Miracle-dz/UniFAR-FTUs huggingface dataset:
ARSeek: Identifying API Resource using Code and Discussion on Stack Overflow

It is not a trivial problem to collect API-relevant examples, usages, and mentions on venues such as Stack Overflow. It requires efforts to correctly recognize whether the discussion refers to the API method that developers/tools are searching for. The content of the Stack Overflow thread, which consists of both text paragraphs describing the involvement of the API method in the discussion and the code snippets containing the API invocation, may refer to the given API method. Leveraging this observation, we develop ARSeek, a context-specific algorithm to capture the semantic and syntactic information of the paragraphs and code snippets in a discussion. ARSeek combines a syntactic word-based score with a score from a predictive model fine-tuned from CodeBERT. In terms of $F_{1}-{score}$, ARSeek achieves an average score of 0.8709 and beats the state-of-the-art approach by 14%.
GA-Based Optimization of Generative Adversarial Networks on Stock Price Prediction

The combinational model of Generative Adversarial Networks (GANs) and Long Short-Term Memory (LSTM) has a good performance for analyzing and predicting time series data. The model performance, however, depends on the hyper-parameters which are defined in advance by the machine learning designers. In this study, Genetic Algorithm (GA) is applied for the design of hyper-parameters in the combinational model of GANs and LSTM in the stock price prediction. GA defines the candidate solutions as chromosomes. The GA chromosome includes the window size of training data and the hyper-parameters of the LSTM employed in the Generator of GANs. The experimental results show that the optimized model has a better performance than the original model.
Multi-Intention-Aware Configuration Selection for Performance Tuning

Automatic configuration tuning helps users who intend to improve software performance. However, the auto-tuners are limited by the huge configuration search space. More importantly, they fo-cus only on performance improvement while being unaware of other important user intentions (e.g., reliability, security). To re-duce the search space, researchers mainly focus on pre-selecting performance-related parameters which requires a heavy stage of dynamically running under different configurations to build per-formance models. Given that other important user intentions are not paid attention to, we focus on guiding users in pre-selecting performance-related parameters in general while warning about side-effects on non-performance intentions. We find that the con-figuration document often, if it does not always, contains rich in-formation about the parameters' relationship with diverse user intentions, but documents might also be long and domain-specific. In this paper, we first conduct a comprehensive study on 13 representative software containing 7,349 configuration parame-ters, and derive six types of ways in which configuration parame-ters may affect non-performance intentions. Guided by this study, we design SAFETUNE, a multi-intention-aware method that pre-selects important performance-related parameters and warns about their side-effects on non-performance intentions. Evaluation on target software shows that SAFETUNE correctly identifies 22–26 performance-related parameters that are missed by state-of-the-art tools but have significant performance impact (up to 14.7x). Furthermore, we illustrate eight representative cases to show that SAFETUNE can effectively prevent real-world and critical side-effects on other user intentions.
A new SOCMINT framework for Threat Intelligence Identification

Social Media platforms have changed the way of communication between people. These platforms allow users and organizations to communicate with each other, sharing their ideas, interests, and knowledge, becoming a part of their daily routine. On the other hand, social media platforms represent good channels to exploit for perpetrating various kinds of crimes and crimes illegal activities in several criminal areas, including cyber-crimes and terrorism. The goal of this work is to present a novel framework for Threat Intelligence that adopts Machine Learning and Artificial Intelligence techniques for OSINT (Open–source intelligence) investigation can extract actionable intelligence for threats.
Refactoring Debt: Myth or Reality? An Exploratory Study on the Relationship Between Technical Debt and Refactoring

To meet project timelines or budget constraints, developers intentionally deviate from writing optimal code to feasible code in what is known as incurring Technical Debt (TD). Furthermore, as part of planning their correction, developers document these deficiencies as comments in the code (i.e., self-admitted technical debt or SATD). As a means of improving source code quality, developers often apply a series of refactoring operations to their codebase. In this study, we explore developers repaying this debt through refactoring operations by examining occurrences of SATD removal in the code of 76 open-source Java systems. Our findings show that TD payment usually occurs with refactoring activities and developers refactor their code to remove TD for specific reasons. We envision our findings supporting vendors in providing tools to better support developers in the automatic repayment of technical debt.
XAI4FL: Enhancing Spectrum-Based Fault Localization with Explainable Artificial Intelligence

Manually finding the program unit (e.g., class, method, or statement) responsible for a fault is tedious and time-consuming. To mitigate this problem, many fault localization techniques have been proposed. A popular family of such techniques is spectrum-based fault localization (SBFL), which takes program execution traces (spectra) of failed and passed test cases as input and applies a ranking formula to compute a suspiciousness score for each program unit. However, most existing SBFL techniques fail to consider two facts: 1) not all failed test cases contribute equally to a considered fault(s), and 2) program units collaboratively contribute to the failure/pass of each test case in different ways. In this study, we propose a novel idea that first models the SBFL task as a classification problem of predicting whether a test case will fail or pass based on spectra information on program units. We subsequently apply eXplainable Artificial Intelligence (XAI) techniques to infer the local importance of each program unit to the prediction of each executed test case. Applying XAI to the failed test case, we retrieve information about which program statements within the test case that are considered the most important (i.e., have the biggest effect in making the test case failed). Such a design can automatically learn the unique contributions of failed test cases to the suspiciousness of a program unit by learning the different and collaborative contributions of program units to each test case's executed result. As far as we know, this is the first XAI-supported SBFL approach. We evaluate the new approach on the Defects4J benchmark dataset. We compare the performance of our approach against five popular SBFL techniques: DStar, Tarantula, Barinel, Ochiai, and OP. We measure their performance using the Top-K and EXAM scores. In particular, we focus on the result of the Top-1, which importance has been highlighted in automated program repair domain, where the proposed methods often assume perfect fault localization (i.e., the fault must be found at the first rank of the suspiciousness list). Our results show that our approach, named XAI4FL, has a statistically significant and substantially better performance in terms of Top-1 than the SBFL approaches. We also compare our approach with a simpler approach to get feature importance in a tree-based model (i.e., using the Mean Decrease in Impurity method). Our results show that XAI4FL statistically significantly outperforms the MDI method in Top-K and EXAM score. Our results and findings highlight that the utilization of XAI for fault localization can improve the overall results of fault localization techniques.
Multimodal Medical Image Fusion Based on Hybrid Bilateral Filter and Contrast Adjustment Model

The purpose of multimodal medical image fusion (MMIF) is to integrate images of different modes with different details into a result image with rich information, which is convenient for doctors to accurately diagnose and treat the diseased tissues of patients. Encouraged by this aim, this paper proposes a novel method based on a hybrid bilateral filter (HBF) and contrast adjustment model. First, HBF is applied to decomposing the input images to obtain the structure layer and energy layer, which have the property of detail preservation. Then, two fusion rules based on structure tensor operator (STO) and contrast adjustment model are designed, which greatly improve the image performance from the perspective of balancing between information retention and enhancing contrast. Experiments demonstrate that the proposed method has superior performance compared with the state-of-the-art fusion methods.
Estimating Developers' Cognitive Load at a Fine-grained Level Using Eye-tracking Measures

The comprehension of source code is a task inherent to many software development activities. Code change, code review and debugging are examples of these activities that depend heavily on developers' understanding of the source code. This ability is threatened when developers' cognitive load approaches the limits of their working memory, which in turn affects their understanding and makes them more prone to errors. Measures capturing humans' behavior and changes in their physiological state have been proposed in a number of studies to investigate developers' cognitive load. However, the majority of the existing approaches operate at a coarse-grained task level estimating the difficulty of the source code as a whole. Hence, they cannot be used to pinpoint the mentally demanding parts of it. We address this limitation in this paper through a non-intrusive approach based on eye-tracking. We collect users' behavioral and physiological features while they are engaging with source code and train a set of machine learning models to estimate the mentally demanding parts of code. The evaluation of our models returns F1, recall, accuracy and precision scores up to 85.65%, 84.25%, 86.24% and 88.61%, respectively, when estimating the mental demanding fragments of code. Our approach enables a fine-grained analysis of cognitive load and allows identifying the parts challenging the comprehension of source code. Such an approach provides the means to test new hypotheses addressing the characteristics of specific parts within the source code and paves the road for novel techniques for code review and adaptive e-learning.
Relevant pitch features selection for voice disorders families classification

Identification of voice disorders plays a major role in our life nowadays. In this context, voice analysis can be used, as a complementary technique with other traditional invasive methods, such as laryngoscopy, to identify voice disorders. This paper explores a set of acoustic features extracted from the vocal folds signals namely the pitch, to this purpose. First, 49 pitch-based features are extracted from speech recordings. Then, relevant ones are selected according to their discriminatory power between normal and pathological voice classes. To do so, different feature selection techniques have been used and compared. Afterward, KNN, SVM, Random Tree and Naive Bayes classifiers are applied to decide on the existence of a voice disorder or not and which pathological class is detected. The experimental results denote the usefulness of retained features and despite, the simplicity of classification technique (KNN for instance), the best performance in term of accuracy rate reached 91.5%.
Usability Heuristics for Early Primary Children: A Case Study in Sri Lanka

Tablet-mediated learning becomes popular among kindergarten children with the encroachment of technology . Most educational app developers target pre-school children in their app market. Therefore developers increasingly create educational apps that target this age group. The study of usability aspects of such applications is important due to the lack of cognitive and physical development of children in this age group. However, as per the literature most of the current applications for pre-school children are not successful due to a lack of consideration of usability. Hence, preschool children face many usability issues when using these applications. Due to limited cognitive and physical development, children of early primary education find it difficult to use those applications. There are many usability frameworks developed but most of them have been developed targeting adult users. So, there is no standard framework for early primary children. Hence, this research aims to propose a set of usability heuristics that can be used by the designers of tablet-mediated applications for early primary children.
LineVul: A Transformer-based Line-Level Vulnerability Prediction

Software vulnerabilities are prevalent in software systems, causing a variety of problems including deadlock, information loss, or system failures. Thus, early predictions of software vulnerabilities are critically important in safety-critical software systems. Various ML/DL-based approaches have been proposed to predict vulnerabilities at the file/function/method level. Recently, IVDetect (a graph-based neural network) is proposed to predict vulnerabilities at the function level. Yet, the IVDetect approach is still inaccurate and coarse-grained. In this paper, we propose LINEVUL, a Transformer-based line-level vulnerability prediction approach in order to address several limitations of the state-of-the-art IVDetect approach. Through an empirical evaluation of a large-scale real-world dataset with 188k+ C/C++ functions, we show that LINEVUL achieves (1) 160%-379% higher F1-measure for function-level predictions; (2) 12%-25% higher Top-10 Accuracy for line-level predictions; and (3) 29%-53% less Effort@20%Recall than the baseline approaches, highlighting the significant advancement of LINEVUL towards more accurate and more cost-effective line-level vulnerability predictions. Our additional analysis also shows that our LINEVUL is also very accurate (75%-100%) for predicting vulnerable functions affected by the Top-25 most dangerous CWEs, highlighting the potential impact of our LINEVUL in real-world usage scenarios.
Search-based Diverse Sampling from Real-world Software Product Lines

Real-world software product lines (SPLs) often encompass enormous valid configurations that are impossible to enumerate. To understand properties of the space formed by all valid configurations, a feasible way is to select a small and valid sample set. Even though a number of sampling strategies have been proposed, they either fail to produce diverse samples with respect to the number of selected features (an important property to characterize behaviors of configurations), or achieve diverse sampling but with limited scalability (the handleable configuration space size is limited to 1013). To resolve this dilemma, we propose a scalable diverse sampling strategy, which uses a distance metric in combination with the novelty search algorithm to produce diverse samples in an incremental way. The distance metric is carefully designed to measure similarities between configurations, and further diversity of a sample set. The novelty search incrementally improves diversity of samples through the search for novel configurations. We evaluate our sampling algorithm on 39 real-world SPLs. It is able to generate the required number of samples for all the SPLs, including those which cannot be counted by sharpSAT, a state-of-the-art model counting solver. Moreover, it performs better than or at least competitively to state-of-the-art samplers regarding diversity of the sample set. Experimental results suggest that only the proposed sampler (among all the tested ones) achieves scalable diverse sampling.
MOBDroid2: An Improved Feature Selection Method for Detecting Malicious Applications in a Mobile Cloud Computing Environment

This paper presents an ensemble machine learning (ML) based system for the detection of malicious applications in the Mobile Cloud Computing (MCC) Environment. The proposed system named MOBDroid2 applies a static feature analysis approach using the permissions and intents demanded by Android apps. The experiments conducted showed that the proposed system was able to effectively detect malicious and benign apps, achieving a classification accuracy rate of 98.16%, a precision rate of 98.95%, a recall rate of 98.20%, and a false alarm rate of 1.85%. The results obtained in our experiment compared well with other results reported in extant literature.
Atomic Layer Deposition Optimization Using Convolutional Neural Networks

Atomic layer deposition (ALD) is a chemical engineering process used to coat surfaces with a thin film. It is a versatile process able to deposit a wide range of films using different chemical reagents. When developing novel ALD processes, a technician must determine the dosing time of each reagent. To accelerate this development process, we trained convolutional neural networks to predict the reagent saturation times of novel ALD reactions given the reagent dosing times and film growth rates of example reactions. We generated two kinds of models. Single reaction models made predictions based on a single example ALD reaction. Multiple reaction models made predictions based on ten example reactions using the same reagents with different dosing times.
A Novel Approach to Detect Fake News Using eXtreme Gradient Boosting

The usage of social media has expanded in recent years, allowing them to get news from around the world at any time. This in turn, is questioning the authenticity of the news that is being spread both globally and locally. Fake news such as misinformation, gossips is widely disseminated on social media having a negative impact on society and lives of the people. As a result, much study is being is carried out in order to detect them. The data can be clustered into smaller groups based on the type of news using a few learning approaches. A novel method has been proposed for prediction of the authenticity of the news of the LIAR dataset [1] using Logistic Regression and a boosting algorithm eXtreme Gradient Boosting (XGBoost) for efficacy, computational pace and performance of the model. This method detects fake news by analyzing the semantic and syntactic connections between sentences. Various graphs (like heat maps, bar charts) are plotted to show the distribution of the authenticity of news and also to compare the predicted result with the actual one. The proposed strategy addresses the effects of the hoax's global spread. People are hungry for information to defend themselves and others in a community where humans are confronting large-scale risks from harms. Some key traits such as Sentimental features, Content-based features, Frequency features, and Hybrid features (combinations of two or more features) are incorporated for early prediction of fake news spread via social media. The liar dataset is used to train the method and tested for accurate results. The experimental accuracy is found out to be 98%.
Effects of Social Distancing Intention, Affective Risk Perception, and Cabin Fever Syndrome on Perceived Value of E-learning : Type of submission: Late Breaking Paper / Most relevant symposium: CSCI-ISED

Universities have switched from traditional face-to-face lectures to e-learning to prevent the spread of COVID-19. E-learning provides the health value of prevention of infection along with the purpose of education. In this context, this study aims to identify factors affecting perceived value of e-learning. It assumes that social distancing intention, affective risk perception, and cabin fever syndrome have a significant effect on the perceived value of e-learning. A comparison between groups between males and females was also performed. A survey was conducted on university students and samples were analyzed by using the PLS technique. The findings showed that social distancing intention determines perceived value. The analysis results revealed that affective risk perception has a significant positive effect on perceived value and social distancing intention. In addition, cabin fever syndrome harms the perceived value. There was a significant difference between male and female groups in the influence of social distancing intention on perceived value. Moreover, there was a significant difference between men and women in the path coefficient of affective risk perception on perceived value. Academic contributions and practical implications are discussed.
Automatic Loop Invariant Generation for Data Dependence Analysis

Parallelization of programs relies on sound and precise analysis of data dependences in the code, specifically, when dealing with loops. State-of-art tools are based on dynamic profiling and static analysis. They tend to over- and, occasionally, to under-approximate dependences. The former misses parallelization opportunities, the latter can change the behavior of the parallelized program. In this paper we present a sound and highly precise approach to generate data dependences based on deductive verification. The central technique is to infer a specific form of loop invariant tailored to express dependences. To achieve full automation, we adapt predicate abstraction in a suitable manner. To retain as much precision as possible, we generalized logic-based symbolic execution to compute abstract dependence predicates. We implemented our approach for Java on top of a deductive verification tool. The evaluation shows that our approach can generate highly precise data dependences for representative code taken from HPC applications.
A deep learning-enhanced botnet detection system based on Android manifest text mining

Android botnets remain a significant threat to mobile and IoT systems and networks as they continue to infect millions of devices worldwide. Therefore, there is a need to develop more effective solutions to tackle their spread. Hence, in this paper we propose a system for detecting Android botnets through automated text mining of the manifest files obtained from apps. The proposed method utilizes NLP techniques to extract features from the manifest files and a deep learning-based classification model is used to detect botnet applications. The classification model is implemented using CNN and a traditional machine learning classifier such as SVM, Random Forest or KNN. We performed experiments to evaluate the proposed system with 3858 Android applications consisting of 1929 botnet and 1929 benign samples. The results showed the best overall performance with the CNN-SVM hybrid model which had an average accuracy of 96.9% thus outperforming the singular machine learning classifiers.
Towards Language Models for AI Mental Health Assistant Design

Advances in Artificial Intelligence (AI) Language Models (LMs) and their new applications are continuously reported. LMs, respond to plain text that is readily human interpretable. Based on these human-like responses, hopes are created for achieving human-level performance for various language tasks soon. This paper discusses challenges in applying current LMs to design an AI Mental Health Assistant. The results of experiments are encouraging but show that significant research and development efforts are necessary to reach the practical usefulness of AI. We discuss that chaining multiple LMs might be needed to filter or post-process the results. Additionally, the models themselves might need to go through enhanced training with a more significant emphasis on empathy, ethics, and moral standards, especially in the very sensitive mental health area.