Reviewerly/exHarmony huggingface dataset:
An Ensemble Architecture Based on Deep Learning Model for Click Fraud Detection in Pay-Per-Click Advertisement Campaign

With the rapid development of online advertising, click fraud is a serious issue for the internet market. Click fraud is a dishonest attempt to improve a website’s profit or deplete an advertiser’s budget by clicking on pay-per-click advertisements. For an extended period, this illegal act has a threat to the industrial sectors. As a result, these businesses hesitate to advertise their items on mobile apps and websites, as numerous groups attempt to take advantage of themes. To safely advertise their services and products online, a robust mechanism is needed for efficient click fraud detection. To tackle this issue, an ensemble architecture of machine learning and deep learning is proposed to detect click fraud in online advertisement campaigns. The proposed ensemble architecture consists of a Convolutional Neural Network (CNN), and a Bidirectional Long Short-Term Memory network (BiLSTM) is used to extract hidden features, while the Random Forest (RF) is used for classification. The main objective of the proposed research study is to develop a hybrid DL model for automatic feature extraction from clicks data and then process through an RF classifier into two classes, such as fraudulent and non-fraudulent clicks. Furthermore, a preprocessing module is developed to preprocess data by dealing with categorical attributes and imbalanced data to enhance the reliability and consistency of the clicks data. In addition, different evaluation criteria are used to evaluate and compare the performance of the proposed CNN-BiLSTM-RF with the ensemble and standalone models. The experimental results indicate that our ensemble architecture achieved the accuracy of 99.19 ± 0.08%, precision 99.89 ± 0.03%, sensitivity 98.50 ± 0.11%, F1-score 99.19 ± 0.08% and specificity 99.89 ± 0.03%. Furthermore, our proposed architecture produced superior results compared to other developed ensemble and conventional models. Moreover, our proposed ensemble architecture can be used as a safeguard against click fraud for pay-per-click advertising to facilitate industries for the safe and reliable promotion of their products.
Verifiable Relation Sharing and Multi-verifier Zero-Knowledge in Two Rounds: Trading NIZKs with Honest Majority

We introduce the problem of Verifiable Relation Sharing (VRS) where a client (prover) wishes to share a vector of secret data items among k servers (the verifiers) while proving in zero-knowledge that the shared data satisfies some properties. This combined task of sharing and proving generalizes notions like verifiable secret sharing and zero-knowledge proofs over secret-shared data. We study VRS from a theoretical perspective and focus on its round complexity. As our main contribution, we show that every efficiently-computable relation can be realized by a VRS with an optimal round complexity of two rounds where the first round is input-independent (offline round). The protocol achieves full UC-security against an active adversary that is allowed to corrupt any t-subset of the parties that may include the client together with some of the verifiers. For a small (logarithmic) number of parties, we achieve an optimal resiliency threshold of $$t<0.5(k+1)$$ , and for a large (polynomial) number of parties, we achieve an almost-optimal resiliency threshold of $$t<0.5(k+1)(1-\epsilon )$$ for an arbitrarily small constant $$\epsilon >0$$ . Both protocols can be based on sub-exponentially hard injective one-way functions. If the parties have an access to a collision resistance hash function, we can derive statistical everlasting security, i.e., the protocols are secure against adversaries that are computationally bounded during the protocol execution and become computationally unbounded after the protocol execution. Previous 2-round solutions achieve smaller resiliency thresholds and weaker security notions regardless of the underlying assumptions. As a special case, our protocols give rise to 2-round offline/online constructions of multi-verifier zero-knowledge proofs (MVZK). Such constructions were previously obtained under the same type of assumptions that are needed for NIZK, i.e., public-key assumptions or random-oracle type assumptions (Abe et al., Asiacrypt 2002; Groth and Ostrovsky, Crypto 2007; Boneh et al., Crypto 2019; Yang, and Wang, Eprint 2022). Our work shows, for the first time, that in the presence of an honest majority these assumptions can be replaced with more conservative "Minicrypt"-type assumptions like injective one-way functions and collision-resistance hash functions. Indeed, our MVZK protocols provide a round-efficient substitute for NIZK in settings where honest-majority is present. Additional applications are also presented.
Unpaired Cartoon Image Synthesis via Gated Cycle Mapping

In this paper, we present a general-purpose solution to cartoon image synthesis with unpaired training data. In contrast to previous works learning pre-defined cartoon styles for specified usage scenarios (portrait or scene), we aim to train a common cartoon translator which can not only simultaneously render exaggerated anime faces and realistic cartoon scenes, but also provide flexible user controls for desired cartoon styles. It is challenging due to the complexity of the task and the absence of paired data. The core idea of the proposed method is to introduce gated cycle mapping, that utilizes a novel gated mapping unit to produce the category-specific style code and embeds this code into cycle networks to control the translation process. For the concept of category, we classify images into different categories (e.g., 4 types: photo/cartoon portrait/scene) and learn finer-grained category translations rather than overall mappings between two domains (e.g., photo and cartoon). Furthermore, the proposed method can be easily extended to cartoon video generation with an auxiliary dataset and a new adaptive style loss. Experimental results demonstrate the superiority of the proposed method over the state of the art and validate its effectiveness in the brand-new task of general cartoon image synthesis.
MSF-Model: Multi-Scale Feature Fusion-Based Domain Adaptive Model for Breast Cancer Classification of Histopathology Images

One of the most common causes of mortality for women globally is breast cancer. Early breast cancer identification could make it possible for people to receive the appropriate treatment to save their lives and return to their routine lives. Breast cancer diagnosis by histopathology is referred to as the gold standard. In recent years, convolutional neural network-based techniques are used for breast cancer classification. However, they faced domain adaptation, small objects retention, and feature extraction issues of complex microscopic images. In this study, we introduced multi-scale feature fusion-based domain adaptive model for breast cancer classification using histopathology images. It has two blocks and six lightweight sub-models where each block contains three models. Dilated layers are used in sub-models to overcome the disappearing of small objects in deep layers. Reducing the disappearing of small objects helped to extract better features for higher performance. Multiple heterogeneous feature extractors are used in this study which helped to extract various features. Extracted features are fused and reduced by retaining better features. Learning of model from natural images to complex microscopic images has limitation of domain adaptation. Same domain transfer learning is used in this study to overcome the limitations of different domain transfer learning. Model is trained on patchcamelyon17 dataset and weights of this training are further used for same domain transfer learning. Pre-trained weights are further used for the training of proposed model on BreaKHis dataset. A number of conventional data augmentation techniques are used as complex models require higher number of samples for the tuning of weights. Local window based CLAHE contrast enhancement technique is used to increase foreground-background contrast and remove noise. The proposed model achieved 98.00% precision, 98.15% recall, 98.08% f-measure, and 98.23% accuracy on test data. To best of our knowledge, it surpassed state-of-the-art models.
Locating Multiple Equivalent Feature Subsets in Feature Selection for Imbalanced Classification

Feature selection can be used to solve imbalanced classification problems encountered in big data projects. There often exist multiple feature subsets achieving the same accuracy. These subsets tend to exhibit different acquisition difficulty and reliability, thus offering decision-makers with multiple choices if they can be well-identified. This work formulates feature selection as a Multimodal Multiobjective Problem (MMOP), where a point on Pareto front in objective space has multiple equivalent feature subsets in decision space. To seek more equivalent feature subsets, this work proposes a new multiobjective fireworks algorithm. It extends a latest single-objective fireworks algorithm to a multiobjective version such that it becomes suitable for solving MMOP. An adaptive strategy and special archive guidance are newly designed to improve its performance. A weighted extreme learning machine is chosen to classify datasets and return classification accuracy due to its fast learning speed. Experimental results show that the proposed algorithm outperforms its compared ones on 15 imbalanced classification datasets including 5 low-dimensional, 5 high-dimensional feature selection problems and 5 large-scale problems with larger imbalanced ratio, and its runtime is the least among them. Also, fault diagnosis in self-organizing cellular networks, as an important imbalance classification problem, is performed by the proposed algorithm and the results show that it can perform fault diagnosis well.
Authenticated Garbling from Simple Correlations

We revisit the problem of constant-round malicious secure two-party computation by considering the use of simple correlations, namely sources of correlated randomness that can be securely generated with sublinear communication complexity and good concrete efficiency. The current state-of-the-art protocol of Katz et al. (Crypto 2018) achieves malicious security by realizing a variant of the authenticated garbling functionality of Wang et al. (CCS 2017). Given oblivious transfer correlations, the communication cost of this protocol (with 40 bits of statistical security) is comparable to roughly 10 garbled circuits (GCs). This protocol inherently requires more than 2 rounds of interaction. In this work, we use other kinds of simple correlations to realize the authenticated garbling functionality with better efficiency. Concretely, we get the following reduced costs in the random oracle model: Finally, we show that by using recent constructions of pseudorandom correlation generators (Boyle et al., CCS 2018, Crypto 2019, 2020), the simple correlations consumed by our protocols can be securely realized without forming an efficiency bottleneck.
Learning to Learn and Remember Super Long Multi-Domain Task Sequence

Catastrophic forgetting (CF) frequently occurs when learning with non-stationary data distribution. The CF issue remains nearly unexplored and is more challenging when meta-learning on a sequence of domains (datasets), called sequential domain meta-learning (SDML). In this work, we propose a simple yet effective learning to learn approach, i.e., meta optimizer, to mitigate the CF problem in SDML. We first apply the proposed meta optimizer to the simplified setting of SDML, domain-aware meta-learning, where the domain labels and boundaries are known during the learning process. We propose dynamically freezing the network and incorporating it with the proposed meta optimizer by considering the domain nature during meta training. In addition, we extend the meta optimizer to the more general setting of SDML, domain-agnostic meta-learning, where domain labels and boundaries are unknown during the learning process. We propose a domain shift detection technique to capture latent domain change and equip the meta optimizer with it to work in this setting. The proposed meta optimizer is versatile and can be easily integrated with several existing meta-learning algorithms. Finally, we construct a challenging and large-scale benchmark consisting of 10 heterogeneous domains with a super long task sequence consisting of 100K tasks. We perform extensive experiments on the proposed benchmark for both settings and demonstrate the effectiveness of our proposed method, outperforming current strong baselines by a large margin.
Real-Time Surveillance System for Detection of Social Distancing

As the corona virus can mutate and due to other scientific factor associated to it, experts believe that COVID-19 will remain with us for decades. Therefore, one has to keep social distancing measures. Accepting the pandemic situation, the paper presents a mechanism for detecting violations of social distancing using deep learning to estimate the distance between individuals to diminish the influence of COVID-19. The focus of this paper is to understand the effect of social distancing on the spread of COVID-19 by using YOLOv3 and Faster-RCNN and proposes IFRCNN (improved faster region – convolution neural network). The proposed method IFRCNN is checked on a live streaming video of pedestrians walking on the street. This paper keeps the live updates of the recorded video along with social distancing violation records on a location, so how many people in a location are maintaining social distancing. Updates will be stored in a cloud-based storage system and any organization or firm can get live updates of that location in their digital devices.
A Fully Interpretable First-Order TSK Fuzzy System and Its Training With Negative Entropic and Rule-Stability-Based Regularization

While interpretable antecedent parts of first-order Takagi–Sugeno–Kang (TSK) fuzzy rules can be properly acquired by adopting some clustering methods, this study aims at avoiding the commonly used yet fully incomprehensive consequent parts and their intractable training, and simultaneously seeking for enhanced generalization performance by determining the weight of each rule. The central idea is to build a mathematically equivalent bridge between a Gaussian mixture model (GMM) and a fully interpretable first-order TSK fuzzy system called FIMG-TSK, with the help of Gaussian-mixture's mean. The resultant FIMG-TSK has a simple expected output expression without summation-to-one defuzzification, which will be helpful in inducing both smaller output variance and a negative entropic and rule-stability-based regularizer for enhancing the generalization performance. After revealing three factors affecting the output stability of FIMG-TSK, the negative entropic and rule-stability-based regularizer is designed through both these factors and the squared entropy to make the output variance of FIMG-TSK as small as possible. Accordingly, a novel training method, whose objective function takes the proposed regularizer as an additional term and hence compromises both accuracy and output stability of FIMG-TSK, is developed to quickly provide an analytical solution to the weight of each rule. The effectiveness of the proposed training method is manifested by the experimental results on ten regression datasets.
Neural Global Shutter: Learn to Restore Video from a Rolling Shutter Camera with Global Reset Feature

Most computer vision systems assume distortion-free images as inputs. The widely used rolling-shutter (RS) image sensors, however, suffer from geometric distortion when the camera and object undergo motion during capture. Extensive researches have been conducted on correcting RS distortions. However, most of the existing work relies heavily on the prior assumptions of scenes or motions. Besides, the motion estimation steps are either oversimplified or computationally inefficient due to the heavy flow warping, limiting their applicability. In this paper, we investigate using rolling shutter with a global reset feature (RSGR) to restore clean global shutter (GS) videos. This feature enables us to turn the rectification problem into a deblur-like one, getting rid of inaccurate and costly explicit motion estimation. First, we build an optic system that captures paired RSGR/GS videos. Second, we develop a novel algorithm incorporating spatial and temporal designs to correct the spatial-varying RSGR distortion. Third, we demonstrate that existing image-to-image translation algorithms can recover clean GS videos from distorted RSGR inputs, yet our algorithm achieves the best performance with the specific designs. Our rendered results are not only visually appealing but also beneficial to downstream tasks. Compared to the state-of-the-art RS solution, our RSGR solution is superior in both effectiveness and efficiency. Considering it is easy to realize without changing the hardware, we believe our RSGR solution can potentially replace the RS solution in taking distortion-free videos with low noise and low budget.
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection

Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) images can provide complementary information to handle light variations, the paired images are used in many fields, such as multispectral pedestrian detection, RGB-IR crowd counting and RGB-IR salient object detection. Compared with natural RGB-IR images, we find detection in aerial RGB-IR images suffers from cross-modal weakly misalignment problems, which are manifested in the position, size and angle deviations of the same object. In this paper, we mainly address the challenge of cross-modal weakly misalignment in aerial RGB-IR images. Specifically, we firstly explain and analyze the cause of the weakly misalignment problem. Then, we propose a Translation-Scale-Rotation Alignment (TSRA) module to address the problem by calibrating the feature maps from these two modalities. The module predicts the deviation between two modality objects through an alignment process and utilizes Modality-Selection (MS) strategy to improve the performance of alignment. Finally, a two-stream feature alignment detector (TSFADet) based on the TSRA module is constructed for RGB-IR object detection in aerial images. With comprehensive experiments on the public DroneVehicle datasets, we verify that our method reduces the effect of the cross-modal misalignment and achieve robust detection results.
Learning to Collaborate in Decentralized Learning of Personalized Models

Learning personalized models for user-customized computer-vision tasks is challenging due to the limited private-data and computation available on each edge device. Decentralized learning (DL) can exploit the images distributed over devices on a network topology to train a global model but is not designed to train personalized models for different tasks or optimize the topology. Moreover, the mixing weights used to aggregate neighbors' gradient messages in DL can be suboptimal for personalization since they are not adaptive to different nodes/tasks and learning stages. In this paper, we dynamically update the mixing-weights to improve the personalized model for each node's task and meanwhile learn a sparse topology to reduce communication costs. Our first approach, “learning to collaborate (L2C) ”, directly optimizes the mixing weights to minimize the local validation loss per node for a predefined set of nodes/tasks. In order to produce mixing weights for new nodes or tasks, we further develop “meta-L2C‘, which learns an attention mechanism to automatically assign mixing weights by comparing two nodes' model updates. We evaluate both methods on diverse benchmarks and experimental settings for image classification. Thorough comparisons to both classical and recent methods for IID/non-IID decentralized and federated learning demonstrate our method's advantages in identifying collaborators among nodes, learning sparse topology, and producing better personalized models with low communication and computational cost.
SS3D: Sparsely-Supervised 3D Object Detection from Point Cloud

Conventional deep learning based methods for 3D object detection require a large amount of 3D bounding box annotations for training, which is expensive to obtain in practice. Sparsely annotated object detection, which can largely reduce the annotations, is very challenging since the missing-annotated instances would be regarded as the background during training. In this paper, we propose a sparsely-supervised 3D object detection method, named SS3D. Aiming to eliminate the negative supervision caused by the missing annotations, we design a missing-annotated instance mining module with strict filtering strategies to mine positive instances. In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision. The proposed SS3D is a general framework that can be used to learn any modern 3D object detector. Extensive experiments on the KITTI dataset reveal that on different 3D detectors, the proposed SS3D framework with only 20% annotations required can achieve onpar performance comparing to fully-supervised methods. Comparing with the state-of-the-art semi-supervised 3D objection detection on KITTI, our SS3D improves the benchmarks by significant margins under the same annotation workload. Moreover, our SS3D also out-performs the state-of-the-art weakly-supervised method by remarkable margins, highlighting its effectiveness.
Optical Flow Estimation for Spiking Camera

As a bio-inspired sensor with high temporal resolution, the spiking camera has an enormous potential in real applications, especially for motion estimation in high-speed scenes. However, frame-based and event-based methods are not well suited to spike streams from the spiking camera due to the different data modalities. To this end, we present, SCFlow, a tailored deep learning pipeline to estimate optical flow in high-speed scenes from spike streams. Importantly, a novel input representation is introduced which can adaptively remove the motion blur in spike streams according to the prior motion. Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic Highspeed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes. Experimental results show that the SCFlow can predict optical flow from spike streams in different high-speed scenes. Moreover, SCFlow shows promising generalization on real spike streams. Codes and datasets refer to https://github.com/Acnext/Optical-Flow-For-Spiking-Camera.