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18265 papers
Objectives: To assess how patients’ dependent parameters may affect [68Ga]Ga-DOTANOC image quality and to propose a theoretical body mass index (BMI)-adjusted injected activity (IA) scheme, to improve imaging of high weight patients. Methods: Among patients prospectively enrolled (June-2019 and May-2020) in an Institutional Ethical Committee-approved electronic archive, we included those affected by primary gastro-entero-pancreatic (GEP) or lung neuroendocrine tumour and referred by our Institutional clinicians (excluding even minimal radiopharmaceutical extravasation, movement artefacts, renal insufficiency). All PET/CT images were acquired following EANM guidelines and rated for visual quality (1 = non-diagnostic, 2 = poor, 3 = moderate, 4 = good). Collected data included patient’s body mass, height, BMI, age, IA (injected activity), IA/Kg (IAkg), IA/BMI (IABMI), liver SUVmean, liver SUVmax standard deviation, liver-signal-to-noise (LSNR), normalised_LSNR (LSNR_norm) and contrast-to-noise ratio (CNR) for positive scans and were compared to image rating (poor vs moderate/good). Results: Overall, 77 patients were included. Rating concordance was high (agreement = 81.8%, Fleiss k score = 0.806). All patients’ dependent parameters resulted significantly different between poor-rated and moderate/good-rated scans (IA: p = 0.006, IAkg: p =< 0.001, body weight: p =< 0.001, BMI: p =< 0.001, IABMI: p =< 0.001). Factors significantly associated with moderate/good rating were BMI (p =< 0.001), body weight (p =< 0.001), IABMI (p =< 0.001), IAkg (p = 0.001), IA (p = 0.003), LSNR_norm (p = 0.01). The BMI-based model presented the best predictive efficiency (81.82%). IABMI performance to differentiate moderate/good from poor rating resulted statistically significant (IA-AUC = 0.78; 95% CI: 0.68–0.89; cut-off value of 4.17 MBq*m2/kg, sensitivity = 81.1%, specificity = 66.7%). If BMI-adjusted IA (=4.17*BMI) would have been applied in this population, the median IA would have slightly inferior (−4.8%), despite a different IA in each patient. Advances in knowledge: BMI resulted the best predictor of image quality. The proposed theoretical BMI-adjusted IA scheme (4.17*BMI) should yield images of better quality (especially in high-BMI patients) maintaining practical scanning times (3 min/bed).
Abstract Background and aims: Poor quality of life is a main complaint among individuals with irritable bowel syndrome (IBS). Self-rated health (SRH) is a powerful predictor of clinical outcomes, and also reflects psychological and social aspects of life and an overall sense of well-being. This population-based twin study evaluates how IBS affects ratings of physical and mental health, and influences perceptions of hindrance of daily activity by physical or mental health. Further, we examine how IBS is related to these SRH measures. Methods: The sample included 5288 Norwegian twins aged 40–80, of whom 575 (10.9%) suffer from IBS. Hierarchical regressions were used to estimate the impact of IBS on perceptions of health, before and after accounting for other chronic physical and mental health conditions. Two dimensions of SRH, physical and mental, and two aspects of functional limitations, the extent to which physical or mental health interferes with daily activities, were included as outcomes in separate models.Co-twin control analyses were used to explore whether the relationships between IBS and the four measures of SRH are causal, or due to shared genetic or shared environment effects. Results: IBS was an independent predictor of poor self-rated physical health (OR = 1.83 [1.42; 2.35]), the size of this effect was comparable to that predicted by chronic somatic conditions. However, in contrast to somatic diseases, IBS was associated with the perception that poorer ratings of mental health (OR=1.46 [1.03; 2.07]), but not physical health (OR = 1.24 [0.96; 1.59]), interfered with daily activity. The co‐twin control analyses suggest that causal mechanisms best explain the relationships between IBS with self-rated physical health and with hindrance of daily activities. In contrast, the relationship between IBS and self-rated mental health was consistent with shared genetic effects. Conclusion: IBS is predictive of poor self-rated physical health. The relationship between IBS and self-rated mental health is best explained by shared genetic effects which might partially explain why mental health interferes with daily activity to a larger degree among those with IBS.
Disgust is an aversive reaction protecting an organism from disease. People differ in how prone they are to experiencing it, and this fluctuates depending on how safe the environment is. Previous research has shown that the recognition and processing of disgusting words depends not on the word's disgust per se but rather on individual sensitivity to disgust. However, the influence of dynamically changing disgust on language comprehension has not yet been researched. In a series of studies, we investigated whether the media's portrayal of COVID-19 will affect subsequent language processing via changes in disgust. The participants were exposed to news headlines either depicting COVID-19 as a threat or downplaying it, and then rated single words for disgust and valence (Experiment 1; N = 83) or made a lexical decision (Experiment 2; N = 86). The headline type affected only word ratings and not lexical decisions, but political ideology and disgust proneness affected both. More liberal participants assigned higher disgust ratings after the headlines discounted the threat of COVID-19, whereas more conservative participants did so after the headlines emphasized it. We explain the results through the politicization and polarization of the pandemic. Further, political ideology was more predictive of reaction times in Experiment 2 than disgust proneness. High conservatism correlated with longer reaction times for disgusting and negative words, and the opposite was true for low conservatism. The results suggest that disgust proneness and political ideology dynamically interact with perceived environmental safety and have a measurable effect on language processing. Importantly, they also suggest that the media's stance on the pandemic and the political framing of the issue may affect the public response by increasing or decreasing our disgust.
Ten years ago, when the META-NET Network of Excellence conducted a study on language technology support for European languages, Latvian was included in the category of languages with little or no support. During the last decade, notable progress has been made in the development of language resources and tools for Latvian, particularly regarding the creation of advanced datasets like speech corpora and treebanks, state-of-the-art neural language models, machine translation systems, speech technology, and technologies for natural language understanding and human-computer interaction. This paper provides an overview of the most recent activities in the language technology field in Latvia: national and international initiatives, key language resources and tools, key projects and initiatives. We summarize both the recent activities and the most significant achievements after the publication of the META-NET White Paper on Latvian.
In their provocative article, Nelson Flores and Jonathan Rosa have captured how dominant notions of competence reinforce normative whiteness as universal and fail to account for processes of racialization in language learning. Building upon their goal of “shifting the locus of enunciation in ways that provide a glimpse into alternative worlds beyond colonial logics,” in this commmentary, we illustrate one such alternative in which language and languaging are anchored in Indigenous notions of relationality, the worldview that everything is interrelated, and by extension, interdependent. This view aligns with the deep connection that Indigenous communities make to their languages and the specific geographical, sociopolitical, and cultural contexts of their use. Relational frameworks thus counter the dispossession experienced by many Indigenous communities due to the colonial practice of separating languages from these contexts (see Davis, 2017, pp. 40–42). Similarly, by facilitating knowledge coproduction in ways that are locally specific and accountable, a relational approach serves to undo practices of teaching and assessing language learning in ways that uncritically adopt Eurocentric (“universal”) norms (McIvor, 2020; Mellow, 2000). We enter this discussion as scholar–practitioners based at a public university in the lands of the Cahuilla, Tongva, Serrano, and Luiseño peoples. Melissa Venegas is a white settler, PhD student, and former K–12 Spanish instructor. Her current research involves critical approaches to language education that examine language hierarchies and validate US varieties of Spanish. Wesley Y. Leonard is a citizen of the Miami Tribe of Oklahoma and a linguist who serves as a Native American Studies faculty member. His experiences being told that his community's efforts to learn their “extinct” language from documentation would not succeed inspired his current work in language reclamation, a mode of language recovery that replaces colonial logics with Indigenous community needs, goals, and worldviews. Clearly, in its narrow conceptualization, linguistic competence is theoretically lacking. Its assumptions about language ignore the social contexts that are fundamental to language learning and use, and its focus on an ideal speaker–hearer as the unit of analysis misaligns with how languaging actually occurs. In contrast, an analytic that considers language users and learners as networks of relations points to different metrics and units of analysis––language ecologies rather than languages-as-objects and diverse communities rather than an abstract prototype. Below, we explore examples of how language learning can be framed through an approach anchored in relationality and the ensuing notion of relational accountability, the responsibility of being accountable to relationships such as those between people and nonhuman relations, institutions, and lands. While this principle applies for all language communities, we draw special attention to those that have experienced severe ruptures to core relationships due to colonial dispossession and cultural genocide. In these contexts, exercising relational accountability entails active interventions to restore the relationships that have been disrupted or severed. The initial goal may not be “proficiency,” but rather to strengthen cultural ties or relationships with Elders (Lukaniec & Palakurthy, 2022, p. 344). As Flores and Rosa have pointed out, narrow definitions of language, as well as dominant notions of linguistic competence, render racialized students as “deficient” in academic language because of their supposed reliance on home or community language patterns. In addition to erasing the legitimacy of current language users’ practices, we further note how these dominant frameworks have replicated colonial logics that inhibit language reclamation potential. For instance, Miami people at one point had shifted fully to English, thus becoming “incompetent” in relation to myaamiaataweenki (speaking Miami) with relationships anchored in language similarly damaged. Decolonial framings of “competence,” however, might instead reference a community's collective ability to use their language(s), including future use as a result of reclamation (Leonard, 2008). Relational accountability entails building capacity to realize this potential through appropriate interventions in language teaching, development, and assessment. This is exemplified in ANA ‘ŌLELO, a Hawaiian proficiency scale developed by and for Hawaiians to reflect community values and ways of being (Kahakalau, 2017). The tool was designed to not only measure linguistic proficiency but also to perpetuate the Native Hawaiian culture. For example, the scale considers the ability to perform protocol, an important aspect of Native Hawaiian culture. Moreover, as Flores and Rosa have described, normative notions of competence elevate some people to a fully human status while diminishing the humanity of racialized Others. We observe that this conceptualization also advances colonial violence by erasing the nonhuman relatives that have central roles in many Indigenous cultures. However, appropriate interventions can counteract these erasures. For example, Engman and Hermes (2021) described an ecological approach to language learning that recognizes land as a relative. Young Ojibwe learners participated in forest walks near what is now Hayward, Wisconsin, and the Lac Courte Oreilles Ojibwe Reservation. The participants engaged in collective meaning-making involving discussions of naming items, with the land as an interlocutor. Requests for names of items in Ojibwe went beyond lexical labeling, instead serving as invitations to consider broader relationships, for example, How did it get here? Who put it in this configuration? What is our relationship to the object? As another example, Corntassel and Hardbarger (2019) described land-based pedagogies with Cherokee youth and Elders in the territory of the Cherokee Nation in Oklahoma. Participants took photographs of items meaningful to them that could exemplify Cherokee community sustainability and perpetuation of Cherokee lifeways (p. 96), which they then presented at a community symposium. Such an approach exemplifies relational accountability to the land, community, and intergenerational knowledge, and acknowledges learners’ experiences and expertise as vital to community well-being. As Opaskwayak Cree scholar Shawn Wilson concluded in his foundational Research Is Ceremony: Indigenous Research Methods, knowledge production and sharing become accountable to Indigenous ways of being and knowing through a relational framework because “relationships do not merely shape reality, they are reality” (Wilson, 2008, p. 7). The activities described above demonstrate language learning as a process anchored in relationality. People are learning language, but rather than this being a decontextualized goal assessed through normative notions of linguistic competence, it represents an outcome of cultivating relationships that allow communities to thrive.
BACKGROUND AND AIMS: Poor quality of life is a main complaint among individuals with irritable bowel syndrome (IBS). Self-rated health (SRH) is a powerful predictor of clinical outcomes, and also reflects psychological and social aspects of life and an overall sense of well-being. This population-based twin study evaluates how IBS affects ratings of physical and mental health, and influences perceptions of hindrance of daily activity by physical or mental health. Further, we examine how IBS is related to these SRH measures. METHODS: The sample included 5288 Norwegian twins aged 40-80, of whom 575 (10.9%) suffer from IBS. Hierarchical regressions were used to estimate the impact of IBS on perceptions of health, before and after accounting for other chronic physical and mental health conditions. Two dimensions of SRH, physical and mental, and two aspects of functional limitations, the extent to which physical or mental health interferes with daily activities, were included as outcomes in separate models. Co-twin control analyses were used to explore whether the relationships between IBS and the four measures of SRH are causal, or due to shared genetic or shared environment effects. RESULTS: IBS was an independent predictor of poor self-rated physical health (OR = 1.82 [1.41; 2.33]), the size of this effect was comparable to that predicted by chronic somatic conditions. However, in contrast to somatic diseases, IBS was associated with the perception that poorer ratings of mental health (OR = 1.45 [1.02; 2.06]), but not physical health (OR = 1.23 [0.96; 1.58]), interfered with daily activity. The co-twin control analyses suggest that causal mechanisms best explain the relationships between IBS with self-rated physical health and with hindrance of daily activities. In contrast, the relationship between IBS and self-rated mental health was consistent with shared genetic effects. CONCLUSION: IBS is predictive of poor self-rated physical health. The relationship between IBS and self-rated mental health is best explained by shared genetic effects which might partially explain why mental health interferes with daily activity to a larger degree among those with IBS.
On the CIFAR-10 (Canadian Institute for Advanced Research-10), ImageNet, and Penn Treebank datasets, Neural Architecture Search (NAS) algorithms obtained better results by computerizing the process of architectural design on the CIFAR-10, ImageNet, and Penn Treebank datasets. Even though the search time has been simplified, search algorithms count on performance prediction or controllers. When used on a new task with a fresh dataset, this may necessitate optimal structuring. The problem of architecture search is not solved because this is done by hand. Using continuous relaxation and gradient descent methods, Differentiable Architecture Search (DARTS) [1] avoids this issue. There are, however, plenty of intriguing methods to make DARTS better. In this paper, we first split the DARTS’s supernet into three (03) sub-supernets and applied neural message passing so that each node in the graph has information from other nodes. The three (03) sub-supernets by using gradient descent to find the best graph represent the whole search space. By adding parameter sharing and transfer learning, our method enhances the final accuracy of one-shot-based DARTS systems consistently. On CIFAR-10, it achieves 98.25% test set accuracy, according to the results.
Artiklis arutleme inimestelt semantilist leksikaalset infot koguva uurimuse peamiste probleemide üle. Kirjeldame katset, millega kogume konkreetsushinnanguid eestikeelsetele sõnadele. Artikli eesmärk on analüüsida semantiliste tunnuste hinnangutena kogumist kui meetodit tervikuna. Hinnangute kogumise metoodikat on vaja sisuliselt ja kriitiliselt hinnata, sest sellise info kogumine on nii keelepsühholoogia kui ka keeletehnoloogia valdkonnas aina olulisemal kohal. Esmalt käsitleme konkreetsust ja abstraktsust kui mõisteid ning seda, kuidas neid varem uuritud on. Seejärel anname ülevaate uuringutest, mis on kogunud konkreetsushinnanguid teiste keelte sõnade kohta ning toome välja selliste hinnangute peamised kasutusalad. Kolmandaks anname ülevaate eestikeelsete sõnade konkreetsushinnanguid koguvast katsest ning sellega kaasnevatest probleemidest nii sisu kui ka vormi osas. *** Collecting concreteness ratings for Estonian words This paper analyzes introspection as a method of collecting large amounts of semantic data from human participants. Semantic indexes, such as lexical concreteness ratings, are becoming increasingly predominant in current psycholinguistic and language technology research. However, human introspection and categorized semantic properties also constitute fields over which researchers traditionally quarrel. Hence, an analysis of the issues introduced by this methodology is needed, with the aim of sparking discussion and introducing doubt into overly confident frameworks, hopefully even leading to more uniform and solid research designs where fewer classic mistakes are made. In this paper, we first discuss the concepts of concreteness and abstractness as well as the usefulness of such lexical indexes, followed by the description of a vast experiment collecting concreteness ratings from at least 2000 Estonian speakers. In the main part of the paper, however, we discuss a number of stimulus-based and organisatory issues, which this method either inherently entails or might introduce, as well as make suggestions for overcoming them.
Universal Dependencies is an international community project and a collection of morphosyntactically annotated data sets (“treebanks”) for more than 100 languages. The collection is an invaluable resource for various linguistic studies, ranging from grammatical constructions within one language to language typology, documentation of endangered languages, and historical evolution of language. In the tutorial, I will first quickly show the main principles of UD, then I will present the actual d...
The article considers the role of tax administration digitalization bodies in the context of current challenges and threats caused by the pandemic in the world and war in Ukraine. The importance of developing the electronic interaction channels between the State Tax Service of Ukraine, tax administrations of OECD countries and taxpayers in the context of Covid-19 and war action is outlined. The focus is on expanding the list of electronic services (for business, private entrepreneurs, IT services), which will increase opportunities for the implementation of the principle of convenience in fulfilling the tax obligation to the state. Continuous development and improvement of services provided by state tax authorities increase the image rating of state institutions, in particular, carry out electronic taxation, which allows: automate internal tax functions; to build electronic information interaction between taxpayers and state tax authorities in the field of taxation; to form effective online communication and ensure fast and secure data exchange between government agencies in the field of taxation; to ensure effective international cooperation in electronic format. The directions for the tax authorities digitalization strategy change for the purpose of effective taxes and fees administration process are offered: granting equal access of citizens, business representatives, tax administrators to digital technologies and new opportunities (to reduce digital gaps); advanced training of personnel for the full development of digitalization of the State Tax Service; increasing the level of automation and digitalization of public services together with the motivation of government agencies.
RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its importance, one of the most prevailing limitations in modern day discourse parsing is the lack of large-scale datasets. To overcome the data sparsity issue, distantly supervised approaches from tasks like sentiment analysis and summarization have been recently proposed. Here, we extend this line of research by exploiting distant supervision from topic segmentation, which can arguably provide a strong and oftentimes complementary signal for high-level discourse structures. Experiments on two human-annotated discourse treebanks confirm that our proposal generates accurate tree structures on sentence and paragraph level, consistently outperforming previous distantly supervised models on the sentence-to-document task and occasionally reaching even higher scores on the sentence-to-paragraph level.
Sequence labeling, in which a class or label is assigned to each token in a given input order, is a fundamental task in natural language processing. Many advanced neural network architectures have recently been proposed to solve the sequential labeling problem affecting this task. By contrast, only a few approaches have been proposed to address the sequential ensemble problem. In this paper, we resolve the sequential ensemble problem by applying the sequential alignment method in a proposed ensemble framework. Specifically, we propose a simple but efficient ensemble candidate generation framework with which multiple heterogeneous systems can easily be prepared from a single neural sequence labeling network. To evaluate the proposed framework, experiments were conducted with part-of-speech (POS) tagging and dependency label prediction problems. The results indicate that the proposed framework achieved accuracy values that were higher by 0.19 and 0.33 than those achieved by the hard-voting method on the Penn-treebank POS-tagged and Universal dependency-tagged datasets, respectively.
We created a Slovak language model in the Spacy library. When creating the model, we used pretraining and training in the Spacy library. During the pretraining, we used Fasttext vectors and the text we collected. We used data from Slovak Dependency Treebank during the training. We created a model with an accuracy of 86.249%. We also examined the effect of pretraining on the model. All achieved results are described in the final part.
Many studies in the literature attempt recognition of emotions through the use of videos or images, but very few have explored the role that sounds have in evoking emotions. In this study we have devised an experimental protocol for elicitation of emotions by using, separately and jointly, images and sounds from the widely used International Affective Pictures System and International Affective Digital Sounds databases. During the experiments we have recorded the skin conductance and pupillary signals and processed them with the goal of extracting indices linked to the autonomic nervous system, thus revealing specific patterns of behavior depending on the different stimulation modalities. Our results show that skin conductance helps discriminate emotions along the arousal dimension, whereas features derived from the pupillary signal are able to discriminate different states along both valence and arousal dimensions. In particular, the pupillary diameter was found to be significantly greater at increasing arousal and during elicitation of negative emotions in the phases of viewing images and images with sounds. In the sound-only phase, on the other hand, the power calculated in the high and very high frequency bands of the pupillary diameter were significantly greater at higher valence (valence ratings > 5). Clinical relevance- This study demonstrates the ability of physiological signals to assess specific emotional states by providing different activation patterns depending on the stimulation through images, sounds and images with sounds. The approach has high clinical relevance as it could be extended to evaluate mood disorders (e.g. depression, bipolar disorders, or just stress), or to use physiological patterns found for sounds in order to study whether hearing aids can lead to increased emotional perception.
Focus on language-specific properties with insights from formal minimalist syntax can improve universal dependency (UD) parsing. Such improvements are especially sensitive for low-resource African languages, like Wolof, which have fewer UD treebanks in number and amount of annotations, and fewer contributing annotators. For two different UD parser pipelines, one parser model was trained on the original Wolof treebank, and one was trained on an edited treebank. For each parser pipeline, the accuracy of the edited treebank was higher than the original for both the dependency relations and dependency labels. Accuracy for universal dependency relations improved as much as 2.90%, while accuracy for universal dependency labels increased as much as 3.38%. An annotation scheme that better fits a language's distinct syntax results in better parsing accuracy.
OBJECTIVE: The purpose of this study was to examine implicit affect toward suicide (i.e., how good/bad suicide is perceived). Some people might be more likely to think about/choose suicide because they perceive it as a good option (to gain relief) relative to available alternatives. METHOD: Implicit affect toward suicide among adults (N = 72) and adolescents (N = 174) with and without suicidal thoughts was examined using first-person (FP) perspective suicide pictures in the affect misattribution procedure (AMP). RESULTS: Suicidal adults' implicit positive affect toward suicide was associated with STB variables, such as explicit valence (r = 0.34) and arousal (r = 0.44) ratings of suicide pictures, and implicit affect differentiated groups above and beyond explicit valence ratings. Contrary to our hypothesis, suicidal participants did not display higher implicit positive affect toward suicide than nonsuicidal participants. However, suicidal participants displayed consistent implicit affect toward different suicide pictures, whereas nonsuicidal participants evaluated some pictures as more pleasant than others (ORs = 1.92-2.27). CONCLUSIONS: Implicit affect toward suicide may relate to STB, but stimuli characteristics (e.g., color) likely influence the accuracy of assessment with the AMP and should be a focus of future research involving this and other implicit measures.
Stress is omnipresent in our everyday lives. It is therefore critical to identify potential stress-buffering behaviors that can help to prevent the negative effects of acute stress in daily life. Massages, a form of social touch, are an effective buffer against both the endocrinological and sympathetic stress response in women. However, for other forms of social touch, potential stress-buffering effects have not been investigated in detail. Furthermore, the possible stress-buffering effects of social touch on men have not been researched so far. The present study focused on embracing, one of the most common forms of social touch across many cultures. We used a short-term embrace between romantic partners as a social touch intervention prior to the induction of acute stress via the Socially Evaluated Cold Pressor Test. Women who embraced their partner prior to being stressed showed a reduced cortisol response compared to a control group in which no embrace occurred. No stress-buffering effect could be observed in men. No differences between the embrace and control group were observed regarding sympathetic nervous system activation measured via blood pressure or subjective affect ratings. These findings suggest that in women, short-term embraces prior to stressful social situations such as examinations or stressful interviews can reduce the cortisol response in that situation.
In this paper, we launch a new Universal Dependencies treebank for an endangered language from Amazonia: Kakataibo, a Panoan language spoken in Peru. We first discuss the collaborative methodology implemented, which proved effective to create a treebank in the context of a Computational Linguistic course for undergraduates. Then, we describe the general details of the treebank and the language-specific considerations implemented for the proposed annotation. We finally conduct some experiments on part-of-speech tagging and syntactic dependency parsing. We focus on monolingual and transfer learning settings, where we study the impact of a Shipibo-Konibo treebank, another Panoan language resource.
Building computational resources and tools for the under-resourced languages is strenuous for any Natural Language Processing task. This article presents the first dependency parser for an under-resourced Indian language, Nepali. A prerequisite for developing a parser for a language is a corpus annotated with the desired linguistic representations known as a treebank. With an aim of cross-lingual learning and typological research, we use a Bengali treebank to build a Bengali-Nepali parallel corpus and apply the method of annotation projection from the Bengali treebank to build a treebank for Nepali. With the developed treebank, MaltParser (with all algorithms for projective dependency structures) and a Neural network-based parser have been used to build Nepali parser models. The Neural network-based parser produced state-of-the-art results with 81.2 Unlabeled Attachment Score, 73.2 Label Accuracy, and 66.1 Labeled Attachment Score on the gold test data. The parser models have also been evaluated with the predicted Part-of-speech (POS)-tagged test data. A statistical POS tagger using Conditional Random Field has been developed for predicting the POS tags of the test data.
International audience
PURPOSE: Adolescence is a delicate phase during life in which self-stigmatization increases and acceptance by peers becomes more important. However, little is known about how adolescents with cleft lip and/or palate experience this stage of life. Therefore, the purpose of this study was to investigate how cleft lip and/or palate (CLP) adolescents are looked at by their peers and how they look at others with/without CLP. METHODS: In this prospective, cross-sectional study 54 observers (CLP versus control) performed an eye-tracking task and gave attractiveness/ valence ratings. For this purpose, they were shown pictures of patients with and without CLP with neutral or smiling facial expressions. RESULTS: Adolescents with CLP were looked at differently compared to their unaffected peers, with shorter fixations of the eyes and longer fixations of the nose and mouth. Smiling altered the scan path toward the mouth for all faces. Contrary to the control group, adolescents with CLP tended to spend less time fixating the eyes. In the attractiveness/valence ratings, CLP adolescents were rated more negatively. CONCLUSIONS: Adolescents with cleft lip and/or palate look differently at peers and are also viewed with an alternate scan path.
Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence arousal space. Four additional songs were selected from DEAM to act as a benchmark in this study and filter out low quality ratings. A total of 277 participants participated in annotating the dataset, and their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.
People experience the same event but do not feel the same way. Such individual differences in emotion response are believed to be far greater than those in any other mental functions. Thus, to understand what makes people individuals, it is important to identify the systematic structures of individual differences in emotion response and elucidate how such structures relate to what aspects of psychological characteristics. Reflecting this importance, many studies have attempted to relate emotions to psychological characteristics such as personality traits, psychosocial states, and pathological symptoms across individuals. However, systematic and global structures that govern the across-individual covariation between the domain of emotion responses and that of psychological characteristics have been rarely explored previously, which limits our understanding of the relationship between individual differences in emotion response and psychological characteristics. To overcome this limitation, we acquired high-dimensional data sets in both emotion-response (8 measures) and psychological-characteristic (68 measures) domains from the same pool of individuals (86 undergraduate or graduate students) and carried out the canonical correlation analysis in conjunction with the principal component analysis on those data sets. For each participant, the emotion-response measures were quantified by regressing affective-rating responses to visual narrative stimuli onto the across-participant average responses to those stimuli, while the psychological-characteristic measures were acquired from 19 different psychometric questionnaires grounded in personality, psychosocial-factor, and clinical-problem taxonomies. We found a single robust mode of population covariation, particularly between the 'accuracy' and 'sensitivity' measures of arousal responses in the emotion domain and many 'psychosocial' measures in the psychological-characteristics domain. This mode of covariation suggests that individuals characterized with positive social assets tend to show polarized arousal responses to life events.
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يقدم هذا البحث نظاما لاكتشاف وتصحيح الأخطاء الإملائية للغة العربية للشبكة العنكبوتية (Web Spell Checker) قمنا بتصميمه باستخدام نظام WebSpellChecker Engine. نظامنا متاح للمستخدمين على شكل خدمة ويب سحابية (Cloud Web Service) يمكن دمجها مع أي موقع أو تطبيق متاح على الشبكة العنكبوتية، كما يمكن دمجه مع تطبيقات الأجهزة الذكية وذلك من خلال واجهة لبرمجة التطبيقات (Application Programming Interface) تتيح التدقيق الإملائي لنصوص اللغة العربية المدخلة إلى مواقع وتطبيقات الشبكة العنكبوتية وتطبيقات الأجهزة الذكية. يستطيع نظامنا التعامل مع نسبة كبيرة من الكلمات التي تغطي العربية الفصحى بشكل عام والعربية الفصحى الحديثة (Modern Standard Arabic) بشكل خاص باستخدام معجم حاسوبي (Lexicon). صُمم هذا المعجم باستخدام قائمة كلمات ضخمة (Word List) مفتوحة المصدر (Open Source). بُنِيَت هذه القائمة باستخدام قاعدة بيانات معجمية (Lexical Database) مفتوحة المصدر مخصصة للتحليل الصرفي (Morphological Analysis) للأسماء والأفعال العربية صُمِّمَت باستخدام تقنية الآلات منتهية الحالات (Finite State Automata). تحتوي قائمة الكلمات المذكورة على الصيغ الصرفية والاشتقاقية (Inflected and Derived Forms) المحتملة لكلمات اللغة العربية الفصحى (على سبيل المثال: كَتَبَ، ويكتبان، كتبوا، فسيكتبن، كاتِبة، للكاتِبَين، المكتوب). كما تم تزويد النظام بالقدرة على إعادة ترتيب (Re-Ranking) مقترحات التصحيح الآلي الناتجة من تطبيق خوارزمية مسافة تحرير ليفينستين (Levenshtein Edit Distance Algorithm) المستخدمة في التصحيح الحاسوبي الآلي للأخطاء الإملائية من خلال إعطاء الأولوية لإظهار مقترحات التصحيح الآلي للأخطاء الإملائية الشائعة لدى مستخدمي اللغة العربية وذلك باستخدام قوانين إملائية وصوتية سياقية (Context Sensitive Orthographic and Phonological Rules). استُخدِم المعجم الحاسوبي والقوانين الإملائية والصوتية السياقية المذكورة لتزويد النظام بالمعرفة اللغوية التي تمكنه من اكتشاف وتصحيح الأخطاء الإملائية في نصوص اللغة العربية الفصحى المدخَلة إلى مواقع الشبكة العنكبوتية.
Collection of Ancient Greek annotated trees of Artemidorus' Oneirocritica Book 5. Part of the Open Projects in Digital Classics at the College of Letters and Sciences of the State University of São Paulo in Araraquara, São Paulo, Brazil. The trees were annotated manually on Perseids Platform using the Arethusa tool. The treebank tagset and guidelines used were those from The Ancient Greek Dependency Treebank with the morphological and syntactic layer, which was based on Bamman's and Crane's 2008 Guidelines for the Syntactic Annotation of the Ancient Greek Dependency Treebank (1.1). We translated it into Portuguese with a few additions from some specifications provided by a forum maintained in 2013 by Alpheios.net, which is no longer online. The trees are visible in Perseids Collection as UNESP-trees at https://perseids-publications.github.io/unesp-trees.
Abstract This paper presents the SAGT Turkish–German code-switching treebank, and observations and annotation challenges we encountered during its development. The treebank consists of transcriptions of bilingual conversations annotated with several layers: language IDs, lemmas, POS tags, morphological features, and dependency relations. The annotations follow the Universal Dependencies annotation scheme and the conventions used in monolingual treebanks as much as possible. We present and discuss a number of issues that arise because of the need for consistent multilingual annotation within a single treebank, as well as the informal language, which is where code-switching is observed most. Besides proposing solutions to these issues, we present some observations about code-switching phenomena that are only possible to observe in a data set with rich linguistic annotation. The treebank was annotated with a focus on quality of annotations through an iterative process of detecting and correcting annotation errors. We also present quantitative measures for indication of annotation quality. The code-switching treebank created in this study is released to the public through Universal Dependencies repositories.
This article describes an ongoing project for the development of a novel Italian treebank in Universal Dependencies format: VALICO-UD. It consists of texts written by Italian L2 learners of different mother tongues (German, French, Spanish and English) drawn from VALICO, an Italian learner corpus elicited by comic strips. Aiming at building a parallel treebank currently missing for Italian L2, comparable with those exploited in Natural Language Processing tasks, we associated each learner sentence with a target hypothesis (i.e. a corrected version of the learner sentence written by an Italian native speaker), which is in turn annotated in Universal Dependencies. The treebank VALICO-UD is composed of 237 texts written by non-native speakers of Italian (2,234 sentences) and the related target hypotheses, all automatically annotated using UDPipe. A portion of this resource (36 texts corresponding to 398 learner sentences and related target hypotheses)-firstly released on May 2021 in the Universal Dependencies repository-is associated with error annotation and the automatic output is fully manually checked. In this article, we focus especially on the challenges addressed in treebanking a resource composed of learner texts. In addition, we report on a preliminary data exploration that makes use of three quantitative measures for assessing the quality of the data and for better understanding the role that this resource can play in tasks lying at the intersection of Computational Linguistics and learner corpus studies.
Individuals who produce few spoken words (“minimally-speaking” individuals) often convey rich affective and communicative information through nonverbal vocalizations, such as grunts, yells, babbles, and monosyllabic expressions. Yet, little data exists on the affective content of the vocal expressions of this population. Here, we present 78,624 arousal and valence ratings of nonverbal vocalizations from the online ReCANVo (Real-World Communicative and Affective Nonverbal Vocalizations) database. This dataset contains over 7,000 vocalizations that have been labeled with their expressive functions (delight, frustration, etc.) from eight minimally-speaking individuals. Our results suggest that raters who have no knowledge of the context or meaning of a nonverbal vocalization are still able to detect arousal and valence differences between different types of vocalizations based on Likert-scale ratings. Moreover, these ratings are consistent with hypothesized arousal and valence rankings for the different vocalization types. Raters are also able to detect arousal and valence differences between different vocalization types within individual speakers. To our knowledge, this is the first large-scale analysis of affective content within nonverbal vocalizations from minimally verbal individuals. These results complement affective computing research of nonverbal vocalizations that occur within typical verbal speech (e.g., grunts, sighs) and serve as a foundation for further understanding of how humans perceive emotions in sounds.
Background: Numerous studies have investigated emotion in virtual reality (VR) experiences using self-reported data in order to understand valence and arousal dimensions of emotion. Objective physiological data concerning valence and arousal has been less explored. Electroencephalography (EEG) can be used to examine correlates of emotional responses such as valence and arousal in virtual reality environments. Used across varying fields of research, images are able to elicit a range of affective responses from viewers. In this study, we display image sequences with annotated valence and arousal values on a screen within a virtual reality theater environment. Understanding how brain activity responses are related to affective stimuli with known valence and arousal ratings may contribute to a better understanding of affective processing in virtual reality. Methods: We investigated frontal alpha asymmetry (FAA) responses to image sequences previously annotated with valence and arousal ratings. Twenty-four participants viewed image sequences in VR with known valence and arousal values while their brain activity was recorded. Participants wore the Oculus Quest VR headset and viewed image sequences while immersed in a virtual reality theater environment. Results: Image sequences with higher valence ratings elicited greater FAA scores than image sequences with lower valence ratings ( F [1, 23] = 4.631, p = 0.042), while image sequences with higher arousal scores elicited lower FAA scores than image sequences with low arousal ( F [1, 23] = 7.143, p = 0.014). The effect of valence on alpha power did not reach statistical significance ( F [1, 23] = 4.170, p = 0.053). We determined that only the high valence, low arousal image sequence elicited FAA which was significantly higher than FAA recorded during baseline ( t [23] = −3.166, p = 0.002), suggesting that this image sequence was the most salient for participants. Conclusion: Image sequences with higher valence, and lower arousal may lead to greater FAA responses in VR experiences. While findings suggest that FAA data may be useful in understanding associations between valence and arousal self-reported data and brain activity responses elicited from affective experiences in VR environments, additional research concerning individual differences in affective processing may be informative for the development of affective VR scenarios.
This paper presents the first publicly available treebank of Odia, a morphologically rich low resource Indian language. The treebank contains approx. 1082 tokens (100 sentences) in Odia selected from "Samantar", the largest available parallel corpora collection for Indic languages. All the selected sentences are manually annotated following the ``Universal Dependency (UD)" guidelines. The morphological analysis of the Odia treebank was performed using machine learning techniques. The Odia annotated treebank will enrich the Odia language resource and will help in building language technology tools for cross-lingual learning and typological research. We also build a preliminary Odia parser using a machine learning approach. The accuracy of the parser is 86.6% Tokenization, 64.1% UPOS, 63.78% XPOS, 42.04% UAS and 21.34% LAS. Finally, the paper briefly discusses the linguistic analysis of the Odia UD treebank.
How are emotions perceived through human body language in social interactions? This study used point-light displays of human interactions portraying emotional scenes (1) to examine quantitative intrapersonal kinematic and postural body configurations, (2) to calculate interaction-specific parameters of these interactions, and (3) to analyze how far both contribute to the perception of an emotion category (i.e. anger, sadness, happiness or affection) as well as to the perception of emotional valence. By using ANOVA and classification trees, we investigated emotion-specific differences in the calculated parameters. We further applied representational similarity analyses to determine how perceptual ratings relate to intra- and interpersonal features of the observed scene. Results showed that within an interaction, intrapersonal kinematic cues corresponded to emotion category ratings, whereas postural cues reflected valence ratings. Perception of emotion category was also driven by interpersonal orientation, proxemics, the time spent in the personal space of the counterpart, and the motion-energy balance between interacting people. Furthermore, motion-energy balance and orientation relate to valence ratings. Thus, features of emotional body language are connected with the emotional content of an observed scene and people make use of the observed emotionally expressive body language and interpersonal coordination to infer emotional content of interactions.
The Bulgarian Treebank Corpus is composed of 156,149 tokens (11,138 sentences) coming from three main sources in the domain of Grammar Notebooks (1,391 sentences), News (6,698 sentences), Other (3,049 sentences). It is available with syntactical and morphological annotation on a sentence basis in Universal Dependencies format. This subset of BulTreeBank excludes ellipses and some rare phenomena. The conversion of BulTreeBank into Universal Dependency format was supported by the EU Project QTLeap (http://qtleap.eu/).
This study focuses on the negativity bias theory displayed by individuals scoring high on depressive symptoms when viewing emotional stimuli. Based on previous research on negativity bias in depressed individuals, two hypotheses were tested: (1) The valence scores are lower for individuals scoring higher on depressive symptoms when rating high arousal, low valence images, and (2) a magnified LPP amplitude is observed for more depressed individuals when viewing high arousal images with low valence. Participants (N=131) were university students (Mean age= 20.8). The International Affective Picture System (IAPS) images were used as stimuli, and scalp event-related potentials (ERPs) were recorded. The Patient Health Questionnaire-9 (PHQ-9) was used to determine levels of depressive symptoms and categorize participants into low, middle, and high levels of depressive symptoms groups. Results showed significantly lower LPP amplitudes and higher mean valence ratings for individuals scoring higher on depressive symptoms compared to the low and mid depressed groups. Based on the results, both hypotheses are rejected as more depressed individuals show flattened emotional responses to highly arousing, unpleasant stimuli.