Reviewerly/exHarmony huggingface dataset:
CalD3r and MenD3s: Spontaneous 3D facial expression databases

Authors:
raw_author_name: Luca Ulrich
raw_affiliation_string: Department of Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino 10129, Italy
raw_author_name: Federica Marcolin
raw_affiliation_string: Department of Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino 10129, Italy
raw_author_name: Enrico Vezzetti
raw_affiliation_string: Department of Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino 10129, Italy
raw_author_name: Francesca Nonis
raw_affiliation_string: Department of Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino 10129, Italy
raw_author_name: Daniel Mograbi
raw_affiliation_string: Department of Psychology, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente 225, Gávea, Rio de Janeiro, Brazil
raw_author_name: Giulia Wally Scurati
raw_affiliation_string: Department of Mechanical Engineering, Blekinge Tekniska Högskola, Valhallavägen 1, Karlskrona 371 41, Sweden
raw_author_name: Nicolo Dozio
raw_affiliation_string: Department of Mechanical Engineering, Politecnico di Milano, Via Privata Giuseppe La Masa 1, Milano 20156, Italy
raw_author_name: Francesco Ferrise
raw_affiliation_string: Department of Mechanical Engineering, Politecnico di Milano, Via Privata Giuseppe La Masa 1, Milano 20156, Italy

Abstract: In the last couple of decades, the research on 3D facial expression recognition has been fostered by the creation of tailored databases containing prototypical expressions of different individuals and by the advances in cost effective acquisition technologies. Though, most of the currently available databases consist of exaggerated facial expressions, due to the imitation principle which they rely on. This makes these databases only partially employable for real world applications such as human-computer interaction for smart products and environments, health, and industry 4.0, as algorithms learn on these ‘inflated’ data which do not respond to ecological validity requirements. In this work, we present two novel 2D + 3D spontaneous facial expression databases of young adults with different geographical origin, in which emotions have been evoked thanks to affective images of the acknowledged IAPS and GAPED databases, and verified with participants’ self-reports. To the best of our knowledge, these are the first three-dimensional facial databases with emotions elicited by validated affective stimuli.
Certificate-based authenticated encryption with keyword search: Enhanced security model and a concrete construction for Internet of Things

Authors:
raw_author_name: Danial Shiraly
raw_affiliation_string: Department of Data and Computer Science, Shahid Beheshti University, G. C., Tehran, Iran
raw_author_name: Ziba Eslami
raw_affiliation_string: Department of Data and Computer Science, Shahid Beheshti University, G. C., Tehran, Iran
raw_author_name: Nasrollah Pakniat
raw_affiliation_string: Information Science Research Center, Iranian Research Institute for Information Science and Technology (IRANDOC), Tehran, Iran

Abstract: The Internet of Things (IoT) devices produce a humongous amount of data frequently stored on cloud servers, and as a result, cryptographic techniques to guarantee the privacy of outsourced data while preserving search ability on servers are becoming indispensable research topics. A prominent example of such topics is the concept of public key authenticated encryption with keyword search (PAEKS). In spite of the large number of PAEKS schemes in the literature, most existing schemes exhibit issues in certificate management, key escrow, or even key distribution. To address these issues, recently, PAEKS schemes in the certificate-based setting have gained attention. To the best of our knowledge, there exist only two Certificate-Based Authenticated Encryption with Keyword Search (CBAEKS) schemes, both presented with rather weak security models based on single keyword challenges. In this paper, we propose an enhanced security model for CBAEKS which captures notions of multi-ciphertext and multi-trapdoor indistinguishability, then proceed to devise a concrete instantiation for a CBAEKS scheme and formally prove its security under our enhanced model. Furthermore, we prove that the existing CBAEKS schemes are insecure under the enhanced security model. Comparisons with related schemes in the literature are also provided to demonstrate that the enhanced security is achieved at some affordable costs.
Artificial intelligence and machine learning applications in the Spanish nuclear field

Authors:
raw_author_name: A. Carrasco
raw_affiliation_string: ENUSA Industrias Avanzadas S. A, Santiago Rusiñol 12, Madrid 28040, Spain
raw_author_name: L.E. Herranz
raw_affiliation_string: Unit of Nuclear Safety Research, CIEMAT, Avda. Complutense 40, Madrid 28040, Spain
raw_author_name: D. Ramos
raw_affiliation_string: AUDIAS Laboratory – Audio, Data Intelligence and Speech, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Francisco Tomás y Valiente 11, Madrid 28049, Spain
raw_author_name: M. Díaz
raw_affiliation_string: TECNATOM, Avda. Montes de Oca 1, San Sebastián de los Reyes, Madrid 28703, Spain
raw_author_name: J.M. Zazo
raw_affiliation_string: TECNATOM, Avda. Montes de Oca 1, San Sebastián de los Reyes, Madrid 28703, Spain

Abstract: Machine Learning and Artificial Intelligence techniques are increasingly applied in the nuclear field. In this way, Spanish companies and research institutions have been developing different projects in the field of nuclear energy applying such techniques. A brief overview of some of these projects is presented, which cover areas as diverse as nuclear data, nuclear fuel manufacturing, simulation codes, fuel performance monitoring, nuclear inspection, and operation training. Among the techniques used to develop them are deep neural networks for building surrogate models, convolutional neural networks for computer vision, Bayesian networks and Gaussian processes for predictive models, and speech recognition with large language models.
DUIANet: A double layer U-Net image hiding method based on improved Inception module and attention mechanism

Authors:
raw_author_name: Xintao Duan
raw_affiliation_string: College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, Henan, China
raw_author_name: Guoming Wu
raw_affiliation_string: College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, Henan, China
raw_author_name: Chun Li
raw_affiliation_string: College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, Henan, China
raw_author_name: Zhuang Li
raw_affiliation_string: College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, Henan, China
raw_author_name: Chuan Qin
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Abstract: Image hiding is the process of hiding secret image into cover image, and revealing secret image from steganographic image. However, the quality of generated images and the ability to resist steganalysis detection of steganographic image can be further improved. We propose a novel double-layer U-Net image hiding method based on improved Inception and attention mechanism. The secret image undergoes Haar wavelet transform, hiding network hides the wavelet sub-band of secret image into cover image. We use improved Inception while cascading convolution and attention mechanisms to better extract and fuse information, resulting in high-quality steganographic image. In the revealing stage, Attention mechanism can help networks extract more features about secret image, then we using inverse Haar wavelet transform to revealing high-quality secret image. The experimental results show that the method can obtain high quality images and have good security. When hiding multiple images, the method still has good performance.
Batch image encryption using cross image permutation and diffusion

Authors:
raw_author_name: Wei Song
raw_affiliation_string: School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China
raw_author_name: Chong Fu
raw_affiliation_string: Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, China; School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China
raw_author_name: Yu Zheng
raw_affiliation_string: Department of Information Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong Special Administrative Region
raw_author_name: Yanfeng Zhang
raw_affiliation_string: School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China
raw_author_name: Junxin Chen
raw_affiliation_string: School of Software, Dalian University of Technology, Dalian, 116621, China
raw_author_name: Peipei Wang
raw_affiliation_string: School of Electrical Information Engineering, Ningxia Institute of Technology, Shizuishan, 753000, China

Abstract: Chaotic encryption mostly has been proposed to protect a single image or batch images of identical size. Typically, it splices these images into a huge one, and later performs batch image encryption. Yet, this solution may not be feasible given arbitrary-size images, which could not be directly connected one by one. To address it, we propose an arbitrary-size encryption scheme via chaos, for efficiently protecting batch/multiple images at one go. Our scheme supports to automatically read size of images, and then perform cross image permutation and diffusion. The cross image permutation is generic, which could output each permutation with equal expectation. We newly propose a bi-direction cross image diffusion, which has superior performance with only 1 round operation to spread the encryption influence to all images. At last, we conduct extensive experiments and security analyses. The experimental results show that our algorithm can mask the statistical information and resist differential attacks, with metrics such as information entropy, correlation coefficients, NPCR, and UACI closely approximating their theoretical values, namely 8, 0, 99.609%, and 33.464%, respectively. Our code is available at [https://github.com/TcSong/CrossImgEncryption].
DCCNMF: Deep Complementary and Consensus Non-negative Matrix Factorization for multi-view clustering

Authors:
raw_author_name: Khanh Luong
raw_affiliation_string: Queensland University of Technology, Brisbane, Australia
raw_author_name: Thirunavukarasu Balasubramaniam
raw_affiliation_string: Queensland University of Technology, Brisbane, Australia
raw_author_name: Richi Nayak
raw_affiliation_string: Queensland University of Technology, Brisbane, Australia

Abstract: Deep non-negative matrix factorization-based methods have recently been explored in multi-view clustering due to their ability to deal with complex non-linear data. Although a few methods exist that learn both complementary and consensus information simultaneously by adding new regularizations or adjusting hyperparameters without providing a solid architecture, they do not fully exploit this information. This paper proposes a novel method called Deep Complementary and Consensus Non-negative Matrix Factorization (DCCNMF) that combines the strengths of Non-negative Matrix Factorization and Coupled Non-negative Matrix Factorization using a novel architecture to simultaneously learn both complementary and consensus information present in multi-view data. Two manifold regularization terms, namely complementary manifold and consensus manifold are introduced to preserve the view-specific and view-shared geometric structures during the dimensionality reduction from higher to lower order. Further, smoothness regularization is employed to achieve distinct low-order representations by maximizing the cosine similarity among data points with similar orientations and minimizing it among data points with dissimilar orientations. DCCNMF and benchmark methods are evaluated on several real-world datasets and the results demonstrate that DCCNMF significantly outperforms state-of-the-art multi-view learning approaches.
En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology

Authors:
raw_author_name: Umair bin Waheed
raw_affiliation_string: Department of Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
raw_author_name: George Karniadakis
raw_affiliation_string: Department of Applied Mathematics, Brown University, Providence, 02912, RI, USA

Abstract: The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems. Despite recent progress, real-time earthquake localization remains challenging due to the need to learn a generalizable Eikonal operator. We introduce a novel deep learning architecture, Enriched-DeepONet (En-DeepONet), addressing the limitations of current operator learning models in dealing with moving-solution operators. Leveraging addition and subtraction operations and a novel ‘root’ network, En-DeepONet is particularly suitable for learning such operators and achieves up to four orders of magnitude improved accuracy without increased training cost. We demonstrate the effectiveness of En-DeepONet in earthquake localization under variable velocity and arrival time conditions. Our results indicate that En-DeepONet paves the way for real-time hypocenter localization for velocity models of practical interest. The proposed method represents a significant advancement in operator learning that is applicable to a gamut of scientific problems, including those in seismology, fracture mechanics, and phase-field problems.
An electronic medical record access control model based on intuitionistic fuzzy trust

Authors:
raw_author_name: Rong Jiang
raw_affiliation_string: Institute of Intelligence Applications, Yunnan University of Finance and Economics, Kunming, China; Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, China
raw_author_name: Rui Liu
raw_affiliation_string: Institute of Intelligence Applications, Yunnan University of Finance and Economics, Kunming, China; Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, China
raw_author_name: Weiping Ding
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong, China

Abstract: Electronic Medical Records (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and use, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet in the dynamic and complex healthcare environment. We create a model to solve the privacy leakage problem of healthcare big data. The model utilizes the Latent Delicacy Allocation (LDA) topic model to quickly search documents and extract the topic probability distributions between the target patient and other patients' electronic medical records, which improves the efficiency of medical record document analysis; Calculating physician access similarity, we quantify physician access behavior to more accurately describe physician access behavior in the form of data. We combined the intuitionistic fuzzy theory with the trust evaluation method to create a novel trust evaluation method-- Intuitionistic Fuzzy Trust Evaluation Method, which can reflect the uncertainty characteristics that exist in the physician's diagnostic and treatment process when quantifying the trust of the access by using this method, and it is more flexible and practical, which enhances the accuracy of the quantification of the trust. Improving the traditional trust aggregation method by adding a time window and time decay function, the optimized method improves the accuracy of trust aggregation, and the experimental results are more in line with the actual law. We created a new intuitionistic fuzzy trust access control model using a relative entropy-based intuitionistic fuzzy clustering algorithm and a reward and punishment mechanism to achieve adaptive and dynamic access control for physician access behavior. Experiments show that the access control model based on intuitionistic fuzzy trust proposed in this article achieves 95% correctness in identifying user access behaviors and 94.29% correctness in identifying excessive user access behaviors. Experiments have proved that this model has obvious advantages over other models in the process of privacy protection of electronic medical record systems and has certain control effects.
Rationalizing Graph Neural Networks with Data Augmentation

Authors:
raw_author_name: Gang Liu
raw_affiliation_string: University of Notre Dame, USA
raw_author_name: Eric Inae
raw_affiliation_string: University of Notre Dame, USA
raw_author_name: Tengfei Luo
raw_affiliation_string: University of Notre Dame, USA
raw_author_name: Meng Jiang
raw_affiliation_string: University of Notre Dame, USA

Abstract: Graph rationales are representative subgraph structures that best explain and support the graph neural network (GNN) predictions. Graph rationalization involves the joint identification of these subgraphs during GNN training, resulting in improved interpretability and generalization. GNN is widely used for node-level tasks such as paper classification and graph-level tasks such as molecular property prediction. However, on both levels, little attention has been given to GNN rationalization and the lack of training examples makes it difficult to identify the optimal graph rationales. In this work, we address the problem by proposing a unified data augmentation framework with two novel operations on environment subgraphs to rationalize GNN prediction. We define the environment subgraph as the remaining subgraph after rationale identification and separation. The framework efficiently performs rationale–environment separation in the representation space for a node’s neighborhood graph or a graph’s complete structure to avoid the high complexity of explicit graph decoding and encoding. We conduct experiments on 17 datasets spanning node classification, graph classification, and graph regression. Results demonstrate that our framework is effective and efficient in rationalizing and enhancing GNNs for different levels of tasks on graphs.
An Improved Heterogeneous Comprehensive Learning Symbiotic Organism Search for Optimization Problems

Authors:
raw_author_name: Aminu Onimisi Abdulsalami
raw_affiliation_string: Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria; School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China
raw_author_name: Mohamed Abd Elaziz
raw_affiliation_string: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
raw_author_name: Farhad Soleimanian Gharehchopogh
raw_affiliation_string: Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
raw_author_name: Ahmed Tijani Salawudeen
raw_affiliation_string: Faculty of Engineering, University of Jos, Plateau State, Nigeria; Institute for Automation of Complex Power System, RWTH, Aachen, Germany
raw_author_name: Shengwu Xiong
raw_affiliation_string: School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China

Abstract: The Symbiotic Organism Search (SOS) algorithm, and most of its variants, exhibit the same search behaviour. That is, each organism or individual in the ecosystem applies the same update rules according to 3 phases: mutualism, commensalism, and parasitism. By applying this strategy, an organism is capable of crippling the search effort of another organism. To overcome this problem, this paper proposes a Heterogeneous Comprehensive Learning SOS (HCLSOS) which divides the population into two distinct subpopulations namely: the exploration and exploitation subpopulations. HCLSOS allows each organism to follow one of two search behaviour according to into its subpopulation: whether to explore or exploit. This addresses the problem of balancing exploration and exploitation in SOS. The proposed algorithm employs a comprehensive learning strategy for the exploration group to generate a new type of mutual vector called the multispecies mutual vector capable of preserving organisms' diversity and discouraging premature convergence. Information exchange between the two groups is unidirectional and managed through a random elite learning strategy. Through this collaboration, HCLSOS can effectively evolve organisms to explore the search space and properly exploit the discovered optimal regions. The study tested HCLSOS on 23 benchmark functions, CEC2014, CEC2017, and the recent CEC 2022 test suites. The outcome of HCLSOS is compared with 15 state-of-the-art algorithms, and the results obtained showed that HCLSOS can attain competitive or even better results. Furthermore, we applied HCLSOS to solve 3 constrained engineering problems and to design an efficient frequency control of a two-area islanded microgrid system.
Dual-resolution transformer combined with multi-layer separable convolution fusion network for real-time semantic segmentation

Authors:
raw_author_name: Zongxia Xie
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
raw_author_name: Qinghua Hu
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

Abstract: Environmental perception is crucial for unmanned mobile platforms such as autonomous vehicles and robots. Precise and fast semantic segmentation of the surrounding scene is a key task to enhance this capability. Existing real-time semantic segmentation networks are typically based on convolutional neural networks (CNNs), which have achieved good results, but they still lack control over global context features. In recent years, the Transformer architecture has achieved significant success in capturing global context, which is beneficial for improving segmentation accuracy. However, Transformers tend to ignore local connections, and their computational complexity makes real-time segmentation challenging. We propose a lightweight real-time semantic segmentation network called DTMC-Net, which combines the advantages of CNNs and Transformers. We design a special residual convolution module called the Lightweight Multi-layer Separable Convolution Attention module (LMSCA) to reduce the parameter count and perform multi-scale feature fusion to capture local features effectively. We introduce the Simple Dual-Resolution Transformer (SDR Transformer) that utilizes lightweight attention mechanisms and residual feed forward networks to capture and maintain features, with multiple bilateral fusions between two branches to exchange information. The proposed Anti-artifact Aggregation Pyramid Pooling Module (AAPPM) optimizes the upsampling process, refines features, and performs multi-scale feature fusion again. DTMC-Net only contains 4.2M parameters and achieves good performance on multiple public datasets with different scenarios.