Reviewerly/exHarmony huggingface dataset:
Decision Support System for Selection of the Superior Mango Seeds Using Web-based Analytical Hierarchy Process (AHP) Hybrid Simple Additive Weighting (SAW) Method

Indonesia is a horticultural country that agricultural production, one of which is mango production. Mango (Mangifera indica L) is one of the leading horticultural commodities in Indonesia. The use of high-quality seeds has made an impact influence on the productivity of farming, to increase the productivity of farming, it is very necessary to provide superior seeds for farmers so that farmers can increase yields and quality of production. With so many manga seeds available, a Decision Support System is needed or often called a Decision Support System (DSS). DSS is a model-based system consisting of procedures in processing and considerations to assist farmers (users) in making decisions on the selection of high-quality manga seeds. In this research, the method used is the Analytical Hierarchy Process (AHP) in searching for the weighting criteria and the Simple Additive Weighting (SAW) method in performing alternative rankings. The results of this study are to make it easier for farmers and the community in choosing superior manga seeds.
Quantum-enhanced filter: QFilter

Convolutional neural networks (CNN) are used mainly to treat problems with many images characteristic of deep learning. In this work, we propose a hybrid image classification model to take advantage of quantum and classical computing. The method will use the potential that convolutional networks have shown in artificial intelligence by replacing classical filters with variational quantum filters. Similarly, this work will compare with other classification methods and the system’s execution on different servers. The algorithm’s quantum feasibility is modelled and tested on Amazon Braket Notebook instances and experimented on the Pennylane’s philosophy and framework.
GRADES-NDA'22: 5th International Workshop on Graph Data management Experiences and Systems (GRADES) and Network Data Analytics (NDA)

GRADES-NDA is the premier workshop on graph data management and analytics that aims to bring together researchers from academia, industry, and government. GRADES-NDA'22 is a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards addressing domain specific challenges or handling noise in real-world graphs. In 2022, GRADES-NDA is in its fifth edition.
PENINGKATAN MINAT BELAJAR AKSARA BALI MELALUI MEDIA INTERAKTIF BERBASIS TEKNOLOGI INFORMASI DI LINGKUNGAN PENDIDIKAN ANAK USIA SEKOLAH DASAR

The influence of culture and foreign languages that enter to the life of local communities makes the use of regional languages and scripts increasingly shifting in the lives of Balinese people especially in this information technology era. Information technology media based on augmented reality applications on smartphones can be used as alternative learning media. the availability of smart phones and the habits of elementary school age children in playing gadgets can be used as tools to encourage children's interest in learning Balinese script. The method of playing while learning is an effective method to develop the desire to learn Balinese script. An interactive learning model by combining three-dimensional animation and sound (Augmented Reality) can make elementary school-aged children interested in playing and practicing it. The application also provides examples of the use of characters in a word and the use of pasang-pageh with their explanations.
Automated Checking of Knowledge of Lab Works on the Course “Thermodynamics”

The task was set to automate the process of protection to control knowledge in the process of defending laboratory work in the course "Thermodynamics". The article proposes the experience of the Department of Theoretical Foundations of Heat Engineering. M.P. Vukalovich (TOT named after M.P. Vukalovich) NRU MPEI for automated testing of students' knowledge on the performed topic of laboratory work. A feature of automated work protection is the ability to organize the protection process in a laboratory equipped with computers, and organize the protection process in a remote form. Remote access allows you to check level of knowledge on-line.
Webinar gives tips on starting legacy giving programs

Nonprofit Business AdvisorVolume 2022, Issue 394 p. 9-9 Resources Webinar gives tips on starting legacy giving programs First published: 10 June 2022 https://doi.org/10.1002/nba.31263Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2022, Issue394July 2022Pages 9-9 RelatedInformation
Technical Perspective - No PANE, No Gain

The machine learning community has traditionally been proactive in developing techniques for diverse types of data, such as text, audio, images, videos, time series, and, of course, matrices, tensors, etc. "But what about graphs?" some of us graph enthusiasts may have asked ourselves, dejectedly, before transforming our beautiful graph into a brutalistic table of numbers that bore little resemblance to its parent, nor the phenomena it represented, but could at least be shovelled into the machine learning frameworks of the time. Thankfully those days are coming to an end.
Engineering Education in the Conditions of Transition to Distance Learning in the Pandemic Period: Empirical Assessments and Forecasts

The article discusses the organization of the transition of engineering education from traditional education to distance learning in the emergency conditions of a pandemic, its legislative form, problems and development prospects. The main attention in the work focused on the results of an online survey of students of the Federal State Budgetary Educational Institution of Higher Education "NRU" MPEI "on the level of their satisfaction with the educational activities of the university in the context of the exclusive use of e-learning and distance educational technologies. In the process of the sociological study, the problems of a sharp transition to a remote format identified. The experience of problems must take into account in the future. The opinion about the effectiveness of distance learning in the implementation of the educational process was confirmed.
Bookbinding

Booksellers were not necessarily concerned with describing physical properties, and sometimes gave no condition details at all; but in their catalogues can be traced something of the changing fortunes of some well-known early printed books, reflecting in turn the changing tastes and preoccupations of successive collectors. This serves as background to a very gradual change in understanding of old bindings, particularly by W.H.J.Weale.
Conclusion and Outlook

This chapter gives a summary, conclusion, and outlook on the interplay between AI and physics overall. As an impact of physics on AI, the most recent quantum computer is described. It is the first to exhibit more than 100 qubits and is thus so superior that it can no longer be simulated by a conventional supercomputer. A quantum computer breaking through the “sound barrier” of 1,000 qubits is announced for 2023; experts claim this is the barrier a quantum processor must overcome to be of first efficient use. These advancements will allow the current shortcomings of AI to be overcome and make it more similar to human intelligence which is still superior to AI in various ways. Most of all, human brains are a million times more energy efficient than AI and thus really high-tech! However, the power of AI is increasing exponentially, and technological singularity may be achieved within a few decades. AI researchers expect that artificial general intelligence would be developed by 2040–50. Following thoughts by Stephen Hawking, we could come to the point at which AI would drive science forward by itself, for the sake of technology and human knowledge; the power of AI for physics would then fully unfold.
AI-enabled Bot and Social Media

The impact of AI-enabled bot is a subject of discussion in academic venues, while the general public is not much aware of it. The spread of fake news on Online Social Networks has been under investigation for several years by the research community. Initially, socialbots were designed to perform repetitive tasks faster and more efficiently than humans, such as automated content aggregators. This chapter provides a review of the latest AI technologies supporting bots. It discusses the malicious activities of socialbots documented in the literature and reviews the state-of-the-art of existing detection methodologies. The chapter analyses the AI-based technological pillars supporting socialbots, presents the state-of-the-art of existing socialbots, and lists the most important toolkits, frameworks, and SDKs publicly available for developing socialbots.
Teachers' Representations of Digital Education in Morocco

Communication and information technologies in education have proven to be effective in teaching. But what is the opinion of teachers about the integration of digital technology in education in Morocco? Therefore, it is legitimate to ask, what are the social representations of primary school teachers on digital education? Is it perceived as an education or is it limited to a tool? Indeed, recent research on digital literacy and on digital technology in general has led the authors to ask some questions: Is digital education an education to or a medium for learning? Why is there a gap between ministerial prescriptions and teaching practices? What are the representations of digital technology in the teaching world? In order to carry out the study, the authors conducted a survey for teachers of different cycles.
A Face Recognition Security Model Using Transfer Learning Technique

Nowadays security plays a vital role in day-to-day life. Without it, so many problems will arise and it also pave way for the occurrences of illegal entries and information theft, security attacks. Due to this, confidential information gets lost and this led to many issues. To avoid this, we have created a system of facial recognition which completely detects and recognizes the face by capturing images of user, crop them and saves them in a database. If any unauthorized user enters the security system, then it will detect them, and prevent does not allow them from entering into the security system. In this project, we have applied a transfer learning model based on Alexnet Deep learning application to develop a real time facial recognition system which has good robustness to cope with pose and lighting, reduce dimensionality, complexity and better recognition accuracy. This system accurately detects and compares the image that are stored in the database. And accurately provides the results at faster rates. Also, this system would be able to create and read a database of different users.
A Corpus-Based Study on Syntactic Features of Maritime English News Headlines

The purpose of this study is to explore the syntactic features of maritime news headlines from three aspects: sentence structure patterns, sentence voices and omissions, with the help of SPSS, WordSmith Tools and CLAWS4. Three findings are noted. First, simple sentences are used much more frequently in the Maritime News Headline Corpus (MNHC) than the General News Headlines Corpus (GNHC). Among the seven basic modes of simple sentences, SVOA, SVO, and SVA are the most common patterns used in the two corpora. Second, there were no significant differences between the two corpora in sentence voice. However, the active voice is used more frequently in the two corpora than the passive voice. Third, there were no significant differences between the corpora in omissions. However, the data showed a significant similarity in that the omission of relative verbs, auxiliary verbs, articles, conjunctions and pronouns in the MNHC, as seen in the GNHC.
Supplemental Material: Geoheritage: Progress toward Preservation

&lt;p&gt;Table S1. Geoheritage video and Story Maps, aligned to the Next Generation Science Standards Earth and Space Science Disciplinary Core Ideas, with suggested free resources from SERC Carlton Teach the Earth Collection. &lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
Numerical Analysis of Synchronous Dislocation Scheduling Algorithm Optimization in Shared Cloud Computing Environment

This paper studies the numerical analysis of the optimization of the synchronization misalignment scheduling algorithm under the shared cloud computing environment. First, the user submitted job is initialized, and then the job is inserted into the execution queue according to the appropriate strategy of the user synchronization misalignment scheduling algorithm. Establish multiple simultaneous multiple vacations Queuing model. The steady-state distribution of the system model is given by the quasi-birth-death process and the matrix geometric solution method, and the expressions of the average response time of the task and the average power of the system are derived. The test results show that in the case of multiple virtual machines, compared with Xen's Credit scheduling algorithm, the synchronization optimization scheduling system CCHybrid improves the performance of parallel loads by 15% to 50%, and the performance impact on non-parallel loads does not exceed 5%.
Image recoloring of printed fabric based on the salient map and local color transfer

Factories must proof different colors on fabric in order to verify the feasibility of the color scheme. This proofing process is time-consuming and laborious. To recolor printed fabric images, a novel image recoloring method was proposed in this paper. This method can provide convenience for printed fabric designers and can be used to simulate printed fabric proofing. The relative total variation model was implemented to remove the fabric image texture and noise. Next, in the CIE1976 L* a* b* color space, the mean-shift clustering algorithm was utilized to segment the reference image and the target image to obtain the separated color regions. Then, the color regions of the reference image and the target image were matched based on the salient map and the values of the L* a* b* channels. Finally, local color transfer was performed between the matched color regions based on the matching results. Experiments were conducted on 100 printed fabrics with different color schemes. Results indicated that the proposed method can transfer the color appearance of the reference printed fabric image to the target printed fabric image and realize printed fabric image recoloring.
Optical quantum state engineering with multimode resources: between a drawback to be circumvented and an advantage to be exploited

In this work, we study the generation of non-Gaussian quantum states of light using multimode quantum resources. We first propose a theoretical framework to describe photon subtraction operation - a well known non-Gaussian operation - applied to a multimode Gaussian state. We apply this framework to the case of Schr&ouml;dinger kitten state generation using a multimode squeezed vacuum as input state. We show that a monomode non-Gaussian state with high purity can be generated by a better choice of the different experimental parameters. Then, we propose a new protocol to generate high amplitude non-Gaussian states using spectral multiplexing of input states and Gaussian operations. We show that this protocol can implement the cat breeding operation with less complexity. We show also that this protocol can be used for the generation of approximated GKP state.
ω- Fuzzy Translation and Multiplication in BH-Algebras

This paper focuses on the novel exploration of \(\omega\) - Fuzzy Translation and Multiplication in \(B H-\) Algebras. To explore this \(\omega\)-Fuzzy is defined first in \(B H\) - Algebras and used in Fuzzy Translation and Multiplication in \(B H-\) Algebras by tracing its algebraic properties.
Recognition of facial expression using spatial transformation network and convolutional neural network

With the development of artificial intelligence, the field of sentiment analysis can be used in various industries such as computer-human interaction technology, personal status monitoring, criminal investigation, and entertainment. In the field of sentiment analysis, various methods such as facial expression, voice, EEG signal, and text are being studied. Among these methods, facial expression recognition is one of the approaches being actively studied because it has the advantage of being relatively easy to collect learning data and easy to apply to real life compared to other methods. Recently, research on facial expression recognition using deep learning has been actively conducted, and it shows relatively high performance. The method using deep learning has advantage of being easy to apply to a variety of data, but there is a limitation in large deviation in accuracy depending on the effect of occlusion, pose, and illumination in extracting feature points. In addition, in the case of expression recognition, similar objects such as face always exist in the data, and only some specific regions such as eyes, nose, and mouth have necessary information for learning, and remaining regions such as background and hair is considered as insignificant part of the data. Therefore learning all the features in the data not only takes a long time to learn, but also uses computing resources inefficiently. Therefore, we propose a convolutional neural network algorithm combined with a spatial transformation network which helps facial expression recognition by focusing on a specific part of the face.
Enhancing Product Comparison through Automated Similarity Matching

The volume, variety and velocity of products in software-intensive systems product lines is increasing. One challenge is to understand the range of similarity between products. Reasons for product comparison include (i) to decide whether to build a new product or not (ii) to evaluate how products of the same type differ for strategic positioning or branding reasons (iii) to gauge if a product line needs to be reorganized (iv) to assess if a product falls within the national legislative and regulatory boundaries. We will discuss two different approaches to address this challenge. One is grounded in feature modelling, the other in case-based reasoning. We will also describe a specific product comparison process in which a product configured from a product line feature model is represented as a weighted binary string, the overall similarity between products is compared using a binary string metric, and the significance of individual feature combinations for product similarity can be explored by modifying the weights. We will illustrate our ideas with a mobile phone example, and discuss some of the benefits and limitations of this approach.
SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models

Large scale pre-training models have been widely used in named entity recognition (NER) tasks.However, model ensemble through parameter averaging or voting can not give full play to the differentiation advantages of different models, especially in the open domain.This paper describes our NER system in the SemEval 2022 task11: MultiCoNER.We proposed an effective system to adaptively ensemble pre-trained language models by a Transformer layer.By assigning different weights to each model for different inputs, we adopted the Transformer layer to integrate the advantages of diverse models effectively.Experimental results show that our method achieves superior performances in Farsi and Dutch.
SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes. However, these state-of-the-art approaches face several major limitations, including lacking interpretability, high false positive rates and the weak robustness to evasion techniques, These limitations mean that existing real-world methods for detecting Ponzi schemes are ineffective. In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, we propose a heuristic-guided symbolic execution technique to identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.
Development of Solar Panel Models in Different Countries/Regions

Energy conservation is becoming a major issue worldwide due to increase in the usage of different home appliances that consume high amount of energy. Solar energy projects are affected by several factors while selecting area for those projects. Therefore, selection of area for establishing the infrastructure of these projects is a critical issue. For any country or region that aims to develop its electric network, renewable energy is suggested due to it is clean and environmentally friendly nature. The present research uses a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) method to obtain the required design of solar panels. Using the curve fitting, the ANFIS helps estimate the requirements of different models suitable for different countries and regions. The derived models are formulated to function based on the number of units consumed per day in the considered country or region. Based on the load variation identified using the derived models in the solar panels, the number of panels required to fulfill the increase in the load in the future is predicted. In this chapter, the different models are obtained, and a comparison is made between the considered countries/regions. Finally, it is concluded that the solar energy helps reduce the use of fossil fuels in power generation.
Students’ Social Networks in a Digitalised and Multicultural World

This conceptual piece attempts to evaluate the rising disruptions of students’ social networks due to the digital explosion and massification of education. Digitalised education has made impacts in terms of universalising and internationalising education, but there are corresponding disruptions in students’ social network system. It is perhaps safe to state that students’ social network is about to be exterminated. Thus, this effort is a holistic view of students’ network activities that can enhance interactivity, bonding, and attachment to promote multicultural and intercultural competencies. Education is only holistic when it stimulates the cognitive, affective, and psychomotor domains proportionately. Pathetically, technologisation and digitalisation threaten the existence of holistic education and make it a remnant of the past if scholarship discourse fails to address the challenge now. Hence, this endeavour recommends the inclusion of activities that can constantly bring digital students together from time to time to create room for interaction and socialisation.