Reviewerly/exHarmony huggingface dataset:
Bolt looseness detection based on Canny edge detection algorithm

Summary Aiming at current serious problems that the bolt looseness can cause heavy damages and the manual bolt looseness detection is ineffective, this study presents a bolt looseness detection method based on Canny edge detector. First, HSV (hue, saturation, value) color space is used to extract the area of mark bar. Then, after expanding, eroding and median filtering the image, Canny edge detector is used to segment the marker area to extract its edge. Last, the fitted slope of edge is used to judge whether the bolt is loose. The experimental results show that the proposed method has F1 score of 88.37%, contributing to the automated detection of bolt looseness.
Digital Transformation Model to Improve Educational Processes in Higher Education Applying Big Data

The current study proposes a digital transformation model for the career based on information gathered from teachers of specific subjects and students of the Computer Science Career. The goal is to improve educational processes in higher education by utilising Big Data as an analysis tool, which is extremely useful in decision-making and has recently become widely used in a variety of fields. In the first instance, this model establishes how education is currently conducted. As a result, it determines teachers' and students' capacities and interests to face and accept this improvement in the educational process. The model is a proposal for management, teachers, and students to consider the indicators and variables generated by the model that should be improved. Ideally, the model would be implemented in the career to improve higher education and be replicable for other careers.
RepCPSI: Coordinate-preserving Proximity Spectral Interaction Network with Re-parameterization for Lightweight Spectral Super-Resolution

Existing remarkable models for spectral super-resolution (SSR) achieve higher precision at the expense of computations with larger parameters. These algorithms require the heavy memory footprint and sufficient computing power, limiting their practical deployments and applications on portable devices. In this paper, we propose an efficient re-parameterizing coordinate-preserving proximity spectral interaction (RepCPSI) network for lightweight SSR. Specifically, the basic architecture is constituted of several polymorphic residual context restructuring (PRCR) modules to fully explore spatial and spectral contextual information with a multi-branch topology during the training stage. Using a structural re-parameterization scheme, the training-completed network is converted equivalently to a high-efficiency inference-time model, when it runs in the testing phase. To significantly improve the accuracy of SSR with an extra negligible computational overhead, a lightweight coordinate-preserving proximity spectral-aware attention (CPSA) block is developed. Such CPSA block can adaptively emphasize informative signatures and suppress useless ones among intermediate spatial-spectral features, which effectively enables the model to quickly locate features that are beneficial to the network learning and representation. Furthermore, considering the continuity of spectral variation for capturing real-world HSIs, a spectral physical consistency loss (SPCL) is added to the end-to-end network to constrain the changing trend of the spectral curve to be consistent with the ground-truth objects. Finally, our RepCPSI can accomplish a favorable balance between the reconstructed quality and model complexity. Extensive experimental results on six benchmarks demonstrate that our method obtains excellent performance with fewer parameters in terms of quantitative and qualitative measurements over the current advanced SSR approaches.
When will AI misclassify? Intuiting failures on natural images

Machine recognition systems now rival humans in their ability to classify natural images. However, their success is accompanied by a striking failure: a tendency to commit bizarre misclassifications on inputs specifically selected to fool them. What do ordinary people know about the nature and prevalence of such classification errors? Here, five experiments exploit the recent discovery of “natural adversarial examples” to ask whether naive observers can predict when and how machines will misclassify natural images. Whereas classical adversarial examples are inputs that have been minimally perturbed to induce misclassifications, natural adversarial examples are simply unmodified natural photographs that consistently fool a wide variety of machine recognition systems. For example, a bird casting a shadow might be misclassified as a sundial, or a beach umbrella made of straw might be misclassified as a broom. In Experiment 1, subjects accurately predicted which natural images machines would misclassify and which they would not. Experiments 2 through 4 extended this ability to how the images would be misclassified, showing that anticipating machine misclassifications goes beyond merely identifying an image as nonprototypical. Finally, Experiment 5 replicated these findings under more ecologically valid conditions, demonstrating that subjects can anticipate misclassifications not only under two-alternative forced-choice conditions (as in Experiments 1–4), but also when the images appear one at a time in a continuous stream—a skill that may be of value to human–machine teams. We suggest that ordinary people can intuit how easy or hard a natural image is to classify, and we discuss the implications of these results for practical and theoretical issues at the interface of biological and artificial vision.
Optimal jamming method based on grey wolf optimization and orthogonal decomposition

Jamming attacks can significantly degrade the performance of wireless communication systems and plays a very important role in electronic countermeasures and radio resource management. Most of the relevant research in recent years has been conducted to analyze the optimal jamming of digital modulation signal under the condition of constellation diagram without distortion. However, in non-ideal reception conditions, the optimal jamming scheme will no longer be the conventional one due to the constellation distortion. Therefore, aiming at the optimal jamming scheme for non-ideal received multiple quadrature amplitude modulation (MQAM) signals, a grey wolf optimization-orthogonal decomposition (GWO-OD) based optimal jamming waveform construction algorithm is proposed in this paper. Based on the orthogonal decomposition algorithm, this method can freely distribute the power between the in-phase dimension and the orthogonal dimension. The optimal allocation scheme is selected by using the GWO algorithm, and various unconventional jamming modulation signals are constructed. The simulation results show that the proposed algorithm can intelligently select the jamming scheme with the best jamming effect. In the case of constellation distortion, the jamming modulation scheme constructed by this method has a better jamming effect than the conventional scheme, which is better than the existing optimal jamming methods.
A Literature Review on Solar Energy Output Prediction using Various Machine Learning Techniques

This literature survey explores various machine learning and deep learning models used for solar energy forecasting. A total of 15 papers published between 2020-2023 were reviewed. The objective of the survey was to identify the advancements made in the field of solar energy forecasting using machine learning and deep learning techniques. The survey found that most studies focused on using artificial neural networks (ANN) and deep learning models such as long short-term memory (LSTM) and convolutional neural networks (CNN) for solar energy forecasting. Several studies used ensemble models, such as stacking and bagging, to improve the accuracy of solar energy forecasts. Other studies used feature selection techniques and autoencoders to reduce the dimensionality of data and improve the accuracy of predictions. The survey also found that the availability of data is crucial for accurate solar energy forecasting. Many studies used data from meteorological agencies, such as NASA, NOAA, and ECMWF, along with satellite images and sky cameras to generate accurate forecasts. The use of IoT devices and sensors was also explored in some studies to obtain real-time data for improved forecasting. Overall, the literature survey found that machine learning and deep learning techniques have shown great promise in improving the accuracy of solar energy forecasting. The use of ensemble models, feature selection techniques, and autoencoders has improved the accuracy of forecasts. However, the availability of data is crucial for accurate forecasting. The use of IoT devices and sensors can provide real-time data that can be used for accurate forecasting.
Future Trend of Virtual Machines: A Case Analysis of Broadcom's Acquisition of VMware

On May 26, Broadcom, the U.S. communications chip giant, announced that it will acquire cloud services provider VMware for $61 billion in a stock plus cash deal. Broadcom would assume $8 billion of VMware's debt under the terms of the agreement, while its Broadcom Software Group will continue to operate under the name VMware. In tough economic times, it is customary for large technology companies to double their bets: in the 1990s, the old AA transformed itself from a hardware company into a software and services company during the recession, and in the midst of the global economic downturn, Broadcom has made no secret of its intent to expand and grow its software business by accelerating its investments and acquisitions. If Broadcom and VMware can combine their strengths, not only will customers benefit from the integration of product portfolios, but the synergies between these different products could open up new business models.
Data Storytelling to Communicate Big Data Internally – a Guide for Practical Usage 

Purpose: Data is collected from all aspects of our lives. Yet, data alone is useless unless converted into information and, ultimately, knowledge. Since data analysts, in most cases, are not the ones in charge of making decisions based on their findings, communicating the results to stakeholders is crucial to passing on information of data-driven insights. That is where the discipline of data storytelling comes into play. Often, data storytelling is considered an effective data visualization. Creating data stories is a structured approach to communicating data insights as an interplay of the three elements data, visuals, and narrative. Sharing data-driven insights to support better business decisions require data storytellers skilled in the “art of storytelling”.&#x0D; Design/Method/Approach: In this paper, the authors discuss the use of data storytelling in business to communicate data to stakeholders for improving decision-making. The findings are derived from (1) an extensive literature review and (2) a qualitative analysis of 13 expert interviews with people incorporating data storytelling into their daily work within their jobs in international companies.&#x0D; Findings: These interviews revealed the importance of providing a flexible tool to support knowledge sharing for people communicating complex data to internal stakeholders. Combining literature with qualitative research enabled the authors to create the "data storytelling cheat sheet", a guide for practical data storytelling.&#x0D; Theoretical Implications: Theories like the Psychological distance or the idea of the theory of dual processing dual are used to base our research idea on. There was no new theory built in this paper.&#x0D; Practical Implications: One of the results is an implementation systematic cheat sheet that helps practitioners to implement data storytelling in their daily business.&#x0D; Originality/Value: The theory of data storytelling is overwhelming the first time to use and based on an empirical study with experts in the field a guideline for hands on use was developed under a based on a cleanly defined empirical study.&#x0D; Research Limitations/Future Research: The paper focus on internal data storytelling – maybe with external stakeholders it might be slightly different. The results the data communication part in any data analytics project.&#x0D; Paper Type: Empirical&#x0D; JEL Classification: D7, D8
Governance token distributions of Layer-One.X blockchain

Layer-One.X (L1X) blockchain provides the infrastructure layer on which decentralized applications can be created. L1X's governance token distribution is important and requires a proportional balance between token distribution, nurturing user activity and engagement, and financial incentives. In this paper, we present research in progress of L1X tokenomics describing key concepts and implementations including token velocity and value, incentive scheme, and broad distribution. Particularly the economic design of the native token of the L1X blockchain is presented.
A fault diagnosis method of distributed photovoltaic power generation equipment based on big data mining

Aiming at the massive operation and monitoring data of distributed photovoltaic equipment, this paper builds a real-time database and a relational database platform. Based on big data mining technology, the distributed photovoltaic power generation equipment status data and the related data are preprocessed and cleaned. Then the failure mode of distributed photovoltaic power generation equipment is counted and analyzed with the data mining algorithm, and the failure severity, probability level, severity level and failure rate of each faulty subsystem is calculated to find out the failure rule and corresponding treatment measures. At the same time, a new energy remote expert fault diagnosis software system has been developed to realize early warning, fault cause analysis and fault trend analysis of distributed photovoltaic equipment faults.
Identifiable, But Not Visible: A Privacy-Preserving Person Reidentification Scheme

Person re-identification (Person Re-ID) is widely regarded as a promising technique to identify a target person through surveillance cameras in the wild. Nevertheless, person Re-ID leads to severe personal image privacy concerns as personal images are stipulated by laws and guidelines as private data. To address these concerns, this article explores the first solution for building a privacy-preserving person Re-ID system. Specifically, this article formulizes privacy-preserving person Re-ID as similarity metrics of encrypted feature vectors because the underlying operation of person Re-ID is to compute the similarity of feature vectors that are extracted from person images by a machine learning model. However, feature vectors are generally denoted by floating-point numbers. To this end, this article exploits a series of new encoding mechanisms and secure batch computing protocols to encrypt floating-point feature vectors and achieve the underlying operation of person Re-ID. Rigorous theoretical analyses demonstrate that this work achieves person Re-ID without compromising any personal image privacy. Furthermore, the proposed secure batch protocols significantly enhance the performance of privacy-preserving person Re-ID while outputting the same precision as the previous method.
A Comparative Analysis of Different Models of Deep Learning for Prediction of Renal Cell Carcinoma

RCC (renal cell carcinoma) are one of the most common diseases that can be seen in an old age group today. For this problem, the technologists give the CT SCAN, MRI, and ultrasound images, so that the doctor or nephrologist can predict whether the disease is present or not. For this prediction problem, much research has been done on various approaches in the field of AI. Now researchers are making different models for better accuracy. In this paper, we have taken CT SCAN images from Radiopedia and then applied different models of deep learning for prediction. We compared each model for RCC prediction. We find that our model accuracy is 100%. We have also compared our model with other papers. We have concluded that many papers have used CNN because of huge image datasets and their models are failing when small datasets are applied.
Masked Facial Recognition Using Deep Metric Learning

Due to the Covid-19 disease, masked faces identification has become the current challenge. This type of identification is difficult because masks cover noses and mouths, obscuring important features for facial recognition. A deep learning-based model for recognizing masked faces is presented in this paper. We tested our system on a dataset of 2113 images collected from 179 people with and without masks. The obtained results are analysed using various metrics and appear to be motivating.
An Approach to Workload Generation for Cloud Benchmarking: a View from Alibaba Trace

Finding performance bottlenecks through bench-marking is one of the driving forces to improve the resource provision efficiency of cloud computing. Although existing benchmarks have been designed to improve the effectiveness in system performance evaluation, the following problems still exist in these benchmarks due to insufficient consideration of the characteristics of jobs in the production environment: (i) lacking of understanding for the details of workloads composition in the production environment, which reduces the authenticity of the job. (ii) the design of workloads submission patterns lacks quantization and reproducibility, which often relies on a random setting. In our benchmarking, multiple workloads are generated by analyzing and fine-grained matching the composition of workloads in the real production, and a design of workloads submission pattern based on LSTM time series prediction is proposed to simulate the real submission behavior. We finally demonstrate the effectiveness of our work by evaluating the impact of different workloads submission patterns on system performance evaluation.
Development of Solar Power Generation Prediction System using Artificial Intelligence

In this paper, although photovoltaic power generation has recently shown the most remarkable growth in the field of renewable energy worldwide, defects may occur if power outages or manufacturing facilities remain despite the increase in demand and demand for photovoltaic power generation.A machine learning algorithm that predicts the optimal development to solve these problems was obtained through experiments.By implementing the algorithm in the system, it will be able to contribute to reducing operating costs and popularizing it.Neural network, SVM, and deep learning are used as prediction algorithms, and the optimal algorithm is selected by using the root mean square error (RMSE), which is the most used when identifying prediction errors.We propose a predictive model whose prediction rate is expanded by changing the algorithm structure and modifying constants.Then, a defect detection system is developed by applying the predicted results to the domestic regional data.
Implementation of Agile Management in Construction Industry: A Perspective Towards Existing Structures

Construction projects have high complexity in nature, especially construction work during the renovation or any addition to existing structures.Managing techniques during construction projects have not been transformed considerably over the last few decades.More than 80% of construction projects do not meet deadlines.This is due to multiple reasons including but not limited to some inherited nature of project management techniques.Fixed project scope is also a drawback of traditional project management.In addition, user requirements with respect to lightweight construction, structures, and architectural designs have significantly evolved over the same period.Therefore, an amplified gap has been observed between the current managerial project understanding and project operations along with its execution.Although, all the industries including construction engineering have learned from past experiences and have created frameworks for project implementation.The construction sector has been lagging in proper implementation as compared to other areas like Information Technology, Pharmaceutical, and the mechanical design industry.With recently established frameworks at hand, the construction industry has improved its competitiveness in the market.However, the comprehensive, and absolute implementation of a fully integrated approach is still not onpar.Due to this reason, a vast disparity has been noticed between client demands and project executions.This study investigates the implementation of an agile framework as a major requirement in the construction industry.The application of agile management has been analyzed through the evaluation of its impact on existing projects.The present research explores the following possibilities: Can agile management be implemented at various stages in the construction industry, particularly during the renovation phase of existing structures?What adaptations are necessary to use agile methodology in the understanding of process workflow during a new project, or renovation of an existing and operational building?How could agile or its parts be implemented during the design and planning phase of the renovation project to create proper deliverance?When agile could be implemented for multi-department efficiency improvement for renovation projects framework?How can agile management be integrated with upstream or downstream activities to improve project schedules?The results from this study revealed that agile methodology has great potential in the construction industry, whether it be implemented on newly proposed projects or renovation
The Making of Ship Points and Lines Assisted by the C-MAP Application to Practice Students' Collaboration Skills

One of the causes of accidents in the waters is the result of neglecting the point and the line of the ship. To indicate the presence of a ship using C-MAP, one way is to select a point using the save location in the navigator tools. Then when the ship starts sailing, immediately use the save track in the navigator tools to make a line. The minimum number of points that must be made is 5 pieces. The more places stored in a route, the neater the route will be. The method used is descriptive qualitative with data collected in the form of a display. Although this application can be used offline, it demands that the user is always present and only uses this application and not in conjunction with other applications on one device. It would be more suitable if there is more than one user using this app, working in a teams or taking turns.
Discussion on the Optimization Path of College Student Education Management under the Perspective of Internet

With the continuous development of Internet technology, "Internet +" mode has become an important driving force in all fields of society.It not only provides convenience for people's study, life, work and communication, but also promotes the optimization and innovation of traditional education management mode, making the dissemination of information more convenient and efficient.This paper aims to explore the impact of the Internet on the education management of college students, and put forward a series of reform and development suggestions based on the current challenges, in order to better meet the needs of students.