Social bots are now deeply embedded in online platforms for promotion, persuasion, and manipulation. Most bot-detection systems still treat behavioural features as static, implicitly assuming bots behave stationarily over time. We test that assumption for promotional Twitter bots, analysing change in both individual behavioural signals and the relationships between them. Using 2,615 promotional bot accounts and 2.8M tweets, we build yearly time series for ten content-based meta-features. Augmented Dickey-Fuller and KPSS tests plus linear trends show all ten are non-stationary: nine increase over time, while language diversity declines slightly. Stratifying by activation generation and account age reveals systematic differences: second-generation bots are most active and link-heavy; short-lived bots show intense, repetitive activity with heavy hashtag/URL use; long-lived bots are less active but more linguistically diverse and use emojis more variably. We then analyse co-occurrence across generations using 18 interpretable binary features spanning actions, topic similarity, URLs, hashtags, sentiment, emojis, and media (153 pairs). Chi-square tests indicate almost all pairs are dependent. Spearman correlations shift in strength and sometimes polarity: many links (e.g. multiple hashtags with media; sentiment with URLs) strengthen, while others flip from weakly positive to weakly or moderately negative. Later generations show more structured combinations of cues. Taken together, these studies provide evidence that promotional social bots adapt over time at both the level of individual meta-features and the level of feature interdependencies, with direct implications for the design and evaluation of bot-detection systems trained on historical behavioural features.

Maintaining high Data Quality (DQ) is critical in data-driven approaches; however, it is still an open challenge in Data-Driven Requirements Engineering (DDRE) where data steers the requirements engineering process. The software engineering view of DQ is still limited and is often neglected due to a lack of motivation and the inadequacy of skills in identifying DQ issues. Issues such as bias, inconsistency, noise etc. can compromise the quality of the analysis and product. This work draws upon work in behaviour change, in particular nudge theory, to motivate software engineers to adopt better practices towards DQ. We report on a preliminary controlled experiment, where we empirically evaluate the effect of digital nudging in terms of performance, efficiency, and engagement. Our positive findings provide promising initial empirical insights and pave the way for further research on the use of digital nudging for altering human behaviour in general, and for software engineers in particular.

Blockchain technology has been widely adopted in many areas to provide more dependable and trustworthy systems, including digital infrastructure. Nevertheless, its widespread implementation is accompanied by significant environmental concerns, as it is considered a substantial contributor to greenhouse gas emissions. This environmental impact is mainly attributed to the inherent inefficiencies of its consensus algorithms, notably Proof of Work, which demands substantial computational power for trust establishment. This paper proposes a novel self-adaptive model to optimize the environmental sustainability of blockchain-based systems, addressing energy consumption and carbon emission without compromising the fundamental properties of blockchain technology. The model continuously monitors a blockchain-based system and adaptively selects miners, considering context changes and user needs. It dynamically selects a subset of miners to perform sustainable mining processes while ensuring the decentralization and trustworthiness of the system. The aim is to minimize blockchain-based systems' energy consumption and carbon emissions while maximizing their decentralization and trustworthiness. We conduct experiments to evaluate the efficiency and effectiveness of the model. The results show that our self-optimizing model can reduce energy consumption by 55.49% and carbon emissions by 71.25% on average while maintaining desirable levels of decentralization and trustworthiness by more than 96.08% and 75.12%, respectively. Furthermore, these enhancements can be achieved under different operating conditions compared to similar models, including the straightforward use of Proof of Work. Also, we have investigated and discussed the correlation between these objectives and how they are related to the number of miners within the blockchain-based systems.

Adaptive e-learning is becoming increasingly popular as a tool to help learners with dyslexia. It provides more customized learning experiences based on the learners’ characteristics. Each learner with dyslexia has unique characteristics for which material should ideally be suitably tailored. However, adaptation to the characteristics of learners with dyslexia—in particular, their dyslexia type and reading skill level—is limited. By examining the learning effectiveness of adaptation of learning material based on the learner’s type of dyslexia and reading skill, this study fills a knowledge vacuum in this under-researched area. An empirical evaluation through a controlled experiment with 47 Arabic subjects has been undertaken and assessed using the following metrics: learning gain and learner satisfaction. The findings reveal that adapting learning material to the combination of dyslexia type and reading skill level yields significantly better short- and long-term learning gains and improves the learners’ satisfaction compared to non-adapted material. There is evidence that this benefit also extends to how well learners read unseen material. This paper also discusses implications and important avenues for future research and practice related to how adaptation influences learners with dyslexia.

In digital communication, emoji are essential in decoding nuances such as irony, sarcasm, and humour. However, their incorporation in Arabic natural language processing (NLP) has been cautious because of the perceived complexities of the Arabic language. This paper introduces ArSarcasMoji, a dataset of 24,630 emoji-augmented texts, with 17. 5% that shows irony. Through our analysis, we highlight specific emoji patterns paired with sentiment roles that denote irony in Arabic texts. The research counters prevailing notions, emphasising the importance of emoji’s role in understanding Arabic textual irony, and addresses their potential for accurate irony detection in Arabic digital content.

Blockchain technology has gained recognition in industrial, financial, and various technological domains for its potential in decentralizing trust in peer-to-peer systems. A core component of blockchain technology is a consensus algorithm, most commonly Proof of Work (PoW). PoW is used in blockchain-based systems to establish trust among peers; however, it does require the expenditure of an enormous amount of energy that affects the environmental sustainability of blockchain-based systems. Energy minimization, whilst ensuring trust within blockchain-based systems that use PoW, is a challenging problem. The solution has to consider how energy consumption can be minimized without compromising trust, whilst still ensuring, for instance, scalability, security, and decentralization. In this paper, we represent the problem as a subset selection problem of miners in a blockchain-based system. We formulate the problem of blockchain energy consumption as a Search-Based Software Engineering problem with four objectives: energy consumption, carbon emission, decentralization, and trust. We propose a model composed of multiple fitness functions. The model can be used to explore the complex search space by selecting a subset of miners that minimizes the energy consumption without drastically impacting the primary goals of the blockchain technology (i.e., security/trustworthiness and decentralization). We integrate our proposed fitness functions into five evolutionary algorithms to solve the problem of blockchain miners selection. Our results show that the environmental sustainability of blockchain-based systems (e.g. reduced energy use) can be enhanced with little degradation in other competing objectives. We also report on the performance of the algorithms used.

The global spread of COVID-19 has shifted the learning process towards e-learning. In this context, a critical challenge for researchers is to understand and evaluate the effectiveness of e-learning, especially when the learning is adapted to the needs of individual users. In this work we argue that the learner’s perception of the level of usability of a system is a valuable metric that gives an insight into the learners’ engagement and motivation to learn. Little attention has been paid to this metric. In this paper we explore why this is important and valuable. We present a case study which uses the System Usability Scale (SUS) questionnaire to measure the user’s perception of usability as an indirect (proxy) measure of engagement. A between-subject experiment was conducted with 41 learners with dyslexia. The intervention group used the adaptive version of the e-learning system that matched the material to the needs of the learner. The control group used a standard version. At the end, learning gain and SUS scores were assessed. The correlation between learning performance and the perceived level of usability was positive and moderate (0.517, p < 0.05) among participants in the intervention group. However, learning performance and perceived level of usability were unrelated in the control group (− 0.364, p > 0.05). From this, and other work, it appears that using a learner’s assessment of the usability of a system is an effective way to measure their attitude to their learning. It reflects their perception of its suitability to their needs and this, in turn, is likely to affect their engagement and motivation. As such, this provides an effective instrument to judge whether adaptation based on learner needs has been successful.

Emoji (the digital pictograms) are crucial features for textual sentiment analysis.However, analysing the sentiment roles of emoji is very complex.This is due to its dependency on different factors, such as textual context, cultural perspective, interlocutor's personal traits, interlocutors' relationships or a platforms' functional features.This work introduces an approach to analysing the sentiment effects of emoji as textual features.Using an Arabic dataset as a benchmark, our results confirm the borrowed argument that each emoji has three different norms of sentiment role (negative, neutral or positive).Therefore, an emoji can play different sentiment roles depending upon context.It can behave as an emphasizer, an indicator, a mitigator, a reverser or a trigger of either negative or positive sentiment within a text.In addition, an emoji may have neutral effect (i.e., no effect) on the sentiment of the text.

Dyslexia is a universal reading difficulty independent of ethnicity or race. It is sensitive, though, to language and is affected by the language’s structure and orthography. Also, there are non-reading difficulties that cooccur with dyslexia, such as memory problems and low levels of self-esteem. Due to the lack of research in the Arabic language, this study aims to understand the needs of Arab learners with dyslexia in order to design adaptive interactive educational tools (IET)s based on their needs. To this end, the common reading and non-reading difficulties of learners with dyslexia and the features that the special education teachers (SPET)s prefer to either add or avoid when designing any IET, were explored in schools in Saudi Arabia, from the SPETs’ perspective. It was found that vowel letter dyslexia and visual dyslexia are the most common reading difficulties and that dictation difficulty and attention deficit are the most common non-reading difficulties. Hierarchical and Modelling teaching strategies are the teaching approaches that, from the SPETs’ perspective, most improved the reading of learners with dyslexia. Finally, pictures and sounds should be used in the design of IETs, while using many colours and loud sounds should be avoided.

Gender differences in usages and attitudes toward emoji have been reported previously in many different cultures, but not in Arabic. Here we examine the extent to which Arabic smart-device users, specifically females and males, interpret emoji sentiment differently. A manual sentiment annotation of 1034 emoji was applied by 28 Arabic native speakers (14 females, and 14 males) from seven different Arabic regions (Gulf, Egypt, Levant, Sudan, Magharib, Iraq, and Yemen). For the gender-based analysis, ten emoji groups were used to compare the differences in context-free emoji sentiment interpretation between Arabic women and men. Two approaches were used to perform the comparison: emoji sentiment score correlation coefficient and emoji sentiment score intensity-based clustering. The analysis reveals that, although the correlation results are moderate between the two genders' emoji sentiment interpretations, the clustering results reveal nuanced differences. Arabic females, mainly, indicate positivity through heart and facial expression emoji, while Arabic males indicate positivity through objects, nature, and some heart emoji. Regarding negativity, both genders tend to use facial expression emoji slightly more than any other type of emoji. Moreover, emoji representing flags are more neutral from the Arabic female perspective, while emoji representing symbols are more neutral from the Arabic male perspective.

E-learning has become a popular tool for helping people with dyslexia to improve their reading, as it provides interactivity anywhere and at any time. However, most traditional e-learning systems are designed for a generic learner, regardless of each individual’s differences and needs. In this context, a successful e-learning system needs to consider the learner’s individual characteristics – in particular, their dyslexia type and reading skill level. This can lead to a more appropriate learning experience and interaction. There is, however, a need to understand the value of this adaptation, particularly on the learning gain. This study contributes to research by bridging this under-investigated gap by evaluating the learning effectiveness of adapting material based on the learner’s dyslexia type and reading skill. A controlled, between-subjects experiment with 47 subjects is described and the results presented and analysed. The findings indicate that adaptation based on the combination of dyslexia type and reading skill level results in significantly better short-term and long-term learning gains and greater learner satisfaction than non-adapted material. There is also evidence that this benefit transfers to learners’ reading performance on unseen material.

Dyslexia is a universal reading difficulty. Each dyslexic suffers from individual reading problems. Some may not understand what is written, while others may omit or transpose letters while reading. Most research has treated all dyslexics as a single class, especially in Arabic. Therefore, the aim of this research is to overcome these problems by training dyslexics using learning material that matches their individual needs, in order to improve their reading. To achieve this, an online training system for Arabic dyslexics has been developed and evaluated (in terms of learning gain and learner satisfaction).

Blockchain technology holds several promises for many application areas; however, it is not without its limitations. One of the most significant weaknesses of blockchain technology is its substantial energy consumption. Many researchers have proposed solutions to reduce the energy demands of this technology - such as the use of alternative consensus algorithms and the use of renewable energy. However, the use of alternative trust and reputation models to improve sustainability (by, for instance, selecting miners based on these trust or reputation values) has not been widely investigated. In this paper, we propose a reputation model that quantifies and compares the trustworthiness of miners based on their behaviours within a blockchain network. The model is evaluated analytically and compared to other trust and reputation models for miners. The evaluation shows that our model fulfils several desirable properties that should always be satisfied by reputation models, whereas other models do not always meet these requirements. In addition, we perform experimental evaluations to represent the performance of our model and its accuracy in detecting malicious miners. We also report the effectiveness of using the model in reducing the energy consumption of blockchain-based systems.

The Nature Inspired Creative Design network brings together people from three different main areas: Nature and Biology, Art and Design, and Science and Computing. It aims to establish a forum for crossfertilization between the disciplines, within the overall topic of adopting nature inspired approaches to creative design. Its members explore a wide range of subjects, including evolution, growth and development, emergence and self organization, robustness, natural structures, and human design behaviour and performance1. Nature Inspired Creative Design is a research cluster under the 'Designing for the 21st Century' program. It is sponsored by the British Arts and Humanities Research Council and the Engineering and Physical Sciences Research Council. The network brings together people from a variety of disciplines to exchange ideas about art, design, nature and science [Schnier06]. This chapter presents the background and ideas of the network, predominantly from a computational viewpoint. It also presents some of the result of the initial one-year funded phase of the network. 2. NATURE INSPIRED CREATIVE DESIGN Nature is the ultimate designer. Every species, every individual, can be seen as the result of an implicit design process. At the same time, the results of this design process perform extremely well, in whatever measure of performance one might want to use. This even often includes aesthetical measures – there are many extremely beautiful species on earth. The main goal of this network is to explore what can be learned from Nature for the human design and engineering activities. However, this is not a one-way process. The nature of the funding puts an emphasis on applications to design, and this chapter will maintain this emphasis. However, there is potential for transfer of ideas between all the groups participating in the network (Figure 1). Without this, the network would be one-sided and of questionable use to perhaps the majority of the members. 2.2 Promises of Nature Inspired Design The design process is becoming increasingly complex and demanding, and will continue to do so into the 1 This is an extended version of a paper originally published at ACDM 2006 (IP-CC, Bristol) future. Individual designs are composed of more and more parts; designs are subject to more and more constraints and objectives; at the same time the rapid technical innovations and shorter lifespans increase the demand for design. By adopting nature inspired methods, we hope to solve some of these problems. Nature inspired methods have a number of important potentials, as described in the following sections. 2.2.1 Produce better designs Comparing natural and artificial designs, it is clear that natural designs often have a number of advantages. For example, natural designs are usually: • Resource efficient: Through continuous adaptation, natural designs have become parsimonious in their use of resources. • Resilient to faults: Artificial designs often fail when individual components break. Natural designs, on the other hand, usually show graceful degradation: a small injury is usually not fatal to an animal, and the death of a number of individuals will not seriously endanger a colony of insects. • Adaptable: Natural designs are usually able to adapt to the environment; sometimes this happens as part of the development process from genetic code to individual, sometimes this happens during the lifetime as physical change, or as behavioural change. • Extremely varied: Natural design processes have produced individuals that span a massive range of scales, complexities, shapes and forms. They can be found in a vast range of environments. Nature inspired processes can help designers to create very novel designs. 2.2.2 Create better design processes By taking inspiration from nature's methods, we will be able to find processes that are Figure 1: Nature Inspired Creative Design Network • More scalable: Nature has developed systems that show a very high complexity: human nervous systems, colonies of insects, large ecosystems are all examples. Very often, these systems are composed of a large number of elements with complex interactions. Designing artificial systems of similar complexity is usually a very difficult process. • Better parallelizable: In natural evolution, there is no top-down design process – every species evolves on its own, and individual parts of a design (e.g. the beak of a Darwin Finch) can often be optimized fairly much independently of other parts. • More reliable: A very simple system consisting of 10 components interacting in 10 different ways has the state space of 10,000,000,000 possible states. A designer using conventional tools simply does not have the time to search the entire state space. Natural systems have developed various emergent methods for searching the sate space of the system. • More efficient. Natural design processes are unsupervised processes – no conscious designer is involved. Instead, processes of evolution, emergence, self-organization and interaction with the environment determine the outcome. Together with the increasing availability of fast computer clusters, nature inspired approaches have the potential to provide efficient alternatives to labour-intensive manual design processes. 2.2.3 Create better design tools Nature inspired design tools have the potential to • Allow the user to search a larger design space: designers are often limited to a small subset of the total design space, for a variety of reasons: lack of knowledge, lack of time, lack of design methods. Nature inspired methods may be able to help both producing designs in a larger search space, and evaluating these designs. • Provide better support: systems that know about the design process, and learn the user's preferences, will be able to provide better support to the designer. Nature inspired techniques can provide both languages to describe designs, as well as the learning algorithms. 2.2.4 Provide knowledge about human design activities • Humans are a part of nature. They are also a result of an evolutionary process – our artistic perception, and our creative abilities, are formed by this process. By studying aspects of human design activities, we can learn more about human and animal creative processes. Human creativity is a many-faceted process, and attempts at creating creative computer programs can benefit from emulating one or more of these aspects. Human creativity is also of course a result of human evolution. • Capture design knowledge. Human designers have developed a large pool of knowledge about design, both explicit and implicit. This knowledge can be applied in other disciplines, for example in data visualization. • Gain insights into ethical aspects of design. Natural life has survived many drastic changes, in terms of climate changes, natural disasters, or rapid evolutionary changes. Studying natural design dynamics may give us some ideas about possible interactions between the natural and human designed worlds. 3. AREAS OF INTEREST With about 40 members with a variety of backgrounds actively participating in the network, there are nearly as many subjects discussed. The following sections attempt to categorise and summarise these subjects.

The flexibility of online courses means that people are able to fit them around other commitments, often studying during holiday times. However, there is limited research examining the behaviour of online learners during holidays and special events. It is important to understand this in order to know when learners will be active and therefore when they might be contactable or require online support. We conducted this study to examine the changes in behaviour of online learners during Ramadan, a period when Muslims are required to fast between sunrise and sunset. This change in mealtimes affects Muslims’ wake-sleep cycles and work patterns as well as all other aspects of their lives. The results can be used to infer an understanding of their lifestyle during this period. The insights that this provides are important in the context of online learning but also many other application domains where users’ availability and openness to interaction are important.

Usability is now widely recognised as a critical factor to the success of e-learning systems.A highly usable e-learning system increases students' satisfaction and engagement, thereby enhancing learning performance.However, one challenge in e-learning is poor engagement arising from a "one-size-fits-all" approach that presents learning content and activities in the same way to all students.Each student has different characteristics and, therefore, the content should be sensitive to these differences.This study evaluated the students' perceived level of usability of an e-learning system that matches content to reading skill levels of students with dyslexia.41 students rated their perceived usability of an e-learning system using the System Usability Scale (SUS).Results indicated that when the e-learning system matches content to students' skill level, students perceive greater usability than when the learning is not matched.There was also a moderate, positive correlation between perceived usability and learning gain when e-learning was matched to their skill level.Thus, students assessment of the usability of a system is affected by the degree to which it is suited to their needs.This may be reflected in increased engagement and is associated with higher learning gain.

In this paper, we represent the problem of selecting miners within a blockchain-based system as a subset selection problem. We formulate the problem of minimising blockchain energy consumption as an optimisation problem with two conflicting objectives: energy consumption and trust. The proposed model is compared across different algorithms to demonstrate its performance.

Emoji (the popular digital pictograms) are sometimes seen as a new kind of artificial and universally usable and consistent writing code. In spite of their assumed universality, there is some evidence that the sense of an emoji, specifically in regard to sentiment, may change from language to language and culture to culture. This paper investigates whether contextual emoji sentiment analysis is consistent across Arabic and European languages. To conduct this investigation, we, first, created the Arabic emoji sentiment lexicon (Arab-ESL). Then, we exploited an existing European emoji sentiment lexicon to compare the sentiment conveyed in each of the two families of language and culture (Arabic and European). The results show that the pairwise correlation between the two lexicons is consistent for emoji that represent, for instance, hearts, facial expressions, and body language. However, for a subset of emoji (those that represent objects, nature, symbols, and some human activities), there are large differences in the sentiment conveyed. More interestingly, an extremely high level of inconsistency has been shown with food emoji.

Dyslexia is a universal reading difficulty where each individual with dyslexia can have a different combination of underlying reading difficulties. For instance, errors in letter identification or omissions or transpositions within or between words. Nowadays, adaptive e-learning and gamification have become more common. Different learner characteristics have been used when adapting e-learning systems, such as the user’s learning style or knowledge level. However, little attention has been directed towards understanding the benefits of using dyslexia type or reading skill level when adapting systems for learners with dyslexia. This, despite dyslexia type and reading skill level being significant factors in their education and learning. This paper reports on research which aims to improve this understanding through empirical studies designed to evaluate the benefits of adaptation with native Arabic speaking children with dyslexia. A mixed-methods approach was used. In the first experiment the focus is on a qualitative understanding of the effects of adaptation based on dyslexia type. The second experiment provides a quantitative analysis of the effects of adaptation based upon the reading skill level of learners with dyslexia. Findings revealed that the majority of learners are motivated when adapting learning material to dyslexia type. Analysis of the results indicated that adapting based on reading skill level does achieve improved learning gain and lead to greater learner satisfaction compared to a non-adaptive version. Implications of these experiments are discussed.

Dyslexia is a universal specific learning disability that is characterised by poor spelling, word reading, and fluency. Adaptive e-learning is becoming more common, but there is little work on adaptation focused on the needs and characteristics of dyslexic students. In particular, there is very little work that investigates adaptivity based on a student’s dyslexia type. This despite the type of dyslexia being a critical factor in determining the most appropriate teaching and learning. Also, previous research has overlooked rigorously designed and controlled evaluations of the system’s effectiveness. Therefore, there is a great deal of progress yet to be achieved for dyslexia, especially for Arabic dyslexics. The contribution of this paper is investigating whether adapting learning material based upon an individual’s dyslexia type will improve learning performance, and also the perception of the usability of the system. An experiment was conducted with 40 Arabic dyslexic children producing statistically significant results. They indicate that adapting learning material according to dyslexia type yields significantly better learning gain (short- and long-term) and perceived level of usability than without adaptation.