Reviewerly/exHarmony huggingface dataset:
The attitude of university students to distance learning in the context of the spread of coronavirus

The article condemns the problem of the attitude of university students to distance learning in the conditions of the spread of coronavirus. It describes the results of a study, the main purpose of which is to show how the attitude of students to the conditions for obtaining education in a distance format is changing. The article indicates that distance learning has become a forced form of student training due to the pandemic and the widespread lockdown in April 2020. Neither teachers nor students had been prepared for distance learning, this caused a negative attitude to the educational process, which could not but affect its results. The article gives the concept of distance learning, briefly describes its history, discusses the advantages and disadvantages. The results of a survey of students in May 2020 and in May 2021 are presented in order to clarify the changes in their attitude to distance learning. The analysis of the questionnaire showed that for many students, adaptation to distance learning was quite difficult. The majority of the surveyed students preferred traditional contact training and with great problems and reluctance studied in a distance format, which has both subjective and objective reasons. In May 2021, a repeated survey of students was conducted, which showed significant changes over the past year in relation to remote technologies. Despite the fact that the value of daily contact with teachers and classmates remains high for students, they do not deny the opportunity to study remotely. The results of the study show that the development of distance learning skills by students affects the attitude to it. The article describes the conditions for the effectiveness of distance learning in the modern realities of education.
Predictive Control-Based Completeness Analysis and Global Calibration of Robot Vision Features

This paper provides an in-depth study and analysis of robot vision features for predictive control and a global calibration of their feature completeness. The acquisition and use of the complete macrofeature set are studied in the context of a robot task by defining the complete macrofeature set at the level of the overall purpose and constraints of the robot vision servo task. The visual feature set that can fully characterize the macropurpose and constraints of a vision servo task is defined as the complete macrofeature set. Due to the complexity of the task, a part of the features of the complete macrofeature set is obtained directly from the image, and another part of the features is obtained from the image by inference. The task is guaranteed to be completely based on a robust calibration-free visual serving strategy based on interference observer that is proposed to complete the visual serving task with high performance. To address the problems of singular values, local minima, and insufficient robustness in the traditional scale-free vision servo algorithm, a new scale-free vision servo method is proposed to construct a dual closed-loop vision servo structure based on interference observer, which ensures the closed-loop stability of the system through the Q-filter-based interference observer, while estimating and eliminating the interference consisting of hand-eye mapping model uncertainty and controlled robot input interference. The equivalent interference consisting of hand-eye mapping model uncertainty, controlled robot input interference, and detection noise is estimated and eliminated to obtain an inner-loop structure that presents a nominal model externally, and then an outer-loop controller is designed according to the nominal model to achieve the best performance of the system dynamic performance and robustness to optimally perform the vision servo task.
PANDA: Population Automatic Neural Distributed Algorithm for Deep Leaning

Deep neural network models perform very brightly in the field of artificial intelligence, but their success is affected by hyperparameters, and the learning rate schedule is one of the most important hyperparameters, and the search for the learning rate schedule is often time-consuming and computationally resource-intensive. In this paper, we propose a Population Automatic Neural Distributed Algorithm (PANDA) based on population joint optimization, which uses distributed data parallel deep neural network training to implement a dynamic learning rate schedule optimization strategy based on the population idea, with almost no loss of test accuracy, PANDA is able to dynamically refine the learning rate schedule during model training instead of following the usual suboptimal strategy. We conducted experiments on typical AlexNet, VGG16, and ResNet18 using the Tianhe-3 supercomputing prototype, and the results show that using PANDA to dynamically update the learning rate greatly reduces the learning rate schedule search time while ensuring close performance with the latest population hyperparameter algorithm, and in our experiments, PANDA lead to at max 123.85x speedup, and the experimental results prove the effectiveness and robustness of PANDA.
Random Variables

Abstract Specializing the concepts of Chapter 7 to the case of real variables, this chapter introduces distribution functions, discrete and continuous distributions, and describes examples such as the binomial, uniform, Gaussian, Cauchy, and gamma distributions. It then treats multivariate distributions and the concept of independence.
Correcting Defective Trajectories using Conditional GAN

The end-user mobility patterns play a key role in the process of 5G network design. During tracking end-users via GPS, errors can appear. While we are still constrained with a very limited number of commercially available trajectory datasets, a possible solution is to extend an existing dataset using a generative adversarial network (GAN). Our previous work showed that the utilization of GAN in a form of artificial trajectory generator is possible but not flawless as it introduces the issue with unreasonable gaps between trajectory’s consecutive GPS coordinates. To overcome this issue with a generated dataset and with authentic data as well a special type of GAN called conditional GAN can be used. By leveraging this approach we are not only able to generate a potentially unlimited number of new data samples but as well to correct the existing ones. The number of missing datapoints in the trajectory can go as low as 95% of all points. This artificial intelligence approach has the potential to be used in various use cases where trajectory data are defective and need to be corrected.
A Black Box Attack Based on Visual Sensitivity

A bstract-Adversarial attack is an important research direction of neural network security, and the black box attack in the unknown model is an important application scenario against the attack. The adversarial samples generated by existing black-box attack algorithms often have drastic hue changes, are easy to perceive by human eyes, and the algorithm computational efficiency is generally low. Therefore, this paper proposes a multi-scale grid search for the image gray attack strategy, disturbs different intensity of different scales, and then makes the generated sample hue change more natural. Experiments show that the performance of the method is better than the baseline algorithm, and greatly improves the attack efficiency, increasing the neural network query number over 50% comparing with the fastest baseline algorithm.
Research on Scenario Analysis in Aircraft e-Service

Advanced aircraft manufactures are taking digital operation support as their main objective to providing value-added services for airlines during aircraft lifecycle. This paper puts forward a concept of aircraft e-Service, which aims at providing aircraft operation support environment with "data interconnection, systems integration, resources dynamic allocation, people coordination, service digitalization and networking, and intelligent decision and analysis", and then improving operation efficiency of airlines. E-Service is the integration of several different systems, so system engineering approaches will be mainly used. This paper focuses on the scenario analysis method, selecting several typical e-Service scenarios and implementing the corresponding scenario analysis and functional analysis, which provided a research methodology for e-Service full system research and development.
HINet: Hierarchical Point Cloud Frame Interpolation Network

Intelligent agents utilize various sensors such as LiDAR, cameras to perceive the surroundings. However, the frame rate difference among sensors seriously affects both safety and efficiency of intelligent agents. Recently some research concerning point cloud frame interpolation is conducted to solve the frame rate inconsistency problem by interpolating low frame rate point cloud sequences up to high frame rate ones. To improve the performance of current state-of-the-art method, we come up with a novel Hierarchical Point Cloud Frame Interpolation Network (HINet). By proposed hierarchical warping module, coarse intermediate frames are generated hierarchically to reach closer toward the target position. Besides, we propose spatial aware fusion strategy to hierarchically restore local geometric distribution by attention mechanism and positional offset. Finally, hierarchical supervision module is applied to efficiently train the HINet in two stages, guaranteeing the quality of predicted intermediate frames. We employ HINet in a large outdoor autonomous driving dataset and provide convincing qualitative and quantitative evaluation results.
Impact of the type, orientation, and temperature of solar panels on observed efficiency in Latvian climate conditions

Abstract As solar panel technologies become more and more popular and are increasingly used in nearly zero-energy building solutions, one must make sure that the panels are able to achieve performance indicators similar to those determined by manufacturers under standard testing in real-world conditions. To determine the efficiency of poly- and monocrystalline panels, depending on their spatial orientation and other parameters, a set of test panels was installed in Riga, Latvia in 2018 for long-term monitoring of their power output. This article summarizes the results for the first two years. In the autumn of the second year of monitoring, temperature sensors were installed on the solar panels to study the effects of temperature on panel’s efficiency. The data show that the panel’s spatial positioning is a crucial element affecting the amount of energy produced, although the type of panels and climate conditions are also important.
A Comparative Experiment of Document Security Level on Parallel Encryption With Digit Arithmetic of Covertext and Parallel Encryption using Covertext

Documents for some entities contain important information. Cryptography and steganography are a mechanism that is often used to maintain confidentiality. Parallel Encryption With Digit Arithmetic of Covertext (PDAC) and Parallel Encryption using Covertext (PECT) are two encryption models for securing text-based documents. Both have similarities and differences in the mechanism. This research is experimenting with the models to see which is better for securing documents. This study also aims to find the important factors inside PDAC and PECT. Parameters used to compare are entropy and file size. As a result, it is found that the PDAC and PECT ciphertexts have the same size, but PDAC has a higher entropy value than PECT. This experiment also found factors that affect the level of security.
Energy-Efficient CNNs Accelerator Implementation on FPGA with Optimized Storage and Dataflow

Convolutional neural networks (CNNs) has been widely used in computer vision and speech processing, and has achieved great success. However, the deployment of large-scale CNN model is limited by computing and memory in the smart embedded system. Through the current high parallel computing paradigm of CNNs accelerator, the computing requirements can be effectively met to achieve high throughput. However, because the communication cost may be higher than the computing cost for small smart platform, the energy consumption is still very high. In order to solve this problem, a new CNNs accelerator based on storage and data flow is proposed, which realizes energy-saving CNNs inference acceleration by minimizing data access and maximizing data reuse. This paper implements the accelerator on the Zynq UltraScale+ MPSoC ZCU102 evaluation board, and evaluates the throughput and energy efficiency of the accelerator for typical vgg16 and tiny Yolo benchmark networks. Compared with other accelerators, the our accelerator improves the system energy efficiency by 6.3&#x00D7;, the system throughput by 41&#x00D7;, and the throughput of a single DSP by 7.63&#x00D7;.
Efficient Analysis for Extracting Feature and Evaluation of Text Mining using Natural Language Processing Model

Text mining is the process of extracting the word from the document which is collected from various sources. It is used to transform the text into normalized data for performing machine learning operations. Feature extraction process in the text considers various words in the text then converted into features. Traditional extraction methods extract the features without semantic meaning of the data due to different data formats. Because of the natural language representation, the text mining process uses the document as an input and evaluates the meaning and relationship between the documents. The main objective of the proposed analysis for extracting the features forms the text with various models and parameters. Bag of Words, GloVe, Word2Vec, TF-IDF and Doc2Vec models are analyzed. This analysis considers parameters like cost, number of iteration, learning rate, similarity measure and object relationship for performance measurement. This model uses the Wikipedia data source for analysis and gives raw data with high volume
Terroristic Content Detection using a Multi-scene classification system

The spread of terroristic images on the World Wide Web will bring significant risks to social security. Terroristic image detection technology can help image filtering. Otherwise, due to the lack of high-quality terrorist image dataset, deep learning based recognition methods have not been popularized. Furthermore, characteristics of occlusions and diversity of scenes, automatic approaches to terroristic content detection need to be well-designed. In this paper, a multi-model system is intended to detect for various types of terroristic content. For an input image, the system will locate and identify the terrorist organization leader or flag, determine whether the text information in the image belongs to terrorist slogan, and distinguish a terroristic picture from an ordinary one. For a terroristic image, the system will further detect sensitive objects, e.g. guns or flames, and subdivide the scene of brutal terrorist events. We build a terroristic dataset containing over 41,000 images for experimentation. The experiment result shows the accuracy of the multi-model terroristic content detection system.
PUPy: A Generalized, Optimistic Context Detection Framework for Implicit Authentication

Devices like smartphones and laptops employ some form of user authentication to ensure that access to confidential data by the wrong user is avoided. Implicit authentication aims to limit the number of explicit authentications that a user is subjected to by using passive approaches to authenticate the user. Context detection frameworks aim to reduce explicit authentications by disabling explicit authentication entirely when appropriate. Since explicit and implicit authentication are not mutually exclusive, we can also use context detection frameworks to decide whether explicit or implicit authentication should be used when authentication is required. We present a novel context detection framework, PUPy, that uses sensed context data to infer and make available three values–privacy, unfamiliarity, and proximity–allowing clients of our framework, like authentication services, to better adapt to different contexts. As opposed to existing work, our context detection framework is based on an optimistic approach to context detection. Our assumption is that the absence of data, like the inability to detect nearby people or devices, can be taken as a sign that a context is safe. Such an optimistic approach may provide less security than a pessimistic approach, but provides a significantly improved user experience due to reducing the number of explicit authentications. We provide an Android implementation of the framework, including an API that allows other developers to contribute modules to the system. We also conduct a statistical analysis of our framework based on a large real-world dataset. We find that PUPy compares favourably to existing works, permitting a 77.2% reduction in the number of explicit authentications.
1st International Workshop on Digital Language Archives

This virtual workshop on digital language archives - digital libraries that preserve, curate, and provide online access to language data - seeks to address the growing need. It will explore a broad scope of issues related to digital language archives. This includes challenges and opportunities, strategies and solutions for: facilitating depositing and improving access; information organization, architecture, and retrieval; quality assurance; usability; ethical issues; ways of encouraging reuse of deposited data in research, and education. Workshop is expected to support interdisciplinary collaboration among information professionals, linguists, educators, representatives of language communities (including indigenous and other underrepresented), other interested audiences.
Actuator Response Model of 3DOF Solar Tracking Based on Tetrahedron Sensor

One of the problems of not optimal output power from a solar panel is not being perpendicular to the incidence angle. The shift in the light source's position causes the light's incidence angle to be not perpendicular to the solar panel. Therefore, a solar panel requires a solar tracking system equipped with actuators or gearboxes to maintain the tilt of the solar panel perpendicular to the incidence angle of the light. Actuators play an essential role in moving the solar panels following the displacement of the light source. This paper presents a servo actuator response model used in 3 Degree of Freedom (3DOF) solar tracking. In the 3DOF solar tracking, we use three servo motors as actuators, which are kinematically connected with three parallel arms to align the panel with the light source. Besides that, a light source position tracking sensor is also used, namely a tetrahedron sensor. The method used in the experiment is to direct the incidence angle of the light in 6 states on a tetrahedron sensor to see the actuator's response in tracking changes in the incidence angle of the light. From the test results, the actuator can respond to changes in the direction of the incidence angle of the light with the average absolute deviation level (MAD) of light on the sensor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\leq 1$</tex> Lux.
Shadow Removal using GTA Road Dataset

In this paper, we propose a end-to-end Road Shadow Removal Network (RSRNet) on GTA road scene. Proposed network consists of shadow detection part and removal part. The shadow detection network separately predicts edges and regions of the shadow to accurately predict shadow masks. Given the mask, the shadow removal network removes shadows by predicting parameters of the shadow region between shadow free and shadow. The RSR network effectively removes the shadow while preserving the non-shadow region information. We evaluate proposed network quantitatively and qualitatively to confirm the performance on shadow removal in complex scenes.
Relative Pose Estimation for RGB-D Human Input Scans via Human Completion

Relative pose estimation for human scans enjoys a promising prospect. However, most existing methods mainly focus on indoor or outdoor scenes, requiring considerable overlap between the inputs. We present a technique for estimating the relative pose whatever the overlap between the human RGB- D input scans is. For non-overlapping scans, the insight is to take advantage of the underlying human geometry prior as much as possible. We utilize the implicit function model for human reconstruction, enriching abundant hidden cues for unseen regions, then we use the completed human geometry to get a stable pose estimation. Our evaluation shows that our approach outperforms considerably than standard pipelines in non-overlapping setting, without compromising performance over overlapping input scans.
Watermarking with recovery capability for HGI image compression

The paper proposes a new semi-fragile watermarking system with tamper localization and recovery capabilities, adapted for the HGI image compression method. The method exploits the hierarchical structure of the image when embedding: it replaces a step of post-interpolation residual quantization by a special quantizer based on quantization index modulation (QIM). The proposed watermarking system consists of authentication and recovery parts. Both parts work at different hierarchical levels. Our approach makes it possible not only to detect the area of local distortions but also to recover the original image in this area with acceptable quality. The developed watermarking system, compatible with the HGI compression method, is applicable to protect remote sensing and medical images against malicious distortions.
Real-Time Detection and Classification of Porous Bone Structures Using Image Segmentation and Opening Operation Techniques

When treating bone diseases such as osteoarthritis, rheumatoid arthritis, bone fractures, and osteoporosis, the postoperative recovery of patients becomes more efficient by customizing bone implants with different bone densities. In this study, the non-contact surface profile measurement system based on image segmentation and opening operation techniques is proposed to measure the exterior geometric configurations of porous bone implants. We compile the image processing program for distinguishing the bone surface profiles from the original bone images captured by a CMOS camera. First, the original bone images are converted into gray-scale images. Then, the gray-scale images are normalized by k-means clustering for improving the accuracy of binarizations. The opening operation of erosion and dilation is applied to remove image noises and enhance porous features. The Canny edge detection is used to precisely obtain the contours and pixel sizes of the porous bone features. Finally, the clear surface contours of the porous structures without background noises are acquired by the Gaussian blur technique. The geometric dimensions of artificial porous joints and implants can be inspected by this surface profile measurement system after the metal three-dimensional printing process. The experimental results are excessively helpful to real-time implant corrections and accurate treatments.
Modeling Method of Multi-Dimensional Feature Distributed System

Aiming at the multi-dimensional feature of distributed system in military and related fields, a modeling method is proposed and corresponding graphical representation is given. Based on the "4+1" view modeling method, the method focuses on increasing the modeling ability of each view to multi-dimensional feature. It proposes a multi-dimensional relationship coordination mechanism based on fault type transfer, and a multi-dimensional feature multi view coupling method based on multi view component relationship.It realizes the expansion of the "4+1" modeling method to the multi-dimensional feature modeling ability of the system. Finally, the feasibility and modeling ability of the method are verified by a relevant example.