Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and additional training procedures. Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance. We argue that various data augmentations should be adjusted to better adapt to the semi-supervised scenarios instead of directly applying these techniques from supervised learning. Specifically, we adopt a simplified intensity-based augmentation that selects a random number of data transformations with uniformly sampling distortion strengths from a continuous space. Based on the estimated confidence of the model on different unlabeled samples, we also randomly inject labelled information to augment the unlabeled samples in an adaptive manner. Without bells and whistles, our simple AugSeg can readily achieve new state-of-the-art performance on SSS benchmarks under different partition protocols.

We introduce TeSS (Text Similarity Comparison using Sentence Encoder), a framework for zero-shot classification where the assigned label is determined by the embedding similarity between the input text and each candidate label prompt. We leverage representations from sentence encoders optimized to locate semantically similar samples closer to each other in embedding space during pre-training. The label prompt embeddings serve as prototypes of their corresponding class clusters. Furthermore, to compensate for the potentially poorly descriptive labels in their original format, we retrieve semantically similar sentences from external corpora and additionally use them with the original label prompt (TeSS-R). TeSS outperforms strong baselines on various closed-set and open-set classification datasets under zero-shot setting, with further gains when combined with label prompt diversification through retrieval. These results are robustly attained to verbalizer variations, an ancillary benefit of using a bi-encoder. Altogether, our method serves as a reliable baseline for zero-shot classification and a simple interface to assess the quality of sentence encoders.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddosian's algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is the most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Also, we propose a threshold optimization algorithm, which determines whether the driver is drowsy or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiment, we have determinend a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.

热分析在不同的温度场景下提供了对电子芯片行为的更深入见解，并可以更快地设计探索。但是，使用FEM或CFD，在芯片上获得详细而准确的热曲线非常耗时。因此，迫切需要加快片上热溶液以解决各种系统方案。在本文中，我们提出了一个热机学习（ML）求解器，以加快芯片的热模拟。热ML-Solver是最近的新型方法CoAemlSim（可组合自动编码器的机器学习模拟器）的扩展，并对溶液算法进行了修改，以处理常数和分布式HTC。在不同情况下，针对商业求解器（例如ANSYS MAPDL）以及最新的ML基线UNET验证了所提出的方法，以证明其增强的准确性，可伸缩性和概括性。

语义上有意义的句子嵌入对于自然语言处理中的许多任务都很重要。为了获得此类嵌入，最近的研究探讨了利用验证语言模型（PLM）作为训练语料库的合成生成数据的想法。但是，PLM通常会产生与人类写的句子大不相同的句子。我们假设将所有这些合成示例同样地用于训练深层神经网络可能会对学习语义上有意义的嵌入产生不利影响。为了分析这一点，我们首先训练一个分类器来识别机器编写的句子，并观察到机器编写的句子的语言特征与人写的句子的语言特征大不相同。基于此，我们提出了一种新颖的方法，该方法首先训练分类器来衡量每个句子的重要性。然后，分类器的蒸馏信息用于训练可靠的句子嵌入模型。通过对四个现实世界数据集的广泛评估，我们证明了我们的合成数据训练的模型可以很好地概括并表现优于现有基线。我们的实现可在https://github.com/ddehun/coling2022_reweighting_sts上公开获得。

最近，神经主题模型（NTMS）已纳入预训练的语言模型（PLM）中，以捕获用于文本摘要的全局语义信息。但是，在这些方法中，它们捕获和整合全局语义信息的方式仍然存在局限性。在本文中，我们提出了一个新颖的模型，即图形对比主题增强语言模型（GRETEL），该模型将图形对比主题模型与预训练的语言模型结合在一起，以充分利用长文档提取的全球和本地上下文语义摘要。为了更好地捕获并将全局语义信息纳入PLM，图形对比主题模型集成了层次变压器编码器和图形对比度学习，以从全局文档上下文和金摘要中融合语义信息。为此，Gretel鼓励该模型有效提取与黄金摘要有关的显着句子，而不是涵盖亚最佳主题的多余句子。对通用域和生物医学数据集的实验结果表明，我们所提出的方法优于SOTA方法。

特征图的分辨率对于医学图像分割至关重要。大多数现有用于医疗图像分割的基于变压器的网络都是U-NET样体系结构，其中包含一个编码器，该编码器利用一系列变压器块将输入医疗图像从高分辨率表示形式转换为低分辨率特征图和解码器这逐渐从低分辨率特征图中恢复了高分辨率表示。与以前的研究不同，在本文中，我们利用高分辨率网络（HRNET）的网络设计样式，用变压器块替换卷积层，并从变压器块生成的不同分辨率特征图中连续交换信息。本文介绍的新基于变压器的网络表示为高分辨率SWIN Transformer网络（HRSTNET）。广泛的实验表明，HRSTNET可以与基于最新的变压器类似于脑肿瘤分割的U-NET样结构（BRATS）2021和Medical Sementation Decathlon的肝数据集实现可比的性能。 HRSTNET代码将在https://github.com/auroua/hrstnet上公开获得。

Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device DNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple DNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19$\times$ speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy; over 20$\times$ speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training.

Cybercriminals are moving towards zero-day attacks affecting resource-constrained devices such as single-board computers (SBC). Assuming that perfect security is unrealistic, Moving Target Defense (MTD) is a promising approach to mitigate attacks by dynamically altering target attack surfaces. Still, selecting suitable MTD techniques for zero-day attacks is an open challenge. Reinforcement Learning (RL) could be an effective approach to optimize the MTD selection through trial and error, but the literature fails when i) evaluating the performance of RL and MTD solutions in real-world scenarios, ii) studying whether behavioral fingerprinting is suitable for representing SBC's states, and iii) calculating the consumption of resources in SBC. To improve these limitations, the work at hand proposes an online RL-based framework to learn the correct MTD mechanisms mitigating heterogeneous zero-day attacks in SBC. The framework considers behavioral fingerprinting to represent SBCs' states and RL to learn MTD techniques that mitigate each malicious state. It has been deployed on a real IoT crowdsensing scenario with a Raspberry Pi acting as a spectrum sensor. More in detail, the Raspberry Pi has been infected with different samples of command and control malware, rootkits, and ransomware to later select between four existing MTD techniques. A set of experiments demonstrated the suitability of the framework to learn proper MTD techniques mitigating all attacks (except a harmfulness rootkit) while consuming <1 MB of storage and utilizing <55% CPU and <80% RAM.