Reviewerly/exHarmony huggingface dataset:
Analysis of target detection algorithms at different stages

Target detection as a part of computer vision occupies an important position in the field of recognition. It has seen significant improvements in algorithm performance at every stage. You only look Once (YOLO), for example, seems to have the greatest advantage as a target detection model. It is clear that it only needs to be viewed once to identify the class and location of objects in an image. As YOLO continues to improve, it exhibits even faster and more accurate recognition. This paper discusses the features and advantages shown by the different target detection algorithms at each stage. From the analysis results, YOLO shows more advantages in object detection. YOLO detection is fast and can process streaming video in real-time. Also, the number of false background detections is less than half compared to other algorithms while showing good generalization.
An overview of the vessels for electrical submarine cable laying

Abstract The cable laying vessel is a key factor for the submarine laying routing, insta llation, and layout. The loading and laying capabilities of a submarine cable vessel alw ays determine the speed and costs of corresponding cable construction engineering, wh ich will significantly affect the electricity transmission of the islands, wind farms, and other offshore energy engineering. Therefore, an overview on the recent development of domestic and international cable laying vessels and related construction equipment i s conducted with the summarizations of the development trends. Furthermore, a perspe ctive of the cable laying vessels in the future is also proposed.
Radar operator’s oculomotor activity at working with group targets

Computer oculography (eye-tracking) technologies make it possible to effectively study the work of the human oculomotor system in the operator's activity in the man-machine system. The data obtained in this way helps to increase the system efficiency, especially in conditions of extreme activities associated with vital and emotional stress at a high cost of error. Within the process of the interface ergonomic design of the radar complex for detecting and tracking, the features of the human oculomotor system are considered when working with group targets.
Retrieval of exudate‐affected retinal image patches using Siamese quantum classical neural network

Deep neural networks were previously used in the arena of image retrieval. Siamese network architecture is also used for image similarity comparison. Recently, the application of quantum computing in different fields has gained research interest. Researchers are keen to explore the prospect of quantum circuit implementation in terms of supervised learning, resource utilization, and energy-efficient reversible computing. In this study, the authors propose an application of quantum circuit in Siamese architecture and explored its efficiency in the field of exudate-affected retinal image patch retrieval. Quantum computing applied within Siamese network architecture may be effective for image patch characteristic comparison and retrieval work. Although there is a restriction of managing high-dimensional inner product space, the circuit with a limited number of qubits represents exudate-affected retinal image patches and retrieves similar patches from the patch database. Parameterized quantum circuit (PQC) is implemented using a quantum machine learning library on Google Cirq framework. PQC model is composed of classical pre/post-processing and parameterized quantum circuit. System efficiency is evaluated with the most widely used retrieval evaluation metrics: mean average precision (MAP) and mean reciprocal rank (MRR). The system achieved an encouraging and promising result of 98.1336% MAP and 100% MRR. Image pixels are implicitly converted to rectangular grid qubits in this experiment. The experimentation was further extended to IBM Qiskit framework also. In Qiskit, individual pixels are explicitly encoded using novel enhanced quantum representation (NEQR) image encoding algorithm. The probability distributions of both query and database patches are compared through Jeffreys distance to retrieve similar patches.
Unfree Flow with No Trust: The Implications of Geoeconomics and Geopolitics for Data and Digital Trade

The recognition that the modern economy is increasingly driven by data and on-line commerce has led to a number of initiatives to establish a global data governance regime that promises “data free flow with trust” or DFFT, and an associated open e-commerce regime. Since then, notwithstanding progress in formulating proposals to build the trust that is the key to enabling free flow, international frictions have pushed in the opposite direction and today’s reality is closer to unfree flow with no trust than it is to DFFT. The issues go beyond the technical and legal frameworks required to build trust at the level of private participants in the digital economy – the household/consumer and corporations. They extend to the geoeconomics and geopolitics of the knowledge-based and data-driven economy that emerge from the contest for the rising economic rents that this economy generates and the parallel contest for leadership/dominance of the new general purpose and dual-use technologies built on the nexus of big data/machine learning/artificial intelligence. This note discusses how data and digital trade policies are shaped by the new geoeconomics and geopolitics and how these are in fact endogenous to the technological conditions of the age.
COFH—An improved feature descriptor for RGB information based on OVFH

In this paper, we propose a novel point cloud feature descriptor for object recognition based on 3D RGB point cloud. Due to its strong recognition power, OVFH(Orthogonal Viewpoint Feature Histogram) feature descriptor is widely used in real-time 3D point cloud object recognition task, especially when dealing with the object of symmetric placement of mirror image, and has achieved good results. However, this descriptor does not take the color information of the point cloud into consideration, which is an essential information for object recognition. To solve this problem, we propose a novel feature descriptor named COFH(Color-OVFH). It consists of two parts, which are viewpoint component and extended color shape feature. The extended color shape feature integrates the surface shape feature and color feature. The performance of COFH descriptor is validated on the common dataset. The experimental result shows that COFH significantly improved the recognition ability in RGB point clouds with similar shapes but large color differences. It can be effectively applied to object recognition with RGB information.
Adjuvant diagnosis of breast cancer by deep learning

The rapid development of deep learning has sparked a lot of interest in its application to medical-image processing issues. Cancer, as one of the most intractable diseases, needs high accurate and efficient adjuvant detection. Our study compared and developed a deep learning algorithm by using CNN models from multiple dimensions that can accurately detect breast cancer. One of our depthwise separable convolutional network methods for classifying breast cancer based on histopathologic scans of lymph node data attain excellent performance compared to the previous investigation to other applications. This method achieves 93.41% in test accuracy, 0.9568 in recall lastly. The remarkable recall performance shows the diagnosis is almost completely correct for benign tumors. However, there is still a small chance of misdiagnosis for malignant tumors so that no patient is spared. Such results are ideal for the adjuvant diagnosis of breast cancer. We also demonstrate that the depth of the network and the computation and number of parameters play critical roles. These findings demonstrate that deep learning approaches can be readily trained to achieve high accuracy and efficiency in breast cancer classification. They hold enormous promise for enhancing clinical diagnosis methods and being more portable.
A signal modulation classification algorithm based on convolutional neural network

As a hot research direction in the field of communication reconnaissance, signal modulation classification plays an increasingly important role in the field of national defense. Traditional signal modulation style classification methods are mostly based on the combination of feature engineering and pattern recognition. First, the expert features are extracted by manual design, and then the signal modulation is recognized by the pattern recognition algorithm. The limitation of this method is that an expert feature can only effectively identify a few specific modulation signals. Besides, the deficiency of the number of expert features will lead to a low classification accuracy. In order to improve the accuracy of signal modulation classification, a signal modulation classification algorithm based on convolutional neural network is proposed. Convolutional neural networks can achieve end-to-end classification without manually designing and extracting features. Convolutional neural networks can automatically extract various levels of abundant features through learning, which can improve classification accuracy. The convolutional neural network architecture designed in this paper includes: three convolutional layers, three pooling layers, and the last layer is the softmax classification layer, which outputs the classification results. Experimental results show that on a data set containing 32 I/O signals, when the signal-to-noise ratio is 6dB, the algorithm has a training accuracy rate of 92.7% and a test accuracy rate of 90.2%.
Performance Comparison of User-Estimated and Learned Task Recognition Models for Maintenance Procedures

Maintenance procedures of technical installations are crucial to keep systems in a healthy state. Unfortunately, sometimes rare technical installations require certain types of maintenance that are not known to all technicians. We are investigating an idea that enables those technicians to still perform maintenance by using an assistance system (AS). An important part of such an AS, which we concentrate on in this work, is a task recognition engine. It keeps track of the tasks performed within a maintenance procedure, so that the AS can provide the technician with adequate information and help. We show how to build two types of task recognition models for this task recognition engine. The first was developed by Honecker & Schulte and is based on the Dempster-Shafer theory (DST). These models can be well estimated by experts, but may not represent the sequence of tasks in maintenance procedures because they are designed for parallel tasks. But still, they may be useful if there is not enough training data available. The second is based on Hidden Markov models (HMM). They are harder to estimate, but they may perform better for the sequential tasks in maintenance procedures if they are trained from a sufficient data set. Depending on the training data set’s size, we investigate which kind of model performs better by evaluating them on an example maintenance procedure.
Submission to Canadian Government Consultation on a Modern Copyright Framework for AI and the Internet of Things

We are grateful for the opportunity to participate in the Canadian Government’s consultation on a modern copyright framework for AI and the Internet of Things. Below, we present some of our research findings relating to the importance of flexibility in copyright law to permit text and data mining (“TDM”). As the consultation paper recognizes, TDM is a critical element of artificial intelligence. Our research supports the adoption of a specific exception for uses of works in TDM to supplement Canada’s existing general fair dealing exception.Empirical research shows that more publication of citable research takes place in countries with “open” research exceptions -- that is, research exceptions that are open to all uses (e.g. reproduction and communication), to all works, and to all users. Empirical research also shows that text and data mining research is promoted through exceptions that more specifically authorize text and data mining research. While these studies are preliminary and we are still improving on them, they provide evidence that supports the approach of combining a general research exception with a more specific data mining exception.
An Advanced Hybrid Algorithm for Real-World Optimization Problem

Between various metaheuristic algorithms, DE (differential evolution) and PSO (particle swarm optimization) is established to be efficient and powerful optimization techniques. Likewise, it has been perceived that their hybrid algorithms afford a consistent estimate to global optimum. Thus, based on multi-swarm tactic an advanced hybrid algorithm (haDEPSO) is introduced to solve engineering optimization problems, instead of naïve way. Proposed aDE and aPSO (an advanced DE and PSO) are incorporated in haDEPSO. Also, the population of one is combined with the other in a predefined way in haDEPSO, to create balance between global and local search capability. Introduced hybrid haDEPSO and its participating component aDE and aPSO has been applied over two engineering optimization problems. Results show supremacy of introduced algorithms comparing to so some modern algorithms. Finally, on basis of performance the introduced haDEPSO is endorsed in solving engineering optimization problems.
User Perceptions of Security and Privacy Risks with Contact Tracing Apps

This article explores Canadians’ response to their federal government’s release of the COVID-19 Tracing app for smartphones in 2020 during the pandemic. Researchers and industry have proposed cellphone-based contact tracing to help contain the spread of SARS-COV-2 virus in people during the COVID-19 pandemic. However, the efficacy of contact tracing requires a certain threshold of participants and for people to enable specific access to communication ports on their mobile devices. Privacy and confidentiality concerns over users’ data on their personal devices have existed for years. Rightfully, industry watchdogs have raised concerns about the long-term consequences of contact tracing. As well questioning the potential confidentiality risks, privacy experts have also raised questions about whether contact tracing can curb the spread of the SARS-COV-2 virus. The uncertainty of this technology and pre-existing privacy concern could affect the adoption by the public. It is unknown how people perceive their security regarding contact tracing during the COVID-19 pandemic. This article presents early theoretical work and survey data taken from an ongoing study on participants’ perceptions of security and privacy perceptions of the COVID Alert contact-tracing app released in several Canadian provinces and territories during the 2020–2021 pandemic.
Decentralized Voting: A Blockchain-Based Voting System

As internet has become one of the important key technologies for today’s generation, every system like banking, education, healthcare, agriculture, and others seems to leverage the usage of the internet by migrating the corresponding system to an online-based system. When we talk about the term democracy, election and voting are one of the key elements for creating constituency by leaders, where voters (citizens) choose their leader. This voting system can also be leveraged to an online system, many countries have already introduced Online Voting/E-Voting System, but security, privacy, transparency is still a question in such a system. In this, the authors have proposed a blockchain-based voting system, integrated with the Face Recognition module to address all such issues that exist in today’s voting system. Also, as a whole in an online election/voting system, end users are required to provide their confidential ID proofs (UUID/Voter Card or any other valid Government ID Proofs), in a traditional online-based voting system, all these confidential data(s) are vulnerable to breach as central processing agencies (those who are in charge of managing/administrating the voting process) have full control to all these confidential data, and there is no guarantee that all these confidential data are not misused/breached. This Blockchain-based system will also address the above-mentioned issue, by giving control to end user about who will have the access to these confidential data and what level of access they will be having (with whom all these data are being shared), in short, will help to achieve more trust in the whole voting process and make the entire operations transparent. Also, the proposed system will publish the result of voting on a real-time basis.
Low Rank Regularized Equivalent Source Method for Sound Field Reconstruction

Aiming at the numerical instability problem that probably exists in the process of calculating the equivalent source strengths, this paper proposes a low rank regularized equivalent source method to improve the anti-noise characteristics and sound field reconstruction accuracy under the condition of low SNR by the means of multiple measurements. In the method, a matrix that contains certain multiply measured data vectors on the hologram surface is firstly composed. After that, a low rank matrix recovery problem of computing the equivalent source strengths is established by minimizing a convex optimization function. Furthermore, the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> norm is simultaneously added to the noise matrix formed by multiple noise vectors corresponding to the measurements to make the matrix more column sparse for suppressing the influence of the noise on the reconstruction accuracy. Numerical simulation results show that the proposed method can obtain higher reconstruction accuracy than the equivalent source method with Tikhonov regularization and prove the effectiveness and robustness of the method.
Chinese Medical Question Answering Model Based on Lattice CNNs

The application demand of intelligent medicine is growing, and medical question answering (QA) is an important part of it. Chinese medical question answering has difficulty in word segmentation, which leads to insufficient feature extraction. To solve this problem, this paper constructs a user-defined dictionary and uses LCN s to build a Chinese medical question answering model. First, a user-defined dictionary for Chinese medical QA is constructed based on ICD-10 professional medical thesaur-us, and the electronic medical record is processed by using Chine-se word segmentation tool and user-defined dictionary. Secondly, use the EMR (electronic medical record) after word segmentation as the data of word vector training, and use the Skip-gram model to train the medical word vector. Finally, taking the medical word vector as the input of the model, LCNs are used to extract sentence features, calculate the similarity between question feat-ures and answer features, and then train the Chinese medical question answering model. Experiments show that the accuracy of LCNs model can reach 88%, and its performance is better than other deep learning methods such as LSTM.
Bringing Industry to the Classroom Trough Virtual Reality: Enhancing Learning and the Undergraduate Experience

The requirements of Industry 4.0 provide a new opportunity for universities. As it will lead to a substantial transformation of their educational programs and methods to enhance students’ skills and competencies required for a new labor market given the rapidly emerging industry changes. This called Fourth Industrial Revolution is based on cutting-edge technological tools and data collection. Motivating new and innovative ways to operate and transform processes. These new tools and innovative education strategies, using trailblazing technologies, in engineering programs can potentially transform our society for the better. Well oriented, these revolutionary transformations, supported by updated universities’ study programs, could lead to better and faster replies to global environmental, economic and social challenges the world had been facing during the last century. In this paper we present the last advances of a whole assessment method applied to an undergraduate industrial engineering program to measure the impact of virtual reality use for the enhancement of industrial processes in a set of eleven transversal student's competencies, focused on the Fourth Industrial Revolution. The importance of measuring this impact is of the major relevance for the educational challenge of improving study programs; which plan to include front-line technologies to respond to the skill set required by Industry 4.0. Our measures were tested statistically and qualitatively in a class of statistical engineering with a group of students in their last semester of the industrial engineering program in the Tecnologico de Monterrey Mexico. The group was exposed to challenges related to the enhancement of industrial processes, these challenges were presented to the students; first traditionally using descriptions and 2D video films of the processes and in a second part using immersive 3D films. The results obtained were a positive impact on five of the set of eleven competencies, the statistical validation was made using traditional hypotheses tests. We also added a qualitative measure by means of a survey opinion pool, which showed the great satisfaction of students experimenting with virtual reality environments. This assessment method was iteratively applied and improved during the last three years. This closes the experience of measuring the impact of the use of virtual reality environments to enhance industrial processes on relevant competencies regarding Industry 4.0. We present the last achievements related to tests, rubrics and results. We have added a direct evaluation on competencies which complement our previous attempts and experiences.
Establishment and Design of English Translation Memory and Terminology Database Based on Artificial Intelligence

In the application field of professional translation and localization market, since the quality of computer translation basically meets actual needs, most of the translation products that can be sold in the market use computer-assisted translation technology. However, due to the complexity and variability of linguistic knowledge, some people have proposed translation memory, which makes full use of the powerful functions of files and databases to improve the reliability and reuse of existing translation materials. Therefore, in some professional translation technology applications with high data and repetitiveness, the use of this type of technology can effectively eliminate the double operation of translators, avoid multiple translation results of the same word or two words, and significantly improve its work efficiency. This article is based on artificial intelligence-based English translation memory and terminology establishment and design. First of all, it uses literature research to explain the functions of English translation memory technology and English translation screening methods. After analyzing the memory and terminology, according to analyzing the results of the design of the memory bank and the design library, this article locates the granularity of the memory bank at the simple sentence level, and establishes a correspondence between the various components of a sentence based on the establishment of a simple word-sentence alignment. The English sentence similarity algorithm based on syntax and semantics can well measure the structural and semantic similarity between two English sentences, and can correctly return the most similar example sentences in a memory bank. After we obtained the most similar example sentences, we translated them according to the corresponding relationship between the sentence to be translated and the various components of the most similar example sentences.