Reviewerly/exHarmony huggingface dataset:
GSCV-XGBoost based information reconstruction and fusion method for SINS/DVL integrated navigation system

Abstract An underwater navigation system has specific requirements for reliability. In the frequently used strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integrated navigation system, the process of the DVL measuring velocity is always disturbed in a complex underwater environment. Thus the velocity measurements on acoustic beam directions are prone to get lost, which seriously affects system reliability. This paper proposes a novel information reconstruction and integration algorithm to solve this problem, based on the tree boosting model. The algorithm is mainly divided into two consecutive stages: training and reconstruction. When the DVL velocity in all beam directions is available, the GSCV-XGBoost (extreme gradient boosting based on the grid-search and cross-validation theory) method is used to train the mapping model between DVL velocity and SINS/DVL integrated navigation system velocity. When the DVL velocity is incompletely available, the mapping model, that has been trained in advance, is used to reconstruct the missing DVL velocity measurements and resume normal SINS/DVL integration conditions. Simulation and underwater vehicle-mounted experiments are applied to verify that the proposed algorithm reconstructs more accurate velocity information than traditional XGBoost, which shows great prospects for application and reliability in the SINS/DVL underwater integrated navigation system.
Adversarial examples based on object detection tasks: A survey

Deep learning plays a critical role in the applications of artificial intelligence. The trend of processing images or videos as input data and pursuing execution efficiency in practical applications is unstoppable. However, the vulnerability due to the complex structure of deep networks makes it at risk of attacks. Object detection, as the significant product impacted by the deep learning frame, corresponds to this weakness implicated by its multiple-tasks property. Besides, these applications involving object detection techniques are integrated deeply into our lives, potentially leading to unimaginable loss. Adversarial example attacks, as the mainstream attack method, provide an efficacious and comprehensible idea to generate perturbation. In this survey, we review the existing adversarial example attacks in object detection tasks and inductively discuss the similarity and differences among these approaches. Finally, we construct this survey for discussing the attacks in the object detection field and point out the possible direction for adversarial defenses in future studies.
Autonomous robot navigation based on a hierarchical cognitive model

Abstract We propose a hierarchical cognitive navigation model (HCNM) to improve the self-learning and self-adaptive ability of mobile robots in unknown and complex environments. The HCNM model adopts the divide and conquers approach by dividing the path planning task into different levels of sub-tasks in complex environments and solves each sub-task in a smaller state subspace to decrease the state space dimensions. The HCNM model imitates animal asymptotic properties through the study of thermodynamic processes and designs a cognitive learning algorithm to achieve online optimum search strategies. We prove that the learning algorithm designed ensures that the cognitive model can converge to the optimal behavior path with probability one. Robot navigation is studied on the basis of the cognitive process. The experimental results show that the HCNM model has strong adaptability in unknown and environment, and the navigation path is clearer and the convergence time is better. Among them, the convergence time of HCNM model is 25 s, which is 86.5% lower than that of HRLM model. The HCNM model studied in this paper adopts a hierarchical structure, which reduces the learning difficulty and accelerates the learning speed in the unknown environment.
Unifying Framework for Optimizations in Non-Boolean Formalisms

Abstract Search-optimization problems are plentiful in scientific and engineering domains. Artificial intelligence (AI) has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search-optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements. Recall integer linear programming, MaxSAT, optimization satisfiability modulo theory, (constraint) answer set programming. These paradigms vary significantly in their languages in ways they express quality conditions on computed solutions. Here we propose a unifying framework of so-called extended weight systems that eliminates syntactic distinctions between paradigms. They allow us to see essential similarities and differences between optimization statements provided by distinct automated reasoning languages. We also study formal properties of the proposed systems that immediately translate into formal properties of paradigms that can be captured within our framework.
A model with pseudo-label correction and distribution alignment for image clustering

Self-supervised learning has recently created a surge of research interest in image clustering with the help of pseudo-labels. However, most of methods have to face the challenge of dealing with noisy labels since the noisy pseudo-labels might provide erroneous guidance for model training and deteriorate the clustering performance. To solve this problem, a Pseudo-label Correction and Distribution Alignment-based deep Clustering (PCDAC) model is proposed in this paper. To correct the noisy pseudo-labels, PCDAC embeds the historical cluster assignment by maintaining a memory bank. Additionally, a distribution alignment operation is employed to impose the cluster probability distribution of strong-augmented instances close to those of weak-augmented instances, to reduce the possibility of generating incorrect pseudo-labels at the initial training phase. Extensive comparisons experiments are conducted and the results illustrate that PCDAC is superior to the SOTA methods. The code is available on GitHub at https://github.com/LiuZiweiAI/PCDAC.
Pwnable-Sherpa: An interactive coaching system with a case study of pwnable challenges

• Analyzing the 2020 pwnable CTF’s results held by our team and demonstrating the problem with the existing evaluation method • Analyzing the pwnable challenge-solving workflow using the Task, Knowledge, and Skill framework for the detailed feedback • Proposing a cybersecurity training platform called Pwnable-Sherpa which sets three detailed evaluation points for a challenge • Design with a multi-container architecture and LLVM dummy pass to save the evaluation time needed for the detailed assessment • Dealing with Post-Implementation issues, such as integrating Pwnable-Sherpa with a course and measuring students’ improvement To improve their cybersecurity knowledge and skills, students participate in a competitive game called capture the flag (CTF). CTF is used as an educational tool to improve students’ cybersecurity competency by solving challenges. As system vulnerabilities are the leading cause of cyberattacks on critical systems, cybersecurity personnel with knowledge and skills to detect system vulnerabilities are increasingly in demand. In this context, the importance of challenges concerning system vulnerabilities, such as pwnable, is gradually increasing in CTF competitions. Unlike other CTF challenges, solving a pwnable challenge requires considerable knowledge and skill. However, traditional evaluation methods in CTF (i.e., pass or non-pass) provide limited feedback regarding knowledge and skill gaps. To investigate this issue, we analyzed the results of the CTF competitions held by our research team over the past three years (2017, 2018, and 2020). Our analysis revealed the necessity for a new evaluation system that can provide detailed feedback to students, while reducing the grading burden on educators. Thus, to provide detailed feedback, we propose a cybersecurity training platform, Pwnable-Sherpa, which sets three detailed evaluation points for a given pwnable challenge. In addition, we designed our training platform with a multi-container architecture and an LLVM dummy pass, thereby saving time by grading each detailed assessment simultaneously rather than sequentially.
A robust feature reinforcement framework for heterogeneous graphs neural networks

In the real world, various kinds of data are able to be represented as heterogeneous graph structures. Heterogeneous graphs with multi-typed nodes and edges contain rich messages of heterogeneity and complex semantic information. Recently, diverse heterogeneous graph neural networks (HGNNs) have emerged to solve a range of tasks in this advanced area, such as node classification, knowledge graphs, etc. Heterogeneous graph embedding is a crucial step in HGNNs. It aims to embed rich information from heterogeneous graphs into low-dimensional eigenspaces to improve the performance of downstream tasks. Yet existing methods only project high-dimensional node features into the same low-dimensional space and subsequently aggregate those heterogeneous features directly. This approach ignores the balance between the informative dimensions and the redundant dimensions in the hidden layers. Further, after the dimensionality has been reduced, all kinds of nodes features are projected into the same eigenspace but in a mixed up fashion. One final problem with HGNNs is that their experimental results are always unstable and not reproducible. To solve these issues, we design a general framework named Robust Feature Reinforcement (RFR) for HGNNs to optimize embedding performance. RFR consists of three mechanisms: separate mapping, co-segregating and population-based bandits. The separate mapping mechanism improves the ability to preserve the most informative dimensions when projecting high-dimensional vectors into a low-dimensional eigenspace. The co-segregating mechanism minimizes the contrastive loss to ensure there is a distinction between the features extracted from different types of nodes in the latent feature layers. The population-based bandits mechanism further assures the stability of the experimental results with classification tasks. Supported by rigorous experimentation on three datasets, we assessed the performance of the designed framework and can verify that our models outperform the current state-of-the-arts.
Price Prediction of Ethereum Using Time Series and Deep Learning Techniques

Ethereum, a blockchain platform inspired by Bitcoin, was introduced in 2015. It is a worldwide computing platform fueled by Ether (ETH), its native currency. As the demand for processing power on the Ethereum blockchain rises, so will the price of ETH. Several studies are working to project its price based on previous price inflations of the cryptocurrency. This topic has become a prominent research topic all around the world. In this work, the price of ETH is predicted using a hybrid model consisting of Long short-term memory (LSTM) and Vector Auto Regression (VAR). The hybrid model gave the least values for the evaluation metrics compared to the standalone models.
Application of Blockchain Technologies in Digital Agriculture

Blockchain is one of the latest developments in information and communication technology that has begun to revolutionize our planet. This technology is designed to increase transparency and accountability, which is why it is seen as an important element of modern agriculture. Blockchain in agriculture can be used as a system that helps to increase the amount of reliable information available regarding stocks, agricultural contracts and the general condition of farms. Thanks to blockchain in agriculture, it is now possible to improve the quality of the food supply chain and maintain trust between customers and farmers by constantly tracking both the origin of various products and the formation of the supply as a whole. Because it is a reliable and proven data storage method, it enables various data-driven innovations to usher in the era of smart farming. Combined with the concept of smart contracts, this also helps to smooth out the nature of transactions between different stakeholders, making them more timely. This article is devoted to an overview of the impact of blockchain technologies on the development of agriculture, as well as the advantages and disadvantages of this innovation.
Models of Recognition Algorithms Based on Construction of Two-Dimensional Logical Classifiers

The paper considers issues related to the construction of a model of recognition algorithms (MRA) designed to solve the problem of object classification in conditions of interconnectedness of features. A new approach to the construction of the MRA is proposed on the basis of the construction of two-dimensional threshold classifiers (TDTC). The main idea of the proposed MRAs is to form a set of preferred two-dimensional classifiers. A distinctive feature of the proposed model is to determine a suitable set of TDTCs when constructing an extreme recognition algorithm (RA). The purpose of this paper is to develop MRAs based on the construction of a TDTC in the subspace of representative features. In scientific terms, the results of this work together represent a new solution to a scientific problem related to the issues of increasing the reliability of RA based on the construction of two-dimensional threshold classifiers. The practical significance of the results lies in the fact that the developed MRAs can expand the area of their application in the context of the interconnectedness of characteristics.
Digital Tools in Post-pandemic Education: Useful Lessons

COVID-19 pandemic gave rise to new didactics—new ways of teaching were actively introduced due to global closures of schools. Teachers faced a new challenge connected with the quality of distance learning. Having access to a multitude of digital tools, it was quite confusing for them to choose the most efficient ways of teaching which could satisfy the learning needs of modern students. The experience of distance learning during the pandemic showed that not all digital techniques are worth incorporating into post-pandemic education. Having analyzed the experience of various countries including Russia, the authors single out the digital tools which, in their opinion, can be combined with traditional teaching methods to provide high quality education to cater for the needs of modern labour market. On the example of a lesson plan of an English class the authors show what digital resources and what kinds of activities involving them can be used in the classroom.
<scp>IPFS</scp> based file storage access control and authentication model for secure data transfer using block chain technique

Summary The block chain is the emerging domain that offers the features like shared, immutable, and decentralized dataset that saves the asset's history. Security and storage are the most challenging aspect. The classical techniques' issues and challenges are an encouragement for developing a fresh block chain‐assisted IPFS model with access control policies. Hence, this research paper introduces an effective access control and authentication based on an interplanetary file system (IPFS)‐based block chain and access control system for attaining secured transactions. The developed method comprises five entities, that is smart contract, data owner, data requester, block chain and IPFS along with eight phases, to be exact setup, user registration, initialization, storage and data encryption, authentication, testing, access control, and decryption phase, to provide better access control and storage capacity. The proposed IPFS‐based block chain and access control system offered effective storage and secure transactions in the block chain network. The highest detection rate (0.870), lowest communication overhead (0.766 MB), and highest privacy rate (0.719) are all achieved by the proposed IPFS‐based block chain and access control system.
Motivation Vectors for Choosing an Individual Educational Route in the Digital Environment

The article presents the characteristics of the determinants of students’ choice of a technical profile of an individual educational route in a digital environment. The analysis of the features of motivational vectors of the choice of this training format is given. The introduction provides an analytical overview of research on the problem of motivational resources and risks of choosing an educational route in the context of digitalization, systematizes and typifies the main clusters of factors that motivate the effectiveness of this format of vocational education. The purpose of the study is to study the peculiarities of the representations of technical subjects about the vectors of motivation for choosing an individual educational route in the digital environment. The methods of questionnaires, essays and content analysis were used in the study. The paper describes the main results of studying the ideas of students of a technical profile, reflecting an understanding of the motivation, advantages and risks of individual educational routes in the digital environment. It is shown that students emphasize the resourcefulness of internal motivational motives associated with the possibility of satisfying professional cognitive needs and modeling the content and format of training. The novelty of the research lies in the description and systematization of students’ ideas about the resources and risks, advantages and limitations of choosing an adequate individual educational route in the digital space. For the first time, the vectors of motivation for choosing an individual educational route by technical students in a digital environment are described.
Alzheimer ’s disease identification from 3D SPECT brain scans by variational analysis

The application of a radioactive tracer and following brain Single Positron Emission Computed Tomography (SPECT) is a standard technique used in neurodegenerative disease investigation. Alzheimer’s disease is the most common form of neurodegenerative disease. In this paper, a novel 3D linear classifier is developed to classify Alzheimer’s disease. The classification problem is formulated as the variational task with periodic boundary conditions, which is easy to discretize and solve using Fast Fourier Transform, and therefore, the resulting learning algorithm is very fast. Thanks to linearity of the classifier, weights obtained by 3D classifier learning are easy to visualize and bring understanding the most important features. The proposed classifier exhibits accuracy, sensitivity, and specificity of at least 90%. • New linear classifier standardized 3D images. • Regularized learning process generates smooth 3D weight maps. • Fast implementations of variational classifier using FFT. • Classifier application to 3D SPECT scans of human brain with intuitive visualization of brain activity changes. • High sensitivity and specificity in diagnosis of Alizheimer’s disease.
A Systematic Review on Underwater Image Enhancement and Object Detection Methods

In the last decade, the number of underwater image processing research has increased significantly. This is primarily due to society's dependency on the precious resources found underwater and to protect the underwater environment. Unlike regular imaging in a normal environment, underwater images suffer from low visibility, blurriness, color casts, etc. due to light scattering, turbidity, darkness, and wavelength of light. For effective underwater exploration, excellent approaches are necessary. This review study discusses the survey of "underwater image enhancement and object detection" methods. These methods are outlined briefly with the available dataset and evaluation metrics used for underwater image enhancement. A wide range of domain applications is also highlighted.
A System for Monitoring the Effective Operation of Employee Access to Computer Systems Based on Biometric Authentication

The article describes the process of developing a control system for the effective work of employees’ access to computer systems, based on biometric authentication. Special attention is paid to the methods of implementing this technology from the point of view of machine learning. In the course of this work, a computer vision module based on the OpenCV library was implemented, written in C#, used to detect people, recognize and identify the identity of employees in the database and train for these operations. Also, software was created that demonstrates the capabilities of this module, and a simple employee database based on an XML document was implemented, and programmatically implemented Person objects for programmatically storing information about employees. This program was implemented using Visual Studio. The C# programming language was used. This software tool is intended for offices and offices, in which it is necessary to control access to data among employees. The created application serves as an example showing the real possibilities of using these technologies, which are just beginning to gain maximum popularity and their application is still only expanding.
Non-interactive quantum homomorphic encryption scheme based on the rotation operator

量子同态加密是量子密码学的一个重要分支，它可以直接对密文量子态进行计算，同时保证计算的正确性和数据的安全性. 对量子门<i>T</i> 进行量子同态加密会产生额外的相位门<i>S</i>，如果不消除该错误则不能得到正确的输出。使用量子门隐形传态可以非交互地消除相位门错误，但是增加了解密复杂度. 本文利用旋转算子实现了<i>T/T<sup>†</sup></i>门的量子同态加密，提出了非交互式量子同态加密方案. 该方案解密复杂度为<i>O(1)</i>，加密复杂度为<i>O(N)</i>，其中<i>N</i>是量子线路中量子门的数量. 本文证明了该方案是信息论安全的以及能够实现对任意量子线路的同态加密，并且在IBM Quantum Experience上实现了对Toffoli门分解线路的量子同态加密.
MALIBOO: When Machine Learning meets Bayesian Optimization

Bayesian Optimization (BO) is an efficient method for finding optimal cloud computing configurations for several types of applications.On the other hand, Machine Learning (ML) methods can provide useful knowledge about the application at hand thanks to their predicting capabilities.In this paper, we propose a hybrid algorithm that is based on BO and integrates elements from ML techniques, to find the optimal configuration of time-constrained recurring jobs executed in cloud environments.The algorithm is tested by considering edge computing and Apache Spark big data applications.The results we achieve show that this algorithm reduces the amount of unfeasible executions up to 2-3 times with respect to state-of-the-art techniques.
A Semantic Segmentation Algorithm for Distributed Energy Data Storage Optimization based on Neural Networks

There are many kinds of energy data, how to realize unified storage, processing and sharing of energy data is a big problem. As the national energy data center, State Grid aims to build a database that can store distributed heterogeneous asynchronous energy data. The storage of image files in the big energy database will take up a lot of space in the system, but not all parts of the image are needed. Therefore, it is very necessary to accurately segment the effective area of the image to store it so as to achieve the purpose of data compression. This paper proposes the Attention U-Net framework, which combines the traditional semantic segmentation network U-Net with the Attention module to focus on the region of interest in the image, emphasize foreground information, and suppress background information. The results show that compared with U-Net, the accuracy is improved by 1.77% and after the segmentation is completed, each image saves an average of 2MB of storage space.