Reviewerly/exHarmony huggingface dataset:
Barriers to Technology Adoption Among K-12 Teachers During the COVID-19 Crisis

This chapter aims to explore challenges and barriers to technology adoption faced by secondary school teachers during the COVID-19 crisis. Pandemic-related school shutdowns created a compulsory and rapid transition to online learning for most teachers, facilitating closer examination of technology adoption factors. The main questions asked were: To what extent did teachers’ perceptions of self-efficacy influence their adoption of technology during the COVID-19 pandemic? What barriers commonly impeded teachers’ technology implementation? Interviews with twenty secondary school teachers across multiple states in the United States revealed barriers to technology adoption and factors that impacted technology implementation. An understanding of these factors is critical for building a technological environment that can effectively support future educational use of technology. Findings are also important for understanding the level to which change and uncertainty may impact educational technology needs and opportunities. This study demonstrates the importance of preparation for future crisis events that may lead to widespread virtual learning.
Linear Regression Model for Predicting Virtual Machine Consolidation Within the Cloud Data Centers (LrmP_VMC)

Cloud data centers have been growing rapidly in both capacity and number to meet the ever-increasing demands for the highly responsive computing and its massive storage space. The data centers of this kind utilize a large amount of electricity which in turn leads to high operating costs and CO2 emissions from these cloud data centers. This consolidation of virtual machines involves live migration of VMs and thus the ability of transferring a virtual machine (VM) between the physical servers with a close to zero down time. In our proposed model (LrmP_VMC), we are going to use Roulette–Roulette Wheel Method, in which the virtual machine selects a certain instance type and the physical machine using this Roulette Wheel Selection Mechanism. We put forward an approach where the allocation of virtual machines is done, but the genetic approach reduces energy consumption and consolidates the whole process of virtual machine placement. As per the results, the genetic approach gives off better results when there are a greater number of virtual machines involved in the allocation process, and it is said that LrmP_VMC approach is 15% more efficient with higher number of VMs compared to Random Forest Method on the basis of accuracy when a large number of inputs are taken.
A Concept for QoS Management in SOA-Based SoS Architectures

This paper identifies discrepancies in the sole use of classic QoS calculation in order to determine the quality of a service within the Arrowhead framework, as a representative of SOA-based system of systems architectures. To substantiate the research, several scenarios from an industrial manufacturing environment are used. New key requirements for a QoS management system in SOA-based SoS architectures are identified and based on them a novel concept for an enhanced QoS management system within such architectures is proposed.
Facial Expression Recognition from Low Resolution Facial Segments Using Pre-trained Networks

Understanding the different categories of facial expressions is crucial to comprehend human cognition as well as for designing these computational interfaces. Deep learning is an exciting area in machine learning and has seen an exponential rise in recent years in various applications. In this paper, we use deep learning networks to identify facial expressions in low resolution images. The major contribution of this paper is in analysing the effect of spatial resolution reduction on the facial expression recognition rates. There is not much work reported in this area. Detailed analysis to establish a relationship between recognition rates and drop in spatial resolution is carried out. We have successfully demonstrated that even with extreme spatial resolution reduction in full face images accurate facial expression recognition can be achieved using the proposed frameworks, and there is negligible effect on recognition rates. In addition to full face images, we have also successfully demonstrated that the proposed networks work well for various segments of the face. The results obtained stay consistent even for extreme reduction in spatial resolution of these facial segments. In order to evaluate performance of the proposed frameworks, rigorous sets of experiments are presented in the paper. We can conclude that even when human computer interface systems have access to entire fontal face images, only a small segment of the image is required for accurate facial expression recognition, and the results obtained are consistent even under extreme spatial resolution reductions.
Unsupervised Data Drift Detection Using Convolutional Autoencoders: A Breast Cancer Imaging Scenario

Imaging AI models are starting to reach real clinical settings, where model drift can happen due to diverse factors. That is why model monitoring must be set up in order to prevent model degradation over time. In this context, we test and propose a data drift detection solution based on unsupervised deep learning for a breast cancer imaging setting. A convolutional autoencoder is trained on a baseline set of expected images and controlled drifts are introduced in the data in order to test if a set of metrics extracted from the reconstructions and the latent space are able to distinguish them. We prove that this is a valid tool that manages to detect subtle differences even within these complex kind of images.
BrainFuse: Self-Supervised Data Fusion Augmentation for Brain MRI’s via Frame Interpolation

Deep learning has been successfully applied to medical image analysis tasks, however, the field faces unique challenges related to data scarcity. Medical datasets tend to be considerably smaller than what is accessible in the general computer vision domain. Recent state-of-the-art data augmentation techniques take advantage of combining multiple samples to create new ones. In this paper, we present such a technique and show that it can significantly improve brain age prediction from Magnetic Resonance Imaging (MRI) data. We use self-supervision to learn spatially coherent brain MRI features, and utilize that to fuse together different brain samples.
REST Security

With SPAs (single page web applications), it's impossible to reliably enforce security using the security constraints defined in WEB-INF/web.xml. The reason for this is that these security constraints have been especially tailored to HTTP requests and responses of web pages, not JSON snippets. Also, although annotations exist to mark REST classes and methods as subject to security checks, REST implementations are not required to actually perform those checks. The JAX-RS specification for RESTful services just does not handle security.
A Unified Framework for Knowledge Inference Based on Heterogeneous Graph Neural Networks

Heterogeneous graph neural networks show powerful transfer aggregation in multi-relational directed graphs, which play a role in knowledge inference and recommendation tasks. Both of these tasks are applications of heterogeneous graph neural networks on knowledge graphs and have a commonality in many aspects. However, there are no relevant articles for comparative analysis. So we analyze the commonality and critical factors of the two tasks through many literature reviews and propose a unified framework for knowledge inference based on heterogeneous graph neural networks. We conduct experimental comparisons on the WN18RR dataset to verify the key factors that make it effective in knowledge inference, and the results prove the correctness of the unified framework. We draw three conclusions as follows. Firstly, taking explicit modeling relationships is better than invisible modeling relationships; Secondly, taking three directions modeling is better than one-direction modeling; Lastly, considering node attention is better than not considering node attention.
Clustering for Global and Local Outliers

Outlier detection is a fundamental data analysis task. Identifying outliers from data is a challenging task. In this paper, we decompose the data using hierarchical clustering to identify potential outliers in a hierarchy. Such an approach gives us an understanding of likely outliers at multiple levels, which can be further validated using a standard outlier technique. In this work, we use the isolation forest outlier detection technique as the post-processing step for better generalization of our approach. Experiments on real data show that the proposed approach is good enough for the data decomposition into likely outliers. In addition, this approach helps us to understand two levels of hierarchy of outliers, which are crucial in many real-world applications. A numerical experiment on a real dataset is provided to illustrate the applicability of the proposed algorithm.
Emotionally Intelligent Image to Audible Prompt Generation for Visually Challenged People Using AI

There has been an increased interest in synthesizing expressed speech over recent years, mainly due to advances in artificial intelligence and human-computer interaction involving voice command-based systems. This in turn has precipitated great interest in automated captioning of computer images which can help the visually impaired. We present a deep convolutional neural network (CNN)-based technique for generating audible captions from plaintext with pre-processing the image to caption. The pre-processing method utilizes the strength of recurrent neural networks (RNN) along the lines of the long short-term memory (LSTM) method. A generative end-to-end TTS model for captioning images is presented along with an investigation on the optimization of plaintext caption to audible prompt synthesis with the help of a labeled emotions dataset to render the caption in verbal form with an emotion. We will also try to check if it is possible to adapt our model to have emotional TTS by fine-tuning the neutral TTS model with a small emotional dataset.
Empirical Analysis of Hybrid Classical Variational Quantum Neural Networks for Target Classification from SAR Data

The advancements in the field of quantum machine learning and the ability of quantum-enhanced networks to acquire a higher effective dimension are promising for remote sensing applications and synthetic aperture radar (SAR) data. This paper aims to incorporate variational quantum circuits with existing classification-based deep learning state-of-the-art architectures. Tests are proposed by using ResNets, DenseNets, and a SqueezeNet-based convolutional neural network. Various dressed quantum circuits associated with these encoders are created and tested for novel applicability analysis. A famous publicly available X-Band SAR image-based dataset called moving and stationary target acquisition and recognition (MSTAR) is utilized to demonstrate image-centric target classification. The utility of quantum-enhanced and hybrid classical-quantum transfer learning models is provided.
Research on Data Security Access Control Mechanism in Cloud Computing Environment

Cloud computing is an Internet-based virtualization application model that has been in development for these years. Cloud computing technology provides users with a large number of virtualized computing and storage resources. As a continuously developing emerging technology, some theoretical and technical systems have been formed in countries all over the world. However, the data security in the virtualization environment of cloud service providers has attracted extensive attentions of users over the years. In the cloud computing environment, data access control mainly includes two aspects: one is the safe transmission of required information resources in the network, and the other is the reasonable control of user permissions. In this paper, we analyze the cloud computing platform based on OpenStack and the related data security access control model is proposed. The data access control process is studied where trust of the cloud user is evaluated. An access control system based on the studied model is then designed whose architecture and its components are described. The proposed model and system is expected to provide a new technical method for data security protection so as to lower the risks of data leakage.
Cryptoasset Investment Types

For any investment, buying at a low price and selling at a higher price is one form of return, capital gain. However, it is often not the only way to benefit from the investment. Equity investments (stocks) typically also deliver returns in dividends. Fixed-income investments (bonds) mainly deliver returns through interest. Real estate investments can deliver returns through rent.
Blockchain Basics

Understanding cryptoassets involves understanding blockchain. The first cryptoasset, Bitcoin, is only possible through blockchain, and blockchain was developed to make Bitcoin possible. While alternative technologies were subsequently developed to make cryptoassets technically possible, blockchain remains the core behind most cryptoassets.
Beyond the horizon: immersive developments for animal ecology research

More diverse data on animal ecology are now available. This "data deluge" presents challenges for both biologists and computer scientists; however, it also creates opportunities to improve analysis and answer more holistic research questions. We aim to increase awareness of the current opportunity for interdisciplinary research between animal ecology researchers and computer scientists. Immersive analytics (IA) is an emerging research field in which investigations are performed into how immersive technologies, such as large display walls and virtual reality and augmented reality devices, can be used to improve data analysis, outcomes, and communication. These investigations have the potential to reduce the analysis effort and widen the range of questions that can be addressed. We propose that biologists and computer scientists combine their efforts to lay the foundation for IA in animal ecology research. We discuss the potential and the challenges and outline a path toward a structured approach. We imagine that a joint effort would combine the strengths and expertise of both communities, leading to a well-defined research agenda and design space, practical guidelines, robust and reusable software frameworks, reduced analysis effort, and better comparability of results.
Overcoming Catastrophic Forgetting in Lane Detection

With the development of autonomous driving in recent years, algorithms for lane detection emerge in endlessly. In this paper, lane detection has improved the performance of lane detection and overcome the catastrophic forgetting problem of machine learning[20]. The study of classical physics describes a static world, while physiology focuses on evolutionary systems related to the time dimension, that is, the non-stationary world. We propose ways of lifelong learning to alleviate the catastrophic forgetting of machine learning in lane marking detection, because lifelong learning can accumulate experience over time. In real and complex road conditions, lane detection needs to take into account the data imbalance in multiple scenarios, including the dynamic changes in the number and type of lane. By comparing with traditional deep learning, we introduced the most advanced lifelong learning strategies (EWC, MAS, SCP), and applied them to the advanced backbone network ENet. A large number of experimental results under the CULane datasets prove that our model significantly improves the average performance of models, which adopts the methods of lifelong learning in a variety of complex real-world scenarios. (Abstract)
Design and implementation of Handwritten Character Recognition System Based on Deep Learning

With the development of technology, it is more and more common to store data in electronic form. In order to facilitate the conversion of paper forms into electronic information storage, this project has implemented a handwritten Chinese character recognition system for table pictures, which reduces the pressure of manual input of handwritten Chinese character information. This project preprocesses the scanned image, extracts the handwritten text information from the image, and cuts the handwritten Chinese characters. In order to improve the accuracy of single character cutting, this project uses the combination of vertical projection and single character length width ratio to determine the cutting position. ResNext50 model was used for model training, and the two models were trained to recognize numbers, letters and handwritten Chinese characters respectively. The accuracy of the Chinese character recognition model is more than 90%, and the accuracy of the number and letter recognition model is 98%. According to the input content, the proper noun list is used to correct the recognition results and improve the accuracy of recognition. Find the specific noun with the highest similarity by calculating Levenshtein distance. The method proposed in this paper effectively completes the separation and recognition of handwritten Chinese characters in table images.
MAPPO method based on attention behavior network

In the research of multi-agent reinforcement learning algorithm, in order to accelerate the convergence of algorithm strategies, the agent actor network in MAPPO is improved by drawing on the attention mechanism in the Transformer model. The feature extraction method of the agent actor for environmental observations is changed from linear network learning to high-dimensional features of data extracted through attention mechanism to learn the sequence information between values under fixed attribute areas. The correlation of its own information, friendly information, and environmental information in the observed values is improved, and a clearer policy distribution is obtained, so as to improve the training efficiency in a fixed complexity environment, accelerate the strategic convergence of the agent, and obtain a reasonable multi-agent strategy. The modified multi-agent method is trained and tested in the SMAC multi-agent reinforcement learning environment. The final experimental results show that compared with the MAPPO algorithm, the strategy can converge faster, and the game strategy obtained by the final convergence can achieve an average win rate of 95%, which meets the performance requirements of the multi-agent algorithm.
An Anchor-free Detection Framework for Object Detection in Autonomous Driving Scenarios

As the number of car ownership is rising year by year, traffic congestion and traffic accidents are increasingly frequent, and in order to solve such problems, more and more autonomous driving systems based on object detection are proposed, which mainly contain tasks such as road detection, traffic sign detection, and vehicle-pedestrian detection. Among them, detecting key targets such as vehicles and pedestrians is one of the most fundamental and important research branches in autonomous driving scenarios. The existing mainstream research is mainly based on object detection algorithms with predefined anchor frames. Although better results have been achieved, a large number of hyper-parameters need to go to a manual setting, which leads to some limitations in the performance, robustness and generality of the model in complex scenarios. In this paper, an anchor-free based target detection algorithm is applied to investigate the detection of targets such as pedestrians, vehicles and other targets on the road, which has better robustness and generality due to its simple structure and avoidance of hyper-parameters and complex operations associated with anchor frames, and ultimately faster detection rate and higher accuracy. Through experiments on the KITTI dataset, it is demonstrated that the method in this paper has a faster detection rate while the mean confidence score (mCS) can reach 69.1%, and detection performance is significantly better than other previous models.
BCH Encoding and Decoding Algorithm and Digital Demodulation Technology based on DVB-S2 Standard

By comparing the research status of BCH codec based on DVB-S2 standard at home and abroad, it is pointed out that it is important to realize high-speed and small size BCH parallel codec circuit to improve system performance. Finally, two kinds of data synchronization techniques using unified time reference between receiver and transmitter are introduced, which are symbol synchronization and frame synchronization.
Qos-Sure: Qos-Assurance AutoScaling of Sharing GPU For DNN Inference in Multi-Task

With ensuring Quality-of-Service (QoS) of inference tasks, how to improve the utilization of GPU by sharing GPU resources in data centers, is very important yet challenging. The key challenge is that when the inference and training task are co-located on GPU, the interference performance because of resource competition can influence QoS of inference tasks. In addition, the stochastic and bursty inference loads will also bring great challenges to QoS due to coarse-grain resource allocation. To address these open problems, we first present a novel method of sharing GPU resources, QoS-Sure, based on the prediction model to predict the performance interference between tasks and the resource demand of the inference task in the next moment. Moreover, our evaluation on real-world datasets shows that QoS-Sure reduces time duration of inference task by about 20s and improves GPU utilization by about 50% compared to the state-of-the-art approach.
A Study on Telecommunication Network, Internet, and Internet of Things

This paper elaborates on the history of the telecommunication networks, the Internet, and the Internet of Things, as well as their protocols and other characteristics. In addition, the connection between these three types of networks and the trend of their subsequent development are discussed. Most importantly, we have analyzed in depth the underlying principles of the development of the three networks, such as the social needs, the system architecture, and the network operation. This paper is the results of our pondering on the future and we hope the insights would turn out to be useful for other researchers.
Semi-supervised Dense Object Counting via Mutual Consistency Learning

By reason of its wide range of applications, object counting is increasingly recognized as a prominent branch of computer vision. Moreover, object counting has an active role in poultry statistics and fruit yield prediction in the agricultural field. However, in a dense target counting study, annotating a large-scale object counting dataset at the pixel level is labor-intensive and time-consuming. To alleviate dependence on labeled data and take advantage of unlabeled data, we take dense crowd counts as the subject of our study and propose a novel mutual consistency learning model (MCL) for semi-supervised crowd counting. This model includes two networks with the same structure but different initialization. By imposing consistency constraints, the two networks can be encouraged to interact effectively in high-density regions. We further introduce an observation-based random image recombination (RIR) approach, which splits an image into several equal patches, and then recombines these patches into an image with the same shape as the initial image without changing the population. The RIR disrupts the scene spatial order in the image, enhancing the model's ability to adapt to a deceptive environment and improving the ability to retrieve crowd characteristics. The presented method delivers comparable performance on three challenging benchmarks compared with other semi-supervised methods. The outcomes also indicated that the suggested method could achieve excellent results and provide an algorithmic basis for future applications in agriculture, such as sheep counting, duck counting, fruit counting, etc.