Reviewerly/exHarmony huggingface dataset:
SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers

Data augmentation has been proven effective which, by preventing overfitting, not only enhances the performance of a deep neural network but also leads to a better generalization even with limited dataset. Recently introduced regional dropout based data augmentation strategies remove (or replace) some parts of an input image with a desideratum to make the network focus on less discriminative portions of an image, which results in an improved performance. However, such approaches usually possess' strong-edge' problem caused by an obvious change in the pixels at the positions where the image is manipulated. It may not only impact on the local convolution operation but can also provide clues for the network to latch on to, which do not align well with the fundamental philosophy of augmentation. In order to minimize such peculiarities, we introduce Smoothmix in which blending of images is done based on soft edges and the training labels are computed accordingly. Extensive analysis performed on CIFAR-10, CIFAR- 100 and ImageNet for image classification demonstrates state-of-the-art results. Furthermore, Smoothmix significantly increases the robustness of a network against image corruption which is validated by the experiments carried out on CIFAR-100-C & ImageNet-C corruption datasets.
A Survey on Image Forgery Detection Using Different Forensic Approaches

Recently, digital image forgery detection is an emergent and important area of image processing.Digital image plays a vital role in providing evidence for any unusual incident.However, the image forgery my hide evidence and prevents the detection of such criminal cases due to advancement in image processing and availability of sophisticated software tamper of an image can be easily performed.In this paper, we provide a comprehensive review of the work done on various image forgeries and forensic technology.Many techniques have been proposed to detect image forgery in the literature such as digital watermarking, digital signature, copy-move, image retouching, and splicing.The investigation done in this paper may help the researcher to understand the advantage and handles of the available image forensic technology to develop more efficient algorithms of image forgery detection.Moreover, the comparative study surveys the existing forgery detection mechanisms include deep learning and convolution neural networks concerning it is on benefits and demerits.
Advance image encryption technique utilizing compression, dynamical system and S-boxes

Chaos theory is initially developed in the 1970s and has been utilized in the field of cryptography for the last three decades. In present work, an implementation of image encryption technique using chaotic system, compression and S-boxes is discussed. In this encryption scheme, compression is used to hide the original size of (plain text) image and also reduce the time complexity of the scheme. In addition to this, a particular S-box is selected for the encryption process with the help of an improved range chaotic map. The proposed scheme is discussed in detail, along with its standard security analysis and experimental results. This improved chaotic map and S-boxes provide large key space and high level of security. The size of cipher text generated by this scheme is different from plain text and is easy to implement for practical application of secure transmission of confidential data.
Deep Joint Deinterlacing and Denoising for Single Shot Dual-ISO HDR Reconstruction

HDR images have traditionally been obtained by merging multiple exposures each captured with a different exposure time. However, this approach entails longer capture times and necessitates deghosting if the captured scene contains moving objects. With the advent of modern camera sensors that can perform per-pixel exposure modulation, it is now possible to capture all of the required exposures within a single shot. The new challenge then becomes how to best combine different pixels with different exposure values into a single full-resolution and low-noise HDR image. We propose a joint multi-exposure frame deinterlacing and denoising algorithm powered by deep convolutional neural networks (DCNN). In our algorithm, we first train two DCNNs, with one tuned for reconstructing low exposures and the other for high exposures. Each DCNN takes the same mosaicked dual-ISO input image and outputs either the low exposure or high exposure depending on the type of the network. The resulting exposures can be demosaicked and converted to the desired target color space prior to HDR assembly. Our evaluations indicate that the quality of our results significantly surpasses the state-of-the-art in single-image HDR reconstruction algorithms.
Parallel and distributed architecture of genetic algorithm on Apache Hadoop and Spark

The genetic algorithm (GA), one of the best-known metaheuristic algorithms, has been extensively utilized in various fields of management science, operational research, and industrial engineering. The efficiency of GAs in solving large-scale optimization problems would be enhanced if the iterative processes required by the genetic operators can be implemented in a parallel and distributed computing architecture. Apache Hadoop has recently been one of the most popular systems for distributed storage and parallel processing of big data. By integrating the GA highly into Apache Hadoop, this study proposes an advanced GA parallel and distributed computing architecture that achieves the effectiveness and efficiency of GA evolution. Characterized by the sophisticated mechanism of dispatching the GA core operators into Apache Hadoop, the developed computing framework fits well with the cloud computing model. The presented GA parallelization architecture outperforms the state-of-the-art reference architectures according to the computational experiments where the testing instances of traveling salesman problems are employed. Our numerical experiments also demonstrate that the proposed architecture can readily be extended to Apache Spark.
Context-Aware Graph Label Propagation Network for Saliency Detection

Recently, a large number of existing methods for saliency detection have mainly focused on designing complex network architectures to aggregate powerful features from backbone networks. However, contextual information is not well utilized, which often causes false background regions and blurred object boundaries. Motivated by these issues, we propose an easyto-implement module that utilizes the edge-preserving ability of superpixels and the graph neural network to interact the context of superpixel nodes. In more detail, we first extract the features from the backbone network and obtain the superpixel information of images. This step is followed by superpixel pooling in which we transfer the irregular superpixel information to a structured feature representation. To propagate the information among the foreground and background regions, we use a graph neural network and self-attention layer to better evaluate the degree of saliency degree. Additionally, an affinity loss is proposed to regularize the affinity matrix to constrain the propagation path. Moreover, we extend our module to a multiscale structure with different numbers of superpixels. Experiments on five challenging datasets show that our approach can improve the performance of three baseline methods in terms of some popular evaluation metrics.
ALBATROSS: Publicly AttestabLe BATched Randomness Based On Secret Sharing

In this paper we present ALBATROSS, a family of multiparty randomness generation protocols with guaranteed output delivery and public verification that allows to trade off corruption tolerance for a much improved amortized computational complexity. Our basic stand alone protocol is based on publicly verifiable secret sharing (PVSS) and is secure under in the random oracle model under the decisional Diffie-Hellman (DDH) hardness assumption. We also address the important issue of constructing Universally Composable randomness beacons, showing two UC versions of Albatross: one based on simple UC NIZKs and another one based on novel efficient "designated verifier" homomorphic commitments. Interestingly this latter version can be instantiated from a global random oracle under the weaker Computational Diffie-Hellman (CDH) assumption. An execution of ALBATROSS with n parties, out of which up to $$t=(1/2-\epsilon )\cdot n$$ are corrupt for a constant $$\epsilon >0$$ , generates $$\varTheta (n^2)$$ uniformly random values, requiring in the worst case an amortized cost per party of $$\varTheta (\log n)$$ exponentiations per random value. We significantly improve on the SCRAPE protocol (Cascudo and David, ACNS 17), which required $$\varTheta (n^2)$$ exponentiations per party to generate one uniformly random value. This is mainly achieved via two techniques: first, the use of packed Shamir secret sharing for the PVSS; second, the use of linear t-resilient functions (computed via a Fast Fourier Transform-based algorithm) to improve the randomness extraction.
Contrast Enhancement Using Novel White Balancing Parameter Optimization for Perceptually Invisible Images

A novel white balancing algorithm is proposed in this paper to automatically enhance the global contrast degraded imperceptible images. The technique is applied on four publicly available image dataset, CSIQ, KADID, TID and SIPI. Colour images consist of three channels viz. Red, Blue and Green. A contrast degraded colour image visually appears similar to an image with one or more distorted channel. 12 images are obtained by enhancing one channel of the contrast degraded image at the cost of other channel using White Balancing algorithm. Four images with best quantitative performance metrics, visual similarity index (VSI), gradient magnitude similarity index (GMSD), patch-based contrast quality index (PCQI) and peak signal-to-noise ratio (PSNR) determines the pair of weak and prominent channels. An optimization algorithm then enhances these channels and the image with the best quantitative performance metrics is chosen as the enhanced image. Quantitative and qualitative results demonstrate that the proposed method produces an enhanced image with superior perceptual quality, and gives the best average results for all the parameters across every dataset as compared to the state-of-the-art methods.
Ethereum-Blockchain-Based Technology of Decentralized Smart Contract Certificate System

Traditional paper certificates and electronic certificates have difficulties in preservation and management, not to mention other problems concerning inconvenient verification, poor reliability, anti-counterfeiting and anti-tampering. This paper proposes a scheme designed to build a decentralized certificate system that is based on blockchain technology and smart contract, in which a set of blockchain certificate system aiming at providing blockchain certificate services for college students' innovation and entrepreneurship competition is developed. In this system, certain functions of the certificate about management, issuing, verification and revocation are realized via smart contract. Signer information, certificate template and certificate information are stored in a smart contract that adopts structured data, thereby realizing more convenient callings in querying and validating certificate.
Investigating bias in deep face analysis: The KANFace dataset and empirical study

Deep learning-based methods have pushed the limits of the state-of-the-art in face analysis. However, despite their success, these models have raised concerns regarding their bias towards certain demographics. This bias is inflicted both by limited diversity across demographics in the training set, as well as the design of the algorithms. In this work, we investigate the demographic bias of deep learning models in face recognition, age estimation, gender recognition and kinship verification. To this end, we introduce the most comprehensive, large-scale dataset of facial images and videos to date. It consists of 40K still images and 44K sequences (14.5M video frames in total) captured in unconstrained, real-world conditions from 1,045 subjects. The data are manually annotated in terms of identity, exact age, gender and kinship. The performance of state-of-the-art models is scrutinized and demographic bias is exposed by conducting a series of experiments. Lastly, a method to debias network embeddings is introduced and tested on the proposed benchmarks.
A Novel Zero-Watermarking Scheme Based on Variable Parameter Chaotic Mapping in NSPD-DCT Domain

In this paper, a novel image zero-watermarking scheme against rotation attacks is proposed based on nonsubsampled pyramid decomposition (NSPD) and discrete cosine transform (DCT). It utilizes the intrinsic characteristics of NSPD and DCT to extract the robust feature of an image as the original zero-watermark. To increase the security of the proposed scheme, a variable parameter chaotic mapping (VPCM) is designed for the processes of watermark encryption and robust feature extraction. Firstly, the host gray-scale image is decomposed by NSPD, and the low-frequency sub-band image is divided into non-overlapping blocks. After the blocks are transformed by DCT, the signs of the first AC coefficients from all the blocks are used to construct a binary feature image. Then an exclusive-or operation is performed between the binary feature image and the encrypted watermark image to obtain the verification zero-watermark image. Furthermore, a method against arbitrary rotation attacks is employed to improve the robustness of the scheme against geometric attacks. The experimental results demonstrate that the proposed scheme is highly robust against various image processing attacks such as filtering, JPEG compression, scaling, translation, rotation and Checkmark attacks.
Graph-Based CNNs With Self-Supervised Module for 3D Hand Pose Estimation From Monocular RGB

Hand pose estimation in 3D space from a single RGB image is a highly challenging problem due to self-geometric ambiguities, diverse texture, viewpoints, and self-occlusions. Existing work proves that a network structure with multi-scale resolution subnets, fused in parallel can more effectively shows the spatial accuracy of 2D pose estimation. Nevertheless, the features extracted by traditional convolutional neural networks cannot efficiently express the unique topological structure of hand key points based on discrete and correlated properties. Some applications of hand pose estimation based on traditional convolutional neural networks have demonstrated that the structural similarity between the graph and hand key points can improve the accuracy of the 3D hand pose regression. In this paper, we design and implement an end-to-end network for predicting 3D hand pose from a single RGB image. We first extract multiple feature maps from different resolutions and make parallel feature fusion, and then model a graph-based convolutional neural network module to predict the initial 3D hand key points. Next, we use 2D spatial relationships and 3D geometric knowledge to build a self-supervised module to eliminate domain gaps between 2D and 3D space. Finally, the final 3D hand pose is calculated by averaging the 3D hand poses from the GCN output and the self-supervised module output. We evaluate the proposed method on two challenging benchmark datasets for 3D hand pose estimation. Experimental results show the effectiveness of our proposed method that achieves state-of-the-art performance on the benchmark datasets.
Wasserstein based transfer network for cross-domain sentiment classification

Automatic sentiment analysis of social media texts is of great significance for identifying people's opinions that can help people make better decisions. Annotating data is time consuming and laborious, and effective sentiment analysis on domains lacking of labeled data has become a problem. Cross-domain sentiment classification is a promising task, which leverages the source domain data with rich sentiment labels to analyze the sentiment polarity of the target domain lacking supervised information. Most of the existing researches usually explore algorithms that select common features manually to bridge different domains. In this paper, we propose a Wasserstein based Transfer Network (WTN) to share the domain-invariant information of source and target domains. We benefit from BERT to achieve rich knowledge and obtain deep level semantic information of text. The recurrent neural network with attention is used to capture features automatically, and Wasserstein distance is applied to estimate feature representations of source and target domains, which could help to capture significant domain-invariant features by adversarial training. Extensive experiments on Amazon datasets demonstrate that WTN outperforms other state-of-the-art methods significantly. Especially, the model behaves more stable across different domains.
Sleep Ecologies

Autoethnographic and other first-person research methods are a topic of increasing interest in design and HCI. This focus parallels the boom in self-tracking and personal informatics, perhaps most intriguingly in the intersection of quantitative and qualitative data and the noticing of patterns in one's own life and everyday wellbeing. But how can design support this? One opportunity is for research probes, or tools, which enable forms of self-inquiry, by design researchers themselves, or others. In this paper-with the broad scope of healthier student sleep as a domain-we present a series of artifacts designed by undergraduates as tools to enable autoethnographic exploration, and detail how they have been used to investigate bedtime routines, personal scheduling of time, focus, sleep data, and sleeping in non-traditional places. We also reflect on the notion of combination autoethnographic 'kits' as a way forward for forms of self-inquiry.
IncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection

Elastic weight consolidation (EWC) has been successfully applied for general incremental learning to overcome the catastrophic forgetting issue. It adaptively constrains each parameter of the new model not to deviate much from its counterpart in the old model during fine-tuning on new class data sets, according to its importance weight for old tasks. However, the previous study demonstrates that it still suffers from catastrophic forgetting when directly used in object detection. In this article, we show EWC is effective for incremental object detection if with critical adaptations. First, we conduct controlled experiments to identify two core issues why EWC fails if trivially applied to incremental detection: 1) the absence of old class annotations in new class images makes EWC misclassify objects of old classes in these images as background and 2) the quadratic regularization loss in EWC easily leads to gradient explosion when balancing old and new classes. Then, based on the abovementioned findings, we propose the corresponding solutions to tackle these issues: 1) utilize pseudobounding box annotations of old classes on new data sets to compensate for the absence of old class annotations and 2) adopt a novel Huber regularization instead of the original quadratic loss to prevent from unstable training. Finally, we propose a general EWC-based incremental object detection framework and implement it under both Fast R-CNN and Faster R-CNN, showing its flexibility and versatility. In terms of either the final performance or the performance drop with respect to the upper bound of joint training on all seen classes, evaluations on the PASCAL VOC and COCO data sets show that our method achieves a new state of the art.
Learning Symmetry Consistent Deep CNNs for Face Completion

Deep convolutional networks (CNNs) have achieved great success in face completion to generate plausible facial structures. These methods, however, are limited in maintaining global consistency among face components and recovering fine facial details. On the other hand, reflectional symmetry is a prominent property of face images and benefits face analysis and consistency modeling, yet remaining uninvestigated in deep face completion. In this work, we leverage two kinds of symmetry-enforcing modules to form a symmetry-consistent CNN model (i.e., SymmFCNet) for effective face completion. For missing pixels on only one of the half-faces, an illumination-reweighted warping subnet is developed to guide the warping and illumination reweighting of the other half-face. As for missing pixels on both of half-faces, we present a generative reconstruction subnet together with a perceptual symmetry loss to enforce symmetry consistency of recovered structures. The SymmFCNet is constructed by stacking generative reconstruction subnet upon illumination-reweighted warping subnet, and can be learned in an end-to-end manner. Experiments show that SymmFCNet can generate globally consistent results on images with synthetic and real occlusions, and performs favorably against state-of-the-arts.
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the ML model is presented with an input containing an embedded "trigger " that provides a predetermined response advantageous to the adversary. Our work builds upon prior backdoor data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup, Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model's performance.
On relaxed greedy randomized coordinate descent methods for solving large linear least-squares problems

The greedy randomized coordinate descent (GRCD) method is an effective iterative method for solving large linear least-squares problems. In this work, we construct a class of relaxed greedy randomized coordinate descent (RGRCD) methods by introducing a relaxation parameter in the probability criterion. Then, we prove the convergence properties of these methods when the coefficient matrix of the linear least-squares problems is of full column rank, with the number of rows being no less than the number of columns. In addition, we propose a max-distance coordinate descent (CD) method, and study its convergence properties and accelerated version. Finally, we provide some numerical experiments to confirm the effectiveness of our new methods.