Reviewerly/exHarmony huggingface dataset:
Logical Topology Inference via CPGCN Joint Optimizing With Pedestrian Re-Id

With the rise of artificial intelligence, deep learning has become the main research method of pedestrian recognition re-identification (re-id). However, most of the existing researches usually just determine the retrieval order based on the geographical location of cameras, which ignore the spatio-temporal logic characteristics of pedestrian flow. Furthermore, most of these methods rely on common object detection to detect and match pedestrians directly, which will separate the logical connection between videos from different cameras. In this research, a novel pedestrian re-identification model assisted by logical topological inference is proposed, which includes: 1) a joint optimization mechanism of pedestrian re-identification and multicamera logical topology inference, which makes the multicamera logical topology provides the retrieval order and the confidence for re-identification. And meanwhile, the results of pedestrian re-identification as a feedback modify logical topological inference; 2) a dynamic spatio-temporal information driving logical topology inference method via conditional probability graph convolution network (CPGCN) with random forest-based transition activation mechanism (RF-TAM) is proposed, which focuses on the pedestrian's walking direction at different moments; and 3) a pedestrian group cluster graph convolution network (GC-GCN) is designed to measure the correlation between embedded pedestrian features. Some experimental analyses and real scene experiments on datasets CUHK-SYSU, PRW, SLP, and UJS-reID indicate that the designed model can achieve a better logical topology inference with an accuracy of 87.3% and achieve the top-1 accuracy of 77.4% and the mAP accuracy of 74.3% for pedestrian re-identification.
Offline Handwritten Hindi Character Recognition Using Deep Learning with Augmented Dataset

It is highly desirable to develop an offline handwritten character recognition system for the Hindi script with an acceptable accuracy level. This script has many characters, including numerals and different shape variations of these characters. Moreover, the Hindi script also has compound characters as well as characters with modifiers. It makes the task of character recognition more complex. Even the shape of characters written by an individual may have significant variation at different times. Hence, incorporating all these issues is very challenging. This paper proposes the augmented data set to overcome these problems up to an extent. The augmented data set introduces the hypothetical changes in the original data set to increase the variations in the data samples, which may increase the variety of samples. Thus, the accuracy of the recognition system improves. For the classification of characters, Convolutional Neural Networks (CNNs) are employed. Based on CNN, classification models are more suitable for recognition tasks where the images are taken as inputs. So, utilizing deep learning algorithms for recognition of Hindi script characters with the projected scheme will improve the accuracy of the system.
Single-Server Delegation of Ring Multiplications from Quasilinear-time Clients

We investigate the problem of delegating operations in cryptography solutions from client devices that only perform quasilinear-time or lower-order computations (e.g., additions/subtractions, modular reductions with a small modulus, etc.) to a single, possibly malicious, server. All previous work considered clients capable of computing higher-order operations, such as fully homomorphic encryption, group exponentiations or several group multiplications. In this model, we show protocols to delegate the computation of ring multiplications while satisfying desirable result correctness, input privacy and result security requirements. The main technical component, of independent interest, is a family of probabilistic tests that extends a classical test by Pippenger. The asymptotic improvement in our multiplication delegation protocols is also backed up by concrete implementation results, demonstrating that the client's online computation is strictly smaller than non-delegated computation of the same function, for input lengths of interest in cryptography solutions.
Exploring Versatile Prior for Human Motion via Motion Frequency Guidance

Prior plays an important role in providing the plausible constraint on human motion. Previous works design motion priors following a variety of paradigms under different circumstances, leading to the lack of versatility. In this paper, we first summarize the indispensable properties of the motion prior, and accordingly, design a framework to learn the versatile motion prior, which models the inherent probability distribution of human motions. Specifically, for efficient prior representation learning, we propose a global orientation normalization to remove redundant environment information in the original motion data space. Also, a two-level, sequence-based and segment-based, frequency guidance is introduced into the encoding stage. Then, we adopt a denoising training scheme to disentangle the environment information from input motion data in a learnable way, so as to generate consistent and distinguishable representation. Embedding our motion prior into prevailing backbones on three different tasks, we conduct extensive experiments, and both quantitative and qualitative results demonstrate the versatility and effectiveness of our motion prior. Our model and code are available at https://github.com/JchenXu/human-motion-prior.
Central Bank Digital Currencies (CBDCs)

Central Bank Digital Currencies (CBDCs) constitute a response by sovereign monetary authorities to the emergence of cryptocurrencies. Although they share commonalities as “virtual assets,” they do diverge significantly in key monetary, oversight/surveillance, and philosophy aspects. As such, the aim of this paper is to consider the monetary, social, and political role of CBDCs as a complement and substitute to cryptocurrencies. The findings of the paper suggest that CBDCs resolve key issues in cryptocurrencies, but raise concerns of their own in turn.
Ambiguity rate of hidden Markov processes

The ε-machine is a stochastic process's optimal model-maximally predictive and minimal in size. It often happens that to optimally predict even simply defined processes, probabilistic models-including the ε-machine-must employ an uncountably infinite set of features. To constructively work with these infinite sets we map the ε-machine to a place-dependent iterated function system (IFS)-a stochastic dynamical system. We then introduce the ambiguity rate that, in conjunction with a process's Shannon entropy rate, determines the rate at which this set of predictive features must grow to maintain maximal predictive power over increasing horizons. We demonstrate, as an ancillary technical result that stands on its own, that the ambiguity rate is the (until now missing) correction to the Lyapunov dimension of an IFS's attracting invariant set. For a broad class of complex processes, this then allows calculating their statistical complexity dimension-the information dimension of the minimal set of predictive features.
A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs

Autonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work introduces an obstacle avoidance architecture specifically for sprayer UAVs. This architecture has generality in the spraying UAV problem, and it reduces the reliance on the global mapping of farmland. This approach computes the avoiding path based on the onboard sensor fusion system in real-time. Moreover, it autonomously determines the transition of several maneuver states using the current spraying liquid data and the UAV dynamics data obtained by offline system identification. This approach accurately tracks the avoidance path for the nonlinear time-variant spraying UAV systems. To verify the performance of the approach, we performed multiple simulations with different spraying missions, and the method demonstrated a high spraying coverage of more than 98% while successfully avoiding all vertical obstacles. We also demonstrated the adaptability of our control architecture; the safe distance between the UAV and obstacles can be changed by specifying the value of a high-level parameter on the controller. The proposed method adds value to precision agriculture, reduces mission time, and maximizes the spraying area coverage.
Improving U-Net Kidney Glomerulus Segmentation with Fine-Tuning, Dataset Randomization and Augmentations

The recent success of deep learning methods for Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) provide a possibility to speed up some specific medical procedures, for example, medical image segmentation and analysis, and suggests ways to perform it in the very effective ways. In this study of the Human Kidney segmentation task, we sought to improve the efficiency of our previously proposed method based on some U-Net architectures. The investigation measures the impact of several techniques on the model performance, like test time data augmentations (TTAs) and train time data augmentations (TrTAs), shuffling, randomization, normalization, hyperparameter fine-tuning, and others. It was shown that a major improvement of the Dice Score for the test dataset - from 0.9127 as in initial research up to 0.934 (almost by 3%) - was achieved by applying dataset shuffling. The reliability of the obtained results was checked by K-Fold cross-validation for some optimization techniques. These findings can be used for different kinds of precision-focused segmentation tasks in the CADe/CADx context.
The Application of Graph Neural Network in Natural Language Processing and Computer Vision

Graphs can represent information transformation via geometrical relations, which have been well studied and applied in various research areas. A graph-based learning network named Graph Neural Network (GNN) arose with the vast development of deep learning in recent years. Unlike traditional deep learning networks such as CNN and RNN, GNN is superior in dealing with non-Euclidean graph data. This survey focuses on two widespread application fields of GNN, natural language processing (NLP) and computer vision (CV). Firstly, based on the tasks they perform, we categorize the most popular research sub-domains of NLP and CV, purpose a detailed review on the application of GNN in these areas. Secondly, we thoroughly analyzed the benchmark datasets applied in the GNN models while comparing them with different evaluation metrics. Finally, we briefly discuss the potential future direction of GNN according to its model building procedure and related application branches.
Multiple UAVs Forest Fire Fighting using a hierarchical task planning method

A task planning approach is proposed for multiunmanned aerial vehicles (UAVs) in a forest fire fighting mission considering obstacles and simultaneous arrival. The approach consists of three stages, in the first stage, the estimate arrival time between UAVs and targets are calculated by the rapidly-exploring random tree (RRT) algorithm, which serves as input to solve task assignment problem. Then, a distributed task assignment method using auction mechanism is employed to allocate these fire spots aiming to minimize the fire-fighting mission time, the value and resources requirement of fire spots are considered. In the last stages, a modified cooperative particle swarm optimization algorithm is designed for these UAVs to reach the fire spot simultaneously, kinematic constraint of UAV and collision avoidance with mountains are considered. Simulation examples are used to validate the proposed methods.
A Deep Neural Network (DNN) Approach for Recommendation Systems

In different commercial platforms, Recommendation System (RS) is widely used for providing recommendations to users. In various areas, RS is utilized broadly and in E-Commerce sites, product recommendations are discovered during an active user interaction by RS. In recent decades, some key challenges are faced due to tremendous growth in user as well as products. Moreover, in RS, computation of right product and active user is a major task. User inclination and socio-demographic behavior are considered in existing works for recommending a product. In recommendation systems, one of the major algorithm used is Collaborative Filtering (CF) algorithms. This algorithm is simple as well as effective. However, further enhancement of recommendation result's quality is limited by data sparsity and scalability of this technique. Previous technique's problems are addressed effectively in proposed technique and user preference on balance feature analysis and products are evaluated. Therefore, proposed a model using the combination of deep learning technology and CF recommendation algorithm with three major stages, namely, preprocessing, representation of features and rating score prediction using DNN. At first, from log files, redundant and unnecessary data are removed in preprocessing module. There is an unwanted files like repeated tags, repeated similar products, removing invalid values, last visit and elapsed time. In feature representation stage, Quadric Polynomial Regression—QPR-based feature representation technique is used. The traditional matrix factorization algorithm is enhanced for obtaining accurate latent features. At last, DNN model is fed using these latent features as input data, which is a second stage of proposed model. Rating scores are predicted using this. From Amazon dataset, user data based on behavior is obtained and used in experimentation. There are 18,501 product reviews in Amazon product dataset. From Amazon web services, collected the dataset information that joins with administrative services. Based on metrics like F1-measure, Recall (R) and Precision (P) proposed Deep Neural Network (DNN) method is evaluated experimentally and highest value of those metrics are produced when compared with state-of-the art techniques like K-Nearest Neighbor (K-NN), Artificial Neural Networks (ANN).
BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection

Small object detection becomes a challenging problem in computer vision due to low resolution and less feature information. Making full use of high-resolution features is an important factor in improving small object detection. In this paper, to improve the utilization of high-resolution features, this work proposes the Bidirectional Multi-scale Feature Enhancement Network (BFE-Net) based on RetinaNet. First, this work introduces a bidirectional feature pyramid structure to shorten the propagation path of high-resolution features. Then, this work utilizes residually connected dilated convolutional blocks to fully extract high-resolution features of low-feature layers. Finally, this work supplements the high-resolution features lost in the high-level feature propagation process by leveraging the high-level guided lower-level features. Experiments show that our proposed BFE-Net achieves stable performance gains in the object detection task. Specifically, the improved method improves RetinaNet from 34.4 AP to 36.3 AP on the challenging MS COCO dataset and especially achieves excellent results in small object detection with an improvement of 2.8%.
Crawling and Mining the Dark Web: A Survey on Existing and New Approaches

The last two decades have seen a marked increase in the illegal activities on the Dark Web. Prompt evolvement and use of sophisticated protocols make it difficult for security agencies to identify and investigate these activities by conventional methods. Moreover, tracing criminals and terrorists poses a great challenge keeping in mind that cybercrimes are no less serious than real life crimes. At the same time, computer security societies and law enforcement pay a great deal of attention on detecting and monitoring illegal sites on the Dark Web. Retrieval of relevant information is not an easy task because of vastness and ever-changing nature of the Dark Web; as a result, web crawlers play a vital role in achieving this task. Thereafter, data mining techniques are applied to extract useful patterns that would help security agencies to limit and get rid of cybercrimes. The aim of this paper is to present a survey for those researchers who are interested in this topic. We started by discussing the internet layers and the properties of the Deep Web, followed by explaining the technical characters of The Onion Routing (TOR) network, and finally describing the approaches of accessing, extracting and processing Dark Web data. Understanding the Dark Web, its properties and its threats is vital for internet servers; we do hope this paper be of help in that goal.
Learning HDR video reconstruction for dual-exposure sensors with temporally-alternating exposures

High-dynamic range (HDR) video reconstruction using conventional single-exposure sensors can be achieved by temporally alternating exposures. This, in turn, requires computing exposure alignment which is difficult to achieve due to the exposure differences that notoriously creates problems for moving content, in particular, in larger saturated and dis-occluded regions. An attractive alternative are dual-exposure sensors that capture, in a single-shot, differently exposed and spatially interleaved half-frames, so that they are perfectly spatially and temporally (up to varying motion blur) aligned by construction. In this work, we demonstrate that we successfully compensate for reduced spatial resolution and aliasing in such sensors, and we improve overall the quality and dynamic range of reconstructed HDR video with respect to single-exposure sensors for a given number of alternating exposures. Specifically, we consider low, mid, and high exposures, and we propose that the mid exposure is captured for every frame, and serves as a spatial and temporal reference. We capitalize here on neural networks for denoising, deblurring, and upsampling tasks, so that effectively we obtain two clean, sharp, and full-resolution exposures for every frame, which are then complemented by warping a missing third exposure. High-quality warping is achieved by learning optical flow that merges the individual flows found for each specific exposure. Such flow merging is instrumental in handling saturated/dis-occluded image regions, while dense temporal sampling of mid exposure improves motion quality reproduction between more sparsely sampled exposures. We demonstrate that by capturing only a limited amount of sensor-specific data and a novel use of histograms, instead of common parametric noise statistics, we are able to generate synthetic training data that lead to a better denoising and deblurring quality than can be achieved by existing state-of-the-art methods. As there is not enough high-quality HDR video available, we devise a method to learn from LDR video instead. Our approach compares favorably to several strong baselines, and can boost existing HDR image and video methods when they are re-trained on our data.
TAPE: Task-Agnostic Prior Embedding for Image Restoration

Learning a generalized prior for natural image restoration is an important yet challenging task. Early methods mostly involved handcrafted priors including normalized sparsity, $$\ell _0$$ gradients, dark channel priors, etc. Recently, deep neural networks have been used to learn various image priors but do not guarantee to generalize. In this paper, we propose a novel approach that embeds a task-agnostic prior into a transformer. Our approach, named Task-Agnostic Prior Embedding (TAPE), consists of two stages, namely, task-agnostic pre-training and task-specific fine-tuning, where the first stage embeds prior knowledge about natural images into the transformer and the second stage extracts the knowledge to assist downstream image restoration. Experiments on various types of degradation validate the effectiveness of TAPE. The image restoration performance in terms of PSNR is improved by as much as 1.45 dB and even outperforms task-specific algorithms. More importantly, TAPE shows the ability of disentangling generalized image priors from degraded images, which enjoys favorable transfer ability to unknown downstream tasks.
Redactable Signature-Based Public Auditing Scheme With Sensitive Data Sharing for Cloud Storage

To guarantee data security, the data owner needs to check the integrity of data stored remotely in the cloud server with the public auditing technique. However, the auditing result will be invalid if the data have been modified dynamically in the process of data anonymization when sharing data to others with sensitive information. In existing solutions, a data sanitizer is needed to anonymize the data and transform the signature. However, such data sanitizers introduce new security risks, and the static anonymous strategy is not flexible to different application scenarios. Therefore, we propose a new scheme based on redactable signature. In our proposed scheme, the cloud server can transform the signature directly without the additional sanitizer while sharing sensitive data. The signature transformation does not influence the integrity checking of the stored data. The signature not only can be used to authenticate the source of sharing data, but can also be used to check the integrity of the stored data in the cloud. Both the security proof and experimental analysis show that our proposed scheme is secure and more efficient than the existing schemes.
SciNoBo: A Hierarchical Multi-Label Classifier of Scientific Publications

Classifying scientific publications according to Field-of-Science (FoS) taxonomies is of crucial importance, allowing funders, publishers, scholars, companies and other stakeholders to organize scientific literature more effectively. Most existing works address classification either at venue level or solely based on the textual content of a research publication. We present SciNoBo, a novel classification system of publications to predefined FoS taxonomies, leveraging the structural properties of a publication and its citations and references organized in a multilayer network. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multidisciplinarity potential. By unifying publications and venues under a common multilayer network structure made up of citing and publishing relationships, classifications at the venue-level can be augmented with publication-level classifications. We evaluate SciNoBo on a dataset of publications extracted from Microsoft Academic Graph, and we perform a comparative analysis against a state-of-the-art neural-network baseline. The results reveal that our proposed system is capable of producing high-quality classifications of publications.