Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Relationship between Frames and Emotionality in Text

Emotions, which are responses to salient events, can be realised in text implicitly, for instance with mere references to facts (e.g., “ That was the beginning of a long war ”). Interpreting emotions thus relies on the readers’ background knowledge, but that is hardly modeled in computational emotion analysis. Much work in the field is focused on the word level and treats individual lexical units as the fundamental emotion cues in written communication. We shift our attention to the event knowledge they evoke. We leverage frame semantics, a prominent theory for the description of event meanings, and show it is well-suited for the study of emotions: frames build on a “semantics of understanding” whose assumptions rely precisely on people’s world knowledge. Our overarching question is if the events that are represented by frames possess an emotion dimension. We hypothesise that they do, and that such a dimension can be distinguished qualitatively for different groups of frames. To carry out a large corpus-based correspondence analysis, we automatically annotate texts with emotions as well as with FrameNet frames and roles, and we analyse the correlations between them. Our main finding is that substantial groups of frames have an emotional import. With an extensive qualitative analysis, we show that they capture several properties of emotions that are purported by theories from psychology. These observations contribute to advancing the two strands of research that we combine: emotion analysis can profit from the event-based perspective of frame semantics; in return, frame semantics gains a better grip of its position vis-`a-vis emotions, an integral part of word meanings.
Presidential Cycles and Exchange Rates*

This paper shows that US presidential cycles can predict dollar-based exchange rate returns. Armed with nearly 40 years of data and a large cross-section of currency pairs, we document an average US dollar appreciation during Democratic presidential terms and an average US dollar depreciation during Republican presidential mandates. The difference in these average exchange rate returns is larger than 5% per annum and is primarily linked to trade tariffs. In contrast, we find no relationship with cross-country interest rate differentials, inflation differentials, and pre-existing economic conditions. We relate these findings to trade policy within a model of exchange rate determination with constrained financiers.
Some Universals of Grammar with Particular Reference to the Order of Meaningful Elements

A log slasher includes an elongated log receiving bunk and a saw assembly movable longitudinally along the bunk to saw logs supported in the bunk into shortened lengths. The saw is pivotally supported at one end on a traveling trolley for movement with the trolley along the bunk and in a vertical plane toward and away from the bunk. A guide moves with the saw to hold the saw against whip as the saw is moving into, and away from, a cut. A lift crane lifts logs into and out of the bunk and includes clam fingers having opposite angled surfaces adjacent the free ends thereof for engaging logs of different diameter. The trolley drive and that for moving the saw toward and away from the bunk are carried on the trolley and an articulated arm assembly, extendible and contractible in response to trolley movement, supports the power supply conduits in a compact arrangement throughout the range of movement of the trolley and saw.
Shaping information and knowledge on climate change technologies: A cross‐country qualitative analysis of carbon capture and storage results on Google search

Commercial search engines play a central role in shaping, defining, and promoting the information people have access to in contemporary societies. This is particularly true when it comes to emergent technologies, for which there is often limited available information in legacy media and other sources, thus having a strong bearing on public perceptions. In this article, we focus on how the Google search engine promotes information on carbon capture and storage (CCS). We explore how Google's ranking parameters and interface shape the information people access when searching for CCS through a qualitative analysis comparing the results in three countries (France, Spain, and Portugal). We focus on the content of the first search engine result pages (SERP) and consider both Google's ranking criteria and the content and format of promoted sources. The study reveals Google's influence in highlighting Wikipedia pages, Q&A‐formatted sources, and prioritizing online specialized media and private corporations. Additionally, we observe country‐specific variations in terms of actors and types of content, reflecting the level of interest and investment in the topic at the national level. These findings underscore the significant role of search engine mediations in shaping public perceptions and knowledge about emergent climate change technologies.
Revisión del género" agrostis" L. ("poaceae") en la Península Ibérica

Se realiza la revision taxonomica del genero agrostis l. En la peninsula iberica considerandose constituido por tres subgeneros tres secciones dieciocho especies siete variedades y tres hibridos dichos taxones se han estudiado desde el punto de vista palinologico (destacan las diferencias en tamano y superficie exinica) citotaxonotica (con los niveles diploide tetra hexoploide) anotomico y epidermico. Asi como un detallado analisis morfologico. Teniendo en cuenta todos estos datos se proponen como taxones nuevos: subgenero zingrostis romero blanca y morales. Subgenero jurzssi romero blanca y morales. Agrostis hesperica romero blanca y morales. Agrostis canina l. Subespecie granatensis romero blanca y morales. Agrostis castellana boiss. Y reuter var. Mutica (boiss-y reuter) kerguzlen ex romero blanca y morales.
Components of Health Surveillance System in Natural Disasters that Affect Military Health Services: A Systematic Review

Background: Military health surveillance system, as the first responder in natural disasters, plays an important role in public health. This study aimed to identify those components of the health surveillance system, which influence military health services in natural disasters. Methods: Five databases of Medline, SCOPUS, Web of Science, ProQuest, Cochrane Library, and search engines of Scholar Google, scienceopen.com, openGery.eu, and WHO.int were reviewed from Jan 1990 to Jan 2022. Due to the heterogeneity of the included studies, various JBI quality assessment tools were applied and the extracted data were analyzed by meta-synthesis method. Results: Out of 6538 retrieved studies, after the duplicates and irrelevant studies identified in screening stage were removed, 174 studies extracted from the electronic search of databases and 16 studies retrieved from the manual search of other sources were reviewed based on the study inclusion criteria. Finally, 24 studies with inclusion criteria were selected for data extraction. Ten criteria of monitoring, detecting, data collection and reporting, tracking, type of surveillance system, operational readiness, coordination and interaction, feasibility, flexibility, and acceptability were identified as effective components in the surveillance system of military health services. The identified components were classified into two main categories of structural and operational components. Conclusion: The structural components of the military health surveillance system describe the important infrastructural features of the health surveillance system to preparing for natural disasters; and the operational components explains the effective functional features of military health surveillance system in response of natural disasters. The results of this study help policymakers in military health services implement a more effective health surveillance system in natural disasters.
Future-proof: Monitoring the development, deployment, and impacts of Artificial Intelligence

Recent developments in Artificial Intelligence (AI) pose a complex challenge for policymakers, who are tasked with regulating a technology which is poorly understood, highly multi-use, of potentially enormous economic impact, and which becomes more powerful at an extraordinary rate. In response to this challenge, this policy position paper outlines two recommended actions for national governments to monitor the AI supply chain: (1) Invest in infrastructure for monitoring the AI supply chain, and (2) establish key AI standards. This will allow policymakers to prepare for current technological challenges, as well as to have the infrastructure for unforeseen ones. Importantly, these recommendations are directly informed by technical research at the frontiers of AI and AI forecasting, to help policymakers make decisions that are robust to future technological changes.
Encyclopaedia of Nuclear Energy Magnetic Confinement Fusion - Development Facilities

Roadmaps have been developed to achieve fusion electricity as quickly as possible. All roadmaps have as the central element the construction and exploitation of ITER, which will, for the first time, exhibit a fusion gain of Q ≥ 10. ITER will test essential technologies that are required for the next step, including tritium breeding, and will demonstrate that fusion is feasible on a scale appropriate to practical exploitation. A variety of ideas are being evaluated for the steps after ITER. Some countries are designing relatively large demonstration plants that are based on an extrapolation from ITER, thereby using proven technologies as much as possible. Japan, Korea and the EU have labeled their devices JA-DEMO, K-DEMO and EU-DEMO, while China is working on the Chinese Fusion Experimental Test Reactor. The USA and the UK are focusing their activities on smaller devices based on the spherical tokamak, and a similar approach is followed by a number of privately funded enterprises that focus on spherical tokamaks featuring high-field superconductors. Conceptual studies of stellarator-based fusion power plants are also being conducted in Europe and Japan. Since materials in future devices will experience much higher neutron loads than in ITER, new materials must be developed or the operational space of existing materials must be expanded. It is critical, therefore, that the materials are tested and validated in a dedicated neutron source exhibiting a similar spectrum to that in a fusion reactor. The most prominent of these (accelerator-based) facilities are IFMIF/DONES in Europe and A-FNS in Japan. Some countries focus also on volumetric neutron sources based on plasma devices. This chapter will give an overview of the major development devices within the magnetic confinement fusion program. It also includes a brief description of several non-nuclear devices that are currently being constructed and that will give support to the main line of nuclear fusion devices.
A FLEXIBLE BARRIER TO AVOID SOLID SKIN APPEARANCE IN CELLULAR POLYMERS PRODUCED BY GAS DISSOLUTION FOAMING

Gas dissolution foaming is one of the most employed methods to produce polymer foams. Despite all the advantages of this environmentally friendly method based on physical gas dissolution, the obtained cellular polymers generally present a non-foamed solid skin surrounding the foamed core. This feature is related to the diffusivity mechanisms which are involved in the pressure drop stage and represents a significant drawback, limiting many potential applications of these materials. This work proposes the incorporation of a flexible gas diffusion barrier during the foaming process which allows reducing the solid skin without limiting the expansion during the foaming stage.
An improved image steganography model based on Deep Convolutional Neural Networks

In this paper, we propose an image steganography model with the use of a DeepCNN grounded autoencoder which allows extracting spatiotemporal features from images. It tends to hide four images in one other image taking into consideration equivalency in terms of size. Thus we try to encode and decode multiple secret images within a single, high-resolution cover image. The quantitative result of this model was arranged using the quantitative index "error per pixel"
Knowledge Interchange Format Version 3.0 Reference Manual

Knowledge Interchange Format (KIF) is a computer-oriented language for the interchange of knowledge among disparate programs. It has declarative semantics (i.e. the meaning of expressions in the representation can be understood without appeal to an interpreter for manipulating those expressions); it is logically comprehensive (i.e. it provides for the expression of arbitrary sentences in the rst-order predicate calculus); it provides for the representation of knowledge about the representation of knowledge; it provides for the representation of nonmonotonic reasoning rules; and it provides for the de nition of objects, functions, and relations.
Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

The emergence of the Internet of Things (IoT) accelerated the implementation of various smart city applications and initiatives. The rapid adoption of IoT-powered smart cities is faced by a number of security and privacy challenges that hindered their application in areas such as critical infrastructure. One of the most crucial elements of any smart city is safety. Without the right safeguards, bad actors can quickly exploit weak systems to access networks or sensitive data. Security issues are a big worry for smart cities in addition to safety issues. Smart cities become easy targets for attackers attempting to steal data or disrupt services if they are not adequately protected against cyberthreats like malware or distributed denial-of-service (DDoS) attacks. Therefore, in order to safeguard their systems from potential threats, businesses must employ strong security protocols including encryption, authentication, and access control measures. In order to ensure that their network traffic remains secure, organizations should implement powerful network firewalls and intrusion detection systems (IDS). This article proposes a blockchain-supported hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) to provide privacy and security for smart city users and systems. Blockchain is used to provide trust, and CNN enabled with KPCA is used for classifying threats. The proposed solution comprises three steps, preprocessing, feature selection, and classification. The standard features of the datasets used are converted to a numeric format during the preprocessing stage, and the result is sent to KPCA for feature extraction. Feature extraction reduces the dimensionality of relevant features before it passes the resulting dataset to the CNN to classify and detect malicious activities. Two prominent datasets namely ToN-IoT and BoT-IoT were used to measure the performance of this anticipated method compared to its best rivals in the literature. Experimental evaluation results show an improved performance in terms of threat prediction accuracy, and hence, increased security, privacy, and maintainability of IoT-enabled smart cities.
Applying the Theory of Planned Behavior to Predicting Nurse’s Intention and Behavior in Using Health Literacy Strategies in Patient Education

Background & Aims: Health literacy is one of the most important priorities for improving health care quality through enhancing patient- provider communication. Implementing health literacy strategies enables nurses to provide information and instructions for patients in a manner that is more commensurate and understandable. The purpose of this study was to investigate the factors affecting nurses’ intention to implement health literacy strategies in patient education based on theory of planned behavior. Material & Methods: A cross- sectional study conducted among 148 nurse practitioners of Al-Zahra Hospital affiliated to Isfahan University of Medical Sciences, Isfahan, Iran, using descriptive-analytical method. Data collected via standardized questionnaire based on theory of planned behavior constructs and analyzed by SPSS v.17 using ANOVA, Independent T-test, Pearson correlation and linear regression. Results: There were statistically significant correlation between using health literacy strategies and marriage status, attending in retraining courses, employment type, job history, and job status. Perceived behavioral control was the most powerful predictor of intention (β= 0/417) and use health literacy strategies in patient education and behavior of nurses (β=0/319). Conclusion: According to the findings of this study, perceived behavioral control is a powerful determinant of nurses’ intention and behavior of using health literacy strategies in patient education. Hence we recommend nurse educators to pay special attention to the constructs of this theory mainly perceived behavioral control in retrain courses about patient education and health literacy strategies.
Journal of Technology Education

Facilitating student growth and development in diagnosing and solving technical problems remains a challenge for technology and engineering educators. With funding from the National Science Foundation, this team of researchers developed a self-guided, computer-based instructional program to experiment with conceptual mapping as a treatment to improve undergraduate technology and engineering students’ abilities to develop a plan of action when presented with real technical problems developed in collaboration with industrial partners. The pilot-testing and experimental portions of the project confirmed that the subjects found the training interesting and useful. However, the experimental data did not support the conclusion that the treatment caused an increase in diagnostic performance. Analysis of individual data demonstrated that for a portion of the sample, significant gains occurred. Qualitative analysis demonstrated that the majority of the students treated the experience as a trivial academic exercise, which seriously limited their efforts. A major step forward occurred in the ability to automatically compare student maps with the expert’s reference map using a modified version of the similarity flooding algorithm. Student feedback was based on this automatic comparison. In all, the experience has encouraged the team to continue and expand the use of conceptual mapping as a tool for improving diagnostic and problem-solving skills.
Building a Better Mouse Trap : Item Characteristics Associated with Rating Discrepancies in Multi-source Assessment Instruments

The authors hypothesized that the low level of convergence characteristic of 360-degree performance ratings is partly a function of the quality of the items used in these instruments. Considering rating items as “stimuli” to raters, three linguistic characteristics are identified that can cause raters to attach different meanings to the same item. In turn, these different interpretations can lead to discrepant ratings from multiple judges of the same target. The results of this study indicate that inter-rater reliability is lower for items that describe complex abstractions of behaviors and for items that refer to more than one discrete behavior. By contrast, inter-rater reliability is higher for items that describe less descriptively complex dispositional qualities and those that are grammatically complex enough to provide contextual cues yet focus on only one specific behavior. Thus, the authors of multi-rater instruments can enhance the quality of feedback provided with their tools by paying more attention to how the items are written. The emerging research base on 360-degree feedback or multi-source assessment (MSA) ratings paints a discouraging picture. Specifically, analyses of the psychometric properties of these measures indicate that the ratings are plagued with measurement error. Not only are inter-rater reliability estimates dubiously low (Conway & Huffcut, 1997; Gregarus & Robie, 1998; LeBreton et al., 2001), but as much as 55% of the variance in the latent structure of MSA ratings can be attributed to idiosyncratic rater effects (Scullen et al., 2000). These data suggest that MSA ratings tell us more about the people who provided ratings than the people they were assessing. One reason for the lack of convergence between multiple raters of the same individual may be that the items on MSA instruments are written in such a way as to invite different interpretations. Although rules of thumb for writing survey items have existed for some time (e.g., Crocker & Algina, 1986), we are aware of no published empirical research on the relation between the linguistic characteristics of rating items and their measurement properties. In this study, we empirically assessed the psychometric effects of the following item characteristics: syntax (number of linguistic elements), “multi-barreledness” (referring to more than one behavior), and degree of behavioral abstraction (versus specificity). Theoretical Argument MSA ratings can be thought of as raters’ responses to presented stimuli (items). To respond to the item, it is assumed that raters use the stimulus as a cue to instigate an information retrieval
Computer Malicious Code Signal Detection based on Big Data Technology

The article addresses the challenges modelled by the inadequacy of traditional detection methods in effectively handling the substantial volume of software behavior samples, particularly in big data. A novel approach is proposed for leveraging big data technology to detect malicious computer code signals. Additionally, it seeks to attack the issues associated with machine learning-based mobile malware detection, namely the presence of a large number of features, low accuracy in detection, and imbalanced data distribution. To resolve these challenges, this paper presents a multifaceted methodology. First, it introduces a feature selection technique based on mean and variance analysis to eliminate irrelevant features hindering classification accuracy. Next, a comprehensive classification method is implemented, utilizing various feature extraction techniques such as principal component analysis (PCA), Kaehunen-Loeve transform (KLT), and independent component analysis (ICA). These techniques collectively contribute to enhancing the Precision of the detection process. Recognizing the issue of unbalanced data distribution among software samples, the study proposes a multi-level classification integration model grounded in decision trees. In response, the research focuses on enhancing accuracy and mitigating the impact of data imbalance through a combination of feature selection, extraction techniques, and a multi-level classification model. The empirical results highlight the effectiveness of the proposed methodologies, showcasing notable accuracy improvements ranging from 3.36% to 6.41% across different detection methods on the Android platform. The introduced malware detection technology, grounded in source code analysis, demonstrates a promising capacity to identify Android malware effectively.