Reviewerly/exHarmony huggingface dataset:
Optimization Design of Camera Pose for Network Base on Multi-view Stereo Vision

Abstract When designing a multi-vision stereo vision network, the camera’s positional information has a critical impact on the measurement accuracy. In order to solve the problem of optimizing the camera pose during network design, in this paper, we split the multi-vision stereo vision system into binocular stereo vision system. Based on the mathematical model of binocular stereo vision system, the error function of the positional parameters is constructed. By simulation analyzing the function models the field of view angle, the angle between the optical axis and the baseline, the baseline length, the law of the influence of each parameter on the measurement accuracy and the optimal range of values are obtained. Then we compare the actual measurement results with the simulation analysis results. The results show that as the field of view angle, optical axis and baseline angle, baseline length increases, the measurement error first decreases and then increases, and the increase is gradually accelerated. When each parameter is located in the optimal range, the measurement error meets the precision engineering measurement accuracy requirement of 0.05mm, and the minimum error is reduced by 0.114mm, 0.120mm and 0.061mm compared to the maximum error. In the network design, the optimization of the pose parameters can effectively obtain better pose information of the camera and thus improve the accuracy of the measurement.
Design and Implementation of SPARQL Engine Based on Heuristic Algorithm

To solve the problem of long execution time of SPARQL query statements in current large-scale RDF, this paper proposes a query statement optimization based on heuristic LKH algorithm, and optimizes query statements according to different shapes. Finally, experiments are carried out based on Apache Jena ARQ query engine. Comparative experimental results show that, This method has a good optimization effect on a wide range of query statements, and a good optimization effect on the query statements with a significant number of triplet patterns, and the optimization effect is stable.
Automatic 3D Reconstruction of Carotid Vessels Based on Region Growing Method

Segmentation and 3D visualization of carotid vessels is an important part of the treatment of carotid stenosis conditions. In this paper, we propose a novel method for automatic 3D reconstruction of carotid vessels by combining the region growing algorithm and the marching cubes algorithm. The automatic segmentation and 3D reconstruction of carotid vessels can be achieved by only manually and interactively selecting a point in the target vessel in human CTA images. The experiments demonstrate that this method has good practicality, can reduce a large amount of manual intervention, and has the advantage of saving time and effort.
Developing Intending Teachers’ Creativity in the Process of Their Professional Training in the Context of Educational Transformations

The article considers the problem of developing creativity of intending teachers in pedagogical universities of Ukraine. In the context of the research the state of intending Mathematics teachers’ creativity development in educational theory and practice was found out. The essence of the concept «creativity of intending teacher» was clarified. The pedagogical system of developing creativity of intending teachers in the course of their professional training in higher education institution was substantiated. In order to be experimentally checked theoretical results of the research have been implemented into educational practice of higher educational institutions of Ukraine by means of the pedagogical experiment. The results of the experimental study of the level of creativity of intending Mathematics teachers at the stating and forming stages are presented. The dynamics of the process of developing creativity of intending teachers is highlighted. Statistical analysis of the experimental data confirmed the validity and objectivity of the obtained results and proved the effectiveness of the author's pedagogical system of developing creativity of intending teachers.
Multi-surface measurement at arbitrary positions based on four-surface interferometry

Wavelength-tuning phase-shifting interferometry (WPI) is a feasible method to measure transparent parallel plates. But the existing algorithms are effective when the measurement positions are fixed. To overcome the limitations, this paper proposes a multi-surface measurement method at arbitrary positions based on the four-surface interferometry. The proposed method consists of two parts: a multi-surface phase demodulation algorithm, and a selection technique of the optimal phase-shifting sampling parameters for the measured plates at arbitrary positions. The results of the simulations verify the effectiveness of the proposed algorithm.
Ship detection in infrared images based on YOLOV4 and salient regions

Infrared image ship detection has important applications in military and civil affairs. Because infrared images are not easy to acquire in large quantities, deep neural networks cannot directly use infrared images for training; if the pre-trained model of visible light images is directly used for detection, the phenomenon of missed detection will be caused due to different imaging conditions. In response to this problem, this paper proposes a detection method that combines a deep convolutional neural network and salient region. Firstly, we proposed a method extracting salient region based on anchor and saliency map, then multiple new images are formed by salient regions, and the newly formed images and the original image are input to the deep convolutional neural network for parallel processing, and finally the results of the detection are integrated to produce the final detection results by the non-maximum suppression (NMS) method. The comparison results show that the method proposed in this paper can effectively reduce the rate of missed detection and thus improve the accuracy of detection.
Research on deformation field reconstruction of elastic thin plate based on fiber Bragg grating strain monitoring

In this paper, a new deformation measurement method based on fiber bragg grating strain monitoring is proposed. Aiming the four-sided fixed elastic thin plate structure, an algorithm for reconstructing deformation field based on fiber bragg grating strain monitoring is proposed. The mechanical model of elastic thin plate was established. According to the degree of deformation, it can be divided into small deflection deformation and large deflection deformation. The relationship between surface strain and curvature of a thin plate was deduced. On this basis, a curvature iteration method was proposed to reconstruct the deformation field of thin plates. Aiming at the non-linear problem of large deflection deformation, a subsidence correction algorithm based on curvature iteration method was proposed. The experimental platform for strain monitoring of thin plate and several different loading conditions was established. Based on the discrete point strain data obtained by fiber bragg grating strain monitoring, the strain field is reconstructed to obtain detailed strain information. Based on the curvature iteration method and strain information, the deformation curves of thin plates under different loads are obtained. The experimental results show that the average error of reconstruction of the elastic thin plate structure is 6%, and the reconstructed results can accurately reflect the deformation surface of elastic thin plate. It also provides a basis for the study of deformation field reconstruction under complex constraints.
Few-pattern defocusing fringe projection profilometry for high-speed 3-D imaging

Fringe projection profilometry (FPP) based on the binary defocusing technique (BD) shows great potential in high-speed 3-D imaging. Due to the constant defocusing degree, existing binary defocusing operations require adopting similar-wavelength fringe patterns, thereby forming a long imaging sequence in multi-frequency temporal phase unwrapping (TPU). In this paper, we propose a few-pattern defocusing FPP for efficient and accurate 3-D imaging. The imaging sequence consists of only 6 hybrid images, namely 2 unit-frequency ramp images, 2 low-frequency, and 2 high-frequency sinusoidal fringe images. Combining unit-frequency ramp and low-frequency fringe images, the unknown average intensity and fringe orders of fringe images can be determined. Consequently, the final absolute phase map can be extracted from the high-frequency fringe images. Moreover, a kernel-optimized dithering technique is presented to generate the projected patterns of hybrid images. In this dithering technique, a dynamic kernel and a dual-objective function ensure the optimal binarization of defocused images with different grayscale variations. Experiment results verify the proposed few-pattern defocusing FPP achieves efficient 3-D imaging with a measurement accuracy of 0.02 mm.
Combined deep centralized coordinate learning and hybrid loss for human activity recognition

Abstract Human activity recognition has been a popular research topic in recent years. The rapid development of deep learning techniques has greatly helped researchers to achieve success in this field. During the training process with deep learning techniques, features and time dependencies between them are well learned. However, researchers generally ignore the distribution of extracted features in the coordinate space despite their significant effect on classification and network convergence status. The present article utilizes a simple but effective centralized coordinate learning method that dispersedly spans extracted features across the coordinate space. This method causes the angle between the features of different classes to increase significantly. A hybrid loss function is also suggested to enhance the discriminative power of learned features. Some experiments were carried out on the OPPORTUNITY and the PAMAP2 datasets. The results showed that the proposed method outperformed the recently proposed deep learning methods, including the Deep ConvLSTM, CNN‐LSTM‐ELM, and Hybrid methods. This high efficiency was due to the identification of discriminative features.
Exploring the role of machine learning in Email Filtering

Spam, commonly known by various names as spam, commercial, or junk email, is the process or practice of sending some unsolicited bulk email messages to a randomly selected group of recipients, often to promote the sale of something. Phishing messages try to get your private and personal information by disguising it as legitimate messages from multiple trusted sources. They take you to many fraudulent websites, to fill in and use the private information that can be used for identity theft and many other things. Spam spreads across the Internet quickly because the value and transaction cost of electronic communications is much lower than that of any other and different type of communication. Spam is more than just annoying. It can be dangerous, especially if it is part of a phishing message. There are many agents used to filter spam through different methods to identify and mark an incoming message as spam, ranging from whitelisting, mail header analysis, content scanning, etc. With all the spam every day. This is not because the filters are not powerful enough, but rather because of the total and rapid adoption of all new technologies by spammers and the inflexibility of spam with changes. Therefore, modern machine learning algorithms offer many huge possibilities to help many organizations extract business value from the wealth and amount of data available today. All developers when starting the machine learning process rely on their knowledge and experience with statistics and the creation of many successful models. In this paper, we look at machine learning spam filtering strategies currently in use, as well as a few in development. We have discussed the use of three commonly used machine learning algorithms, namely C 4.5 decision tree, Na&#x00EF;ve Bayes, and Multilayer Perceptron.
Neural network pruning based on channel attention mechanism

Network pruning facilitates the deployment of convolutional neural networks in resource-limited environments by reducing redundant parameters. However, most of the existing methods ignore the differences in the contributions of the output feature maps. In response to the above, we propose a novel neural network pruning method based on the channel attention mechanism. In this paper, we firstly utilise the principal component analysis algorithm to reduce the influence of noisy data on feature maps. Then, we propose an improved Leaky-Squeeze-and-Excitation block to evaluate the contribution of each output feature map using the channel attention mechanism. Finally, we effectively remove lower contribution channels without reducing the model performance as much as possible. Extensive experimental results show that our proposed method achieves significant improvements over the state-of-the-art in terms of FLOPs and parameters reduction with similar accuracies. For example, with VGG-16-baseline, our proposed method reduces parameters by 83.3% and FLOPs by 66.3%, with only a loss of 0.13% in top-5 accuracy. Furthermore, it effectively balances pruning efficiency and prediction accuracy.
HELO DarkSide

The pedagogy of cybersecurity education presents an exciting challenge. Although cyber-warfare has existed for nearly four decades, we fail to adequately model the chaos of offensive cyber attacks in the classroom. Instead, coursework focuses on studying choreographed cyber-attack patterns. In this paper, we present an undergraduate cybersecurity course design that balances theoretical learning with an emphasis on exploring offensive tactics, techniques, and procedures. Labs consist of writing payloads and channels to evade detection, cobbling together operating system internals to achieve attack functionality, and developing survivable post-exploitation tools. In the exams, students develop malware capable of avoiding static and dynamic analysis and identify the strategic and tactical flaws that lead to the discovery of highly successful attack campaigns. We believe that sharing this experience will prove valuable for instructors who wish to explore offensive cyber tactics in the classroom.
Fast algorithm for quantum polar decomposition and applications

The polar decomposition of a matrix is a key element in the quantum linear algebra toolbox. We show that the problem of quantum polar decomposition, recently studied in Lloyd et al. [arXiv:2006.00841], has a simple and concise implementation via the quantum singular value transform (QSVT). We focus on the applications to pretty-good measurements, a close-to-optimal measurement to distinguish quantum states, and the quantum Procrustes problem, the task of learning an optimal unitary mapping between given ``input'' and ``output'' quantum states. By transforming the state-preparation unitaries into a block-encoding, a prerequisite for QSVT, we develop algorithms for these problems whose gate complexity exhibits a polynomial advantage in the size and condition number of the input compared to alternative approaches for the same problem settings [Lloyd et al., arXiv:2006.00841; Gily\'en et al., arXiv:2006.16924]. For these applications of the polar decomposition, we also obtain an exponential speedup in precision compared to Lloyd et al. [arXiv:2006.00841], as the block-encodings remove the need for the costly density matrix exponentiation step. We contribute a rigorous analysis of the approach of Lloyd et al. [arXiv:2006.00841].
Talking during a test?! Embracing mobile instant messaging during assessment

This study reports on the incorporation of mobile instant messaging (MIM) in assessments, as a collaborative learning tool, to enable students to socially construct knowledge and develop their collaborative problem solving competence, while being assessed individually. In particular, this study explores: what is the extent and timing of students’ use of MIM to communicate with other students while being assessed individually? What communicative activities are evident in the content of students’ MIM communications while being assessed individually? How do students experience being able to use MIM while being assessed individually? The results of this study’s analysis of the messages sent during various assessments suggests that when incorporating MIM into assessments, instructors should consider the objective of those assessments together with the nature (e.g. essay style) and the stakes of the assessments as these appear to influence the extent, timing and content of the instant messaging communications by the students during the assessment. A survey of the students suggested that their experiences of being able to use MIM during assessments were largely positive due to the learning opportunities, collaboration and teamwork, authenticity and equity that the introduction of instant messaging during assessment enabled.
Realities of Implementing Community-Based Learning during Lockdown

At King’s College, Nepal, the “Integrated Course” is an experimental project-based learning program, which aims to integrate learning and action in the community with classroom-based education. Connecting classroom learning with the world beyond academia can increase motivation for students, provide familiar examples for study, open the classroom to a trove of resources, promote integration across subject areas, and facilitate transfer of learning. However, it is often challenging to make those connections, and the challenges became more acute during the COVID-19 lockdown. We discuss here the design and implementation of the course, including both positive outcomes and difficulties as well as the experiences of participants. In particular, we use both successes and failures of the course as a guide for understanding how this kind of educational reform can be accomplished.
Integrated Generative-Model Domain-Adaptation for Object Detection under Challenging Conditions

Realistic object detection scenarios, most notably the detection of objects such as pedestrians and traffic signs by autonomous vehicles, present a real challenge for state-of-the-art methods. Domain shifts further aggravate this problem when an autonomous vehicle has to operate and detect objects under more challenging conditions (e.g., rainy weather). In this paper, we demonstrate that the performance of challenging object detection scenarios can be improved rather significantly using a novel integrated Generative-model based unsupervised training and Domain Adaptation (GDA) framework. In particular, we exploit unsupervised image-to-image translation to generate annotated visuals that are representatives of a target challenging domain. Then, we use these generated annotated visuals in addition to unlabeled target domain data to train a domain adaptive object detection method. We show that using this integrated approach outperforms both methods, unsupervised image translation, and domain adaptation, when they are used separately. We evaluate the performance of the proposed GDA framework using real visuals captured by vehicles driving under rainy weather. Our simulation results show that GDA can achieve significant improvements in the detection of pedestrians and traffic signs when tested under real challenging conditions in comparison with state-of-the-art methods.
Blockchain Data Management for IoT Applications

The Internet of Things (IoT) revolution has significantly increased the number of sensor-enabled devices in an ever-growing spectrum of intelligent surroundings. A critical component of future IoT scenarios is the necessity for all devices to function cooperatively, openly, immutably, properly, and with performance guarantee. The community has advocated using blockchain databases to address these concerns, however present blockchain designs suffer from performance limitations. The objective of this Ph. D. research is to propose <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SCULPT$</tex> , a unique permissioned blockchain database system that abstracts machine learning models into fundamental data blocks that are then stored and retrieved from the blockchain. As such, it doesn't store detailed records on a medium, like blockchains, which is fundamentally very slow due to the expensive verification process. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SCULPT$</tex> consensus protocol incorporates technological advances, particularly the concept of Proof-of-Federated-Learning (PoFL). The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SCULPT$</tex> prototype system is built on the Hyperledger Fabric blockchain platform, which has shown impressive first results.
DeepDish on a diet

Intelligent sensors using deep learning to comprehend video streams have become commonly used to track and analyse the movement of people and vehicles in public spaces. The models and hardware become more powerful at regular and frequent intervals. However, this computational marvel has come at the expense of heavy energy usage. If intelligent sensors are to become ubiquitous, such as being installed at every junction and frequently along every street in a city, then their power draw will become non-trivial, posing a severe downside to their usage. We explore Multi-Object Tracking (MOT) solutions based on our custom system that use less power while still maintaining reasonable accuracy.
Handling Class Imbalance in Multiclass Datasets by using a Neighborhood based Adaptive Heterogeneous Oversampling Ensemble Classifier

Classification of multiclass datasets with the complexity of skewed data distribution is a widely discussed research area. In this paper, a novel Neighborhood based Adaptive Heterogeneous Oversampling Ensemble classifier is proposed to address the class imbalance in multidass datasets. The proposed algorithm is examined on five datasets. The performance results are compared with the benchmarking algorithms. The results revealed that the proposed method performs better than the benchmarking algorithms.
Unmanned Aerial Vehicle Object Detection and Recognition using Deep Learning

The methodologies of profound learning-based discovery and acknowledgment of dangers by UAV are analyzed as far as military and safeguard enterprises. To begin with, CNNs, one of the DL strategies, are utilized to prepare for ML on the articles in the proposed strategy. It is trusted that by utilizing the Faster-RCNN and YoloV4 profound learning models, the exactness accomplished all through the preparation stage can measure up. Informational indexes containing photographs gathered from shifted climate, land conditions, and time spans of the not entirely settled for use in the preparation and testing phases of the suggested techniques. The model for identifying and perceiving hazardous things has been prepared utilizing 2595 photographs. The innovation for identifying and perceiving things is being assessed utilizing military activity photographs and information caught by UAVs. While the Faster-RCNN engineering scored a precision pace of 93&#x0025; in object location and acknowledgment, the YoloV4 design procured an exactness pace of 88&#x0025;.