Reviewerly/exHarmony huggingface dataset:
Class-Prompting Transformer for Incremental Semantic Segmentation

Class-incremental Semantic Segmentation (CISS) aims to learn new tasks sequentially that assign a specific category to each pixel of a given image while preserving the original capability to segment the old classes even if the labels of old tasks are absent. Most existing CISS methods suppress catastrophic forgetting by directly distilling on specific layers, which ignores the semantic gap between training data from the old and new classes with different distributions and leads to distillation errors, thus affecting segmentation performance. In this paper, we propose a Class-prompting Transformer (CPT) to introduce external prior knowledge provided by a pre-trained vision-language encoder into CISS pipelines for bridging the old and new classes and performing more generalized initialization and distillation. Specifically, we proposed a Prompt-guided Initialization Module (PIM), which measures the relationships between the class prompts and old query parameters to initialize the new query parameters for relocating the previous knowledge to the learning of new tasks. Then, a Semantic-aligned Distillation Module (SDM) is proposed to incorporate class prompt information with the class-aware embeddings extracted from the decoder to prevent the semantic gap problem between distinct class data and conduct adaptive knowledge transfer to suppress catastrophic forgetting. Extensive experiments on Pascal VOC and ADE20K datasets for the CISS task demonstrate the superiority of the proposed method, which achieves state-of-the-art performance.
PolyLU: A Simple and Robust Polynomial-Based Linear Unit Activation Function for Deep Learning

The activation function has a critical influence on whether a convolutional neural network in deep learning can converge or not; a proper activation function not only makes the convolutional neural network converge faster but also can reduce the complexity of convolutional neural network architecture and gets the same or better performance. Many activation functions have been proposed; however, various activation functions have advantages, defects, and applicable network architectures. A new activation function called Polynomial Linear Unit (PolyLU) is proposed in this paper to improve some of the shortcomings of the existing activation functions. The PolyLU meets the following basic properties: continuously differentiable, approximate identity near the origin, unbounded for positive inputs, bounded for negative inputs, smooth, monotonic, and zero-centered. There is a polynomial term for the negative inputs and no exponential terms in the PolyLU that reduces the computational complexity of the network. Compared to those common activation functions like Sigmoid, Tanh, ReLU, LeakyReLU, ELU, Mish, and Swish, the experiments show that the PolyLU has improved some network complexity and has better accuracy over MNIST, Kaggle Cats and Dogs, CIFAR-10 and CIFAR-100 datasets. Test by the CIFAR-100 dataset with batch normalization, PolyLU improves by 0.62%, 2.82%, 2.44%, 1.33%, 2.08%, and 4.26% of accuracy than ELU, Swish, Mish, Leaky ReLU, ReLU, and Tanh respectively. Test by the CIFAR-100 dataset without batch normalization, PolyLU improves by 1.24%, 4.39%, 2.12%, 5.43%, 15.51%, and 8.10% of accuracy than ELU, Swish, Mish, Leaky ReLU, ReLU, and Tanh respectively.
Distributed Stochastic Gradient Descent With Compressed and Skipped Communication

This paper introduces CompSkipDSGD, a new algorithm for distributed stochastic gradient descent that aims to improve communication efficiency by compressing and selectively skipping communication. In addition to compression, CompSkipDSGD allows both workers and the server to skip communication in any iteration of the training process and reserve it for future iterations without significantly decreasing testing accuracy. Our experimental results on the large-scale ImageNet dataset demonstrate that CompSkipDSGD can save hundreds of gigabytes of communication while maintaining similar levels of accuracy compared to state-of-the-art algorithms. The experimental results are supported by a theoretical analysis that demonstrates the convergence of CompSkipDSGD under established assumptions. Overall, CompSkipDSGD could be useful for reducing communication costs in distributed deep learning and enabling the use of large-scale datasets and models in complex environments.
Progressive Direction-Aware Pose Grammar for Human Pose Estimation

Human pose estimation is challenged by lots of factors such as complex articulation, occlusion and so on. Generally, message passing among human joints plays an important role in rectifying the wrong detection caused by referred challenges. In this paper, we propose a progressive direction-aware pose grammar model which performs message passing by building the pose grammar in a novel fashion. Firstly, a multi-scale Bi-C3D pose grammar module is proposed to promote message passing among human joints within a local range. We propose to conduct message passing by means of 3D convolution (C3D) which proves to be more effective compared with other sequential modeling techniques. To facilitate the message passing, we devise a novel adaptive direction guidance module where explicit direction information is embedded. Besides, we propose to fuse final results with attention maps to make full use of the bidirectional information and the fusion can be regarded as an ensemble process. Secondly, a more economic global regional grammar is introduced to build the relationships among human joints globally. The local-to-global modeling scheme promotes the message passing in a progressive manner and boosts the performance by a large margin. Promising results are achieved on MPII, LSP and COCO benchmarks.
Secure Semantic Communication Model for Black-Box Attack Challenge Under Metaverse

The Metaverse is the post-reality universe, a perpetual and persistent multiuser environment merging physical reality with digital virtuality. It is a new type of Internet application and social form that integrates a variety of new technologies. The realization of the Metaverse has extremely high demands on the communication traffic density, which needs a new communication technology. Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, will be one of the key research fields of the Metaverse. It aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of every single symbol or bit regardless of its meaning. However, semantic communication faces the challenge of black-box attacks and poses security risks of information leakage and tampering. For black-box attacks, this article points out three approaches to secure semantic communication: secure communication model based on semantic block, semantic variable coding, and hybrid channel with hidden task. The security of semantic communication can be improved through these methods.
Are All Text News Just a Noise for Investors? Impact of Online Texts on Bitcoin Returns

Abstract The paper demonstrates the power of alternative data. Relying on the indicators obtained by mining online publicly available news articles, authors analyze their impact on Bitcoin returns. This research shows that in the first quarter of 2022 Bitcoin returns could be explained by the sentiment of information obtained from news published on online portals. However, we find negative relation between Bitcoin news sentiment and its returns. Such result can be explained as anomaly of researched period which is characterized by inception of global political crisis caused by the war in Eastern Europe and turmoil on crypto market. Our research also confirms that the news about Ethereum, Bitcoins’ investment substitute, affected Bitcoin's returns as well. On the other hand, the obtained results show that there is no relation between the lexical readability of the news (i.e., the clarity with which the text is written, measured by the fog index) and the returns on Bitcoin in the analyzed period. Collected evidences speak in favor of Bitcoin’s market inefficiency. In this paper we also demonstrate that returns forecasts based on online news are more accurate in comparison to those generated by ARMA-GARCH model, a conventional financial tool for predicting returns.
Improved Interpretation of Impulse Frequency Response Analysis for Synchronous Machine Using Life long Learning Based on iCaRL

Winding inter-turn and ground short circuit (SC) faults are common fault types of synchronous machines. The sweep frequency response analysis (SFRA) has been recently introduced to detect winding SC faults. Additionally, impulse FRA (IFRA) provides an alternative that serves the same objective. Whether SFRA or IFRA, so far, there is still no standard and reliable interpretation code. The present interpretation of frequency response still calls for experienced personnel, which could be subjective. Thus, many researchers have used machine or deep learning-based models to detect winding SC faults from the frequency response automatically. However, most proposed models cannot provide real-time feedback and update. Therefore, this study proposes an improved model using life long learning strategy based on incremental classifier and representation learning (iCaRL) to interpret and analyze the IFRA curves. This study artificially simulates and records winding SC faults on a 5 kW synchronous machine. The proposed method is then verified on the test set and compared with other traditional life long learning strategies. The experimental results show that the accuracy of the proposed model is higher than 90% under all types of fault data streams in real-time. The comparative experimental results show that the proposed model performs better than other life long learning strategies. (Github code: https://github.com/cy1034429432/Impro ved-Interpretation-of-IFRA-based-on-iCaRL/tree/main).
Cross-Modal Local Calibration and Global Context Modeling Network for RGB–Infrared Remote-Sensing Object Detection

RGB-infrared object detection in remote sensing images is crucial for achieving around-clock surveillance of unmanned aerial vehicles. RGB-infrared remote sensing object detection methods based on deep learning usually mine the complementary information from RGB and infrared modalities by utilizing feature aggregation to achieve robust object detection for around-the-clock applications. Most of the existing methods aggregate features from RGB and infrared images by utilizing element-wise operations ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eg.,</i> element-wise addition or concatenation). The detection accuracy of these methods is limited. The main reasons can be concluded as follows: local location misalignment across modalities and insufficient non-local contextual information extraction. To address the above issues, we propose a cross-modal local calibration and global context modeling network (CLGNet), consisting of two novel modules: a cross-modal local calibration (CLC) module and a cross-modal global context modeling (CGC) module. The CLC module first aligns features from different modalities, and then aggregates them selectively. The CGC module is embedded into the backbone network to capture cross-modal non-local long-range dependencies. Experimental results on popular RGB-infrared remote sensing object detection datasets, namely DRoneVehicle and VEDAI demonstrate the effectiveness and efficiency of our CLGNet.
A Verifiable and Efficient Secure Sharing Scheme in Multiowner Multiuser Settings

Data security has remained a challenging problem in cloud storage, especially in multiowner data sharing scenarios. As one of the most effective solutions for secure data sharing, multikeyword ranked searchable encryption (MRSE) has been widely used. However, most of the existing MRSE schemes have some shortcomings in multiowner data sharing, such as index trees generated by data owners, relevance scores in plaintext form, and lack of verification function. In this article, we propose a verifiable and efficient secure sharing scheme in multiowner multiuser settings, where the index tree is generated by the trusted authority. To achieve verifiable functionality, the blockchain-based smart contract is adopted to execute the search algorithm. Based on a distributed two-trapdoor public-key cryptosystem, the data uploaded and used are in ciphertext form, and the proposed algorithms are secure in our scheme. For improving efficiency, the encrypted data are aggregated according to the category and the Category ID-based index tree is generated. Extensive experiments are conducted to demonstrate that it can reduce the time cost of index construction by 75% and the time cost of search by 53%, approximately. Moreover, multithreaded optimization is introduced in our scheme, which can reduce the time cost of index construction by 76% and the time cost of search by 67%, approximately (with 16 threads).
YOLO-MSA: A Multiscale Stereoscopic Attention Network for Empty-Dish Recycling Robots

As the global population ages and the labor force shrinks, using Artificial Intelligence (AI) technology to promote labor productivity growth has become a hot topic. The emergence of Empty-Dish Recycling Robots has effectively alleviated the impact of the decline in labor productivity. This paper proposes a Multi-scale Stereoscopic Attention (MSA) network YOLO-MSA to detect postprandial dishes for Empty-Dish Recycling Robots. First, the standard convolution is replaced with a Res2Net module, which improves the multi-scale expressiveness of the network at a finer-grained level. Second, we adopt a Res2Net with different dilation rates and a novel stereoscopic attention mechanism to propose an MSA module, which is used for coarse-grained multi-scale expression. Thirdly, for multi-scale feature learning in the dimensionality reduction process, a Dimension Reduction Spatial Pyramid Pooling (DRSPP) is proposed to fuse feature maps of different scales. Extensive experiments demonstrate the effectiveness of the proposed MSA module for multi-scale feature learning. Furthermore, YOLO-MSA has achieved 98.47% mean Average Precision ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> ) on Dish-21, a dataset of the postprandial dishes, which is much higher than other state-of-the-art models, and has achieved an inference speed of 33.93 frames per second ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FPS</i> ), which meets the needs of real-time detection of the postprandial dish for the Empty-Dish Recycling Robot. Test results on other public datasets show that the proposed YOLO-MSA has a better generalization ability. In summary, YOLO-MSA exhibits satisfactory multi-scale feature expression ability, demonstrates effectiveness and robustness in postprandial dish detection, and has far-reaching significance for the development of Empty-Dish Recycling Robots.
Examining explainable clinical decision support systems with think aloud protocols

Machine learning tools are increasingly used to improve the quality of care and the soundness of a treatment plan. Explainable AI (XAI) helps users in understanding the inner mechanisms of opaque machine learning models and is a driver of trust and adoption. Explanation methods for black-box models exist, but there is a lack of user studies on the interpretability of the provided explanations. We used a Think Aloud Protocol (TAP) to explore oncologists' assessment of a lung cancer relapse prediction system with the aim of refining the purpose-built explanation model for better credibility and utility. Novel to this context, TAP is used as a neutral methodology to elicit experts' thought processes and judgements of the AI system, without explicit prompts. TAP aims to elicit the factors which influenced clinicians' perception of credibility and usefulness of the system. Ten oncologists took part in the study. We conducted a thematic analysis of their verbalized responses, generating five themes that help us to understand the context within which oncologists' may (or may not) integrate an explainable AI system into their working day.
Connecting Maker Education to Secondary School Technology Education in Korea

This chapter introduces trends and cases of maker education in South Korean technology education. In particular, this study investigates a technology teachers’ professional learning community for maker education. Four themes were drawn from a qualitative analysis of the four technology teachers’ interviews and relevant literature reviews: sharing and communication, we are makers, technology teachers as practitioners for maker education, and diffusion and movement. MAKERS, a technology teachers’ professional learning community is focused on sharing and communication. In addition, MAKERS became makers and experienced making and collaboration presenting a balanced interest and practical ability in both hardware and software parts. Lastly, MAKERS is very active in practicing and spreading maker education and technology education.
Dynamic weight HiLo attention network for medical image multiple organ segmentation

Abstract In recent years, there has been a surge of research in the field of medical image segmentation using hybrid CNN‐Transformer network architectures. Most of these studies leverage the attention mechanism of ViT to overcome the limitations of CNN architectures in capturing long‐range dependencies. However, these hybrid model approaches also have some potential drawbacks. First, due to the heavy reliance of the Transformer's attention mechanism on global information, it can lead to a significant increase in computational cost when dealing with high‐resolution input images. Furthermore, the convolutional and attention mechanisms in hybrid models have different interpretability in information extraction and decision‐making, which poses a challenge for the interpretability of the convolutional part. Our proposed model, DWHA, addresses these limitations and outperforms state‐of‐the‐art models in a range of medical image segmentation tasks, such as abdominal multiorgan segmentation, automatic cardiac diagnosis, neurostructure segmentation, and skin lesion segmentation, achieving significantly superior performance. Specifically, on the abdominal multiorgan segmentation dataset, DWHA outperformed the previous state‐of‐the‐art baseline by 0.57%; on the neurostructure segmentation dataset, it achieved an improvement of 1.17%; and on the skin lesion segmentation dataset, it achieved an improvement of 0.91%. These significant improvements suggest that DWHA may become the preferred model in the field of medical image segmentation.
Short-term PV power prediction based on the 24 traditional Chinese solar terms and adaboost-GA-BP model

High-precision, short-term power forecasting for photovoltaic systems not only reduces unnecessary energy consumption but also provides power grid security. To this end, in this paper we propose a photovoltaic short-term power forecasting model based on the division of data of the 24 traditional Chinese solar terms and the Adaboost-GA-BP model. The 24 solar terms were condensed from the laws of meteorology, phenology, and seasonal changes to adapt to agricultural times in ancient China and have become intangible cultural heritage. This article first analyzes the numerical characteristics of meteorological factors and demonstrates their close correlation with the turning points of the 24 solar terms. Second, using Standardized Euclidean Distance and Spearman’s Correlation Coefficients to analyze data similarity between the Gregorian half-months and the 24 solar terms divisions for comparative analysis purposes, it is shown that the intragroup data under the division of the 24 solar terms have a higher similarity, leading to an average decrease of 15.68%, 40.57%, 14.68%, and 14.64% in the MAE, MSE, RMSE, and WMAPE of the predicted results, respectively. Finally, based on the data derived from the 24 solar terms, the combined algorithm was compared with the Adaboost-GA-BP model and then was verified. The genetic algorithm and Adaboost were used to optimize the BP neural network algorithm in initial value assignment and neural network structure, resulting in a 23.42%, 18.12%, and 22.28% reduction in the mean values of the MAE, RMSE, and WMAPE of the predicted results, respectively. Analysis of the results show that using the Adaboost-GA-BP model based on the 24 solar terms for short-term photovoltaic power forecasting can improve the accuracy of photovoltaic power forecasting and significantly improve the predictive performance of the model.
Sample-efficient multi-agent reinforcement learning with masked reconstruction

Deep reinforcement learning (DRL) is a powerful approach that combines reinforcement learning (RL) and deep learning to address complex decision-making problems in high-dimensional environments. Although DRL has been remarkably successful, its low sample efficiency necessitates extensive training times and large amounts of data to learn optimal policies. These limitations are more pronounced in the context of multi-agent reinforcement learning (MARL). To address these limitations, various studies have been conducted to improve DRL. In this study, we propose an approach that combines a masked reconstruction task with QMIX (M-QMIX). By introducing a masked reconstruction task as an auxiliary task, we aim to achieve enhanced sample efficiency-a fundamental limitation of RL in multi-agent systems. Experiments were conducted using the StarCraft II micromanagement benchmark to validate the effectiveness of the proposed method. We used 11 scenarios comprising five easy, three hard, and three very hard scenarios. We particularly focused on using a limited number of time steps for each scenario to demonstrate the improved sample efficiency. Compared to QMIX, the proposed method is superior in eight of the 11 scenarios. These results provide strong evidence that the proposed method is more sample-efficient than QMIX, demonstrating that it effectively addresses the limitations of DRL in multi-agent systems.
Artificial Intelligence for Drug Discovery: A New Frontier for Patent Law

The artificial intelligence (AI) revolution is here and is changing healthcare as we know it. In the world of biotechnology and pharmaceuticals, AI systems are increasingly being used to discover new drugs, with multiple AI-designed drugs already entering clinical trials. AI for drug discovery represents the convergence of software and life science technologies—two industries that are heavily reliant on patents—and poses many unprecedented questions under U.S. patent law. In general, a new invention must be useful, novel, and non-obvious to meet the requirements for patentability. In addition, the application for a patent must be written so as to enable a person “skilled in the art” to make and use the invention and must “describe the invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention.” Walking step-by-step through the patent requirements laid out in the Patent Act, this Article (i) demonstrates how each and every one of these requirements will be put to the test by the increasing use of AI in drug discovery, (ii) analyzes how each of these questions is likely to be resolved under current law, and (iii) explores the resulting implications for the drug discovery industry. Where the existing law would lead to undesirable effects, this Article also proposes preferable alternatives and policy suggestions for fostering innovation. Finally, for the patent practitioner, this Article further provides practical recommendations for those seeking to prosecute patents in the current realm of legal uncertainty.