Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs' capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models' chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA.

Charts are widely used for data visualization across various fields, including education, research, and business. Chart Question Answering (CQA) is an emerging task focused on the automatic interpretation and reasoning of data presented in charts. However, chart images are inherently difficult to interpret, and chart-related questions often involve complex logical and numerical reasoning, which hinders the performance of existing models. This paper introduces VProChart, a novel framework designed to address these challenges in CQA by integrating a lightweight Visual Perception Alignment Agent (VPAgent) and a Programmatic Solution Reasoning approach. VPAgent aligns and models chart elements based on principles of human visual perception, enhancing the understanding of chart context. The Programmatic Solution Reasoning approach leverages large language models (LLMs) to transform natural language reasoning questions into structured solution programs, facilitating precise numerical and logical reasoning. Extensive experiments on benchmark datasets such as ChartQA and PlotQA demonstrate that VProChart significantly outperforms existing methods, highlighting its capability in understanding and reasoning with charts.

Charts are high-density visualization carriers for complex data, serving as a crucial medium for information extraction and analysis. Automated chart understanding poses significant challenges to existing multimodal large language models (MLLMs) due to the need for precise and complex visual reasoning. Current step-by-step reasoning models primarily focus on text-based logical reasoning for chart understanding. However, they struggle to refine or correct their reasoning when errors stem from flawed visual understanding, as they lack the ability to leverage multimodal interaction for deeper comprehension. Inspired by human cognitive behavior, we propose ChartSketcher, a multimodal feedback-driven step-by-step reasoning method designed to address these limitations. ChartSketcher is a chart understanding model that employs Sketch-CoT, enabling MLLMs to annotate intermediate reasoning steps directly onto charts using a programmatic sketching library, iteratively feeding these visual annotations back into the reasoning process. This mechanism enables the model to visually ground its reasoning and refine its understanding over multiple steps. We employ a two-stage training strategy: a cold start phase to learn sketch-based reasoning patterns, followed by off-policy reinforcement learning to enhance reflection and generalization. Experiments demonstrate that ChartSketcher achieves promising performance on chart understanding benchmarks and general vision tasks, providing an interactive and interpretable approach to chart comprehension.

In this study, an experiment is conducted to compare the usability of two in-vehicle information systems (IVISs), namely, the newly developed “Calink” and the commercially available “Carlife.” The interface style of Calink is similar to that of smartphones, while that of Carlife is similar to the interface style of traditional IVISs. This study establishes a usability index system based on the International Organization for Standardization’s definition of usability, and each evaluation index has a corresponding subjective or objective measurement method. Five new indexes, namely, angular velocity, velocity, turn angle, distance of finger movements, and valence and arousal value of facial expression, are used in the usability test. Participants are asked to complete three tasks (namely, music, telephone, and navigation) to test the usability of the IVIS interface. The results show that in the case of most of the indexes Calink performs better than Carlife. Finally, the consistency of subjective experience and objective performance is discussed.

BACKGROUND: Reconstructing evolution provides valuable insights into the processes of gene evolution and function. However, while there have been great advances in algorithms and software to reconstruct the history of gene families, these tools do not model the domain shuffling events (domain duplication, insertion, transfer, and deletion) that drive the evolution of multidomain protein families. Protein evolution through domain shuffling events allows for rapid exploration of functions by introducing new combinations of existing folds. This powerful mechanism was key to some significant evolutionary innovations, such as multicellularity and the vertebrate immune system. A method for reconstructing this important evolutionary process is urgently needed. RESULTS: Here, we introduce a novel, event-based framework for studying multidomain evolution by reconciling a domain tree with a gene tree, with additional information provided by the species tree. In the context of this framework, we present the first reconciliation algorithms to infer domain shuffling events, while addressing the challenges inherent in the inference of evolution across three levels of organization. CONCLUSIONS: We apply these methods to the evolution of domains in the Membrane associated Guanylate Kinase family. These case studies reveal a more vivid and detailed evolutionary history than previously provided. Our algorithms have been implemented in software, freely available at http://www.cs.cmu.edu/˜durand/Notung.

Requirements Elicitation (RE) is a critical process in system/software engineering. Its goal is to capture the stakeholders’ expectations, needs and constraints, which can be elicited, analyzed and specified as requirements. Gathering the requirements correctly, clearly and completely in a natural way is a typical challenging problem, because requirements analysts always play key roles in the elicitation process dominantly while stakeholders participate in passively. In this paper, we propose a collective intelligence driven business process oriented requirements acquisition and refining method. Its aim is to reduce the requirements analysts’ dominance and promote stakeholders’ self-expression and self-improvement to elicit requirements clearly and completely. It adopts the group storytelling method to promote the collaboration and communication among stakeholders, utilizes the narrative network model to enhance the associations among story fragments, and introduces dialogue game theory to guide the progressive refining. At the same time, the activity theory is adopted as the description framework to present the method and an application example is introduced. Finally, a pilot experiment is carried out to evaluate its perceived usefulness and perceived ease of use; and the actual quality of the requirements based on the degree of completeness and understandability in comparison with JAD. The results show that the requirements elicited by BPCRAR are more complete and understandable than JAD. In addition, the BPCRAR is perceived usefulness and ease of use in the experiment.

With the pervasiveness of the success cases of SaaS applications, outsourcing the manuscript submission and review system of periodical presses to SaaS providers becomes a new trend. This study proposes a solution for constructing the journal manuscript submission and review system based on level-3 maturity model of SaaS. In this article, the requirements of the system have been elicited, the architecture has been designed, and the key implementation has been presented. By renting the services provided by the system instead of buying or developing, the periodical presses can not only obtain better technical support, but also save costs. In addition, the periodical press can utilize its agglomeration effect based on SaaS platform and subscribe the value-added services such as journal recommendation service to implicit contributors and reviewer recommendation service to periodical presses which will be helpful to promote the quality and impact factor of the journal.

Wikis, as typical well-known knowledge management tools that support collaborative work, are adopted by more and more practitioners and researchers as the basis to develop Distributed Requirements Engineering (DRE) tools. Thus, many wikis which are enhanced specially for supporting various activities in Distributed Requirements Engineering (namely DRE-specific wikis) are developed. The main goal of this study is to discover all the available DRE-specific wikis, gain an insight into how and to what degree current DRE-specific wikis can support the DRE activities, and identify the future research directions. We adopt the methodology of systematic literature review to find DRE-specific wikis, analyze the features embodied in them, identify DRE activities supported by them, and cluster the users' feedbacks from their literatures. The results show that 1) distributed requirements elicitation is the most popular DRE activity supported by current DRE-specific wikis, 2) enhanced features are mainly designed for this phase, 3) the well recognized advantage for using DRE-specific wikis is that they can facilitate the collaborative work, and the disadvantages mainly lie in the organization of the content and the usability. Based on the findings of this review, the possible future research directions of DRE-specific wikis have been pointed out, especially in distributed requirements elicitation, negotiation, validation, and management. The importance of cross-over studies and empirical research are both emphasized at the end of the paper.

MOTIVATION: Gene duplication (D), transfer (T), loss (L) and incomplete lineage sorting (I) are crucial to the evolution of gene families and the emergence of novel functions. The history of these events can be inferred via comparison of gene and species trees, a process called reconciliation, yet current reconciliation algorithms model only a subset of these evolutionary processes. RESULTS: We present an algorithm to reconcile a binary gene tree with a nonbinary species tree under a DTLI parsimony criterion. This is the first reconciliation algorithm to capture all four evolutionary processes driving tree incongruence and the first to reconcile non-binary species trees with a transfer model. Our algorithm infers all optimal solutions and reports complete, temporally feasible event histories, giving the gene and species lineages in which each event occurred. It is fixed-parameter tractable, with polytime complexity when the maximum species outdegree is fixed. Application of our algorithms to prokaryotic and eukaryotic data show that use of an incomplete event model has substantial impact on the events inferred and resulting biological conclusions. AVAILABILITY: Our algorithms have been implemented in Notung, a freely available phylogenetic reconciliation software package, available at http://www.cs.cmu.edu/~durand/Notung. CONTACT: [email protected].