Reviewerly/exHarmony huggingface dataset:
Towards Rough Set Theory for Outliers Detection in Questionnaire Data

Manual processing of questionnaire surveys takes a lot of time and effort. This article aims at the automatic detection of corrupted or inappropriate responses in questionnaire data using unsupervised outliers detection methods. Unlike numerical data, which are usually assessed by distance-based methods, the entries in questionnaires need to be assessed from multiple perspectives. This paper proposes a novel algorithm utilizing the rough sets that capture relations among attributes/questions. The rough set theory is based on the granularity of data and is used to find combinations of attributes identifying the discernible questionnaires. The method is compared with standard and recent outlier detection algorithms that are based on distance, entropy, correlation, and probability. The tests are computed on the real-world HBSC dataset using several experiments. The rough set score computed on combinations of three attributes is preferred as it returns significant outliers that even reflect multiple perspectives investigated by other types of methods.
Facial Expression Estimation Using Convolutional Neural Network with Upper and Lower Segmented Face Images

In a convolutional neural network (CNN) for facial expression recognition, it is important to focus attention on the parts of a face where facial expressions are likely to be expressed. We propose two methods to focus on the parts of a face where facial expressions are likely to be expressed: one is to merge face images divided into the upper and lower parts by facial feature points as inputs to separate convolutional layers, and the other is to superimpose the two divided images and use them as 6-channel inputs to the convolutional layer. We evaluated the performance of the two methods by comparing the facial expression estimation results of the model trained with the two methods and the model trained without image segmentation. By segmenting the image, we were able to improve the accuracy of the proposed model with respect to anger, disgust, and neutral.
Investigating the Use of Machine Learning Methods for Levels of Sense of Presence Classification Based on Eye Tracking Data

This paper presents a study aimed at developing a framework for classifying levels of user satisfaction in Immersive Virtual Environments (IVEs). As an initial step, we conducted an experiment to explore the potential for using machine learning methods to classify levels of Sense of Presence (SOP). Participants performed a task in two virtual environments with varying levels of SOP, and their eye tracking data were analyzed. Our study found that Support Vector Machine (SVM) achieved the best performance based on pupil dilation-based features, with an accuracy of $$79.1\%$$ . However, relying solely on pupil dilation-based features may not be reliable due to the sensitivity of pupil dilation to light intensity. Eye movement-based features were also analyzed, and Random Forest achieved the best result with an accuracy of $$64.6\%$$ . In addition, we confirmed that texture resolution affects the perception of SOP by analyzing participants’ IPQ scores. Our results showed that higher resolution leads to a higher perception of SOP. These findings suggest the potential for using machine learning methods to classify levels of SOP based on eye tracking data and provide a positive step towards developing a framework for classifying levels of user satisfaction in IVEs.
Analytic Techniques for Automated Analysis of Writing

Abstract Analysis of academic writing has long been of interest for pedagogical and research purposes. This involves the study of students’ writing products and processes , often enabled by time-consuming manual analysis in the past. With the advent of new tools and analytic techniques, analysis and assessment of writing has become much more time and resource efficient. Advances in machine learning and artificial intelligence also provide distinct capabilities in supporting students’ cognitive writing processes. This chapter will review analytical approaches that support the automated analysis of writing and introduce a taxonomy, from low-level linguistic indices to high-level categories predicted from machine learning. A list of approaches including linguistic metrics, semantic and topic-based analysis, dictionary-based approaches, natural language processing patterns, machine learning, and visualizations will be discussed, along with examples of tools supporting their analyses. The chapter further expands on the evaluation of such tools and links above analysis to implications on writing research and practice including how it alters the dynamics of digital writing.
Small Target Helmet Wearing Detection Algorithm Based on Improved YOLO V5

To solve problems such as the low detection accuracy of helmet wearing, missing detection and poor real-time performance of embedded equipment in the scene of remote and small targets at the construction site, the text proposes an improved YOLO v5 for small target helmet wearing detection. Based on YOLO v5, the self-attention transformer mechanism and swin transformer module are introduced in the feature fusion step to increase the receptive field of the convolution kernel and globally model the high-level semantic feature information extracted from the backbone network to make the model more focused on helmet feature learning. Replace some convolution operators with lighter and more efficient Involution operators to reduce the number of parameters. The connection mode of the Concat is improved, and 1 × 1 convolution is added. The experimental results compared with YOLO v5 show that the size of the improved helmet detection model is reduced by 17.8% occupying only 33. 2 MB, FPS increased by 5%, and mAP@0.5 reached 94.9%. This approach effectively improves the accuracy of small target helmet wear detection, and meets the deployment requirements for low computational power embedded devices.
Automated Text Generation and Summarization for Academic Writing

Abstract In this chapter, we discuss the implications of automatic text generation for academic writing. We first review the current state of the technology and how it is being used. We then discuss the implications of using automatic text generators for academic writing, including the need for users to be aware of the limitations of the technology and how to use it effectively. We also discuss how the use of automatic text generation can change the traditional stages of writing, and how the content generated by these systems is not justified by semantic or extra-linguistic criteria. We finally argue that notions that have been useful for explaining, analyzing, and teaching academic writing will need to be re-examined in the light of human–machine-interaction. (This abstract has been automatically generated using OpenAI and slightly post-edited; see this article’s Appendix for an explanation.)
The Future of Intelligent Tutoring Systems for Writing

Abstract Writing is essential for success in academics and everyday tasks, but the development of writing skills depends on consistent access to high-quality instruction, extended practice, and personalized feedback. To address these demands and meet students’ needs, educators and researchers have turned to technology-based writing tools. Ideally, these tools integrate the core components of intelligent tutoring, including a domain model, student model, tutor model, and interface model to engage students with individualized feedback that is linked to adaptive writing instruction. However, the landscape of writing tools still has much room for improvement in terms of incorporating advanced artificial intelligence-enabled features to better approximate intelligent tutoring systems (ITSs). This chapter describes the key elements of ITS technologies and how they can be integrated to further develop ITS tools for writing. To this end, this chapter (1) summarizes evidence-based aspects of successful ITSs and how they might be integrated into computer-based tools for writing, (2) reviews how existing systems have leveraged intelligent tutoring approaches, and (3) articulates how future technology-based writing tools could implement advanced intelligent tutoring features to better meet students’ needs. The chapter concludes with the implications and future directions of intelligent tutoring for the teaching and learning of writing.
Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering

In the field of recommender systems, shallow autoencoders have recently gained significant attention. One of the most highly acclaimed shallow autoencoders is easer, favored for its competitive recommendation accuracy and simultaneous simplicity. However, the poor scalability of easer (both in time and especially in memory) severely restricts its use in production environments with vast item sets. In this paper, we propose a hyperefficient factorization technique for sparse approximate inversion of the data-Gram matrix used in easer. The resulting autoencoder, sansa, is an end-to-end sparse solution with prescribable density and almost arbitrarily low memory requirements — even for training. As such, sansa allows us to effortlessly scale the concept of easer to millions of items and beyond.
A Game-Based Approach for Mitigating the Sybil Attacks on Blockchain Applications

The data explosion of this digital century encourages the necessity of data security and maintaining privacy. The generated data through online transactions in different sectors have been increased a million times. Therefore, the trustful transaction and reliability of data became necessary for its analysis and future use in the online platform. The change of data platform from a centralized to distributed nature enhances the utility of the data use, and the security threats are also increased side by side. The Sybil attack is one of the significant security threats in the distributed network environment. Blockchain is a peer-to-peer distributed ledger that works in a distributed environment. However, the blockchain framework, which aims to provide tamper-proof security on data suffers from Sybil attacks too. This paper aims to analyze the implementation of blockchain technology in different applications and propose a new game-based model to effectively detect and mitigate Sybil attacks on the blockchain.
A New Approach to the Architecture of Hybrid and Multilayer Steganographic Methods

Audio steganography is a rapidly growing aspect of broad information protection. However, most of the designed steganographic algorithms are vulnerable to stegoanalysis and detecting the presence of hidden messages in stegoobjects. High hopes for solving this problem are associated with multilayer and hybrid secret message embedding techniques because such mixed techniques can withstand a wide range of attacks. The proposed article expands the knowledge of multilayer methods and introduces the concept of a hybrid method based on the embedding place in different domains of steganographic methods. A comparative analysis of a single-layer, operating in two domains, and a hybrid method that combines the two embedding methods, was made. The comparative analysis shows the advantage of hybrid methods over single-layer methods. The hybrid methods have the exciting potential of creating more flexible methods better suited to a specific purpose or improving one parameter at the expense of another. In addition, combined hybrid and multilayer methods are more secure due to the greater difficulty in extracting a secret message by an intruder.
Beyond MS Word: Alternatives and Developments

Abstract Microsoft Word, the word processing software developed by Microsoft in 1983, established itself as the market leader in the 1990s and 2000s and remained the gold standard for many years. Despite its obvious benefits, it always faced criticism from various quarters. We address the persistent criticism that MS Word is overloaded with features and distracts from writing rather than facilitating it. Alternatives, mainly distraction-free editors and text editors for use with a markup language, are briefly reviewed and compared to MS Word. A serious challenger emerged in 2006 with Google Docs, a cloud-based writing software that has moved text production into the platform era, enabling files to be shared and creating collaborative writing spaces. Even though Google Docs failed to break the dominance of MS Word, it became the trend-setter in online writing. Microsoft and Apple soon followed by designing complex web environments for institutions and companies rather than individual writers. We give an overview of technologies that have evolved to challenge the supremacy of MS Word or compete for market share. By this, we hope to provide clues as to the future development of word processing.
Relationship Between Degree of Appropriation of ICT and Learning Styles of Teachers in Training

The incursion of Information Technology and Communications (ICT) in education is shaping and modifying teaching-learning processes. Therefore, educational institutions have been forced to offer contextualized training programs according to the personal characteristics of the actors. The article presents the results of a research carried out at Barranquilla-Colombia, whose objective was to analyze the relationship between the appropriation of ICT mediations and learning styles in an initial teacher training program. For this, the process of appropriation of ICT was characterized from the profile of mediation agents in initial teacher training, and the learning styles of students who are being trained as teachers were identified. Under a quantitative approach, non-experimental design, and a sample of 63 teachers in training for the first and fourth semester to whom a REATIC (Cognitive and Affective Reactions to Information and Communication Technology) questionnaire and a CHAEA (Honey-Alonso Questionnaire of Learning Styles) questionnaire. Data was analyzed using R Studio statistical software. The Pearson, Kendall, T-Student, Wilcoxy ANOVA (Analysis of Variance) tests were applied. The results indicate a higher degree of educational appropriation of ICT in individuals with a pragmatic learning style.
Information Retrieval and Knowledge Extraction for Academic Writing

Abstract The amount of unstructured scientific data in the form of documents, reports, papers, patents, and the like is exponentially increasing each year. Technological advances and their implementations emerge at a similarly fast pace, making for many disciplines a manual overview of interdisciplinary and relevant studies nearly impossible. Consequently, surveying large corpora of documents without any automation, i.e. information extraction systems, seems no longer feasible. Fortunately, most articles are now accessible through digital channels, enabling automatic information retrieval by large database systems. Popular examples of such systems are Google Scholar or Scopus. As they allow us to rapidly find relevant and high-quality citations and references to previous work, these systems are particularly valuable in academic writing. However, not all users are aware of the mechanisms underlying relevance sorting, which we will address in this chapter. For example, in addition to searching for specific terms, new tools facilitate the discovery of relevant studies by using synonyms as well as similar works/citations. The near future holds even better tools for the creation of surveys, such as automatic summary generation or automatic question-answering systems over large corpora. In this chapter, we will discuss the relevant technologies and systems and their use in the academic writing context.
LGHAE: Local and Global Hyper-relation Aggregation Embedding for Link Prediction

The Knowledge Graph (KGs) have profoundly impacted many research fields. However, there is a problem of low data integrity in KGs. The binary-relational knowledge graph is more common in KGs but is limited by less information. It often has less content to use when predicting missing entities (relations). The hyper-relational knowledge graph is another form of KGs, which introduces much additional information (qualifiers) based on the main triple. The hyper-relational knowledge graph can effectively improve the accuracy of predicting missing entities (relations). The existing hyper-relational link prediction methods only consider the overall perspective when dealing with qualifiers and calculate the score function by combining the qualifiers with the main triple. However, these methods overlook the inherent characteristics of entities and relations. This paper proposes a novel Local and Global Hyper-relation Aggregation Embedding for Link Prediction (LGHAE). LGHAE can capture the semantic features of hyper-relational data from local and global perspectives. To fully utilize local and global features, Hyper-InteractE, as a new decoder, is designed to predict missing entities to fully utilize local and global features. We validated the feasibility of LGHAE by comparing it with state-of-the-art models on public datasets.
A New File Format for Binarized Images

In this paper we present a new lossless file format specialized in binarized images. The proposed algorithm creates files smaller in average than standard lossless formats. We propose a modification to the algorithm which allows it to be used in every type of images, not only the binarized ones. We compare the file sizes with the PNG format. In order to encourage the use and compatibility with the most common image libraries, we provided the implementation details and the idea behind the algorithm.
Digital policing in Nigeria: A study of Jigawa State Police Command

The application of digital technologies in police practices can engender a number of benefits, which include round-the-clock surveillance, intelligence collection (in real time), community policing (through regular online briefings and trust-building by policing agencies) and disruption of crimes, among others. This study used situational crime prevention theory and examined the use, benefits and challenges of digital policing in Jigawa State Police Command, Nigeria. A sample of 230 respondents was utilized in the study. From the sample, 215 police personnel were administered questionnaires, and 15 in-depth interviews were conducted with 5 police personnel, 5 information technology professionals and 5 policing scholars. The study revealed that use of digital technologies in police practice is low in Jigawa State, because the study discovered insufficient digital technologies in the police command. Furthermore, the majority of police personnel sampled were not skilled in information and communications technologies. No doubt, poor use of digital technologies may pose a serious setback to crime prevention in Jigawa State, Nigeria. Therefore, it is recommended that the police should acquire and leverage on emerging policing technologies. Police personnel should be given adequate training and retraining on the use of digital technologies. Collaboration and information sharing with relevant agencies should also be improved.
Writing Processes in the Digital Age: A Networked Interpretation

Abstract The concept of writing processes is clearly useful for the study of digital writing when we see writing as a complex, integrated set of actions that are organized in time and integrated into both digital and physical spaces. With the increased digitalization of writing, the processes of writing are becoming more integrated into a variety of tools, workspaces, and platforms, adding new layers to how we perform writing. This chapter explores how writers and researchers can conceptualize digital writing activities as new platforms and software continue to evolve, changing the way writers adapt processes to complex writing situations. The proliferation of digital spaces and tools also raises the visibility of writing processes formerly difficult to assess and evaluate due to their cognitive nature. This chapter will summarize primary approaches to writing processes and several heuristics currently used to understand and analyze writing as a dynamic and networked activity. These tools are useful for examining the new ways digital technologies make writing processes visible for both research and reflection. The chapter will also discuss some implications for theory, pedagogy, and research.
Disentangling Motives behind Item Consumption and Social Connection for Mutually-enhanced Joint Prediction

Item consumption and social connection, as common user behaviors in many web applications, have been extensively studied. However, most current works separately perform either item consumption or social link prediction tasks, possibly with the help of the other as an auxiliary signal. Moreover, they merely consider the behaviors in a holistic manner yet neglect the multi-faceted motives behind them. For example, the intention of watching a movie could be killing time or watching it with friends; Likewise, one might connect with others due to friendships or colleagues. To fill this gap, we propose to Disentangle the multi-faceted Motives in each network (i.e., the user-item interaction network and social network) defined respectively by the two types of behaviors, for mutually-enhanced Joint Prediction (DMJP). Specifically, we first learn the disentangled user representations driven by motives of multi-facets in both networks. Thereafter, the mutual influence of the two networks is subtly discriminated at the facet-to-facet level. The fine-grained mutual influence is then exploited asymmetrically to help refine user representations in both networks, with the goal of achieving a mutually-enhanced joint item and social link prediction. Empirical studies on three public datasets showcase the superiority of DMJP over state-of-the-arts (SOTAs) on both tasks.
On the Consistency of Average Embeddings for Item Recommendation

A prevalent practice in recommender systems consists of averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates the relevance of such a practice. For this purpose, we propose an expected precision score, designed to measure the consistency of an average embedding relative to the items used for its construction. We subsequently analyze the mathematical expression of this score in a theoretical setting with specific assumptions, as well as its empirical behavior on real-world data from music streaming services. Our results emphasize that real-world averages are less consistent for recommendation, which paves the way for future research to better align real-world embeddings with assumptions from our theoretical setting.
Delivery Hero Recommendation Dataset: A Novel Dataset for Benchmarking Recommendation Algorithms

In this paper we propose Delivery Hero Recommendation Dataset (DHRD), a novel real-world dataset for researchers. DHRD comprises over a million food delivery orders from three distinct cities, encompassing thousands of vendors and an extensive range of dishes, serving a combined customer base of over a million individuals. We discuss the challenges associated with such real-world datasets. By releasing DHRD, researchers are empowered with a valuable resource for building and evaluating recommender systems, paving the way for advancements in this domain.