With the rapid development of short videos, multi-modal sentiment analysis (MSA) has gradually become a hot research topic. However, most of the existing MSA research focuses on designing multimodal fusion frameworks by leveraging the complementary and shared information of different modalities, while paying insufficient attention to the in-depth exploration of unimodal features. The noise information hidden in a single modality often interferes with the accuracy of sentiment analysis. To address this issue, we propose an attention-based denoising cross-modal multi-level fusion network. The network includes a modality denoising module, which leverages the attention mechanism and other strategies to perform deep denoising and feature enhancement for single modalities. Subsequently, cross-modal information interaction is achieved using a cross-attention mechanism, and an improved variant of the MLP network, MLP-Fusion, is proposed to perform multi-level intra-modal and inter-modal fusion. Furthermore, a specially designed fusion loss function is introduced to optimize the fusion process. Finally, a feature focus module dynamically adjusts the weights of each modality’s representation to achieve the final joint feature fusion. Our method was tested on two publicly available datasets, CMU-MOSEI and CMU-MOSI, and achieved state-of-the-art performance compared to baseline methods.

BACKGROUND: In an online issue tracking system, e.g., GitHub Issue Tracker, feature requests and the associated comment stream provide valuable crowd-generated knowledge for requirements elicitation. To decide whether a feature request should be accepted or not, stakeholders need to identify the comments for/against the feature and understand the two-sided opinions, which is time- and effort-consuming considering the abundant information embedded in lengthy comment stream per feature request. AIMS: This paper proposes VoteBot for automatically detecting stance (for/against) and summarizing the related opinions on a feature request, which can facilitate the decision making (i.e., voting) of feature requests. To our best knowledge, such an approach is previously unexplored for crowd-based requirements elicitation. METHOD: VoteBot is a relation-aware approach, which incorporates three types of relations among the comments or among the comment sentences to better understand the discussions about feature requests. Specifically, it extracts the reply-to relation among the comments, and incorporates it into a BERT-based classifier for stance detection. It also designs a graph-based ranking algorithm, and incorporates semantic relevance and argumentative relations for stance summarization. RESULTS: The automatic evaluation on 250 feature requests with 6,598 comments from five GitHub projects, and the evaluation with practitioners on five new projects, show the promising results. CONCLUSIONS: VoteBot is effective in stance detection and stance summarization, and potentially useful for understanding feature requests and associated discussions in real-world practice.

Nowadays increasing AI techniques, e.g., reinforcement learning, imitation learning, etc., are applied to solve sequential decision-making problems by modeling them as Markov Decision Process (MDP), and achieve superior performance in areas, such as video games, robotics and autonomous driving etc. The reliability of such models is facing severe challenges especially in some safety-critical areas, where failures would bring intolerable disasters. Existing works testing episodic decision-making models are not workable, since they neglect the nature of sequentiality and interactivity in MDP. While other works testing sequential decision-making models are challenged by low testing efficiency because the interaction of MDP is time-consuming. In this paper, we propose an optimized fuzzing framework SeqDivFuzz which infers the sequence diversity during the MDP interaction process to effectively and efficiently test sequential decision-making models in blackbox settings. It adapts the existing fuzzing framework, including Seed Selection, Seed Mutation, Feedback Analysis and integrating a module of Diversity Inference to accelerate the fuzzing procedure. The module learns historical in-process information to check the diversity of test cases when running up to checkPoint in the course of MDP, and early terminating those non-diverse ones. We conduct experimental evaluation with four models involving three simulation environments. The results reflect that SeqDivFuzz exposes 12.3%~49.1% more crashes during a 12-hour testing procedure in four pairs of models and environments compared with the state-of-the-art fuzzing framework. The idea of in-process terminating can potentially boost other techniques for testing sequential decision-making models.

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://github.com/APandaThief/FastLNGC.