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Despite recent increase in research on improvement of non-functional properties of software, such as energy usage or program size, there is a lack of standard benchmarks for such work. This absence hinders progress in the field, and raises questions about ...

Recent years have seen a rise in Neural Program Repair (NPR) systems in the software engineering community, which adopt advanced deep learning techniques to automatically fix bugs. Having a comprehensive understanding of existing systems can facilitate ...

Open-ended questions test a more thorough understanding compared to closed-ended questions and are often a preferred assessment method. However, open-ended questions are tedious to grade and subject to personal bias. Therefore, there have been efforts to ...

Since the 1990s, many implementations of polyhedral compilers have been written and distributed, either as source-to-source translating compilers or integrated into wider-purpose compilers. This article provides a survey on those various available ...

The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems: In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of ...

We present VLMEvalKit: an open-source toolkit for evaluating large multi-modality models based on PyTorch. The toolkit aims to provide a user-friendly and comprehensive framework for researchers and developers to evaluate existing multi-modality models ...

Engaging in conversational recommendations within a specific scenario represents a promising paradigm in the real world. Scenario-relevant situations often affect conversations and recommendations from two closely related aspects: varying the ...

With the increased attention to model efficiency, post-training sparsity (PTS) has become more and more prevalent because of its effectiveness and efficiency. However, there remain questions on better practice of PTS algorithms and the sparsification ...

A plethora of text-guided image editing methods has recently been developed by leveraging the impressive capabilities of large-scale diffusion-based generative models especially Stable Diffusion. Despite the success of diffusion models in producing high-...

In recent years, with the widespread attention of academia and industry on the application of large language models (LLMs) to code-related tasks, an increasing number of large code models (LCMs) have been proposed and corresponding evaluation benchmarks ...

Large language models (LLMs) have revolutionized code completion tasks. IDE plugins such as MarsCode can generate code recommendations, saving developers significant time and effort. However, current evaluation methods for code completion are limited by ...

With large neural models becoming increasingly accurate and powerful, they have raised privacy and transparency concerns on data usage. Therefore, data platforms, regulations and user expectations are rapidly evolving leading to enforcing privacy via ...

While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poor substitute for causation. The conventional way to discover causal ...

Time series anomaly detection is of significant importance in many real-world applications, including finance, healthcare, network security, industrial equipment, complex computing systems, and space probes. Most of these applications involve multi-...

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving ...

While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior ...

Compared with an extensive list of automotive radar datasets that support autonomous driving, indoor radar datasets are scarce at a smaller scale in the format of low-resolution radar point clouds and usually under an open-space single-room ...

In recent years, an increasing number of companies and institutions have begun the process of digitizing their physical records to promote digital access and searchability of their collections. For cost-efficiency, documents are often scanned in ...

Log data have facilitated various tasks of software development and maintenance, such as testing, debugging and diagnosing. Due to the unstructured nature of logs, log parsing is typically required to transform log messages into structured data for ...

The Universal Variability Language (UVL) is a textual format for specifying feature models. UVL has optional language levels (i.e., extensions) that add more expressive functionality to the base language, such as numerical constraints over attributes. ...