Reviewerly/exHarmony huggingface dataset:
Methodological Strategies to Potentiate the Teaching-Learning Process in Virtual Modality of Business Administration Students

This study is developed to identify the methodological strategies used in the business administration program of a private university located in the region of the Colombian Caribbean Coast to promote the teaching-learning process in virtual modality of students. At the methodological level it is developed as a field research, quantitative, non-experimental research, which data was collected during the first semester of 2021 from a sample of 28 university professors who teach in the administration program of a university located on the Colombian Caribbean Coast, to which a survey was applied consisting of 13 items divided into five dimensions: competences, teaching, pedagogical strategies, meaningful learning, and education quality. The presentation of the results that underpins the authors’ research to diagnose and evaluate data sources is formulated so it can assure efficient support that allows maximum theoretical advantage. The results show important shortcomings within the institution under study related to the recognition of practical elements of meaningful learning in virtual media. It is concluded that education in recent years has undergone extremely important changes; therefore, universities must adapt to these new dynamics with new educational proposals that allow to maintain quality standards in higher education.
Human Action Recognition Algorithm of Non-local Two-Stream Convolution Network Based on Image Depth Flow

Action recognition has wide prospects in the field of video surveillance. At present, the popular Temporal Segment Networks (TSN) has been applied to action recognition and has achieved good results. However, TSN uses RGB images as the input of CNN, which can be easily affected by the illumination, complex backgrounds, and local occlusion, resulting in poor robustness and low recognition accuracy. In this paper, we design a two-stream network for action recognition by adding a depth flow to fuse with the original RGB image, which we term as the depth flow TSN (D-TSN). In addition, to increase the receptive fields of both the spatial and temporal CNN flows for recognizing actions with long-term dependencies, we further propose an N-TSN model by embedding a non-local attention module into the shallow layer of the network. Experiments show that both the proposed D-TSN and N-TSN models achieve superior performance to the original TSN method on the widely used action recognition dataset.
Deep Learning Based Image Semantic Feature Analysis and Image Classification Techniques and Models

The theoretical foundation for the subsequent research is laid. Deep learning methods are widely used to extract and analyze image semantic features, and lay a solid foundation for the research of image classification techniques. Based on the relationship between big data and artificial intelligence technology, this paper firstly outlines the process of generation and development of deep learning, and elaborates the restricted Boltzmann machine model from the perspective of energy model, and then introduces the application principle and specific application scheme of deep belief network model and convolutional neural network model in image classification field in detail, which lays a theoretical foundation for the subsequent research.
An Online Testing Technique for the Detection of Control Nodes Displacement Faults (CNDF) in Reversible Circuits

With the advancements of Quantum Computing and its implementing technologies like NMR, IoN trap, the necessity of constructing fault-free quantum circuit is observed. But in way to ensure fault-free circuit, appropriate testing model to be invoked and here in this paper we present an online testing technique that effectively detects control node displacement fault (CNDF) in quantum circuit designed with reversible gates. Our testing approach involves two steps. In the very first step, the input circuit is transformed to its corresponding testable design by appending additional gates and lines (auxiliary lines). Next, appropriate test vectors are generated and subsequently are applied to find possible node displacement faults in the circuit. The proposed online testing approach is suitable for all type of quantum circuits built with reversible gates (MCT gates). More interestingly, some small changes in the design turn this scheme very effective for ESOP based representation as well. We have extensively tested our approach over a large spectrum of benchmarks and comparison with existing testing algorithms is also summarized in the result tables.
Conclusion and Extensions

Dynamic provisioning is an effective technique for reducing server energy consumption in datacenters, by turning off unnecessary servers to save energy. In this monograph, we design online dynamic provisioning algorithms with zero or partial future workload information available. We reveal an elegant structure of the off-line dynamic provisioning problem, under the cost model that a running server consumes a fixed amount of energy per unit time. Exploiting such structure, we show its optimal solution can be achieved by the datacenter adopting a simple last-empty-server-first job-dispatching strategy and each server independently solving a classic ski-rental problem.
Supervised learning: classification with neural networks

Abstract So far we have discussed unsupervised learning, the inference of the underlying statistical structure (and adequate representations) of a set of data. Another fundamental field of application of machine learning is supervised learning, which is the task of learning an input-output relationship. Supervised learning assumes the existence of a function that associates a label, e.g. dog or cat, to each data point, e.g. an image. In practice, the machine tries to learn this function from a limited set of input-output examples. At the end of learning, one should be able to predict the right class for any input, not necessarily belonging to the data set: does this new image correspond to a dog or to a cat? In this chapter, we address two questions: How can supervised learning and classification be done in practice? How many data does one need to generalise, i.e. to avoid overfitting the examples? We will start by discussing perceptrons, also called support vector machines (SVM), which are neural networks with no hidden layer and have set the state of the art in classification for decades, and we will then turn to more complex neural networks.
Power System State Estimation Based on Improved Strong Tracking Unscented Kalman Filter

Considering the question of low estimation accuracy, poor tracking performance and weak robustness of the unscented Kalman filter (UKF) applied in the state estimation process of power system when the measurement noise is non-Gaussian distribution, an improved method, modified strong tracking unscented Kalman filter(MSTUKF), is proposed. This method adjusts the measurement error covariance matrix by introducing an fading factor and makes the information sequence orthogonal, so that the actual non-Gaussian measurement noise approximately satisfies the Gaussian distribution, and adjusts the Kalman filter gain to reduce the impact on subsequent filtering. The influence of system noise will be reduced via correcting the prediction covariance before the measurement update of UKF. The results of simulation show that compared with the traditional UKF method, when the measurement noise of power system is non-Gaussian, the results of MSTUKF are significantly improved and have better robustness.
LightGBM-RF: A Hybrid Model for Anomaly Detection in Smart Building

Smart building uses sophisticated and integrated building technology and allows numerous IoT systems to interact as well as provide convenience to its users. Unfortunately, smart buildings have become a point of attraction for cybercriminals. Due to the fact that the majority of these IoT devices lack the memory and computing power required for robust security operations, they are inherently vulnerable. IoT devices are consequently vulnerable to various attacks. Therefore, a single attack on network systems or devices can cause serious harm to the security of data as well as privacy in a smart building. This paper presents LightGBM-RF, a machine learning model that accurately detects anomalies in a smart building by utilizing a combination of Light Gradient Boosting Machine and Random Forest algorithms. The model detects anomalies with an accuracy of 99.19%, thereby providing an effective scheme for detecting different attack families, and the potential to significantly improve security in smart buildings.
Intelligent Detection Model for Power Grids Based on Graph Neural Networks

With the rapid development of artificial intelligence and machine vision technology, power grid inspection system based on vision is widely used. However, the power grid intelligent inspection system has the problem of unsatisfactory accuracy of small target detection. To address this problem, this paper proposes an intelligent detection model for power grids based on graph neural networks. Firstly, the VGG16 model is used to construct the trunk feature extraction network. Secondly, the graph relation network between target and background is constructed. Finally, the defect detection model based on graph neural network is constructed. The model proposed in this article is compared with the existing object recognition model in the simulation of the different datasets. The average recognition accuracy of the proposed model is 0.858, which is improved by9.71%.
Ubiquitous Art: Hybrid Aspects of Technology-Oriented Art (Part 1)

Art subgenres defined media art, new media and interrnedia, and various genres of computer digital art including complex artistic environments or projects, is now a universal platform that offers a natural environment for interdisciplinary and hybrid artistic overlaps. Research into the field of the natural sciences will clearly play an irreplaceable role in the art of the coming years. It is highly likely that we will witness an even greater intertwining of the virtual and technological worlds with the original natural environment.
Towards Explainable AutoML Using Error Decomposition

The important process of choosing between algorithms and their many module choices is difficult, even for experts. Automated machine learning allows users at all skill levels to perform this process. It is currently performed using aggregated total error, which does not indicate whether a stochastic algorithm or module is stable enough to consistently perform better than other candidates. It also does not provide an understanding of how the modules contribute to total error. This paper explores the decomposition of error for the refinement of genetic programming. Automated algorithm refinement is examined through choosing a pool of candidate modules and swapping pairs of modules to reduce the largest component of decomposed error. It is shown that a pool of candidates that are not examined for diversity in targeting different components of error can provide inconsistent module preferences. Manual algorithm refinement is also examined by choosing refinements based on their well-understood behaviour in reducing a particular error component. The results show that an effective process should exploit both the advantages of targeted improvements identified using a manual process and the simplicity of an automated process by choosing a hierarchy of the most important modules for reducing error components.
Contrast Enhancement Technique for Efficient Detection of Cloud from Remote Sensing Images

Satellite imaging is essential for various applications, including disaster management and recovery, agriculture, and military intelligence. Clouds are a severe impediment to all of these applications, and they must be customarily identified and removed from a dataset before satellite images can be used for further processing. The quality of the satellite images is affected by various factors during the acquisition process. In this paper, an enhancement approach is proposed to improve the quality of the satellite images to improve the accuracy of the cloud detection process. The enhancement process utilizes the edge information extracted from the input image. The extracted edge information creates a variational map to equalize the intensities by distributing them to occupy the whole dynamic gray scale. Experiments have been performed to validate the efficiency of the enhancement process on the segmented results. The analysis shows that the enhancement process aids in improving the cloud detection, which is indicated by the high values of the performance measures such as accuracy, F1-score, Dice, and Jaccard coefficient compared with the un-processed images from the sentine1-2 remote sensing dataset.
Chinese Electronic Medical Record Retrieval Method Using Fine-Tuned RoBERTa and Hybrid Features

Electronic medical records are an essential basis for doctors in clinical diagnosis and treatment. Accurate and effective retrieval of similar medical records can not only offer great help to clinical decision-making but also bring benefits and convenience to case-based patient research and the unearthing of similar patient groups. However, the existing electronic medical record retrieval model cannot accurately and efficiently retrieve similar medical records. In this paper, we propose a Chinese electronic medical record retrieval method using fine-tuned Roberta and hybrid features. Firstly, RoBERTa pre-trained language model was adopted and finetuned by using real clinical Chinese electronic medical records to make character-level embedding more suitable for Chinese electronic medical records. Then, the character vectors generated from Fine-tuned RoBERTa model are input to BiLSTM and CNN respectively and the features of electronic medical records respectively output from BiLSTM and CNN are combined. Finally, the similarities between combined features of Chinese electronic medical records are calculated to obtain the most similar medical records. A comparative experiment conducted on a real dataset shows that our method can make a progress in the accuracy of Chinese electronic medical record retrieval.
Shape of Pill Recognition Using Mask R-CNN

Convolutional Neural Networks (CNNs) architecture are widely used in machine learning and deep learning, but its application in pill shape detection and recognition is still a challenge. This paper proposes a method to detect and recognize the shape of a pill with the Mask R-CNN network. Through experimenting and checking the results on some traditional and proposed methods to evaluate the efficiency of the construction model. The CURE dataset was used to both train and test. The proposed method achieved 94.13% IoU score.
Comparison of Measured and Modeled Snow Losses for Photovoltaic Systems in Colorado

We quantify measured and modeled snow losses at four utility-scale single-axis tracking photovoltaic (PV) power plants in Colorado. Across 50 site-months of data collected over three winters, the Marion and Townsend snow loss models exhibited similar absolute bias errors, although the Townsend model had lower monthly root mean square error. Based on our results, we recommend that, for PV systems in Colorado and similar climates, the Townsend and Marion model predictions be averaged together to generate the most accurate snow loss predictions for monofacial tracking PV facilities, and that solely the Townsend model be used for bifacial tracking PV facilities.
Performance and analysis of detecting image forgery block processing and determination of pixel for images forensics in video frames

Digital forensics (now and later called digital forensic science) forensic is a branch of science that involves retrieving and testing objects initiated on cardinal procedures. Images and videos need to develop significant data exporters in the current era. Doctor Images is gradually approaching as an approach that affects individuals and changes their feelings and emotions in the light of different occasions. In the present case, the video counterfeit size has expanded with the expansion of malware work on the Internet, which has made it possible for any customer to download and share items, including images, audio, and video on the Internet. Digital image forensics aims to recognize the legitimacy of images by retrieving data about their history. There are two basic problems: the unique source of the image imaging device and the detection of duplicate notes. Image duplication detection techniques are expected to ensure the validity of computerized images lacking past data about the novel image. There are various courses for modifying an image, for example, rearrangement, splitting, and copy moving. These days, due to the promising results of early selections and constantly growing applications, digital image forensics speaks of an attractive selection space for some researchers. This research study is a comprehensive study of image forensic image processing detection and pixel resolution in video frames.
XAI for Communication Networks

Explainable AI (XAI) is a topic of intense activity in the research community today. However, for AI models deployed in the critical infrastructure of communications networks, explainability alone is not enough to earn the trust of network operations teams comprising human experts with many decades of collective experience. In the present work we discuss some use cases in communications networks and state some of the additional properties, including accountability, that XAI models would have to satisfy before they can be widely deployed. In particular, we advocate for a human-in-the-Ioop approach to train and validate XAI models. Additionally, we discuss the use cases of XAI models around improving data preprocessing and data augmentation techniques, and refining data labeling rules for producing consistently labeled network datasets.
Dpnet: end-to-end Aerial Image Segmentation Via Deformable Point Network

Aerial image Segmentation segmentation faces intrinsic foreground-background imbalance and background clutter distraction. To guide the segmentation model to learn more discriminative foreground ability and more invariant back-ground representation features, we design a Deformable Point Network (DPNet). It is an end-to-end segmentation network and consists of a multi-head deformable attention module that simultaneously considers foreground object information and background suppression. Specifically, we first employ a feature pyramid network to aggregate multiple-layer features to handle scale variants. And then, we further investigate deformable convolution to select some representative points for each layer and propose a differential module to implement it automatically instead of traditional dense fusion. Moreover, we incorporate the multi-head mechanism in the feature fusion to focus on the key contents from different representation regions. Experimental results on the representative iSAID, Vaihingen, and Postdam datasets demonstrate that our DPNet achieves competitive performance. Also, the multiple-head deformable attention facilitates the network convergence significantly.
Boundary-Aware Polyp Segmentation Network

Colorectal cancer (CRC) caused by polyps has a high mortality rate worldwide. Accurately segmenting the polyp from colonoscopy images is important for the clinical treatment of CRC. The traditional method of polyp segmentation involves the physician manually marking the location of the polyp, resulting in unreliable segmentation results. The complex structure of polyps, the low contrast with mucosal tissue, and the fact that polyp boundary is usually hidden in the background make the task of polyp segmentation extremely challenging. To address these issues, we propose a boundary-aware polyp segmentation network. Specifically, we first propose an attention-aware location module to accurately identify the primary location of polyp. In order to improve the missing polyp portion in the initial region prediction and to mine the polyp boundary hidden in the background, we propose a residual pyramid convolution. Further, we propose a boundary-guided refinement module for more accurate segmentation in order to use the boundary information provided from residual pyramid convolution for constrained polyp region prediction. Extensive experiments show that our proposed network has advantages over existing state-of-the-art methods on five challenging polyp datasets.
How far are German companies in improving security through static program analysis tools?

As security becomes more relevant for many com-panies, the popularity of static program analysis (SPA) tools is increasing. In this paper, we target the use of SPA tools among companies in Germany with a focus on security. We give insights on the current issues and the developers' willingness to configure the tools to overcome these issues. Compared to previous studies, our study considers the companies' culture and processes for using SPA tools. We conducted an online survey with 256 responses and semi-structured interviews with 17 product owners and executives from multiple companies. Our results show a diversity in the usage of tools. Only half of our survey participants use SPA tools. The free tools tend to be more popular among software developers. In most companies, software developers are encouraged to use free tools, whereas commercial tools can be requested. However, the product owners and executives in our interviews reported that their developers do not request new tools. We also find out that automatic security checks with tools are rarely performed on each release.
Developing Self-Directed Learning Skills of Future Engineers in Digital Educational Environment

Self-directed learning skills are crucial today, as life requires a high level of self-organization, responsibility and the ability to manage everyday activities. The goal of the research is to examine the influence of advanced assessment on developing students’ responsibility as a vital constituent of self-directed learning in digital learning environment, categorize the type of their responsibility, and determine the ways of its boost via assessment in e-learning courses. The survey was conducted among 116 1st and 2nd year students of South Ural State National Research University majoring in engineering. The research has revealed that 2nd year students have promoted comprehensive responsibility compared to 1st year students with situational responsibility. The results of the study prove the efficient impact of advanced assessment in electronic courses aimed at improving student responsibility. The findings of the research will contribute to further development of relevant soft skills requested by self-directed learning of future engineers in digital educational environment.
Introduction to Intellectual Property: Cases and Questions (2022 Edition)

This open access textbook for a survey course on intellectual property law contains 28 chapters, each with introductory material, learning outcomes, and one or two judicial opinions. Each opinion is accompanied by a set of questions to focus the reading and guide further discussion. This set of materials is designed for instructors who seek to have a discussion on one or two cases per class session, supplemented by lectures and active-learning activities. This edition is updated through fall 2022.