Reviewerly/exHarmony huggingface dataset:
AUTOMATIC IMAGE CAPTIONING USING DEEP LEARNING

Past years have definitely been very crucial to development of artificial intelligence and scene recognition has become the most contributed field of Computer Vision, and it has attracted the attention of many young researchers. Generating natural language descriptions automatically according to the content observed in a picture, is an important part of scene understanding, which combines the knowledge of computer vision along with natural language processing. The application of image captioning is vast and profound. Tremendous progress in scene recognition problems is credited to the availability of large databases like MS COCO, Places,ImageNet, etc, and the development of CNNs (Convolutional Neural Networks) for gathering high-level features. This paper aims to describe the methodology used and possible improvements.
Let’s Create!

Good day, dear readers! Now we’re here at Chapter 2 of our book. This chapter covers the topics related to Modeling tools and features of Blender. I’ll also be discussing a bit about some basic stuff for beginners of Blender so they can follow along without difficulty.
Review on Resume Analysis and Job Recommendation using AI

Applications of AI technology-based 60 minutes meeting have step by step attracted public attention and have become fashionable. The system developed within the same study may be explained in 2 elements, particularly from the attitude of the work person which of the work recruiter. we'll essentially take the majority of input resume from the consumer company which consumer company will offer the necessity and therefore the constraints to that the resume ought to be hierarchical by our system. This study depends on resume analysis and therefore the technique adopted area unit machine learning and text-mining primarily based artificial intelligence-NLP. The system works as talent recommendation system for the businesses and job recommendation system for the work candidates.
Powers and Perils

This article refers to:Design Bioethics: A Theoretical Framework and Argument for Innovation in Bioethics Research
Analisis Perangkat Lunak Penilaian Digital Pertandingan Pencak Silat Menggunakan ISO 9126

This study aims (1) to know the quality of software possessed by eventsilat.com aplications, which is good or bad according to ISO 9126, (2) to know how to measure the quality characteristics of functionality, reliability, usability, efficiency, maintainability, and portability on eventsilat.com applications and (3) to know the quality of the last result of functionality, reliability, usability, efficiency, maintainability, and portability on eventsilat.com aplications based on ISO 9126. The analysis was conducted through a test case of each ISO 9126 characteristic. The results of this study show (1) the eventsilat.com digital assessment applications as a whole get good interpretation according to ISO 9126 with a value of 3.09 (2) How to measure the quality characteristics of functionality, reliability, usability, efficiency, maintainability, and portability at eventsilat.com aplications is by measuring the interpretation value using Likert scale of each character and sub character ISO 9126 and then test case, and (3) the quality of the last result of functionality is very good with the value of 3,26, the quality of reliability is good with a value of 2.74, usability quality is very good with a value of 3.52, the quality of efficiency is good with a value of 2.91, the quality of maintainability is good with a value of 2.99, and quality portability is good with value of 3.09.
Deep Multi-Objective Multi-Stakeholder Music Recommendation

Many successful recommendation approaches rely on the optimization of a single objective function and focus on predicting ratings by considering customer and product features. In this paper, we consider the multi-objective recommendation problem for several stakeholders and introduce a large-scale recommender system that aims at satisfying multiple, potentially conflicting, objectives of consumers and vendors. We propose a "two-tower" neural network model for music recommendation that: (a) learns each of the stakeholders' objectives in a different tower, (b) shares the latent information that was learned in each tower to predict each stakeholder's objective, and (c) aggregates the predicted objectives to generate rating-based recommendations. Specifically, we focus on the following criteria: user satisfaction objectives, such as saves, likes, and degrees of engagement with songs; and artist satisfaction objectives, such as acquiring new fans. We apply our proposed deep architecture to music recommendation and examine the performance of our model on two proprietary industrial-scale datasets provided by Spotify and on a public music listening history dataset from Last.fm. We find that our model is effective in solving the multi-objective problem and achieves the best performance trade-off for the two stakeholders (users and artists), when compared to the original single-objective model and state-of-the-art multi-objective and multi-stakeholder methods.
Mobile Phones in Classrooms and in Professor-Student Communication: Ukrainian, Omani, and U.S. American College Students’ Perceptions and Practices

&lt;p&gt;The unprecedented expansion of wireless technologies and the global pandemic of 2020-2021, which forced many educational establishments out of traditional face-to-face and into online instructional environments, have created an urgency for achieving a better understanding of the various education-related uses of mobile phones, and students’ attitudes toward them, worldwide. We conducted a questionnaire-based study to explore college undergraduate students’ perceptions and uses of mobile phones, with a focus on instructor-student communication and classroom use, across three diverse cultural contexts: Ukraine, Oman, and the United States. Based on our findings, we suggest that conceptualizing mobile phones as cultural tools and situating their use within cultural discourses illuminates how – and explains why – mobile phones are not “the same” tools for all students. The findings offer insights into students’ (developing) perspectives on uses of mobile phones, and provide grounds from which to formulate productive, and culturally appropriate, means of using them for educational purposes.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
A Novel Deep Learning Model Based on Yolo-V2 and Resnet for Pedestrian Detection

Person on foot discovery is one of the significant errands in item location innovation. The person on foot identification calculation has been utilized in applications like shrewd video reconnaissance, traffic examination, and self-governing driving. Lately, numerous passer by discovery calculations have been proposed however the key downside is the precision and speed, which can be improved my coordinating productive calculations. The proposed model improves the person on foot identification calculation by incorporating two proficient calculations together. The model is created utilizing the joint rendition of ResNet and YOLO v2, which preforms highlight extraction and grouping separately. By utilizing this model, the effectiveness of the framework is expanded by improving the precision rate so it tends to be utilized with constant applications. The model has been contrasted and existing models like SSD, Faster R-CNN and Mask R-CNN. Contrasting and these models, the proposed model gives mAP esteem higher than these current models with less misfortune work when tried on the INRIA dataset.
WRITTEN CORRECTIVE FEEDBACK IN ACADEMIC WRITING CLASS: HOW DOES EXPERIENCED TEACHER LEAD THE EFFECTIVE PRACTICE?

&lt;p class="StyleAuthorBold"&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The research aimed to investigate an Indonesian academic writing teacher’s practice on written corrective feedback (WCF) in academic writing class. A case study involving an experienced Indonesian academic writing teacher and teacher students were employed. To gather data, a semi-structured interview was conducted. The data were then analyzed using the content analysis method. The findings indicated that the teacher’s practice on WCF was mediated by her language learning experience. Thus, the teacher provided WCF on her students’ writing drafts by considering the students’ personalities and their level of writing ability. In correcting student writing errors, the teacher used several types of WCF; direct and indirect correction; metalinguistic clues to the errors; and the reformulation of the wrong words. The relevant pedagogical implications for teachers in conceptualizing WCF and in learning and practicing it on their daily instruction based on their knowledge, experience, and reflection-on-practice.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p class="StyleAuthorBold"&gt;&lt;strong&gt;&lt;em&gt;Abstrak&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Penelitian bertujuan untuk mendeskripsikan praktik dosen Indonesia dalam penulisan akademik tentang umpan balik korektif tertulis (WCF) di kelas penulisan akademik. Penelitian termasuk studi kasus yang melibatkan seorang dosen Indonesia dalam menulis akademik yang berpengalaman dan mahasiswa. Untuk mengumpulkan data, dilakukan wawancara semi-terstruktur. Data tersebut kemudian dianalisis dengan menggunakan metode analisis isi. Temuan menunjukkan bahwa praktik dosen pada WCF dimediasi oleh pengalaman belajar bahasanya. Dosen memberikan WCF pada draf tulisan mahasiswanya dengan mempertimbangkan kepribadian mahasiswa dan tingkat kemampuan menulisnya. Dalam mengoreksi kesalahan menulis mahasiswa, dosen menggunakan beberapa jenis WCF; koreksi langsung dan tidak langsung; petunjuk kesalahan metalinguistik; dan perumusan ulang kata yang salah. Implikasi pedagogis yang relevan bagi dosen dalam membuat konsep WCF dan dalam mempelajari serta mempraktikkannya pada instruksi harian berdasarkan pada pengetahuan, pengalaman, dan refleksi pada praktik yang dilaksanakan.&lt;/em&gt;&lt;/p&gt;
The Impact of ICT on Financial Development: An Empirical Evidence from Syria

The digital revolution affects all economic aspects and the financial sector is not an exception. Despite the wide evidence on the impact of Information, Communication, and Technology (ICT) on the financial sector development, this topic has yet to be researched in Syria. We investigate the impact of five proxies of ICT on financial development using multiple regression model for the period 2005 to 2017. We also conduct marginal effect analysis to identify the impact of penetration of mobile phones on the financial development with war and without it. We find that there is a significant positive impact of the number of mobile subscribers on the financial development index in Syria. This result is consistent with Alshubiri et al. (2019) who find that an increase in fixed broadband has a statistically significant and positive effect on financial development in GCC countries. However, the number of telephone subscribers has insignificant impact on the Syrian financial development. The war seems to have a significant negative impact on the financial development in Syria. In addition, we find that the marginal effect of mobile subscription on the financial development index without war is larger compared to the case with war. We conclude that digital transformation has great potentials for advancing the Syrian financial sector especially if accompanied with more investment in ICT infrastructure.
Data Hiding in Digital Image for Efficient Information Safety Based on Residue Number System

The mass dispersal of digital communication requires the special measures of safety. The need for safe communication is greater than ever before, with computer networks now managing almost all of our business and personal affairs. Information security has become a major concern in our digital lives. The creation of new transmission technologies forces a specific protection mechanisms strategy particularly in data communication state.&#x0D; We proposed a steganography method in this paper, which reads the message, converting it into its Residue Number System equivalent using the Chinese Remainder Theorem (CRT), encrypting it using the Rivest Shamir Adleman (RSA) algorithm before embedding it in a digital image using the Least Significant Bit algorithm of steganography and then transmitting it through to the appropriate destination and from which the information required to reconstruct the original message is extracted. These techniques will enhance the ability to hide data and the hiding of ciphers in steganographic image and the implementation of CRT will make the device more efficient and stronger. It reduces complexity problems and improved execution speed and reduced the time taken for processing the encryption and embedding competencies.
SQL Generation from Natural Language: A Sequence-to-Sequence Model Powered by the Transformers Architecture and Association Rules

Using Natural Language (NL) to interacting with relational databases allows users from any background to easily query and analyze large amounts of data.This requires a system that understands user questions and automatically converts them into structured query language such as SQL.The best performing Text-to-SQL systems use supervised learning (usually formulated as a classification problem) by approaching this task as a sketch-based slot-filling problem, or by first converting questions into an Intermediate Logical Form (ILF) then convert it to the corresponding SQL query.However, non-supervised modeling that directly converts questions to SQL queries has proven more difficult.In this sense, we propose an approach to directly translate NL questions into SQL statements.In this study, we present a Sequence-to-Sequence (Seq2Seq) parsing model for the NL to SQL task, powered by the Transformers Architecture exploring the two Language Models (LM): Text-To-Text Transfer Transformer (T5) and the Multilingual pre-trained Text-To-Text Transformer (mT5).Besides, we adopt the transformationbased learning algorithm to update the aggregation predictions based on association rules.The resulting model achieves a new state-of-the-art on the WikiSQL DataSet, for the weakly supervised SQL generation.
Experimental realization of phase-controlled dynamics with hybrid digital–analog approach

Quantum simulation can be implemented in pure digital or analog ways, each with their pros and cons. By taking advantage of the universality of a digital route and the efficiency of analog simulation, hybrid digital-analog approaches can enrich the possibilities for quantum simulation. We use a unique hybrid approach to experimentally perform a quantum simulation of phase-controlled dynamics resulting from a closed-contour interaction (CCI) within certain multi-level systems in superconducting quantum circuits. Due to symmetry constraints, such systems cannot host an inherent CCI. Nevertheless, by assembling analog modules corresponding to their natural evolutions and specially designed digital modules constructed from standard quantum logic gates, we can bypass such constraints and realize an effective CCI in these systems. Based on this realization, we demonstrate a variety of related and interesting phenomena, including phase-controlled chiral dynamics, separation of chiral enantiomers, and a new mechanism to generate entangled states based on CCI.
General Theory and Tools for Proving Algorithms in Nominative Data Systems

Summary In this paper we introduce some new definitions for sequences of operations and extract general theorems about properties of iterative algorithms encoded in nominative data language [20] in the Mizar system [3], [1] in order to simplify the process of proving algorithms in the future. This paper continues verification of algorithms [10], [13], [12], [14] written in terms of simple-named complex-valued nominative data [6], [8], [18], [11], [15], [16]. The validity of the algorithm is presented in terms of semantic Floyd-Hoare triples over such data [9]. Proofs of the correctness are based on an inference system for an extended Floyd-Hoare logic [2], [4] with partial pre- and postconditions [17], [19], [7], [5].
A Research Review on Artificial Intelligence in Marketing

Today, AI is playing an increasingly important role in optimizing marketing decisions, improving consumer experience, and establishing and maintaining customer relationship. Although researchers in this field have made certain achievements, due to the short application history of AI in marketing, there are some obstacles for research in this field such as lack of systematic integration of research results and core literature, confusion of related concepts, unclear research focus and future direction. This review focuses on the above deficiencies, helps readers to systematically sort out the knowledge framework of AI research in marketing and to comprehensively understand the current research progress, and hopefully provides reference or inspiration for future interdisciplinary research.Firstly, in order to show the overview of the existing research intuitively, this review analyzes 690 papers from the WOS database, through the CiteSpace knowledge maps. We find that the “marketing + AI” research is mainly based on three aspects: the introduction of basic AI technology, marketing data analysis using AI, and the emerging “AI+service” ideas. Besides, the research in the marketing field can be summarized into four hot topics: service interaction, product design, controversial issues, and data analysis.Secondly, we switch to a micro perspective to sort out the concept of AI. As previous research argues, the definition of AI can be summarized into two categories, namely, a human-centered approach and a rationalist approach, and scholars can focus on different definition perspectives according to what they emphasize. In order to define AI more clearly, this review compares AI concept with AI related algorithms or models like machine learning, and with other technology like IoT, robotics, etc. Researchers in this area should pay attention to these differences.Thirdly, this review further summarizes and comments on previous research in theories, themes and methodologies. The theories involve anthropomorphism, social presence, compensatory consumption, etc. Based on the four hot topics obtained above, this review separately discusses the role of AI in the service, the possibility of AI in product design, the controversy arising from the application of AI, and the significance of AI’s data analysis function for marketing. Furthermore, the coding result of research methods shows that empirical papers dominate now and few researches use actual AI technologies to prove its conclusions.Finally, we discuss future research directions in terms of themes and methodologies. For example, how will consumers’ expectation of the relationship between themselves and AI change over time? What characteristics of consumers and environmental factors can make the humanoid appearance and social skills of service robots produce better results? How to benefit from AI technology without hurting consumer welfare? In each stage of marketing, how to use AI technology to conduct more accurate data analysis? How to further standardize the overall measurement tools and variables in this area? etc.
Robust and Efficient Estimation of Relative Pose for Cameras on Selfie Sticks

Taking selfies has become one of the major photographic trends of our time. In this study, we focus on the selfie stick, on which a camera is mounted to take selfies. We observe that a camera on a selfie stick typically travels through a particular type of trajectory around a sphere. Based on this finding, we propose a robust, efficient, and optimal estimation method for relative camera pose between two images captured by a camera mounted on a selfie stick. We exploit the special geometric structure of camera motion constrained by a selfie stick and define this motion as spherical joint motion. Utilizing a novel parametrization and calibration scheme, we demonstrate that the pose estimation problem can be reduced to a 3-degrees of freedom (DoF) search problem, instead of a generic 6-DoF problem. This facilitates the derivation of an efficient branch-and-bound optimization method that guarantees a global optimal solution, even in the presence of outliers. Furthermore, as a simplified case of spherical joint motion, we introduce selfie motion, which has a fewer number of DoF than spherical joint motion. We validate the performance and optimality of our method on both synthetic and real-world data. Additionally, we demonstrate the applicability of the proposed method for two applications: refocusing and stylization.
Rethinking Comprehensive Physical Education for Young Learners with COVID-19

COVID-19 pandemic is causing drastic changes in the exiting educational environment. Korean government guidelines on the physical distance between people, banning indoor physical activities, and restricting even outdoor physical education in educational institutions promote physical inactivity among young learners. General subjects in schools have been replaced by virtual education, but physical education still exposes the following challenges: First of all, physical education teachers and students often experience a lack of systematic platforms in virtual physical education. It is difficult to change the contents of physical education for virtual learning even if they change the contents, the environment imposed on young students varies too much by an individual to carry out and learn the contents correctly. Secondly, physical education teachers are not familiar with legal issues of various educational content needed for virtual learning, and there is a great fear of legal punishment regarding infringement of copyright and intellectual property of educational contents. Lastly, for young students, effective communication with teachers is becoming more difficult compared to the traditional face-to-face physical education setting. The paradigm shift, which boldly breaks away from existing learning objectives and provides sufficient infrastructure to prevent learners from receiving inequality in physical education, and maintains healthy physical strength through easy content, will be the way to overcome the changing educational environment due to COVID-19 pandemic.
Aplikasi Spoxtech Untuk Penyandang Tuna Rungu – Wicara Menggunakan Algoritma Hidden Markov Model dan Metode Finite State Automata (FSA)

Communication is a very important thing to establish social relations. In communicating, deaf and mute persons have difficulty conveying what information they want to convey. The Android-based Spoxtech application is an application specially designed for deaf and speech impaired persons. As the name implies, Spoxtech is taken from the word speech to text - text to speech. This application can make it easier for users to communicate because it is equipped with a voice menu that functions to detect the spoken voice which is then converted into text and a typed menu which functions to detect writing which is then converted into voice form. This application was created using the Hidden Markov Model algorithm in generating sound into text and using the finite state automata method in generating text into sound by performing two-level decapitation. This results of this study were carried out by testing the whitebox and blackbox. In this research, the text normalization test on the typewriter menu was also carried out, namely each word containing currency, time, temperature, and number units. The accuracy value obtained in testing this application uses Indonesian by testing the voice menu and typing menu and testing the two-level decapitation, the value is 100% of the 300 words in Indonesian.