Reviewerly/exHarmony huggingface dataset:
Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

Authors:
raw_author_name: Kai Zhang
raw_affiliation_string: College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China
raw_author_name: Dong Zhang
raw_affiliation_string: Key Laboratory of Gas Hydrate, Qingdao Institute of Marine Geology, Ministry of Natural Resources, Qingdao, 266237, Shandong, China

Abstract: Conventional machine learning (CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning (DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network (CNN) and an adaptive particle swarm optimization-least squares support vector machine (APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models. This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.
SPACE: Senti-Prompt As Classifying Embedding for sentiment analysis

Authors:
raw_author_name: Jinyoung Kim
raw_affiliation_string: Department of Artificial Intelligence, Sungkyunkwan University 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Republic of Korea
raw_author_name: Youngjoong Ko
raw_affiliation_string: Department of Computer Science and Engineering, Sungkyunkwan University 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Republic of Korea

Abstract: In natural language processing, the general approach to sentiment analysis involves a pre-training and fine-tuning paradigm using pre-trained language models combined with classifier models. Recently, numerous studies have applied prompts not only to downstream generation but also to classification tasks as well. However, to fully utilize the advantages of prompts and incorporate the context-dependent meaning of class representations into the prompts, it is necessary for the prompts to be learned similarly to the sentiment representations of each class. To achieve this, We introduce a novel method to learn soft prompts during fine-tuning. In this method, the class prompts are initialized with sentiment-related embeddings and are trained by a denoising task, which replaces them with masked tokens, just like the conventional masked language model (MLM) approach. Furthermore, a novel attention pattern is designed to tune attention between class prompts effectively. As a result, we demonstrate that our approach outperforms state-of-the-art models through experiments on four common datasets, achieving superior performance on sentiment analysis.
Extracting key topics from massive COVID-19 information on social networks: An integrated deep learning and LDA framework

Authors:
raw_author_name: Dan Li
raw_affiliation_string: Department of Economics and Information Management, Shanghai University of Finance&Economics Zhejiang College, Jinhua, 321013, China
raw_author_name: Zheng Qu
raw_affiliation_string: Faculty of Business Information, Shanghai Business School, Shanghai, 200235, China
raw_author_name: Chen Lyu
raw_affiliation_string: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China

Abstract: Quickly discovering and processing unmeaningful information is especially important in the current social network, where public opinion is rapidly fermenting. In this study, a novel topic extraction framework is proposed to extract key topics for the special event of COVID-19 on social networks. First, huge and noisy COVID-19 information is pre-processed for our framework. Second, a model of key information identification is designed for COVID-19 information, which combines the deep learning model of Text Convolutional Neural Networks (TextCNN) with Bi-directional Long Short-Term Memory (BiLSTM) to perform a multi-class classification task. Finally, a model of key topic extraction is developed with the Latent Dirichlet Allocation (LDA) model, which is able to summarize topics and keywords from a large amount of information. we crawled the public opinion information of the COVID-19 on weibo as our dataset. experimental results demonstrate that our model outperforms the other benchmark models in identifying important information related to COVID-19. the key topic extraction model based on LDA can further divide key information into multiple topics ►And describe the topics through keywords for fast tracking of COVID-19 pandemic on social networks.
FL-OTCSEnc: Towards secure federated learning with deep compressed sensing

Authors:
raw_author_name: Leming Wu
raw_affiliation_string: College of Information Sciences and Technology, Donghua University, Shanghai, 201620, China; Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Donghua University, Shanghai, 201620, China
raw_author_name: Yaochu Jin
raw_affiliation_string: College of Information Sciences and Technology, Donghua University, Shanghai, 201620, China; Department of Computer Science, University of Surrey, Guildford, Surry, GU2 7XH, UK; Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Donghua University, Shanghai, 201620, China; Trustworthy and General AI Lab, School of Engineering, Westlake University, Hangzhou, 310024, China
raw_author_name: Yuping Yan
raw_affiliation_string: Faculty of Informatics, Eötvös Loránd University, Budapest, 310030, Hungary; Trustworthy and General AI Lab, School of Engineering, Westlake University, Hangzhou, 310024, China
raw_author_name: Kuangrong Hao
raw_affiliation_string: College of Information Sciences and Technology, Donghua University, Shanghai, 201620, China; Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, Donghua University, Shanghai, 201620, China

Abstract: In recent years, federated learning has made significant progress in preserving data privacy. In this paradigm, clients train local models without sharing their raw data, thereby substantially mitigating the vulnerability to private data exposure. However, it is still possible to infer clients’ raw data by leveraging the gradient parameters exchanged between the clients and the server. To address this problem, this paper proposes a novel algorithm that introduces deep compressed sensing into federated learning to support one time encryption, called FL-OTCSEnc, to secure the communication data exchanged between the clients and the server. The process starts by creating a dataset of deep learning model parameters and training a system for both encryption and decryption using deep compressed sensing. This system is then used to secure the communication between clients and the server in federated learning, by encrypting and decrypting the data exchanged. To enhance the security of the proposed algorithm, we introduce an assessment method for evaluating the security level of the clients, facilitating the selection of suitable candidates for deployment within distributed training encryption and decryption models that are updated in real time. To enhance the accuracy of the decrypted deep network model, we introduce a tandem loss function in the training process. Moreover, this paper proves that the proposed end-to-end encryption method satisfies additive homomorphic encryption properties. Extensive experiments demonstrate that the deep compressed sensing encryption in federated learning achieves promising results without increasing the computational complexity.
Learning disentangled representations in signed directed graphs without social assumptions

Authors:
raw_author_name: Jinhong Jung
raw_affiliation_string: School of Software, Soongsil University, Seoul, 07027, Republic of Korea

Abstract: Signed graphs can represent complex systems of positive and negative relationships such as trust or preference in various domains. Learning node representations is indispensable because they serve as pivotal features for downstream tasks on signed graphs. However, most existing methods often oversimplify the modeling of signed relationships by relying on social theories, while real-world relationships can be influenced by multiple latent factors. This hinders those methods from effectively capturing the diverse factors, thereby limiting the expressiveness of node representations. In this paper, we propose DINES, a novel method for learning disentangled node representations in signed directed graphs without social assumptions. We adopt a disentangled framework that separates each embedding into distinct factors, allowing for capturing multiple latent factors, and uses signed directed graph convolutions that focus solely on sign and direction, without depending on social theories. Additionally, we propose a new decoder that effectively classifies an edge's sign by considering correlations between the factors. To further enhance disentanglement, we jointly train a self-supervised factor discriminator with our encoder and decoder. Throughout extensive experiments on real-world signed directed graphs, we show that DINES effectively learns disentangled node representations, and significantly outperforms its competitors in predicting link signs.
Towards trustworthy remaining useful life prediction through multi-source information fusion and a novel LSTM-DAU model

Authors:
raw_author_name: Rui Bai
raw_affiliation_string: School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China
raw_author_name: Khandaker Noman
raw_affiliation_string: School of Civil Aviation, Northwestern Polytechnical University, Xi’an, 710072, China
raw_author_name: Yu Yang
raw_affiliation_string: Aircraft Strength Research Institute, Aviation Industries of China, Xi’an, 710065, China
raw_author_name: Yongbo Li
raw_affiliation_string: School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China
raw_author_name: Weiguo Guo
raw_affiliation_string: School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China

Abstract: Remaining useful life (RUL) prediction is a key part of the prognostic and health management of machines, which can effectively prevent catastrophic faults and decrease expensive unplanned maintenance. A good health indicator (HI) can ensure the accuracy and reliability of RUL prediction. However, most of the existing HI construction methods use only a single signal and rely heavily on prior knowledge, making it difficult to capture critical information about mechanical degradation, which in turn affects the performance of RUL prediction. To solve the above problems, a novel adaptive multi-source fusion method based on genetic programming is proposed for building a HI that can effectively reflect the health state of machines. Subsequently, a new LSTM model with a dual-attention mechanism is developed, which differentially handles the network input information and the recurrent information to improve the prediction performance and reduce the time complexity at the same time. Experimental validation is carried out on the real PRONOSTIA bearing dataset. The comparative results validate that the constructed fusion HI has better comprehensive performance than other fusion HIs, and the proposed prediction method is competitive with the current state-of-the-art methods.
Blind image quality assessment with semi-supervised learning

Authors:
raw_author_name: Xiwen Li
raw_affiliation_string: Shool of Information Science and Engineering, Ningbo University, Ningbo 315211, China
raw_author_name: Zhihua Wang
raw_affiliation_string: Department of Engineering, Shenzhen MSU-BIT University, Shenzhen 518000, China
raw_author_name: Binwei Xu
raw_affiliation_string: Shool of Information Science and Engineering, Ningbo University, Ningbo 315211, China

Abstract: Blind image quality assessment (BIQA) aims to automatically predict the perceptual quality of an image without requiring access to its pristine reference counterpart. BIQA models are typically developed through supervised learning, optimizing and testing them by comparing their predictions to human ratings, usually expressed as mean opinion scores (MOS), which can be labor-intensive to collect. The performance of these BIQA models is significantly reliant on the amount of labeled training data. When there is a shortage of human-rated data, these BIQA models may perform inadequately. In this study, we investigate the potential of incorporating unlabeled data to mitigate this issue and enhance the performance of BIQA models. To achieve this, we propose a deep ensemble-based BIQA model (referred to as the “target model”) with two heads: one for quality estimation and the other for pseudo-label generation. Initially, we train it on a small set of human-rated images where the supervisory signals are binary labels indicating the pairwise ranking of perceptual quality for image pairs. Then, the head responsible for pseudo-label generation assigns pseudo-binary labels to unlabeled pairs. Subsequently, we re-train the target model using a combination of labeled and pseudo-labeled datasets. This process can be iterated, allowing for the progressive improvement of the target model’s performance. We conduct comprehensive case studies to illustrate the advantages of utilizing unlabeled data for BIQA, particularly in terms of model generalization and identifying cases of model failure.
Voice-based Age, Gender, and Language Recognition Based on ResNet Deep model and Transfer learning in Spectro-Temporal Domain

Authors:
raw_author_name: Samira Mavaddati
raw_affiliation_string: Electronic Department, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran

Abstract: In personal identity recognition systems, detecting a person's age, gender, and language using voice signal characteristics is a crucial issue, especially because of the importance of security considerations. Age, gender, and language classification problems are important in signal processing because they are used to analyze and understand human behavior, interactions, and preferences. This can be especially useful in the fields of human-computer interaction, psychology, and social science research. In this paper, a new system for detecting a speaker's age, gender, and language based on deep learning models is presented. Deep learning models have shown great efficacy in various fields of signal processing. For this paper, a range of deep models were tested, including convolutional neural networks (CNNs), recurrent neural network (RNN), and a fine-tuning ResNet34 architecture. Additionally, techniques such as transfer learning were applied to improve the efficiency of the proposed system. The input voice signals are preprocessed by applying the spectro-temporal transform to obtain additional features that can be fed to the ResNet34 model, which is designed specifically for the task of voice signal processing. The dataset used in this paper was sourced from the Mozilla common voice initiative, which is dedicated to advancing speech recognition and language identification technologies. The performance of the proposed algorithm was evaluated in the presence of Gaussian noise to determine its robustness. The experimental results demonstrated that the proposed algorithm significantly outperformed basic algorithms and other deep neural networks in terms of age and gender recognition from voice signals.
Sliding mode control for discrete interval type-2 fuzzy semi-Markov jump models with delay in controller mode switching

Authors:
raw_author_name: Runkun Li
raw_affiliation_string: School of Engineering, Qufu Normal University, Rizhao 276826, China
raw_author_name: Wenhai Qi
raw_affiliation_string: School of Engineering, Qufu Normal University, Rizhao 276826, China
raw_author_name: Ju H. Park
raw_affiliation_string: School of the Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea
raw_author_name: Jun Cheng
raw_affiliation_string: College of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China
raw_author_name: Kaibo Shi
raw_affiliation_string: School of Information Science and Engineering, Chengdu University, Chengdu 610106, China

Abstract: The problem of asynchronous sliding mode control (SMC) for discrete nonlinear semi-Markov jump models is investigated by the semi-Markov kernel strategy, where asynchronous mechanism means that there is a one-step delay in the controller mode switching related to the system mode. Unlike traditional T-S fuzzy model, the interval type-2 fuzzy model is applied to represent discrete semi-Markov jump systems with uncertain parameters. The main novelty is to design a suitable asynchronous SMC mechanism for the attainment of the quasi-sliding mode, overcoming the difficulty caused by parameter uncertainties. According to the fuzzy Lyapunov function related to the elapsed time and the current mode, the stability criterion of nonlinear model is developed by additional matrix variables, realizing the σ-error mean-square stability of the underlying augmented system. An appropriate fuzzy asynchronous SMC mechanism is established to achieve the accessibility of the quasi-sliding mode. Finally, a tunnel diode circuit model validates the proposed control algorithm.
Learning-Augmented Maximum Flow

Authors:
raw_author_name: Adam Polak
raw_affiliation_string: Jagiellonian University, ul. Lojasiewicza 6, 30-348, Krakow, Poland; Max-Planck Institute for Informatics, Saarland Informatics Campus, 66123, Saarbrucken, Germany

Abstract: We propose a framework for speeding up maximum flow computation by using predictions. A prediction is a flow, i.e., an assignment of non-negative flow values to edges, which satisfies the flow conservation property, but does not necessarily respect the edge capacities of the actual instance (since these were unknown at the time of learning). We present an algorithm that, given an m-edge flow network and a predicted flow, computes a maximum flow in O(mη) time, where η is the ℓ1 error of the prediction, i.e., the sum over the edges of the absolute difference between the predicted and optimal flow values. Moreover, we prove that, given an oracle access to a distribution over flow networks, it is possible to efficiently PAC-learn a prediction minimizing the expected ℓ1 error over that distribution. Our results fit into the recent line of research on learning-augmented algorithms, which aims to improve over worst-case bounds of classical algorithms by using predictions, e.g., machine-learned from previous similar instances. So far, the main focus in this area was on improving competitive ratios for online problems. Following Dinitz et al. (NeurIPS 2021), our results are among the firsts to improve the running time of an offline problem.
Knowledge graph-based image classification

Authors:
raw_author_name: Christel Vrain
raw_affiliation_string: LIFO - Laboratoire d'Informatique Fondamentale d'Orléans (Bâtiment IIIA, Rue Léonard de Vinci, B.P. 6759, F-45067 ORLEANS Cedex 2 - France)
raw_author_name: Frédéric Ros
raw_affiliation_string: PRISME - Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (8, rue Léonard de Vinci - 45067 ORLEANS CEDEX 2 - France - France)
raw_author_name: Thi-Bich-Hanh Dao
raw_affiliation_string: LIFO - Laboratoire d'Informatique Fondamentale d'Orléans (Bâtiment IIIA, Rue Léonard de Vinci, B.P. 6759, F-45067 ORLEANS Cedex 2 - France)

Abstract: This paper introduces a deep learning method for image classification that leverages knowledge formalised as a graph created from information represented by pairs attribute/value. The proposed method investigates a loss function that adaptively combines the classical cross-entropy commonly used in deep learning with a novel penalty function. The novel loss function is derived from the representation of nodes after embedding the knowledge graph and incorporates the proximity between class and image nodes. Its formulation enables the model to focus on identifying the boundary between the most challenging classes to distinguish. Experimental results on several image databases demonstrate improved performance compared to state-of-the-art methods, including classical deep learning algorithms and recent algorithms that incorporate knowledge represented by a graph.