Reviewerly/exHarmony huggingface dataset:
Readiness and Potential Application of Smart Contracts in the Indonesian Construction Industry

A construction project requires a contract to run safely by minimizing costs, schedules and maintaining project quality. In traditional contracts, contractual transactions between trustless parties are generally conducted in a centralized form, requiring a trusted third party to act as witnesses and make them legally binding, enforceable, and trustworthy. However, involving third parties is associated with high costs and delays. In this case, Smart Contracts can be a solution. A smart contract is a code that contains rules that will execute a transaction itself according to the agreement of two parties. Smart Contracts contained in Blockchain Technology have received much attention globally, including in the construction industry. Smart Contracts can enable automatic payments without delays, hassles and without third parties (mediators) carried out in a decentralized network to ensure integrity and transparency by preventing the potential of record manipulation existed in traditional contracts. This paper presents an overview of the potential application of Smart Contracts in Indonesian construction industry. This paper was prepared based on a literature review of several existing studies to obtain data related to the current state of Blockchain Technology, technological readiness, and the potential areas that Smart Contracts can be applied. It is hoped that the findings from the literature review will explain various aspects of the potential for adopting Smart Contracts in the construction industry and can assist in the development of further research that will focus on the application of Smart Contracts in the Indonesian construction industry.
Drop Shadows and Product Lightness Perceptions: An Abstract

Marketing research evidences that despite their stylistic and peripheral nature, product shadows can have a tangible impact on consumer perceptions and their behavioral inclinations (Sharma, 2016, 2018; Sharma & Romero, 2020). However, prior research has only explored the effects of shadows that touch the casting object as well as the background surface. This research investigates the impact of a unique type of shadows (known as drop shadows), that do not touch the casting object or the background and hence, lend a floating appearance to the object (Adelson, 2001; Friedlander, 2012; Kliever, 2021). Building on multidisciplinary research such as that in the field of Augmented Reality (AR) evidencing an effective use of drop shadows in facilitating grasping interactions of free-floating objects (Al-Kalbani et al., 2019, 2020), this research proposes that advertising a product with its drop shadow should increase product lightness perceptions. Furthermore, for many product categories like electronics where lightness is a desirable product attribute, the current work proposes a positive impact of advertising a product with its drop shadow on critical downstream consumer outcomes like product purchase intentions (Deng & Kahn, 2009; Sharma & Romero, 2020). Findings from multiple experimentally designed studies testing various products evidence a significant positive impact of the presence versus absence of drop shadow in the ad on product lightness perceptions as well as on the tested downstream outcomes. The current study findings also reveal that lower product weight expectations serve as a boundary condition for the positive effects of drop shadow on downstream consumer outcomes. Drop shadows are a common occurrence in many product ads but this research is the first to systematically study their impact on consumer product perceptions. Counter to previous marketing research investigating shadows that touch the casting product in evidencing how they increase product heaviness perceptions (Sharma & Romero, 2020), results from this research reveal that drop shadows do the opposite and enhance product lightness perceptions. The current findings have important implications for advertising design in educating marketers about drop shadows as stylistic visual cues that can not only influence product weight perceptions but also impact consumers’ behavioral inclinations, particularly in distant online shopping contexts.
Text Me If You Can! The Influence of Modality in Consumer Interactions with AI Digital Assistants: An Abstract

AI-based digital assistants are Internet-enabled devices which provide daily technical, administrative and social assistance to their users and can be used as tools for online shopping, learning, controlling other smart applications and devices as well as for communications and companionship (Han & Yang, 2018; Santos et al., 2016). In particular, major tech companies are heavily investing in the development of AI digital assistant primarily designed to facilitate voice interactions between users and the systems (e.g., Amazon Alexa, Apple Siri, Google Assistant, Microsoft Cortana). However, many of the current voice-activated digital assistants offer additional modality options to interact with their users, including “type-in” features. For example, recent versions of Amazon Alexa come with a keyboard option that allows users to type in their requests or read Alexa’s replies. While the ways in which we interact with technology, and particularly with digital voice-assistants, is variegate, few studies have explored the influence that digital voice assistant modality has on consumers responses and behaviors (Melzner et al., 2020). The present research aims to fill this gap and, by focusing on consumers-digital assistants’ interactions, it investigates the effect of interaction modality (i.e., voice vs text) on consumers feelings of power, comfort and recommendation acceptance. Specifically, we posit that the modality through which consumers interact with the conversational digital assistant (text vs voice) may change consumers’ responses and impact their psychological states and subsequent behavior (Melumad et al., 2020). Since higher level of perceived anthropomorphism have been proved to be detrimental to consumers perceptions of power and control (Jörling et al., 2019), we expect that when individuals interact with AI digital assistants through voice (vs text) interfaces, their feelings of power over the technology as well as comfort decrease (increase) resulting in lower (higher) recommendation acceptance. Moreover, we investigate the moderating role of users’ previous experience with the digital assistant. Previous studies showed that when interacting with conversational agents, consumers may perceive the interaction differently depending on whether they already have experience of communicating with a digital assistant (Hildebrand et al., 2019). Building on this we suggest that previous experience moderates the effect of the digital assistant interaction modality on perceived power, comfort, and recommendation acceptance. Specifically, we expect the negative effect of voice-based interactions to be reversed with increasing level of prior experience. Preliminary findings from two experimental studies provide support to hour hypotheses and show that voice (vs text) interactions with AI conversational digital assistants decrease consumers’ willingness to accept recommendation through a decrease of comfort and power.
Image Denoising using Wavelet Cycle Spinning and Non-local Means Filter

Removing as much noise as possible in an image while preserving its fine details is a complex and challenging task.We propose a wavelet-based and non-local means (NLM) denoising method to overcome the problem.Two well-known wavelets: dual-tree complex wavelet transform (DT-CWT) and discrete wavelet transform (DWT), have been used to change the noise image into several wavelet coefficients sequentially.NLM filtering and universal hard thresholding with cycle spinning have been used for thresholding on its approximation and detail coefficients, respectively.The inverse two-dimensional DWT was applied to the modified wavelet coefficients to obtain the denoised image.We conducted experiments with twelve test images on the set12 data set, adding the additive Gaussian white noise with variances of 10 to 90 in increments of 10.Three evaluation metrics, such as peak signal noise to rate (PSNR), structural similarity index metric (SSIM), and mean square error (MSE), have been used to evaluate the effectiveness of the proposed denoising method.From these measurement results, the proposed denoising method outperforms DT-CWT, DWT, and NLM almost in all noise levels except for the noise level of 10.At that noise level, the proposed denoising method is lower than NLM but better than DT-CWT and DWT.
Text-based Sarcasm Detection on Social Networks: A Systematic Review

Sarcasm is a sophisticated phenomenon used for conveying a meaning that differs from what is being said, and it is usually used to express displeasure or ridicule others.Sentiment analysis is a process of uncovering the subjective information from a text.Detecting figurative language such as irony or sarcasm, is a focused challenging research field of sentiment analysis.Detecting and understanding the use of sarcasm in social networks could provide businesses and politicians with significant insight, since it reflects people's opinions about certain topics, news, and products.This has especially become relevant recently because sarcastic texts have been trending on social networks and are being posted by millions of active users.As a result of this situation, there is now an increasing amount of research on the detection of sarcasm in social network posts.Many works have been published on sarcasm detection, and they include a wide variety of techniques based on rules, lexicons, traditional machine learning, deep learning, and transformers.However, sarcasm detection is a challenging task due to the ambiguity and non-straightforward nature of sarcastic text.In addition, very few reviews have been conducted on the research in this area.Therefore, this systematic review mainly aims at exploring the newly published sarcasm detection articles on social networks in the years between 2019 and 2022.Several databases were extensively searched, and 30 articles that met the criteria were included.The selected articles were reviewed based on their approaches, datasets, and evaluation metrics.The findings emphasized that deep learning is the most commonly used technique for sarcasm detection in recent literature, and Twitter and F-measure are the most used source and performance metric, respectively.Finally, this article presents a brief discussion regarding the challenges in sarcasm detection and future research directions.
SEDITION LAW: A FRIEND OR FOE?

Freedom of opinion and expression is essential for the entire development of a person. They are the foundation of any free and democratic society. Because it gives meaning to life, freedom of speech and expression is the first and basic human right, the basic condition of freedom, and the mother of all rights. However, when exercising freedom of speech, questions often arise, such as how far the state can regulate individual behavior. Personal autonomy is the cornerstone of basic freedom; therefore, every constraint is strictly evaluated. However, this right can always be appropriately restricted to ensure that it is used correctly and that all people have equal access to it. Section 124A of the Indian Penal Code of 1860 defines sedition as a criminal offence. The importance of this part in an independent and democratic country is a hotly debated topic. This article looks at sedition and why it cannot be compared with Article 19 (Right of Freedom of Speech and Expression) of the Indian Constitution. It also raises the issue of application in a democratic world where freedom of speech and expression is regarded as the mother of all rights, and compares India’s sedition legislation with those of Australia, the United Kingdom, and the United States of America, as India’s sedition law viewpoint Different from other countries.
Effects of Literacy Environment and Quantity and Quality of Reading Interaction on Preschoolers’ Early Literacy Skills: Latent Profile Analysis

본 연구의 목적은 음운론적 인식, 어휘력, 이야기 이해력을 토대로 유아의 기초문해력을 유형화하고, 가정의 물리적 문해환경과 책 읽기 상호작용이 유아의 기초문해력 유형에 영향을 미치는지 검증하는 것이다. 이를 위해 만 4세 유아와 어머니 125쌍을 대상으로 유아의 기초문해력 유형화를 위해 잠재프로파일 분석을 실시하고, 유형에 따른 차이를 확인하기 위해 일원배치분산분석, 다항로지스틱 회귀분석을 실시하였다. 연구문제에 따른 분석 결과는 다음과 같다. 첫째, 기초문해력의 유형은 ‘평균’, ‘지연’, 음운론적 인식과 어휘력이 높은 ‘우수’ 집단으로 구분되었다. 둘째, 가정의 물리적 문해환경은 ‘평균’과 ‘우수’ 집단이 ‘지연’ 집단보다 풍부했다. 질적 책 읽기 상호작용은 ‘평균’ 집단이 ‘지연’ 집단보다 풍부했다. 셋째, 질적 책 읽기 상호작용의 수준이 낮을수록 ‘평균’ 집단보다 ‘지연’ 집단에 속할 가능성이 높았고, 물리적 문해환경이 풍부할수록 ‘지연’ 또는 ‘평균’ 집단보다 ‘우수’ 집단에 속할 가능성이 높았다. 이러한 결과는 가정의 물리적 문해환경과 질적 책 읽기 상호작용이 유아의 기초문해력 향상에 중요한 요인임을 보여준다. 본 연구는 유아기 자녀가 있는 가정 대상 부모교육 및 중재 프로그램 개발에 활용 될 수 있을 것이다.This study aims to identify the profile of early literacy skills based on phonological awareness, vocabulary knowledge, and story comprehension and verifies the effects of home literacy environment and reading interaction on early literacy skills. Participants of this study were 125 pairs of four-years-old preschoolers and their mothers. Latent profiles analysis was conducted to classify children into different groups according to levels of early literacy skills. One-way ANOVA and multinomial logistic regression was conducted to verify the differences between groups. First, we identified three different groups of early literacy skills: average, delayed, and excellent groups. Second, children in the excellent group showed higher levels of phonological awareness and vocabulary knowledge when compared to children in the average and delayed groups. Home literacy environment was richer in the average and excellent groups than the delayed group, whereas quality of reading interaction was higher in the average group than the delayed group. Third, lower quality of reading interaction predicted the higher possibility of classification into the delayed group rather than the average group and richer home literacy environment predicted the higher possibility of classification into the partially excellent group rather than the delayed and average groups. This indicates that home literacy environment and quality of reading interaction are important to enhance preschoolers’ early literacy skills. The results of this study can be used to develop parent education or a child intervention program for preschoolers and their parents.
Mobile Cloud Computing Based Technologies for Enhancing E-learning Content Delivery and Sharing in Higher Learning Institutions in Tanzania using Learner-Centered Approach

Electronic learning (E-learning) in Higher Learning Institutions (HLIs) offers a cost-effective teaching and learning that support social interactivity, flexibility, context sensitivity, and active participation of learners in learning activities.The objective of this study was to investigate the challenges facing the traditional E-learning tools and leverage the advanced capacity of Mobile Cloud Computing (MCC) to enhance E-learning service delivery and sharing of learning resources focusing in learner-centered approach.Also, the evolvement of mobile computing devices such as smartphones, Personal Digital Assistance (PDA), and laptops owned by learners bring prospects in overcoming the inherent challenges facing HLIs in developing countries such as shortage of computer laboratories and network resources.Consequently, this study proposes MCC-based E-learning content delivery and sharing to augment higher learning institutions with limited resource setting in developing countries.The main benefits of MCC-based E-learning include, first, augment traditional LMS by provisioning abundant processing capacity and storage in the cloud that guarantee unlimited learning materials available for learners and instructors; Second, improves performance in local Learning Management System (LMS) servers by outsourcing execution and storage into the cloud especially when resource-intensive E-learning contents such as games, Virtual Reality (VR), and video streaming are used for learning; third, supports multi-platforms to execute the workload of various E-learning applications in the cloud which is potential for E-learning resource sharing; and fourth, guarantee costeffective E-learning content delivery and sharing.
Anderson localization without eigenstates in photonic quantum walks

Anderson localization is ubiquitous in wavy systems with strong static and uncorrelated disorder. The delicate destructive interference underlying Anderson localization is usually washed out in the presence of temporal fluctuations or aperiodic drives in the Hamiltonian, leading to delocalization and restoring transport. However, in one-dimensional lattices with off diagonal disorder, Anderson localization can persist for arbitrary time-dependent drivings that do not break a hidden conservation law originating from the chiral symmetry, leading to the dubbed “localization without eigenstates.” Here it is shown that such an intriguing phenomenon can be observed in discrete-time photonic quantum walks with static disorder applied to the coin operator and can be extended to non-Hermitian dynamics as well.
Jeu de mots paronomasia: a StackOverflow-driven bug discovery approach

Abstract Locating bug code snippets (short for BugCode) has been a complex problem throughout the history of software security, mainly because the constraints that define BugCode are obscure and hard to summarize. Previously, security analysts attempted to define such constraints manually (e.g., limiting buffer size to detect overflow), but were limited to the types of BugCode. Recent researchers address this problem by extracting constraints from program documentation, which shows the potential for API misuse. But for bugs beyond the scope of API misuse, such an approach becomes less effective since the corresponding constraints are not defined in documents, not to mention the programs without documentation In this paper, inspired by the fact that expert programmers often correct the BugCode on open forums such as StackOverflow, we design an approach to automatically extract knowledge from StackOverflow and leverage it to detect BugCode. As we all know, the contexts in StackOverflow come from ordinary developers. Their writing tends to be loosely organized and in various styles, which are more challenging to analyze than program documentation. To address the challenges, we design a custom tokenization approach to segment sentences and employ sentiment analysis to find the Controversial Sentences (CSs) that typically contain the constraints we need for code analysis. Then we use constituency parsing to extract knowledge from CSs, which helps locate BugCode. We evaluated our system on 41,144 comments from the questions tagged with Java and Android. The results show that our approach achieves 95.5% precision in discovering CSs. We have discovered 276 pieces of BugCode proved to be true through manual validation including an assigned CVE. 89.3% of the discovered bugs remained in the current version of answers, which are unknown to users.
Artificial Intelligence Technology in the Assessment of Teachers' Music Teaching Skills Training

The development of modern technology and economy has fully improved people's daily life, and at the same time, the disposable income of residents has also increased rapidly.At this time, people began to pay more and more attention to art education such as music for the next generation.Education.At this time, the education and training enterprises of music and various art disciplines have a broader development market, and at the same time, the improvement of the market competition environment has also made the training mode used by various training enterprises to train the teaching skills of music teachers there are bigger deficiencies.At this time, the rise and rapid progress of artificial intelligence (AI) technology has also brought more ideas and development opportunities for the training of music teachers' professional skills.Relevant companies have begun to pay attention to the relationship between AI technology and teachers' music teaching skills training.This combination can not only fully improve teachers' music teaching skills, but also greatly shorten the period required for music teacher training.In this paper, through the in-depth exploration of AI technology, a comprehensive model for the training and assessment of music teachers' professional teaching skills based on AI technology is proposed to provide efficient training for relevant teachers' majors, so that Enable related enterprises to achieve higher quality development.At the same time, this AI technology-based training and assessment mode for teachers' music teaching skills also greatly improves the professionalism of teachers, thereby producing better teaching effects.
GLDAP: Global Dynamic Action Persistence Adaptation for Deep Reinforcement Learning

In the implementation of deep reinforcement learning (DRL), action persistence strategies are often adopted so agents maintain their actions for a fixed or variable number of steps. The choice of the persistent duration for agent actions usually has notable effects on the performance of reinforcement learning algorithms. Aiming at the research gap of global dynamic optimal action persistence and its application in multi-agent systems, we propose a novel framework: global dynamic action persistence (GLDAP), which achieves global action persistence adaptation for deep reinforcement learning. We introduce a closed-loop method that is used to learn the estimated value and the corresponding policy of each candidate action persistence. Our experiment shows that GLDAP achieves an average of 2.5%~90.7% performance improvement and 3~20 times higher sampling efficiency over several baselines across various single-agent and multi-agent domains. We also validate the ability of GLDAP to determine the optimal action persistence through multiple experiments.
Socio-pedagogical Technologies of Interaction with Childrenʼs Public Associations in the Activities of the Adviser on Education

The creation of various childrenʼs public associations and movements in modern Russia actualizes the problem of scientific substantiation of ways to strengthen the influence of child and adolescent activity on the processes of education and socialization of students. The purpose of this article is to substantiate socio-pedagogical technologies aimed at improving the processes of education in an educational organization in interaction with childrenʼs public associations. The focus is on the socio-pedagogical technologies of the activities of the adviser to the Director for education and interaction with childrenʼs public associations. The article clarifies the concept of “socio-pedagogical technology in the activity of an adviserˮ. Three groups of socio-pedagogical technologies are presented: the inclusion of students in socially significant social projects of childrenʼs public associations; the interaction of participants in educational relations when they are included in various public associations; the involvement of socially significant adults in educational work in cooperation with childrenʼs public associations. The theoretical idea of the relationship between the use of socio-pedagogical technologies by the adviser to the director for education and interaction with childrenʼs public associations and the education of the individual in accordance with spiritual and moral, family, socio-cultural values is substantiated.