Reviewerly/exHarmony huggingface dataset:
Making sense of distributional semantics

Abstract This chapter focuses on the ‘semantics’ in ‘distributional semantics’: what do clusters in distributional models look like and what do they mean? Based on analyses performed on 32 Dutch nouns, adjectives, and verbs from a synchronic, semasiological perspective, the chapter shows that there is no parameter setting for distributional models that returns an optimal solution across the board and second, that the token clouds, that is, the clusters within the models, do not systematically match senses as one would find them in dictionaries. Token clusters in distributional models may take different shapes, depending on the frequency and distinctiveness of the context words, and such clusters do not coincide unambiguously with lexicographic senses.
Visual analytics for token-based distributional semantics

Abstract The modelling workflow described in Chapter 3 produces token-level distance matrices: one matrix per model, each indicating the pairwise dissimilarity between the occurrences of a certain word in a sample, according to that model. However, because of the large number of tokens in the sample and the diversity of models produced by multiple parameters, such output is challenging to interpret. In this chapter we will describe the steps followed to process the distance matrices and obtain a more manageable format, as well as a visual analytics tool designed to explore the results. The chapter introduces a number of notions that are relevant for the way in which the models discussed in Chapter 5 are analysed and how the token-level plots in Chapters 6, 9, and 10 can be interpreted.
<i>À Propos de Pierre,</i> Does He … or Doesn’t He?

Abstract In Frege’s Puzzle (1986), Nathan Salmón analyzed ‘a withholds believing p’ in terms of a ternary relation BEL of x believing a proposition p under a guise g. The analysis is the following: There is a proposition guise g such that a grasps p by means of g but a does not stand in BEL to p and g. Sean Crawford has made a proposal for Millians to evade propositional guises through second-order belief. Specifically, in effect, Crawford proposes to analyze the crucial notion of withheld belief instead as believing that one does not believe: a believes that a does not believe p. Crawford’s clever proposal thus avoids explicit quantification over guises. However, it is shown that the proposal is inadequate to avoid guises. The resulting notion is too weak to capture the relevant notion of withheld belief.
Big Data as Tracking Technology and Problems of the Group and Its Members

Abstract Digital data help data scientists and epidemiologists track and predict outbreaks of disease. Mobile phone GPS data, social media data, or other forms of information updates such as the progress of epidemics are used by epidemiologists to recognize disease spread among specific groups of people. Targeting groups as potential carriers of a disease, rather than addressing individuals as patients, risks causing harm to groups. While there are rules and obligations at the level of the individual, we have to reach a stage in the development of data analytics where groups are protected as entities. This chapter offers a deeper examination of harms to the groups.
Transparency

Abstract What do we need to know about an algorithm to justify using it to make decisions about how to treat others? In all cases, we need to know how accurate the algorithm is, and how it distributes the risk of error. If the prediction is about what the decision-subject will do, we also need to know which facts influence that output. We can assess the former statistically, by quantifying predictive accuracy across datasets. And we can assess the latter counterfactually, by adjusting inputs to isolate statistically significant variables. We do not need to ‘open the black box’, in the sense of unpacking the code itself.
The False Binary of Reason and Emotion in Data Visualisation

Abstract In this chapter, D’Ignazio and Klein call for the incorporation of emotions and embodied experiences into data visualisation practices. They ask: how did the field of data communication arrive at conventions that prioritise rationality and exclude emotion? Ignazio and Klein argue that data selection and presentation is always political, and should be visualised in such a way as makes this evident. The piece features a number of stunning data visualisations created by artists and designers that bring to life their feminist emphasis on ‘elevating emotion’ and ‘stories from a standpoint’. Ultimately, D’Ignazio and Klein argue that rebalancing emotion and reason enlarges the data communication toolbox and allows one to focus on what truly matters in a data design process: honouring context, gathering attention and taking action in service of rebalancing social and political power.
Afrofeminist Data Futures

Abstract This overview of many years of research, which aims to improve government service-delivery, locates injustices in specific areas of AI development and deployment across Africa. It provides qualitative and quantitative evidence of its claims, as well as recommendations for policy and governance responses. As the authors explain, data collection in African contexts is both the poison and the remedy: colonialist data practices extract data from African regions for the benefit of Western tech companies, and datafication poses significant risks to women and girls’ privacy. However, data—especially Big Data—can improve NGO and governmental responses to issues affecting African women by providing insight into elements of their lives that are difficult to capture through other data collection modalities. This is why, they argue, it is so important to work with feminist methods and theory when creating, processing and interpreting data.
Design Practices

Abstract This essay explores the paradox of ‘user-centred design’, a dominant design practice that prioritises ‘real-world’ users but excludes those who currently have no access to the technology in question. Which potential user groups are catered for and which are not is a political choice that often goes unnoticed in tech development. They ask that more attention is paid to the demographic from which the ‘unmarked’ generic user persona derives, and how it matches up with the average tech worker: white, cis-male, person with internet access, and digitally literate. As a remedy, they point to participatory practices and asks that keener attention is paid to how ‘users’ themselves innovate products. Costanza-Chock’s rallying call for ‘design justice’ paved the way in the artificial intelligence (AI) sector for work that did not merely attempt to neutralise harmful systems but called for their active re-orientation towards redress. This remains a go-to text for practitioners and academics alike when drawing up ethical design processes.
Virtual Assistant for Thesis Technical Guide Using Artificial Neural Network

This study focuses on finding best practice for Artificial Neural Network (ANN) implementation in the information system for student’s thesis technical instructions. The machine learning model applied sequential model, it means ANN only use 1 input layer, a hidden/dense layer and 1 output layer. The Stochastic Gradient Decent (SGD) method was applied into data training process. The results of this study are chatbot applications, and model testing using the confusion matrix. The result of model evaluation are 99,49% accuracy and 91% in F-1 score.
Comparison of Naïve Bayes Algorithm, Support Vector Machine and Decision Tree in Analyzing Public Opinion on COVID-19 Vaccination in Indonesia

The spread of COVID-19 in Indonesia has caused many negative impacts. Therefore, the government is taking vaccination measures to suppress the spread of COVID-19. Public response to vaccinations on Twitter has been mixed, with some supporting it and some not. The data for this study comes from the Twitter feed of the drone portal Emprit Academy (dea). Classification is performed using SVM, decision tree and Naive Bayes algorithm. The purpose of this study is to inform the public about whether vaccination against COVID-19 is inclined toward positive, neutral, or negative opinions. Moreover, this study compares the accuracy of the three algorithms used, namely Naive Bayes (NB), Support Vector Machine (SVM) and Decision Tree, and the validation performed using the K-Fold Cross-Validation method, AdaBoost feature selection, and the TF-IDF Transformer feature extraction test. The result obtained from this study is that the accuracy of the 90:10 data keeps improving, dividing by 82.86% on the SVM algorithm, 81.43% on the Naive Bayes and 78.57% on the decision tree.
Prompting Large Language Models to Power Educational Chatbots

The recent rise in both popularity and performance of large language models has garnered considerable interest regarding their applicability to education. Technologies like ChatGPT, which can engage in human-like dialog, have already disrupted educational practices given their ability to answer a wide array of questions. Nevertheless, integrating these technologies into learning contexts faces both technological and pedagogical challenges, such as providing appropriate user interfaces and configuring interactions to ensure that conversations stay on topic. To better understand the potential large language models have to power educational chatbots, we propose an architecture to support educational chatbots that can be powered by these models. Using this architecture, we created a chatbot interface that was integrated into a web application aimed at teaching software engineering best practices. The application was then used to conduct a case study comprising a controlled experiment with 26 university software engineering students. Half of the students interacted with a version of the application equipped with the chatbot, while the other half completed the same lesson without the chatbot. While the results of our quantitative analysis did not identify significant differences between conditions, qualitative insights suggest that learners appreciated the chatbot. These results could serve as a starting point to optimize strategies for integrating large language models into pedagogical scenarios.
The Maximal Length of <i>q</i>-ary MDS Elliptic Codes Is Close to

Abstract Determining the maximal length of MDS codes with certain dimension is one of the central topics in coding theory and finite geometry. The MDS Main Conjecture states that the maximal length of a non-trivial $q$-ary MDS code of dimension $k$ is $q+1$ except when $q$ is even and $k=3$ or $k=q-1$. We prove that the maximal length of non-trivial $q$-ary MDS elliptic codes is close to $\frac{q}{2}$, which gives an affirmative answer to a conjecture of Li, Wan, and Zhang. Moreover, we apply our result to derive an answer to a question on subset sums in finite abelian groups from elliptic curves.
Corpus-Based Translation Pedagogy: A Preliminary Case Study

This research is grounded in the practical experiences of teaching translation at a specific local university and aims to introduce an innovative corpus-based pedagogical framework tailored for translation courses. Through a comparative study involving undergraduate students’ training experiments, the research seeks to provide valuable insights into the effective integration of corpus technology in translation education, meticulously examining aspects such as course design, corpus resources, teaching content, and implementation strategies. The results of the teaching practice reveal that the newly proposed pedagogical model significantly enhances the expression of translations, elevates students’ English proficiency as translators, diversifies learning methods, and fosters increased interest and self-learning capabilities among students. Building upon these findings, future translation education should prioritize the enrichment of corpus teaching resources and dynamic tracking of students’ daily corpus usage. Translation instructors must continually refine their corpus usage skills and enhance their digital integrated capabilities to effectively adapt to the evolving needs of translation course reforms in the current context. This research contributes to the ongoing discourse on the integration of corpus technology and Data-Driven Learning (DDL) in language teaching, offering insights into how educators can harness the potential of corpus tools and DDL pedagogy in their classrooms.
Tracking the Adaptive Learning Process with Topics Ontology

We focus on an ontological model of the learner’s context in the organized education process at a university level. Extending an existing temporal context model to cover topics of study, their mutual hierarchy, and their relations with other entities in the course we yield a complex model of the learner’s context. Such a model enables tracking of the student’s learning goals within a course and providing personalized recommendations w.r.t. the current level of students’ topic understanding as well w.r.t. the current point in time within the course. We also outline how the unified knowledge-graph representation allows for a more flexible design of visualizations and user interfaces.
A study of behavioral decay in design patterns

Abstract Design patterns represent a means of communicating reusable solutions to common problems, provided they are implemented and maintained correctly. However, many design pattern instances erode as they age, sacrificing qualities they once provided. Identifying such instances of pattern decay is valuable because it allows for proactive attempts to extend the longevity and quality attributes of pattern components. Apart from structural decay, design patterns can exhibit symptoms of behavioral decay. We utilized a taxonomy that characterizes these negative behaviors and designed a case study wherein we measured structural and behavioral decay, hereafter referred to as pattern grime, as well as pattern quality and size, across pattern evolutions. We evaluated the relationships between structural and behavioral grime and found statistically significant cases of strong correlations between specific types of structural and behavioral grime. Furthermore, we extended the QATCH operational software quality model to incorporate design pattern evolution metrics and measured and correlated software quality to the presence of behavioral grime in software systems. Our results suggest a strong inverse relationship between software quality and behavioral grime.
Strategies for improving efficiency and developing human capital in the context of digitalization

The rapid development of technology has led to revolutionary changes in various spheres of society, including education. Educational technologies, commonly referred to as EdTech, have become a powerful tool for improving the efficiency of human capital formation. The purpose of this article is to explore strategies for using EdTech to optimize human capital development. It will discuss the benefits of educational technology, the challenges encountered in its implementation, and suggest effective strategies for integrating EdTech into educational systems. In addition, this paper will emphasize the importance of collaboration among stakeholders such as educators, policy makers, and technology developers to ensure successful implementation and maximize the potential and in education. This article discusses strategies for increasing the efficiency of human capital formation through educational technologies. It will primarily focus on the use of educational institutions such as schools and universities. However, the potential of digitalization in education in non-formal and continuing education institutions will also be considered..
The Future of Work: Automation, Artificial Intelligence and Information Technology

Rapid advances in automation, artificial intelligence (AI), and information technology (IT) are transforming the world of work, leading to a paradigm shift in how we perceive and perform our work. This article examines the implications of these technological innovations for the future of work, exploring the potential benefits and challenges they bring to the labor market. By analyzing current trends in automation and AI, we discuss the potential displacement of jobs, the emergence of new roles, and the need to reskill and upskill the workforce. The article also highlights the importance of IT in enabling remote working and fostering collaboration, as well as the ethical considerations surrounding the use of AI in the workplace. We offer strategies for businesses, policy makers and individuals to adapt to this rapidly changing landscape, ensuring a more inclusive and sustainable future of work.
Digital Technologies and Education of the Future

It is not the first year in the educational environment that there has been a tendency for students to have a better command of modern technologies than their parents and teachers. The constant use of various digital technologies in the process of growing up by the iGen generation to implement or simplify various life processes leads to the fact that the absence or insufficiency of their use in the educational process makes classes less interesting and comfortable than for previous generations of students. The issues of digital transformation of universities, rethinking of their main processes, the use of a pool of modern technologies as a tool for involving a modern student in the lifelong learning process are increasingly being raised. This article presents the results of bibliometric mapping on selected keywords related to digital learning, and discusses technologies that can accelerate the transition to smart learning. The results of an online survey of students concerning the level of students' awareness of learning opportunities in the virtual world are demonstrated, the project of creating objects and scenarios for educational activity of a master class in the metaverse is illustrated.
Enhancing Chemistry Education’s Relevance and Comprehension through Immersive Virtual Reality

The study is focused on evaluating the effectiveness of integrating virtual reality (VR) with the principles of chemistry education (CE) in order to improve students' understanding of chemistry. A mixed method approach, including literature review, empirical research, and analysis of VR sessions, was used to identify the impact of VR on students' conceptual understanding and motivation. The results confirmed that VR integration significantly increases conceptual understanding and student engagement. VR provides interactive and practice-oriented learning, breaking the abstraction of chemical concepts. The approach also highlights the need to rethink traditional pedagogical methods in favor of active, contextualized learning. Emerging issues, including equity of access and job training, require attention to successfully integrate VR into education.
Differential, Linear, and Meet-in-the-Middle Attacks on the Lightweight Block Cipher RBFK

Randomized butterfly architecture of fast Fourier transform for key cipher (RBFK) is the lightweight block cipher for Internet of things devices in an edge computing environment. Although the authors claimed that RBFK is secure against differential cryptanalysis, linear cryptanalysis, impossible differential attack, and zero correlation linear cryptanalysis, the details were not explained in the literature. Therefore, we have evaluated the security of RBFK by application of differential cryptanalysis, linear cryptanalysis, and meet-in-the-middle (MITM) attack and have found that RBFK is not secure against these attacks. This paper introduces not only a distinguish attack but also key recovery attacks on full-round RBFK. In the distinguish attack scenario, data for differential cryptanalysis are two, and the time complexity is one for an exclusive-OR operation. In the key recovery attack scenario, the data for linear cryptanalysis are one pair of known plaintext–ciphertext. The time complexity is one operation for a linear sum. Data for an MITM attack are two. The time complexity is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mn>2</mn> <mn>48</mn> </msup> </math> encryptions; the memory complexity is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mn>2</mn> <mn>45</mn> </msup> </math> bytes. Because the vulnerabilities are identified in the round function and the key scheduling part, we propose some improvements for RBFK against these attacks.
An adaptive steganography insertion technique based on wavelet transform

Abstract Over the past few decades, there have been several successful methods developed for steganography. One popular technique is the insertion method, which is favored for its simplicity and ability to hold a reasonable amount of hidden data. This study introduces an adaptive insertion technique based on the two-dimensional discrete Haar filter (2D DHF). The technique involves transforming the cover image into the wavelet domain using 2D DWT and selecting a predetermined number of coefficients to embed the binary secret message. The selection process is carried out by analyzing the cover image in two non-orthogonal domains: 2D discrete cosine transform and 2D DHF. An adaptive algorithm is employed to minimize the impact on the unrepresented parts of the cover image. The algorithm determines the weights of each coefficient in each domain, and coefficients with low weights are chosen for embedding. To evaluate the effectiveness of the proposed approach, samples from the BOSSbase and custom databases are used. The technique’s performance is measured using three metrics: mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Additionally, a visual inspection by humans is conducted to assess the resulting image. The results demonstrate that the proposed approach outperforms recently reported methods in terms of MSE, PSNR, SSIM, and visual quality.
Point-Based Learnable Query Generator for Human–Object Interaction Detection

Transformer-based and interaction point-based methods have demonstrated promising performance and potential in human-object interaction detection. However, due to differences in structure and properties, direct integration of these two types of models is not feasible. Recent Transformer-based methods divide the decoder into two branches: an instance decoder for human-object pair detection and a classification decoder for interaction recognition. While the attention mechanism within the Transformer enhances the connection between localization and classification, this paper focuses on further improving HOI detection performance by increasing the intrinsic correlation between instance and action features. To address these challenges, this paper proposes a novel Transformer-based HOI Detection framework. In the proposed method, the decoder contains three parts: learnable query generator, instance decoder, and interaction classifier. The learnable query generator aims to build an effective query to guide the instance decoder and interaction classifier to learn more accurate instance and interaction features. These features are then applied to update the query generator for the next layer. Especially, inspired by the interaction point-based HOI and object detection methods, this paper introduces the prior bounding boxes, keypoints detection and spatial relation feature to build the novel learnable query generator. Finally, the proposed method is verified on HICO-DET and V-COCO datasets. The experimental results show that the proposed method has the better performance compared with the state-of-the-art methods.