Reviewerly/exHarmony huggingface dataset:
Extending and Analyzing Self-supervised Learning Across Domains

Self-supervised representation learning has achieved impressive results in recent years, with experiments primarily coming on ImageNet or other similarly large internet imagery datasets. There has been little to no work with these methods on other smaller domains, such as satellite, textural, or biological imagery. We experiment with several popular methods on an unprecedented variety of domains. We discover, among other findings, that Rotation is the most semantically meaningful task, while much of the performance of Jigsaw is attributable to the nature of its induced distribution rather than semantic understanding. Additionally, there are several areas, such as fine-grain classification, where all tasks underperform. We quantitatively and qualitatively diagnose the reasons for these failures and successes via novel experiments studying pretext generalization, random labelings, and implicit dimensionality. Code and models are available at https://github.com/BramSW/Extending_SSRL_Across_Domains/ .
Reliable Multi-Object Tracking Model Using Deep Learning and Energy Efficient Wireless Multimedia Sensor Networks

Presently, sensor-cloud based environment becomes highly beneficial due to its applicability in several domains.Wireless multimedia sensor network (WMSN) is one among them, which involves a set of multimedia sensors to collect data about the deployed region.Compared to traditional object tracking models, animal tracking in WMSN is a tedious process owing to the harsh, dynamic, and energy limited sensors.This paper introduces a new Reliable Multi-Object Tracking Model using Deep Learning (DL) and Energy Efficient WMSN.Initially, the fuzzy logic technique is employed to determine the cluster heads (CHs) to attain energy efficiency.Next, in the second stage, a novel tracking algorithm by the use of Recurrent Neural Network (RNN) with a tumbling effect called RNN-T is developed.The proposed RNN-T model gets executed by every sensor node and the CHs execute the tracking algorithm to track the animals.Finally, the tracking results are transmitted to the cloud server for investigation purposes.In order to assess the performance of the presented model, an extensive experimental analysis is carried out by the use of a realtime wildlife video.The obtained results ensured that the RNN-T model has achieved better performance over the compared methods in different aspects.
Coherence effects on estimating two-point separation

The quantum Fisher information (FI), when applied to the estimation of the separation of two point sources, has been shown to be non-zero in cases where the coherence between the sources is known. Although it has been claimed that ignorance of the coherence causes the quantum FI to vanish (a resurgence of Rayleigh’s curse), a more complete analysis including both the magnitude and phase of the coherence parameter is given here. Partial ignorance of the coherence is shown to potentially break Rayleigh’s curse, whereas complete ignorance guarantees its resurgence.
Mask-Guided Attention Network and Occlusion-Sensitive Hard Example Mining for Occluded Pedestrian Detection

Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving other pedestrians and inter-class occlusions caused by other objects, such as cars and bicycles. These result in a multitude of occlusion patterns. We propose an approach for occluded pedestrian detection with the following contributions. First, we introduce a novel mask-guided attention network that fits naturally into popular pedestrian detection pipelines. Our attention network emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features. Second, we propose the occlusion-sensitive hard example mining method and occlusion-sensitive loss that mines hard samples according to the occlusion level and assigns higher weights to the detection errors occurring at highly occluded pedestrians. Third, we empirically demonstrate that weak box-based segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts. Experiments are performed on CityPersons, Caltech and ETH datasets. Our approach sets a new state-of-the-art on all three datasets. Our approach obtains an absolute gain of 10.3% in log-average miss rate, compared with the best reported results on the heavily occluded HO pedestrian set of the CityPersons test set. Code and models are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Leotju/MGAN</uri> .
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization

Emergent hardwares can support mixed precision CNN models inference that assign different bitwidths for different layers. Learning to find an optimal mixed precision model that can preserve accuracy and satisfy the specific constraints on model size and computation is extremely challenge due to the difficult in training a mixed precision model and the huge space of all possible bit quantizations. In this paper, we propose a novel soft Barrier Penalty based NAS (BP-NAS) for mixed precision quantization, which ensures all the searched models are inside the valid domain defined by the complexity constraint, thus could return an optimal model under the given constraint by conducting search only one time. The proposed soft Barrier Penalty is differentiable and can impose very large losses to those models outside the valid domain while almost no punishment for models inside the valid domain, thus constraining the search only in the feasible domain. In addition, a differentiable Prob-1 regularizer is proposed to ensure learning with NAS is reasonable. A distribution reshaping training strategy is also used to make training more stable. BP-NAS sets new state of the arts on both classification (Cifar-10, ImageNet) and detection (COCO), surpassing all the efficient mixed precision methods designed manually and automatically. Particularly, BP-NAS achieves higher mAP (up to 2.7% mAP improvement) together with lower bit computation cost compared with the existing best mixed precision model on COCO detection.
Learning Latent Representations Across Multiple Data Domains Using Lifelong VAEGAN

The problem of catastrophic forgetting occurs in deep learning models trained on multiple databases in a sequential manner. Recently, generative replay mechanisms (GRM) have been proposed to reproduce previously learned knowledge aiming to reduce the forgetting. However, such approaches lack an appropriate inference model and therefore can not provide latent representations of data. In this paper, we propose a novel lifelong learning approach, namely the Lifelong VAEGAN (L-VAEGAN), which not only induces a powerful generative replay network but also learns meaningful latent representations, benefiting representation learning. L-VAEGAN can allow to automatically embed the information associated with different domains into several clusters in the latent space, while also capturing semantically meaningful shared latent variables, across different data domains. The proposed model supports many downstream tasks that traditional generative replay methods can not, including interpolation and inference across different data domains.
Profitable Double-Spending Attacks

Our aim in this paper is to investigate the profitability of double-spending (DS) attacks that manipulate an a priori mined transaction in a blockchain. It was well understood that a successful DS attack is established when the proportion of computing power an attacker possesses is higher than that of the honest network. What is not yet well understood is how threatening a DS attack with less than 50% computing power used can be. Namely, DS attacks at any proportion can be a threat as long as the chance to make a good profit exists. Profit is obtained when the revenue from making a successful DS attack is greater than the cost of carrying out one. We have developed a novel probability theory for calculating a finitetime attack probability. This can be used to size up attack resources needed to obtain the profit. The results enable us to derive a sufficient and necessary condition on the value of a transaction targeted by a DS attack. Our result is quite surprising: we theoretically show how a DS attack at any proportion of computing power can be made profitable. Given one’s transaction value, the results can also be used to assess the risk of a DS attack. An example of profitable DS attack against BitcoinCash is provided.
Design of a Scalable and Fast YOLO for Edge-Computing Devices

With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% mAP@0.5 in the MS COCO dataset than YOLOv4-tiny model.
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer

In this paper, we propose an effective knowledge transfer framework to boost the weakly supervised object detection accuracy with the help of an external fully-annotated source dataset, whose categories may not overlap with the target domain. This setting is of great practical value due to the existence of many off-the-shelf detection datasets. To more effectively utilize the source dataset, we propose to iteratively transfer the knowledge from the source domain by a one-class universal detector and learn the target-domain detector. The box-level pseudo ground truths mined by the target-domain detector in each iteration effectively improve the one-class universal detector. Therefore, the knowledge in the source dataset is more thoroughly exploited and leveraged. Extensive experiments are conducted with Pascal VOC 2007 as the target weakly-annotated dataset and COCO/ImageNet as the source fully-annotated dataset. With the proposed solution, we achieved an mAP of $$59.7\%$$ detection performance on the VOC test set and an mAP of $$60.2\%$$ after retraining a fully supervised Faster RCNN with the mined pseudo ground truths. This is significantly better than any previously known results in related literature and sets a new state-of-the-art of weakly supervised object detection under the knowledge transfer setting. Code: https://github.com/mikuhatsune/wsod_transfer .
Superpixel Image Classification with Graph Attention Networks

This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regions are superpixels and edges connect neighboring superpixels. Our experiments suggest that Graph Attention Networks (GATs), which combine graph convolutions with self-attention mechanisms, outperforms other GNN models. Although raw image classifiers perform better than GATs due to in-formation loss during the RAG generation, our methodology opens an interesting avenue of research on deep learning beyond rectangular-gridded images, such as 360-degree field of view panoramas. Traditional convolutional kernels of current state-of-the-art methods cannot handle panoramas, whereas the adapted superpixel algorithms and the resulting region adjacency graphs can naturally feed a GNN, without topology issues.
Domain-Specific Mappings for Generative Adversarial Style Transfer

Style transfer generates an image whose content comes from one image and style from the other. Image-to-image translation approaches with disentangled representations have been shown effective for style transfer between two image categories. However, previous methods often assume a shared domain-invariant content space, which could compromise the content representation power. For addressing this issue, this paper leverages domain-specific mappings for remapping latent features in the shared content space to domain-specific content spaces. This way, images can be encoded more properly for style transfer. Experiments show that the proposed method outperforms previous style transfer methods, particularly on challenging scenarios that would require semantic correspondences between images. Code and results are available at https://github.com/acht7111020/DSMAP .
Low-Cost Fredkin Gate with Auxiliary Space

Effective quantum information processing is tantamount in part to minimization of the quantum resources needed by quantum logic gates. Here, we propose an optimization of an $n$-controlled-qubit Fredkin gate with a maximum of $2n+1$ two-qubit gates and $2n$ single-qudit gates by exploiting auxiliary Hilbert spaces. The number of logic gates required improves on earlier results on simulating arbitrary $n$-qubit Fredkin gates. In particular, the optimal result for a one-controlled-qubit Fredkin gate (which requires three qutrit-qubit partial-swap gates) breaks the theoretical nonconstructive lower bound of five two-qubit gates. Furthermore, using an additional spatial-mode degree of freedom, we design a possible architecture to implement a polarization-encoded Fredkin gate with linear optical elements.
FogChain: A Fog Computing Architecture Integrating Blockchain and Internet of Things for Personal Health Records

The Internet of Things (IoT) adoption grows significantly and is successful in many different domains.Nevertheless, the ever-growing demand for more connected devices pushes the requirement for scalable IoT architectures capable of maintaining the security and privacy of collected data.The latter is a particularly critical aspect when considering sensitive data, e.g., medical records.One solution to address this challenge is to modify the centralized back-end model to one based on a Blockchain, changing the way IoT data is stored and shared by providing a decentralized peer-to-peer network.This technology enables naming and tracking for connected devices, and in the case of this article, features a high availability of Personal Health Records, yet protecting patients' privacy through the use of cryptography.Furthermore, the addition of Fog computing mechanisms helps to achieve real-time data processing, supports precision medicine, and avoids single points of failure.As a result, devices have a local and more resilient ecosystem for operation.In this context, this work proposes an architecture model named FogChain, which combines the technologies Blockchain, Fog computing, and the IoT for the healthcare domain.Our main contribution is the FogChain model itself, and its concept of overcoming IoT constraints by employing a differential approach, adding an intermediary Fog layer near to the edge to improve their capabilities and resources.Experiments demonstrate that FogChain can achieve a 62.6% faster response time when compared to Cloudlike Blockchain infrastructures.The results obtained from the evaluation endorses the capacity of our model in achieving its goals while retaining application performance.
Unpaired Image-to-Image Translation Using Adversarial Consistency Loss

Unpaired image-to-image translation is a class of vision problems whose goal is to find the mapping between different image domains using unpaired training data. Cycle-consistency loss is a widely used constraint for such problems. However, due to the strict pixel-level constraint, it cannot perform shape changes, remove large objects, or ignore irrelevant texture. In this paper, we propose a novel adversarial-consistency loss for image-to-image translation. This loss does not require the translated image to be translated back to be a specific source image but can encourage the translated images to retain important features of the source images and overcome the drawbacks of cycle-consistency loss noted above. Our method achieves state-of-the-art results on three challenging tasks: glasses removal, male-to-female translation, and selfie-to-anime translation.
Censored Planet: An Internet-wide, Longitudinal Censorship Observatory

Remote censorship measurement techniques offer capabilities for monitoring Internet reachability around the world. However, operating these techniques continuously is labor-intensive and requires specialized knowledge and synchronization, leading to limited adoption. In this paper, we introduce Censored Planet, an online censorship measurement platform that collects and analyzes measurements from ongoing deployments of four remote measurement techniques (Augur, Satellite/Iris, Quack, and Hyperquack). Censored Planet adopts a modular design that supports synchronized baseline measurements on six Internet protocols as well as customized measurements that target specific countries and websites. Censored Planet has already collected and published more than 21.8 billion data points of longitudinal network observations over 20 months of operation. Censored Planet complements existing censorship measurement platforms such as OONI and ICLab by offering increased scale, coverage, and continuity. We introduce a new representative censorship metric and show how time series analysis can be applied to Censored Planet's longitudinal measurements to detect 15 prominent censorship events, two-thirds of which have not been reported previously. Using trend analysis, we find increasing censorship activity in more than 100 countries, and we identify 11 categories of websites facing increasing censorship, including provocative attire, human rights issues, and news media. We hope that the continued publication of Censored Planet data helps counter the proliferation of growing restrictions to online freedom.
Unsupervised adversarial domain adaptation with similarity diffusion for person re-identification

Due to the scarcity of human identities labels, unsupervised person re-identification (re-ID) draws much attention recently, which attempts to learn discriminative person representation without labels. Domain adaptation based methods utilize the labeled sample in source domain and transfer the knowledge to the unlabeled target domain. However, for person re-ID, no identities overlapping between source and target domain leads to the difficulty during adaptation. To address this problem, we propose an unsupervised adversarial domain adaptation method for person re-ID which exploits the pixel level and feature level alignments. Specifically, we utilize CycleGAN to transfer the target domain style to the source domain for the pixel level, and we propose an adversarial metric adaptation method which aligns between the source domain and target domain for the feature level. We further explore the feature similarity laying on manifold structure revealed by the features through the similarity diffusion. To verify the efficacy of our proposed method, we conduct extensive experiments on three benchmark datasets: Market1501, Duke-MTMC, and MSMT17. Comparing with state-of-the-art unsupervised domain adaptation approaches, we have comparable performance on ranking metric and significant improvement on mAP metric, which validates the efficacy of the proposed technique for person re-identification tasks.
Variational quantum simulations of stochastic differential equations

Stochastic differential equations (SDEs), which model uncertain phenomena as the time evolution of random variables, are exploited in various fields of natural and social sciences such as finance. Since SDEs rarely admit analytical solutions and must usually be solved numerically with huge classical-computational resources in practical applications, there is strong motivation to use quantum computation to accelerate the calculation. Here, we propose a quantum-classical hybrid algorithm that solves SDEs based on variational quantum simulation. We first approximate the target SDE by a trinomial tree structure with discretization and then formulate it as the time-evolution of a quantum state embedding the probability distributions of the SDE variables. We embed the probability distribution directly in the amplitudes of the quantum state whereas the previous studies took the square-root of the probability distribution in the amplitudes. Our embedding enables us to construct simple quantum circuits that simulate the time-evolution of the state for general SDEs. We also develop a scheme to compute the expectation values of the SDE variables and discuss whether our scheme can achieve quantum speedup for the expectation-value evaluations of the SDE variables. Finally, we numerically validate our algorithm by simulating several types of stochastic processes. Our proposal provides a new direction for simulating SDEs on quantum computers.
SVD Based Robust Unsighted Video Watermarking Technique for different attacks

Abstract Nowadays, digital watermark protects intellectual property in the digital world. In this article, a new singular value decomposition (SVD) based robust unsighted video watermarking technique is used for protection of copyright or owns individual work. The Singular Value Decomposition is used in this method to embed the information in a video file. In the embedded process the information generally referred to as watermark, which is of the form either audio, text, video, or any binary image or digital image. The embedded information can be identified in this process without considering the mechanisms of the original video, or any other knowledge about the encoded video ‘s main singular values. Singular value decomposition is used in embedding the information as well as retrieving the information from the embedded watermarked video. SVD based robust unsighted video technique shows that it bears required robustness on various attacks like MPEG-2 compressed video, median filtered, small rescaled, and rotational videos. In the experimental result, it is noticed that the unsighted video watermarking technique has visible quality and robustness for various attacks. In which watermark is installed in uniquely chose particular estimations of the video, and the first request can be kept up to recovering the data in our method and it has been indicated roust to different attacks.