Reviewerly/exHarmony huggingface dataset:
Multicriteria-Based Trustworthiness Testing of Web Sites

In this age, Internet being affordable and fast has become the foremost popular means to retrieve any sort of information. Presently, people depend upon the Web for everything. The gathering of data from the Internet witnessed to be a standard phenomenon within the present technological advanced era since any information can be easily accessed and available to everyone. In fact, Internet has been used as a source for spreading awareness about anything among the public. People often visit online sources to accumulate details about something. Most people tend to believe that the information available online is reliable. When any user searches for something, he gets the results within a flash. But the question to ponder over is, “Are the results obtained be trustable?”. Unfortunately, there is no surety for accuracy of results because of contrasting data provided by distinct sites about the equivalent thing. So, the matter is, “How does a user ensure which Web site is reliable?”. In the current scenario, where the majority of the population relies on Web sources for any information, the necessity to test the correctness of data available online requires utmost attention. Hence, this paper proposed a scheme to ensure the trustworthiness of Web sites. In this scheme, a tool to check and display genuine Web sites for a particular keyword is made.
Virtual Office Prototype: A Unity-2D work Simulation

In today’s world virtual office have become the talk of the town. In a very less span of time, the number of people using it have grown exponentially. Virtual Office management is a science that requires the use of a scientific tool that can be used for the management of the virtual office. Many tools and applications can be used for the management of the virtual office. The most important of them is a virtual office management application that uses unity and agora.io. The main purpose of a virtual office management application is to increase work effectiveness and to complete standard operating procedures in it. The virtual office management application should be based on a standard operating procedure. The application should have a user interface that is simple and easy to use. Though the problems of missing customer letters, conflicting schedules are there but it can be rectified as we move on. So, to overcome the problem faced by today’s world, we have designed a prototype of virtual office which can be downloaded from Heroku and after creating a server would be ready to use.
Strengthening Indonesian Farmers’ Resilience Capacity of Disasters and Climate Change Through Development of Decision Support System: Challenges Lie Ahead

Abstract Farmers are the vanguard of national food providers. However, they still have not yet received much serious attention and assistance. This condition is exacerbated by unpredictable climate change. Therefore, it is crucial to have an analytics tool to assist farmers in resolving production problems with some variables such as soil condition, season, and plant species. Decision support system (DSS) in agriculture helps farmers in making decisions based on previous research results. However, DSS in some countries are available, but not widely used by the farmers. This study aims to analyze the possibility of developing DSS to increase the resilience capacity of farmers in Indonesia. The method used in this research was a literature study and descriptive analysis. The results showed that there is still a long way to go in achieving the robust DSS, referring to the quality of research results so far. To elaborate on this, it is necessary to make a research roadmap in this field by understanding the current research status. Moreover, DSS will be a form of contribution from researchers in providing reliable and updated public information packages for a better agricultural management.
A Penalty Function Semi-Continuous Thresholding Methods for Solving Large-Scale Data Hash Coding Problems

Hashing process is an effective tool for handling large-scale data (for example images, videos or multi-model data) retrieval problems. To obtain better retrieval accuracy, hashing models usually are imposed with three rigorous constraints, i.e., discrete binary constraint, uncorrelated condition and the balanced constraint, which will lead the models to be NP-hard. In this study, we divide the whole constraints set into the uncorrelated (orthogonality) constraint and the binary discrete balance constraint, and propose a fast and accurate penalty function semi-continuous thresholding (PFSCT) hash coding algorithm based on forward-backward algorithms. In addition, we theoretically analyze the equivalence between the relaxed model and the original problems. Extensive numerical experiments on diverse large-scale benchmark datasets demonstrate comparable performance and effectiveness of the proposed method.
Developing an Indoor Location Guidance System for a College Campus Environment

Automation and digital technologies are revolutionizing how we live, and smart systems are increasingly becoming viable alternatives to indoor guidance and navigation. This paper discusses the development of an autonomous indoor location guidance system designed to guide visitors with navigating a college campus department. Advanced programming features and electronic tools aligned with the 4th industrial revolution are integrated to create a wireless, motor-driver circuit, which acts as a guidance system. Using motion graphics technology it displays a guidance visualization navigation running on embedded systems-including a Raspberry Pi. Users interact with the system by accessing an interface through a capacitive screen. This prototype demonstrated the potential of indoor guidance as essential and efficient, with some commercial prospects.
Informal learning outcomes certification based on blockchain Technology: Framework and mechanism

Effective certification of informal learning outcomes is a key link in building a learning society. As one of the innovative means of deep integration of information technology and teaching practice, blockchain technology has begun to operate in some teaching institutions. This paper reviews the connotation and certification concept of informal learning outcomes, and briefly introduces the application of blockchain technology in the field of education, This paper analyzes the application framework of blockchain technology from the dimensions of learning outcomes certification protocol, platform and interface, and discusses the influence of blockchain technology on informal learning outcomes record, learning motivation incentive, learning resource evaluation and learning process sharing, so as to provide useful reference for the development and innovation of informal learning.
Predicting the J1 League Standings Using Pythagorean Expectations

The purpose of this study is to develop a prediction formula for the winning points of the J-League, a Japanese soccer league, and compare it with the actual winning points. The Pythagorean expectation formula, which predicts winning percentages in baseball and basketball, was modified for soccer for this purpose. We used the ggplot2 package of the statistical software R to visualize and compare the actual winning points with the winning points calculated using the Pythagorean expectation formula for soccer.
Equipment and Software for Energy Supply Monitoring and Control Process

The parameters of probability and operation of control units are mainly determined on the basis of research of state of work and failure of elements conversion-formation of control signal. During research of reliability and probability of operational parameters of control units of monitoring and controlling devices, necessary to determine probability parameters of functioning elements, involved to formation of output signal, based on primary electrically, magnetically, thermal and other quantities and values. The results of control are recorded in non-volatile memory for storage if device operates in the measurement mode, and also displayed on the display.
Efficient Heterogeneous Acceleration Using Single-core Histograms of Oriented Gradients

As the key to image processing, text localization has a wide range of applications in intelligent detection. Given the complexity of text recognition algorithms, a large amount of data processing will consume too much time and energy. Aiming at the high latency defect of the Histogram of oriented gradients (HOG) algorithm in text localization technology, this paper designs an efficient single-core heterogeneous acceleration scheme. This scheme overcomes the problem of global memory synchronization caused by multiple cores and avoids complicated protocol operations. Experiments show that the scheme saves more than 50% of resources than the multi-core acceleration scheme, and the calculation speed is increased by more than twice. Compared with the central processing unit (CPU), the speedup ratio and energy efficiency ratio of the hog algorithm on the graphics processing unit (GPU) platform are 28 and 22.8, and on the field programmable gate array (FPGA) platform it is 6.9 and 42.5. The acceleration scheme meets the real-time and low energy consumption requirements of text localization and will play an important role in the field of image processing.
Design of intelligent semantic recognition framework for English reading APP based on waveform matching algorithm

Design of the intelligent semantic recognition framework for English reading APP based on the waveform matching algorithm is studied in this paper. The vector elements used are not normalized. This is because some signal amplitudes are distributed positively and negatively, which may make the sum of signal amplitudes zero or very small. In this model, the focus will be on APP software structure optimization. With the popularity of modern Ethernet and IP technology. The general collection and transmission of data information and monitoring information has become more and more inseparable from the open network. The machine recognition model is combined for the sematic analysis of the words. The system is then implemented with the optimized structure and the simulation is then conducted.
Counting and Randomly Generating &amp;lt;i&amp;gt;k&amp;lt;/i&amp;gt;-Ary Trees

k-ary trees are one of the most basic data structures in Computer Science.A new method is presented to determine how many there are with n nodes.This method gives additional insight into their structure and provides a new algorithm to efficiently generate such a tree randomly.
Remote Sensing Image Target Detection Algorithm Based on Multi-dimension Attention

Aiming at the problem of missing detection caused by the densely arranged targets in remote sensing images, a target detection algorithm based on multi-dimension attention is proposed. The proposed algorithm uses the RetinaNet target detector as the main framework. Firstly, the backbone network is used to extract feature maps of different scales. Secondly, the Feature Pyramid Net (FPN) is used to realize the fusion of feature maps of different scales. Then, the feature map through the attention module is used to suppress the boundary noise and highlight the target features. Finally, the class and box subnets are used to classify and locate the target. The experimental results show that, compared with the RetinaNet algorithm, the mean Average Precision (mAP) of the improved algorithm on small-DOTA is improved by 5.55%.
Summary of Research on Intelligent Alarm Technology of Substation

Alarm is one of the important application scenarios of intelligent technology in the operation of substations. The study of intelligent technologies such as expert systems, Petri nets, fuzzy set theory, and machine learning has great significance to promote the development of intelligent substations. This article briefly explains the basic concepts of the above-mentioned intelligent technology, introduces the research results of intelligent technology in the field of substation alarm and fault diagnosis, points out the problems in the current research institute and looks forward to the future development direction of this field.
Migrating Software Towards Mobile Technologies

New paradigms such as pervasive computing, cloud computing, and the internet of things (IoT) are impacting the business world. Smartphones are at the core of these paradigms by allowing us interaction with the world around us. In light of this, it is imperative to migrate a lot of existing non-mobile software to adapt it to the new technological reality. The main challenge to achieve this goal is the proliferation of mobile platforms. An integration of ADM (Architecture Driven Modernization), cross-platform development and formal metamodeling to face this kind of migration is described. The proposal was validated with the migration of object-oriented software to different mobile platforms through the multiplatform language Haxe. A comparison of the approach with traditional migration processes and the description of existing challenges in real projects of the scientific and industrial field are included.
On AutoMLs for Short-Term Solar Radiation Forecasting in Brazilian Northeast

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Machine learning models applied to solar radiation time series helps to improve forecast results. AutoMLs are becoming very popular for industrial application, because it simplifies some the challenge of finding the best suited model. This work presents three different optimizations of known models applied for solar radiation forecasting, ACOLSTM, ACO-CLSTM and AGMMFF. A methodology was used to obtain the results, with Brazilian INMET hourly scaled data, which were compared to the open sources AutoMLs TPOT, HPSKLEARN and AutoKeras. The obtained results for the presented models is promising for use in automatic solar radiation forecasting systems since ACOLSTM was found to outperform the compared models.
Fast and lightweight binary and multi-branch Hoeffding Tree Regressors

Incremental Hoeffding Tree Regressors (HTR) are powerful non-linear online learning tools. However, the commonly used strategy to build such structures limits their applicability to real-time scenarios. In this paper, we expand and evaluate Quantization Observer (QO), a feature discretization-based tool to speed up incremental regression tree construction and save memory resources. We enhance the original QO proposal to create multi-branch trees when dealing with numerical attributes, creating a mix of interval and binary splits rather than binary splits only. We evaluate the multi-branch and strictly binary QO-based HTRs against other tree-building strategies in an extensive experimental setup of 15 data streams. In general, the QO-based HTRs are as accurate as traditional HTRs, incurring one-third of training time at only a fraction of the memory resource usage. The obtained numerical multi-branch HTRs are shallower than the strictly binary ones, significantly faster to train, and they keep predictive performance similar to the traditional incremental trees.
A Sentence Classification Method for Chinese Spelling Error Detection Based on BERT

Chinese Spelling Check(CSC) is a crucial task aiming to detect and correct spelling errors in a text. Most previous methods focus on detecting and correcting the characterlevel errors. In this paper, we consider detecting spelling errors at the sentence-level and treat this problem as a binary classification. We propose a new paradigm to detect errors, which is a binary classifier based on the BERT. This classifier is fine-tuned with sentences containing errors as negative examples. We also propose a pair-shuffle training method to improve fine-tuning. Experimental <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/jiangjin1999/Sentence-level-detection-on-CSC results on the SIGAHN2015 dataset demonstrate that our paradigm and training method outperform the SOTA model.
Ensemble-based Noise Filter for Noisy and Imbalanced Data

Class Imbalance problems have an unequal distribution of classes within a dataset. The noise filter is a preprocessing technique that aims to identify and delete noisy examples in the training data to improve the performance of an induced classifier. In our previous work, we proposed a novel method deletes noisy examples, located inside the borderline region, from the majority class. The method combines the advantages of thresholding with noise filtering. In that method, the identification of noise is based on classification filter approach. One of the main drawbacks of this approach is that classifiers are organized in parallel way so they cannot benefit each other through noise identification. In this paper, we evaluate our proposed method by investigating the use of ensemble filter approach to identify noisy examples. The extended method has been assessed on 14 real imbalanced datasets. The results show that our proposal is significant in identifying noise and performs better in term of AUC scores based on random forest.
Fine-grained Object Recognition based on Multi-Scale Destruction and Construction Learning

With the development of image processing technology, how to effectively use computer vision technology for image classification has become an important research branch in the field of computer vision. Traditional image classification is to identify coarse-grained categories, such as using computer vision technology to distinguish meta-categories such as "dog", "grape" and "bird". In many practical applications, images need to be classified at a fine-grained level, such as distinguishing which subcategory the image belongs to in the "dog" category: "husky", "Samoyed" and "Alaska". Due to the characteristics of large intra-class differences and small inter-class differences in fine-grained image classification, fine-grained image classification has become a challenging research. This paper proposes multi-scale input (MS_DCL) and multi-scale detail boosting (MSDB_DCL) destruction and construction learning methods, so that the network can learn object and component-level discriminative information for fine-grained image classification. The experimental results show that MS_DCL is finer than other existing ones. The granular image classification method has higher recognition accuracy, and the test accuracy rate can reach 87.4% on the CUB200-2011 dataset.
Genetic algorithms and grammatical inference

This chapter presents an algorithm for grammatical inference using the genetic algorithm. First, the author introduces a bit-masking oriented genetic algorithm and discusses mask-file reproduction operators. Three different crossover operators and one mutation operator are discussed comprehensively. Then, another grammatical inference algorithm is discussed. The second grammatical inference algorithm is a modified version of the bit-masking oriented genetic algorithm with a minimum description length principle. Flowcharts and examples are presented so that one can easily understand the steps involved in the algorithms.
Patch-Based Convolutional Neural Networks for TCGA-BRCA Breast Cancer Classification

The current study automatically identified regions of interest and classified breast tumors in whole slide images from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) using patch-based convolutional neural networks (CNNs). Pre-processing techniques were applied on whole slide images. Then, whole slide images were tiled into patches, and patches containing regions of interest (ROIs) like nuclei-rich areas were identified. Afterwards, features from patches containing ROIs were extracted using CNNs and used to train patch-level classifiers. Finally, patch-level predictions were aggregated into slide-level predictions. Classification metrics like accuracy, precision, recall, and f1-score were used to evaluate results.
Learning Discriminative Representation with Attention and Diversity for Large-Scale Face Recognition

Recent improvements of face recognition mainly come from investigations on metric or loss functions rather than the structure of CNNs and interpretation of face representation. By analyzing the application of face representation (e.g. age estimation), we find some principles (utilizing low-level features and preserving enough localization information) of pixel-level prediction approaches would help to enhance the discriminative ability of face representation. Therefore, we propose a new plug-and-play attention module to integrate low-level features and propose two types of diversity regularizers to maintain localization information while reduce redundant correlation. Moreover, our diversity regularizers can achieve decorrelation without eigenvalue decomposition or the approximation process. Visualization results illustrate that models with our attention module and diversity regularizers capture more critical localization information. And competitive performance on large-scale face recognition benchmark verifies the effectiveness of our approaches.
Talking-heads attention-based knowledge representation for link prediction

State-of-the-art methods for link prediction, also known as knowledge graph embedding, aim to represent both entities and relations in the given knowledge graphs (KGs) into a continuous low-dimensional vector space and thus could be used to fill the missing facts or identify the spurious facts in KGs, where a fact is represented as a triple in the form of (head entity, relation, tail entity). Most previous attempts solely learn triples independently and thus fail to utilize the rich hidden inference and semantic information in the local neighbourhood existed surrounding each triple in KGs. To this effect, this paper proposes a talking-heads attention-based knowledge representation method, a novel graph attention networks-based method for link prediction which learns the knowledge graph embedding with talking-heads attention guidance from multi-hop neighbourhood triples. We evaluate our model in Freebase, WordNet and Kinship datasets on link prediction, experimental results demonstrate that the injection of talking-heads attention mechanism could better capture the semantic relationship of the neighbourhood surrounding triples and indeed achieve promising performance on link prediction.
On line monitoring method of operation environment safety for substation maintenance

Aiming at the problems of low accuracy of monitoring results, long monitoring time and poor stability of monitoring results in traditional methods, an on-line monitoring method for operation environment safety for substation maintenance is proposed. An on-line monitoring platform for substation operation environment safety is constructed. The platform realizes signal acquisition through A/D conversion and transmits it to the data display unit for unified packaging and display of data. The point distribution optimization model is established with the minimum average monitoring distance as the objective function and the constraints are set. Finally, the monitoring model is constructed by using fuzzy mathematics method, which realizes the on-line monitoring of substation operation environment safety. The experimental results show that this method effectively solves the problems existing in the traditional methods, the overall monitoring time is less than 1.5s, and the accuracy of the monitoring results is high, which fully verifies the effectiveness of this method.