Lane Detection Based on Object Segmentation and Piecewise Fitting

A lane detection algorithm for complex environment was proposed. It was concerned on selecting candidate lane region by object segmentation. Then redundancy edges were extracted by Sobel operator. Furthermore, candidate lane markers were obtained by threshold selection from the edges. Finally lane markers were detected by piecewise fitting. The proposed algorithm was simulated in MATLAB. Experiments showed that lane markers could be detected correctly. Piecewise linear transformation in preprocessing has enhanced performance of detection while the environment was dim. And limited region of interest helps to identification lane in an appropriate region, which have the effect of enhancement in the speed of operation. Feature-based method is usually affected by intensity of image. Several characteristics of roads need to be considered in further for detection more precisely. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.324

Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity

Recent breakthroughs in natural language processing (NLP) have permitted the synthesis and comprehension of coherent text in an open-ended way, therefore translating the theoretical algorithms into practical applications. The large language models (LLMs) have significantly impacted businesses such as report summarization software and copywriters. Observations indicate, however, that LLMs may exhibit social prejudice and toxicity, posing ethical and societal dangers of consequences resulting from irresponsibility. Large-scale benchmarks for accountable LLMs should consequently be developed. Although several empirical investigations reveal the existence of a few ethical difficulties in advanced LLMs, there is little systematic examination and user study of the risks and harmful behaviors of current LLM usage. To further educate future efforts on constructing ethical LLMs responsibly, we perform a qualitative research method called ``red teaming'' on OpenAI's ChatGPT\footnote{In this paper, ChatGPT refers to the version released on Dec 15th.} to better understand the practical features of ethical dangers in recent LLMs. We analyze ChatGPT comprehensively from four perspectives: 1) \textit{Bias} 2) \textit{Reliability} 3) \textit{Robustness} 4) \textit{Toxicity}. In accordance with our stated viewpoints, we empirically benchmark ChatGPT on multiple sample datasets. We find that a significant number of ethical risks cannot be addressed by existing benchmarks, and hence illustrate them via additional case studies. In addition, we examine the implications of our findings on AI ethics and harmal behaviors of ChatGPT, as well as future problems and practical design considerations for responsible LLMs. We believe that our findings may give light on future efforts to determine and mitigate the ethical hazards posed by machines in LLM applications.Comment: Technical Repor

Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing

Automated GUI testing is widely used to help ensure the quality of mobile apps. However, many GUIs require appropriate text inputs to proceed to the next page which remains a prominent obstacle for testing coverage. Considering the diversity and semantic requirement of valid inputs (e.g., flight departure, movie name), it is challenging to automate the text input generation. Inspired by the fact that the pre-trained Large Language Model (LLM) has made outstanding progress in text generation, we propose an approach named QTypist based on LLM for intelligently generating semantic input text according to the GUI context. To boost the performance of LLM in the mobile testing scenario, we develop a prompt-based data construction and tuning method which automatically extracts the prompts and answers for model tuning. We evaluate QTypist on 106 apps from Google Play and the result shows that the passing rate of QTypist is 87%, which is 93% higher than the best baseline. We also integrate QTypist with the automated GUI testing tools and it can cover 42% more app activities, 52% more pages, and subsequently help reveal 122% more bugs compared with the raw tool.Comment: Accepted by IEEE/ACM International Conference on Software Engineering 2023 (ICSE 2023

Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI Testing via Functionality-aware Decisions

Automated Graphical User Interface (GUI) testing plays a crucial role in ensuring app quality, especially as mobile applications have become an integral part of our daily lives. Despite the growing popularity of learning-based techniques in automated GUI testing due to their ability to generate human-like interactions, they still suffer from several limitations, such as low testing coverage, inadequate generalization capabilities, and heavy reliance on training data. Inspired by the success of Large Language Models (LLMs) like ChatGPT in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process. Within this framework, we have also introduced a functionality-aware memory prompting mechanism that equips the LLM with the ability to retain testing knowledge of the whole process and conduct long-term, functionality-based reasoning to guide exploration. We evaluate it on 93 apps from Google Play and demonstrate that it outperforms the best baseline by 32% in activity coverage, and detects 31% more bugs at a faster rate. Moreover, GPTDroid identify 53 new bugs on Google Play, of which 35 have been confirmed and fixed.Comment: Accepted by IEEE/ACM International Conference on Software Engineering 2024 (ICSE 2024). arXiv admin note: substantial text overlap with arXiv:2305.0943

Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language Model

Mobile applications have become a ubiquitous part of our daily life, providing users with access to various services and utilities. Text input, as an important interaction channel between users and applications, plays an important role in core functionality such as search queries, authentication, messaging, etc. However, certain special text (e.g., -18 for Font Size) can cause the app to crash, and generating diversified unusual inputs for fully testing the app is highly demanded. Nevertheless, this is also challenging due to the combination of explosion dilemma, high context sensitivity, and complex constraint relations. This paper proposes InputBlaster which leverages the LLM to automatically generate unusual text inputs for mobile app crash detection. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. In detail, InputBlaster leverages LLM to produce the test generators together with the mutation rules serving as the reasoning chain, and utilizes the in-context learning schema to demonstrate the LLM with examples for boosting the performance. InputBlaster is evaluated on 36 text input widgets with cash bugs involving 31 popular Android apps, and results show that it achieves 78% bug detection rate, with 136% higher than the best baseline. Besides, we integrate it with the automated GUI testing tool and detect 37 unseen crashes in real-world apps from Google Play.Comment: Accepted by IEEE/ACM International Conference on Software Engineering 2024 (ICSE 2024

Gastrointestinal protozoa in pet cats from Anhui province: prevalence and molecular characterization

IntroductionTo investigate the prevalence of Tritrichomonas foetus, Pentatrichomonas hominis, Giardia intestinalis, Cryptosporidium, Microsporidium, and Sarcocystis in domestic cats in Anhui Province, China, and their potential role as zoonotic hosts for human infection, a total of 304 fecal samples from two different sources were screened for the presence of related pathogens.MethodsUsing microscopy, along with polymerase chain reaction (PCR) or nested PCR amplification, followed by genotyping through sequence analysis.ResultsThe infection rates of T. foetus, P. hominis, G. intestinalis, Cryptosporidium, Enterocytozoon bieneusi, and Sarcocystis were 5.6%, 0%, 1.7%, 0.7%, 2.6%, and 0%, respectively. The evolutionary relationships and genetic characteristics of G. intestinalis based on the GDH gene, Cryptosporidium based on the SSU rRNA gene, and E. bieneusi based on the ITS sequence were assessed: five cases of G. intestinalis were identified, with four belonging to assemblage F and one to zoonotic assemblage B, two Cryptosporidium cases were identified as Cryptosporidium felis, and all eight E. bieneusi cases were identified as belonging to group 1, with three cases being genotype D, three EbpA, and two EbpC.DiscussionAge, neutering status, and deworming were identified as potential risk factors. Further analysis revealed that diarrhea, as a clinical symptom, could serve as an indicator for pathogen infection. Although the pathogen infection rates detected in this study were relatively low, their zoonotic transmission potential cannot be ignored. Therefore, special attention should be paid, and it is essential to establish targeted prevention plans

MicroRNA-503 inhibits the G1/S transition by downregulating cyclin D3 and E2F3 in hepatocellular carcinoma

Abstract Background Increasing evidence indicates that deregulation of microRNAs (miRNAs) is involved in tumorigenesis. Downregulation of microRNA-503 has been observed in various types of diseases, including cancer. However, the biological function of miR-503 in hepatocellular carcinoma (HCC) is still largely unknown. In this study we aimed to elucidate the prognostic implications of miR-503 in HCC and its pathophysiologic role. Methods Quantitative reverse transcriptase polymerase chain reaction was used to evaluate miR-503 expression in HCC tissues and cell lines. Western blotting was performed to evaluate the expression of the miR-503 target genes. In vivo and in vitro assays were performed to evaluate the function of miR-503 in HCC. Luciferase reporter assay was employed to validate the miR-503 target genes. Results miR-503 was frequently downregulated in HCC cell lines and tissues. Low expression levels of miR-503 were associated with enhanced malignant potential such as portal vein tumor thrombi, histologic grade, TNM stage, AFP level and poor prognosis. Multivariate analysis indicated that miR-503 downregulation was significantly associated with worse overall survival of HCC patients. Functional studies showed miR-503 suppressed the proliferation of HCC cells by induction of G1 phase arrest through Rb-E2F signaling pathways, and thus may function as a tumor suppressor. Further investigation characterized two cell cycle-related molecules, cyclin D3 and E2F3, as the direct miR-503 targets. Conclusion Our data highlight an important role for miR-503 in cell cycle regulation and in the molecular etiology of HCC, and implicate the potential application of miR-503 in prognosis prediction and miRNA-based HCC therapy. </jats:sec

A response to commenter Ke Lan's comment on our paper published in Nature Communications (2023)14:5782 by J. Yan et al

A response to commenter Ke Lan's comment on our paper published in Nature Communications (2023)14:5782 by J. Yan et a