Miracle-dz/UniFAR-FTUs huggingface dataset:
Positive, Negative or Both? Assessing Emotional Appeals Effectiveness in Anti-Drink Driving Advertisements

Background: The use of advertising to influence social behavior is one element of social marketing campaigns. While it is known that informational appeals are less effective in changing social perceptions and behaviors, the literature presents mixed and inconsistent results of which appeal (i.e. positive negative or both) is more effective in changing social behavior. The purpose of this study is to investigate the effectiveness of positive, negative and a combination of both appeals on young adults’ drink driving perceptions. Focus: This article is related to research and evaluation of the social marketing field. Research Question: Which, if any, of positive, negative or combination of both emotional appeals, is most effective in changing young adults’ drink driving attitudes, norms and intentions and how well do intentions explain behavior? Importance to the Field: The study builds on, and extends previous research exploring the effectiveness of emotional appeals, contributing to the literature by exploring both emotional appeals along the testing of each appeal separately. The research also validates the TRA’s utility beyond previously explored contexts. At a practical level, the research informs the work of advertising message design. It is recommended that, social marketers consider combined emotional appeals approach when designing messages. Methods: Respondents were randomly exposed to one of three emotional appeals (positive, negative or both) and asked to complete a questionnaire about their attitudes, norms and intentions to drink drive before and after exposure. Two analyses were used, Partial Least Squares Structural Equation Modeling to validate the Theory of Reasoned Action (TRA), and paired sample t-tests to assess change of perceptions over time between the three groups. Results: First the study found the TRA to have a strong predictive utility in explaining drink driving intentions. Second, the combination of both positive and negative emotional appeals was found more effective in changing drink driving attitudes, norms and intentions of young adults. Recommendations for Research or Practice: The study recommends that, social marketers consider combined emotional appeals approach when designing messages. However, further investigation is required to confirm this recommendation and as in any campaign, pre-testing the advertisements with the target audience is a must. Limitations: The key limitations of the study include the use of non-probability sampling, self-reported data, and varied exposure times. Future research should utilize autonomic measures to accurately assess perceptions and emotions as well as unifying the time of exposure for all emotional appeals.
Interpreting experience enhances the use of lexical stress and syllabic structure to predict L2 word endings

Abstract Prediction underlies many life’s situations including language. Monolinguals and advanced L2 learners use prosodic cues such as stress and tone in a word’s first syllable to predict the word’s suffix. To determine whether the same findings extend to words with non-morphological endings, we investigate whether Spanish monolinguals and advanced learners of Spanish with and without interpreting experience use stress (stressed, unstressed) and syllabic structure (CV, CVC) in a word’s initial syllable to predict its ending. This is crucial to understand whether associations underlying prediction are morphophonolexical associations or purely phonolexical. Interpreters were included due to their extensive experience predicting incoming speech. Participants completed an eye-tracking study where they listened to a sentence while seeing two words and selected the word they heard. Results revealed that monolinguals and interpreters predicted word endings under all conditions, but non-interpreters only predicted in the CVC oxytone condition. These findings are relevant for (1) prediction accounts, showing that phonolexical associations trigger prediction; (2) phonological models, revealing that stress and syllable information in the initial syllable are key for accessing and predicting meaning; and (3) L2 processing models, indicating that L2 learners with interpreting experience use suprasegmental information to access and predict lexical items similar to monolinguals.
Examining the growth trajectories and cognitive predictors of reading in a consistent orthography: Evidence from a 10-year longitudinal study

Abstract We examined the growth trajectories of reading in a consistent orthography (Greek) in two developmental periods (from Grade 1 to Grade 4 and from Grade 4 to Grade 10) and what cognitive skills predict the growth patterns. Seventy-five Greek-speaking children were assessed in Grades 1, 2, 4, 6, and 10 on word-, nonword-, and text-reading fluency. In Grades 1 and 4, they were also assessed on phonological awareness, rapid naming, phonological memory, orthographic knowledge, and articulation rate. Results of growth curve modeling showed that during the first developmental period, there was a rapid initial growth from Grade 1 to Grade 2 followed by a less rapid growth from Grade 2 to Grade 4. In the second developmental period, the slow growth continued. In both developmental periods, rapid naming and orthographic knowledge predicted the initial status of all reading outcomes and phonological memory predicted the initial status of nonword-reading fluency. Phonological awareness predicted the initial status of nonword-reading fluency in the first developmental period and the initial status of word- and text-reading fluency in the second developmental period. None of the cognitive skills predicted the growth rate in reading skills. Theoretical and practical implications of these findings are discussed.
Transfer facilitation effects of morphological awareness on multicharacter word reading in Chinese as a foreign language

Abstract This study examined the transfer facilitation effects from English morphological awareness on Chinese multicharacter word reading in English-speaking adult learners of Chinese as a foreign language (CFL). Fifty English-speaking American university CFL learners participated in our study who measured their English morphological awareness, Chinese morphological awareness, Chinese linguistic knowledge, Chinese word reading, and working memory. There were three major findings: (1) with approximately three years of formal Chinese instruction and limited Chinese print input, English-speaking adult learners of Chinese developed sensitivity to the internal morphological structure of multicharacter words in Chinese. (2) English morphological awareness did not directly contribute to Chinese bimorphemic three-character pseudoword reading yet contributed indirectly via the joint serial mediation by Chinese morphological awareness and Chinese monomorphemic two-character real word reading. (3) There was no additional influence from Chinese linguistic knowledge on Chinese word reading. And, English morphological awareness explained about 3.64% of the variance in second language Chinese bimorphemic three-character pseudoword reading. Discussion is provided regarding the transfer facilitation mechanism through which first language morphological awareness contributes to adult second language reading acquisition.
Foundations of early literacy among Arabic-speaking pre-school children

Abstract The present study aimed to shed light on (i) the most accessible phonological unit and (ii) the nature of letter knowledge among native Arabic-speaking preschool children living in Israel. One hundred and sixty-seven children were assessed on phonological awareness with initial and final isolation tasks as well as knowledge of the standard names and sounds of Arabic letters. Children’s responses in these tasks were categorized in accordance with the phonological unit that the child supplied. Regarding phonological unit accessibility, the novel finding of this study was the prevalence of a tri-phonemic /ʔεC/ unit that begins with the prefix /ʔε-/ and ends with the target (consonantal) phoneme which we have termed the “demi-phoneme” (e.g., /ʔεs/ for the consonant /s/). Awareness of the consonant–vowel unit was the next most prevalent unit followed lastly by the “smallest unit” – the phoneme. It appears that the demi-phoneme functions as a psycholinguistic aid to facilitate phoneme perception and pronunciation (as proposed by the 8th-century scholar – Al-Khalil ibn Ahmad al-Farahidi) and both phoneme and demi-phoneme responses are underpinned by the same knowledge. With regard to letter knowledge, the standard name for Arabic letters was the preferred response and letter sounds were retrieved as a demi-phoneme unit.
The different fates of two Asian horseshoe crab species with different dispersal abilities

Impending anthropogenic climate change will severely impact coastal organisms at unprecedented speed. Knowledge on organisms’ evolutionary responses to past sea‐level fluctuations and estimation of their evolutionary potential is therefore indispensable in efforts to mitigate the effects of future climate change. We sampled tens of thousands of genomic markers of ~300 individuals in two of the four extant horseshoe crab species across the complex archipelagic Singapore Straits. Carcinoscorpius rotundicauda Latreille, a less mobile mangrove species, has finer population structure and lower genetic diversity compared with the dispersive deep‐sea Tachypleus gigas Müller. Even though the source populations of both species during the last glacial maximum exhibited comparable effective population sizes, the less dispersive C. rotundicauda seems to lose genetic diversity much more quickly because of population fragmentation. Contra previous studies’ results, we predict that the more commonly sighted C. rotundicauda faces a more uncertain conservation plight, with a continuing loss in evolutionary potential and higher vulnerability to future climate change. Our study provides important genomic baseline data for the redirection of conservation measures in the face of climate change and can be used as a blueprint for assessment and mitigation of the adverse effects of impending sea‐level rise in other systems.
Skeletal approximation enumeration for SMT solver testing

Ensuring the equality of SMT solvers is critical due to its broad spectrum of applications in academia and industry, such as symbolic execution and program verification. Existing approaches to testing SMT solvers are either too costly or find difficulties generalizing to different solvers and theories, due to the test oracle problem. To complement existing approaches and overcome their weaknesses, this paper introduces skeletal approximation enumeration (SAE), a novel lightweight and general testing technique for all first-order theories. To demonstrate its practical utility, we have applied the SAE technique to test Z3 and CVC4, two comprehensively tested, state-of-the-art SMT solvers. By the time of writing, our approach had found 71 confirmed bugs in Z3 and CVC4,55 of which had already been fixed.
Transport Infrastructure and the Output in the Hotel Sector: Evidence of the Chinese Case

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library. None of the papers from this Proceeding should be cited in the literature because of the questionable integrity of the peer review process for this Conference.
Design of the Face Recognition System for Criminals in Taxi Based on Cloud Platform

Taxi safety incidents cause serious threats to the personal and property safety of drivers and passengers. An effective solution was installed video surveillance in taxis. For privacy issues and the cost of using equipment, only a few taxis were equipped with surveillance cameras. However, the uploaded video didn't be transmitted to the management platform for inspection, and it doesn't have the function of criminal identification. To solve the problem mentioned above, the cloud platform combined with face recognition technology, transmit the required video to the cloud server database through wireless transmission technology. The web client would check uploaded videos online and passengers pass through criminal face recognition system before boarding the taxi. This design could monitor, review and verify the entire process of incidents online, would effectively reduce the occurrence of taxi safety incidents.
Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction

The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We envision a two-tier architecture to produce such predictions. First, a routing and travel-time-prediction subsystem generates a suggested route and predicts how the speed will vary along the route. Next, the expected energy use is predicted from the speed profile and other contextual characteristics, such as weather information and slope. To this end, the paper proposes deep-learning models that are built from EV tracking data. First, as the speed profile of a route is one of the main predictors for energy use, different simple ways to build speed profiles are explored. Next, eight different deep-learning models for energy-use prediction are proposed. Four of the models are probabilistic in that they predict not a single-point estimate but parameters of a probability distribution of energy use on the route. This is particularly relevant when predicting EV energy use, which is highly sensitive to many input characteristics and, thus, can hardly be predicted precisely. Extensive experiments with two real-world EV tracking datasets validate the proposed methods. The code for this research has been made available on GitHub.
Makina: a new QuickCheck state machine library

This article presents Makina, a new library and a domain specific language for writing property-based testing models for stateful programs. Models written in the new domain specific language are, using Elixir macros, rewritten into normal QuickCheck state machines. Our main goals with Makina are to facilitate the task of developing correct and maintainable models, and to encourage model reuse. To meet these goals, Makina provides a declarative syntax for defining model states and model commands. In particular, Makina encourages the typing of specifications, and ensures through its rewrite rules that such type information can be used by, e.g., the Dialyzer tool, to effectively typecheck models. Moreover, to promote model reuse, the domain specific language provides constructs to permit models to be defined in terms of collections of previously defined models.
How can a serious game be designed to provide engagement with and awareness of the plastic crisis as part of UN's SDGs

The aim of this study was to provide engagement with and awareness of the plastic crisis through a serious game, as part of the UN Sustainable Development Goal 12 by targeting students ages 17-21. The study is based on 32 participants, who were included in a formative evaluation within two evaluations (E1 and E2). E1 included an evaluation of 10 university students. E2 consisted of 22 students from a Danish gymnasium, where the game was part a project related to the plastic crisis. Both evaluations followed the same self-reporting methodology, which used a questionnaire and interviews. The findings reveal that most of the participants were engaged and immersed in the serious game, and that they had a high attentional focus. The findings also revealed that the game needs to be improved in terms of usability, aesthetic appeal, and the implementation of additional learning objectives. This study's novelty derives from its measurement of high engagement and immersion in a rather simple platform game about the plastic crisis. Furthermore, this study is intended to emphasize important elements when designing serious games, including content, learning objectives, and the quality of the game's title.
Frontmatter: mining Android user interfaces at scale

We introduce Frontmatter: the largest open-access dataset containing user interface models of about 160,000 Android apps. Frontmatter opens the door for comprehensive mining of mobile user interfaces, jumpstarting empirical research at a large scale, addressing questions such as "How many travel apps require registration? ", "Which apps do not follow accessibility guidelines? ", "Does the user interface correspond to the description? ", and many more. The Frontmatter UI analysis tool and the Frontmatter dataset are available under an open-source license.
Fairea: a model behaviour mutation approach to benchmarking bias mitigation methods

The increasingly wide uptake of Machine Learning (ML) has raised the significance of the problem of tackling bias (i.e., unfairness), making it a primary software engineering concern. In this paper, we introduce Fairea, a model behaviour mutation approach to benchmarking ML bias mitigation methods. We also report on a large-scale empirical study to test the effectiveness of 12 widely-studied bias mitigation methods. Our results reveal that, surprisingly, bias mitigation methods have a poor effectiveness in 49% of the cases. In particular, 15% of the mitigation cases have worse fairness-accuracy trade-offs than the baseline established by Fairea; 34% of the cases have a decrease in accuracy and an increase in bias. Fairea has been made publicly available for software engineers and researchers to evaluate their bias mitigation methods.
Ethylene/POSS copolymerization behavior of postmetallocene catalysts and copolymer characteristics

Copolymerization of ethylene with iso-butyl substituted monoalkenyl(siloxy)- or monoalkenylsilsesquioxane (POSS) comonomers over bis(phenoxy-imine) and salen-type titanium and zirconium catalysts was studied. It was found that the polyreaction performance was significantly depended by the kind of the catalyst and by the structure and concentration of POSS in the feed. The POSS comonomer was efficiently incorporated into the polymer chain at up to 0.2 mol %. The differences in the copolymer compositions as the functions of the catalyst kind and the POSS comonomer were observed, including the varied number-average sequence length of ethylene and unsaturated end groups, as determined by 1H NMR and FT-IR. The presence of POSS comonomers affected also the melting and crystallization behavior of the copolymers, as evidenced by DSC, because of influence on the polymer chain arrangement. The POSS units could act as the nucleating agents. Moreover, the crystal and structural parameters of ethylene/POSS copolymers were evaluated on the basis of X-ray results, and the limited self-aggregation of POSS incorporated into the polymer chain, the small number and size of POSS aggregates, and the increased crystallinity degree of copolymers were demonstrated. The ethylene/POSS copolymers produced by postmetallocenes offered also high thermal stability and interesting morphological properties. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017
An Auction and Witness Enhanced Trustworthy SLA Model for Decentralized Cloud Marketplaces

Cloud computing has become one of the most important technologies that have changed the traditional application development and operation (DevOps) lifecycle. However, current cloud software DevOps often faces the following key challenges: 1) selecting the best fitting service providers, customizing services and planning capacities for large-scale distributed applications; 2) guaranteeing high-quality and trustworthy service level agreements (SLAs) among multiple service providers; 3) enhancing the interoperability of cloud services across providers; 4) designing incentive model effectively among players. In this study, a framework called AWESOME is proposed to build a decentralized cloud marketplace and to address the above challenges. The proposed framework contains four subsystems including a customizable auction model, an incentive witness mechanism, and a social behavior-based simulator as one automated framework. We also provide a proof of concept to demonstrate that the AWESOME framework is feasible.
Estimating residual risk in greybox fuzzing

For any errorless fuzzing campaign, no matter how long, there is always some residual risk that a software error would be discovered if only the campaign was run for just a bit longer. Recently, greybox fuzzing tools have found widespread adoption. Yet, practitioners can only guess when the residual risk of a greybox fuzzing campaign falls below a specific, maximum allowable threshold. In this paper, we explain why residual risk cannot be directly estimated for greybox campaigns, argue that the discovery probability (i.e., the probability that the next generated input increases code coverage) provides an excellent upper bound, and explore sound statistical methods to estimate the discovery probability in an ongoing greybox campaign. We find that estimators for blackbox fuzzing systematically and substantially under-estimate the true risk. An engineer—who stops the campaign when the estimators purport a risk below the maximum allowable risk—is vastly misled. She might need execute a campaign that is orders of magnitude longer to achieve the allowable risk. Hence, the key challenge we address in this paper is adaptive bias: The probability to discover a specific error actually increases over time. We provide the first probabilistic analysis of adaptive bias, and introduce two novel classes of estimators that tackle adaptive bias. With our estimators, the engineer can decide with confidence when to abort the campaign.
Nucleoporin107 mediates female sexual differentiation via Dsx

We recently identified a missense mutation in Nucleoporin107 (Nup107; D447N) underlying XX-ovarian-dysgenesis, a rare disorder characterized by underdeveloped and dysfunctional ovaries. Modelling of the human mutation in Drosophila or specific knockdown of Nup107 in the gonadal soma resulted in ovarian-dysgenesis-like phenotypes. Transcriptomic analysis identified the somatic sex-determination gene doublesex (dsx) as a target of Nup107. Establishing Dsx as a primary relevant target of Nup107, either loss or gain of Dsx in the gonadal soma is sufficient to mimic or rescue the phenotypes induced by Nup107 loss. Importantly, the aberrant phenotypes induced by compromising either Nup107 or dsx are reminiscent of BMP signaling hyperactivation. Remarkably, in this context, the metalloprotease AdamTS-A, a transcriptional target of both Dsx and Nup107, is necessary for the calibration of BMP signaling. As modulation of BMP signaling is a conserved critical determinant of soma-germline interaction, the sex and tissue specific deployment of Dsx-F by Nup107 seems crucial for the maintenance of the homeostatic balance between the germ cells and somatic gonadal cells.