Binbin Huang

Shuo Wang, Jing Li, Zibo Zhao et al.

Holistic scene understanding includes semantic segmentation, surface normal estimation, object boundary detection, depth estimation, etc. The key aspect of this problem is to learn representation effectively, as each subtask builds upon not only correlated but also distinct attributes. Inspired by visual-prompt tuning, we propose a Task-Specific Prompts Transformer, dubbed TSP-Transformer, for holistic scene understanding. It features a vanilla transformer in the early stage and tasks-specific prompts transformer encoder in the lateral stage, where tasks-specific prompts are augmented. By doing so, the transformer layer learns the generic information from the shared parts and is endowed with task-specific capacity. First, the tasks-specific prompts serve as induced priors for each task effectively. Moreover, the task-specific prompts can be seen as switches to favor task-specific representation learning for different tasks. Extensive experiments on NYUD-v2 and PASCAL-Context show that our method achieves state-of-the-art performance, validating the effectiveness of our method for holistic scene understanding. We also provide our code in the following link https://github.com/tb2-sy/TSP-Transformer.

Zengyang Li, Binbin Huang, Yimeng Li et al.

Industrial Internet of Things (IIoT) has become a prominent topic recently, with an increasing number of IIoT OSS projects emerging, also within the Eclipse Foundation. Code cloning is a common practice that can adversely affect software maintenance. In the IIoT OSS domain, developers frequently reuse code and configurations for efficiency, which can lead to code clone proliferation and maintenance challenges. However, the extent and effects of code clones in the IIoT OSS domain remain understudied. This study aims to investigate the prevalence, evolution, and co-modification of code clones within the Eclipse IIoT OSS ecosystem. We collected 90 release versions from 15 projects in the Eclipse IIoT OSS ecosystem, and investigated their code clone situations based on source code and change history using the NiCad tool and our custom analysis module. The investigation covered clone distribution, patterns, evolution trends, co-modified clones, and cross-project clones. 1) Code clones are prevalent in Eclipse IIoT OSS projects, with 16.3% of code lines involved in clones - nearly twice the proportion observed in traditional OSS projects; 2) Most code clones occur between commits, while there are still a significant proportion of code clones that each clone pair happens within a commit; 3) Most Eclipse IIoT projects remain stable in clone numbers during version iterations; 4) An average of 0.17% of the clones have been co-modified, which negatively affect maintenance; and 5) Cross-project clone pairs are prevalent, more in Java than in C projects, with rare co-modifications (0.02%) only in Java projects. Our findings highlight the potential negative impacts of these clones on software maintenance, emphasizing the need to address these issues to improve overall software quality.

Binbin Huang, Dongze Lian, Weixin Luo et al.

An LBYL (`Look Before You Leap') Network is proposed for end-to-end trainable one-stage visual grounding. The idea behind LBYL-Net is intuitive and straightforward: we follow a language's description to localize the target object based on its relative spatial relation to `Landmarks', which is characterized by some spatial positional words and some descriptive words about the object. The core of our LBYL-Net is a landmark feature convolution module that transmits the visual features with the guidance of linguistic description along with different directions. Consequently, such a module encodes the relative spatial positional relations between the current object and its context. Then we combine the contextual information from the landmark feature convolution module with the target's visual features for grounding. To make this landmark feature convolution light-weight, we introduce a dynamic programming algorithm (termed dynamic max pooling) with low complexity to extract the landmark feature. Thanks to the landmark feature convolution module, we mimic the human behavior of `Look Before You Leap' to design an LBYL-Net, which takes full consideration of contextual information. Extensive experiments show our method's effectiveness in four grounding datasets. Specifically, our LBYL-Net outperforms all state-of-the-art two-stage and one-stage methods on ReferitGame. On RefCOCO and RefCOCO+, Our LBYL-Net also achieves comparable results or even better results than existing one-stage methods.

Binbin Huang, Zhongjin Lia, Peng Tang et al.

It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge computing (MEC) can provide abundant computing resources to execute all or parts of the tasks of MDs and thereby can greatly reduce the energy of MD and improve the QoS of applications. However, offloading workflow tasks to the MEC servers are liable to external security threats (e.g., snooping, alteration). In this paper, we propose a security and energy efficient computation offloading (SEECO) strategy for service workflows in MEC environment, the goal of which is to optimize the energy consumption under the risk probability and deadline constraints. First, we build a security overhead model to measure the execution time of security services. Then, we formulate the computation offloading problem by incorporating the security, energy consumption and execution time of workflow application. Finally, based on the genetic algorithm (GA), the corresponding coding strategies of SEECO are devised by considering tasks execution order and location and security services selection. Extensive experiments with the variety of workflow parameters demonstrate that SEECO strategy can achieve the security and energy efficiency for the mobile applications.