Reviewerly/exHarmony huggingface dataset:
Alexa Skills

Every year hundreds of students and their far miles visit the universities physically for enrollment. As the parents are curious and concerned about the future of their kid, they want to know everything about the University. Parents utilize their time to ask all types of questions to the university representative present on the day. The whole process is key for the students and even University is keen to share all the information and knowledge about the University to help the students make a well-informed decision about their future. The whole physical process is very time con summing due to the high volume of queries. There is a slight risk of not getting the time to ask the question during the visit as university representatives are very busy during the admission time. Based on the sample data set of questions that have been asked in the past, we gather that many of the questions are basic and can be easily answered virtually. That means, students and their parents can get an answer to all the basic questions before coming to the campus and can utilize their time effectively on the campus
Energy Evaluation of Bugs vs Birds Path Planning Strategies for Robots

The paper analyzes two path-planning strategies from the optimal energy perspective. The first strategy is reactive navigation and is very similar to a bug trying to find food, while the second is based on a map like navigation imitating birds' movement. Both approaches are evaluated in environments with and without obstacles, and their energy consumption profile is extracted.
Blockchain Technology: A case study of its Decentralized Use

The world of technology has experienced a paradigm shift in the span of two decades which in turn demands better and more effective measures for data security. Blockchain technology is used for serving this purpose widely. It is a tool which can be used for Peer to Peer (P2P) transactions and exchange assets digitally without the need of a central administration to govern and mediate, i.e. a decentralized system. It works by integrating various technologies like digital signatures, consensus algorithms, hashing etc. All these technologies make it possible to craft a system that provides a way for easy tracking of the previous transactions, adding a feature of security and transparency. Blockchain has revolutionized and transformed the outlook of business functionality in terms of transparency and decentralization. Apart from being the core of crypto-currencies which have impacted the global economy drastically, blockchain can prove to be beneficial in the healthcare sector, bringing reforms in social services, revolutionizing the financial sector, the education system and many more. This study aims to discuss the architecture of Blockchain, the features including privacy, security and transparency, the various domains in the modern world which can make use of blockchain technology to overcome the prevailing challenges.
Virtual Reality Application for Acute Pain Therapy - User Experience

The use of Virtual Reality applications under the form of serious games is gaining traction in the medical domain. The development is pushed by new technological advancements in immersive Virtual Reality technology, improved game-engines and improved hardware capabilities. The user experience is an important aspect that needs to be taken into consideration while developing serious games for medical purpose. In this article we show the methods we have applied in our custom-made serious game application for pain therapy, in order to obtain and to analyse relevant user experience data. The two main tools that we have used were the System Usability Scale and the Short User Experience Questionnaire. We have used the mentioned tools to get information regarding the complexity of our system, integrity, usability, ease of use and most importantly the effects on pain. Other information that we took in consideration were game performance, user friendliness, user motivation, and user enjoyment. The results show that our initial prototype has a consistent and sustainable development as well as a noticeable effect on reducing the pain felt by the users.
Transfer Learning for Behavioral Biometrics-based Continuous User Authentication

The cybersecurity industry is developing innovative solutions to avoid cyber-attacks. One such upcoming technology is continuous user authentication. It uses keystrokes and mouse movement behavioral patterns to authenticate the user continuously in the background. This technique uses machine learning to classify users based on the behavioral pattern. It requires a lot of data to find the user’s behavioral pattern and plenty of time is required to gather the data which extends the start of continuously authenticating the new user. In this research, the transfer learning technique was used for a feed-forward neural network model to overcome this issue for new users. Experiments were done using only one behavioral pattern with a set of 5 users to find the difference in accuracy between the model trained with transfer learning and the model trained without any previous learning. The results showed that the model using transfer learning had 9.76% more accuracy than the model trained from scratch. This implies that using transfer learning improves the accuracy with a small amount of data which will help to speed up the onboarding process for new users. This work generates new knowledge which will allow the researchers to implement various machine learning techniques with multiple behavioral patterns, thereby providing the best model performance for transfer learning.
Ultra Light-weight Encryption for Securing D2D Communication of ESP8266 IoT devices in Wireless Mesh Networks

System-on-chip (SoC) design is extensively used in the Internet of Things (IoT) devices, cyber-physical systems, and embedded systems. However, due to the increasing complexity of on-chip components and the long supply chain, SoC devices, such as ESP8266 IoT devices, are vulnerable to numerous cyberattacks. Consequently, developing and implementing security solutions for protecting these devices and their Device-to-Device (D2D) communications is increasingly becoming crucial. For the first time, this paper presents and investigates securing D2D communication of limited resources ESP8266 IoT devices in Wireless Mesh Network(WMN) using ultra light-weight encryption algorithms, including Speck Small and Diffie-Hellman key exchange using Curve 25519. This is important for protecting these limited-resources devices’ data confidentiality and integrity in critical applications. The experiment results prove that Speck Small and Curve25519 are more efficient than other core algorithms in computation time, demonstrating their suitability for ESP8266 IoT devices in WMN.
Facial Privacy Preservation using FGSM and Universal Perturbation attacks

Research done in Facial Privacy so far has entrenched the scope of gleaning race, age, and gender from a human’s facial image that are classifiable and compliant biometric attributes. Noticeable distortions, morphing, and face-swapping are some of the techniques that have been researched to restore consumers’ privacy. By fooling face recognition models, these techniques cater superficially to the needs of user privacy, however, the presence of visible manipulations negatively affects the aesthetic of the image. The objective of this work is to highlight common adversarial techniques that can be used to introduce granular pixel distortions using white-box and black-box perturbation algorithms that ensure the privacy of users’ sensitive or personal data in face images, fooling AI facial recognition models while maintaining the aesthetics of and visual integrity of the image.
Cat Swarm Optimization for Solving the N-Queens Problem

Many swarm intelligence algorithms study the behavior of animals and make animal-based systems for solving various tasks. Cat swarm optimization (CSO) is a swarm intelligence algorithm which was originally inspired by the resting and tracking behaviors observed naturally in cats. The N-Queens problem is a classical and complex constraint satisfaction problem which has been used as a benchmark for testing AI techniques for years. This paper aims to adapt the original CSO algorithm to the N-Queens problem by replacing the continuous addition/subtraction operations with swapping operations. The results report very fast convergence at smaller numbers of queens and point towards quicker convergence than similar work at larger numbers of queens, with improvements still needed for perfecting the algorithm.
ФОРМУВАННЯ ІНШОМОВНОЇ КОМУНІКАТИВНОЇ КОМПЕТЕНТНОСТІ СТУДЕНТІВ - МАЙБУТНІХ ІНЖЕНЕРІВ З ВИКОРИСТАННЯМ ІННОВАЦІЙНИХ ТЕХНОЛОГІЙ

The paper deals with actual problems of EFL communication competence under the conditions of flipped education with help of innovative teaching technologies. The optimum conditions to use IT instruments in order to teach English as EFL are regarded. The ways to improve students’ motivation are given. Real professional communicative situations are offered to simulate involving IT in order to improve communicative skills.
NepalEEW: Testing the feasibility of an earthquake early warning system in Nepal

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyNepalEEW: Testing the feasibility of an earthquake early warning system in NepalAuthors: Vaclav M. KunaVaclav M. KunaInstitute of Geophysics of the Czech Academy of SciencesSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751420.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractOwing to large earthquakes, a dense population, and vulnerable residential buildings, Nepal ranks among countries with the highest seismic risk in the world. In 2015, a magnitude 7.8 earthquake hit Nepal, claiming about 9000 human lives and leaving many homeless. Yet, even larger earthquakes are likely to come in the future. The impact of those earthquakes could potentially be limited by implementing an earthquake early warning (EEW) system, that is, a system that detects early stages of an earthquake, issues alerts, and provides critical time for users to seek cover or exit a building before the shaking arrives. Recent advances in the Internet of Things and cloud-based technologies have significantly reduced the cost of EEWs, making it available around the world. A startup company Grillo, Inc. together with an open-source initiative OpenEEW developed a straightforward, low-cost EEW solution available to the public. We test the new technology by establishing a real-time earthquake monitoring system in central Nepal, with the ultimate goal of testing the feasibility of EEW in the region.Keywords: earthquake early warning, internet of things, real-time seismic data acquisitionPermalink: https://doi.org/10.1190/image2022-3751420.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Vaclav M. Kuna, (2022), "NepalEEW: Testing the feasibility of an earthquake early warning system in Nepal," SEG Technical Program Expanded Abstracts : 3077-3080. https://doi.org/10.1190/image2022-3751420.1 Plain-Language Summary Keywordsearthquake early warninginternet of thingsreal-time seismic data acquisitionPDF DownloadLoading ...
Integration of multiscale attributes using a semisupervised machine learning algorithm

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyIntegration of multiscale attributes using a semisupervised machine learning algorithmAuthors: Salma AlsinanPhilippe NivletYazeed AltowairqiHarald KargSalma AlsinanSaudi AramcoSearch for more papers by this author, Philippe NivletSaudi AramcoSearch for more papers by this author, Yazeed AltowairqiSaudi AramcoSearch for more papers by this author, and Harald KargSaudi AramcoSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3728196.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe paper presents a machine learning workflow to integrate data from basin modelling, rock physics, geochemistry, and production to predict regional sweet spots in unconventional reservoirs. The presented workflow uses a combination of different statistical learning techniques to reduce the dimensionality of the attributes and combine them into a joint qualitative label at well locations. A semi-supervised algorithm is then used to propagate the classified labels to the unlabeled data whilst adhering to a set of predefined spatial constraints. The results show the strength of data integration in reducing the uncertainty associated with each data type.Keywords: machine learning, semisupervised learning, sweetspot, maturity, integrationPermalink: https://doi.org/10.1190/image2022-3728196.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Salma Alsinan, Philippe Nivlet, Yazeed Altowairqi, and Harald Karg, (2022), "Integration of multiscale attributes using a semisupervised machine learning algorithm," SEG Technical Program Expanded Abstracts : 1815-1819. https://doi.org/10.1190/image2022-3728196.1 Plain-Language Summary Keywordsmachine learningsemisupervised learningsweetspotmaturityintegrationPDF DownloadLoading ...
Text Security Using Vigenere Cipher and Hill Cipher

Data security in the system is a problem that until now has made researchers continue to develop science. cryptography as a data security technique, especially in text data through the implementation of classical cryptography. In this research, a combination of vigenere and hill cipher has been implemented. Both algorithms are algorithms that are easy to implement, but each has its drawbacks. Vigenere is easy to brute force, while hill cipher in the decryption process must use a reversible key so that not all keys are suitable for use. The combination of Vigenere and hill cipher has been implemented and tested using black box testing and the calculation of the Avalanche Effect. Tests using the Avalanche Effect resulted in an average value of 52,46%.
Perspectives on Neural Proof Nets

We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, namely Lambek's pregroup semantics.A key observation is that the monoidal category of (finite dimensional) vector spaces, linear maps and the tensor product, as well as any pregroup, are examples of compact closed categories.Since, by definition, a pregroup is a compact closed category with trivial morphisms, its compositional content is reflected within the compositional structure of any non-degenerate compact closed category.The (slightly refined) category of vector spaces enables us to compute the meaning of a compound well-typed sentence from the meaning of its constituents, by 'lifting' the type reduction mechanisms of pregroup semantics to the whole category.These sentence meanings live in a single space, independent of the grammatical structure of the sentence.Hence we can use the inner-product to compare meanings of arbitrary sentences.A variation of this procedure which involves constraining the scalars of the vector spaces to the semiring of Booleans results in the well-known Montague semantics.
Packer Identification Method for Multi-Layer Executables Using Entropy Analysis with &lt;i&gt;k&lt;/i&gt;-Nearest Neighbor Algorithm

The damage cost caused by malware has been increasing in the world. Usually, malwares are packed so that it is not detected. It is a hard task even for professional malware analysts to identify the packers especially when the malwares are multi-layer packed. In this letter, we propose a method to identify the packers for multi-layer packed malwares by using k-nearest neighbor algorithm with entropy-analysis for the malwares.
PRESTO

Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental or internal changes trigger a system response only after a violation of the system requirements. Despite a broad agreement that prevention is better than cure in self-adaptation, proactive adaptation methods are underrepresented within the repertoire of solutions available to the developers of self-adaptive systems. To address this gap, we present a work-in-progress approach for the <u>pre</u>diction of <u>s</u>ystem-level disrup<u>t</u>i<u>o</u>ns (PRESTO) through parametric model checking. Intended for use in the analysis step of the MAPE-K (Monitor-Analyse-Plan-Execute over a shared Knowledge) feedback control loop of self-adaptive systems, PRESTO comprises two stages. First, time-series analysis is applied to monitoring data in order to identify trends in the values of individual system and/or environment parameters. Next, future non-functional requirement violations are predicted by using parametric model checking, in order to establish the potential impact of these trends on the reliability and performance of the system. We illustrate the application of PRESTO in a case study from the autonomous farming domain.
Analisis Sistem Informasi Order dan Tracking Barang Menggunakan Metode Prototyping pada CV. Untung Maju Bersama

Entering an increasingly advanced era, the role of information technology is very important in various sectors. Currently, many business people have used information technology as a system in an agency or company. CV. Untung Maju Bersama is a company engaged in the sale of snacks and basic necessities. In its journey, the system used is still using a manual system. To maximize the sales system, therefore an analysis of the sales system design was made using the prototyping method to help improve the existing system. The system used on this website has three users, namely admin, sales, and delivery who have different access rights and have different tasks according to their respective job descriptions. In the analysis of this information system design, a prototyping method is used where the design made must be in accordance with the system to be built. Several features will be added in this proposed design system which did not exist in the previous system, namely the addition of an item tracking feature. This feature allows customers to see how far their orders have been processed. It is hoped that the design of this system can help CV. Untung Maju Bersama in maximizing sales and data management. &#x0D; Keywords: information systems, order systems, tracking systems, prototyping
Sistem Rekomendasi Pakaian Muslim Toko Al-Fath Semarang Dengan Collaborative Filtering

The many variations of Muslim clothing make customers confused in determining which clothes to buy. One solution is to use a recommendation system that can help customers find clothes according to their wants and needs. This study aims to create a product recommendation system that can provide product recommendations to Muslim clothing stores using the collaborative filtering method. The recommendation results will only display a maximum of 6 Muslim clothes that have been seen or purchased based on the similarity value of the highest Muslim clothing to the recommendation level with the lowest similarity. The results of Muslim clothing recommendations from customer A are Queen Dress (P0007) with a similarity value of 2.00, Trimuji Toyobo (P0006) with a similarity value of 1.40, Wonder Set 3in1 (P0008) with a similarity value of 1.00, Afnan Atasan Tunik (P0009 ) with a similarity value of 1.00 and Abaya Embroidery Maxi (P0010) with a similarity value of 1.00
Game Edukasi Interaktif Pengenalan Tata Surya Berbasis Animasi 2D untuk Siswa Kelas 6 SD

The solar system is one of the natural science lessons taught in grade 6 elementary school. This material is abstract because in studying it children cannot directly observe the phenomena of celestial bodies in the solar system. So far, only using books or 2D images as media. This study aims to create an educational game about the introduction of the solar system. 2D animation is made with icons and audio so that it can attract students' interest in learning and remembering solar system material. The result of the research is an educational game as an effective means of entertainment and learning. Students become interested in learning because of the application of the dissertation with attractive images and audio that helps increase enthusiasm for learning about the solar system. In testing the respondents as many as 22 people, the calculation results obtained as much as 59.1% strongly agree with this educational game
Kecerdasan Emosi Siswa terhadap Motivasi Belajar Siswa di Masa Pandemi Covid-19 SMP Jakarta Timur

The research aims to (1) describe emotional intelligence on the learning motivation of East Jakarta junior high school students during the Covid-19 pandemic, (2) analyze the effect of emotional intelligence on learning motivation of East Jakarta junior high school students during the Covid-19 pandemic. Using quantitative, with associative types. The sample is 100 junior high school students in Jakarta. The results of simple linear regression analysis, obtained the equation that learning motivation: 26,151 + 0,755 emotional intelligence. The constant value of learning motivation is 26,151, while the regression coefficient for emotional intelligence is 0.755. The constant value is positive, meaning that if there is no emotional intelligence, the learning motivation is 26,151. The regression coefficient is positive, meaning that there is a positive relationship between emotional intelligence variables and learning motivation. If emotional intelligence has increased by 1% then students' learning motivation will increase by 75.5%. Based on the results of the t test, obtained a significance value: 0.000 and t count: 14,404. Significance value: 0.000 less than 0.05, meaning that there is a significant influence between the independent variable of emotional intelligence on the dependent variable of learning motivation of East Jakarta Middle School students during the Covid-19 pandemic.. Research on the analysis of students' emotional intelligence on student learning motivation during the covid-19 pandemic is expected to find out new information about the factors of students' emotional intelligence problems during online learning so that it can increase students' online learning motivation. The method used is purposive random sampling, the use of questionnaires and descriptive analysis through statistical tests&#x0D; Keywords: Emotional Intelligence; Motivation to learn; Covid-19 pandemic