Reviewerly/exHarmony huggingface dataset:
Teacher Perspectives on Blended Learning in a Changing Educational Landscape

New technologies can be regarded as synonymous with future-oriented education. With technological advances being so characteristic of the twenty-first century, it is necessary to redesign and transform the way in which teaching and learning occur within the high school classroom. Technology, and especially new technologies, forms part of the overall frame of reference for today’s learners within the contextual framework of the twenty-first-century classroom. The ever-changing educational landscape urges the teacher to embrace an innovative disposition regarding teaching and learning and to explore ways in which technology can be integrated meaningfully with the curriculum framework. During the past year, the world has experienced enormous change due to the COVID-19 pandemic, which has affected education and, consequently, teachers, learners, parents and school management systems. This chapter focuses on the perspectives of language teachers on using blended learning approaches to teach the current twenty-first-century learner. Two core considerations guided this study. The first relates to the significant role that the teacher’s perspective plays in educational reform efforts, and the second entails blended learning as a teaching and learning strategy for language teaching. During blended learning, new technologies could be combined with the most valued features of traditional learning experiences to ultimately transform the language classroom in terms of the nature and quality of the educational experience.
Two Novel Non-Uniform Quantizers with Application in Post-Training Quantization

With increased network downsizing and cost minimization in deployment of neural network (NN) models, the utilization of edge computing takes a significant place in modern artificial intelligence today. To bridge the memory constraints of less-capable edge systems, a plethora of quantizer models and quantization techniques are proposed for NN compression with the goal of enabling the fitting of the quantized NN (QNN) on the edge device and guaranteeing a high extent of accuracy preservation. NN compression by means of post-training quantization has attracted a lot of research attention, where the efficiency of uniform quantizers (UQs) has been promoted and heavily exploited. In this paper, we propose two novel non-uniform quantizers (NUQs) that prudently utilize one of the two properties of the simplest UQ. Although having the same quantization rule for specifying the support region, both NUQs have a different starting setting in terms of cell width, compared to a standard UQ. The first quantizer, named the simplest power-of-two quantizer (SPTQ), defines the width of cells that are multiplied by the power of two. As it is the case in the simplest UQ design, the representation levels of SPTQ are midpoints of the quantization cells. The second quantizer, named the modified SPTQ (MSPTQ), is a more competitive quantizer model, representing an enhanced version of SPTQ in which the quantizer decision thresholds are centered between the nearest representation levels, similar to the UQ design. These properties make the novel NUQs relatively simple. Unlike UQ, the quantization cells of MSPTQ are not of equal widths and the representation levels are not midpoints of the quantization cells. In this paper, we describe the design procedure of SPTQ and MSPTQ and we perform their optimization for the assumed Laplacian source. Afterwards, we perform post-training quantization by implementing SPTQ and MSPTQ, study the viability of QNN accuracy and show the implementation benefits over the case where UQ of an equal number of quantization cells is utilized in QNN for the same classification task. We believe that both NUQs are particularly substantial for memory-constrained environments, where simple and acceptably accurate solutions are of crucial importance.
Blockchain-based secured payment in IoE

The Internet of things (IoT) is a significant technological advancement that will only improve with time. Having items connected to one another has severaladvantages. The Internet of Everything (IoE) is the intelligent interconnection of four key components: people, processes, data, and things. It is considered as a sub branch of the IoT. It is made up of a large number of connected devices and services that share data. Every domain has its own set of norms for security, privacy, and trust. From the capacity to tap into new business models to making citizen transportation simpler, the IoE promises to offer tremendous prospects for corporations and society. However, it also brings with it several problems, problems that will affect every company and person on the planet. This chapter discusses the role of Blockchain with IoE to provide secured payment over various applications.
User-Elicited Surface and Motion Gestures for Object Manipulation in Mobile Augmented Reality

Recent advancements in mobile and AR technology can facilitate powerful and practical solutions for six degrees of freedom (6DOF) manipulation of 3D objects on mobile devices. However, existing 6DOF manipulation research typically focuses on surface gestures, relying on widgets for modal interaction to segment manipulations and degrees of freedom at the cost of efficiency and intuitiveness. In this paper, we explore a combination of surface and motion gestures to present an implicit modal interaction method for 6DOF manipulation of 3D objects in Mobile Augmented Reality (MAR). We conducted a guessability study that focused on key object manipulations, resulting in a set of user-defined motion and surface gestures. Our results indicate that user-defined gestures both have reasonable degrees of agreement whilst also being easy to use. Additionally, we present a prototype system that makes use of a consensus set of gestures that leverage user mobility for manipulating virtual objects in MAR.
Extended Reality: Meeting the Promise of Real-time High Fidelity Learning Environments

An emerging class of MobileHCI applications demand high-fidelity 3D rendering of everything from accurate geo-spatial information to real-time dynamic data sources. This requires a rethinking of interfaces for everything from visualizing crowd-sourced mapping applications to highly realistic, real-time simulation environments. Whether these applications are running on sophisticated head-mounted mobile devices or commodity phones, these generated environments need to take advantage of spatial cues, and need to deliver uninterrupted immersive user experiences in extended reality (XR).
Understanding Student Perceptions Around Mandatory Use of Smartphone Apps for COVID-19

In this short paper, we present our findings from a survey (n=178) conducted on a college campus to understand student perceptions of the deployment of CoVerified app to regulate COVID-19 spread on campus by limiting access to on-campus locations and events to only those with a “cleared” status. We expect our recommendations could help with future deployments of smartphone apps for managing spread of COVID-19, should there be new variants. Additionally, we also highlight our innovative pedagogy of bringing research into the classroom by incorporating the study data into a class project.
TEXT2030 - Shaping Text Entry Research in 2030

We propose a workshop on the theme of ubiquitous text entry research. Our aim is to address the methodological challenges arising from several decades of experience in this research community. We hope to solicit views, experiences and ideas from researchers across a range of backgrounds, with a view to formulate concrete action plans to move community practices into a strengthened position by 2030.
Blockchain-based security for internet of everything

Internet of Everything (IoE) is a revolutionary information technology discovery impacting the lives of human beings. If we look at how IoE captures the idea of how a human being communicates with an object, including those used in daily life, we’ll see that every device on the IoE uses sensors to detect and record information and forward it to an actuator. For instance, as in today’s market, an electricity gadget (electricity counter) connects to the Wi-Fi and calculates how much electricity has been consumed at home or a corporation. Thus, in the same way, through the IoE, every object has a unique IP that is recognized by IPv6 that offers a large IP address pool and serves as the logical connection between people and people and objects by converting data into different formats that aid in making the right decision. The existing method of exchanging data via the Internet as plain texts or unprotected data may lead to security issues through malicious attacks. With this apparatus in hand, the introduction of blockchain technology with distributed ledgers into the IoE has made it a decentralized, distributed database as opposed to the single database of the traditional method. In IoE, each node has a database to store all copies of the shared information. To this, the blockchain technology adds more advantages, like the security of the IoE data, blockchain ensures cost transparency and true tractability, provides encryption features to assure data confidentiality, integrity, and availability and strengthens the protection of data through the Internet. Thus, the blockchain with distributed ledgers has more security features than centralized management. A blockchain uses consensus protocols between the partner’s networks whereby the device has the ability to participate, is conscious about improving the security of the people and their sensitive data, and protects information from malware like man-in-the-middle, denial of services, network phishing attacks, hackers, and SQL injection threats.
Loan Question Answering Platform Based on ERNIE and Knowledge Graph

At present, the excessive amount of loan consultation has brought great pressure to manual customer service. However, the existing loan question answering (QA) platforms cannot solve this problem well because of their poor understanding ability. Therefore, the authors constructs a loan QA platform based on ERNIE and knowledge graph (KG). Firstly, they use semi-automatic methods to construct KG with data from a loan company. Secondly, they use token-level random mask strategy (TRM), word-level fixed mask strategy (WFM), and fine-tuning strategy integrating knowledge (IK) to train ERNIE. Finally, they construct a QA platform based on KG and trained ERNIE and experiment with proprietary datasets. The results show that ERNIE trained after three strategies achieve average improvements of 14.7% on judging intention similarity of sentence pairs and 14.28% on retrieving the most similar intention problem compared with the baseline. It also shows that their platform achieves an average improvement of 13% on question answering compared with the customer service app of the loan company.
New Mobile Adaptation System for Better Avatar-mediated communication; Facial Expressions in Memoji

Avatars, including Apple’s Memojis, represent users on digital platforms, and future investigations will address questions beyond computer-mediated communication, such as group communication, non-verbal communication, and organizational communication. Currently, Memojis’ emotional conveyance is receiving much attention. We investigated how people perceive and judge the emotional quality of the Memojis. An online survey collected user assessments of emotional expressions in Memojis. We created Memojis of seven emotions based on 28 images from the Japanese and Caucasian facial expressions of emotion (JACFEE) database using an iPhone running iOS 15.2. The emotional appeals of individual Memojis were judged. In general, Memojis were insufficient to communicate emotions. Happiness and sadness are the most robust, while fear and disgust had a low level of conveyance, and anger, contempt, and surprise were relatively inaccurately perceived. The results suggested developing an adaption system that detects the intended emotion and expresses it in the exaggerated way that cartoon-like characters convey emotions. The limitations and challenges of the proper use of Memojis for better figure-based and non-verbal communication are also discussed.
A Proposal Phishing Attack Detection System on Twitter

The security of personal data is crucial for a company or any individual. Phishing is one of the most common and dangerous cybercrime attacks. These attacks aim to steal information used by individuals and organizations using social engineering, which is a key point for the success of the phishing attack. Even though there are several systems and solutions, the amount of personal information stolen continues to increase as cyberattacks become more difficult to detect. This paper consists of a broad review to study the work carried out in the fight against phishing and the identification of vulnerabilities in existing systems to achieve better efficiency. The authors focused on the social medium Twitter to study the phishing attacks passing through this medium, and they present their new design, which is based on new features. The classification of the approach includes 23 features and uses the MLP artificial neural network (ANN MLP) algorithm. Experiments show that the system is effective at detecting phishing sites, with a 96% success rate using recent data.
Combining Elliptic Curve Cryptography and Blockchain Technology to Secure Data Storage in Cloud Environments

Many encryption strategies have been applied to ensure data confidentiality and improve cloud security. The most recent cryptosystems are based on homomorphic (HE), attribute-based (ABE), and hybrid encryption. However, most of them suffer from numerous drawbacks: high time consumption, encrypted message size, and some vulnerabilities. Hence, a secure method is highly required to get a satisfying security level while keeping the computational complexity reduced. This paper outlines a novel technique that combines elliptic curve cryptography (ECC) and Blockchain technology. The data is first encoded using the Elliptic Curve Integrated Encryption Scheme, then signed using signed using the Elliptic Curve Digital Signature Algorithm, and finally confirmed by the blockchain network before being stored in the cloud. The performance evaluation results prove that the proposed system is highly resistant to man-in-the-middle and replay attacks and performs better than a set of existing solutions in terms of cryptography cost, encryption/decryption time, and algortithm complexity.
A Global Equity Assessment System Based on Principal Component Analysis

In this paper, we propose an equity assessment model(EAM) to describe global equity to evaluate the impact of the asteroid mining plan on global equity. First, we define global equity: each country is able to obtain planetary mineral resources according to a certain weight. Second, data from 18 countries are collected and Kaiser-Meyer-Olkin (KMO) test on data is 0.653, which verify the validity of principal component analysis. Third, we use MATLAB to calculate 18 countries’ score with principal component analysis. Finally, equity index p is introduced to measure global equity. Next, we use ridge regression to analyze the impact of asteroid mining on global equity and the model fit level is 0.742, which indicates the model fit is good.
Learning from Mistakes in an Open Source Software Course

Open Source Software development includes many peculiarities, which may not be apparent at first sight. Committing to Open Source projects may be a difficult task without a prior knowledge. This paper describes the experience of a student that took a course on Open Source Software. Much attention was paid to mistakes that were made during the course, and a reflection on decisions was conducted.
Can Reinforcement Learning Learn Itself? A Reply to ‘Reward is Enough’

In their paper ‘Reward is enough’, Silver et al. conjecture that the creation of sufficiently good reinforcement learning (RL) agents is a path to artificial general intelligence (AGI). We consider one aspect of intelligence Silver et al. did not consider in their paper, namely, that aspect of intelligence involved in designing RL agents. If that is within human reach, then it should also be within AGI’s reach. This raises the question: is there an RL environment which incentivises RL agents to design RL agents?
A Fine-Grained Social Bias Measurement Framework for Open-Domain Dialogue Systems

A pre-trained model based on a large-scale corpus can effectively improve the performance of open-domain dialogue systems in terms of performance. However, recent studies have shown various ethical issues in pre-trained models that seriously affect the application of dialogue systems. Social bias is particularly complex among these ethical issues because its negative impact on marginalized populations is often implicit and therefore requires normative reasoning and rigorous analysis. In this paper, we report the solution of the team BERT 4EVER for the NLPCC 2022 Shared Task 7 - Fine-Grain Dialogue Social Bias Measurement, which aims to measure the social bias in dialogue scenario. Specifically, we study fine-grained social bias measurement in open-domain dialogue systems. We construct a framework based on prompt learning and contrastive learning for fine-grained dialogue social bias measurement. We propose a two-stage prompt learning method to identify whether the text involves fairness topics, and then identify the bias of the text involving fairness topics. In order to enable the model to better learn the complete label (i.e. irrelevant, anti-bias, neutral, and biased) information in the first-stage prompt learning, we employ a contrastive learning module to further regularize the text representation of the same labeled samples to the uniform semantic space. On NLPCC 2022 task-7 final test, our proposed framework achieved second place with an $${F}_{macr\mathrm{o}}$$ of 59.02%.
Virtual learning resources in the development of competencies of engineers in Business Management of the Instituto Tecnológico de Campeche

The aim of this paper is to present an experience of using various applications for classroom education at the Technological Institute of Campeche; in this way, as teachers respond to the training needs favorably, involving the use of ICT, linking systemically with knowledge, skills and attitudes of students, linking the pillars of education. Technologies of information and communication technologies (ICT) have become an important tool, especially in education. The use of the internet and different platforms to support teaching activities have been widely accepted in young people, facilitating the process of teaching and learning. It is therefore important that teachers know and use these platforms to implement strategies to promote the construction of learning. The results invite reflection of how ICT help teachers work,
Knowledge Enhanced Pre-trained Language Model for Product Summarization

Automatic summarization has been successfully applied to many scenarios such as news and information services, assisted recommendations, etc. E-commerce product summarization is also a scenario with great economic value and attention, as they can help generate text that matches the product information and inspires users to buy. However, existing algorithms still have some challenges: the generated summarization produces incorrect attributes that are inconsistent with original products and mislead users, thus reducing the credibility of e-commerce platforms. The goal of this paper is to enhance product data with attributes based on pre-trained models that are trained to understand the domain knowledge of products and generate smooth, relevant and faithful text that attracts users to buy.
Developing the Semantic Web via the Resolution of Meaning Ambiguities

The paper presents an interdisciplinary project in cognitive linguistics, computer science, and mathematical logic, aimed at the development of a theoretical framework and correspondent logical tools for the treatment, in the Semantic Web context, of some typical linguistic phenomena in natural languages, such as lexical ambiguities and figures of speech. In particular, we focus on some specific features of metaphor that need to be addressed in order to enhance the overall quality of knowledge representation in the Semantic Web. To this extent, we briefly present PROL (Parametric Relational Ontology Language) as a novel ontological approach to the representation of the whole semantic content of n-ary relations usually expressed in natural language. Lastly, we show how specific instances of metaphorical expressions can be represented and dealt with via PROL.
Regret from Cognition to Code

Regret seems like a very negative emotion, sometimes even debilitating. However, emotions usually have a purpose – in the case of regret to help us learn from past mistakes. In this paper we first present an informal cognitive account of the way regret is built from a wide range of both primitive and more sophisticated mental abilities. The story includes Skinner-level learning, imagination, emotion, and counter-factual reasoning. When it works well this system focuses attention on aspects of past events where a small difference in behaviour would have made a big difference in outcome – precisely the most important lessons to learn. The paper then takes elements of this cognitive account and creates a computational model, which can be applied in simple learning situations. We find that even this simplified model boosts machine learning reducing the number of required training samples by a factor of 3–10. This has theoretical implications in terms of understanding emotion and mechanisms that may cast light on related phenomena such as creativity and serendipity. It also has potential practical applications in improving machine leaning and maybe even alleviating dysfunctional regret.
DrPython–WEB: A Tool to Help Teaching Well-Written Python Programs

A good percentage of students, while learning how to program for the first time in a higher education course, often write inelegant code, i.e., code which is difficult to read, badly organized, not commented. Writing inelegant code reduces the student’s professional opportunities, and is an indication of a non-systematic programming style which makes it very difficult to maintain (or even understand) the code later, even by its own author. In this paper we present DrPython–WEB, a web application capable to automatically extract linguistic, structural and style-related features, from students’ programs and to grade them with respect to a teacher-defined assessment rubric. The aim of DrPython–WEB is to make the students accustomed to good coding practices, and stylistic features, and make their code better. There are other systems able to perform code analysis through quality measures: the novelty of DrPython–WEB, with respect to such systems, is in that it analyzes also linguistic and stylistic features.
Cross-Modal Knowledge Graph Construction for Multiple Food Additives

Aiming at the food safety problems caused by the improper use of food additives, a cross-modal knowledge graph for multiple food additives is constructed based on the data obtained from the Internet. Firstly, we design a model of the Resource Description Framework graph to extract the knowledge from structured text. Subsequently, semantic role tagging and dependency syntactic parsing are used to extract knowledge from unstructured text. Finally, we add images to the knowledge graph as cross-modal attributes by semantic label sets. Overall, we propose a novel cross-modal knowledge graph construction framework, which lays the foundation for intelligent question answering systems and supervision platforms for multiple food additives.