Reviewerly/exHarmony huggingface dataset:
An Intelligent Platform for Threat Assessment and Cyber-Attack Mitigation in IoMT Ecosystems

The increasing connectivity of medical devices along with the growing complexity, heterogeneity and attack surface of healthcare ecosystems has lead to numerous severe cyber-attacks. This paper proposes a novel collaborative security platform for threat assessment, intelligent detection and autonomous mitigation. The solution leverages machine learning(ML) and federated learning for detecting and preventing sophisticated multi-stage attacks, as well as blockchain for supporting integrity verification and accountability to defend against advanced persistent threats. The solution uses a distributed edge approach, performing intensive computations at the edge of the network, where information is generated, to achieve real-time processing of security events. The prevention capabilities employ autonomous decision-making with optimal response strategies towards cyber-attacks and runtime adaptation; these rely on dynamic risk-based models that use real-time information about security incidents.
Data Augmentation Using Mixup and Random Erasing

Deep convolutional neural networks show excellent performance on computer vision tasks. However, these networks rely heavily on large-scale datasets to avoid overfitting. Un-fortunately, except for some datasets used for classical tasks, only small-scale datasets can be acquired in many applications. Data augmentation is a commonly used approach to extend the dataset scale and take advantage of the capabilities of large-scale datasets. Based on Mixup and random erasing, this paper proposes two different combinations of these two methods, namely RSM and RDM, to compensate their respective shortcomings. The RSM method mix up two original images before erasing randomly selected region, while the RDM method performs in opposite order. The two proposed methods are evaluated extensively for object detection and image classification on various datasets. The experimental results show that RSM and RDM achieve over 1% and 1.5% improvements for the detection of small-scale objects and the image classification, respectively, compared to Mixup and random erasing.
Data Augmentation on Intra-Oral Images Using Image Manipulation Techniques

The quality, quantity, and relevance of training data determine how well most ML models perform, and deep learning models. One of the most frequent problems in implementing machine learning, though, is a lack of data. This is because gathering such data can frequently be expensive and time-consuming. The diversity of training data for machine learning algorithms is increased through data augmentation without the need for new data collection. Basic image manipulation techniques, including horizontal flip, Brightness and contrast, Noise injection, and histogram equalization techniques were used in this work to produce an augmented intraoral dataset. Faster R-CNN, a CNN-based model, was used to analyze the performance of the data augmentation strategies. An extensive simulation shows that the augmented dataset achieves better accuracy than the original dataset. The experimental results show a mean average precision (mAP) of 72.4 % on augmentation data.
Deep Reinforcement Learning for Parameter Tuning of Robot Visual Servoing

Robot visual servoing controls the motion of a robot through real-time visual observations. Kinematics is a key approach to achieving visual servoing. One key challenge of kinematics-based visual servoing is that it requires time-varying parameter configuration throughout the entire process of one task. Parameter tuning is also necessary when applying to different tasks. The existing work on parameter tuning either lacks adaptation or cannot automate the tuning of all parameters. Meanwhile, the transferability of existing methods from one task to another is low. This work develops a Deep Reinforcement Learning (DRL) framework for robot visual servoing, which can automate all parameters tuning for one task and across tasks. In visual servoing, forward kinematics focuses on motion speed, while inverse kinematics focuses on the smoothness of motion. Therefore, we develop two separate modules in the proposed DRL framework. One tunes time-varying Forward Kinematics parameters to accelerate the motion, and the other tunes the Inverse Kinematics parameters to ensure smoothness. Moreover, we customize a knowledge transfer method to generalize the proposed DRL models to various robot tasks without reconstructing the neural network. We verify the proposed method on simulated robot tasks. The experimental results show that the proposed method outperforms the state-of-the-art methods and manual parameter configuration in terms of movement speed and smoothness in one task and across tasks.
A Lightweight NFT Auction Protocol for Cross-chain Environment

The NFT market has been booming in recent years. In 2021, digital artist Pak's newest creation, The Merge, fetched US$91.8 million on Nifty Gateway. Since then, many NFT owners have turned to auctions to gain more profits through their collections. Ethereum covers the majority of NFT transactions at the moment. However, it will be hard for them to make profits if they have only one way to sell. To settle this situation, we propose an auction protocol for NFTs which works in a cross-chain environment. We design our protocol by using hash time lock and adding strategies to control users' malicious behaviors. We also optimize the cross-chain asset exchange process to ensure both auction and exchange are successful. Through testing in Ethereum and FISCO BCOS networks, the experimental results show that our scheme is capable of completing auctions in heterogeneous blockchain networks and maintaining low communication costs. The transactions can be confirmed in an average of 4 blocks, and the contract strategies will filter out invalid transactions. We also do additional experiments to prove that our protocol can resist reentrancy.
Snippet-based result merging in federated search

In federated search, the central broker simultaneously forwards the search query to multiple resources. The returned results from those resources are then merged into a single ranked list. An autonomous resource in a federated search system usually does not provide scores for the retrieved documents; even if some of them do, scores from different resources are incomparable due to the heterogeneity in many aspects of those resource involved such as retrieval models and corpus statistics. Many results merging approaches have been proposed in the literature to deal with this problem. However, to the best of our knowledge, none of them has utilised snippets. This article proposes a snippet-based weighting scheme for the query terms involved. It quantifies the importance of each query term from two angles: the frequency of the term and the part in which the term occurs inside a snippet. Three parts – which are URL, title, and description – are given different weights. Experiments are conducted with the TREC 2013 FedWeb data set. The results show that the proposed methods consistently outperform several baseline models. We also find in many instances, a further small slight performance improvement is achievable by an extra measure of weighting each of the resources involved, which can be done in the phase of resource selection.
Russian publishing market and sci-tech library collection development

The Russian publishing market is analyzed from the library’ perspective, as the book publishing and book distribution market dynamics, including both book publishing and trends in distributing in different formats through various channels and technologies, affect significantly library collection development. The following problems are indicated: 1) completeness and accessibility of information on scientific literature being published; 2) procurability. The analysis is based on the statistical data by Russian Book Chamber, sector-specific report “The Russian Book Market”, etc. The dependence of quality of library collection development on the book industry status is emphasized. In assessing quantitative characteristics of scientific book publishing, the acquisitions indicators of RNPLS&amp;T as a Mandatory Copy receiver, were used. The role of electronic library systems, library book pools, as well as of the National Book Platform and National Bibliographic Resource, both under development, is explored.
Improved Hybrid Firefly Algorithm with Probability Attraction Model

An improved hybrid firefly algorithm with probability attraction model (IHFAPA) is proposed to solve the problems of low computational efficiency and low computational accuracy in solving complex optimization problems. First, the method of square-root sequence was used to generate the initial population, so that the initial population had better population diversity. Second, an adaptive probabilistic attraction model is proposed to attract fireflies according to the brightness level of fireflies, which can minimize the brightness comparison times of the algorithm and moderate the attraction times of the algorithm. Thirdly, a new location update method is proposed, which not only overcomes the deficiency in that the relative attraction of two fireflies is close to 0 when the distance is long but also overcomes the deficiency that the relative attraction of two fireflies is close to infinity when the distance is small. In addition, a combinatorial variational operator based on selection probability is proposed to improve the exploration and exploitation ability of the firefly algorithm (FA). Later, a similarity removal operation is added to maintain the diversity of the population. Finally, experiments using CEC 2017 constrained optimization problems and four practical problems in engineering show that IHFAPA can effectively improve the quality of solutions.
Adversarial Artificial Intelligence in Insurance: From an Example to Some Potential Remedies

Artificial intelligence (AI) is a tool that financial intermediaries and insurance companies use or are willing to use in almost all their activities. AI can have a positive impact on almost all aspects of the insurance value chain: pricing, underwriting, marketing, claims management, and after-sales services. While it is very important and useful, AI is not free of risks, including those related to its robustness against so-called adversarial attacks, which are conducted by external entities to misguide and defraud the AI algorithms. The paper is designed to review adversarial AI and to discuss its implications for the insurance sector. We give a taxonomy of adversarial attacks and present an original, fully fledged example of claims falsification in health insurance, as well as some remedies which are consistent with the current regulatory framework.
Sensing Algorithm to Estimate Slight Displacement and Posture Change of Target from Monocular Images

Various types of displacement sensors, which measure position changes of object, have been developed depending on the type and shape of the object under measurement, measurement range of the amount of displacement, required accuracy, and application. We are developing a new type of displacement sensor that is image-based, capable of measuring changes in 6DOF (3D position and orientation) of an object simultaneously, and is compact and low-cost. This displacement sensor measures the 6DOF of an object using images obtained by a monocular vision system. To confirm the usefulness of the proposed method, experimental measurements were conducted using a simple and inexpensive optical system. In this experiment, we were able to accurately measure changes of about 0.25 mm in displacement and 0.1 deg in inclination of the object at a distance of a few centimeters, and thus confirming the usefulness of the proposed method.
Large-scale calibration method for MEMS-based projector 3D reconstruction

Projectors based on Micro-Electro-Mechanical System (MEMS) have the advantages of small size and low cost. Moreover, uniaxial MEMS projectors have high projection accuracy, and have been widely used in structured light 3D reconstruction. However, the existing calibration methods for uniaxial MEMS projectors are not effective in large-scale scenes. To solve this problem, this paper proposes a novel efficient large-scale calibration method, which is easily implemented. The proposed method first calibrates a partial light plane for a fixed sampling period, then obtains the rest of the light plane by exploiting a non-fixed rotating shaft linear interpolation method. Experimental results verify that the proposed method attains high accuracy in a large depth field with only 11 sets of calibration data. Specifically, at a distance of 3000mm, the standard deviation of the plane fitting error reaches 0.2584mm on the standard plane, and the measurement accuracy attains 0.9124mm on the standard step object with 200mm interval.
Semi-Supervised k-Star (SSS): A Machine Learning Method with a Novel Holo-Training Approach

As one of the entropy-based methods, the k-Star algorithm benefits from information theory in computing the distances between data instances during the classification task. k-Star is a machine learning method with a high classification performance and strong generalization ability. Nevertheless, as a standard supervised learning method, it performs learning only from labeled data. This paper proposes an improved method, called Semi-Supervised k-Star (SSS), which makes efficient predictions by considering unlabeled data in addition to labeled data. Moreover, it introduces a novel semi-supervised learning approach, called holo-training, against self-training. It has the advantage of enabling a powerful and robust model of data by combining multiple classifiers and using an entropy measure. The results of extensive experimental studies showed that the proposed holo-training approach outperformed the self-training approach on 13 out of the 18 datasets. Furthermore, the proposed SSS method achieved higher accuracy (95.25%) than the state-of-the-art semi-supervised methods (90.01%) on average. The significance of the experimental results was validated by using both the Binomial Sign test and the Friedman test.
Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients

This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec).
Homomorphisms Between Transfer, Multi-task, and Meta-learning Systems

Transfer learning, multi-task learning, and meta-learning are well-studied topics concerned with the generalization of knowledge across learning tasks and are closely related to general intelligence. But, the formal, general systems differences between them are underexplored in the literature. This lack of systems-level formalism leads to difficulties in coordinating related, inter-disciplinary engineering efforts. This manuscript formalizes transfer learning, multi-task learning, and meta-learning as abstract learning systems, consistent with the formal-minimalist abstract systems theory of Mesarovic and Takahara. Moreover, it uses the presented formalism to relate the three concepts of learning in terms of composition, hierarchy, and structural homomorphism. Findings are readily depicted in terms of input-output systems, highlighting the ease of delineating formal, general systems differences between transfer, multi-task, and meta-learning.
Research on Road Object Detection Algorithm Based on YOLOv5+Deepsort

In the Multiple Object Tracking (MOT) algorithm, usually adopt Sort based on Faster R-CNN as the target detector to identify and track the target. However, Sort hardly processes object occlusion, and the number of ID switches is very high the accuracy is very low in the case of object occlusion. In this paper, Deepsort is used to integrate apparent information and cascade matching to improve the recognition performance of Sort, so that the detection model can better deal with the case that the target is occluded for a long time and reduce the number of ID switches. At the same time, use YOLOv5 as the target detector, the performance of target detection is obviously better than Faster RCNN, and the consumption of GPU resources is less.
A Chinese Short Text Classification Method Based on TF-IDF and Gradient Boosting Decision Tree

To solve the problem of feature extraction and semantic sparsity in Chinese short text classification, this paper uses TF-IDF algorithm to extract category keywords and uses the set of category keywords as the feature set of short text classification. Next, the weight of keyword features is obtained by calculating the maximum similarity between the category keywords and each word in the essay. Based on the weighted keyword feature vector set, the short text is represented by vectors. Finally, we use the GBDT algorithm to train the classifier for short text classification and carry out experiments to verify the effectiveness of this method in improving the classification effect.
Mobile Device Fingerprinting Recognition using Insensitive Information

Device fingerprinting concept is inspired from the forensic value of human fingerprints. It consists of information collected about the software and hardware of a remote computing device for the purpose of identification. This technique can be applied to areas, such as advertisement targeting, user tracking, eCommerce fraud prevention and etc. Sensitive information such as MAC address, hardware serial numbers or phone numbers are unique identifiers, however, they may leak user privacy. Therefore, device fingerprinting based on insensitive information from web browsers or mobile Apps is more favorable. In this work, we propose a passive device fingerprinting approach using a variety of zero permission features. First, a data cleansing process is set up to remove corrupted or inaccurate records. Then entropy-based feature selection method is proposed. Two models are established to create device finger prints. These models are evaluated using two datasets. The experimental results show the effectiveness of our proposed methods. The optimal model can reach average macro-F1 of 0.978 and micro-F1 of 0.982 respectively.
Cyber security: Current threats, challenges, and prevention methods

Objective of this study is to perceive the attacks, threats, and weaknesses of digital framework, which incorporate equipment and programming frameworks, organizations, and also intranets. Cybercrime may happen from any spot whatsoever time and the outcomes could be unending, it isn't restricted to a particular region. Cyber Security is a blend of innovative headways, process cycles and practices. The goal of cyber security is to ensure protection of applications, networks, PCs, and critical information from attack. In a computing context, security incorporates both network safety and actual security. The hacker harms or takes programming or data just as a result of disturbance or confusion of the administrations they mislead. As of now, digital protection is viewed as one of the basic boundaries for the acknowledgment of any systems administration innovation. Any hub enduring an onslaught in an organization presents a peculiar conduct called malevolent conduct. In the present situation, the whole activity of an organization becomes unsafe and to forestall such vindictive practices, a few security goals have been found. This paper reviews research work done in cybersecurity including the types of cybersecurity. The paper also discusses threats and prevention methods and an overview of future threats in cybersecurity.
A Note on “Optimum Sets of Interference-Free Sequences With Zero Autocorrelation Zone”

In this paper, a simple construction of interference-free zero correlation zone (IF -ZCZ) sequence sets is proposed by well designed finite Zak transform lattice tessellation. Each set is characterized by the period of sequences <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$KM$</tex> , the set size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> and the length of zero correlation zone <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M-1$</tex> , which is optimal with respect to the Tang-Fan-Matsufuji bound. Secondly, the transformations that keep the properties of the optimal IF -ZCZ sequence set unchanged are given, and the equivalent relation of the optimal IF -ZCZ sequence set is defined based on these transformations. Then, it is proved that the general construction of the optimal IF -ZCZ sequence set proposed by Popovic is equivalent to the simple construction of the optimal IF -ZCZ sequence set, which indicates that the generation of the optimal IF -ZCZ sequence set can be simplified. Moreover, it is pointed out that the alphabet size for the special case of the simple construction of the optimal IF -ZCZ sequence set can be a factor of the period. Finally, both the simple construction of the optimal IF -ZCZ sequence set and its special case have sparse and highly structured Zak spectra, which can greatly reduce the computational complexity of implementing matched filter banks.
LiDAR-based 3D Object Detection for Autonomous Driving

LiDAR-based 3D object detection has attracted increasing attention recently in various fields, especially in the autonomous driving area, as playing a significate role in perception techniques. One of the most commonly used representations of LiDAR data is the voxel, which converts point clouds into 3D cubes containing points inside. Compared to original points, voxels can save memory and computational resources, which are fundamental to larger and better models. Therefore, in this paper, LiDAR-based 3D object detection approaches in recent years are reviewed, focusing on methods using voxel representation. Firstly, popular datasets and evaluation metrics are introduced. The principles of methods are briefly described. Methods are categorized into two general types, voxel-based and point-voxel-based, and compared inside each representation class. Their performances on different datasets are also presented and analyzed. Then, a discussion about the pros and cons of approaches is achieved by a comprehensive systematic analysis of the statistics of their performances, and several future research trends in this area are mentioned as well.