Reviewerly/exHarmony huggingface dataset:
The efficacy analysis method of input–output for substations based on LCC theory

Along with the deepening of the power system reform, the input and output evaluation and analysis of asset became more and more important. Arounding the goal of comprehensive optimization of safety, efficiency and cost throughout the life cycle of assets, this paper constructed a quantitative evaluation model for asset input–output analysis, and put forward an input–output analysis method combining the boston matrix method and the equivalent analysis method. Actual substations’ input cost and output efficiency analysis verified the method feasibility, which could provide decision-making support for optimizing power grid asset management strategies and enhancing asset value operation benefits.
Robust Object Detection with Multi-input Multi-output Faster R-CNN

Recent years have seen impressive progress in visual recognition on many benchmarks, however, generalization to the out-of-distribution setting remains a significant challenge. A state-of-the-art method for robust visual recognition is model ensembling. However, recently it was shown that similarly competitive results could be achieved with a much smaller cost, by using multi-input multi-output architecture (MIMO). In this work, a generalization of the MIMO approach is applied to the task of object detection using the general-purpose Faster R-CNN model. It was shown that using the MIMO framework allows building strong feature representation and obtains very competitive accuracy when using just two input/output pairs. Furthermore, it adds just 0.5% additional model parameters and increases the inference time by 15.9% when compared to the standard Faster R-CNN. It also works comparably to or outperforms the Deep Ensemble approach in terms of model accuracy, robustness to out-of-distribution setting, and uncertainty calibration when the same number of predictions is used. This work opens up avenues for applying the MIMO approach in other high-level tasks such as semantic segmentation and depth estimation.
Convolution-Based Encoding of Depth Images for Transfer Learning in RGB-D Scene Classification

Classification of indoor environments is a challenging problem. The availability of low-cost depth sensors has opened up a new research area of using depth information in addition to color image (RGB) data for scene understanding. Transfer learning of deep convolutional networks with pairs of RGB and depth (RGB-D) images has to deal with integrating these two modalities. Single-channel depth images are often converted to three-channel images by extracting horizontal disparity, height above ground, and the angle of the pixel's local surface normal (HHA) to apply transfer learning using networks trained on the Places365 dataset. The high computational cost of HHA encoding can be a major disadvantage for the real-time prediction of scenes, although this may be less important during the training phase. We propose a new, computationally efficient encoding method that can be integrated with any convolutional neural network. We show that our encoding approach performs equally well or better in a multimodal transfer learning setup for scene classification. Our encoding is implemented in a customized and pretrained VGG16 Net. We address the class imbalance problem seen in the image dataset using a method based on the synthetic minority oversampling technique (SMOTE) at the feature level. With appropriate image augmentation and fine-tuning, our network achieves scene classification accuracy comparable to that of other state-of-the-art architectures.
Flexible Table Recognition and Semantic Interpretation System

Table extraction is an important but still unsolved problem. In this paper, we introduce a flexible and modular table extraction system. We develop two rule-based algorithms that perform the complete table recognition process, including table detection and segmentation, and support the most frequent table formats. Moreover, to incorporate the extraction of semantic information, we develop a graph-based table interpretation method. We conduct extensive experiments on the challenging table recognition benchmarks ICDAR 2013 and ICDAR 2019, achieving results competitive with state-of-the-art approaches. Our complete information extraction system exhibited a high F1 score of 0.7380. To support future research on information extraction from documents, we make the resources (ground-truth annotations, evaluation scripts, algorithm parameters) from our table interpretation experiment publicly available.
Problems of training engineering personnel in Russia

Mechanical engineering as an industry needs modernizing to maintain a sufficient level of competitiveness. The existing education system is not able to fully provide enterprises with qualified staff. This is due to a gap between the level of knowledge obtained in an educational institution and the enterprise real needs.&#x0D; To bridge the gap, more attention should be paid to personnel training and retraining. The article identifies the problems of educating future engineers, proposes an appropriate set of measures providing for a three-level transformation: at the level of government, regions and enterprises; considers a model for training staff of a machine-building enterprise and also suggests building regional training and internship centres. Such centres will make it possible not only to train workers, to conduct internships for students and employees, but also to provide internships for future engineers, to acquaint them with the production specifics. Such internships will ensure young specialists’ consolidation, allow them to interact more effectively with enterprises, and create for them an additional incentive to stay in the region. Furthermore, a favourable image of a machine-building worker, engineer, innovator, and ordinary worker should be formed.
Testing of a Deep Learning Model Providing Monocular Depth Estimation on Mobile Devices via Web Service

Augmented reality applications running on smartphones or tablets are becoming increasingly common. It is crucial to extract the physical structure of the scene perceived by the device camera in these applications. In such applications, employed in education, navigation and obstacle notification, the distance information between the device camera and the object must be derived and processed with sufficient accuracy and speed. In this study, the deep learning model, named "From Big To Small (BTS)", with superior performance metrics in depth extraction according to the literature reviews, was transformed into a web service and tested on an Android phone. Thus, a deep learning model with a high computational cost will be available on an Android device with average processing power. Test results were examined, and improvements were discussed.
Social media penetration, party politics and elections in Tanzania Emerging practices and challenges

Tanzania has witnessed an increased use of social media in political party campaigning over the last decade. Use of social media was nonetheless curtailed by a changing techno-political framework regulated by acts relating to cybersecurity and statistics. This study was guided by two hypotheses: firstly, that despite restrictive cybersecurity laws, social media in recent years has been effectively institutionalised as a new civic cyberspace for political party campaigns during elections. Secondly, increasing use of social media in elections has had a transformative effect on the way party structure was organised to conduct political mobilisation, promote party ideology and both inter- and intra-party interaction, and for fundraising. The study interviewed party members and leaders from five political parties which participated in the 2015 and 2020 general elections and concluded that social media had a transformative effect on core political party campaign activities.
The Influence of UI UX Design to Number of Users Between ‘Line’ and ‘Whatsapp’

In this modern era, many applications have been used to communicate long distances, both for telephone and chat. UI / UX design in applications affects users, as most of the younger generation prefers an attractive, innovative, and many-choice design, while most of the older generation prefers a simple design appearance. In this research, we discuss the effect of UI / UX design on the number of uses between the LINE and Whatsapp applications. Then we conducted a survey with short questions using Google Forms, where 44 respondents participated in our research, 34 (77.3%) respondents out of 44 respondents used Whatsapp more often. Of 34 respondents, 23 (67.6%) of respondents chose a score of 5 which stated that the WhatsApp application was easy to learn and easy to use, 20 (58.8%) respondents were comfortable using the WhatsApp application by choosing a score of 5, 21 (61.8%) respondents did not experience difficulties in using the features on the Whatsapp application by choosing a score of 1 (not confusing), 14 (41.2%) respondents like the appearance and color selection on the Whatsapp application with a score range of 4–5. The results of the survey show that a good UI / UX design has an effect that can increase the number of users of the application.
Check-In Location System Using NFC Technology

Abstract In COVID 19 pandemic, new norms have been introduced, including, to leave a record when checking-in to a particular place. This new norm is regulated in order to trace locations that have been visited by someone with positive COVID-19. This paper presents a work on development of check-in location system. The system implemented Near Field Communication (NFC) technology which is mainly utilized two NFC compatible devices where an identification card (IC) is used as a smart object (NFC tag) and the NFC detector as an NFC reader to exchange information. Testing has been conducted in order to observe the system performance, and, the results showed that this system is able to collect information of users who were coming to premise. Also, the information can be checked by authority in order to track someone with positive COVID-19. As conclusion, this system can be an alternative to MySejahtera App.
Attribute-Based Adaptive Homomorphic Encryption for Big Data Security

There is a drastic increase in Internet usage across the globe, thanks to mobile phone penetration. This extreme Internet usage generates huge volumes of data, in other terms, big data. Security and privacy are the main issues to be considered in big data management. Hence, in this article, Attribute-based Adaptive Homomorphic Encryption (AAHE) is developed to enhance the security of big data. In the proposed methodology, Oppositional Based Black Widow Optimization (OBWO) is introduced to select the optimal key parameters by following the AAHE method. By considering oppositional function, Black Widow Optimization (BWO) convergence analysis was enhanced. The proposed methodology has different processes, namely, process setup, encryption, and decryption processes. The researcher evaluated the proposed methodology with non-abelian rings and the homomorphism process in ciphertext format. Further, it is also utilized in improving one-way security related to the conjugacy examination issue. Afterward, homomorphic encryption is developed to secure the big data. The study considered two types of big data such as adult datasets and anonymous Microsoft web datasets to validate the proposed methodology. With the help of performance metrics such as encryption time, decryption time, key size, processing time, downloading, and uploading time, the proposed method was evaluated and compared against conventional cryptography techniques such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). Further, the key generation process was also compared against conventional methods such as BWO, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results established that the proposed method is supreme than the compared methods and can be applied in real time in near future.
Challenges of Online Education among University Students, Saudi Arabia

Introduction: Online education during COVID 19 pandemic is witnessing significant growth, particularly in higher education. The aim of the study will explore the challenges that are being encountered related to online education for nursing students.Methods: A correlational study using cross-sectional approach was adopted among 150 participants selected through convenient sampling technique. Data was collected through electronic survey from undergraduate students participated voluntarily studying in College of Nursing, King Khalid University, Abha, Saudi Arabia. The survey forms (Rating Scales on satisfaction and barriers of online education) were used to collect the data. Descriptive and inferential statistics were used to analyze the data by using SPSS version 24.0.Results: The results revealed nearly 92% of participants were highly satisfied with online education. The mean scores and SD related to satisfaction of online education was (37.50+ 8.50). The global perceived barriers mean scores and SD were (35.19+11.19) which indicates that they were able to quickly adopt new strategies by overcoming the limitations.Conclusion: The researcher concludes with the fact that study findings support that, students had positive perception on benefits of online learning by overcoming the limitations. In conclusion, online teaching and learning can be effectively incorporated in future also using blended approach.
Semi‐supervised image super‐resolution with attention CycleGAN

Single-Image Super-Resolution (SISR) has always been an important topic in the field of image processing, which attempts to improve the image resolution and is of great significance in practice. Recently, SISR has made substantial progress aided by deep learning (DL), which has demonstrated impressive potential in many low-level tasks. In the current DL-based SISR approaches, most of them are based on supervised learning. However, in the real world, only low-resolution (LR) images with unknown degradation are provided, which limit the application of current supervised models. To mitigate this problem, in this paper, a two-stage semi-supervised SISR method called SRAttentionGAN, is proposed. First, an upsampling network SRResNet, which is pre-trained in a supervised manner, is employed to scale the LR image to the desired size. Then, the upsampled results are fed into our improved unsupervised CycleGAN framework, which does not need paired samples, to obtain sharper and more realistic super-resolution (SR) images. Specifically, in the improved CycleGAN part, an attention-guided generator is proposed to perceive the discriminative semantic parts between the source and target images, to avoid the impact from low-level information. It also prevents the overall color tone from being changed. A multi-scale discriminator is also adopted to further rich texture details. The effectiveness of the proposed SRAttentionGAN experiments is validated using four benchmarks (Set5, Set14, Urban100, and BSDS100) in both quantitative and qualitative aspects. Compared with the state-of-the-arts, the results are visually promising and show competitive performance in perceptual metrics, Natural Image Quality Evaluator (NIQE) and Perception Index (PI), which have better agreement with the human visual perception.
LRPBFT-Algorithm-Based Consortium Blockchain Traceability Scheme of Dangerous Goods in Commercial Ports

As the increasing throughput of commercial ports and the carrying capacity of large-scale container terminals, the phenomena such as dangerous goods' wrong declaration and false reporting have become increasingly prominent. Intelligent supervision of dangerous goods is essential. Aimed at the problems of poor scalability, high communication overhead, and low efficiency of the existing Practical Byzantine Fault Tolerance (PBFT) algorithm in the consortium blockchain's scenario, we design a hybrid consensus algorithm Link-Identification-Based Raft and PBFT Hybrid Consensus Mechanism (LRPBFT). Compared with the existing traceability platform of dangerous goods based on the blockchain, the LRPBFT algorithm enables the platform to have less delay, greater throughput and higher security while uploads information to the chain.
Change Propagation Path: An Approach for Detecting Co-Changes Among Software Entities

The failure in propagating software changes properly during the maintenance process is one of the main causes of defects and poor software performance. It also increases the time consumed while searching for related changes manually. In addition, incomplete changes increase the cost of the maintenance process, by hiring highly paid senior developers, to give consultations for maintaining the software systems. In this paper, we present an approach called Change Propagation Path (CPP), which is a data mining method that aims at helping developers to predict software complementary changes and perform changes correctly. The CPP approach employs the frequent pattern analysis technique to be used on historical data stored within software repositories. We have designed a web-based tool called Wide Assisting and Leading (WALead) and conducted an experimental study as a proof of concept and to validate the proposed approach. The WALead tool was designed to support developers remotely and through any platform. The tool has been tested in terms of its effects on the maintenance process, and to prove the feasibility of the CPP approach.
Data reconciliation frameworks for dynamic operation of hybrid renewable energy systems

Hybrid renewable energy systems (HRES) are a nexus of various renewable energy sources that have been proposed as a solution to circumvent various issues of renewable energy systems when installed in isolation. During the operation of an HRES, efficient demand-side management of energy and real-time power trading requires accurate estimation of real-time variables for each sub-component. Generally, these variables are corrupted by measurement errors. To address this issue, in this study, we present various frameworks for reconciliation strategies that can be used to rectify the inconsistencies in the sensor measurements of HRES. Specifically, in this study, we evaluate the efficacy of various static and dynamic reconciliation strategies such as Regularized Particle filter (RPF), Ensemble Kalman filter (EnKF), and Extended Kalman filter (EKF) of a candidate HRES system with a solar panel, a fuel cell, and an electrolyzer. Proposed frameworks are evaluated using various simulation-based validation studies. To this end, we have considered different operational scenarios, namely, (i) single-rate sampling, (ii) multi-rate sampling, and (iii) sensor outage, to make the study comprehensive. Simulation results indicate that RPF yields the best estimation accuracy for all three operational scenarios with a performance improvement of 75% from EKF and by 50% from EnKF, with only a fractional increment in computational time.
The Swedish FrameNet++

Large computational lexicons are central NLP resources. Swedish FrameNet++ aims to be a versatile full-scale lexical resource for NLP containing many kinds of linguistic information. Although focused on Swedish, this ongoing effort, which includes building a new Swedish framenet and recycling existing lexicons, has offered valuable insights into general aspects of lexical-resource building for NLP, which are discussed in this book: computational and linguistic problems of lexical semantics and lexical typology, the nature of lexical items (words and multiword expressions), achieving interoperability among heterogeneous lexical content, NLP methods for extending and interlinking existing lexicons, and deploying the new resource in practical NLP applications. This book is targeted at everyone with an interest in lexicography, computational lexicography, lexical typology, lexical semantics, linguistics, computational linguistics and related fields. We believe it should be of particular interest to those who are or have been involved in language resource creation, development and evaluation.Open Access under the CC BY-NC-ND 4.0 license.
Optimizing the Uncertainty of PPM on Small Batch of Quality Data

PPM(Parts per Million) is a quality evaluation standard for the defect rate per million products. It has been widely used in many industry fields, such as automobile manufacturing. In the field of weapon equipment manufacturing, the number of products is under the condition of small batch. PPM shows large uncertainty on such quality data. In this paper, we propose a novel PPM evaluation method on small batch of quality data to approximate PPM results on millions of quality data. We compute the similarity of PPM data by clustering PPM data and a well-defined distance metric. Then, we improve the accuracy of PPM by extracting useful information of similarity PPM data. In our experiments, we show that our method can decrease the computational error of traditional PPM computation method by at least 30% under the condition of small batch.
The Effects of Rain Intensity and Water Elevation with Energy Productivity of Kracak Hydro Power Plant, Bogor Regency – West Java

Abstract This paper was aimed to know the effects of rain intensity and water elevation with energy productivity of Kracak Hydro Power Plant, Bogor Regency – West Java. The method used multiple regression analysis with a quantitative approach to describe the effects of rain intensity and water elevation with energy productivity of Kracak Hydro Power Plant. Based on the data, the highest rain intensity was in February of 13,35 mm with water elevation of 346,09 cm and produced electrical energy of 198.296 kWh. The lowest rain intensity was in July of 0,86 mm with water elevation of 194,02 cm and produced electrical energy of 49.772 kWh. The average rain intensity was 8,9 mm with water elevation of 324,12 cm and produced electrical energy of 156.010 kWh. The results of testing the effect of rain intensity with energy productivity at the Kracak hydropower plant resulted in a significance value of 0.002 (p &lt;0.05) and a t value of 4.306. This value indicates that the significance value is below 0.05 and has a positive coefficient direction. It means that rain intensity has a significant positive effect with energy productivity at the Kracak hydropower plant. And the results of testing the effect of water elevation with energy productivity at the Kracak hydropower plant resulted in a significance value of 0.003 (p &lt;0.05) and a t value of 3.864. This value indicates that the significance value is below 0.05 and has a positive coefficient direction. It means that water elevation has a significant positive effect with energy productivity at the Kracak hydropower plant. The conclusion on this research is the rain intensity and water elevation have effects with energy productivity of Kracak Hydro Power Plant.