Reviewerly/exHarmony huggingface dataset:
FL-OTCSEnc: Towards secure federated learning with deep compressed sensing

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

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

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

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

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

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

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

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.
Textural-compositional evolution of pyrite and metal remobilization during low-grade metamorphism of metapelite: Contribution to gold mineralization in the Luk Ulo Complex, Central Java, Indonesia

The Cretaceous Luk Ulo Complex (LUC) in Central Java, Indonesia, hosts orogenic gold-type ores with S derived from metapelite. The host rock consists of accreted metapelite, metamorphosed under temperature up to 510 ± 30 °C. We investigated the textural and chemical evolution of pyrite and carbonaceous matter (CM), and analyzed the hosts phases for trace elements (TEs) such as As, Pb, Cu, Sb, Te, Ag, Zn, Bi, Se, Hg, and Au, with the aim of documenting the behavior of these elements during prograde metamorphism and evaluate the implications on gold mineralization. With increasing metamorphic grade, the texture and chemical composition of pyrite evolved through different stages: 1) framboidal pyrite and pyrite nodules in the sedimentary–diagenetic facies, 2) anhedral–subhedral pyrite in sub-greenschist facies (SGSF), 3) euhedral pyrite in lower greenschist facies (LGSF), 4) elongated pyrite in middle greenschist facies (MGSF), and 5) replacement of pyrite by pyrrhotite in upper greenschist facies (UGSF). The framboidal pyrite and pyrite nodules contain measurable TEs. Recrystallization of such pyrite into euhedral pyrite in LGSF resulted in significant depletion of TEs, except for Se, by pressure-solution. With increasing metamorphic grade, the recrystallization of organic matter to “cooked” CM in LGSF was accompanied by significant depletion in most TEs, with only Fe and S being retained. The transformation of cooked CM into graphite in MGSF, and of elongated pyrite into pyrrhotite in UGSF resulted in further removal of remaining TEs. These observations and the absence of para-amphibolite in the LUC suggest that remobilization of TEs and S from sedimentary pyrite and CM might have occurred in the LGSF, much earlier than previously proposed (UGSF-amphibolite transition).
A preliminary study on few-shot knowledge reasoning mechanism based on three-way partial order structure

Few-shot knowledge reasoning, as a novel theory that attempts to combine few-shot learning with knowledge reasoning, is a challenging subject. This article intends to explore few-shot knowledge reasoning mechanism based on three-way partial order structure. First, from the perspective of granular computing, three knowledge reasoning operators are defined: granule splitting, granule combination, and granule isomorphism; then, based on the knowledge reasoning operators, the data augmentation operation and knowledge reasoning algorithm are presented; finally, some specific experiments on five datasets are conducted to further illustrate the few-shot knowledge reasoning algorithm. Experiment results show that the method proposed in this article can effectively realize knowledge reasoning based on small sample size data. It can be concluded that the proposed few-shot knowledge reasoning method is effective at some extent, and is expected to bring inspiration to the development of other concept-based knowledge reasoning models.
Revocable certificateless Provable Data Possession with identity privacy in cloud storage

Provable Data Possession (PDP) has gained widespread adoption for ensuring the integrity of data in remote cloud storage, where a data owner can delegate a third party auditor (TPA) to perform data auditing. To eliminate key escrow problem or complicated certificate management in classic solutions, numerous certificateless PDP schemes have been proposed while they failed to achieve efficient user revocation and protect user identity privacy. Therefore, we propose ReCIP, a revocable certificateless PDP scheme with identity privacy, where a TPA can perform public data integrity batch verification for a user while learning no useful knowledge about user identity privacy. Technically, we introduce a new user revocation strategy that directly revokes users’ secret keys, with no correlation to the number of data blocks in place for revocation time cost. To further boost the efficiency of ReCIP, we employ a semi-generic online-offline strategy to obtain an online-offline ReCIP (ReCIPoo) to reduce the time cost of tag generation. Moreover, we conduct a formal security proof of ReCIP, where the security is reduced to simple computational Diffie–Hellman problem and discrete logistic problem. Compared to state-of-the-art solutions, our ReCIPoo achieves comparable computation and communication cost while still achieving user revocation and protecting user identity privacy.
Occlusion-Aware Visual Object Tracking Based on Multi-template Updating Siamese Network

Visual object tracking is a crucial area of computer vision research. It aims to accurately track objects in videos with challenges such as occlusion, deformation, and lighting variations. Existing algorithms face difficulties when objects leave the camera or reappear after being occluded, and they also struggle to track objects with significant appearance changes. To address these issues, this study proposed a novel tracking algorithm. It combines tracking and detection, enabling global searching when objects are occluded or disappear and redetecting them when they reappear. A multi-template updating mechanism was used to adapt to changes in appearance. The study proposed the OTB100-AB and UAV123-AB datasets to evaluate the tracker's ability to handle target disappearance, along with the TNR metric. The proposed algorithm was evaluated on these datasets, as were OTB50 and UAV20L, which outperformed state-of-the-art algorithms and significantly improved tracking performance.
The influence of spatial dimensions of virtual environments on attitudes and nonverbal behaviors during social interactions

Research on physical-world environments has shown that the spatial properties of built worlds are consequential for shaping psychological states and social behavior. However, it has been difficult to empirically test this in natural settings in the physical world. This study uses immersive virtual reality (VR) environments, which have shown to have comparable effects to physical-world environments, to investigate the influence of two spatial dimensions (ceiling height and floor area) on individuals’ attitudes and nonverbal behaviors during social interactions. In the present study, groups of three to four physically remote participants wore VR headsets (n = 110) and took part in discussions every week for four weeks in one of four virtual environments that varied in their spatial dimensions (low or high ceilings, small or large floor areas). Results showed that, when in a virtual environment with a high ceiling, participants reported feeling greater perceived restorativeness, awe, and momentary affective well-being, compared to when they were in virtual environments with low ceilings. Participants also paid more social attention (i.e., looked at other group members), when they were in virtual environments with high ceilings. When in a virtual environment with a large floor area, participants reported having a greater sense of awe, compared to environments with small floor areas. Furthermore, when in a large environment with a high ceiling, participants physically moved their heads more slowly and virtually stood further apart from their group members, compared to the other three conditions. We discuss implications for theoretical work on context and behaviors as well as design of social VR environments.
Federated learning with a Balanced Heterogeneous-Yoke and Loose restriction

The most critical challenges of federated learning for joint modeling via model sharing are the problem of model drift due to the heterogeneous distribution of local data, and the cooperation of a subset of random devices due to communication constraints. To address these dilemmas, we propose a novel Federated Learning algorithm with a Balanced Heterogeneous-Yoke and Loose restriction(FedByL), which chooses to exploit more historical gradients from the client to accelerate model convergence; in addition, a more relaxed local restriction is chosen in local updates, and an incentive clause is proposed to encourage cooperative clients to find the global optimum. Experiments and analyses on 95 sets of five real-world datasets and one synthetic dataset show that FedByL achieves better in terms of convergence speed and accuracy compared to superior algorithms (e.g. FedDyn, SCAFFOLD). In particular, FedByL communication overhead is the same as FedAvg, being lightweight and requiring only one additional storage variable locally, which greatly reduces the communication burden and the risk of information being hijacked.
SelectE: Multi-scale adaptive selection network for knowledge graph representation learning

Most knowledge graphs in the real world suffer from incompleteness which can be addressed through knowledge graph representation learning (KGRL) techniques that use known facts to infer missing links. In this paper, a novel multi-scale adaptive selection network for KGRL, namely SelectE, is developed to learn richer multi-scale interactive features and automatically select important features, thereby achieving promising KGRL performance. Specifically, first, the input feature matrix is redesigned to better cooperate with multi-scale convolution to improve the interaction of entities and relations. Second, a multi-scale learning module is designed to learn richer multi-scale features from the input matrix using multiple branches with different kernel sizes. Third, to automatically strengthen the important features and suppress the useless features, a multi-scale adaptive selection mechanism is proposed to dynamically allocate the weights of the obtained features based on their contained information. The core of SelectE is to maximize interactions while also considering how to better utilize features. Finally, the outstanding performance of SelectE is validated by a series of comparison experiments on seven benchmark datasets (FB15k-237, WN18RR, FB15k, WN18, YAGO3-10, KINSHIP, UMLS). The experimental results show that SelectE outperforms other state-of-the-art models, demonstrating its remarkable performance and generalization.
Extending User Control for Image Stylization using Hierarchical Style Transfer Networks

<h2>Abstract</h2> The field of neural style transfer refers to the re-rendering of content image while fusing the features of a style image. The recent studies either focus on multiple style transfer or arbitrary style transfer while using perceptual and fixpoint content losses in their respective network architectures. The aforementioned losses provide notable stylization results but lack the liberty of style control to the user. Consequently, the stylization results also compromise the preservation of details with respect to the content image. This work proposes the hierarchical style transfer network (HSTN) for the image stylization task that could provide the user with the liberty to control the degree of incurred style via denoising parameter. The HSTN incorporates the proposed fixpoint control loss that preserves details from the content image and the addition of denoising CNN network (DnCNN) and denoising loss for allowing the user to control the level of stylization. The encoder-decoder block, the DnCNN block, and the loss network block make the basic building blocks of HSTN. Extensive experiments have been carried out, and the results are compared with existing works to demonstrate the effectiveness of HSTN. The subjective user evaluation shows that the HSTN's stylization represents the best fusion of style and generates unique stylization results while preserving the content image details, which is evident by acquiring 12% better results than the second-best performing method. It has also been observed that the proposed work is amongst the studies that achieve the best trade-off regarding content and style classification scores, i.e. 37.64% and 60.27%, respectively.