Reviewerly/exHarmony huggingface dataset:
State-Difference Feedback Control for Discrete-Time Takagi–Sugeno Fuzzy Descriptor Systems with Parameter Uncertainties and External Noises

Authors:
raw_author_name: Wen-Jer Chang
raw_affiliation_string: Department of Marine Engineering, National Taiwan Ocean University, Keelung 202, Taiwan
raw_author_name: Che-Lun Su
raw_affiliation_string: Department of Marine Engineering, National Taiwan Ocean University, Keelung 202, Taiwan

Abstract: This research focuses on the development of state-difference feedback controllers for discrete-time (DT) nonlinear descriptor systems. Discrete-time nonlinear DA systems consist of difference and algebraic equations and play a crucial role in describing dynamic behavior and capturing the constraints or relationships within the system. However, analytical stability may pose additional challenges due to the unique characteristics of the system. Utilizing fuzzy model-based techniques, the DT nonlinear DA system discussed in this study can be effectively represented using the Takagi–Sugeno (T-S) fuzzy model. After linearizing the nonlinear system through the T-S fuzzy model, traditional linear control techniques become applicable. These techniques are then applied to T-S fuzzy systems to establish stability criteria. This article chooses the Lyapunov function as the method used to analyze system stability. Additionally, we use a free-weighting matrix to introduce additional degrees of freedom. In summary, this paper presents simulation results and discussions to verify the effectiveness of the proposed design approach.
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians

Authors:
raw_author_name: Jiawei Huang
raw_affiliation_string: Chuzhou University, Chuzhou, China and Void Dimensions, China
raw_author_name: Hajime Tanaka
raw_affiliation_string: Tohoku University, Sendai, Japan
raw_author_name: Taku Komura
raw_affiliation_string: The University of Hong Kong, Hong Kong and Tohoku University, Sendai, Japan
raw_author_name: Yoshifumi Kitamura
raw_affiliation_string: Tohoku University, Sendai, Japan

Abstract: Importance sampling techniques significantly reduce variance in physically-based rendering. In this paper we propose a novel online framework to learn the spatial-varying distribution of the full product of the rendering equation, with a single small neural network using stochastic ray samples. The learned distributions can be used to efficiently sample the full product of incident light. To accomplish this, we introduce a novel closed-form density model, called the Normalized Anisotropic Spherical Gaussian mixture, that can model a complex light field with a small number of parameters and that can be directly sampled. Our framework progressively renders and learns the distribution, without requiring any warm-up phases. With the compact and expressive representation of our density model, our framework can be implemented entirely on the GPU, allowing it to produce high-quality images with limited computational resources. The results show that our framework outperforms existing neural path guiding approaches and achieves comparable or even better performance than state-of-the-art online statistical path guiding techniques.
Tensor-Based Sparse Representation for Hyperspectral Image Reconstruction Using RGB Inputs

Authors:
raw_author_name: Yingtao Duan
raw_affiliation_string: School of Mathematics and Statistics, Hanshan Normal University, Chaozhou 521041, China
raw_author_name: Nan Wang
raw_affiliation_string: School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
raw_author_name: Chao Song
raw_affiliation_string: School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Abstract: Hyperspectral image (HSI) reconstruction from RGB input has drawn much attention recently and plays a crucial role in further vision tasks. However, current sparse coding algorithms often take each single pixel as the basic processing unit during the reconstruction process, which ignores the strong similarity and relation between adjacent pixels within an image or scene, leading to an inadequate learning of spectral and spatial features in the target hyperspectral domain. In this paper, a novel tensor-based sparse coding method is proposed to integrate both spectral and spatial information represented in tensor forms, which is capable of taking all the neighboring pixels into account during the spectral super-resolution (SSR) process without breaking the semantic structures, thus improving the accuracy of the final results. Specifically, the proposed method recovers the unknown HSI signals using sparse coding on the learned dictionary pairs. Firstly, the spatial information of pixels is used to constrain the sparse reconstruction process, which effectively improves the spectral reconstruction accuracy of pixels. In addition, the traditional two-dimensional dictionary learning is further extended to the tensor domain, by which the structure of inputs can be processed in a more flexible way, thus enhancing the spatial contextual relations. To this end, a rudimentary HSI estimation acquired in the sparse reconstruction stage is further enhanced by introducing the regression method, aiming to eliminate the spectral distortion to some extent. Abundant experiments are conducted on two public datasets, indicating the considerable availability of the proposed framework.
Parametric RSigELU: a new trainable activation function for deep learning

Authors:
raw_author_name: Serhat Kiliçarslan
raw_affiliation_string: Department of Software Engineering, Bandırma Onyedi Eylül University, 10200, Bandırma, Balıkesir, Turkey
raw_author_name: Mete Celik
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering, Erciyes University, 38039, Kayseri, Turkey

Abstract: Abstract Activation functions are used to extract meaningful relationships from real-world problems with the help of deep learning models. Thus, the development of activation functions which affect deep learning models’ performances is of great interest to researchers. In the literature, mostly, nonlinear activation functions are preferred since linear activation functions limit the learning performances of the deep learning models. Non-linear activation functions can be classified as fixed-parameter and trainable activation functions based on whether the activation function parameter is fixed (i.e., user-given) or modified during the training process of deep learning models. The parameters of the fixed-parameter activation functions should be specified before the deep learning model training process. However, it takes too much time to determine appropriate function parameter values and can cause the slow convergence of the deep learning model. In contrast, trainable activation functions whose parameters are updated in each iteration of deep learning models training process achieve faster and better convergence by obtaining the most suitable parameter values for the datasets and deep learning architectures. This study proposes parametric RSigELU (P+RSigELU) trainable activation functions, such as P+RSigELU Single (P+RSigELUS) and P+RSigELU Double (P+RSigELUD), to improve the performance of fixed-parameter activation function of RSigELU. The performances of the proposed trainable activation functions were evaluated on the benchmark datasets of MNIST, CIFAR-10, and CIFAR-100 datasets. Results show that the proposed activation functions outperforms PReLU, PELU, ALISA, P+FELU, PSigmoid, and GELU activation functions found in the literature. To access the codes of the activation function; https://github.com/serhatklc/P-RsigELU-Activation-Function .
Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer

Authors:
raw_author_name: Mingyang Yu
raw_affiliation_string: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

Abstract: Semantic segmentation of high-resolution remote sensing urban images is widely used in many fields, such as environmental protection, urban management, and sustainable development. For many years, convolutional neural networks (CNNs) have been a prevalent method in the field, but the convolution operations are deficient in modeling global information due to their local nature. In recent years, the Transformer-based methods have demonstrated their advantages in many domains due to the powerful ability to model global information, such as semantic segmentation, instance segmentation, and object detection. Despite the above advantages, Transformer-based architectures tend to incur significant computational costs, limiting the model’s real-time application potential. To address this problem, we propose a U-shaped network with Transformer as the decoder and CNN as the encoder to segment remote sensing urban scene images. For efficient segmentation, we design a window-based, multi-head, focused linear self-attention (WMFSA) mechanism and further propose the global–local information modeling module (GLIM), which can capture both global and local contexts through a dual-branch structure. Experimenting on four challenging datasets, we demonstrate that our model not only achieves a higher segmentation accuracy compared with other methods but also can obtain competitive speeds to enhance the model’s real-time application potential. Specifically, the mIoU of our method is 68.2% and 52.8% on the UAVid and LoveDA datasets, respectively, while the speed is 114 FPS, with a 1024 × 1024 input on a single 3090 GPU.
A unifying perspective on non-stationary kernels for deeper Gaussian processes

Authors:
raw_author_name: Marcus M. Noack
raw_affiliation_string: Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory 1 , Berkeley, California 94720, USA
raw_author_name: Hengrui Luo
raw_affiliation_string: Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory 1 , Berkeley, California 94720, USA; Department of Statistics, Rice University 2 , Houston, Texas 77005, USA
raw_author_name: Mark D. Risser
raw_affiliation_string: Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory 3 , Berkeley, California 94720, USA

Abstract: The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of machine learning (ML) in the last two decades because of their superior prediction abilities, especially in data-sparse scenarios, and their inherent ability to provide robust uncertainty estimates. Even so, their performance highly depends on intricate customizations of the core methodology, which often leads to dissatisfaction among practitioners when standard setups and off-the-shelf software tools are being deployed. Arguably, the most important building block of a GP is the kernel function, which assumes the role of a covariance operator. Stationary kernels of the Matérn class are used in the vast majority of applied studies; poor prediction performance and unrealistic uncertainty quantification are often the consequences. Non-stationary kernels show improved performance but are rarely used due to their more complicated functional form and the associated effort and expertise needed to define and tune them optimally. In this perspective, we want to help ML practitioners make sense of some of the most common forms of non-stationarity for Gaussian processes. We show a variety of kernels in action using representative datasets, carefully study their properties, and compare their performances. Based on our findings, we propose a new kernel that combines some of the identified advantages of existing kernels.
Quartz: A Template for Quantitative Corpus Data Visualization Tools

Authors:
raw_author_name: Pilar León-Araúz
raw_affiliation_string: Department of Translation and Interpreting, University of Granada, 18002 Granada, Spain

Abstract: Quantitative data visualization is an important element of corpus linguistics, and visualization tools are commonly available in corpus management systems (CMSs) or developed for custom tasks. Nonetheless, the implementation and advancement of visualization methods can be a challenge, both for individual projects and collectively. This article introduces a web application template that integrates with the Sketch Engine application programming interface (API) to encourage the development of new visualization tools that can provide advanced visualization features while directly communicating with the CMS. The application template, written in Python and called Quartz, is described and implemented for the Humanitarian Encyclopedia as part of ongoing efforts to conduct analyses of humanitarian concepts. The application’s features, purpose, and limitations are described, and an example case study utilizing the software is offered, focusing on usage patterns of the concept localization in the humanitarian domain. Some of the challenges of improving visualization techniques for corpus-based analysis are discussed, including data interoperability and the role of visualization in data exploration.
Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities

Authors:
raw_author_name: Hanyue Xu
raw_affiliation_string: Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK; School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215000, China
raw_author_name: Kah Phooi Seng
raw_affiliation_string: School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215000, China; School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia; School of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, QLD 4502, Australia
raw_author_name: Jeremy Smith
raw_affiliation_string: Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK
raw_author_name: Li Minn Ang
raw_affiliation_string: School of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, QLD 4502, Australia

Abstract: In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the collaborative training of deep learning models within these systems encounters significant challenges, chiefly due to data privacy concerns and dealing with communication latency from large-scale IoT devices. To address these issues, multi-level split federated learning (multi-level SFL) has been proposed, merging the benefits of split learning (SL) and federated learning (FL). This framework introduces a novel multi-level aggregation architecture that reduces communication delays, enhances scalability, and addresses system and statistical heterogeneity inherent in large AIoT systems with non-IID data distributions. The architecture leverages the Message Queuing Telemetry Transport (MQTT) protocol to cluster IoT devices geographically and employs edge and fog computing layers for initial model parameter aggregation. Simulation experiments validate that the multi-level SFL outperforms traditional SFL by improving model accuracy and convergence speed in large-scale, non-IID environments. This paper delineates the proposed architecture, its workflow, and its advantages in enhancing the robustness and scalability of AIoT systems in smart cities while preserving data privacy.
Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues

Authors:
raw_author_name: Yibing Zhao
raw_affiliation_string: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
raw_author_name: Dingwei Zhang
raw_affiliation_string: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
raw_author_name: Yadang Chen
raw_affiliation_string: School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract: The purpose of semi-supervised video object segmentation (VOS) is to predict and generate object masks in subsequent video frames after being provided with the initial frame’s object mask. Currently, mainstream methods leverage historical frame information for enhancing the network’s performance. However, this approach faces the following issues: (1) They often overlook important shape information, leading to decreased accuracy in segmenting object-edge areas. (2) They often use pixel-level motion estimation to guide the matching for addressing distractor objects. However, this brings heavy computation costs and struggle against occlusion or fast/blurry motion. For the first problem, this paper introduces an object shape extraction module that exploits both the high-level and low-level features to obtain object shape information, by which the shape information can be used to further refine the predicted masks. For the second problem, this paper introduces a novel object-level motion prediction module, in which it stores the representative motion features during the training stage, and predicts the object motion by retrieving them during the inference stage. We evaluate our method on benchmark datasets compared with recent state-of-the-art methods, and the results demonstrate the effectiveness of the proposed method.
AI in education risk assessment mechanism analysis

Authors:
raw_author_name: Zhuo Luo
raw_affiliation_string: Guangzhou Xinhua University, Guangzhou, China
raw_author_name: Xuedong Zhang
raw_affiliation_string: Department of Humanities Management, Seoul National University of Science and Technology, Seoul, Republic of Korea

Abstract: Artificial intelligence (AI) has had a significant impact on several sectors and fields. Failure Modes and Effects Analysis (FMEA) is a powerful risk management and prevention tool that can help companies identify and address any weaknesses in their practices. The Risk Priority Number (RPN) approach has been criticized for its flaws since it calculates the product of severity, occurrence, and detection ratings of threats. By considering a variety of criteria and aspects, and by thoughtfully and carefully combining expert perspectives, the proposed FMEA model may more accurately evaluate the risks associated with AI in Education. Picture Fuzzy Sets (PFSs) and Grey Relational Analysis-TOPSIS (GRA-TOPSIS) are combined to achieve this. The goal of this research is to use an updated FMEA model to assess and rank the risk hierarchy of the seven identified hazards. According to the data, algorithmic risk is the most significant issue that requires urgent attention. Relevant constraints and suggestions are also provided to encourage research on the dangers of AI in Education. Suggestions include creating a national or international regulatory authority to control the use of artificial intelligence in education, promoting the use of the FMEA technique as a common framework for assessing the risks connected to AI in Education.
Functional quantum abstract detecting systems

Authors:
raw_author_name: Guillermo Lugilde
raw_affiliation_string: Mathematics Department, University of Oviedo, Oviedo, Spain
raw_author_name: Elías F. Combarro
raw_affiliation_string: Computer Science Department, University of Oviedo, Oviedo, Spain
raw_author_name: Ignacio F. Rúa
raw_affiliation_string: Mathematics Department, University of Oviedo, Oviedo, Spain

Abstract: Abstract Quantum abstract detecting systems (QADS) provide a common framework to address detection problems in quantum computers. A particular QADS family, that of combinatorial QADS, has been proved to be useful for decision problems on eigenvalues or phase estimation methods. In this paper, we consider functional QADS, which not only have interesting theoretical properties (intrinsic detection ability, relation to the QFT), but also yield improved decision and phase estimation methods, as compared to combinatorial QADS. A first insight into the comparison with other phase estimation methods also shows promising results.
A multimodal deep learning architecture for smoking detection with a small data approach

Authors:
raw_author_name: Róbert Lakatos
raw_affiliation_string: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, Hungary; Doctoral School of Informatics, University of Debrecen, Hungary; Neumann Technology Platform, Neumann Nonprofit Ltd., Hungary
raw_author_name: Péter Pollner
raw_affiliation_string: Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Semmelweis University, Hungary
raw_author_name: András Hajdu
raw_affiliation_string: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, Hungary
raw_author_name: Tamás Joó
raw_affiliation_string: Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Semmelweis University, Hungary; Neumann Technology Platform, Neumann Nonprofit Ltd., Hungary

Abstract: Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Our model can achieve 74% accuracy for images and 98% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data.