Cross-Document Multi-entity Question Answering (MEQA) demands the integration of scattered information across documents to resolve complex queries involving entities, relationships, and contextual dependencies.Although Large Language Models (LLMs) and Retrieval-augmented Generation (RAG) systems show promise, their performance on crossdocument MEQA remains underexplored due to the absence of tailored benchmarks.To address this gap, we introduce MEBench, a scalable multi-document, multi-entity benchmark designed to systematically evaluate LLMs' capacity to retrieve, consolidate, and reason over scattered and dense information.Our benchmark comprises 4,780 questions which are systematically categorized into three primary categories: Comparative Reasoning, Statistical Reasoning and Relational Reasoning, further divided into eight distinct types, ensuring broad coverage of real-world multi-entity reasoning scenarios.Our experiments on state-of-theart LLMs reveal critical limitations: even advanced models achieve only 59% accuracy on MEBench.Our benchmark emphasizes the importance of completeness and factual precision of information extraction in MEQA tasks, using Entity-Attributed F1 (EA-F1) metric for granular evaluation of entity-level correctness and attribution validity.MEBench not only highlights systemic weaknesses in current LLM frameworks but also provides a foundation for advancing robust, entity-aware QA architectures. 1

This paper introduces significant enhancements to RepoSim4Py and RepoSnipy, advanced semantic tools for deep analysis of software repositories. RepoSim4Py commandline toolbox now supports multi-level embedding, encompassing code, documentation, requirements, README, and comprehensive repository analysis, which enable the understanding of repository dynamics. Concurrently, RepoSnipy webbased search engine facilitates sophisticated repository similarity searches and introduces clustering based on both repository tags (topic_cluster) and code embeddings (code_cluster). We also introduce SimilarityCal, a novel binary classification model trained on these clusters, to predict and quantify repository similarities with high accuracy. These developments provide researchers and developers with powerful tools to navigate the complex landscape of software repositories, improving efficiency in software development and fostering innovation through better reuse of existing resources.

Workflow scheduling is one of the most challenging problems in cloud computing. This paper proposes a heuristic algorithm task type first algorithm (T2FA) for solving deadline-constrained workflow scheduling in cloud with multicore resource (DWS_CMR). The objectives to be minimized are the maximal completion time (i.e., makespan) and the total costs. Firstly, resource model and workflow application model are introduced. Resource model has the configurations of multicore, processing capacity, bandwidth and leasing price, and workflow application model is described by directed acyclic graph (DAG). Based on above models, the mathematical model of DWS_CMR is established, which allows multiple tasks to run concurrently on multicore resources. Secondly, to exploit the characteristics of the problem, the structures of DAG are decomposed and formulated. Merging tasks conforming to the first structure into task blocks can simplify DAG. Four special types of tasks are extracted from the second and third structures, and are preferentially scheduled in task scheduling stage. Then, a new interrelated calculation method of estimated start time and actual start time of tasks is proposed, which can complete the task-to-resource mapping. Finally, T2FA is devised, which incorporates two important phases, including pre-processing and task scheduling. Experimental results show that T2FA can achieve significantly better schedules in most test cases compared to several existing algorithms.