With the rapid advancements in medicine, biology, and information technology, their deep integration has given rise to the emerging field of bioinformatics. In this process, high–throughput technologies such as genomics, transcriptomics, and proteomics have generated massive volumes of biological data. The biological significance of these data heavily relies on bioinformatics software for analysis and processing. Therefore, it is crucial for both scientific research and clinical applications to ensure the quality of bioinformatics software and avoiding errors or hidden defects. However, to date, no dedicated study has systematically analyzed the quality of bioinformatics software. we conduct a comprehensive empirical study that aggregates, synthesizes, and analyzes findings from 167 bioinformatics software projects. Following the Preferred Reporting Items for Systematic Review and Meta–Analysis (PRISMA) protocol, we extract and evaluate quality–related data to answer our research questions (RQs). Our analysis reveals several key findings. The quality of bioinformatics software requires significant improvement, with an average defect density approximately 11.8× higher than that of general-purpose software. Additionally, unlike traditional software domains, a considerable proportion of defects in bioinformatics software are related to annotations. These issues can lead developers to overlook potential security vulnerabilities or make incorrect fixes, thereby increasing the cost and complexity of subsequent code maintenance. Based on these findings, we further discuss the challenges faced by bioinformatics software and propose potential solutions. This paper lays a foundation for further research on software quality in the bioinformatics domain and offers actionable insights for researchers and practitioners alike.

Though Deep Neural Networks(DNNs) have achieved excellent performance in computer vision(CV) tasks such as classification, they are vulnerable to adversarial examples which are generated by adding small-magnitude perturbation to inputs. Recently, different methods have been proposed to produce adversarial examples, most of them are white-box attacks, which assume the adversary knows the internal structure and parameters of the target model which is not practical in the real world. So we proposed using adversarial knowledge distillation to iteratively train a substitute model for the target black model to better learn its high-frequency information processing for giving input and then generate adversarial examples based on the substitute model to attack the target model. According to the experiments, our method can achieve nearly or even a higher attacking success rate than directly attacking the target model in a white-box setting. Besides, most white-box attack methods can achieve black-box attacks using our approach.

Under economic globalization, the exchange between different countries is becoming more frequent. Increasingly more people worldwide start or prepare to learn second (or foreign) languages. In particular, the English language plays a significant role in globalization progress, given that it has been used as a means for international commerce and communication. However, traditional methods for acquiring a second language (e.g., attending tutorial classes) are costly in terms of time, money, and effort to many people. Given the increasing demand for English language learning and traditional learning methods' pitfalls, this paper introduces a virtual-reality-based platform for learning English. The platform provides virtual scenes of daily life, such as hospitals, supermarkets, and schools. Users can instantly communicate with other online users in the virtual world. Besides, aiming to improve users' learning efficiency, every word uttered is translated into English and users' mother language in real-time. This proposed system has five advantages: 1) It is efficient because users can use it to learn a language at any time and space. They can chat with foreigners online whenever they want and wherever they are. 2) Communicating with online users helps practice one's oral English without the need to find a partner in reality. 3) Instant translation facilitates instant learning and acquisition of the phases and vocabularies. 4) The virtual 3D environment simulating real-life scenes creates an enjoyable atmosphere for users to practice their oral English. 5) The system is universal to learners of different languages since it supports English and many other languages.

We study unsupervised multilingual alignment, the problem of finding word-to-word translations between multiple languages without using any parallel data. One popular strategy is to reduce multilingual alignment to the much simplified bilingual setting, by picking one of the input languages as the pivot language that we transit through. However, it is well-known that transiting through a poorly chosen pivot language (such as English) may severely degrade the translation quality, since the assumed transitive relations among all pairs of languages may not be enforced in the training process. Instead of going through a rather arbitrarily chosen pivot language, we propose to use the Wasserstein barycenter as a more informative ``mean'' language: it encapsulates information from all languages and minimizes all pairwise transportation costs. We evaluate our method on standard benchmarks and demonstrate state-of-the-art performances.

With the rapid growing popularity of XML to represent data, how to make good use of XML data in relational databases is worthy of study. Storing XML data as text in relational databases is a traditional strategy which cannot reflect the feature of XML format. In this paper, a mechanism for XML data storage and query in relational databases is proposed. XML data can be stored in relational tables and XQuery expressions can be evaluated as a part of SQL for XML data query. XQuery grammar tree and Query tree model for XML data query in rela-tional databases is presented to gain more efficient performance while querying XML data. Appropriative algorithm for evaluating XPath is also presented in this paper by which XQuery can be evaluated rapidly and efficiency. Finally, experiments invalidate the strategy of XML storage and run the algorithm on real XML datasets to show the efficiency compared with other mechanisms.

This paper presents a real-time system to detect and estimate the pose of human upper body from a monocular video. A novel approach to locate the hands is proposed, which is designed to cope with the complicated situations such as short sleeves, fast motion and occlusion. Human silhouette and skin color blobs are extracted from the frames of the video; then candidate locations of head, hands, and elbows are chosen and evaluated by an inverse kinematics based strategy. Experiments demonstrate the efficacy and robustness of this approach. The algorithm is developed for a camera-based tennis game, in which poses of a player have to be estimated in real time (for avatar animation, action recognition, etc). It can also be applied in other human-computer interaction applications.