Reviewerly/exHarmony huggingface dataset:
Acute lymphoblastic leukemia image segmentation based on modified HSV model

Abstract Image segmentation is a critical step in computer-aided diagnosis that could speed up Leukemia detection. Leukemia is a cancer of the blood that has a reputation for being particularly lethal. Based on the immunohistochemical method, the leukocytes can be manually counted in a stained peripheral blood smear image to detect Acute Lymphoblastic Leukemia (ALL). Regrettably, the manual diagnosis process takes about 3 to 24 hours to complete, which is insufficient. This paper introduced a new and straightforward ALL image segmentation approach based on color image transformation. First, Leukemia, ALL-IDB1, ALL-IDB2, and ALL image datasets were used in this paper. The Leukemia dataset includes 208 ALL-IDB1 and ALL-IDB2 images, while The ALL dataset has 3256 images. Next, we use the HSV model to transform ALL images. In addition, we modified the HSV model by pre-processing the saturation channel for better results. Then, the pre-processed images were segmented based on a fixed threshold. After that, various metrics are utilized to measure the output of the proposed method. Finally, the proposed methodology is compared to currently used benchmarks. The proposed method outperforms previous approaches regarding accuracy, specificity, sensitivity, and time. In addition, results show that the proposed technique improves performance measures significantly.
A novel medical image enhancement technique based on hybrid method

Abstract Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm’s efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics.
Encrypted Data Learning and Prediction Using a BFV-based Cryptographic Convolutional Neural Network

Machine learning services are widely used for big data, cloud computing, and distributed artificial intelligence applications.Multiple parties participating in the provision of these services may access the users' sensitive data because most machine learning models use and share plaintext directly.Therefore, it is necessary to utilize cryptographic mechanisms for protecting user privacy.Homomorphic encryption provides an important information security guarantee for machine learning models.However, the complexity of fully homomorphic encryption increases with the depth of neural networks.Especially with the increase in the number of ciphertext multiplications, the time and space costs will also raise exponentially.Using homomorphic encryption in order to protect the model and data security while ensuring the computational efficiency of the employed model over encrypted data is a challenging problem.This paper proposes a BFV-based cryptographic low-latency convolutional neural network (CLOL-CNN) for solving this problem.This new network model performs deep learning and prediction over encrypted data instead of sharing plaintext data.A series of optimization operations are elaborately presented and implemented, such as cryptographic batch normalization, polynomial approximation, cryptographic convolution, and full cryptographic connection.The performance of the proposed model is evaluated with regard to its accuracy and computational overhead obtained by employing deep learning for homomorphically encrypted data.The experiments were conducted on a MNIST image dataset.The obtained results demonstrated that the proposed model has a higher accuracy and a lower time cost than other models and that it is an effective privacy-preserving deep neural network.
The design of a manipulator drive system based on the main control chip

Abstract This paper mainly studies and designs the control system of a four-degree-of-freedom manipulator. As a motion control system, the control system is mainly oriented to the bottom, striving to develop an industrial manipulator system with high stability, strong reliability, and accurate positioning. Firstly, according to the control requirements of the mechanical arm system, a single CPU system control scheme is designed as a whole, that is, to control the steering gear rotation by controlling the duty cycle of the PWM wave output by the central controller, to achieve the position control of each joint. In terms of hardware, the main components are ARM microprocessor STM32F103ZET6, MG995 steering gear, MG945 steering gear, ultrasonic sensor, and power module. The joint motion control system is built by building a hardware platform and designing a software control program. In software design, the control program is divided into an initialization module and an operation module according to the modular design idea, and the program of each module is designed separately.
OpenCV-based SF6 density controller dial pointer angle recognition

Abstract To solve the problems of the low efficiency and poor accuracy of the manual detection in the factory inspection of the SF6 density controller, this paper proposes an SF6 density controller dial pointer angle identification method. First, the image of the dial pointer at a specific position is acquired according to the working characteristics of the SF6 density controller. The center and radius of the contour circle inside the scale line in the dial are extracted using the Hoff gradient method and the image is coordinate transformed using this parameter. Then the coordinate-transformed image is pre-processed. And finally, the threshold of the image after pre-processing is traversed and the length is used to characterize the pointer angle through the correspondence between the scale line and the angle. The angle of the pointer is characterized by the correspondence between the scale and the angle using the length. This paper uses Python and OpenCV vision library to complete the algorithm and through experiments, the algorithm can achieve high precision pointer angle recognition.
Using algebraic geometry to reconstruct a darboux cyclide from a calibrated camera picture

The task of recognizing an algebraic surface from a single apparent contour can be reduced to the recovering of a homogeneous equation in four variables from its discriminant. In this paper, we use the fact that Darboux cyclides have a singularity along the absolute conic in order to recognize them up to Euclidean similarity transformations.
Communication Interference Signal Recognition Based on Complex Number Convolutional Neural Network

Abstract Modern wireless communication systems are facing an increasingly complex electromagnetic environment, which is affected by a variety of noise and interference signals. In the communication process, if the communication party can effectively identify the type of interference signal, it can take corresponding anti-interference measures to avoid or suppress interference to the maximum extent and reduce the damage of interference to communication quality. In recent years, with the continuous development of deep learning and its outstanding performance in image and speech processing, it has been verified that deep learning has extremely strong nonlinear mapping and data expression capabilities, inspiring researchers to apply deep learning to the field of communication anti-jamming, and improving the antijamming capability of the system. In this paper, an algorithm of interference signal recognition based on a complex convolution neural network is proposed. It also introduces the network architecture. Next, six typical interference signals at each level are identified, and the results are analyzed. The last chapter of the paper summarizes the full text. The experimental results show that the recognition ability of the interference signal recognition algorithm proposed in this paper is better than that of the traditional convolutional neural network.
Obstacle Avoidance Design of Aerial Robot Combining A star algorithm Algorithm and Optical Dynamic Capture Method

Abstract The traditional A star obstacle avoidance algorithm is based on binocular cameras for indoor positioning, which requires rich obstacle feature points and has the risk of explosion in a single indoor environment. In this paper, the method of merging the A star algorithm and optical dynamic capture is proposed. By pasting Marker points on key moving parts, the actual position of the aerial robot is calculated with the optical dynamic capture system, and compared with the target position planned by the A star algorithm and corrected in real time during a flight. Through the experimental analysis of the original acceleration of the aerial robot and different position between the target point and the actual point in the flight process, the stability and flight accuracy of the aerial robot in the flight process can be significantly improved, which has good practical application value.
Variations of polyphony in blogs

Abstract This paper looks at dialogicity in the Slow Art Day blog and focuses on the way the representation of participants encodes the complexity of the communicative action through a polyphony of textual voices. By focusing on posts from the pandemic years (2020 and 2021), and contrasting them with the previous period, we carry out a collocation analysis and a study of semantic preferences ( Sinclair 2004 ) to explore how writers present themselves and how they interact with the reader and other textual voices in a context of cultural intermediation. By looking at forms of address and of self-mention, we trace how this blog enacts different forms of dialogic action with its readers and stakeholders in the extended situational context.
A SIMULTANEOUS PATH PLANNING AND POSITIONING BASED ON ARTIFICIAL DISTRIBUTION OF LANDMARKS IN A GNSS DENIED ENVIRONMENT

In recent years, exploration operations by autonomous robots are expanding into unknown environments on Earth, under the sea, or even on other planets. This paper proposes the idea of Concurrent Path Planning and Positioning (CPPAP) using artificially distributed landmarks, while no GNSS signal is available. The method encompasses an observability-based direction search algorithm for path planning in parallel with Simultaneous Localization and Mapping (SLAM) for localization. Most of the path planning methods utilize offline algorithms; however, the proposed method determines the robot’s direction of motion in real-time, concurrently with the positioning tasks by the inclusion of the system observability, related to the features’ distribution. Same as in all feature-based SLAMs, features play an important role in determination of the most observable direction, and hence the direction of the robot’s motion. Moreover, the effectiveness of the distribution of the features and their pattern in the proposed method is investigated. To evaluate the efficiency and accuracy of the CPPAP, outcomes are compared with an existing random SLAM.
Legal Problems and Solutions in Stablecoins: A Multi-Disciplinary Approach Applied to Euro Stablecoins

Abstract An increasingly important form of electronic currency is the ‘stablecoin’. Unlike other high-profile ‘cryptocurrencies’, such as Bitcoin, stablecoins claim to be matched by a corresponding amount of secure assets in a national currency to which the stablecoin can be converted at par. These crypto-assets, however, create significant legal and economic problems. Notably, issues about trust in both issuers and backing assets, the risk of runs, and particular challenges associated with public and private law regimes resist the adoption of stablecoin both in Europe and beyond. This article draws from previous experiences to analyse these issues and propose solutions. Taking a multi-disciplinary approach, the article argues that the legal and economic issues surrounding today’s stablecoins could be addressed using the lessons from prior centuries.
Nonlinear demosaicking method and apparatus for nonlinear CMOS image sensors exhibiting low-density salt-and-pepper noise

Simple demosaicking methods designed for linear CMOS image sensors, such as MATLAB’s demosaic function, may be used with a monotonic nonlinear sensor having a Bayer color filter array (CFA). However, such methods may be inadequate at handling dynamic salt-and-pepper noise (SPN), i.e., outlier pixels, which is expected in images taken with a nonlinear sensor. Although SPN is present with linear sensors, nonlinear sensors express low-density light-dependent SPN that requires filtering. Extending a recent work on dynamic SPN filtering of a nonlinear sensor, we propose, evaluate, and verify a nonlinear method and apparatus to demosaic images, taken with a Bayer CFA, while simultaneously filtering the SPN. The approach relies on the use of weighted medians to filter the SPN, especially at densities that imply isolated outliers in small neighborhoods, while determining an accurate red, green, and blue (RGB) color at every pixel location. For explanatory purposes, three variants of the proposed method are presented and evaluated. A ground-truth image set, in which RGB channels were not obtained by demosaicking, is subsampled in a Bayer CFA pattern to produce mosaicked images for testing. In varying densities, SPN is introduced to these Kodak images for method and apparatus evaluation. Results of the proposed method and its variants are compared with those obtained with MATLAB’s demosaic function. Considering the alternatives and also apparatus complexity, the proposed nonlinear demosaicking method proves superior in visual quality, for smooth textures and along edges, and in peak signal-to-noise ratio, when there is low-density SPN.
Privacy-preserving transfer learning-based secure quantum image retrieval in encrypted domain for cloud environment

Nowadays, the rapid growth of personal handheld electronic devices encourages the individual and organizations to upload their information to cloud for storage and processing purposes. To ensure privacy, the images are encrypted using cryptographic schemes that are outsourced to the cloud. Though images are encrypted, searching for similar images leads the cloud server to access the image data as it performs computation over plaintext. Thus, to ensure the privacy of the images during image retrieval, the proposed framework presents a transfer learning-based secure quantum image retrieval scheme over encrypted cloud. The confidentiality of the images is guaranteed by introducing quantum-based image encryption. Meanwhile, clustered image feature vectors are extracted through the transfer learning model and protected using secure multiparty computation. During retrieval, the proposed system introduces a similarity comparison model for performing computation on encrypted data. The experiments and performance analysis show the effectiveness and security of the proposed scheme.
A Symmetry-Based Superposition Method for Planning and Surgical Outcome Assessment

Computer-aided surgical planning has been widely used to increase the safety and predictability of surgery. The validation of the target of surgical planning to surgical outcomes on a patient-specific model is an important issue. The aim of this research was to develop a robust superposition method to assess the deviation of planning and outcome by using the symmetrical characteristic of the affected target. The optimal symmetry plane (OSP) of an object is usually used to evaluate the degree of symmetry of an object. We proposed a refined OSP-based contouring method to transfer a complex three-dimensional superposition operation into two dimensions. We compared the typical iterative closest point (ICP) algorithm with the refined OSP-based contouring method and examined the differences between them. The results using the OSP-based method were much better than the traditional method. As for processing time, the OSP-based contouring method was 11 times faster than the ICP method overall. The proposed method was not affected by the metallic artifacts from medical imaging or geometric changes due to surgical intervention. This technique can be applied for post-operative assessment, such as quantifying the differences between surgical targets and outcomes as well as performing long-term medical follow-up.
A RESEARCH PAPER ON STUDY OF MOBILE PAYMENT AND IT’S SECURITY IN INDIA

In India Due to the booming mobile phone market in India, mobile payments have gained popularity as a way to conduct financial transactions.This research paper examines the security issues surrounding India's mobile payment system.The paper provides an overview of the various mobile payment systems available in India.It also looks at issues with fraud, data theft, and hacking that are related to mobile payment security in India.The research paper offers recommendations for enhancing India's mobile payment systems' security.
INTEGRATING LARGE-SCALE ONTOLOGIES FOR ECONOMIC AND FINANCIAL SYSTEMS VIA ADAPTIVE CO-EVOLUTIONARY NSGA-II

The identification, prediction, management, and control of economic and financial systems render extremely challenging tasks, which require comprehensively integrating the knowledge of different expert systems. Ontology, as a state-of-the-art knowledge modeling technique, has been extensively applied in the domain of economics and finance. However, due to ontology engineers’ subjectivity, ontology suffers from the heterogeneity issue, which hampers the co-operation among the intelligent expert system based on them. To address this issue, ontology matching for finding heterogeneous concept pairs between two ontologies has been rapidly developed. It is difficult to find the perfect ontology alignment that satisfies the needs of all decision-makers. Therefore, Multi-Objective Evolutionary Algorithm, such as Non-dominated Sorting Genetic Algorithm (NSGA-II), attracts many researchers’ attention. However, when facing large-scale ontology matching problems, NSGA-II tends to fall into local optimal solutions due to the large search space. To effectively address this drawback, we model the large-scale ontology problem as a nonlinear optimization problem, and propose an Adaptive Co-Evolutionary NSGA-II (ACE-NSGA-II) to deal with it. Compared with NSGA-II, ACE-NSGA-II introduces a co-evolutionary mechanism to increase the diversity of populations in order to decrease the probability of premature convergence. In particular, ACE-NSGA-II uses an adaptive population maintenance strategy to assign more resources toward the dominant ones in order to improve the solution efficiency for solving large-scale ontology matching. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track to test the effectiveness of ACE-NSGA-II, and the resulting experiment demonstrated that compared to NSGA-II and OAEI’s participants, ACE-NSGA-II is able to find better alignment.
Construction and Modification of Topological Tables for Digital Models of Linear Complexes

Building information systems use topological tables to implement the transition from two-dimensional line drawings of the geometry of buildings to digital three-dimensional models of linear complexes. The topological elements of the complex are named and the topological relations of the complex are described by arranging the element names in topological tables. The efficient construction and modification of topological tables for complete buildings is investigated. The topology of a linear complex with nodes, edges, faces, and cells is described with 12 tables. Three of the tables of a complex are independent of each other and form a basis for the construction of the other tables. A highly efficient construction algorithm with complexity O (number of cells) for typical buildings with an approximately constant number of edges per face and faces per cell of is presented. In practice, building designs and their digital models are frequently modified. A modification algorithm is presented, whose complexity equals that of the construction algorithm. Examples illustrate that the efficient algorithms permit the replacement of the conventional focus on the topology of building components by a focus on the topology of the entire building. A set of properties of the original, which are not explicitly described by the topological tables, for example, the orientation of surfaces and multiply connected domains, are analyzed in the paper. An overview of the research dealing with the topological attributes that are not contained in topological tables concludes the paper.
On Pseudorandomness and Deep Learning: A Case Study

Pseudorandomness is a crucial property that the designers of cryptographic primitives aim to achieve. It is also a key requirement in the calls for proposals of new primitives, as in the case of block ciphers. Therefore, the assessment of the property is an important issue to deal with. Currently, an interesting research line is the understanding of how powerful machine learning methods are in distinguishing pseudorandom objects from truly random objects. Moving along such a research line, in this paper a deep learning-based pseudorandom distinguisher is developed and trained for two well-known lightweight ciphers, Speck and Simon. Specifically, the distinguisher exploits a convolutional Siamese network for distinguishing the outputs of these ciphers from random sequences. Experiments with different instances of Speck and Simon show that the proposed distinguisher highly able to distinguish between the two types of sequences, with an average accuracy of 99.5% for Speck and 99.6% for Simon. Hence, the proposed method could significantly impact the security of these cryptographic primitives and of the applications in which they are used.
EVOLUTIONARY MACHINE LEARNING DRIVEN BIG DATA ANALYSIS AND PROCESSING FOR INDUSTRIAL INTERNET

The Industrial Internet is based on the network, the platform is the core, and the security is the guarantee. The Industrial Internet connects all industry elements and the entire industry chain through the large-scale network infrastructure, collects and analyzes industry data in real time, and forms a new application model for a new generation of information communication. With the rapid development of industrial Internet technology, the scale of industrial Internet data will become larger and larger, and the data dimension will become higher and higher. How to efficiently use cluster analysis for industrial Internet big data mining is an urgent problem that needs to be solved. This paper proposes an improved differential evolution particle swarm algorithm for industrial Internet big data clustering analysis. Differential Evolution (DE) strategy can improve the problem that the particle swarm optimization (PSO) algorithm tends to fall into local optimum in the later stage as the number of iterations increases. Considering the influence of the randomness of the arrangement order of the cluster center vectors among the individuals on the learning and updating among individuals, this paper designs a method of adaptively adjusting the arrangement order of the cluster center vectors to optimize the cluster center vector with maximum similarity among individuals. In order to effectively evaluate our method, both industrial and non-industrial datasets are selected. The experimental results verify the feasibility and effectiveness of the proposed algorithm.