Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple-Entry Testing of Android Applications by Constructing Activity Launching Contexts

Existing GUI testing approaches of Android apps usually test apps from a single entry. In this way, the marginal activities far away from the default entry are difficult to be covered. The marginal activities may fail to be launched due to requiring a great number of activity transitions or involving complex user operations, leading to uneven coverage on activity components. Besides, since the test space of GUI programs is infinite, it is difficult to test activities under complete launching contexts using single-entry testing approaches. In this paper, we address these issues by constructing activity launching contexts and proposing a multiple-entry testing framework. We perform an inter-procedural, flow-, context- and path-sensitive analysis to build activity launching models and generate complete launching contexts. By activity exposing and static analysis, we could launch activities directly under various contexts without performing long event sequence on GUI. Besides, to achieve an in-depth exploration, we design an adaptive exploration framework which supports the multiple-entry exploration and dynamically assigns weights to entries in each turn. Our approach is implemented in a tool called Fax, with an activity launching strategy Faxla and an exploration strategy Faxex. The experiments on 20 real-world apps show that Faxla can cover 96.4% and successfully launch 60.6% activities, based on which Faxex further achieves a relatively 19.7% improvement on method coverage compared with the most popular tool Monkey. Our tool also behaves well in revealing hidden bugs. Fax can trigger over seven hundred unique crashes, including 180 Errors and 539 Warnings, which is significantly higher than those of other tools. Among the 46 bugs reported to developers on Github, 33 have been fixed up to now.
The Preliminary Study on the Willingness of Middle-Aged Women of Taiwan Metropolis to Use Social VR and Their Potential Influencing Factors In The Future

Middle-aged women living in the metropolitan area of Taiwan are prone to loneliness in their life style and social contact. Social virtual reality (VR) application service (Social VR) can solve the social barriers in long-distance communication between people, and provide richer information links and emotional exchanges. Therefore, social VR has great developmental potential in providing rich social life and improving loneliness of middle-aged women in Taiwan in the future. Nevertheless, there is no relevant research and in-depth exploration on this group's acceptance and preference of social VR at this stage. Therefore, this study conducts a questionnaire interview with 50 middle-aged women (living in Taipei) aged 50-59 of Taiwan metropolitan form , aiming at exploring their acceptance of social VR and their future development and application in design from the perspective of social life style. The result shows that middle-aged women who have medical needs, often socialize, make good use of communication software and are interested in recollecting applications have positive intentions and attitudes towards social VR, while the ones who like mobile phone communication and static activities tend to be more conservative. Therefore, this study finds that the acceptance of social VR by middle-aged women in Taiwan's metropolitan form is obviously influenced by their personal social life style. This finding will be transformed into references for the development of social VR application design and service content, so as to improve the applicability of social VR in solving woman's loneliness in the future.
Dual-wide band plasmonic filter based on nanocomposite media

Abstract. A dual, wide band pass metal–insulator–metal plasmonic filter based on nanocomposite media in the near-infrared band, 0.75 to 2.5 μm wavelength range, is proposed. Simulation results show that the pass bands can be adjusted to proper ranges by changing the size of the device and the relative permittivity of the dielectric region. Due to the importance of small structures in optical integration, we focused on tuning the transmitted bands by changing the dielectric relative permittivity. Realization of any desired relative permittivity for the dielectric region may be difficult using general dielectrics. In this way, the dielectric region is designed based on nanocomposite media, which no previous studies have focused on. Dielectric region is realized by a polymeric medium, poly-methyl-methacrylate, that consists of random distribution of guest spherical silver nanoparticles. The proper permittivity and consequently, the desired pass bands are obtained by changing the fill fraction of guest nanoparticles.
Improving Bug Detection and Fixing via Code Representation Learning

The software quality and reliability have been proved to be important during the program development. There are many existing studies trying to help improve it on bug detection and automated program repair processes. However, each of them has its own limitation and the overall performance still have some improvement space. In this paper, we proposed a deep learning framework to improve the software quality and reliability on these two detectfix processes. We used advanced code modeling and AI models to have some improvements on the state-of-the-art approaches. The evaluation results show that our approach can have a relative improvement up to 206% in terms of F-1 score when comparing with baselines on bug detection and can have a relative improvement up to 19.8 times on the correct bug-fixing amount when comparing with baselines on automated program repair. These results can prove that our framework can have an outstanding performance on improving software quality and reliability in bug detection and automated program repair processes.
Covid-19: Anti-TNF drug adalimumab to be trialled for patients in the community

A new trial launched at the University of Oxford will investigate whether the anti-tumour necrosis factor (TNF) drug adalimumab is effective for treating covid-19 patients in the community, including in care homes. The AVID-CC trial will enrol up to 750 adult patients from community care settings throughout the UK, who will be randomly allocated to receive either adalimumab plus the standard of care, or just standard of care—as determined by the patient’s doctor. The trial will test two dose levels of adalimumab—two …
Self-Efficacy, Work Engagement, and Job Satisfaction Among Teaching Assistants in Hong Kong’s Inclusive Education

The number of teaching assistants (TAs) working in mainstream schools has soared in recent years as students with special educational needs (SEN) are integrated into regular classrooms. However, research on TAs is rare. This study investigated whether and how work engagement mediates the relationship between self-efficacy and job satisfaction among 292 Chinese TAs working in Hong Kong mainstream schools. Survey data were analyzed using partial least squares structural equation modeling (PLS-SEM). Supported by the social cognitive theory of self-efficacy and self-determination theory of work engagement, our results showed that TAs’ self-efficacy is positively related to their job satisfaction through the mediation of work engagement. Implications for an appropriate focus on enhancing TAs’ self-efficacy through classroom experiences and training courses are discussed.
Species Removal Effects on Movements of Sigmodon hispidus and Reithrodontomys fulvescens

The effects of interspecific interactions on movements of hispid cotton rats (Sigmodon hispidus) and fulvous harvest mice (Reithrodontomys fulvescens) were examined by experimental removal of species. For both Sigmodon and Reithrodontomys, the average distance moved by individuals (AvD,) was greater for males than females. The average distance moved by the population within trap periods (AvD,) was negatively correlated with Sigmodon density only in a field with low resource abundance whereas the AvDp for Reithrodontomys was negatively correlated with density in all fields. Species removal did not affect AvDp of either species. Seasonal differences in AvDp for R. fulvescens suggested an intraspecific effect on movement. Reithrodontomys used the vertical component of the habitat to a significantly greater extent when Sigmodon was present. There was no difference between experimental and control fields in vertical habitat structure, suggesting an interspecific effect of Sigmodon upon movement of Reithrodontomys. Variation in movement may result from differences in available habitat for Sigmodon, but not for Reithrodontomys.
The Sustainability of Quality in Free and Open Source Software

We learned from the history of software that great software are the ones who manage to sustain their quality. Free and open source software (FOSS) has become a serious software supply channel. However, trust on FOSS products is still an issue. Quality is a trait that enhances trust. In my study, I investigate the following question: how do FOSS communities sustain their software qualityƒ I argue that human and social factors contribute to the sustainability of quality in FOSS communities. Amongst these factors are: the motivation of participants, robust governance style for the software change process, and the exercise of good practices in the pull requests evaluation process.
Evaluating Menu Techniques for Handheld AR with a Smartphone & Mid-Air Pen

Adding a mid-air pen to Handheld Augmented Reality creates a new kind of bimanual interaction for which many fundamental interaction design questions have not been answered yet. In particular, menus are an essential component in most visual interfaces, but it is unclear how to best interact with them in this setting: using the pen in mid-air or on a surface, using the touchscreen, or by moving the smartphone itself. We compared basic menus for these methods by analyzing success rates, selection times, device movement, and subjective ratings. Our results indicate that interacting with a mid-air menu using the pen, and operating a menu with the hand holding the smartphone, are sufficiently competitive to the current standard of two-handed touchscreen interaction, so that interaction designers can freely choose among them based on the interaction context of their application.
Adaptive hop-by-hop cone vector-based forwarding protocol for underwater wireless sensor networks

In the recent past, a significant increase has been observed in the use of underwater wireless sensor networks for aquatic applications. However, underwater wireless sensor networks face several challenges including large propagation delays, high mobility, limited bandwidth, three-dimensional deployments, expensive manufacturing, and energy constraints. It is crucial for underwater wireless sensor networks to mitigate all these limitations primarily caused by the harsh underwater environment. To address some of the pertinent challenges, adaptive hop-by-hop cone vector-based forwarding routing protocol is proposed in this article which is based on the adaptive hop-by-hop vector-based forwarding. The novelty of adaptive hop-by-hop cone vector-based forwarding includes increasing the transmission reliability in sparse sensor regions by changing the base angle of the cone according to the network structure. The number of duplicate packets and end-to-end delay is also reduced because of the reduced base angle and a smart selection criterion for the potential forwarder node. The proposed routing protocol adaptively tunes the height and opening of the cone based on the network structure to effectively improve the performance of the network. Conclusively, this approach significantly reduces energy tax, end-to-end delay, and packet delivery ratio.
An Automated Framework For Gaming Platform To Test Multiple Games

Game testing is a necessary but challenging task for gaming platforms. Current game testing practice requires significant manual effort. In this paper, we proposed an automated game testing framework combining adversarial inverse reinforcement learning algorithm with evolutionary multi-objective optimization. This framework aims to help gaming platform to assure market-wide game qualities as the framework is suitable to test different games with minimum manual customization for each game.
Pharmacological Activities of Coumarin Compounds in Licorice: A Review

Licorice is a traditional medicine commonly used in China and many other countries. Over the last 50 years, the structure and pharmacological effects of coumarin compounds in licorice have been investigated. However, a comprehensive review of the literature summarizing current trends is currently lacking. Thus, the aim of the present review is to provide an up-to-date summary of the scientific literature regarding the pharmacological effects of coumarin compounds in licorice, thereby laying the foundation for further research and optimal utilization of licorice. We retrieved 111 articles on the coumarin components of licorice and their potential pharmacological effects, based on titles, keywords, and abstracts from databases (including PubMed and Web of Science). Glycycoumarin, isoglycycoumarin, licoarylcoumarin, licopyranocoumarin, glycyrin, isotrifoliol, glycyrol, and glycyrurol have been investigated for their anticancer, hepatoprotective, antispasmodic, immunosuppressive, anti-inflammatory, and antibacterial properties, and use as therapeutic agents in metabolic syndrome, thereby demonstrating their potential for clinical applications. Future research should further explore the pharmacological mechanisms of action of coumarin compounds, including their antibacterial activities. Investigations into the pharmacological activities of different glycycoumarin isomers might open new research frontiers.
Machine Translation Testing via Pathological Invariance

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. However, machine translation software could return incorrect translations because of the complexity of the underlying network. To address this problem, we introduce a novel methodology called PaInv for validating machine translation software. Our key insight is that sentences of different meanings should not have the same translation (i.e., pathological invariance). Specifically, PaInv generates syntactically similar but semantically different sentences by replacing one word in the sentence and filter out unsuitable sentences based on both syntactic and semantic information. We have applied PaInv to Google Translate using 200 English sentences as input with three language settings: English→Hindi, English→Chinese, and English→German. PaInv can accurately find 331 pathological invariants in total, revealing more than 100 translation errors.
The Impact of New Generation Management Approaches on the Firm Performance: The Moderating Role of Strategic Human Resource Management Applications

The main purpose of this study is to research how and to what degree a company’s performance is affected by the new generation management approaches through strategic human resource applications. Within this framework, of the new generation management approaches, organizational ambidexterity, learning organization, and innovation capability have been included as independent variables, and of the strategic human resource applications, with the moderation of talent management and the recruitment and selection process, the effects on firm performance have been tested empirically. The study was conducted in 2018 by evaluating 387 of the questionnaires collected from the firm owners and middle and upper management of randomly selected large and mid-sized businesses in Istanbul, Ankara, Bursa, and Izmir. The collected data were tested using SPSS and, within the scope of structural equation modeling, using SmartPLS. At the conclusion of the research, of the new generation management approaches, it became evident that innovation capabilities have positive effects on the firm performance and that the learning organization also has positive effects on the firm performance by the effect of the moderating variables. Furthermore, a striking weakness was found in the relationship between the performance of a firm and innovation ambidexterity. It was also found that talent management plays no moderating role in the effects of new generation management approaches on the firm performance.
Complete Genome Sequence of Sulfurospirillum Strain ACSTCE, a Tetrachloroethene-Respiring Anaerobe Isolated from Contaminated Soil

Here, we report the complete genome sequence of the tetrachloroethene-to-trichloroethene dechlorinator Sulfurospirillum sp. strain ACSTCE. The genome consists of a 38.05-kb circular plasmid and a 2.69-Mb circular chromosome, which encodes 3 identical reductive dehalogenases with 91.47% amino acid identity to the PceA of Sulfurospirillum multivorans strain DSM 12446. ABSTRACT Here, we report the complete genome sequence of the tetrachloroethene-to-trichloroethene dechlorinator Sulfurospirillum sp. strain ACSTCE. The genome consists of a 38.05-kb circular plasmid and a 2.69-Mb circular chromosome, which encodes 3 identical reductive dehalogenases with 91.47% amino acid identity to the PceA of Sulfurospirillum multivorans strain DSM 12446.
Finding Client-side Business Flow Tampering Vulnerabilities

The sheer complexity of web applications leaves open a large attack surface of business logic. Particularly, in some scenarios, developers have to expose a portion of the logic to the client-side in order to coordinate multiple parties (e.g. merchants, client users, and third-party payment services) involved in a business process. However, such client-side code can be tampered with on the fly, leading to business logic perturbations and financial loss. Although developers become familiar with concepts that the client should never be trusted, given the size and the complexity of the client-side code that may be even incorporated from third parties, it is extremely challenging to understand and pinpoint the vulnerability. To this end, we investigate client-side business flow tampering vulnerabilities and develop a dynamic analysis based approach to automatically identifying such vulnerabilities. We evaluate our technique on 200 popular real-world websites. With negligible overhead, we have successfully identified 27 unique vulnerabilities on 23 websites, such as New York Times, HBO, and YouTube, where an adversary can interrupt business logic to bypass paywalls, disable adblocker detection, earn reward points illicitly, etc.
DLFix: Context-based Code Transformation Learning for Automated Program Repair

Automated Program Repair (APR) is very useful in helping developers in the process of software development and maintenance. Despite recent advances in deep learning (DL), the DL-based APR approaches still have limitations in learning bug-fixing code changes and the context of the surrounding source code of the bug-fixing code changes. These limitations lead to incorrect fixing locations or fixes. In this paper, we introduce DLFix, a two-tier DL model that treats APR as code transformation learning from the prior bug fixes and the surrounding code contexts of the fixes. The first layer is a tree-based RNN model that learns the contexts of bug fixes and its result is used as an additional weighting input for the second layer designed to learn the bug-fixing code transformations. We conducted several experiments to evaluate DLFix in two benchmarks: Defect4J and Bugs.jar, and a newly built bug datasets with a total of +20K real-world bugs in eight projects. We compared DLFix against a total of 13 state-of-the-art pattern-based APR tools. Our results show that DLFix can auto-fix more bugs than 11 of them, and is comparable and complementary to the top two pattern-based APR tools in which there are 7 and 11 unique bugs that they cannot detect, respectively, but we can. Importantly, DLFix is fully automated and data-driven, and does not require hard-coding of bug-fixing patterns as in those tools. We compared DLFix against 4 state-of-the-art deep learning based APR models. DLFix is able to fix 2.5 times more bugs than the best performing baseline.
Towards Better Technical Debt Detection with NLP and Machine Learning Methods

Technical debt (TD) is an economical term used to depict non-optimal choices made in the software development process. It occurs usually when developers take shortcuts instead of following agreed upon development practices, and unchecked growth of technical debt can start to incur negative effects for software development processes.Technical debt detection and management is mainly done manually, and this is both slow and costly way of detecting technical debt. Automatic detection would solve this issue, but even state-of-the-art tools of today do not accurately detect the appearance of technical debt. Therefore, increasing the accuracy of automatic classification is of high importance, so that we could eliminate significant portion from the costs relating to technical debt detection.This research aims to solve the problem in detection accuracy by bringing in together static code analysis and natural language processing. This combination of techniques will allow more accurate detection of technical debt, when compared to them being used separately from each other. Research also aims to discover themes and topics from written developer messages that can be linked to technical debt. These can help us to understand technical debt from developers’ viewpoint. Finally, we will build an open-source tool/plugin that can be used to accurately detect technical debt using both static analysis and natural language processing methods.
Identification of Cultural Influences on Requirements Engineering Activities

Requirements Engineering (RE) involves critical activities to ensure the accurate elicitation and documentation of clients’ requirements. RE is a socio-technical activity and requires intensive communication with several clients. RE activities might be considerably influenced by individuals’ cultural background because culture has a deep impact on the way in which people communicate. However, there has been limited exploration of this issue. We present a framework that identifies and analyses cultural influences on RE activities. To build the framework, we employed Hofstede’s cultural model and a mixed-methods design comprising two case studies involving two cultures: Saudi Arabia and Australia. The evaluation highlighted that the framework provides high accuracy to identify cultural influences in other cultures as well.
Securing UnSafe Rust Programs with XRust

Rust is a promising systems programming language that embraces both high-level memory safety and low-level resource manipulation. However, the dark side of Rust, unsafe Rust, leaves a large security hole as it bypasses the Rust type system in order to support low-level operations. Recently, several real-world memory corruption vulnerabilities have been discovered in Rust's standard libraries. We present XRust, a new technique that mitigates the security threat of unsafe Rust by ensuring the integrity of data flow from unsafe Rust code to safe Rust code. The cornerstone of XRust is a novel heap allocator that isolates the memory of unsafe Rust from that accessed only in safe Rust, and prevents any cross-region memory corruption. Our design of XRust supports both single- and multi-threaded Rust programs. Our extensive experiments on real-world Rust applications and standard libraries show that XRust is both highly efficient and effective in practice.