Reviewerly/exHarmony huggingface dataset:
An exponential negation of complex basic belief assignment in complex evidence theory

Negation is an important operation in evidence theory, whose idea is to consider the opposite of events, can deal with some problems with uncertainties from the opposite side and obtain information behind probability distribution. In classical D-S theory (Dempster-Shafer's theory), there are already many negation methods existed on real number field and many properties of which have been discovered. However, in complex evidence theory, which based on complex number field, negation is still an open problem. In order to deal with some problems like those in D-S theory, a new negation method for CBBA (Complex Basic Belief Assignment) should be proposed. In this paper, a new negation method called CBBA exponential negation will be presented, which can be seen as a generalization from BBA (Basic Belief Assignment) to CBBA. This proposed negation transforms a CBBA to another one with the entropy increased simultaneously. Also, some properties of this negation will be discussed such as invariance, convergence, fixed point, distribution of Pascal triangle, convergence speed, impact on negation convergence and so on. Besides, most among them will be strictly proved in this paper. Furthermore, a new entropy for CBBA and some numerical examples will be presented, and we will study the proposed negation from the view of entropy. Finally, an application of CBBA exponential negation will be shown in the end.
Scalable CCA-secure public-key authenticated encryption with keyword search from ideal lattices in cloud computing

Public-key authenticated encryption with keyword search (PAEKS) is an important cryptographic primitive for realizing effective and confidential searches on encrypted data in cloud computing. Several PAEKS schemes with various appealing functionalities have been proposed in the literature. Nevertheless, most of them are based on the classical number-theoretic assumptions and are impossible to resist quantum attacks. Their security is only proven against chosen-keyword attacks and is considered insufficient. Fortunately, ring-LWE and ring-ISIS problems reduced from the worst-case ideal lattices are believed to be post-quantum secure and could be improved computational efficiency in constructions. In this paper, we propose a scalable PAEKS scheme based on ring-LWE and ring-ISIS by adopting the ring analog of lattice algorithms of both digital signature and searchable encryption schemes, in which signature algorithms are used to authenticate ciphertext, making our PAEKS resistant to insider keyword guessing attacks. The scheme is also proved secure against chosen-ciphertext attacks and token privacy attacks. In addition, we extend the PAEKS scheme to conjunctive keywords and multi-user settings to allow for realistic promotion. We finally implement our schemes and compare them with related counterparts, which shows that our constructions are efficient in practical applications.
Dynamic Hand Gesture Classification Based on Multichannel Radar Using Multistream Fusion 1-D Convolutional Neural Network

Radar-based dynamic hand gesture classification has been an active research field in recent years. Deep learning methods using radar sensors are widely used to classify dynamic hand gestures. The existing deep learning methods need the overhead of performing the slow- and fast-time Fourier transforms to obtain the various spectrum images as the input of the deep convolutional neural network (DCNN). In this article, a dynamic hand gesture classification method based on the multichannel radar using a multistream fusion 1-D convolutional neural network (MSF-1-D-CNN) is proposed. The proposed MSF-1-D-CNN has four branches in parallel, and each branch has the inception modules to extract features from the raw echo data of each receiving antenna. Then, the extracted features from each branch are concatenated, and the long short-term memory (LSTM) layer is utilized to extract the temporal characteristic of the concatenated features. Finally, the dense layer with the softmax function is utilized to obtain the classification result of hand gestures. The experimental results show that, compared with existing methods, the proposed method using multichannel radar data can provide improved classification accuracy when the hand has a large incident angle and distance from the radar. Moreover, the proposed method can also reduce the network parameters and computational complexity, which has the potential to be implemented on commercial embedded systems.
COVID-19 impact on dairy sector: The mediating role of knowledge sharing and trust on innovation capability

Background: In the current era, innovation has become the basis for the success of all industries. In reality, fast innovation facilitated by rapidly changing technological discoveries is critical to global economic progress. Aim: The primary goal of this article is to examine the effect of knowledge exchange and development of supervisory support, trust, training, information technology, and industrial cluster resources on innovation capabilities in the dairy sector of Pakistan. Setting: From a total of 520 small and medium enterprise (SMEs) dairy farms, 227 owners and managers were carefully chosen to participate in the survey. Method: The current study’s research framework was based on the resources and diffusion of innovation perspective theories. The data were gathered from dairy farm owners and managers in Punjab, Pakistan. SmartPLS-SEM was used to examine the multivariate connection among the variables. Results: The current research finds that training and development, supervisory assistance, and industrial cluster resources strongly influence knowledge sharing. Furthermore, trust has a favourable influence on innovative capabilities. However, the mediation effect of knowledge sharing (KS) did not support information technology (IT) training and development (T&amp;D) and innovative capabilities (IC). Conclusion: According to findings in the study, T&amp;D as a form of learning connect employees through the sharing of new ideas, allowing the business to improve and the concept to be modified. This study found that supervisory assistance significantly impacts innovative capabilities and knowledge sharing.
Spammer detection via ranking aggregation of group behavior

Online review networks have become a popular platform for disseminating information which attracts large numbers of spammers to influence the reputation of the online products by writing fake reviews. Thus, the spammer detection becomes crucial in controlling the fraud reviews which is valuable for practical applications. However, existing ranking-based methods usually focus on the rating value and textual features of reviews ignoring the features of group behavior which results in the sharp degradation of spammer detection's performance in case of large-scale spammer group attack. In this paper, we propose a novel ranking aggregation method based on features of collusive attack of spammer groups to optimize the spammer ranking algorithm by reassigning the spamicity score for each reviewer. Extensive experiments on various real-world datasets with spammer injection demonstrate that our proposed optimization method can significantly improve the accuracy and robustness of the spammer detection. The average improvements of performance for ranking before and after optimization achieve 10.73%, 42.86% and 4.32% respectively on the Recall, Precision and AUC metrics.
RCA-CycleGAN: Unsupervised underwater image enhancement using Red Channel attention optimized CycleGAN

Underwater image enhancement has great significance in underwater vision tasks. In this paper, we propose a novel unsupervised Red Channel Attention optimized CycleGAN (RCA-CycleGAN) to deal with the challenging blurred and color cast underwater scene. The enhancement generator consists of two subnetworks: an auto-encoder and an RCA subnetwork. Firstly, the Red Channel is fed to the RCA subnetwork to generates attention. Secondly, the generated attention map is used to fine-tune the auto-encoder. Specifically, the auto-encoder consists of an encoder, a feature transformation module, and a decoder. The RCA subnetwork has the same structure as the encoder and the feature transformation module to ensure the same receptive field. Furthermore, in addition to the conventional global discriminator, we leverage two local discriminators to enhance the performance on the locally degraded part. Experimental results against the state-of-the-art methods demonstrate that our method has better performance in terms of both qualitative and quantitative evaluations.
DP-TrajGAN: A privacy-aware trajectory generation model with differential privacy

Open Data Processing Services (ODPS) offers vast storage capacity and excellent efficiency, which collects and stores a lot of data. As an essential component of ODPS, location-based services (LBS) are widely used in many aspects. However, LBS generates tens of thousands trajectories, which have a significant likelihood of revealing personal information. In order to address this kind of privacy concerns, a novel model, namely Privacy-Aware Trajectory Generation Model with Differential Privacy (DP-TrajGAN), is proposed in this paper. Firstly, the long short-term memory network (LSTM) is improved and introduced into in the generative adversarial network (GAN) to learn the original distribution. Subsequently, the privacy-preserving of GAN is further enhanced using differential privacy (DP) while retaining the original features of the trajectories, which is called DP-TrajGAN. Furthermore, the privacy budget allocation in the DP is modeled using the Partially Observable Markov Decision Process (POMDP), which takes into account the trade-off between privacy and utility. The experimental findings demonstrate that, when compared to other models, DP-TrajGAN can provide higher quality trajectories with retained statistical features and effective privacy preservation.
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks

One major goal of the AI security community is to securely and reliably produce and deploy deep learning models for real-world applications. To this end, data poisoning based backdoor attacks on deep neural networks (DNNs) in the production stage (or training stage) and corresponding defenses are extensively explored in recent years. Ironically, backdoor attacks in the deployment stage, which can often happen in unprofessional users' devices and are thus arguably far more threatening in real-world scenarios, draw much less attention of the community. We attribute this imbalance of vigilance to the weak practicality of existing deployment-stage backdoor attack algorithms and the insufficiency of real-world attack demonstrations. To fill the blank, in this work, we study the realistic threat of deployment-stage backdoor attacks on DNNs. We base our study on a commonly used deployment-stage attack paradigm - adversarial weight attack, where adversaries selectively modify model weights to embed backdoor into deployed DNNs. To approach realistic practicality, we propose the first gray-box and physically realizable weights attack algorithm for backdoor injection, namely subnet replacement attack (SRA), which only requires architecture information of the victim model and can support physical triggers in the real world. Extensive experimental simulations and system-level real- world attack demonstrations are conducted. Our results not only suggest the effectiveness and practicality of the proposed attack algorithm, but also reveal the practical risk of a novel type of computer virus that may widely spread and stealthily inject backdoor into DNN models in user devices. By our study, we call for more attention to the vulnerability of DNNs in the deployment stage.
Digital Implementation of Radial Basis Function Neural Networks Based on Stochastic Computing

Nowadays Internet of Things (IoT) and mobile systems use more and more Machine Learning based solutions, which implies a high computation cost with a low energy consumption. This is causing a revival of interest in unconventional hardware computing methods capable of implementing both linear and nonlinear functions with less hardware overhead than conventional fixed point and floating point alternatives. Particularly, this work proposes a novel Radial Basis Function Neural Network (RBF-NN) hardware implementation based on Stochastic Computing (SC), which applies probabilistic laws over conventional digital gates. Several complex functions design to implement RBF-NN are presented and theoretically analyzed, such as the squared Euclidean distance and the stochastic Gaussian kernel similarity function between input samples and prototypes. The efficiency and performance of the methodology is tested over well-known pattern recognition tasks, including the MNIST dataset. The results show a low-cost methodology in terms of logic resources and power, along with an inherent capability to implement complex functions in a simple way. This methodology enables the implementation of massively parallel large scale RBF-NN with relatively low hardware requirements while maintaining 96.20% accuracy, which is nearly the same for the floating point and fixed point models (96.4% and 96.25%, respectively).
Likert Scoring with Grade Decoupling for Long-term Action Assessment

Long-term action quality assessment is a task of evaluating how well an action is performed, namely, estimating a quality score from a long video. Intuitively, long-term actions generally involve parts exhibiting different levels of skill, and we call the levels of skill as performance grades. For example, technical highlights and faults may appear in the same long-term action. Hence, the final score should be determined by the comprehensive effect of different grades exhibited in the video. To explore this latent relationship, we design a novel Likert scoring paradigm in-spired by the Likert scale in psychometrics, in which we quantify the grades explicitly and generate the final quality score by combining the quantitative values and the corresponding responses estimated from the video, instead of performing direct regression. Moreover, we extract grade-specific features, which will be used to estimate the responses of each grade, through a Transformer decoder architecture with diverse learnable queries. The whole model is named as Grade-decoupling Likert Transformer (GDLT), and we achieve state-of-the-art results on two long-term action assessment datasets. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project page https://isee-ai.cn/-angchi/CVPR22_GDLT.html
Hierarchical Similarity Alignment for Domain Adaptive Ship Detection in SAR Images

Ship detection from synthetic aperture radar (SAR) images is a hot topic, but the difficulty in collecting labeled SAR images may hinder the development of deep-learning-based detection methods. Inspired by the idea of domain adaptation, in this article, we propose a hierarchical similarity alignment neural network (HSANet) for ship detection in SAR images, which is a domain adaptive (DA) approach with optical remote sensing images as training samples. The kernel target of HSANet is to mine and align both the global structure and the local instance information between SAR and optical images, where two modules, structural alignment module (SAM) and prototype alignment module (PAM), are designed to, respectively, conduct two hierarchies of alignment process. In general, SAM attempts to extract the global structure similarity which exists in image-level feature representation, while PAM tends to extract the local shape similarity which is instance-level representation. To be specific, SAM is developed by Fourier-based feature alignment, which tries to describe the similar structural relationship between optical and SAR images. Meanwhile, PAM is proposed based on the conjoint confidence analysis where the instance-level ship representations of the source and target domains is aligned. SAM and PAM work together to construct a hierarchical domain adaptation network for SAR ship detection. Experiments on several public datasets may indicate the effectiveness of the proposed method.
Multi-View Fuzzy Classification With Subspace Clustering and Information Granules

Multi-view learning becomes increasingly attractive and promising because multimodal or multi-view data are commonly encountered in real-world applications. In this study, we develop a novel multi-view Takagi–Sugeno–Kang (TSK) fuzzy system framework to handle classification problems for such data. We propose an anchor and graph subspace clustering strategy to discover and represent the actual latent data distribution for each view separately. In this way, the discriminate anchors (landmarks) are learned to capture the main structure of the multi-view data. This strategy also provides a computationally efficient clustering algorithm with respect to the number of instances. These resulting anchors are formed as the prototypes of information granules (IGs) for fuzzy modeling. Then we construct an information-granule-based multi-view TSK fuzzy classification model inherited from the natural interpretability of fuzzy rule-based systems. Concretely, the relationship between the multi-view input and label output spaces is depicted by IGs-oriented fuzzy rules. The experimental studies involve various commonly used benchmark datasets, which indicate that our proposed method achieves comparable or better performance compared to the state-of-the-art algorithms.
Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes

Whilst diffusion probabilistic models can generate high quality image content, key limitations remain in terms of both generating high-resolution imagery and their associated high computational requirements. Recent Vector-Quantized image models have overcome this limitation of image resolution but are prohibitively slow and unidirectional as they generate tokens via element-wise autoregressive sampling from the prior. By contrast, in this paper we propose a novel discrete diffusion probabilistic model prior which enables parallel prediction of Vector-Quantized tokens by using an unconstrained Transformer architecture as the backbone. During training, tokens are randomly masked in an order-agnostic manner and the Transformer learns to predict the original tokens. This parallelism of Vector-Quantized token prediction in turn facilitates unconditional generation of globally consistent high-resolution and diverse imagery at a fraction of the computational expense. In this manner, we can generate image resolutions exceeding that of the original training set samples whilst additionally provisioning per-image likelihood estimates (in a departure from generative adversarial approaches). Our approach achieves state-of-the-art results in terms of the manifold overlap metrics Coverage (LSUN Bedroom: 0.83; LSUN Churches: 0.73; FFHQ: 0.80) and Density (LSUN Bedroom: 1.51; LSUN Churches: 1.12; FFHQ: 1.20), and performs competitively on FID (LSUN Bedroom: 3.27; LSUN Churches: 4.07; FFHQ: 6.11) whilst offering advantages in terms of both computation and reduced training set requirements.
SepLUT: Separable Image-Adaptive Lookup Tables for Real-Time Image Enhancement

Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms. However, they embed the complete transform, including the color component-independent and the component-correlated parts, into only a single type of LUTs, either 1D or 3D, in a coupled manner. This scheme raises a dilemma of improving model expressiveness or efficiency due to two factors. On the one hand, the 1D LUTs provide high computational efficiency but lack the critical capability of color components interaction. On the other, the 3D LUTs present enhanced component-correlated transform capability but suffer from heavy memory footprint, high training difficulty, and limited cell utilization. Inspired by the conventional divide-and-conquer practice in the image signal processor, we present SepLUT (separable image-adaptive lookup table) to tackle the above limitations. Specifically, we separate a single color transform into a cascade of component-independent and component-correlated sub-transforms instantiated as 1D and 3D LUTs, respectively. In this way, the capabilities of two sub-transforms can facilitate each other, where the 3D LUT complements the ability to mix up color components, and the 1D LUT redistributes the input colors to increase the cell utilization of the 3D LUT and thus enable the use of a more lightweight 3D LUT. Experiments demonstrate that the proposed method presents enhanced performance on photo retouching benchmark datasets than the current state-of-the-art and achieves real-time processing on both GPUs and CPUs.
Quantum-classical convolutional neural networks in radiological image classification

Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear. However, there are indications that certain quantum machine learning algorithms might result in improved training capabilities with respect to their classical counterparts - which might be particularly beneficial in situations with little training data available. Such situations naturally arise in medical classification tasks. Within this paper, different hybrid quantum-classical convolutional neural networks (QCCNN) with varying quantum circuit designs and encoding techniques are proposed. They are applied to two- and three-dimensional medical imaging data, e.g. featuring different, potentially malign, lesions in computed tomography scans. The performance of these QCCNNs is already similar to the one of their classical counterparts therefore encouraging further studies towards the direction of applying these algorithms within medical imaging tasks.
Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification

Image-to-video person re-identification aims to retrieve the same pedestrian as the image-based query from a video-based gallery set. Existing methods treat it as a cross-modality retrieval task and learn the common latent embeddings from image and video modalities, which are both less effective and efficient due to large modality gap and redundant feature learning by utilizing all video frames. In this work, we first regard this task as point-to-set matching problem identical to human decision process, and propose a novel Temporal Complementarity-Guided Reinforcement Learning (TCRL) approach for image-to-video person re-identification. TCRL employs deep reinforcement learning to make sequential judgments on dynamically selecting suitable amount of frames from gallery videos, and accumulate adequate temporal complementary information among these frames by the guidance of the query image, towards balancing efficiency and accuracy. Specifically, TCRL formulates point-to-set matching procedure as Markov decision process, where a sequential judgement agent measures the uncertainty between the query image and all historical frames at each time step, and verifies that sufficient complementary clues are accumulated for judgment (same or different) or one more frames are requested to assist judgment. Moreover, TCRL maintains a sequential feature extraction module with complementary residual detectors to dynamically suppress redundant salient regions and thoroughly mine diverse complementary clues among these selected frames for enhancing frame-level representation. Extensive experiments demonstrate the superiority of our method.