Search Results (31)

This paper introduces a meta-logical framework—based on the theory of institutions (a categorical version of abstract model theory)—to be used as a tool for the formalization of the two main views regarding the structure of scientific theories, namely the syntactic and the semantic views, as they have emerged from the relevant contemporary discussion. The formalization leads to a proof of the equivalence of the two views, which supports the claim that the two approaches are not really in tension. The proof is based on the Galois connection between classes of sentences and classes of models defined over some institution. First, the history of the syntactic–semantic debate is recalled and the theory of institutions formally introduced. Secondly, the notions of syntactic and semantic theories are formalized within the institution and their equivalence proved. Finally, the novelty of the proposed framework is highlighted with respect to existing formalizations. Full article

Acute pancreatitis (AP) is one of the gastrointestinal pathologies that most frequently requires hospital admission; about half of all deaths occur within the first two weeks and are caused by multi-organ failure. Predicting the degree of severity of AP before 48 h is a challenge. Background/Objectives: Having an early marker, before 48 h after admission, could be useful to avoid or diagnose early complications such as organ failure (OF). A few sentences could place the question addressed in a broader context and highlight the purpose of the study. Methods: A retrospective study conducted in a third-level hospital, during the period from August 2019 to June 2021. Patients aged >18 years, with a diagnosis of PA, who had a complete clinical history and complete biochemical and imaging data were included. The scores of the APACHE II, BISAP, revised Atlanta classification, and modified Marshall scales were recorded. Results: Of the 103 patients included, 60% were women, the mean age was 47.76 years, and the hospital stay was 8 days (IQR 6–12); the most frequent etiology was biliary in 46 (44.7%) patients; the most frequent BMI was overweight with 34 (33%) patients; and 38 (36.9%) patients had a systemic inflammatory response at admission. Hypoalbuminemia was observed in 34 (33%) of the 103 patients at admission; of these, 42 (40.8%) had an APACHE II score > 8 points, 17 (16.3%) a BISAP score > 2, 57 (54.8%) patients were classified as moderate AP according to the revised Atlanta classification, and 54 patients had a score according to the modified Marshall score > 2. A statistically significant difference in the development of death was observed between patients with hypoalbuminemia versus those with normal serum albumin levels. Conclusions: In this study, we show the usefulness of hipoalbuminemia (<3.5 g/dL) at hospital admission in patients with AP, as a severity and mortality indicator. With the results obtained, we conclude that low albumin levels are a good predictor of severity and are useful for establishing timely treatment and close follow-up. Full article

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. During and after COVID-19, audiovestibular symptoms and impairments have been reported. Objectives: This study aimed to investigate the impacts of COVID-19 on the peripheral and central auditory systems of children and adolescents following the acute COVID-19 phase based on behavioral, electroacoustic, and electrophysiological audiological assessments. Methods: This is a primary, prospective, observational, and cross-sectional study of 23 children aged 8 to 15 years who acquired confirmed COVID-19 and who, before infection, had not had any auditory complaints or school complications. The results were compared with pre-pandemic data collected from a similar group of 23 children who had normal peripheral and central hearing and good school performance. Each participant answered a questionnaire about child development, school, and health history and underwent tests including pure-tone audiometry and high-frequency audiometry, imitanciometry, transient evoked otoacoustic emissions, and distortion product otoacoustic emissions. They also received tests of Brainstem Auditory Evoked Potentials, Long Latency Auditory Evoked Potentials, Dichotic Digits Test, Sentence Identification Test, Dichotic Consonant–Vowel Test, Frequency Pattern Test, and Gaps-In-Noise Test. Results: Significant differences were observed between the groups, with the study group showing worse thresholds compared to the control group at both standard audiometric frequencies and at higher frequencies, although both groups were still within normal limits (p ≤ 0.05). In addition, the study group had a higher prevalence of absent responses, as identified by otoacoustic emissions and acoustic reflexes. In terms of central auditory performance, the study group showed ABRs with significantly longer latencies of waves I, III, and V compared to the control group. The study group also performed less well on the Dichotic Digits and Pediatric Speech Identification tests. Conclusions: COVID-19 appears to alter the auditory system, both peripherally at the level of the outer hair cells and more centrally. Full article

Text simplification is crucial in bridging the comprehension gap in today’s information-rich environment. Despite advancements in English text simplification, languages with intricate grammatical structures, such as Greek, often remain under-explored. The complexity of Greek grammar, characterized by its flexible syntactic ordering, presents unique challenges that hinder comprehension for native speakers, learners, tourists, and international students. This paper introduces a comprehensive dataset for Greek text simplification, containing over 7500 sentences across diverse topics such as history, science, and culture, tailored to address these challenges. We outline the methodology for compiling this dataset, including a collection of texts from Greek Wikipedia, their annotation with simplified versions, and the establishment of robust evaluation metrics. Additionally, the paper details the implementation of quality control measures and the application of machine learning techniques to analyze text complexity. Our experimental results demonstrate the dataset’s initial effectiveness and potential in reducing linguistic barriers and enhancing communication, with initial machine learning models showing promising directions for future improvements in classifying text complexity. The development of this dataset marks a significant step toward improving accessibility and comprehension for a broad audience of Greek speakers and learners, fostering a more inclusive society. Full article

This study proposes how to incorporate concurrent purchase data into e-commerce recommendation systems to improve their predictive accuracy. We identified that concurrent purchases account for about $23\%$ of total orders on Katcher’s, a Korean e-commerce platform. Despite the prevalence of concurrent purchases, existing algorithms often overlook this aspect. We introduce a novel transformer-based recommendation algorithm to process a user’s order history, including concurrent purchases. Each order is represented as a natural language sentence, capturing the order timestamp, product names and their attribute values, their corresponding categories, and whether multiple products were purchased together in a single order (i.e., a concurrent purchase). These sentences form a sequence, which serves as a training dataset for fine-tuning Bidirectional Encoder Representations from Transformers (BERT) with the Next Sentence Prediction objective. We validate our ideas by conducting experiments on Katcher’s platform, demonstrating the proposed method’s improved prediction performance compared to existing recommendation systems, with enhanced accuracy and F1 score. Notably, the normalized discounted cumulative gain (NDCG) showed a significant improvement with a large margin. Furthermore, we demonstrate the beneficial impact of integrating concurrent purchase information on prediction performance. Full article

This study introduces an unsupervised term extraction approach that combines non-negative matrix factorization (NMF) with word embeddings. Inspired by a pioneering semantic NMF method that employs regularization to jointly optimize document–word and word–word matrix factorizations for document clustering, we adapt this strategy for term extraction. Typically, a word–word matrix representing semantic relationships between words is constructed using cosine similarities between word embeddings. However, it has been established that transformer encoder embeddings tend to reside within a narrow cone, leading to consistently high cosine similarities between words. To address this issue, we replace the conventional word–word matrix with a word–seed submatrix, restricting columns to ‘domain seeds’—specific words that encapsulate the essential semantic features of the domain. Therefore, we propose a modified NMF framework that jointly factorizes the document–word and word–seed matrices, producing more precise encoding vectors for words, which we utilize to extract high-relevancy topic-related terms. Our modification significantly improves term extraction effectiveness, marking the first implementation of semantically enhanced NMF, designed specifically for the task of term extraction. Comparative experiments demonstrate that our method outperforms both traditional NMF and advanced transformer-based methods such as KeyBERT and BERTopic. To support further research and application, we compile and manually annotate two new datasets, each containing 1000 sentences, from the ‘Geography and History’ and ‘National Heroes’ domains. These datasets are useful for both term extraction and document classification tasks. All related code and datasets are freely available. Full article

Context modeling has always been the groundwork for the dialogue response generation task, yet it presents challenges due to the loose context relations among open-domain dialogue sentences. Introducing simulated dialogue futures has been proposed as a solution to mitigate the problem of low history–response relevance. However, these approaches simply assume that the history and future of a dialogue have the same effect on response generation. In reality, the coherence between dialogue sentences varies, and thus, history and the future are not uniformly helpful in response prediction. Consequently, determining and leveraging the relevance between history–response and response–future to aid in response prediction emerges as a pivotal concern. This paper addresses this concern by initially establishing three context relations of response and its context (history and future), reflecting the relevance between the response and preceding and following sentences. Subsequently, we annotate response contextual relation labels on a large-scale dataset, DailyDialog (DD). Leveraging these relation labels, we propose a response generation model that adaptively integrates contributions from preceding and succeeding sentences guided by explicit relation labels. This approach mitigates the impact in cases of lower relevance and amplifies contributions in cases of higher relevance, thus improving the capability of context modeling. Experimental results on public dataset DD demonstrate that our response generation model significantly enhances coherence by 3.02% in long sequences (4-gram) and augments bi-gram diversity by 17.67%, surpassing the performance of previous models. Full article

According to the World Health Organization, one-third of elderly people aged 65 or over fall annually, and this number increases after 70. Several gait biomechanical parameters were associated with a history of falls. This study aimed to conduct a systematic review to identify and describe the gait biomechanical parameters related to falls in the elderly. MEDLINE Complete, Cochrane, Web of Science, and CINAHL Complete were searched for articles on 22 November 2023, using the following search sentence: (gait) AND (fall*) AND ((elder*) OR (old*) OR (senior*)) AND ((kinematic*) OR (kinetic*) OR (biomechanic*) OR (electromyogram*) OR (emg) OR (motion analysis*) OR (plantar pressure)). This search identified 13,988 studies. From these, 96 were selected. Gait speed, stride/step length, and double support phase are gait biomechanical parameters that differentiate fallers from non-fallers. Fallers also tended to exhibit higher variability in gait biomechanical parameters, namely the minimum foot/toe clearance variability. Although the studies were scarce, differences between fallers and non-fallers were found regarding lower limb muscular activity and joint biomechanics. Due to the scarce literature and contradictory results among studies, it is complex to draw clear conclusions for parameters related to postural stability. Minimum foot/toe clearance, step width, and knee kinematics did not differentiate fallers from non-fallers. Full article

When dementia is diagnosed, it is most often already past the point of irreversible neuronal deterioration. Neuropsychological tests are frequently used in clinical settings; however, they must be administered properly and are oftentimes conducted after cognitive impairment becomes apparent or is raised as a concern by the patient or a family member. It would be beneficial to develop a non-invasive system for approximating cognitive scores which can be utilized by a general practitioner without the need for cognitive testing. To this end, gait, visual, auditory, postural, and olfactory function parameters, reported history of illness, and personal habits were used to train an elastic-net regression model in predicting the cognitive score. Community-dwelling men (N = 104) above the age of sixty-five participated in the current study. Both individual variables and principal components of the motor and sensory functions were included in the elastic-net regression model, which was trained on 70% of the dataset. The years of education, limits of stability testing time, regular ophthalmological exams, postural testing time principal component, better ear score on the sentence recognition test, and olfactory discrimination score largely contributed to explaining over 40% of the variance in the cognitive score. Full article

Sentences are generally understood to be essential communicative units in writing that are built to express thoughts and meanings. Studying sentence production provides a valuable opportunity to shed new light on the writing process itself and on the underlying cognitive processes. Nevertheless, research on the production of sentences in writing remains scarce. We propose a theoretical framework and an open-source implementation that aim to facilitate the study of sentence production based on keystroke logs. We centre our approach around the notion of sentence history: all the versions of a given sentence during the production of a text. The implementation takes keystroke logs as input and extracts sentence versions, aggregates them into sentence histories and evaluates the sentencehood of each sentence version. We provide detailed evaluation of the implementation based on a manually annotated corpus of texts in French, German and English. The implementation yields strong results on the three processing aspects. Full article

Current State-of-the-Art (SotA) chatbots are able to produce high-quality sentences, handling different conversation topics and larger interaction times. Unfortunately, the generated responses depend greatly on the data on which they have been trained, the specific dialogue history and current turn used for guiding the response, the internal decoding mechanisms, and ranking strategies, among others. Therefore, it may happen that for semantically similar questions asked by users, the chatbot may provide a different answer, which can be considered as a form of hallucination or producing confusion in long-term interactions. In this research paper, we propose a novel methodology consisting of two main phases: (a) hierarchical automatic detection of topics and subtopics in dialogue interactions using a zero-shot learning approach, and (b) detecting inconsistent answers using k-means and the Silhouette coefficient. To evaluate the efficacy of topic and subtopic detection, we use a subset of the DailyDialog dataset and real dialogue interactions gathered during the Alexa Socialbot Grand Challenge 5 (SGC5). The proposed approach enables the detection of up to 18 different topics and 102 subtopics. For the purpose of detecting inconsistencies, we manually generate multiple paraphrased questions and employ several pre-trained SotA chatbot models to generate responses. Our experimental results demonstrate a weighted F-1 value of 0.34 for topic detection, a weighted F-1 value of 0.78 for subtopic detection in DailyDialog, then 81% and 62% accuracy for topic and subtopic classification in SGC5, respectively. Finally, to predict the number of different responses, we obtained a mean squared error (MSE) of 3.4 when testing smaller generative models and 4.9 in recent large language models. Full article

Social and cultural changes have brought about a new understanding of the space–time continuum within which modern cities are evolving. A comfortable urban environment contributes to the development of a sustainable urban environment, to the psychological health and social well-being of citizens, as shown by the observation of life in public spaces. In our study, the courtyard is treated as a specific human habitat that satisfies a wide range of people’s needs due to the unity of physical, social, and existential features of the place. It is the environment that is present throughout a person’s life, is biographically tied up with his history and that of his family, and therefore reflects his individuality, expresses identity, and stimulates personal authenticity. To assess Yekaterinburg residents’ perception of the yard space as an element of the urban environment, which is the aim of the study, the authors exploited the method of a questionnaire based on two measures, namely architectural semantic differential and incomplete sentences. It was found that the image My Courtyard was the most uncomfortable and “frozen” of all the urban elements and My City was the most comfortable and dynamic. The respondents perceive the house and the adjacent area as a complete unit. The coincidence of the Ideal Courtyard image in all groups of respondents indicates that this image is universal and does not depend on the place of a person’s actual residence. The study can contribute to formulating recommendation to develop the courtyard space and universal models for improving adjacent areas, taking into account the psychological characteristics and needs of the population. Full article

Personalized recommender systems are used not only in e-commerce companies but also in various web applications. These systems conventionally use collaborative filtering (CF) and content-based filtering approaches. CF operates using memory-based or model-based methods; both methods use a user-item matrix that considers user preferences as items. This matrix denotes information on user preferences, which refers to the user ratings for items. The model-based method exploits the fact that the input matrix is factorized. CF approaches can effectively provide personalized recommendation results to users; however, cold-start problems arise because both these methods depend on the users’ ratings for items to predict users’ preferences. We proposed an approach to alleviate the cold-start problem along with a methodology for utilizing blockchain that can enhance the reliability of the processes of the recommendations. We attempted to predict an average rating for a new item to alleviate item-side cold-start problems. First, we applied the concept of word2vec, treating each user’s item-selection history as a sentence. Then, we derived genre2Vec based on the skip-gram technique and predicted an average rating for a new item by utilizing the vectors and category ratings. We experimentally demonstrated that our approach could generate more accurate results than conventional CF approaches could. We also designed the processes of the recommendation based on the concept of blockchain addressing the smart contract. Based on our approach, we proposed a system that can secure reliability as well as alleviate the cold-start problems in recommender systems. Full article

This essay reflects on the history of the origins of atheism in the late sixteenth century through an analysis of the Inquisition proceedings against Flaminio Fabrizi, which began in Siena in 1587 and ended in Rome in 1591 with the accused being sentenced to death at the stake. This is a very intriguing case because Fabrizi, not a learned man, mixed different forms of heterodoxy and unbelief that surprised and disturbed the judges of the Holy Office. This essay aims to contribute to the history of religious nonconformism in Counter-reformation Italy and in Europe during the so-called “confessional era”. Full article

This study compared the reliability, construct validity, and respondents’ preference of the Chinese version of 8-item eHEALS (C-eHEALS) and 21-item DHLI (C-DHLI) in assessing older adults’ electronic health (eHealth) literacy using a mixed-methods approach. A web-based, cross-sectional survey was conducted among 277 Chinese older adults from September to October 2021, and 15 respondents were subsequently interviewed to understand their preference of scale to use in practice. Results showed that the internal consistency and test-retest reliability of both scales were satisfactory. For the construct validity, the C-DHLI score showed stronger positive correlations with having Internet use for health information and higher educational attainments, occupational skill levels, self-rated Internet skills, and health literacy than the C-eHEALS score. In addition, younger age, higher household income, urban residence, and longer Internet use history were only positively correlated with C-DHLI score. Qualitative data suggested that most interviewees perceived the C-DHLI as more readable than C-eHEALS for its clear structure, specific description, short sentence length, and less semantic complexity. Findings revealed that both scales are reliable tools to measure eHealth literacy among Chinese older adults, and the C-DHLI seemed to be a more valid and favored instrument for the general Chinese older population based on the quantitative and qualitative results. Full article