Reviewerly/exHarmony huggingface dataset:
Qualitative Knowledge Graph Model Construction Method of Transformer Maintenance

The existing transformer operation and maintenance knowledge resources are scattered, and less intelligent in knowledge management and utilization, so they cannot be applied in the occasions which have high requirements to efficiency and accuracy. Knowledge graph not only have the ability to link multi-source knowledge, but also improve the efficiency of knowledge utilization by graph management technology. Therefore, the paper proposed a method for establishing the knowledge graph of transformer operation and maintenance based on the physical structure and physical characteristics of the equipment. Then, the physical equipment, physical quantity, physical phenomena and equipment applications were used as the ontology. Furthermore, the hierarchy of knowledge entity was constructed according to the physical relationship and application relationship between the ontology. As well as, the relationship between the ontology was described by the structural association and functional association between the various components of the transformer. The structural association reflected the structural relationship of the transformer manufacturing, the installation and fixing etc.. The functional association relationship reflected the qualitative relationship in physical quantities of transformer operation. Finally, an oil immersed transformer knowledge graph was constructed, and a graph search method was illustrated as a search path. Combining with qualitative reasoning technology, the search work was behaved from the ontology of abnormal phenomena to the query target along the road provided by the graph. Conclusively, the efficiency and accuracy of the proposed method is verified.
Robust Scheduling for Large-Scale Distributed Systems

In large-scale distributed systems, such as clouds, failures are rather the norm than the exception. These failures include job failures, server failures, network outage and power failure. Among them, server failures are most common. With the wide adoption of cloud computing, the impact of server failures in clouds is far greater than that in traditional computer clusters as jobs of different tenants are often co-located (multi-tenancy). In this paper, we address the problem of robust scheduling, with realistic failure modeling, to minimize such impact on the execution of (co-located) jobs. To this end, we develop four online failure-aware (FA) scheduling algorithms, FAFF-WJ, FAFF-FC, FABF-WJ and FABF-FC, considering the availability and reliability of servers. In particular, FF (First-Fit) and BF (Best-Fit) indicate how the availability of servers is checked while WJ (Waiting Job) and FC (Failure Count) differ primarily in whether the reliability is measured from job's perspective or server's perspective. All four algorithms are designed essentially by combining these availability and reliability check methods. We evaluate our scheduling algorithms with failures generated based on our failure modeling of six real-world server failure traces. Our evaluation results show the effectiveness of our scheduling algorithms in robust job execution, with respect to both performance and cost.
Smart Guest Virtual Assistant with Automatic Guest Registration

Virtual assistants are a key component of smart devices and spaces. Such assistants help users access information and accomplish tasks. In this paper, a smart virtual assistant is presented. The goal of the assistant is to identify guests arriving at organizations, converse with the guests and respond to their inquiries. The assistant makes use of face recognition to identify guests and natural language understanding to interact with them. The novelty in the presented smart assistant comes from the automatic registration of guests. Once the organization invites a guest, the assistant is copied in the email. From the invitation email, the assistant applies natural language understanding to extract guest names and affiliations. Such information is then used to acquire an image for each guest from the Internet. The paper describes a working prototype of the smart virtual assistant, which was successfully tested against a set of real invitation emails used by the authors’ institutions.
Machine Learning Model Based Expert System for Pig Disease Diagnosis

This paper describes the importance of machine learning (ML) algorithms in the design and development of diagnostic expert systems. The designed system is intended on how ML techniques can be used in the prediction of pig diseases through visible symptoms and their past behaviours. These types of systems are very useful in situations where the domain experts in the field are not readily available. It is important to make an accurate diagnosis of pig diseases to provide control strategies for any symptom shows in a pig to prevent related health issues. The main concerned in the study begins with the collection of disease symptoms and related information of pigs during their life span from the domain experts and literatures in the domain. The acquired knowledge and information are then pre-processed to be used for being trained different ML algorithms. The system development involves the training and analysis of different ML classifier models and their performances. It has thus been found in the study that SVM classifier can produce more accurate results in disease prediction. The paper finally presents the design and development of an expert system for the diagnosis of Pig diseases using SVM classifier which is trained with pig disease dataset.
Analysis of Traditional Italian Food Recipes: Experiments and Results

Traditional recipes are among those elements that UNESCO included in its Intangible Cultural Heritage for safeguarding. Traditional recipes are passed down from one generation to the other, and offer strong links with a particular territory. Driven by the important role of food recipes in the cultural heritage domain, we have create CookIt, a web portal with the aim to collect, disseminate and safeguard the knowledge of typical Italian recipes and the Mediterranean diet which is a significant part of the Italian cuisine. In this paper we present some preliminary results in recipe analysis to be used within our web portal to support innovative ways to navigate and browse them. We developed some processing and visualization tools to support the analysis and the presentation of the recipes. Our tools are tailored for the Italian language although they can be generalized.
A CNN-based Traffic Sign Recognition System

The objective of this chapter is to detect and recognize the traffic signs using two methods. Traffic signs are silent speakers to the road users. It gives advance information about road conditions ahead. In this chapter, an effective method is proposed to identify the traffic signs in an automated manner. The traffic sign recognition for advanced driver-assistance system of Method I comprises of three stages: (1) detection using Haar-like features with AdaBoost method, (2) feature extraction using speeded up robust features, and (3) classification using support vector machine. Method II comprises of two stages: (1) detection of traffic sign using convolution neural network and (2) classification using convolution neural network. The performance of the system is analyzed. From the experimental results, it is observed that the Method II using convolution neural network gives a better performance compared with Method I
INFORMATION TECHNOLOGY FOR DETERMINATION OF CHARACTERISTICS ON THE FACE FOR EMOTIONAL RECOGNITION

One of the ways to process an image presented in the form of a set of pixels, in order to further identify, classify the objects present on it is to display the specified set in the form of sets of certain features. Such features are not universal in nature, but rather significantly depend on the tasks under consideration. For certain classes of problems, such features (model) are selected that best allow the application of appropriate methods to solve the problem. The paper considers a class of problems for recognizing the emotional state on a person's face. In, a convolutional neural network (CNN) is used to detect emotions. CNN differs from multilayer perceptron (MLP) in that they have hidden layers called convolutional layers. The proposed method is based on a two-tier CNN system. At the first level, the background of the image is removed to better reflect emotions. A standard CNN network module is used to obtain the primary expression vector (EV). EV is formed by tracking the relevant important points of the face. EV is directly related to changes in facial expression. EV is obtained using a basic perceptron unit plotted on a face image with the background removed. In the proposed model at the last stage, there is a non-evolutionary perceptron layer. Each of the convoluted layers receives input data (images), converts them, and then takes them to the next level. After detecting a face, the CNN filter of the second part captures parts of the face, such as eyes, ears, lips, nose, and cheeks. The authors agree that the method has some limitations, and especially requires high computing power when setting up CNN. The technology of determination of characteristic features on the face for recognition of emotional manifestations is presented and experimentally investigated.
IoT Based Emergency Alert System

There is rapid increase in population, due to which associated challenges in safety and health care are also in raise. No proper assurance is given to safety, especially for women. Moreover, elder people in isolation are facing many health issues due to which continuous monitoring is essential. In order to pave path for these issues, a method is proposed which will ensure road safety, women safety as well elder people safety. On the whole, the major threats and difficulties faced by people will be prevented or solved using this application.
Analysis and Evaluation of Performance Related to Java and PHP Security Codes

In recent years, Internet exposure of applications continuously engenders new forms threats that can endanger the security of the entire system and raises many performance issues related to code security.The safety of information systems becomes essential.This is why the performance linked to security codes is of importance essential in the security systems of all companies.Indeed, as contribution, to carry out measurements, it appropriates tools that are the JMH tool (Java Microbenchmark Harness) and the PHP Benchmark script tool which include unsecure java and PHP codes and secured against SQL (Structured Query Language) injection, XSS (Cross Site Scripting) i.e., using prepared requests, stored procedures, validation of input from white lists, reinforcement of minimum privilege, when sending requests from the last ones to MySQL databases and Postgresql.We recover the times of response to his requests.From java codes and PHP (Hypertext Preprocessor) secure, we also retrieve the response time for requests to databases MySQL and PostgresqL data.We then obtain the curves and interpretations comparing performance related to security and non-security of codes.The goal is to analyze and evaluate the performance comparing secure Java and PHP code against unsecure java and PHP code using MySQL and Postgresql databases.In Section 1, we presented the performance of the code Java and PHP.The configuration of the experiments and the experimental results are discussed in Sections 2 and 3, respectively.Use of suitable tool which is the JMH tool and the PHP Benchmark script tool, we have developed in Java 1.8 and PHP 7.4 secure and non-secure codes that send the queries to the MySQL or Postgresql database to carry out the measurements which led to the conclusion that the insecure PHP and Java codes are faster in terms of response time compared to the PHP and Java secure codes as the number of tables linked to the query increases because the blocking times of SQL injection and XSS preventions linked to its secure codes are increasing.
Detection of Topics and Time Series Variation in Consumer Web Communication Data

Consumers’ personal interests are influenced by new product strategies, such as marketing communication schemes, and these can change over time. Thus, it is important to consider temporal variation in trending consumer interests. We aimed to detect temporal variations in consumer web communication data using weight coefficients between entries and topics obtained from nonnegative matrix factorization. The weight coefficient, which indicates the strength between an entry and a topic, was modeled with a Bayesian network to capture changes in the topic over time. Bayesian networks, commonly used in a wide range of studies such as anomaly detection, reasoning, and time series prediction, build models from data using Bayesian inference for probability computations. The causations can be modeled by representing conditional dependence based on the edges in a directed graph of the Bayesian network.
Motion Control for an Autonomous Personal Mobility Based on Trajectory Prediction of Moving Objects on Shared Space

In recent years, Personal Mobility (PM) has attracted attention as a new means of moving in behalf of foot or car. PM is small electric powered vehicle that runs at low speed, and hence has highly affinity with pedestrians. Therefore, it is suitable to use PM in a space called 'shared space' where pedestrians, cars, and so on coexist. Additionally, research on autonomous driving has also been actively carried out in order to enhance its convenience. In autonomous driving of PM in shared space, it is important to establish a motion control method for safely and comfortably avoiding pedestrians, cars and so on. Therefore, in this paper, we propose a motion control method of PM with trajectory prediction that enables safe and comfort obstacle avoidance. In this method, firstly, the future trajectory of the obstacle is predicted. Next, this predicted trajectory of the future is represented as an oblique prism on the three dimensional space-time temporal space consisting of x, y and time axes and the path and speed of the PM is controlled so as to avoid this oblique prism. Thus, it is possible to control the path and speed of the PM to avoid various obstacles safely based on prediction. From various simulation results, we show the effectiveness of the proposed method.
The Cognitive Processes Involved in Diagnosis

The first disease that comes to a dentist’s mind during diagnosis is thought to be due to intuition. It is a function of “system one,” the area of the brain responsible for the storage of short-term memories. On the other hand, “system two” is responsible for analytical thinking that follows a logical thought process. This area functions during hypothesis formation to create a list of differential diagnoses based on the patient’s history and during hypothesis verification to examine the probability of the patient having a disease from that list. However, its disadvantage is that it takes time. The dual process theory describes the cognitive process of clinical reasoning as an interaction between the fast, subconscious “system one” and time-based, conscious “system two”. This theory is based on the idea that it is useful to think using both systems simultaneously to solve problems. However, it has been found that this leads to the occurrence of a variety of cognitive errors. Dentists should be familiar with the cognitive processes involved in diagnosis and take measures to prevent and correct errors.
Discussion on O+P Landscape Architecture Teaching Mode under the Background of Internet

Abstract The landscape architecture teaching model under the Internet background needs to make full use of the O+P model that combines online teaching with on-site offline practice. The curriculum group of landscape architecture is divided into three types: knowledge, operation, and practice. Each type of course uses different online and offline teaching methods. This teaching mode is conducive to the integration of internationally superior teachers, demonstrates the practical value of landscape architecture, and enhances college students’ awareness of serving the society.
Research on Fatigue Crack Detection Method of Asphalt Concrete Pavement Based on Machine Learning

Based on the original research results, this paper proposes a method for fatigue crack detection of asphalt concrete pavement based on machine learning. Obtain the asphalt concrete pavement fatigue crack data set composed of public data sets and manually collected picture data, and divide the crack pictures into three types: horizontal crack pictures, longitudinal crack pictures and reticular crack pictures. The crack image is pre-processed including grayscale processing, histogram equalization operation processing, noise preprocessing, and Gamma correction processing. After the preprocessing operation, the subsequent target segmentation, extraction and positioning of the crack image are performed. Since there are fractures in the extracted fracture image, it is necessary to splice multiple fracture connected region targets into a complete fracture connected region. On this basis, the fatigue crack detection network model of asphalt concrete pavement is built based on machine learning. The collected data are input into the model to train the model, and the fatigue crack detection of asphalt concrete pavement is realized. The experimental results show that the detection accuracy of this method is high for all kinds of fracture types, and the detection effect is ideal when the model training iterations to 10000 times.
Pre Cervical Cancer Detection on Visual Inspection of Acetic Acid (VIA) Test Image Using K-Means Clustering Method

We built an approach in early detection of cervical cancer using image recognition of Visual Inspection with Acetic Acid (VIA) image. We used k-Means clustering to segment the expected region of cervical cell in the VIA test image. The positive suspect shown by white lesion which are called acetowhite. We used VIA test image captured from mobile phone as the dataset. From the acetowhite area, we extracted the color moment feature and the Gray Level Co-occurence Matrix (GLCM) feature. The color moment and GLCM feature were then classified as positive or negative using Support Vector Machine (SVM) classifier. The best performance were an accuracy of 72,14 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , with sensitivity of 70% and specificity of 74% using k-Means clustering with k=2 and SVM with linear kernel.
Improved Analysis of High-Throughput Sequencing Data Using Small Universal k-Mer Hitting Sets

High-throughput sequencing machines can read millions of DNA molecules in parallel in a short time and at a relatively low cost. As a consequence, researchers have access to databases with millions of genomic samples. Searching and analyzing these large amounts of data require efficient algorithms.Universal hitting sets are sets of words that must be present in any long enough string. Using small universal hitting sets, it is possible to increase the efficiency of many high-throughput sequencing data analyses. But, generating minimum-size universal hitting sets is a hard problem. In this chapter, we cover our algorithmic developments to produce compact universal hitting sets and some of their potential applications.
A Deep Learning Based Visible Knife Detection System to Aid in Women Security

Criminal activities have increased largely over the past couple of years and the security of the commoners especially women have been hugely jeopardized. There has been multifarious cases of threats and assaults with weapons in the present days especially with knives which are one of the most common and readily available household items. Such cases have made CCTV cameras a common sighting in the neighbourhood. The prime idea behind their installation is surveillance. The footage from such cameras can serve as an extremely important source of evidence during investigation. However, such systems only make themselves useful as evidences of a crime and do not aid in prevention of a crime in progress. Standing in such times, making CCTV cameras intelligent can be a solution which can detect weapons and thereafter alert authorities. Here, a deep learning based system is presented which can automatically detect visible knives to alert authorities of a prospective crime and thereby aid in women security. The system has been tested on a freely available dataset [] consisting of over 12000 frames and a highest accuracy of 96.11% has been obtained. We have also tested the performance of handcrafted feature-based framework with grey level co-occurrence matrix (GLCM) and our system produced better result.
WITHDRAWN: Performance analysis of VoIP codecs in interactive streaming data environment

Voice over Internet Protocol (VoIP) is a novel interactive telecommunication equipment. A main challenge of telecom engineering is maintaining quality. VoIP suggests shared, cooperative and quality transactions in the form of communications over the internet. VoIP normally contracts from traditional and conventional circuit based networks in the form of low rate communication. Real time voice data transmission is very difficult when compare with ordinary text data transmission. The VoIP transmission faces lot of inconvenience. It suffers from packet loss, low quality, jitter, delay and delivery. These parameters influence and degrades the performance. The codec plays major role in VoIP transmission. This paper reports the elementary functionalities of VoIP, coder, decoder functioning in the form of quality output, the broadcast technology, and set up connections of VoIP, advantages, applications and forthcoming crazes of VoIP.
Relaxing Pairwise Conditional Dependency of NB Attributes using Hidden Variables

Conditional independence assumption of naive Bayes classifier as one of its major drawbacks, for real-world applications, has been recently tackled by various structural learning approaches. However, finding the optimal structure is itself reported to be an NP-hard problem. Compliance of the devised structure with general rules and concepts of the literature of Bayesian networks is also another challenge facing many researchers. Eventually, the computational complication of structural learning has impelled many scholars to adopt approaches to confine the search space of possible structures. In this paper, we incorporate a set of hidden variables in a predefined Bayesian network structure, for proposing Pairwise Hidden Naive Bayes Classifier (PHNB). In PHNB, a hidden variable is allocated for each pair of features with conditional mutual information higher than a specific threshold in a diverging connection to preserve their dependency whenever the class variable is instantiated. The Expectation-Maximization (EM) algorithm is modified using a Bayesian correction approach so as to consider an infinitesimal possibility for unforeseen events while converging to a local optimum. Experiments on 6 UCI benchmark datasets reveal substantial results based on classification accuracy(ACC) and area under the ROC curve(AUC).