Reviewerly/exHarmony huggingface dataset:
SMILE: Set Membership from Ideal Lattices with Applications to Ring Signatures and Confidential Transactions

In a set membership proof, the public information consists of a set of elements and a commitment. The prover then produces a zero-knowledge proof showing that the commitment is indeed to some element from the set. This primitive is closely related to concepts like ring signatures and "one-out-of-many" proofs that underlie many anonymity and privacy protocols. The main result of this work is a new succinct lattice-based set membership proof whose size is logarithmic in the size of the set. We also give a transformation of our set membership proof to a ring signature scheme. The ring signature size is also logarithmic in the size of the public key set and has size $$16$$ KB for a set of $$2^5$$ elements, and $$22$$ KB for a set of size $$2^{25}$$ . At an approximately 128-bit security level, these outputs are between 1.5 $$\times $$ and 7 $$\times $$ smaller than the current state of the art succinct ring signatures of Beullens et al. (Asiacrypt 2020) and Esgin et al. (CCS 2019). We then show that our ring signature, combined with a few other techniques and optimizations, can be turned into a fairly efficient Monero-like confidential transaction system based on the MatRiCT framework of Esgin et al. (CCS 2019). With our new techniques, we are able to reduce the transaction proof size by factors of about 4X - 10X over the aforementioned work. For example, a transaction with two inputs and two outputs, where each input is hidden among $$2^{15}$$ other accounts, requires approximately 30KB in our protocol .
Performance Modeling of the Weather Impact on a Utility-Scale PV Power Plant in a Tropical Region

Solar photovoltaics and the associated applications are now considered the most promising technologies for a sustainable future. The performance of the PV power plants is not studied in detail with respect to the influence of various weather parameters like rain, relative humidity, and atmospheric pressure on energy generation. The objective of this research work is to analyze and model the weather impact of a utility-scale PV power plant in a tropical region. The methodology involves the detailed analysis of the PV plant performance for various weather seasons and modeling the energy generation based on important weather parameters obtained from a Solar Radiation Resource Assessment (SRRA) station installed at the PV power plant location itself. Solar generation and its performance are affected during the rainy seasons, and it turns out to be a typical phenomenon in the humid tropical region. A regression model of solar generation for all the weather seasons is generated based on different weather parameters.
A Closer Look at Self-training for Zero-Label Semantic Segmentation

Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. However, they are prone to overfitting on the seen classes because there is no training signal for them. In this paper, we study the challenging generalized zero-label semantic segmentation task where the model has to segment both seen and unseen classes at test time. We assume that pixels of unseen classes could be present in the training images but without being annotated. Our idea is to capture the latent information on unseen classes by supervising the model with self-produced pseudo-labels for unlabeled pixels. We propose a consistency regularizer to filter out noisy pseudo-labels by taking the intersections of the pseudo-labels generated from different augmentations of the same image. Our framework generates pseudo-labels and then retrain the model with human-annotated and pseudo-labelled data. This procedure is repeated for several iterations. As a result, our approach achieves the new state-of-the-art on PascalVOC12 and COCO-stuff datasets in the challenging generalized zero-label semantic segmentation setting, surpassing other existing methods addressing this task with more complex strategies. Code can be found at https://github.com/giuseppepastore10/STRICT.
Cryptanalysis of Novel Image Encryption Scheme Based on Multiple Chaotic Substitution Boxes

In current years many chaos-based Substitution boxes (S-boxes) have been proposed. Recently, an image encryption technique based on multiple chaotic S-box was offered. This encryption method was based on the concept of confusion only produced by the implementation of the S-box. The concept of confusion utilized in the understudy technique can be smashed by using just one chosen-plaintext attack and a chosen-ciphertext attack. This article presents a detailed structure of two types of cryptographic attacks on the diffusion-based encryption scheme. The proposed attacks are successfully performed to retrieve the key with very little execution time by using just one chosen image which indicates the vulnerability of multiple chaotic S-boxes-based cryptosystems. The retrieved data is passed through some statistical analysis such as correlation, histogram, and entropy to check the correctness of recovered data.
Learning by Aligning Videos in Time

We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task, while exploiting both frame-level and video-level information. We leverage a novel combination of temporal alignment loss and temporal regularization terms, which can be used as supervision signals for training an encoder network. Specifically, the temporal alignment loss (i.e., Soft-DTW) aims for the minimum cost for temporally aligning videos in the embedding space. However, optimizing solely for this term leads to trivial solutions, particularly, one where all frames get mapped to a small cluster in the embedding space. To overcome this problem, we propose a temporal regularization term (i.e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space. Extensive evaluations on various tasks, including action phase classification, action phase progression, and fine-grained frame retrieval, on three datasets, namely Pouring, Penn Action, and IKEA ASM, show superior performance of our approach over state-of-the-art methods for self-supervised representation learning from videos. In addition, our method provides significant performance gain where labeled data is lacking.
HAOP-Miner: Self-adaptive high-average utility one-off sequential pattern mining

One-off sequential pattern mining (SPM) (or SPM under the one-off condition) is a kind of repetitive SPM with gap constraints, and has been widely applied in many fields. However, current research on one-off SPM ignores the utility (can be price or profit) of items, resulting in some low-frequency but extremely important patterns being ignored. To solve this issue, this paper addresses self-adaptive High-Average utility One-off sequential Pattern (HAOP) mining which has following three characteristics. Any two occurrences cannot share any letter in the sequence. The support (number of occurrences), utility and length of the pattern are considered simultaneously. The HAOP mining discovers patterns with a self-adaptive gap which means that users do not need to set the gap constraints. We propose an effective algorithm called HAOP-Miner that involves two key steps: support calculation and candidate pattern generation. For the support calculation, we propose a heuristic algorithm named the Reverse filling (Rf) algorithm that can effectively calculate the support by avoiding creating redundant nodes and pruning the redundant and useless nodes after finding an occurrence. Since HAOP mining does not satisfy the Apriori property, a support lower bound method combined with the pattern growth strategy is adopted to generate the candidate patterns. The experimental results first validate the effectiveness of HAOP-Miner, and then demonstrate that HAOP-Miner has better performance than other state-of-the-art algorithms. More importantly, HAOP-Miner is easier to mine valuable patterns. The algorithms and datasets are available at https://github.com/wuc567/Pattern-Mining/tree/master/HAOP-Miner.
Multi-level dilated residual network for biomedical image segmentation

Abstract We propose a novel multi-level dilated residual neural network, an extension of the classical U-Net architecture, for biomedical image segmentation. U-Net is the most popular deep neural architecture for biomedical image segmentation, however, despite being state-of-the-art, the model has a few limitations. In this study, we suggest replacing convolutional blocks of the classical U-Net with multi-level dilated residual blocks, resulting in enhanced learning capability. We also propose to incorporate a non-linear multi-level residual blocks into skip connections to reduce the semantic gap and to restore the information lost when concatenating features from encoder to decoder units. We evaluate the proposed approach on five publicly available biomedical datasets with different imaging modalities, including electron microscopy, magnetic resonance imaging, histopathology, and dermoscopy, each with its own segmentation challenges. The proposed approach consistently outperforms the classical U-Net by 2%, 3%, 6%, 8%, and 14% relative improvements in dice coefficient, respectively for magnetic resonance imaging, dermoscopy, histopathology, cell nuclei microscopy, and electron microscopy modalities. The visual assessments of the segmentation results further show that the proposed approach is robust against outliers and preserves better continuity in boundaries compared to the classical U-Net and its variant, MultiResUNet.
An effective method to improve nonlinearity value of substitution boxes based on random selection

In today's digital society, new design alternatives are needed for substitution box structures, which has critical importance in developing secure information systems that are resistant to algebraic and application attacks, as new attacks negatively affect designs based on mathematical transformations. Although random selection-based substitution box structures are a good alternative against algebraic and application attacks, the low value of nonlinearity criterion is an important problem. Solution suggestions using optimization algorithms have been proposed to address this problem. “Information Sciences 523 (2020) 152–166” is the study that reached the highest nonlinearity value based on optimization algorithms to date. However, the high computational costs of optimization algorithms are another challenge that must be overcome. In this study, it has been shown that the nonlinearity values ​​of random (chaotic) selection substitution box structures can be improved as much as mathematically based designs by using a method that is too simple to compare with optimization algorithms. It has been shown that the nonlinearity value can be improved up to 110 for random selection-based substitution box designs. Although this nonlinearity value is the highest achievable value in the literature based on the random selection principle, the fact that it requires only 2×255×255 processes in the worst case has been evaluated as an important success of the proposed method compared to other design approaches. Other advantages of the proposed method are that the improved substitution box structures are resistant to side-channel attacks, simple structure, easy hardware implementation, and successful results in practical applications such as image encryption. It is believed that all these results will be a motivation for new post-processing techniques to be designed in the future for random selection-based substitution box designs.
Depth-Aware Mirror Segmentation

We present a novel mirror segmentation method that leverages depth estimates from ToF-based cameras as an additional cue to disambiguate challenging cases where the contrast or relation in RGB colors between the mirror reflection and the surrounding scene is subtle. A key observation is that ToF depth estimates do not report the true depth of the mirror surface, but instead return the total length of the reflected light paths, thereby creating obvious depth dis-continuities at the mirror boundaries. To exploit depth information in mirror segmentation, we first construct a large-scale RGB-D mirror segmentation dataset, which we subsequently employ to train a novel depth-aware mirror segmentation framework. Our mirror segmentation framework first locates the mirrors based on color and depth discontinuities and correlations. Next, our model further refines the mirror boundaries through contextual contrast taking into account both color and depth information. We extensively validate our depth-aware mirror segmentation method and demonstrate that our model outperforms state-of-the-art RGB and RGB-D based methods for mirror segmentation. Experimental results also show that depth is a powerful cue for mirror segmentation.
Weakly-Supervised Physically Unconstrained Gaze Estimation

A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios. In contrast, videos of human interactions in unconstrained environments are abundantly available and can be much more easily annotated with frame-level activity labels. In this work, we tackle the previously unexplored problem of weakly-supervised gaze estimation from videos of human interactions. We leverage the insight that strong gaze-related geometric constraints exist when people perform the activity of "looking at each other" (LAEO). To acquire viable 3D gaze supervision from LAEO labels, we propose a training algorithm along with several novel loss functions especially designed for the task. With weak supervision from two large scale CMU-Panoptic and AVA-LAEO activity datasets, we show significant improvements in (a) the accuracy of semisupervised gaze estimation and (b) cross-domain generalization on the state-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation benchmark. We open source our code at https://github.com/NVlabs/weaklysupervised-gaze.
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation

Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the depth of scenes. We are the first to explore the learning of a depth-specific structural representation, which captures the essential feature for depth estimation and ignores irrelevant style information. Our S2R-DepthNet (Synthetic to Real DepthNet) can be well generalized to un-seen real-world data directly even though it is only trained on synthetic data. S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domain-invariant structural representation from an image by dis-entangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation. Without access of any real-world images, our method even outperforms the state-of-the-art unsupervised domain adaptation methods which use real-world images of the tar-get domain for training. In addition, when using a small amount of labeled real-world data, we achieve the state-of-the-art performance under the semi-supervised setting.
Blind quality assessment of night-time image

High-quality night-time imaging is crucial to video surveillance, automatic drive and consumer electronics. However, different from day-time imaging, night-time imaging suffers from some disadvantages, such as low light, uneven illumination, difficult focusing, etc., which raises a great concern to the night-time imaging quality. Accordingly, a practical night-time image quality evaluation method is very promising to control and improve the night-time imaging system. Toward this end, in this paper, we propose a blind image quality assessment (BIQA) method to quantify the night-time image quality. Specifically, in the proposed method, we measure the night-time image quality by investigating the fundamental image properties, which are highly relevant to the image quality, such as the brightness, saturation, sharpness, noiseness, contrast and the semantics. Specific features are designed to characterize the image properties properly. Then we employ the support vector regression (SVR) method to infer the image quality with the extracted quality-aware features. The proposed BIQA method for night-time images is thoroughly evaluated on a representative night-time image database. Experimental results demonstrate that the proposed BIQA method for night-time images achieves superior prediction performance to other state-of-the-art BIQA methods.
Multi-stage Aggregated Transformer Network for Temporal Language Localization in Videos

We address the problem of localizing a specific moment from an untrimmed video by a language sentence query. Generally, previous methods mainly exist two problems that are not fully solved: 1) How to effectively model the fine-grained visual-language alignment between video and language query? 2) How to accurately localize the moment in the original video length? In this paper, we streamline the temporal language localization as a novel multi-stage aggregated transformer network. Specifically, we first intro-duce a new visual-language transformer backbone, which enables iterations and alignments among all elements in visual and language sequences. Different from previous multi-modal transformers, our backbone keeps both structure unified and modality specific. Moreover, we also pro-pose a multi-stage aggregation module topped on the trans-former backbone. In this module, we compute three stage-specific representations corresponding to different moment stages respectively, i.e. starting, middle and ending stages, for each video element. Then for a moment candidate, we concatenate the starting/middle/ending representations of its starting/middle/ending elements respectively to form the final moment representation. Because the obtained moment representation captures the stage specific information, it is very discriminative for accurate localization. Extensive experiments on ActivityNet Captions and TACoS datasets demonstrate our proposed method achieves significant improvements compared with all other methods.
A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.
Becoming a blockchain user: understanding consumers’ benefits realisation to use blockchain-based applications

Data breaches and cyber incidents are on the rise, and companies continually research new technologies to defend against attacks and protect customer data. The blockchain is a data store designed to promote data privacy, as well as transaction integrity. Enterprises in several industries, especially banking, have investigated the implementation of blockchain-based databases to replace centralised databases as one mechanism for protecting customers’ data by separating transactional data from personally identifiable information. Despite the blockchain’s privacy protections, consumers remain largely unaware of these benefits. Building on the Health Belief Model (HBM), we include privacy concerns and inertia as critical factors that influence consumers’ perceptions of blockchain-based databases’ benefits. Using a sample of 304 respondents, we test a theoretical model incorporating these factors. Our study results indicate threat severity, threat susceptibility, awareness, and inertia significantly influence the perceived benefits of blockchain, which has a significant positive influence on consumers’ intention to switch to blockchain-based applications. Although consumers’ comfort with the status quo of traditional banking mechanisms is a significant barrier to the realisation of blockchain banking applications benefits, additional awareness of consumer privacy protections can persuade customers to use the blockchain-based applications, especially if they exhibit heightened privacy concerns.
Bengali text document categorization based on very deep convolution neural network

In recent years, the amount of digital text contents or documents in the Bengali language has increased enormously on online platforms due to the effortless access of the Internet via electronic gadgets. As a result, an enormous amount of unstructured data is created that demands much time and effort to organize, search or manipulate. To manage such a massive number of documents effectively, an intelligent text document classification system is proposed in this paper. Intelligent classification of text document in a resource-constrained language (like Bengali) is challenging due to unavailability of linguistic resources, intelligent NLP tools, and larger text corpora. Moreover, Bengali texts are available in two morphological variants (i.e., Sadhu-bhasha and Cholito-bhasha) making the classification task more complicated. The proposed intelligent text classification model comprises GloVe embedding and Very Deep Convolution Neural Network (VDCNN) classifier. Due to the unavailability of standard corpus, this work develops a large Embedding Corpus (EC) containing 969,000 unlabelled texts and Bengali Text Classification Corpus (BDTC) containing 156,207 labelled documents arranged into 13 categories. Moreover, this work proposes the Embedding Parameters Identification (EPI) Algorithm, which selects the best embedding parameters for low-resource languages (including Bengali). Evaluation of 165 embedding models with intrinsic evaluators (semantic & syntactic similarity measures) shows that the GloVe model is more suitable (regarding Spearman & Pearson correlation) than other embeddings (Word2Vec, FastText, m-BERT) in Bengali text. Experimental results on the test dataset confirm that the proposed GloVe + VDCNN model outperformed (achieving the highest 96.96% accuracy) the other classification models and existing methods to perform the Bengali text classification task.