Reviewerly/exHarmony huggingface dataset:
Performance Analysis via Hadoop: A Study of between text and.orc file extensions

In the age of Big Data, access to open data contributes to increasingly in-depth studies on different themes, whether social, economic, or political. A practical example is the analysis on the distribution of the Emergency Aid in Brazil, approved by the National Congress in 2020, due to the pandemic of the new coronavirus. However, as will be seen throughout the text, there are challenges to processing massive databases. Tools capable of processing a large volume of data in a short period of time are increasingly needed. Apache Hadoop is an important framework that allows distributed processing of massive databases, reducing query times. To elucidate this issue, this paper presents a comparison between two file extensions (.txt and.ORC) and checks their performance in processing Hive queries on the Emergency Relief database. The results showed significant differences in average execution time when adopting the.ORC file format. On the other hand, what was thought to be discrepant, the average value of the aid, was not proven, since all States received similar values.
Emotion Recognition of Partial Face Using Star-Like Particle Polygon Estimation

The effectiveness of the existing approaches of facial expression recognition is significantly impacted by the necessity of wearing a facial mask during the Covid-19 outbreak. A large number of significant features on the lower face such as mouth, nose, and cheek, are completely eliminated. In this study, we propose an approach to extract features for recognizing emotions in a partial facial image that is based on Star-Like Particle Polygon Estimation (SLPPE). There are 3 primary steps in the proposed SLPPE. First, the lower part of the original facial input image is obscured by a synthetic mask. The process of representing regions is then used in the upper face image. Secondly, SLPPE is adopted as a feature extraction procedure. It is used to extract probability-based vectors of features that efficiently manifest the occluded facial image’s properties. Unlike other CNN based methods that bring the input image known as a raster form into several Conv modules in order to perform feature extraction process, we extract features from the original input image using our proposed SLPPE feature extraction technique in order to eliminate the issues of extracting unexpected features caused by the facial mask region provided by the Conv modules. Our proposed SLPPE feature extraction module produce the potential transformed data in the form of feature vectors without applying Conv modules. Finally, by utilizing LSTM and ANN networks, the SLPPE is adapted to the datasets of CK+, FER2013, and RAF-DB in order to evaluate the proposed methodology. With the accuracy of 99.01%, 98.7%, and 94.62% on CK+, FER2013, and RAF-DB, respectively, our proposed method outperforms the common CNN approaches which are currently in use and yields better results.
Automatic operation of the optical system in the theatrical show

Theatrical techniques took upon themselves the responsibility of building and organizing the theatrical form for the various forms of performances, and it was the important tool that the show makers could rely on in carrying out the various works at the audio-visual level, and lighting is one of the most important elements of the visual formation of the image in the show, as it is related to the visual process and what it can achieve in operations The contrast that constitutes the aesthetic and intellectual values of the theatrical show, especially since the process of adjusting the element of time and the timings for receiving or delivering, moving, and the movement of the actor is what can determine the rhythm of the scene, which in its totality represents the rhythm of the show, and the processes of delay and confusion in the process of implementing various types of effects or techniques and light are the most important element that can Work to raise the aesthetic, artistic and intellectual values of the display in the case of controlling its implementation and control processes. Therefore, the researcher worked to find a simple technical and technical alternative that works to avoid these errors, accompanied by ease in the process of distribution and design of lighting and the shapes of its projections, since most of the workers now in the field of lighting design and implementation are Technicians with different specializations and far from the stage, which makes the percentage of errors received large. This device can avoid these errors that may appear inadvertently or by an executive error. For this, the researcher proposes a process of manufacturing a device that can perform lighting during the theatrical show, as a technical substitute for the lighting port, which calls for a process to reduce the percentage Errors that may occur during the theatrical show, in addition to the ease of programming and controlling it automatically, which contributes to the process of its spread in various types of theaters, even those in which there are no technicians or lighting technicians, and at a simple manufacturing cost that is not comparable to the cost of traditional control devices found in public theaters
Supplemental Material: Genesis of the Samba Cu deposit of the Central African Copperbelt in Zambia: Constraints from geochemistry and geochronology

&lt;p&gt;Analytical methods.&lt;/p&gt;
Comparative Review of Credit Card Fraud Detection using Machine Learning and Concept Drift Techniques

Credit card fraud is a significant concern for financial institutions and cardholders alike. As fraudulent activities become more sophisticated, traditional rule-based approaches struggle to keep up. This has led to the adoption of machine learning techniques for fraud detection, which have shown promising results. However, the dynamic nature of credit card fraud poses a challenge due to the concept drift phenomenon. Concept drift refers to the changes in the underlying data distribution over time, requiring models to adapt and evolve to maintain their effectiveness. This research paper aims to provide a comprehensive comparative review of credit card fraud detection methods using machine learning and concept drift techniques. This literature review provides an overview of relevant studies comparing credit card fraud detection using machine learning techniques and concept drift handling methods. The paper evaluates the performance, strengths, and limitations of various approaches in addressing credit card fraud detection under concept drift scenarios.
Recommendation model based on intention decomposition and heterogeneous information fusion

&lt;abstract&gt; &lt;p&gt;In order to solve the problem of timeliness of user and item interaction intention and the noise caused by heterogeneous information fusion, a recommendation model based on intention decomposition and heterogeneous information fusion (IDHIF) is proposed. First, the intention of the recently interacting items and the users of the recently interacting candidate items is decomposed, and the short feature representation of users and items is mined through long-short term memory and attention mechanism. Then, based on the method of heterogeneous information fusion, the interactive features of users and items are mined on the user-item interaction graph, the social features of users are mined on the social graph, and the content features of the item are mined on the knowledge graph. Different feature vectors are projected into the same feature space through heterogeneous information fusion, and the long feature representation of users and items is obtained through splicing and multi-layer perceptron. The final representation of users and items is obtained by combining short feature representation and long feature representation. Compared with the baseline model, the AUC on the Last.FM and Movielens-1M datasets increased by 1.83 and 4.03 percentage points, respectively, the F1 increased by 1.28 and 1.58 percentage points, and the Recall@20 increased by 3.96 and 2.90 percentage points. The model proposed in this paper can better model the features of users and items, thus enriching the vector representation of users and items, and improving the recommendation efficiency.&lt;/p&gt; &lt;/abstract&gt;
Comparison of earthquake early warning systems and the national volcano early warning system at the U.S. Geological Survey

First posted August 15, 2023 For additional information, contact: Associate Director, Natural HazardsU.S. Geological Survey12201 Sunrise Valley Dr., Mail Stop 905Reston, VA 20192Contact Pubs Warehouse Every year in the United States, natural hazards threaten lives and livelihoods, resulting in thousands of casualties and billions of dollars in damage. The U.S. Geological Survey (USGS) Natural Hazards Mission Area works with many partners to monitor, assess, and research a wide range of natural hazards, including earthquakes and volcanic eruptions. These efforts aim to enhance community preparedness, response, and resilience. The USGS Earthquake Hazards Program (EHP) provides earthquake monitoring and notifications, assesses seismic hazards, and conducts targeted research to reduce the risk of earthquake hazards nationwide. The USGS Volcano Hazards Program (VHP) delivers forecasts, warnings, and information about volcanic hazards based on proactive monitoring of the nation’s active volcanoes and scientific understanding of volcanic processes. The VHP also conducts targeted research on volcanic processes and creates hazards assessments that inform the level of monitoring required at each of the nation’s active volcanoes. Earthquake and volcano early warning systems are essential to disaster risk reduction: they can save lives and reduce property damage by quickly distributing messages and warnings to communities in harm’s way.
Comparative Analysis of using SIK for Message Cryptography

The process of selecting the private key, which is used in the process of generating the secret key to encrypt messages, is one of the most important processes used in the encryption process, which determines the efficiency of the method used in terms of security and speed of implementation, because part of the encryption time is consumed in generating the secret key. In this paper research we will introduce two methods of using private keys to generate a secret index key, the first key will be used a chaotic logistic map model to generate a chaotic key, the chaotic key will be converted to secret index key. The second way is using a color image as an image_key, an array will be extracted from this image, and the array will be converted to secret index key. A simple procedure of message encryption-decryption will be used, this procedure is based on the secret index key, this key will be used to rearrange the message characters’ binary bits. The two approaches of generating secret index key will be implemented using various messages, the obtained results will be analyzed, and the results of analysis will be used to raise some recommendations regarding the message length, security, quality and the speed of message cryptography.
Intelligent System for Phishing detection on web pages using Random Forest

During the last years there have been many technological problems, among them, one of those that stands out for its impact worldwide is Phishing, which is a Social Engineering technique that seeks as a benefit to illegally appropriate confidential and private information.In Peru, this problem is spreading considerably due to the lack of interest of both state and private entities in being able to make a contingency plan against this problem, and more and more citizens are affected and there is no clear statement or response from the authorities.In response to this major problem, this paper proposes the use of an intelligent system that uses a Random Forest algorithm, which is based on machine learning and can detect in time the URLs that may be malicious.
Identification of a suitable NLP model for the detection of symptoms mentioned in textual conversations of Covid-19 infected persons

In March 2020, the World Health Organization (WHO) declared Covid-19 disease as a global pan-demic. Therefore, the need for reliable information arose, so several Virtual Health Assistants emerged to provide information to the public to teach the population how to prevent or cope with the Covid-19 Alpha variant infection, but progressively emerged the Beta, Delta, Omicron variants with different symptomatology which triggered new waves of infections and deaths in the world. For this reason, the present study promoted the creation of a NLP (Natural Language Processing) model to analyze the experiences of infected people in Guayaquil and to detect the predominant symptoms mentioned in their textual conversations. For this purpose, the Quantitative Methodology was followed by performing surveys through Google form, reaching 2873 people in the city of Guayaquil who had and overcame the Covid-19. Thus, the corpus of textual conversations was generated. On the other hand, the Qualitative Methodology was used by executing interviews to NLP experts, which allowed corroborating the classifiers suggested in this type of software. Finally, twenty-two different NLP models were built based on classifier algorithms like K-Nearest Neighbors, Random Forest, and Long-Short Term Memory (LSTM), and their quality was evaluated using metrics such as Accuracy, Precision, Recall, and AUC, finding that the model based on LSTM version 3 obtained the highest performance.
Research on Video Moving Object Detection Based on Improved Gaussian Mixture Model

Moving object detection is one of the most important and difficult problems in computer vision. How to find and track objects from video images and extract useful information timely and accurately is an urgent problem to be solved. A video moving object detection algorithm based on improved Gaussian mixture model is proposed. The algorithm uses multiple Gaussian distributions to model the background and uses the characteristics of the model to optimize the number of Gaussian mixture model sub-models and background threshold, so as to realize the moving object detection in complex external environment. Experimental results show that the algorithm can better detect the edge details of moving objects in complex environments and complex background changes and can effectively and accurately extract moving objects in videos.
Application of Geolocated Activities for Teaching the Fundamentals of Object-Oriented Programming and Software Modeling

This article describes an experience in the application of geolocated activities to teach first-year students of an engineering university education program, the fundamentals of object-oriented programming and software modeling, generating a motivating and effective environment for their learning. Object-oriented programming is based on general concepts that are known and applied by human beings in general in their daily lives, but its teaching creates challenges that must be faced, mainly due to its abstract nature. Software modeling is a very useful tool for any professional who develops software and it is best to learn it from the beginning of their university studies. It is shown that a teaching based on the application of geolocated activities to teach the concepts of object-oriented programming and software modeling is motivating and effective for students belonging to the Centennial Generation.
Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment

Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.
Extended Reality Model for Accessibility in Learning for Deaf and Hearing Students (Programming Logic Case)

A group of researchers and developers from Colombia and Mexico have recognised that the development of state-of-the-art Extended Reality software, a key technology for the Metaverse, has great potential to improve teachinglearning processes in educational institutions.However, the development process does not take into account accessibility, universal design and inclusion, especially for the deaf student community.An extended reality model is proposed for the creation of this type of software as a tool to support access to knowledge, based on information gathering, requirements analysis, user-centred design and video game programming, including the ludic and didactic.The aim is to minimise the barriers that limit the learning of programming logic by students with hearing disabilities through the use of new technologies, creating spaces in virtual worlds that are understandable, usable and practical in conditions of safety, comfort and as much autonomy as possible.To validate the model, a mixed reality software prototype was designed and programmed to train students in programming logic, both deaf and hearing.User and heuristic tests were carried out, showing how immersion can improve knowledge acquisition processes and develop skills in higher education students.
Corpus-based comparison of lexical complexity in L1 and L2 postgraduate academic writing

This study adopted a corpus-based, contrastive approach to lexical complexity in the academic writing of first language (L1) and second language (L2) postgraduates. Lexical complexity scores were extracted using the Lexical Complexity Analyzer from the Corpus of Arab Proficient Users of English (CAPUE), consisting of Saudi academics’ dissertations in applied linguistics. To investigate the potential differences between this corpus and native speakers’ corpus, the lexical complexity of writing material from the CAPUE and Corpus of English Native Speakers (CENS) were compared. The computational system employed 25 lexical complexity measures to investigate differences in the two groups’ lexical density, sophistication, and variation. The results revealed similar lexical density in the writing of both groups; however, the texts by L1 researchers were more lexically complex for most measures of sophistication and variation. The results have implications for teaching English for academic purposes and highlights areas with inappropriate lexical choices. These findings call for the design of pedagogical interventions to enhance the lexical complexity development of L2 postgraduates.
MENGAMANKAN SERANGAN PACKET SNIFFING PADA JARINGAN KOMPUTER MENGGUNAKAN VPN TUNNEL

Technological developments greatly affect and control our daily activities. Technological developments were followed by the era of the Industrial Revolution 4.0. In the use of this technology there are not a few threats, so we need an adequate and secure computer network that is a priority, such as packet sniffing attacks. In sending data in a network it is very important to ensure the confidentiality of the data to be sent so that it does not fall into the hands of third parties. So this research has the goal of securing packet sniffing attacks on computer networks using a VPN tunnel. The results of VPN tunnel research can be used to secure packet sniffing from attacks and threats on the right computer networks
Knowledge graph augmentation: consistency, immutability, reliability, and context

A knowledge graph is convenient for storing knowledge in artificial intelligence applications. On the other hand, it has some shortcomings that need to be improved. These shortcomings can be summarised as the inability to automatically update all the knowledge affecting a piece of knowledge when it changes, ambiguity, inability to sort the knowledge, inability to keep some knowledge immutable, and inability to make a quick comparison between knowledge. In our work, reliability, consistency, immutability, and context mechanisms are integrated into the knowledge graph to solve these deficiencies and improve the knowledge graph’s performance. Hash technology is used in the design of these mechanisms. In addition, the mechanisms we have developed are kept separate from the knowledge graph to ensure that the functionality of the knowledge graph is not impaired. The mechanisms we developed within the scope of the study were tested by comparing them with the traditional knowledge graph. It was shown graphically and with t-test methods that our proposed structures have higher performance in terms of update and comparison. It is expected that the mechanisms we have developed will contribute to improving the performance of artificial intelligence software using knowledge graphs.
Impact analysis of bug localization accuracy oriented to bug report

The study of bug report-oriented program error location has the characteristics of strong pertinence and low cost, which is an important direction in the current research on program error location. This type of research takes bug reports and source code as input sources, and establishes a mapping relationship between the two through semantic mapping strategies to locate program errors. In the fine-grained program error location scenario, there is a problem that the location accuracy is greatly reduced. Existing empirical studies analyze the difference in location accuracy from two aspects: input source data noise and semantic mapping strategy selection, but most studies take the established location tools and methods as the evaluation object, the evaluation data type is single, and there is a lack of fine-grained analysis of constructing key variables. In order to evaluate the influence of key variables of location method on location accuracy, this paper decouples the location method through pseudo-siamese network, measures the sensitivity of location accuracy by counting the gain of location accuracy under different input source data types, and adds input source data types and a variety of semantic mapping strategies, Based on the evaluation of 23808 bug reports and corresponding source code data in 7 open source projects published on JIRA, this paper provides a more detailed empirical basis for additional data type selection and weight allocation, combined learning of multiple data types and different semantic mapping strategies in fine-grained program error location.