Reviewerly/exHarmony huggingface dataset:
Impartial competitive learning in multi-layered neural networks

The present paper aims to propose a new learning and interpretation method called “impartial competitive learning”, meaning that all participants in a competition should be winners. Due to its importance, the impartiality is forced to be realised even by increasing the corresponding cost in terms of the strength of weights. For the first approximation, three types of impartial competition can be developed: componential, computational, and collective competition. In the componential competition, every weight should have an equal chance on average to win the competition. In the computational competition, all computational procedures should have an equal chance to be applied sequentially in learning. In collective computing for interpretation, all network configurations, obtained by learning, have an equal chance to participate in a process of interpretation, representing one of the most idealised forms of impartiality. The method was applied to a well-known second-language-learning data set. The intuitive conclusion, stressed in the specific science, could not be extracted by the conventional natural language processing methods, because they can deal only with word frequency. The present method tried to extract a main feature beyond the word frequency by competing connection weights and computational procedures impartially, followed by collective and impartial competition for interpretation.
Deep Unfolding Network for Efficient Mixed Video Noise Removal

Existing image and video denoising algorithms have focused on removing homogeneous Gaussian noise. However, this assumption with noise modeling is often too simplistic for the characteristics of real-world noise. Moreover, the design of network architectures in most deep learning-based video denoising methods is heuristic, ignoring valuable domain knowledge. In this paper, we propose a model-guided deep unfolding network for the more challenging and realistic mixed noise video denoising problem, named DU-MVDnet. First, we develop a novel observation model/likelihood function based on the correlations among adjacent degraded frames. In the framework of Bayesian deep learning, we introduce a deep image denoiser prior and obtain an iterative optimization algorithm based on the maximum a posterior (MAP) estimation. To facilitate end-to-end optimization, the iterative algorithm is transformed into a deep convolutional neural network (DCNN)-based implementation. Furthermore, recognizing the limitations of traditional motion estimation and compensation methods, we propose an efficient multistage recursive fusion strategy to exploit temporal dependencies. Specifically, we divide video frames into several overlapping groups and progressively integrate these frames into one frame. Toward this objective, we implement a multiframe adaptive aggregation operation to integrate feature maps of intragroup with those of intergroup frames. Extensive experimental results on different video test datasets have demonstrated that the proposed model-guided deep network outperforms current state-of-the-art video denoising algorithms such as FastDVDnet and MAP-VDNet.
Toward Effective Image Manipulation Detection With Proposal Contrastive Learning

Deep models have been widely and successfully used in image manipulation detection, which aims to classify tampered images and localize tampered regions. Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered image. To exploit such spatial relationships, we propose Proposal Contrastive Learning (PCL) for effective image manipulation detection. Our PCL consists of a two-stream architecture by extracting two types of global features from RGB and noise views respectively. To further improve the discriminative power, we exploit the relationships of local features through a proxy proposal contrastive learning task by attracting/repelling proposal-based positive/negative sample pairs. Moreover, we show that our PCL can be easily adapted to unlabeled data in practice, which can reduce manual labeling costs and promote more generalizable features. Extensive experiments among several standard datasets demonstrate that our PCL can be a general module to obtain consistent improvement. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Sandy-Zeng/PCL</uri> .
SCGNet: Shifting and Cascaded Group Network

Many lightweight networks have been proposed for resource-limited applications, however, they cannot be efficiently applied to neural-network processing units (NPUs) due to the limited operations supported by the NPUs, and few works focus on efficient network design on the NPUs. The basic blocks of networks such as MobileNetV2 and RegNet use smaller convolution kernels with relatively small receptive fields, which are not conducive to capturing large-scale spatial information. To address this weakness, we propose Shifting and Cascaded Group (SCG) block, where we cascade group convolutions with larger kernels to exploit multi-scale information and propose shifting group convolution to communicate channel information between different groups. Besides, we carefully devise our architecture guided by some principles and finally build a very efficient network called Shifting and Cascaded Group Network (SCGNet) on NPUs. To verify the superiority of our method, we conduct extensive experiments on various tasks including image classification, object detection, human pose estimation, person re-identification, and semantic segmentation to comprehensively evaluate the performance. Results on widely used datasets such as ImageNet, PASCAL VOC, COCO, MPII, Market-1501, DukeMTMC-ReID, CUHK03, and Cityscapes demonstrate that the proposed network is a more effective network on the corresponding vision tasks.
Tracking With Sequentially Fused Radar and Acoustic Sensor Data With Propagation Delay

In some special scenarios, the merits of radar tracking may be lost due to great interference, while the passive acoustic signal can be detected. Then, fusing active radar and passive acoustic sensors can improve the tracking performance. However, a problem associated with the existing fusion approaches is that the measurements of radar and acoustic sensor assumed obtained simultaneously are actually from different emission times due to the significant acoustic propagation delay. Then, a sequential cubature Kalman filter (CKF) can be used for fusing heterogeneous sensors with delay. However, in sequential fusion, it is difficult to perform an accurate state prediction or retrodiction due to the unknown delay. Although the emission time is unknown, it satisfies the delay constraint. In view of this, we augment the delay constraint onto the motion equation to estimate the target state and emission time simultaneously. Then, two sequential fusion algorithms, called first-radar-then-acoustic fusion (FRTAF) and first-acoustic-then-radar fusion (FATRF), are developed. A wide range of simulations have been carried, and the performance of the FRTAF, FATRF, and single radar tracking is compared. Illustrative examples demonstrate that both two fusion algorithms outperform the track-to-track fusion (T2TF) and information matrix fusion (IMF) algorithms.
Decomposable pairs and construction of multiwavelets

We study the construction of refinable function vectors and multiwavelets from the perspective of decomposable pairs. There exists a pair of matrices known as the decomposable pair, associated with a refinement mask of the form H(ξ)=12∑k=0lHke−iξk, which gives the spectral information about H(ξ). We show that the existence of a solution to a matrix refinement equation is related to the dimension of 1-eigenspace of the square matrix in the decomposable pair. We also show that the existence of a refinement mask with sum rules of order 1 is related to the dimension of (−1)-eigenspace of the square matrix in the decomposable pair. A systematic procedure is developed to create refinement masks satisfying the sum rules of order 1 using decomposable pairs, such that the corresponding matrix refinement equation has a solution. Several examples are provided to illustrate the theoretical results obtained.
Pattern-based circular reference detection in Python

Circular references keep objects in loops from being released even if they are no longer reachable. Programming languages provide various garbage collection strategies for reclaiming unused objects at runtime. For example, CPython is equipped with a tracing collector as a complement to the count-based collector to recycle circular garbage. However, runtime detection inevitably entails resource consumption. The overhead of circular reference detection can be reduced if the introduction of circular references can be avoided, or if the presence of circular references in code can be detected before the code is run. To reduce the occurrence of circular references, we provide a data-driven approach to discover the common patterns from dynamic data and then automatically detect circular references statically. First, we observe and characterize common circular reference patterns using a large-scale dataset collected from 671 open-source projects at runtime. With these patterns in mind, developers can be aware of where potential circular references might be coming from, leading to more robust Python codes and ultimately lightening the garbage collection workload, and also potentially altering their coding practices. Then, we design CRef, a pattern-based approach for detecting circular references in code. We investigate the effectiveness of CRef on 17 projects. Across all experimental projects, CRef achieves a precision of 0.95 and a recall of 0.98.
Truthful and performance-optimal computation outsourcing for aerial surveillance platforms via learning-based auction

This paper proposes a novel truthful computing algorithm for learning task outsourcing decision-making strategies in edge-enabled unmanned aerial vehicle (UAV) networks. In our considered scenario, a single UAV performs face identification in a monitored target area. The execution of the identification requires a certain computing power, and its complexity and time are dependent on the number of faces in the recorded images. As a consequence, the task cannot be fully executed by a single UAV under high image arrivals or with images that have a high density of faces. In those conditions, UAV can outsource the task to one of the nearby edges. Importantly, the computing task distribution should be energy-efficient and delay-minimal due to the constraints imposed by the UAV platform characteristics and applications. Based on those fundamental requirements, our proposed algorithm conducts sequential decision-making for image sharing with one selected edge. The edge is selected based on a second price auction for truthfulness. Besides the truthfulness guarantees, deep learning based approximation for the auction solution is used for revenue-optimality. Our evaluation demonstrates that the proposed algorithm achieves the desired performance.
Improved domain adaptive object detector via adversarial feature learning

Unsupervised domain adaptation is a promising solution to adapt models to new domains or environments at no additional annotation cost. Such adaptation strategy has been investigated for detection to cope with the domain shift along the detection pipeline. To improve the transferability of object detectors between source and target domains, we propose in this paper to perform multi-level feature alignments via adversarial learning. The alignment process is guided by an uncertainty measure to adapt weights of well-aligned and misaligned target samples, accordingly. Furthermore, the proposed approach includes a consistency regularization to enforce the coherency between image and instance level domain classifiers. We also emphasize the need for leveraging contextual information that captures the scene structure for complementary aspects and to further improve the adversarial training process. These adaptation components are integrated into Faster R-CNN architecture at different levels. The resulting proposed adaptive detector has the advantage of covering different aspects of the domain shift in order to improve the overall performance. Extensive experiments of different domain shift scenarios demonstrate the effectiveness of our proposed adaptation scheme by obtaining better results compared to the baseline detector and to the current state-of-the-art adaptive detectors.
The Past Present and Future of Virtual and Augmented Reality

Mixed Reality Interfaces (MRITFs) like the Hololens and low-cost virtual reality (VR) machineries similar to the Oculus Rift, , Sony PlayStation and HTC Vive VR are attracting the attention of users and researchers, suggesting that they may be the next largest technological innovation stepping stone. However, the VR technology's olden times is elongated than it seems: In the late 1980s, the initial marketable VR tools seemed, and the concept of virtual reality was developed in the 1960s.As a result, thousands of scientific papers have been issued over the past two decades as hundreds of researchers investigated the processes, effects, and applications of this technology. What does this significant research produce? Using cutting-edge scientometric methods, this paper will investigate the field's existing research corpus to answer this question. In the Web of Science Core Collection scientific database, we gathered all existing VR-related articles. The resulting dataset contained 21,667 VR and 9,944 AR records. The author, title, abstract, country, and all of the references (which were required for the citation analysis) were all included in the bibliographic record. The literature was analyzed using network and cluster methods, and the results revealed a symbiotic panorama marked by changes and developments over time. Indeed, whereas conference proceedings and journals were the primary forms of VR publication up until five years ago, journals are now the primary means of communication. In a similar vein, although computer science was initially the most prominent area of research, clinical areas and countries participating in VR research have both grown in recent years. The present work focuses on the anticipated future capabilities, enhancements, and difficulties of virtual reality (VR), as well as the evolution and changes that have occurred over time in the main application areas. We conclude by taking into consideration the disruptive impact that VR/AR/MRITF will have not only on scientific fields but also on human communication and interaction—much like the introduction of mobile phones did—by altering social communication and interaction as well as increasing the use of scientific applications (such as in clinical settings).
VC-DCPS: Verifiable Cross-Domain Data Collection and Privacy-Persevering Sharing Scheme Based on Lattice in Blockchain-Enhanced Smart Grids

With the advent of the era of Industry 4.0, blockchain-enhanced Smart Grids have received widespread attention from academics and industry personnel, with its great advantages in high transparency, flexibility, and decentralization. However, there are still problems of privacy leakage and inefficiency. In this article, we propose a cross-domain verifiable data collection and privacy-persevering sharing scheme based on lattice in blockchain-enhanced Smart Grids, called VC-DCPS. First, lattice-based homomorphic encryption and spatial–temporal aggregation technology are utilized to enable cross-domain collection of data, dynamic addition and deletion of users and improvement of efficiency. Second, a post-verification method is designed to verify whether the outsourcing cloud executes predetermined operations correctly. Using consensus and incentive mechanisms via smart contract will enable flexible access control and efficient transactions on the blockchain. In addition, formal security analysis shows that the proposed scheme can ensure privacy and enhance security. Finally, performance evaluation and experimental comparison are performed and turn out that the proposed scheme is strong security, high efficiency, and low storage overhead.
Improvement of Reinforcement Learning With Supermodularity

Reinforcement learning (RL) is a promising approach to tackling learning and decision-making problems in a dynamic environment. Most studies on RL focus on the improvement of state evaluation or action evaluation. In this article, we investigate how to reduce action space by using supermodularity. We consider the decision tasks in the multistage decision process as a collection of parameterized optimization problems, where state parameters dynamically vary along with the time or stage. The optimal solutions of these parameterized optimization problems correspond to the optimal actions in RL. For a given Markov decision process (MDP) with supermodularity, the monotonicity of the optimal action set and the optimal selection with respect to state parameters can be obtained by using the monotone comparative statics. Accordingly, we propose a monotonicity cut to remove unpromising actions from the action space. Taking bin packing problem (BPP) as an example, we show how the supermodularity and monotonicity cut work in RL. Finally, we evaluate the monotonicity cut on the benchmark datasets reported in the literature and compare the proposed RL with some popular baseline algorithms. The results show that the monotonicity cut can effectively improve the performance of RL.
Learning Performance Models of Distributed Computer Vision Methods for Decision Making in Detection and Tracking Algorithms in UAVs

Unmanned Aerial Vehicles (UAVs) are getting more and more uses in recent times.However, low-cost commercial UAVs may not possess enough computational power to run state of the art algorithms in order to perform certain tasks, negatively affecting performance.Remote computational systems, where heavy processing tasks can be offloaded emerge as a solution.However, they introduce latency, which can be undesirable for real-time tasks.Furthermore, if the task is simple, using a local algorithm with worse performance may be acceptable to avoid latency.As such, a method to decide which algorithm to use is of great importance.We consider the use case of computer vision tasks, in particular detection and tracking.In these tasks, image properties such as brightness, contrast, motion blur and clutter affect the algorithm performance.Our proposed methods use a combination of neural networks and kernel machines to estimate the performance of the algorithm given the input image.An appropriate cost function is then used to identify the best algorithm for the task given the input image, the task deadline, and the uncertainty in the variables of the algorithm, in particular computing time and error rate.Results show that our method matches or outperforms similar state of the art methods, complying with time restrictions while delivering increased performance.
Multi-Frame Compressed Video Quality Enhancement by Spatio-Temporal Information Balance

In recent years, the performance of multi-frame quality enhancement algorithms for compressed videos has been greatly improved compared with single-frame based algorithms. However, the existing methods mainly focus on mining the temporal information of multiple frames. The large number of reference frames reduces the exploration of spatial information, although the existing single-frame based algorithms for enhancement, denoising, and super-resolution demonstrate the significance of the spatial information. To address this problem, we propose a plug-and-play module called Spatio-temporal Information Balance (STIB) to adaptively balance the spatial and temporal information. In our method, we use a feature extractor to exploit richer spatial information, and use a refinement module to refine the aligned temporal information, to be more conducive to the fusion of spatio-temporal information. Finally, we use the deformable convolution based re-alignment module to do alignment and fusion in feature space for balancing the spatio-temporal information. Experiments show that our module can significantly improve the performance of the existing multi-frame based enhancement algorithms.
The Dynamic Vectors-Based Attention Model for Chinese Mathematical Term Extraction

Term extraction aims to extract domain-specific terminology from documents. Secondary school mathematics terminology plays a guiding role in the learning and teaching of secondary school mathematics. We found that there needs to be more work in this field and that there are two main issues in extracting the terms from the middle school mathematics text: the polysemy of one character problem and nested terms problem. Accordingly, we have constructed a secondary school mathematics corpus and proposed a model to resolve these problems, termed BERT-LLA-CRF. Using pretraining to obtain dynamic word vectors to improve the model’s understanding of text semantics and solve the problem of multiple meanings of one word, the proposed model integrates attention mechanisms and Chinese character information to solve the nested terminology problem. The experiment results show that the proposed model effectively solves those problems and achieves good middle-school mathematics term extraction results.