Reviewerly/exHarmony huggingface dataset:
Copywriting life hacks: conceptual structure of electronic gadget advertisements in English

In the consumerist society of the 21st century, advertisement has become an inalienable part of human life. To create it, copywriters apply various strategies and techniques in order to influence the mind of people urging them to buy the item they promote. The aim of the article is expose these mechanisms of influence by analyzing the conceptual structure that underlies English-language written texts advertising electronic devices. This structure is modeled via employing the notion of semantic roles and the methodology of semantics of lingual networks (namely, basic propositional schemas) and theory of conceptual metaphor. As the analyzed empirical data show, advertising texts contain three informational foci/roles: PRODUCT, CONSUMER, and MANUFACTURER. Each role can be described with the help of multiple characteristics that viewed as predicates of five basic propositional schemas. By highlighting these features advertisers showcase the fortes of the gadget thus attracting attention of potential buyers and triggering their desire acquire it. The article exposes the combination patterns of the roles and the frequency of usage of their features (some of which are described metaphorically) to deduce an ideal formula copywriters can apply in creating texts that advertise electronic gadgets. Such a perfect advertisement text must present a novel and user-friendly but affordable gizmo of small size and minimal weight that displays seamless performance because of its high-tech hardware and state-of-the-art software. It is increasingly human-like in its features and operations, which makes it a perfect companion for people both in work and in games enabling them to leverage the gadget in a whole gamut of functions.
Bureaucratic leadership and strategic decision-making at the Department of Trade, Industry and Competition

The relevance of bureaucratic leadership has long been a subject of heated debate among academics and businesses alike. A review of the literature found that bureaucratic leadership entails time-intensive — and often time-wasting — rules and procedures within a rigorous and painstakingly slow framework (Hamel &amp; Zanini, 2017; Bishu &amp; Kennedy, 2020; Mustanir et al., 2019; Berkowitz &amp; Krause, 2020). Employees operating within a bureaucracy are given little choice in how they perform their work. The purpose of the study is to assess the relationship between bureaucratic leadership and strategic decision-making within the Department of Trade, Industry and Competition (DTIC). The researchers employed an explanatory sequential mixed-method research design, conducted across two phases. Phase 1 was a quantitative study comprising an online survey and phase 2 was a qualitative study based on data collected from in-depth interviews. The study found that strategic decision-making at the DTIC is a time-consuming and onerous process and that strategic decisions were not made by the organisation’s bureaucratic leaders, but were relegated to a political level, thus limiting the effectiveness of the DTIC’s operations. To ease this tension in the relationship between bureaucratic leadership and decision-making, the researchers recommended adopting a hybrid leadership framework to engage all levels of management and leadership in the DTIC.
ORGANIZATION OF INDEPENDENT EDUCATIONAL AND COGNITIVE ACTIVITY OF STUDENTS IN THE EDUCATIONAL PROCESS OF THE DISCIPLINARY LEVEL

The purpose of the article is to specify the pedagogical conditions for the organization of independent educational and cognitive activity of students.. Increasing the share of independent work of students today is the task of the university. It is the most important condition for self-organization and self-discipline in mastering the methods of cognitive activity, a means of pedagogical management of the organization and management of independent educational and cognitive activity of students. The quality and intensity of educational and cognitive activity are factors that significantly determine the effectiveness of training, and thereby the quality of the results of training specialists. The solution of this problem is impossible without increasing the role of independent educational and cognitive activity of students, strengthening the responsibility of teachers for the formation of skills and abilities of independent educational and cognitive activity, the organization of educational activities of students carries a pronounced management function. At the same time, there is a need for a clear statement of the educational and cognitive task, which allows the teacher to foresee in advance the nature and level of the student’s independent activity and manage this process.
Building and Analysing an Online Hate Speech Corpus: The NETLANG Experience and Beyond

This preliminary chapter is part of the Introduction to the book, “Online Hate Speech: Object, Approaches, Issues”, and it has a threefold purpose. First, it outlines the general theoretical framework of the volume by offering an overview of the most relevant linguistic approaches to hate speech to date. Then, it describes the genesis of the book, which stems from a four-year research project, netlang, by reporting on the construction and pre-processing of its online hate speech corpus, to which half the chapters resort in their analyses. Finally, it presents the collection by explaining the book’s design and layout, namely the three parts into which it is organised, and by introducing the internal and external contributions which integrate each of them, respectively by members of the project and by participants in its final conference.
First-Person Verbal Aggression in YouTube Comments

To contribute to literature on hateful speech in social media communication, this chapter analyses verbal aggression in YouTube comments. Our text data is the 21.8-million-word Sexism subcorpus of the netlang corpus of YouTube comments. In order to identify instances where YouTube commenters explicitly suggest their personal involvement in carrying out physical violence, we analyse aggression verbs in constructions with the first-person singular subject pronoun (e.g., I slap, I kill). Through manual classification of the instances based on their target and function, we lay out a taxonomy of how aggression verbs are used in online comments and compare the various functions associated with the studied aggression verbs. We find that the pattern with the first-person singular subject and aggression verb frequently indicates the commenter’s subjective aggressive perspective towards the target. The aggression verb constructions are used in threats and threatening wishes targeting specific individuals rather than vulnerable groups, and threatening language contributes to the online norm of aggressive language.
Improving NLP Techniques by Integrating Linguistic Input to Detect Hate Speech in CMC Corpora

Hate speech detection research relies heavily on automatic detection models that make use of machine learning (ML), opinion mining, sentiment analysis and polarity detection. The highly informal and speech-like nature of Computer Mediated Communication (CMC) poses many challenges for electronic processing and automatic detection methods. In this study, we describe details of the natural language processing (NLP) techniques applied to obtain a lemmatised and part-of-speech-tagged Portuguese-English CMC corpus. Considering that automatic analysis and annotation tools are optimised for standard written production, we will address the limitations of these tools due to CMC-specific phenomena and how their performance can be improved by integrating linguistic input. We propose a mixed methods approach in which linguistic knowledge, including lexical, syntactic and pragmatic input, is used in conjunction with NLP techniques to trace and analyse fixed expressions in order to detect potential hate speech in user-generated content. Our focus will be on analysing the behaviour of opinion markers that exhibit a certain degree of fixedness as potential pointers to prejudiced hateful content in netlang’s English Subcorpus as a contribution to the optimisation of hate speech detection NLP and ML models.
Linguistic Markers of Affect and the Gender Dimension in Online Hate Speech

Language is not only a tool for information transfer but also a vehicle of the producer’s subjectivity which adds to the persuasive power of the message. Given the proliferation of discriminatory and hateful content online, it is important to understand the mechanisms of such messages to curb its propagation as efficiently as possible. Looking into a number of factors, such as the characteristics of hate speech producers, contributes to properly addressing the destructive phenomenon of hate speech. This study aims to explore the role of gender in the production of online hate speech by focusing on the use of linguistic markers conveying affect. We analyse selected typographical, grammatical and lexical features in English and Slovene Facebook comments which were identified as conveying socially unacceptable propositions. The results show statistically significant differences in the use of linguistic markers of affect between English-speaking or Slovene-speaking male and female commenters with regard to their hate speech production. This study shows that men are more likely to post shorter and violent comments as opposed to offensive ones, while women tend to include more linguistic markers of affect in their comments on all studied levels.
Negotiating Hate and Conflict in Online Comments: Evidence from the NETLANG Corpus

The chapter seeks to identify how conflict is negotiated within the architecture of discussion forums on online news sites. First, it delimits the relation between the cluster of related concepts ranging from anti-social discourse, hate and aggressive speech, to conflict talk. It adopts a sociopragmatic conception of conflict talk as a multi-dimensional phenomenon with several key elements (structure, form, interaction, meaning) and draws on a framework of discursive strategies that serve to express conflict. Using data on body shaming and physical impairments from the netlang corpus, it documents how commenters deploy conflicting representations, enter into extended conflictual discussions and escalate the mutual conflict, while gradually shifting from idea-oriented to person-oriented strategies. The findings indicate, among other aspects, that conflict in talk can be accompanied by strategies seeking to delegitimise the other, while it simultaneously solidifies the unity of the ingroup.
Derogatory Linguistic Mechanisms in Danish Online Hate Speech

Based on a 400-million-word social media corpus, and exploiting linguistic annotation at the morphological, syntactic and semantic levels, this chapter examines the derogatory linguistic mechanisms found in online Danish hate speech targeting immigrant and refugee minorities. Using only authentic corpus examples, we document the main othering stereotypes and narratives, as well as dehumanising metaphors and target-specific slurs, with a special focus on lexical processes such as compound formation. Other topics are the role of syntax in generalisation and othering, and the role of emojis as vehicles of sentiment and metaphor. Finally, the discussion of minority-targeting hate speech is complemented by also examining what could be called secondary hateful speech, elicited by counterspeech and directed at the establishment, media and left-wing politicians. We instrumentalise quantitative analysis with a multiple purpose. First, it is used to facilitate iterative qualitative inspection of the corpus informed by linguistic pattern searches. Second, we use co-occurrence strengths and relative frequencies to elicit typical attributes of target concepts. Third, we measure concept similarities and perform sentiment ranking based on word vector distances based on machine-learned word embeddings.
Distinguishing Online Hate Speech from Aggressive Speech: A Five-Factor Annotation Model

This chapter is of an introductory nature, offering a definitional and programmatic reflection about the object under focus in the book. How can hate speech be accurately identified? An overview of the extant scholarship reveals a tendency towards imprecision, dismissiveness and all-inclusiveness. Indeed, hate speech seems to lack a consensual definition—or, rather, a consensual awareness of the need for a definition. This need is particularly central to practical, applied areas of detection and control, respectively, natural language processing (NLP) research and legal scholarship, which have striven to circumscribe the concept. In particular, establishing hate speech dataset properties is particularly dependent on definitional issues, just as is distinguishing hate speech from free speech and other competing and cognate concepts. By revisiting the classic communication framework involving senders, messages, channels and receivers, and enlightening it with current linguistic insight, the chapter puts forth an annotation model of five factors, the positive co-occurrence of which signals hate speech. It then tests the model against a range of examples from three subsets of the NETLANG corpus, classified under the variables of sexism, racism and ageism. Finally, it extends the analysis into other, linguistic, features of online hate speech. Admittedly, the identification model proposed in the chapter is but the first step in understanding such an evasive concept as hate speech. It is therefore acknowledged that, so as to fully assess how hate speech works, it is important to know the way it is negotiated by particular speakers in particular communicative situations, meant for specific audiences at specific points in time.
Emotional Deixis in Online Hate Speech

Demonstrative determiners can be used to convey the speaker’s emotional involvement in the subject matter (Lakoff, 1974). They also play a role in establishing solidarity effects between speaker and addressee, thus potentially being used to express complex social meanings and attitudes (Lakoff, 1974; Chen, 1990; Acton and Potts, 2014), a feature that is worth exploring in the context of the characterisation of hate speech in Computer-Mediated Discourse (CMD). This chapter explores the use of demonstrative determiners in a subcorpus of the netlang corpus in Portuguese. All occurrences of demonstrative determiners were encoded according to the affective value retrieved from a sentiment analysis and the variables examined included markers of exclamativity and the type of noun (proper or common) that follows the determiner. The results reveal that the negative sentiment value is higher when the demonstrative is followed by a proper noun and is inserted in an exclamatory sentence. On the contrary, positive-rated comments are more frequent when exclamativity is not present. This entails that the interaction between exclamativity markers and affective use of demonstrative determiners is a possible feature of hate speech in CMD.
Enabling Concepts in Hate Speech: The Function of the Apartheid Analogy in Antisemitic Online Discourse About Israel

This chapter introduces the notion of ‘enabling concepts’: concepts which may or may not themselves constitute a mode of hate speech, but which through their broad social acceptability facilitate or legitimate the articulation of concepts which can be more directly classed as hate speech. We argue that each distinct hate ideology will contain its own, partly overlapping set of ‘enabling concepts.’ In this chapter, we will focus on the enabling role of references to apartheid for the constitution of antisemitism in British online discourse around Israel. This argument does not rest on agreement as to whether the ‘apartheid analogy’—comparisons between contemporary Israel and the former Apartheid regime in South Africa—itself constitutes a form of antisemitism. The chapter draws on qualitative analysis of more than 10,000 user comments posted on social media profiles of mainstream media in the UK, undertaken by the Decoding Antisemitism project in the wake of the May 2021 escalation phase of the Arab-Israeli conflict. We will show how web commenters frequently use the apartheid analogy to trigger more extreme antisemitic stereotypes, including age-old tropes, intensifying and distorting analogies (such as Nazi comparisons) or calls for Israel’s elimination. The results will be presented in detail based on a pragmalinguistic approach taking into account the immediate context of the comment thread and broader world knowledge. Both of these aspects are relevant preconditions for examining all forms of antisemitic hate speech that can remain undetected when conducting solely statistical analysis. Based on this large dataset, we suggest that—under the cover of its widespread social acceptability—the apartheid analogy thus facilitates the articulation and legitimation of extreme antisemitic concepts that would, without this prior legitimation, be more likely to be rejected or countered.
AI Trojan Attacks and Countermeasures

Supply chain vulnerability increases the risk of exposing system-on-chip (SoC) designs to hardware Trojans. While machine learning (ML) based Trojan detection approaches are promising in various domains due to their scalability as well as detection accuracy Arp et al. (Effective and efficient malware detection at the end host. In: 18th USENIX. Montreal, Quebec, 2009); Dahl et al. (Large-scale malware classification using random projections and neural networks. In: ICASSP, 2013, pp 3422–3426); Grosse et al. (Adversarial perturbations against deep neural networks for malware classification. In CoRR, 2016); Saxe and Berlin (Deep neural network based malware detection using two dimensional binary program features. In: 10th MALCON, 2015, pp 11–20); Wang et al. (Adversary resistant deep neural networks with an application to malware detection. In: Proceedings of the 23rd ACM SIGKDD, 2017, pp 1145–1153); Pan and Mishra (Automated detection of Spectre and Meltdown attacks using explainable machine learning. In: 2021 IEEE international symposium on hardware oriented security and trust (HOST). IEEE, 2021, pp 24–34); Witharana and Mishra (Speculative load forwarding attack on modern processors. In: IEEE/ACM international conference on computer-aided design, 2022, pp 1–9), ML-based methods themselves are vulnerable from Trojan attacks. In this chapter, we present a robust backdoor attack on ML-based Trojan detection algorithms to demonstrate this serious vulnerability. The proposed framework is able to design an AI Trojan and implant it inside the ML model that can be triggered by specific inputs. Experimental results demonstrate that the proposed AI Trojans can bypass state-of-the-art defense algorithms. We also discuss a fast and cost-effective solution in achieving 100% attack success rate that outperforms state-of-the-art methods based on adversarial attacks.
Rhetorical Questions as Conveyors of Hate Speech

Hateful ideas have always been expressed during conflict interactions, but it is only in recent years that they have become increasingly pervasive in public domains. This is in large part due to social media which on the one hand facilitate, among other things, anonymity and by extension impunity from prosecution. It is in such a context that the language of hate reproduces itself in various shapes and forms. The present study puts under scrutiny the use of a discursively innovative form of language, namely “rhetorical questions,” in conveying hateful ideas. To this aim, we focus on Instagram comments directed at Afghans. The findings of the study point to the implicative, yet tremendously ubiquitous, use of rhetorical questions in conveying hate. In this respect, various functions of such devices are presented and discussed.
Mitigation of Adversarial Machine Learning

Deep neural networks (DNNs) play an important role in machine learning due to their outstanding performance compared to other alternatives (Pan et al., IEEE Transactions on Computers, 71(12):3308–3321, 2022; Pan and Mishra, 2021 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pages 24–34. IEEE, 2021; Pan and Mishra, Hardware trojan detection using Shapley ensemble boosting, pages 1127–1130, 2021; Pan and Mishra, IEEE Access, 10:49508–49527, 2022). However, DNNs are usually not suitable for safety-critical applications since DNNs can be easily fooled by well-crafted adversarial examples. To address this issue, spectral normalization (SN) technique was proposed to counter adversarial attacks, which ensures that the trained model has low sensitivity toward the disturbance of input samples. Unfortunately, this strategy requires exact computation of spectral norm, which is computation-intensive and impractical for large-scale networks. In this chapter, we introduce an acceleration technique for spectral normalization based on Fourier transform and layer separation. The proposed method provides DNNs with promising security protection while maintaining minimized time cost, which turns SN from a theoretically feasible approach to a practically useful framework. Experimental evaluation using autonomous systems demonstrates that our acceleration method is able to significantly improve both time efficiency (up to 60%) and model robustness (61% on average) compared with the state-of-the-art spectral normalization in real-world applications.
Spectre and Meltdown Detection Using Explainable AI

Spectre and Meltdown attacks exploit security vulnerabilities in architectural features to gain unauthorized memory access. While there are promising defense mechanisms against such attacks, they face three major limitations: vulnerability to obfuscation techniques, lack of transparency, and reliance on mitigation-based methods that can lead to unacceptable performance degradation. To address these limitations, we describe a framework for Spectre and Meltdown attack detection using explainable machine learning. We improve the model’s performance by exploiting the temporal differences of hardware events in sequential timestamps rather than relying on overall statistics. We utilize long-short term memory as well as ensemble boosting to accelerate the training speed. We investigate both model distillation and the Shapley value analysis to improve the model’s explainability.
Explainable Artificial Intelligence

The field of artificial intelligence (AI) utilizes machine learning (ML) algorithms to learn from data to make smart decisions. While these ML algorithms are powerful at carrying out their tasks, they often do not provide any human-understandable explanation as to how the ML model reached its conclusion for a specific input. Explainable AI aims to explain an ML model’s decision-making in a human-comprehensible form. These explanations can be utilized for different scenarios, namely establishing trust between a human and the ML model. This chapter will first introduce popular ML algorithms, such as decision trees and neural networks. Next, it will describe popular explainable AI techniques as well as their advantages and disadvantages.
Hardware Acceleration of Explainable AI

The “black-box” nature of machine learning models raises concerns related to accountability and trustworthiness. Explainable AI is a promising solution that provides insights into the decision-making processes of ML models. Due to compute-intensive nature of explainable AI models, they are not suitable in many real-time applications. This chapter describes recent efforts in hardware acceleration of explainable AI models using Field Programmable Gate Arrays (FPGA) and Graphic Processing Units (GPU). Hardware acceleration enables fast and efficient explainable AI models that provide explainability as well as applicability across diverse domains, including real-time and safety-critical systems.