Reviewerly/exHarmony huggingface dataset:
Persona as Key Component in (Cultural) Person Branding

This article examines the elements and processes involved in the visual construction of person brands, and their personas as key components of those brands, in pursuit of the research question: What pictorial design strategies make person brands succeed? Key findings of the empirical investigation of the iconic artist brand Madonna allow a focus on Madonna’s image and her fans’ co-creative image practice through a visual frame analysis and cultural reading of her self-brand. Madonna has created a complex ‘worldview world’ that is governed by a metanarrative and feeds on the diverse acts of referencing cultural image icons. At the same time, central strategies of her image representations are reflected in the fan artefacts investigated. This article thus focuses not only on the role of the visual in person branding and in a modern-day visual brand culture. It also considers the place and form of such cultural person branding within the persona studies field.
Marketing, Digital Environment and Big Data: A Preliminary Discussion about the Influences within A Company

This article discusses how Big Data influences Brazilian enterprises from the view point of marketing, once there is a deepening in the use of Information Technology (IT) in the business environment and is a differentiated competitive factor, as well as for the consumer. The bibliographical research involved subjects such as the evolution of digital marketing, digital inclusion, co-creation, social media, and Big Data, to have a theoretical vision. Through in-depth qualitative research, using the Focus Group method, the impressions of professionals with academic backgrounds were collected regarding knowledge, abilities and necessary competences for these enterprises to have full results, the need for curve experience, adaptability to the exponential world, as well as the opportunities and challenges stemming from this reality. One of the highlighted points of this research was the need to be dealing in still evolving digital atmospheres and that of ethical behavior, both in the collection, dissemination, and application of digital marketing strategies, as well as in information security.
Learning to Recommend from Sparse Data via Generative User Feedback

Traditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions/ratings are highly scarce. To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback. The proposed framework consists of a recommender and a virtual user. The recommender is formulated as a CF model, recommending items according to observed user preference. The virtual user estimates rewards from the recommended items and generates a feedback in addition to the observed user preference. The recommender connected with the virtual user constructs a closed loop, that recommends users with items and imitates the unobserved feedback of the users to the recommended items. The synthetic feedback is used to augment the observed user preference and improve recommendation results. Theoretically, such model design can be interpreted as inverse reinforcement learning, which can be learned effectively via rollout (simulation). Experimental results show that the proposed framework is able to enrich the learning of user preference and boost the performance of existing collaborative filtering methods on multiple datasets.
Composition of weighted finite transducers in MapReduce

Abstract Weighted finite-state transducers have been shown to be a general and efficient representation in many applications such as text and speech processing, computational biology, and machine learning. The composition of weighted finite-state transducers constitutes a fundamental and common operation between these applications. The NP-hardness of the composition computation problem presents a challenge that leads us to devise efficient algorithms on a large scale when considering more than two transducers. This paper describes a parallel computation of weighted finite transducers composition in MapReduce framework. To the best of our knowledge, this paper is the first to tackle this task using MapReduce methods. First, we analyze the communication cost of this problem using Afrati et al. model. Then, we propose three MapReduce methods based respectively on input alphabet mapping, state mapping, and hybrid mapping. Finally, intensive experiments on a wide range of weighted finite-state transducers are conducted to compare the proposed methods and show their efficiency for large-scale data.
Abstract Contract Synthesis and Verification in the Symbolic 𝕂 Framework

In this article, we propose a symbolic technique that can be used for automatically inferring software contracts from programs that are written in a non-trivial fragment of C, called KERNELC, that supports pointer-based structures and heap manipulation. Starting from the semantic definition of KERNELC in the 𝕂 semantic framework, we enrich the symbolic execution facilities recently provided by 𝕂 with novel capabilities for contract synthesis that are based on abstract subsumption. Roughly speaking, we define an abstract symbolic technique that axiomatically explains the execution of any (modifier) C function by using other (observer) routines in the same program. We implemented our technique in the automated tool KINDSPEC 2.1, which generates logical axioms that express pre- and post-condition assertions which define the precise input/output behavior of the C routines. Thanks to the integrated support for symbolic execution and deductive verification provided by 𝕂, some synthesized axioms that cannot be guaranteed to be correct by construction due to abstraction can finally be verified in our setting with little effort.
Adoption of Cloud Computing Technology for Effective University Administration in Nigeria

Cloud computing is a technology that represents a shift from the traditional ownership of infrastructure and other resources to a more scalable pattern in which computer resources are rented online to organizations on either as a pay-as-you-use basis or by subscription. The researcher is motivated to undertake this study due to cloud computing growing awareness which has led to the increasing adoption of the technology by organizations in Nigeria including educational sector. However, the challenges that comes with the technology slow down the rate of its adoption by many universities in Nigeria and making them unconvinced to entrust into the cloud technology. This work investigates the use of cloud computing technology for effective administration in Nigerian universities. From analysis, the research reveals the strengths, weaknesses, opportunities and threats in the adoption of cloud technology and points out the concerns and challenges impeding its adoption. It further discusses the enormous benefits the universities stand to gain for adopting the cloud technology and proffers threat mitigation techniques to prevent the occurrence of the impediment towards the cloud adoption. The finding also recommends some cloud platforms as a means of IT delivery services for effective university administration.
Observation of Hong-Ou-Mandel interference with scalable Yb<sup>+</sup>-photon interfaces

We present a compact optical design for a scalable trapped ion quantum processor employing a single high numerical aperture lens for the excitation of ions and collection of photons, both of which are essential for remote entanglement generation. We verified the design by performing a quantum interference experiment between two photons generated by two sets of the proposed design and observed a 82(3) % suppression of coincidence within 8.13 ns time window when the two photons became indistinguishable. This design can be extended for the simultaneous generation of multiple pairs of entangled qubits with existing fiber-array devices.
Application for Selection of Student Final Project Supervisors Based on the Selected Category and Expertise of Lecturers Using the Naive Bayes Classifier Method

At the end of the task the supervisor has an important role for the success achieved and graduation of students. For this reason, ideal supervisors are needed for students. As discussed in the STMIK Hang Tuah Pekanbaru in the process of submitting the title of this thesis, so are some of the problems that arise, namely regarding the matter of coaching because the process is still using conventional methods that is based on personal knowledge of the Head of Study Program, the difficulty of the development process of submitting the Student's final position for difficulties check the final supervisor's assignment. The application of selecting the final project supervisor for students is the solution of the debate. The supervisor lecturer recommendation system that can utilize the naïve bayes classifier algorithm as a determinant of the probability of the lecturer results students can choose. Naive Bayes is a prediction technique based on simple probabilities based on the application of the Bayes theorem (Bayes rule) with a strong assumption of independence. The selection is based on the final criteria for the assignment and expertise of the lecturer. From the application of this recommendation is obtained from the recommendations of supervisors in accordance with the concept of the student's final project. With reference data, training and Bayes rules obtained sufficient results to satisfy students in getting a supervisor who is in accordance with the topic of the student's final project.
Sistem Pakar untuk Mengukur Tingkat Akurasi Dalam Mengidentifikasi Kepribadian Menggunaakan Metode Forward Chaining

Personality identification is one of the important things to know yourself and others. This identification is carried out based on the trait symptoms a person has based on the big five personality theory. This study aims to measure the level of accuracy in identifying personality based on visible trait symptoms. The data processed in this study were 10 patients. This data comes from psychology experts at the Faculty of Psychology, Riau Islamic University. The method used to process data is forward chaining. Based on the sample data processing that has been done to identify a person's personality and character, it has an accuracy rate of 100%. The results of this study obtained the type of personality which will then be understood by everyone to be able to identify their personality more deeply
Improved PSO Performance using LSTM based Inertia Weight Estimation

Particle Swarm Optimization (PSO) is first introduced in the year 1995.It is mostly an applied populationbased meta-heuristic optimization algorithm.PSO is diversely used in the areas of sciences, engineering, technology, medicine, and humanities.Particle Swarm Optimization (PSO) is improved its performance by tuning the inertia weight, topology, velocity clamping.Researchers proposed different Inertia Weight based PSO (IWPSO).Every Inertia Weight based PSO in excelling the existing PSOs.A Long Short Term Memory (LSTM) predicting inertia weight based PSO (LSTMIWPSO) is proposed and its performance is compared with constant, random, and linearly decreasing Inertia Weight PSO.Tests are conducted on swarm sizes 50, 75, and 100 with dimensions 10, 15, and 25.The experimental results show that LSTM based IWPSO supersedes the CIWPSO, RIWPSO, and LDIWPSO.
KEMAMPUAN MEMBACA MEMAHAMI TEKS DIALOG BAHASA MANDARIN SISWA KELAS XII SMA INSAN CENDEKIA SYECH YUSUF GOWA

Abstract. The research was conducted to obtain data and information about the ability to read and understand Mandarin in Class XII SMA Insan Cendekia Syech Yusuf Gowa. This type of research uses quantitative descriptive. The population in this study were students of class XII SMA Insan Cendekia Syech Yusuf Gowa which consisted of 2 classes with a total of 47 students. Sampling using random sampling with a sample size of 1 class consisting of 22 students. The research data were obtained through a reading skills test to understand Mandarin. Data analysis using average and percentage search techniques with results that show sufficient (75.2%). Thus, this study shows that the ability to read and understand students of class XII SMA Insan Cendekia Syech Yusuf Gowa is sufficient (75.2%).
PENINGKATAN KEMAMPUAN MEMBACA PERMULAAN MELALUI MEDIA BUKU ”MEMBACA ITU MENGASYIKKAN” DI TK PLUS AL BURHAN KECAMATAN BUARAN KABUPATEN PEKALONGAN

This study was designed to describe and analyze the increase in early reading skills using the media "Membaca Itu Mengasyikkan" at TK Plus Al Burhan, by analyzing the media application of books that use Early Childhood learning. The study was conducted in TK Plus Al Burhan in group B, the data collection method used observation, interviews, and documentation, the data analysis technique used descriptive qualitative. The application of the book "Membaca Itu Mengasyikkan" in TK Plus Al Burhan is as follows: Providing special time, classical techniques and individual techniques, Using the media of books "Membaca Itu Mengasyikkan", Providing a means of playing literacy. the advantage of this book is that learning to read will be more systematic and controlled so that parents at home can know until the development of children's reading skills. While the drawbacks / challenges are the need for caution in their application so that it is not even bad for children. development, information and measurement systems must be objective.
Semantics Altering Modifications for Evaluating Comprehension in Machine Reading

Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans. In this paper, we investigate whether state-of-the-art MRC models are able to correctly process Semantics Altering Modifications (SAM): linguistically-motivated phenomena that alter the semantics of a sentence while preserving most of its lexical surface form. We present a method to automatically generate and align challenge sets featuring original and altered examples. We further propose a novel evaluation methodology to correctly assess the capability of MRC systems to process these examples independent of the data they were optimised on, by discounting for effects introduced by domain shift. In a large-scale empirical study, we apply the methodology in order to evaluate extractive MRC models with regard to their capability to correctly process SAM-enriched data. We comprehensively cover 12 different state-of-the-art neural architecture configurations and four training datasets and find that -- despite their well-known remarkable performance -- optimised models consistently struggle to correctly process semantically altered data.
An Analysis of Derivational and Inflectional Morpheme in Selected News From Tempo.Co

The objectives of this research are to know the derivational and inflectional morphemes and to know the functions of morpheme in the selected news from Tempo.co. This research uses qualitative method. The analysis shows that the derivational and inflectional morphemes found in Tempo.co as 357 words. Derivational shows 97 data (27.17 %) and inflectional shows 260 data (72.83 %). Derivational changes the grammatical categories of words which consists of suffix and prefix, for example, noun-forming suffix changes the words/morphemes to nouns in the suffix –ion, -ment, -ist, -ship and –er. Adjective-forming suffix changes the words/ morphemes to adjectives in the suffix –able, -al, -ful, -ic, -cal, and –ous. Adverb-forming suffix changes the words/morphemes to an adverb in the suffix –ly. While, verb-forming prefix changes adjective to new adjective or form verb to new verb in the prefix un- and mis-. The inflectional does not change the grammatical categories of words which consists of “-s, -‘s, -er, -est, -s, -ed, -ing, and –en”. The suffix –s means plural. The suffix -‘s, -s’ means possessive. The suffix –er means comparative. The suffix –est means superlative. The suffix –ed means past. The suffix –ing means Ving (continuous). The suffix –en means participle.
Intelligent Voice Assisstant for Desktop using NLP and AI

In the coming future, virtual assistants will automate nearly all of the manual and time-consuming tasks. This project is an implementation of an intelligent voice assistant for Windows which includes the functionality of security through facial recognition. Until this day, there has not been any good alternative for Windows, so this project aims to implement a voice assistant for the Windows platform while describing the difficulties and challenges that lies in this task.
Intellectual Property on Works of Art Made by Artificial Intelligence

Rembrandt is considered as one of the greatest painters in the history of art in Europe and the world.Artificial Intelligence (AI) has been gaining a field of academic study due to the possibilities of its use in several fields of knowledge.Its application can be in facial recognition, in music composition, art painting, among others.The objective of the work is to verify the ownership of a work of art made by AI based on the traces made by Rembrandt.The methodology research used was qualitative, exploratory and descriptive, bibliographic and case study.AI made a painting with similar characteristics to Rembrandt's works and a doubt arose about the intellectual property of the work.We conclude that because it is a new fact, there is no legislation to support the deed and as intellectual property is related to human invention, it cannot be attributed to the machine.
Application of Modern Information Systems in the Framework of the Educational Course “Self-Determination and Professional Orientation of the Student’s Personality”

This publication reveals the possibilities of using modern information technologies as a part of the educational process at the university. The authors analyze the modern information technologies used in higher education. The paper formulates the didactic principles of applying modern information technologies and determines their significance. Information technologies implemented in the framework of the educational course “Self-determination and professional orientation of the student’s personality” are examined in detail. The paper describes the conditions for the implementation of this discipline and detailed steps for constructing it within the educational process. It also provides an analysis of the InfoDa distance learning system which is implemented at the FSBEI HE “Moscow State Pedagogical University” and allows running the entire training cycle, starting with the presentation of lecture material and ending with the monitoring of acquired knowledge and skills. Possibilities and features of the application of the InfoDa system are presented on the example of the educational course “Self-determination and professional orientation of the student’s personality”. The paper also shows the effectiveness of using the InfoDa system as modern information technology in the educational process. The conclusion is clear that modern information technologies make the educational process more productive, having a positive effect both on the level of students’ knowledge and on the formation of professionally important competencies.
Children as Crowbar? Justifying Censorship on the Grounds of Child Protection

This article studies how possible it is to use the need to protect vulnerable populations, such as children, as a justification to limit freedom of expression in democratic societies. The research was designed and conducted based on the idea that the regulation of speech and access to content is not only a matter of law and legislative interpretations, but also a question of social norms and values. The study is based on two surveys, one implemented with a representative sample of Norwegians aged 15 and older, the other with a sample of journalists. The results show that for the general population sample, 76% of respondents agreed that the protection of weak groups, such as children, is more important than freedom of expression. The data analysis also shows that gender, education, religious affiliation, trust in media and fear of a terrorist attack are all linked to the likelihood of agreeing with this statement. Women are 66% more likely than men to be in favour of limiting freedom of expression to protect weak groups, such as children. While the numbers of those who agree are lower among journalists, up to 50% of journalists still totally or partially agree that protecting weak groups is more important than freedom of expression. We discuss the policy implications of these results for democratic societies.
PENGGUNAAN GOOGLE FORM DALAM MENINGKATKAN EFEKTIVITAS EVALUASI PEMBELAJARAN DARING SISWA PADA MASA COVID19 DI SD IT BAITUL MUSLIM WAY JEPARA

This article aims to determine the advantages and disadvantages of using Google Forms in evaluating student online learning during the Covid19 pandemic at SD IT Baitul Muslim Way Jepara, East Lampung. This study uses qualitative methods and data collection techniques using observation, interviews, and documentation. The learning process at SD IT Baitul Muslim was carried out using Whatsapp, Zoom, and cellphone media. However, when the learning evaluation arrives, both the daily, mid-semester, and end-semester evaluations of SD IT BM use Google Form because the application from Google is very easily accessible to anyone provided they have a gmail.com account. The results showed that the learning evaluation using Google Form implemented by SD IT Baitul Muslim Way Jepara East Lampung was less effective. Due to feature limitations, design, and lack of editing history. In the realm of assessment, teachers are expected to not only refer to one type of Learning Evaluation but need to apply assessment (evaluation), namely continuity, objectivity, comprehensive, practical and cooperative which aims to optimize the ongoing learning. The positive contribution of this article is expected to provide information to education actors to improve skills and integrity in various learning activities in general, as well as in distance learning (online) specifically to facilitate the learning evaluation process during the current Covid19 pandemic.
ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION

The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with imbalanced data in the real situation. To handling imbalanced data issues, the most popular technique is resampling the dataset to modify the number of instances in the majority and minority classes into a standard balanced data. Many resampling techniques, oversampling, undersampling, or combined both of them, have been proposed and continue until now. Resampling techniques may increase or decrease the classifier performance. Comparative research on resampling methods in structured data has been widely carried out, but studies that compare resampling methods with unstructured data are very rarely conducted. That raises many questions, one of which is whether this method is applied to unstructured data such as text that has large dimensions and very diverse characters. To understand how different resampling techniques will affect the learning of classifiers for imbalanced data text, we perform an experimental analysis using various resampling methods with several classification algorithms to classify articles at the Indonesian Scientific Journal Database (ISJD). From this experiment, it is known resampling techniques on imbalanced data text generally to improve the classifier performance but they are doesn’t give significant result because data text has very diverse and large dimensions.