UI automation tests play a crucial role in ensuring the quality of mobile applications. Despite the growing popularity of machine learning techniques to generate these tests, they still face several challenges, such as the mismatch of UI elements. The recent advances in Large Language Models (LLMs) have addressed these issues by leveraging their semantic understanding capabilities. However, a significant gap remains in applying these models to industrial-level app testing, particularly in terms of cost optimization and knowledge limitation. To address this, we introduce CAT to create cost-effective UI automation tests for industry apps by combining machine learning and LLMs with best practices. Given the task description, CAT employs Retrieval Augmented Generation (RAG) to source examples of industrial app usage as the few-shot learning context, assisting LLMs in generating the specific sequence of actions. CAT then employs machine learning techniques, with LLMs serving as a complementary optimizer, to map the target element on the UI screen. Our evaluations on the WeChat testing dataset demonstrate the CAT's performance and cost-effectiveness, achieving 90% UI automation with $0.34 cost, outperforming the state-of-the-art. We have also integrated our approach into the real-world WeChat testing platform, demonstrating its usefulness in detecting 141 bugs and enhancing the developers' testing process.

In traditional non-orthogonal multiple access (NOMA) systems, the secondary user often encounters decoding challenges caused by a lower signal-to-noise ratio (SNR) as it is assigned less power to prevent the primary user from decoding errors. One way to address this issue is by employing semantic communication, which is known for its robustness in low SNR conditions. In this paper, multi-user semantic communication is investigated in a traditional-semantic hybrid NOMA (TSH-NOMA) system, enabling simultaneous transmissions from both the bit user and the semantic user at the same frequency. However, the compatibility between continuous semantic signals and discrete bit signals remains an open problem. Quantization of semantic features has been attempted to address this problem, but it often results in noticeable performance degradation. To tackle this issue, a novel digital semantic constellation design that allows the encoder to generate a semantic constellation similar to typical digital modulation is proposed. This enables the semantic user’s signal to be readily transmitted over the digital channel without affecting the traditional bit user. Simulation results demonstrate that the proposed method allows for the transmission of the semantic user’s signal on the digital channel with negligible performance degradation. Furthermore, the secondary user in the proposed TSH-NOMA system exhibits considerable performance improvement over its counterpart in traditional NOMA, particularly at low-to-medium SNR, without compromising the performance of the primary user.

Semantic communication systems have been proposed for efficient text transmissions in recent years. However, most existing methods mainly focus on word-level recovery, which fails to reflect the semantic fidelity of the model. By leveraging the sentence-level semantic information, a semantic communication system architecture is proposed within the framework of deep learning based joint source-channel coding. Then, in order to evaluate the semantic fidelity of the system, a new metric of Semantic Similarity is proposed, which is more sensitive to semantic differences as compared to existing evaluation metrics, e.g., bilingual evaluation understudy. For guaranteeing the consistency between model training and performance evaluation, the proposed new metric is then incorporated into the objective function, based on which end-to-end performance optimization is performed. Extensive simulation results on European Parliament dataset demonstrate the effectiveness and necessity of the proposed method. Compared with the state-of-the-art, e.g., DeepSC, an improvement of up to 10% is achieved in terms of Semantic Similarity. Furthermore, significant performance gains are achieved by the proposed method in both regimes of low and medium signal-to-noise ratio, as compared to traditional separate source-channel coding methods.

MOTIVATION: Bulked segregant analysis by deep sequencing (BSA-seq) has been widely used for quantitative trait locus (QTL) mapping in recent years. A number of different statistical methods for BSA-seq have been proposed. However, determination of significance threshold, the key point for QTL identification, remains to be a problem that has not been well solved due to the difficulty of multiple testing correction. In addition, estimation of the confidence interval is also a problem to be solved. RESULTS: In this paper, we propose a new statistical method for BSA-seq, named Block Regression Mapping (BRM). BRM is robust to sequencing noise and is applicable to the case of low sequencing depth. Significance threshold can be reasonably determined by taking multiple testing correction into account. Meanwhile, the confidence interval of QTL position can also be estimated. AVAILABILITY AND IMPLEMENTATION: The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Bulked segregant analysis combined with next generation sequencing has proven to be a simple and efficient approach for fast mapping of quantitative trait loci (QTLs). However, how to estimate the proportion of phenotypic variance explained by a QTL (or termed QTL heritability) in such pooled QTL mapping is an unsolved problem. Results: In this paper, we propose a method called PQHE to estimate QTL heritability using pooled sequencing data obtained under different experimental designs. Simulation studies indicated that our method is correct and feasible. Four practical examples from rice and yeast are demonstrated, each representing a different situation. Availability and implementation: The R scripts of our method are open source under GPLv3 license at http://genetics.fafu.edu.cn/PQHE or https://github.com/biotangweiqi/PQHE. The R scripts require the R package rootSolve. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Different from the existing approaches that usually utilize single view information of image sets to recognize persons, multi-view information of image sets is exploited in this paper, where a novel method called Co-Learned Multi-View Spectral Clustering (CMSC) is proposed to recognize faces based on image sets. In order to make sure that a data point under different views is assigned to the same cluster, we propose an objective function that optimizes the approximations of the cluster indicator vectors for each view and meanwhile maximizes the correlations among different views. Instead of using an iterative method, we relax the constraints such that the objective function can be solved immediately. Experiments are conducted to demonstrate the efficiency and accuracy of the proposed CMSC method.

In this paper, we propose a new collaborative reconstruction-based manifold-manifold distance (CRMMD) method for face recognition with image sets, where each gallery and probe sample is a set of face images captured from varying poses, illuminations and expressions. Given each face image set, we first model it as a nonlinear manifold and then the recognition task is converted as a manifold-manifold matching problem. For each manifold, we divide it into several clusters and describe each cluster by using a local model. Then, we use the local models from each gallery manifold to collaboratively reconstruct each local model of the testing manifold and the minimal reconstruction error is used for classification. Experimental results on three widely used face datasets are presented to show the effectiveness of the proposed method.

Recent research in visual data classification often involves image sets and the measurement of dissimilarity between each pair of them. An effective solution is to model each image set using a subspace and compute the distance between these two subspaces as the dissimilarity between the sets. Several subspace similarity measures have been proposed in the literature. However, their relationships have not been well explored and most of them do not fully utilize the different importance of individual bases of each subspace. To consolidate this family of subspace-based measures, we propose a generalized subspace distance (GSD) framework and show that most existing subspace similarity measures can be considered as its special cases. To better utilize the different importance, we further propose a new fractional order weighted subspace distance (FOWSD) method within the GSD framework, by assigning different weights to the bases of each subspace and thus characterizing their different importance in similarity measurement. Experimental results on two image classification tasks including face recognition and object recognition are presented to show the effectiveness of the proposed method.

One of problems with incremental learning approaches is that the quality of learning samples cannot be controlled since they are provided on-line. Current incremental learning algorithms ignore the issue by accepting each sample unconditionally and assimilating it into the existing recognition system. However, improper sample, caused by a partial occlusion, badly illumination condition and unusual pose, deteriorates the overall performance of the recognition system significantly. In order to overcome this issue, in this paper, based on hypothesis and verification paradigm, we propose a criterion for sample selection, and present a novel system for automatically evaluating the qualities of new added samples during the incremental learning procedure. Following this, a subspace is learned by using only the samples that are considered to be good after verification. The high computation cost, incurred by inclusion of sample verification into incremental learning framework, is greatly reduced using relative distance filtering. The experimental results demonstrated the superiority of our approach, in both recognition accuracy and robustness.