum cardinality of the resolving set. Clearly, this is at least the metric dimension of a graph. In this paper, we study the properties of the multiset dimension of graphs.

Software product line engineering (SPLE) is a paradigm to promote systematic software reuse. A Feature Model (FM) is a common means to illustrate the commonality and variability of software products in a family. In most existing FM extraction approaches, keywords in the requirement document or certain types of system behavior or external events are considered features. The resulting FM is a combination of user activities and system actions (SAs), making it hard to understand. In this paper, we present an automatic approach to generate a product line FM from multiple requirement documents. We consider user activity and SAs separately in our approach and focus on the expected behaviors of the software system, together with the data being processed. The resulting FM clearly illustrates the expected functionalities of the software system and their variability in the product line. We also compared our approach with existing techniques by processing the same textual documents, and noted improvements in our results.

Motivated by the connection with the genus of the corresponding link and its application on DNA polyhedral links, in this paper, we introduce a parameter smax(G), which is the maximum number of circles of states of the link diagram D(G) corresponding to a plane (positive) graph G. We show that smax(G) does not depend on the embedding of G and if G is a 4-edge-connected plane graph then smax(G) is equal to the number of faces of G, which cover the results of S. Y. Liu and H. P. Zhang as special cases.

Diagnosability is a significant metric to measure the reliability of multiprocessor systems. In 2012, a new measure for fault tolerance of the system was proposed by Peng et al. This measure is called the g-good-neighbor diagnosability that restrains every fault-free node to contain at least g fault-free neighbors. The Cayley graph CKn generated by the complete graph Kn has many good properties as other Cayley graphs. In this paper, we show that the connectivity of CKn is [Formula presented], the 1-good-neighbor connectivity of CKn is n2-n-2 and the 1-good-neighbor diagnosability of CKn under the PMC model is n2-n-1 for n=4 and under the MM* model is n2-n-1 for n=5.

The anti-forcing number of a connected graph G is the smallest number of edges such that the remaining graph obtained by deleting these edges has a unique perfect matching. In this paper, we show that the anti-forcing number of every fullerene has at least four. We give a procedure to construct all fullerenes whose anti-forcing numbers achieve the lower bound four. Furthermore, we show that, for every even n = 20 (n 6= 22; 26), there exists a fullerene with n vertices that has the anti-forcing number four, and the fullerene with 26 vertices has the anti-forcing number five.

A (k, g)-cage is a graph that has the least number of vertices among all k-regular graphs with girth g. It has been conjectured (Fu et al. in J. Graph Theory, 24:187-191, 1997) that all (k, g)-cages are k-connected for every k = 3. A k-connected graph G is called superconnected if every k-cutset S is the neighborhood of some vertex. Moreover, if G-S has precisely two components, then G is called tightly superconnected. In this paper, we prove that every (4, g)-cage is tightly superconnected when g = 11 is odd. © 2011 Springer.

The proliferation of fake news on social media platforms poses significant challenges to society and individuals, leading to negative impacts. As the tactics employed by purveyors of fake news continue to evolve, there is an urgent need for automatic fake news detection (FND) to mitigate its adverse social consequences. Machine learning (ML) and deep learning (DL) techniques have emerged as promising approaches for characterising and identifying fake news content. This paper presents an extensive review of previous studies aiming to understand and combat the dissemination of fake news. The review begins by exploring the definitions of fake news proposed in the literature and delves into related terms and psychological and scientific theories that shed light on why people believe and disseminate fake news. Subsequently, advanced ML and DL techniques for FND are dicussed in detail, focusing on three main feature categories: content-based, context-based, and hybrid-based features. Additionally, the review summarises the characteristics of fake news, commonly used datasets, and the methodologies employed in existing studies. Furthermore, the review identifies the challenges current FND studies encounter and highlights areas that require further investigation in future research. By offering a comprehensive overview of the field, this survey aims to serve as a guide for researchers working on FND, providing valuable insights for developing effective FND mechanisms in the era of technological advancements.