Reviewerly/exHarmony huggingface dataset:
WhatsApp application for teaching and learning in higher education institutions

Purpose The purpose of this study is to investigate WhatsApp application for teaching and learning in higher education institutions (HEIs). The consideration for the use of the WhatsApp is based on its usefulness, ease of use, low cost of data and maintenance practices for the functionality of the mobile device used for the WhatsApp. Design/methodology/approach This paper applied systematic literature review in conceptualizing how WhatsApp application could be used for enhanced teaching and learning in HEIs. Most of the articles used in carrying out this study were harvested from Google Scholar. This study was carried out within three weeks internal Findings This study established that WhatsApp has also served as a learning and teaching tool which has made majorities of university students, irrespective of their geographical locations, become tech-savvy, self-expressive and open to new ways of doing things, including the use of social network applications. The exposition of the authors based on the findings from this study indicates that through the use of WhatsApp, activity theory of mobile learning postulated by Vygotsky and theory of connectives by George Siemens gave them the understanding surrounding how humans interact with technology through continuous use of tools. Research limitations/implications This research carried out has a lot of implications to global HEI due to innovation and practices of teaching learning and research. Social implications WhatsApp application has become the avenue through which people learn and retain and share information and knowledge among themselves for a better quality live scenarios. It has brought a shift from traditional to virtual platform of user experience (UX) in the use of digital technologies. Originality/value WhatsApp application for teaching and learning in HEIs established that the impact of WhatsApp on teaching, learning and retention of collocation of knowledge among school students or learners cannot be undermined. The result revealed from this study indicates that there is a shift from traditional to virtual platform which has brought diverse opportunities of transformation the educational systems, enhance productivity and quality service delivery and challenges, where lecturers would have to learn and unlearn through UX of the use of digital technologies.
Specifying Blockchain-Based Resource-Exchange Systems by Business-Level Users Using a Generic Easy-To-Use Framework

Blockchain technology has been rapidly emerging in the past years. The notion of decentralization, enabling ecosystems that provide true ownership of resources without requiring a trusted third party, is gaining interest and is applied in use cases daily. Such use cases vary from simple currency-exchange applications to complex smart contract applications. Designing, building, and deploying blockchain-based applications mostly requires software developers. This introduces a technological burden for organizations who would like to benefit from such systems in their own use case(s) but lack time, financial means, or qualified people. To overcome this burden, we developed a software framework allowing an easy and quick setup of blockchain-based systems for use cases dealing with the exchange of resources. To set up such a system, an easy-to-use user interface is provided that allows business-level people to give the specifications of the system without the need for technical software-, blockchain- or smart contract knowledge. From these specifications an implementation is generated. In this paper, we present the architecture of the framework and discuss the principles used, and the user interface developed for specifying such use cases.
Sound as an Interface, Methods to Evaluate Voice User Interface (VUI) Experiences in Various Contexts

This research evaluates the user experience of an audio feature called “Quick Commands” that is used to control a virtual Assistant with natural invocation phrases. The feature is an application of NLP (Natural Language Processing) that removes hotwords such as “Hey Google”, “Hey Siri”, and “Alexa”, allowing for natural voice interactions. The primary research goal is to validate the value proposition of Quick Commands for VUI (voice user interface) interactions in terms of usability defined as: feature helpfulness, naturalness, and comfort using the feature in public and private spaces. The study design included recruiting a group of over 80 users to test Quick Commands in different contexts, followed by a focus group of 18 users to provide detailed feedback. The study ran 1 week and encouraged users to test Quick Commands in scenarios with a range of ambient audio conditions (ranging from 50–80 dBA). During the week of testing participants completed check-in surveys at the mid-week point, at the end of the week, and then some participants in a focus group to debrief the study. These methods can be replicated to validate future NLP advances and Assistant features before implementing public facing applications. Results of this study demonstrate the value proposition for Quick Commands and user comfort with voice interactions in a range of audio environments.
Generating Human-Like Motion to Defeat Interaction-Based CAPTCHAs

As more companies implement CAPTCHA systems to try to prevent automated attacks, CAPTCHA creators are increasingly using machine learning to try to filter out unwanted traffic. These systems are increasingly important in the development and maintenance of many web-based applications. As machine learning has evolved, so have the detection methods to block automated web traffic. As a result, some image-based CAPTCHAs are being replaced with systems that analyze mouse movements of the user to identify how likely it is that the user is human. In this research, we develop and evaluate a 2-layer convolutional neural network driven framework that generates human-like motions. These types of movements are tracked by some CAPTCHA systems. We demonstrate that the framework’s automatically generated movement paths can effectively and efficiently trick a classifier trained on features that are extracted from paths generated by humans. Using a 2-feature classifier as a CAPTCHA that was trained to recognize 91% of the human paths as valid human paths from our dataset, we are able to successfully bypass the CAPTCHA 89.25% of the time.
The Acrobatics of BQP

One can fix the randomness used by a randomized algorithm, but there is no analogous notion of fixing the quantumness used by a quantum algorithm. Underscoring this fundamental difference, we show that, in the black-box setting, the behavior of quantum polynomial-time ($\mathsf{BQP}$) can be remarkably decoupled from that of classical complexity classes like $\mathsf{NP}$. Specifically: -There exists an oracle relative to which $\mathsf{NP^{BQP}}\not\subset\mathsf{BQP^{PH}}$, resolving a 2005 problem of Fortnow. As a corollary, there exists an oracle relative to which $\mathsf{P}=\mathsf{NP}$ but $\mathsf{BQP}\neq\mathsf{QCMA}$. -Conversely, there exists an oracle relative to which $\mathsf{BQP^{NP}}\not\subset\mathsf{PH^{BQP}}$. -Relative to a random oracle, $\mathsf{PP}=\mathsf{PostBQP}$ is not contained in the "$\mathsf{QMA}$ hierarchy" $\mathsf{QMA}^{\mathsf{QMA}^{\mathsf{QMA}^{\cdots}}}$. -Relative to a random oracle, $\mathsf{\Sigma}_{k+1}^\mathsf{P}\not\subset\mathsf{BQP}^{\mathsf{\Sigma}_{k}^\mathsf{P}}$ for every $k$. -There exists an oracle relative to which $\mathsf{BQP}=\mathsf{P^{\# P}}$ and yet $\mathsf{PH}$ is infinite. -There exists an oracle relative to which $\mathsf{P}=\mathsf{NP}\neq\mathsf{BQP}=\mathsf{P^{\# P}}$. To achieve these results, we build on the 2018 achievement by Raz and Tal of an oracle relative to which $\mathsf{BQP}\not \subset \mathsf{PH}$, and associated results about the Forrelation problem. We also introduce new tools that might be of independent interest. These include a "quantum-aware" version of the random restriction method, a concentration theorem for the block sensitivity of $\mathsf{AC^0}$ circuits, and a (provable) analogue of the Aaronson-Ambainis Conjecture for sparse oracles.
Nested Named Entity Recognition Based on Dual Stream Feature Complementation

Named entity recognition is a basic task in natural language processing, and there is a large number of nested structures in named entities. Nested named entities become the basis for solving many tasks in NLP. A nested named entity recognition model based on dual-flow features complementary is proposed for obtaining efficient feature information after text coding. Firstly, sentences are embedded at both the word level and the character level of the words, then sentence context information is obtained separately via the neural network Bi-LSTM; Afterward, two vectors perform low-level feature complementary to reinforce low-level semantic information; Sentence-local information is captured with the multi-head attention mechanism, then the feature vector is sent to the high-level feature complementary module to obtain deep semantic information; Finally, the entity word recognition module and the fine-grained division module are entered to obtain the internal entity. The experimental results show that the model has a great improvement in feature extraction compared to the classical model.
Multi-modal transformer for video retrieval using improved sentence embeddings

With the explosive growth of the number of online videos, video retrieval becomes increasingly difficult. Multi-modal visual and language understanding based video-text retrieval is one of the mainstream framework to solve this problem. Among them, MMT (Multi-modal Transformer) is a novel and mainstream model. On the language side, BERT (Bidirectional Encoder Representation for Transformers) is used to encode text, where the pretrained BERT will be fine tuned during training. However, there exists a mismatch in this stage. The pre-training tasks of BERT is based on NSP (Next Sentence Prediction) and MLM(masked language model) which have weak correlation with video retrieval. For text encoder will encode text into semantic embeddings. On the visual side, Transformer is used to aggregate multimodal experts of videos. We find that the output of visual transformer is not fully utilized. In this paper, a sentence- BERT model is introduced to substitute BERT model in MMT to improve sentence embeddings efficiency. In addition, a max-pooling layer is adopted after Transformer to improve the utilization efficiency of the output of the model. Experiment results show that the proposed model outperforms MMT.
AI-Driven IoT Systems and Corresponding Ethical Issues

Artificial intelligence (AI) is already gaining major attention among societies. The enduring development of automation offered a wide range of technological services and applications within other advanced systems, one of which is Internet of Things (IoT). While AI enables devices to analyze and learn from experiences, IoT uses devices to connect people and objects with each other through the internet. With efforts to render IoT systems AI-enabled and automated, attention should be given to the potential ethical risks and concerns alongside the designed enhancing features and the benefits predicted. The synergy of AI and IoT can result in advanced and smart applications within smart cities, driver-less vehicles, and robotics and can initiate new job markets and demands. However, the converged system can also threaten general ethical norms and human rights through algorithms bias, cybercrimes, unemployment, liability concerns, and autonomy questioning. This chapter will explain both systems, AI and IoT, in terms of definitions, history, and methods and elaborate on the advantages, disadvantages, and corresponding recommendations of their union to offer a better understanding of the advanced AI-enabled IoT system and reach a clear resolution regarding the matter.
Blockchain and Its Integration in IoT

IoT devices have become an integral part of our lives. The world has witnessed an exponential growth in the number of IoT devices. Managing these devices and the data generated by them has become very crucial. Data security and users’ privacy are becoming more difficult as the number of devices grows. Blockchain, the technology behind Bitcoin, is known for data security and managing and efficiently maintaining huge amounts of data. Blockchain stores data in a chronological manner and in an immutable way. Integration of blockchain in IoT infrastructure has many advantages. This paper discusses various applications and challenges in blockchain. It highlights the adoption of blockchain in IoT infrastructure and reviews recent papers in this field.
Recommendation System Using Deep Learning with Text Mining Algorithm in E-commerce Framework

E-commerce has always been thought of as a fast-growing business, and even while online purchasing has not followed the same boom patterns as in the past, it is now being recognized for its potential. One of the current study topics in the field of textual content mining is sentiment evaluation. Digging normal language for conclusions and feelings is a dreary cycle. The best solution is to conduct a sentiment analysis. This gives crucial data to decision production in an assortment of fields. There are a variety of sentiment detection algorithms available, each of which has an impact on the quality of the final result. In this study, we investigate people’s feelings toward the services offered by e-commerce websites. Reviews, ratings, and emoticons are among the sentiments. The main purpose is to identify bogus reviews using user and product ids in order to purchase the best products and analyze which one is the best. For this, we deploy a mixed learning system that analyzes numerous service feedbacks. To track down the scores of each word, a text mining calculation is utilized. The sentiments are then divided into three categories: negative, positive, and neutral. It is possible to study a website to figure out whether there are any bogus reviews. Finally, anticipate the bogus reviews left by users. Only paid users can leave reviews, and duplicates are checked using the user id and booking id. Credible client criticism is incorporated when making item suggestions.
A Delicate Authentication Mechanism for IoT Devices with Lower Overhead Issues

IoT sensor/edge nodes are vulnerable to many well-known threats. Maintaining the absolute reliability of any IoT LAN requires validating edge nodes before entering a network, mainly post entering a sleep state. These IoT nodes possess limited resources and measurable limits, making this a complicated issue. IoT equipment is frequently exposed to the elements since many IoT installations occur in unregulated settings. Equipment cloning and the theft of private keys from edge nodes are among the most common threats on IoT networks because of their easy physical access. These issues motivate to design a highly authentic device that intends to connect with other devices to preserve the stored and incoming data. When the authentication information matches, the connection is established with requesting device. This work aims to create an ultra-thin verification mechanism for IoT LAN-connected end equipment. The gateway, which serves as edge computing equipment, authenticates the end-user equipment. Formal and informal safety checks are performed on the recommended verification process. The simulation is done in MATLAB 2020a environment, where the proposed delicate authentication scheme works well with superior outcomes.
Analysis of the constructs of Competitive learning as perceived by elementary school students

Purpose: In order for Competitive learning(CL) to be usefully employed in school, It is important to know how students perceive competitive learning. The purpose of this study was to reveal how elementary school students perceive CL so that CL can be positively employed in classrooms.&#x0D; Methods: To achieve this purpose, the characteristics of the CL recognized by elementary school students were analyzed using the Repertory Grid technique, which is a representative research method of Personal Construct Psychology.&#x0D; Results: Of the constructs of CL the following were found: 'A sense of achievement', 'Ability and Effort', 'Focus on results or activity', 'Recognition of rivals', 'Competition burden', 'Damage of sociality', 'Operation of rules', 'Deprivation according to the result', 'Responsiveness of people around', 'Pursuit of Competence', and 'Competition preference'. The degree of recognition differed according to the competitive-cooperation tendency, and the recognition was similar based on the activity.&#x0D; Conclusion: CL has a positive aspect, and it can be one of the effective learning methods if the positive aspects are maximized and the negative aspects are minimized. The focus of CL should be on the activity, not the reward. Furthermore, CL should be approached differently depending on the subject, environment, content and other areas.
Data Paper as a Reward? Motivation, Consideration, and Perspective behind Data Paper Submission

Abstract Data papers, as one of the channels to encourage researchers to open up research data under the open science movement, are expected to provide strong incentives through formal citations. However, few studies have investigated the drivers of this emerging type of publication. This study examines researchers' motivations, and considerations for data paper submission, as well as their perspectives on this scholarly publication. Through an in‐depth interview approach with ten data paper authors, our preliminary results found that, researchers are often driven by extrinsic factors to increase their publications, and data papers are sometimes viewed as territory claims before further research. Although the academic community widely recognizes the benefits of publishing data papers, some still cast a doubtful eye on its academic value and impact. We anticipate such insights on the driving forces and point of views of data papers could provide opportunities for stakeholders to fill gaps and strengthen the open science ecosystem.
Predicting Search Task Difficulty through a <scp>Discrete‐Time</scp> Action Log Representation on Spectrum Kernel

ABSTRACT Predicting perceived difficulty on a web search task is an open problem in the interactive information retrieval field. A common approach to tackle it, is through features obtained from full search sessions, which are then used to train classification models. In this poster we attempt to predict perceived task difficulty at different stages of the search process. To do so, we use the spectrum kernel for support vector machine (SVM) classification. Our preliminary results suggest that by using behavioral data from the first query segment, it is possible to provide timely classifications of whether a search task is perceived as hard or easy.
Historical development of CIPP as a curriculum evaluation model

CIPP, which stands for Context, Input, Process and Product, an evaluation model, is one of the most widely applied curriculum evaluation models in education. This document-based study sought to determine the historical development of CIPP as a curriculum evaluation model. Here, the reasons why the CIPP evaluation model was conceptualised are discussed. In addition, the original major features of the CIPP Model are compared with the changes in the model since it was conceptualised. Moreover, reasons for making changes to the CIPP Model are explained and factors that are considered influential are analysed. Further, contributions of the CIPP Model to the broadening of curriculum evaluation in recent years are discussed. This is to provide essential insights into the role of the CIPP Model in forming a sound evaluation practice in education.
Guided Text-based Item Exploration

Exploratory Data Analysis (EDA) provides guidance to users to help them refine their needs and find items of interest in large volumes of structured data. In this paper, we develop GUIDES, a framework for guided Text-based Item Exploration (TIE). TIE raises new challenges: (i) the need to abstract and query textual data and (ii) the need to combine queries on both structured and unstructured content. GUIDES represents text dimensions such as sentiment and topics, and introduces new text-based operators that are seamlessly integrated with traditional EDA operators. To train TIE policies, it relies on a multi-reward function that captures different textual dimensions, and extends the Deep Q-Networks (DQN) architecture with multi-objective optimization. Our experiments on Amazon and IMDb, two real-world datasets, demonstrate the necessity of capturing fine-grained text dimensions, the superiority of using both text-based and attribute-based operators over attribute-based operators only, and the need for multi-objective optimization.
IMPROVING READING AND WRITING LITERACY IN ENGLISH TEXT-BASED TECHNOLOGY

The research focused on the effects of using technology such as e-books and Google Docs on EFL students. It looked at how beginning readers approach reading e-books, examining the g speed of reading in particular, comprehension, and writing text. The information for this exemplary classroom action research was gathered through initial observations, reading tests, and student writings. The findings show that employing an online library, gadgets for reading and writing English text, and involving literacy competitions based on technology have positive benefits for socio-cognitive development and pedagogical-cognitive literacy.
FPSRS: a fusion approach for paper submission recommendation system

Recommender systems have been increasingly popular in entertainment and consumption, and they are evident in academics, especially for applications that suggest submitting scientific articles to scientists. However, due to the various acceptance rates, impact factors, and rankings in different publishers, searching for a proper venue or journal to submit a scientific work usually takes a lot of time and effort. In this paper, we aim to present two new approaches extended from our paper (Huynh et al. 2021) presented at IAE/AIE 2021. In the first approach, we employ RNN structures besides using Conv1D. In the second approach, we introduce a new method, using DistillBert for two cases of uppercase and lowercase words. It can help vectorize features (such as Title, Abstract, and Keywords) and then use Conv1d to perform feature extraction. Furthermore, we propose a new calculation method for similarity score for Aim & Scope with other features (we name this approach is DistilBertAims). It can help keep the weights of similarity score calculation continuously updated and then continue to fit more data. The experimental results show that the second approach could obtain a better performance, which are 62.46%, 90.32%, 94.89%, 97.96% while the best performance in previous studies barely gained 50.02%, 78.89%, 86.27%, 93.23% in terms of Top K Accuracy (K = 1, 3, 5, 10). Interestingly, our best approach in this paper is higher than 12.44% the best of the previous study (Huynh et al. 2021) in terms of the Top 1 accuracy, which was presented in the conference IEA/AIE 2021.