Reviewerly/exHarmony huggingface dataset:
Graph Learning BFT: A Design of Consensus System for Distributed Ledgers

Distributed ledger technology faces scalability problems due to a long commit time despite recent successes for cryptocurrency. Small group consensus studies have improved this scalability of distributed ledgers. However, they still have problems of the consensus process itself. For example, most blockchain systems perform serialized block proposal and consensus processing, guarantee the finality with high overhead, and handle byzantine nodes inefficiently. To address these problems, we propose a consensus system, named graph learning byzantine fault tolerance (GL BFT), which offers high parallelism and low latency under Byzantine fault. To do this, we enable a parallel pipelined agreement by separating the block proposal and the consensus process. Second, we devise two techniques of merging blocks and commit learning to guarantee the finality with little overhead. Finally, we present a path learning approach which chooses optimal paths to handle Byzantine fault. The proposed GL BFT can achieve instant finality with low message overhead among a small group of nodes even if Byzantine nodes exit. Also, we evaluate its performance on an open source blockchain protocol. Experimental results show that our design reduces data traffic required by the consensus up to 30%, one transaction is finalized within a few seconds, and optimal performance is maintained.
The Nested Subset Differential Attack

In 2017, Ward Beullens et al. submitted Lifted Unbalanced Oil and Vinegar [3], which is a modification to the Unbalanced Oil and Vinegar Scheme by Patarin. Previously, Ding et al. proposed the Subfield Differential Attack [22] which prompted a change of parameters by the authors of LUOV for the second round of the NIST post quantum standardization competition [4]. In this paper we propose a modification to the Subfield Differential Attack called the Nested Subset Differential Attack which fully breaks half of the parameter sets put forward. We also show by experimentation that this attack is practically possible to do in under 210 min for the level I security parameters and not just a theoretical attack. The Nested Subset Differential attack is a large improvement of the Subfield differential attack which can be used in real world circumstances. Moreover, we will only use what is called the "lifted" structure of LUOV, and our attack can be thought as a development of solving "lifted" quadratic systems.
Efficient Segmentation of Cell Nuclei in Histopathological Images

Computer-aided cell nuclei segmentation in histology images is essential for image analysis. There is a demand for methods that accurately detect cell nuclei in large images. We introduce the FECS method for automatic cell nuclei segmentation in Hematoxylin and Eosin (H&E) stained histology images. Our method accurately segments cell nuclei, even in large images, efficiently. We use bimodal-like histograms to perform image binarization via the fast Otsu algorithm. We introduce a super-pixel based filter for cell nuclei boundary detection. A Gaussian blur filter allows us to identify cell nuclei centers, which are understood as local minima in the individual cell nuclei regions. We have evaluated our method for two publicly available datasets. Out tests have produced average Jaccard index values of 0.963 and 0.914, respectively, supporting a high degree of segmentation accuracy. We have compared our method against a state-of-the-art method; our method produced better results for both datasets. The average processing time of FECS was approximately just one second for images of 1k x 1k pixel resolution and about three minutes for larger images of 15k x 15k pixel resolution.
Estimates on the number of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:math>–rational solutions of variants of diagonal equations over finite fields

In this paper we study the set of -rational solutions of equations defined by polynomials evaluated in power-sum polynomials with coefficients in . This is done by means of applying a methodology which relies on the study of the geometry of the set of common zeros of symmetric polynomials over the algebraic closure of . We provide improved estimates and existence results of -rational solutions to the following equations: deformed diagonal equations, generalized Markoff-Hurwitz-type equations and Carlitz's equations. We extend these techniques to more general variants of diagonal equations over finite fields.
Implementation of the 3d Digitalized Brochure using Marker-based Augmented Reality for Real Estates

Nowadays, due to increasing growth in all sectors, be it in healthcare, infrastructure, finance, education, and logistics, etc. the world has come closer to foster their needs. Even though in the era of smart technology constructors, builders are using the traditional method, i.e., usage of brochures by generating 2D sketches of project layout. Builders use these catalogs to sell their construction sites like flats, buildings, or any property to customers by just showing them various floor plans, multiple views, etc. This procedure aims to be unproductive and inefficient because it does not give a clear idea regarding how exactly the property will look. Augmented Reality, shifting to new emerging technology, gives an entirely new perspective for development. This paper discusses the AR implementation that provides many advantages for construction planning, which will help customers to draw a clear view regarding the properties before making deals without actually visiting the sample flats. The paper's focus is to develop an AR-based brochure as a planning tool that will serve as an application to intelligently increase the customers by minimizing their efforts and easing their decision-making process and maximizing the overall productivity.
A First Approach to Path Planning Coverage with Multi-UAVs

In this paper, a multi-UAV system is applied to explore a searching area. The influence of the partition of the surface and the effects of varying the number of UAVs are analyzed. The covering of the area is based on small rectangular polygon area decomposition. Each sector is assigned to an UAV and efficient coverage algorithms are applied. The UAV follows a zig-zag navigation strategy to go through the way-points located at the center of the cells of the corresponding area. The performance of the multi-UAV system is discussed for different scenarios. Simulation results in terms of travel time are presented.
Multi-frame Image Super-Resolution Algorithm Based on Small Amount of Data

In this paper, a novel multi-frame image super-resolution algorithm for small amount of data is proposed. Our method solve the problem that the spatial resolution of the reconstructed image is low and the visual quality of it is poor when the number of input low-resolution images is small. In order to improve the quality of the initial estimation, we construct the initial estimation with multi-frame low-resolution images according to the registration parameter and interpolate the missing pixels by directional Gaussian-like filtering. In order to solve the problem of fuzzy initial estimation, the enhancement method is used to highlight the image details. A large number of qualitative and quantitative evaluation results show that our method has strong reconstruction performance for various types of low-resolution images under different amount of data.
Improving Knowledge Distillation of CTC-Trained Acoustic Models With Alignment-Consistent Ensemble and Target Delay

Knowledge distillation (KD) has been widely used to improve the performance of a simpler student model by imitating the outputs or intermediate representations of a more complex teacher model. The most commonly used KD technique is to minimize a Kullback-Leibler divergence between the output distributions of the teacher and student models. When it is applied to compressing acoustic models trained with a connectionist temporal classification (CTC) criterion, an assumption is made that the teacher and student share the same frame-level feature-transcription alignment. However, frame-level alignments learned by teachers can be inaccurate and unstable due to the lack of fine-grained frame-level guidance during CTC training. Forcing student to learn inaccurate alignments will lead to limited performance improvements. In this article, we investigate building powerful teacher models with more accurate and stable feature-transcription alignments. We achieve this goal by using a novel alignment-consistent ensemble (ACE) technique, where all models within an ensemble are jointly trained along with a regularization term to encourage consistent and stable alignments. With well-trained deep bidirectional LSTM (DBLSTM) ACE as a teacher, we can directly use the traditional frame-wise KD method to train DBLSTM students. When applying KD to transfer knowledge from a DBLSTM ACE to a deep unidirectional LSTM (DLSTM) student, a simple yet effective target delay technique is proposed to handle the alignment difference between bidirectional and unidirectional models. Experimental results on Switchboard-I speech recognition task show that, with DBLSTM ACE as a teacher, the simple frame-wise KD method can achieve competitive or better performance than other complex KD methods on DBLSTM students. When applying KD to build DLSTM students from DBLSTM teachers, our proposed target delay technique can achieve relative word error rate reductions of 14.2%$\sim$14.8% compared with the models trained from scratch, which outperforms other carefully-designed KD methods.
A Novel Approach on Segmentation of AgNOR-Stained Cytology Images Using Deep Learning

Cervical cancer is the second most common cancer type in women. This is a deadly disease that could benefit from early detection methods. Cytology is a possible, noninvasive, alternative to biopsy for diagnosis of malignant lesions, where information about the cells can be obtained. This process consists of staining the slides being able to visualize the nuclei of the cells and then be properly diagnosed. In this work, silver-stained slides, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR), is employed. This method shows great potential for the diagnosis of the malignancy of lesions. However, it has not yet been much explored, mainly using computational methods. In this work, we propose the use of Convolutional Neural Networks on silver stained images to measure AgNOR quantitative image information. We will present the entire process, starting from pre-processing, passing through neural network training and results evaluation. The best results we obtained were 0.87 of Mean Intersection Over Union (mIoU) and 0.99 of Dice Similarity Coefficient (DSC) using an U-Net model with a ResNet18 as its backbone. The code and the dataset employed in this work are publicly available.
Unobtrusive and Non-Invasive Human Activity Recognition using Kinect Sensor

The main objective of this paper is to present a depth imaging human activity recognition system that is basically used to monitor and detect normal activities of the person in the indoor environment. The Proposed system runs in real-time and is capable of detecting moving falls, sitting fall accurately and robustly without taking into account any false positive activities. Human activity recognition with high accuracy is a huge challenge in the research world. Fall detection is an important technology in health care and elderly person activity monitoring. The proposed method is feasibility and workability are illustrated throughout the experimental result that shows the perfect human tracking and activity detection. System works well with low lighting conditions; lighting does not affect the accuracy of detecting activities. Moreover, the proposed method is able to avoid false positive as: lying down, retrieve something from the floor.
A New Image Encryption Algorithm Based on 2D-LSIMM Chaotic Map

Due to the properties of unpredictability, ergodicity and sensitivity to initial values and parameters, chaotic systems are widely employed in image encryption and multimedia security. This paper proposes a new two-dimensional Logistic Sine ICMIC modulation map (2D-LSIMM) based on a close-loop coupling modulation model. Chaotic performance analysis shows that it has better ergodicity, randomness and wider chaotic range. To better investigate the application of 2D-LSIMM, an algorithm based on the chaotic ring transform (CRT-IEA) is designed, the rows and columns permutation are performed by CRT confusion operation, which effectively changed the pixels position of the image. The values of pixels are change by diffusion operation. The security analysis denotes that the algorithm has a high security level and strong ability to resist various security attacks.
Video Prediction and Anomaly Detection Algorithm Based On Dual Discriminator

In order to make full use of the useful information in the massive video data and provide early warning of abnormal events, we propose a video prediction and abnormal detection algorithm. The algorithm designed a generation adversarial network with a single generator and dual discriminator to predict the video, and then performs anomaly detection on the basis of the video prediction frame. For training the model, various loss functions such as perceptual loss and optical flow loss are added to constrain the network. Extensive experiments on three publicly available datasets validate the effectiveness of our method in terms of various evaluation criteria.
Next Step in Online Querying and Visualization of Word-Formation Networks

In this paper, we introduce a new and improved version of DeriSearch, a search engine and visualizer for word-formation networks. Word-formation networks are datasets that express derivational, compounding and other word-formation relations between words. They are usually expressed as directed graphs, in which nodes correspond to words and edges to the relations between them. Some networks also add other linguistic information, such as morphological segmentation of the words or identification of the processes expressed by the relations. Networks for morphologically rich languages with productive derivation or compounding have large connected components, which are difficult to visualize. For example, in the network for Czech, DeriNet 2.0, connected components over 500 words large contain of the vocabulary, including its most common parts. In the network for Latin, Word Formation Latin, over 10 000 words ( of the vocabulary) are in a single connected component. With the recent release of the Universal Derivations collection of word-formation networks for several languages, there is a need for a searching and visualization tool that would allow browsing such complex data.
Online Multi-Object Tracking based on Improved Kernel Correlation Filter

In multi-target tracking, the target's scale change and occlusion problems easily lead to target loss. At the same time, multi-target tracking algorithm of data association based on deep learning for target detection requires a high hardware platform and cannot run on a multi-core DSP. Aiming at the above problems, this paper proposes an online multi-target tracking algorithm based on improved kernel correlation filter, using a multi-core parallel mechanism to establish a tracker for each target for tracking. The improved algorithm integrates multiple features of Hog, CN and LBP at the feature level to enhance the ability to represent the target; for the problem of target scale change, a 1D scale filter is introduced to estimate the new scale of the target; In terms of model updating, the APCE indicator is used to measure the confidence of the response map in order to detect whether the target is occluded or lost in time. By adopting a high-confidence model update method, the correlation filter model is prevented from being polluted by the wrong samples when the target is occluded. When the target tracking fails, the detection module is triggered to detect the target: firstly, the correlation matching algorithm based on the hog feature is used to preliminarily screen, and the relevant surface is analyzed, and then the kernel correlation filter based on the ridge regression classifier is used to detect the multi-peak of the correlation surface. Experimental results show that the improved algorithm is more robust to target occlusion, scale changes, background interference, target loss re-tracking, etc. compared to standard algorithms. This paper effectively extends the improved kernel correlation filter algorithm to multi-target tracking, which can be further transplanted to multi-core DSP platforms.
Taking Shared Print to the Next Level: The Partnership for Shared Book Collections

This article introduces the Partnership for Shared Book Collections, a federation of monograph shared print programs. Collaboration of programs at the network level allows for:Reducing the cost of retaining the scholarly record through shared distribution of commitments;De-duplicating efforts;Developing and promoting evidence-based best practices;Increasing the effectiveness of communication with end-users and funders;Tackling large-scale projects and research that are out of reach for individual shared print programs. This shared stewardship of collection is critical as local resources are depleted due to the COVID-19 pandemic and access to print for digitization increases.
Dynamic character graph via online face clustering for movie analysis

Abstract An effective approach to automated movie content analysis involves building a network (graph) of its characters. Existing work usually builds a static character graph to summarize the content using metadata, scripts or manual annotations. We propose an unsupervised approach to building a dynamic character graph that captures the temporal evolution of character interaction. We refer to this as the character interaction graph (CIG). Our approach has two components: (i) an online face clustering algorithm that discovers the characters in the video stream as they appear, and (ii) simultaneous creation of a CIG using the temporal dynamics of the resulting clusters. We demonstrate the usefulness of the CIG for two movie analysis tasks: narrative structure (acts) segmentation and major character retrieval. Our evaluation on full-length movies containing more than 5000 face tracks shows that the proposed approach achieves superior performance for both the tasks.
An Innovative Tool for the Chaotic Image Encryption, Decryption and Security Tests

A comprehensive GUI tool has been designed and implemented for the cryptographic applications. The tool enables to encrypt and decrypt the colorful images by applying several algorithms. It also contains innovative algorithms using several encryption techniques. Any image can initially be ciphered by the help of chaotic systems, digitally and that process can improve the security of the encryption process. The tool also contains many security tests including noise attack, differential attack, key sensitivity, etc. The tool is easy to maintain and fast enough to make serial encryption procedure. It can give graphical and numerical data to apply for academic researches, too.
Shortest Distance Orientation Algorithm for Robot Path Planning using Low-Cost IR Sensor System

This paper demonstrates a new algorithm in the path planning field for indoor robot navigation, focusing on finding the shortest and most safe path from the robot source position to its target. Robot localization matters are solved by using an IR transmitter fixed to the robot and a low-cost IR sensor array that is regularly distributed in the environment. The robot location depends on a group of IR receiver sensors that sense the IR transmitter signal where the location of each IR receiver is dependent on it is the location in the IR receivers array. The robot initial position is computed by using the modified binary search algorithm to scan the IR receivers array. A path planning problem is solved by using a new algorithm called the shortest distance orientation algorithm. A chain of the IR receivers within the sensing range of the virtual path is activated to be scan through the robot moves to the target. This process helps in reducing the localization time when the robot moves toward the target. At each step of the robot movement, it is direction is corrected dependent on the angle between its orientation and the direction of the line between its location and the target location. The simulation results for different types of IR array environments are shown good performance for this algorithm.
I Can Get Some Satisfaction: Fuzzy Ontologies for Partial Agreements in Blockchain Smart Contracts

This paper proposes a novel extension of blockchain systems with fuzzy ontologies. The main advantage is to let the users have flexible restrictions, represented using fuzzy sets, and to develop smart contracts where there is a partial agreement among the involved parts. We propose a general architecture based on four fuzzy ontologies and a process to develop and run the smart contracts, based on a reduction to a well-known fuzzy ontology reasoning task (Best Satisfiability Degree). We also investigate different operators to compute Pareto-optimal solutions and implement our approach in the Ethereum blockchain.
Blockchain-Enabled Secure Internet of Things

This century is the time of ubiquitous, smart, and intelligent devices. These devices have a wide variety of applications in different fields like business, manufacturing, healthcare, retail, education, security, transportation, etc. Internet of things is now becoming the inexorable part of the all these fields. But security has always been a major concern in embracing these technologies. The blockchain technology is the next frontier for securing the internet of things. It will play a pivotal role to secure the communication in internet of things ecosystem. This chapter discusses the blockchain-enabled secure internet of things (IoT).
An Overview on Protecting User Private-Attribute Information on Social Networks

Online social networks enable users to participate in different activities, such as connecting with each other and sharing different contents online. These activities lead to the generation of vast amounts of user data online. Publishing user-generated data causes the problem of user privacy as this data includes information about users' private and sensitive attributes. This privacy issue mandates social media data publishers to protect users' privacy by anonymizing user-generated social media data. Existing private-attribute inference attacks can be classified into two classes: friend-based private-attribute attacks and behavior-based private-attribute attacks. Consequently, various privacy protection models are proposed to protect users against private-attribute inference attacks such as k-anonymity and differential privacy. This chapter will overview and compare recent state-of-the-art researches in terms of private-attribute inference attacks and corresponding anonymization techniques. In addition, open problems and future research directions will be discussed.