Search Results (2,537)

AI-generated content—spanning text, imagery, and music—is becoming increasingly commonplace. As the newest generation of song-producing AI systems garner attention, serious questions emerge regarding the role and place of music producers, particularly in the area of non-artistic, or “utility music”. While it might seem that human skills and creativity are unlikely to be replaced entirely by generative AI in domains such as art music or live performance, recent developments in the field suggest that human efforts in creation of advertisement or background music are already being challenged by generative AI systems. However, there is a number of alternative, more balanced forms of human–machine co-creativity. It is in this regard that I am posing a question: can commercial generative AI systems really be classified as tools in the strict sense of the term? In this paper, I am attempting to answer this question by introducing the “Control–Convenience Spectrum”—a concept I believe applies to all human creative processes that utilize tools. It bears some similarities to earlier ideas in complexity theory or flow psychology—particularly, it proposes that the extremes of this spectrum are unlikely to produce compelling aesthetical outcomes or satisfying creative practice. I argue that prompt-driven commercial generative AI systems occupy one of the far ends of the spectrum, thus failing to meet the criteria for a creative expression tool. Full article

Sentiment analysis of Arabic text, particularly on social media platforms, presents a formidable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of emojis to convey emotional context. This paper presents SiAraSent, a hybrid framework that integrates traditional text representations, emoji-aware features, and deep contextual embeddings based on Arabic transformers. Starting from a strong and fully interpretable baseline built on Term Frequency–Inverse Definition Frequency (TF–IDF)-weighted character and word N-grams combined with emoji embeddings, we progressively incorporate SinaTools for linguistically informed preprocessing and AraBERT for contextualized encodings. The framework is evaluated on a large-scale dataset of 58,751 Arabic tweets labeled for sentiment polarity. Our design works within four experimental configurations: (1) a baseline traditional machine learning architecture that employs TF-IDF, N-grams, and emoji features with an Support Vector Machine (SVM) classifier; (2) an Large-language Model (LLM) feature extraction approach that leverages deep contextual embeddings from the pre-trained AraBERT model; (3) a novel hybrid fusion model that concatenates traditional morphological features, AraBERT embeddings, and emoji-based features into a high-dimensional vector; and (4) a fully fine-tuned AraBERT model specifically adapted for the sentiment classification task. Our experiments demonstrate the remarkable efficacy of our proposed framework, with the fine-tuned AraBERT architecture achieving an accuracy of 93.45%, a significant 10.89% improvement over the best traditional baseline. Full article

The advancement of large language models (LLMs) has facilitated the generation of user interface (UI) code from natural language prompts, thereby supporting low-code development paradigms. Despite these capabilities, ensuring brand consistency remains a significant challenge, particularly when style data is unstructured. We investigated the impact of three data formats—plain text, structured cascading style sheets (CSS), and structured natural language (NL) guide—on the effectiveness of retrieval-augmented generation (RAG) in producing brand-consistent UI components, with No-RAG serving as the baseline for comparison. The findings indicate that RAG substantially enhances brand alignment. Although the structured NL guide yielded the highest CSS recall rate, participants expressed a preference for outputs derived from plain text, suggesting that the optimal data format may depend on specific design contexts and evaluative criteria. Full article

Despite the growing adoption of generative artificial intelligence (GenAI) tools in higher education, limited research has examined how future educators perceive and use these technologies in their academic practices. This study investigates the practices, perceived benefits, and challenges associated with the use of GenAI tools—such as ChatGPT—among undergraduate students enrolled in programs that confer teaching qualifications. Using a mixed-methods design, data were collected from 314 students from the Early Childhood Education, Philosophy, and Philology departments. The findings indicate that the majority of students use GenAI tools primarily for academic purposes, most commonly for information searching, data analysis, study advice, and exam preparation. Students reported several perceived benefits, including rapid access to information, time efficiency, improved comprehension of complex concepts, enhanced study organization, and support with assignments and research-related tasks such as summarizing or translating academic texts. At the same time, participants expressed notable concerns, particularly regarding over-reliance on AI, reduced personal effort, risks to academic integrity, diminished critical thinking, and weakened research skills. Additional challenges included misinformation, reduced creativity, improper use of AI-generated content, skill underdevelopment, and potential technological dependence. The study concludes that teacher education programs should systematically integrate AI literacy and responsible-use training to prepare future educators to address the pedagogical and ethical implications of GenAI in educational settings. Full article

In recent decades, governments have invested in strategic territories focused on knowledge production and application, which are strategic for socioeconomic development, particularly in urban areas. However, conceptual and terminological diversity hinders aspects such as the systematization of the literature, the advance of theoretical conceptualizations, and the formulation of coherent policies, especially in the context of socioenvironmental challenges. In this study, with the aim of consolidating this literature, we have conducted a systematic review with bibliometric and content analysis, examining publications on eight denominations associated with these territories. The literature reveals the existence of an established field; nonetheless, themes and denominations are still dispersed in the corpus. Among 400 authors, 339 published a single article, and only 13 authors have three or more studies in the sample. We identified a core of 11 journals that concentrate 73 of the 214 analyzed texts. We propose the term “knowledge territories” as an umbrella concept. A total of 114 case studies were identified. Governance is the most recurrent dimension (53% of the texts). Topics such as climate change, food production and diffuse effects of territorial occupation are rarely explored, as are the cases analyzed in the context of semi-peripheral and peripheral countries, indicating gaps and opportunities for future research. Full article

News data have become essential resources across various disciplines. Still, access to full-text news corpora remains challenging due to high costs and the limited availability of free alternatives. This paper presents a novel Python package (gdeltnews) that reconstructs full-text newspaper articles at near-zero cost by leveraging the Global Database of Events, Language, and Tone (GDELT) Web News NGrams 3.0 dataset. Our method merges overlapping n-grams extracted from global online news to rebuild complete articles. We validate the approach on a benchmark set of 2211 articles from major U.S. news outlets, achieving up to 95% text similarity against original articles based on Levenshtein and SequenceMatcher metrics. Our tool facilitates economic forecasting, computational social science, information science, and natural language processing applications by enabling free and large-scale access to full-text news data. Full article

The Israel–Hamas War, which erupted with the horrifying events of 7 October 2023, stands as one of the pivotal breaking points in the Israeli–Palestinian conflict since its inception. Both sides have been left battered, pained, and devoid of any trust or hope for peace. Among the local immediate social grassroots responses to repairing the fractured relationship was the production of a special prayer booklet focused on coexistence and shared life liturgy produced by the Reform Movement in Israel, a non-Orthodox Jewish community. In this article, I analyze four prayers for peace included in this booklet, written by Israeli female religious leaders. I examine how these women crafted prayers to promote a message of peace. The texts establish a maternal dialogue to foster a space of trust and security, aiming to replace the exclusive focus on the God of Israel with a deity encompassing all nations. Through these liturgical creations, the authors challenge both the Israeli Orthodox establishment, which excludes non-Orthodox identities and expressions, and the hegemonic national order, which suppresses discussions of coexistence during times of conflict and marginalizes women’s political involvement. Therefore, I conclude that the creators of these prayers emerge as significant gendered political actors in an era marked by distrust, anger, hostility, and fear. They demonstrate that a message of coexistence can resonate within the religious sphere. Full article

This study provides direct evidence from university professors’ experiences regarding the key features they use to identify artificial intelligence (AI)–generated texts and ranks these features by their perceived importance. The research was conducted in two phases. In Phase 1, online interviews were used to identify the most salient features professors reported using to detect AI-generated texts. In Phase 2, an online survey asked professors to rate the extent to which each identified feature contributes to the successful detection of AI-generated text. The interview data yielded seven features that professors reported using when they suspected a text was AI-generated. Survey ratings varied across features, with hallucinated facts or explanations, nonexistent sources, and the absence of language errors receiving the highest mean ratings in this sample. The use of difficult words received the lowest mean rating. These results have important pedagogical implications, as they can inform the development of more effective detection tools and guide the design of academic integrity policies and instructional strategies to address the challenges posed by AI-generated content. Full article

Background: This study examined how patients and caregivers perceive and experience AI-based care technologies through text mining analysis. The goal was to identify major themes, sentiments, and value-oriented interpretations embedded in their narratives and to understand how these perceptions align with key dimensions of patient-centered care. Methods: A corpus of publicly available narratives describing experiences with AI-based care was compiled from online communities. Natural language processing techniques were applied, including descriptive term analysis, topic modeling using Latent Dirichlet Allocation, and sentiment profiling based on a Korean lexicon. Emergent topics and emotional patterns were mapped onto domains of patient-centered care such as information quality, emotional support, autonomy, and continuity. Results: The analysis revealed a three-phase evolution of care values over time. In the early phase of AI-mediated care, patient narratives emphasized disruption of relational care, with negative themes such as reduced human connection, privacy concerns, safety uncertainties, and usability challenges, accompanied by emotions of fear and frustration. During the transitional phase, positive themes including convenience, improved access, and reassurance from diagnostic accuracy emerged alongside persistent emotional ambivalence, reflecting uncertainty regarding responsibility and control. In the final phase, care values were restored and strengthened, with sentiment patterns shifting toward trust and relief as AI functions became supportive of clinical care, while concerns related to depersonalization and surveillance diminished. Conclusions: Patients and caregivers experience AI-based care as both beneficial and unsettling. Perceptions improve when AI enhances efficiency and information flow without compromising relational aspects of care. Ensuring transparency, explainability, opportunities for human contact, and strong data protections is essential for aligning AI with principles of patient-centered care. Based on a small-scale qualitative dataset of patient narratives, this study offers an exploratory, value-oriented interpretation of how relational care evolves in AI-mediated healthcare contexts. In this study, care-ethics values are used as an analytical lens to operationalize key principles of patient-centered care within AI-mediated healthcare contexts. Full article

With the rapid development of intelligent computing and artificial intelligence, there is an increasing demand for efficient, interpretable, and interactive frameworks for fine-grained text analysis. In the field of argument mining, existing approaches are often constrained by sentence-level processing, limited exploitation of key linguistic markers, and a lack of human–AI collaborative mechanisms, which restrict both recognition accuracy and computational efficiency. To address these challenges, this paper proposes KFF-Transformer, a computing-oriented human–AI collaborative framework for fine-grained argument element identification based on Toulmin’s model. The framework first employs an automatic key marker mining algorithm to expand a seed set of expert-labeled linguistic cues, significantly enhancing coverage and diversity. It then employs a lightweight deep learning architecture that combines BERT for contextual token encoding with a BiLSTM network enhanced by an attention mechanism to perform word-level classification of the six Toulmin elements. This approach leverages enriched key markers as critical features, enhancing both accuracy and interpretability. It should be noted that while our framework leverages BERT—a Transformer-based encoder—for contextual representation, the core sequence labeling module is based on BiLSTM and does not implement a standard Transformer block. Furthermore, a human-in-the-loop interaction mechanism is embedded to support real-time user correction and adaptive system refinement, improving robustness and practical usability. Experiments conducted on a dataset of 180 English argumentative essays demonstrate that KFF-Transformer identifies key markers in 1145 sentences and achieves an accuracy of 72.2% and an F1-score of 66.7%, outperforming a strong baseline by 3.7% and 2.8%, respectively. Moreover, the framework reduces processing time by 18.9% on CPU and achieves near-real-time performance of approximately 3.3 s on GPU. These results validate that KFF-Transformer effectively integrates linguistically grounded reasoning, efficient deep learning, and interactive design, providing a scalable and trustworthy solution for intelligent argument analysis in real-world educational applications. Full article

Background/Objectives: Intensive postoperative pain is a common challenge after knee surgeries such as total knee arthroplasty, arthroscopy, cruciate ligament or meniscus repair, and fixation of tibial plateau or distal femoral fractures. This scoping review mapped and summarized non-regional postoperative analgesia strategies to provide an overview of available approaches when regional blocks or neuraxial anesthesia are not feasible. Methods: We followed established methodological guidance for scoping reviews and report the data in accordance with the PRISMA-ScR checklist. We searched PubMed/MEDLINE, EMBASE, Scopus, and ClinicalTrials.gov in January 2025. Eligible designs included randomized controlled trials, non-randomized trials, observational studies, case series, and pilot studies. Results: We screened 3390 records and assessed 332 in full text. A total of 43 studies met the inclusion criteria, and the literature was grouped into: (1) arthroplasty, (2) arthroscopy, (3) cruciate ligament or meniscus repair, and (4) tibial plateau or distal femoral fractures. We identified substantial heterogeneity in interventions, comparators, and outcome measures across the first three sets of literature but found no focused articles for tibial plateau or distal femoral fractures. Most studies evaluated multimodal approaches combining systemic analgesics with local periarticular or intraarticular techniques. Evidence on functional recovery and mobilization was limited. Conclusions: Current evidence on non-regional postoperative analgesia in knee surgery is fragmented and varies considerably in design, intervention, and reported outcomes. Multimodal regimens and pre-emptive NSAID use were frequently associated with reduced early postoperative pain and lower opioid requirements, although comparability across studies remains limited. As existing evidence largely focuses on outcomes during hospitalization, future research should prioritize standardized pain and functional outcome reporting and directly compare systemic and local multimodal strategies, while extending follow-up beyond discharge to better characterize sustained clinical relevance. Full article

Large Language Models (LLMs) have demonstrated exceptional performance in knowledge-based dialogue generation and text evaluation. Synthetic data serves as a cost-effective alternative for generating high-quality datasets. However, it often plagued by hallucinations, inconsistencies, and self-anthropomorphized responses. Concurrently, manual construction of knowledge-based dialogue datasets remains bottlenecked by prohibitive costs and inherent human subjectivity. To address these multifaceted challenges, we propose ACE (Automatic Construction of Knowledge-Grounded and Engaging Human–AI Conversation Dataset), a hybrid method using hierarchical prompt engineering. This approach mitigates hallucinations and self-personalization while maintaining response consistency. Furthermore, existing human and automated evaluation methods struggle to assess critical factors like factual accuracy and coherence. To overcome this, we introduce the Truthful Answer Score (TAS), a novel metric specifically designed for knowledge-based dialogue evaluation. Our experimental results demonstrate that the ACE dataset achieves higher quality than existing benchmarks, such as Wizard of Wikipedia (WoW) and FaithDial. Additionally, TAS aligns more closely with human judgment, offering a more reliable and scalable evaluation framework. Our findings demonstrate that leveraging LLMs through systematic prompting can substantially reduce reliance on human annotation while simultaneously elevating the quality and reliability of synthetic datasets. Full article

Depression represents a major global health challenge, yet traditional clinical diagnosis faces limitations, including high costs, limited coverage, and low patient willingness. Social media platforms provide new opportunities for early depression screening through user-generated content. However, existing methods often lack systematic integration of clinical knowledge and fail to leverage multi-modal information comprehensively. We propose a DSM-5-guided methodology that systematically maps clinical diagnostic criteria to computable social media features across three modalities: textual semantics (BERT-based deep semantic extraction), behavioral patterns (temporal activity analysis), and topic distributions (LDA-based cognitive bias identification). We design a hierarchical architecture integrating BERT, Bi-LSTM, hierarchical attention, and multi-task learning to capture both character-level and post-level importance while jointly optimizing depression classification, symptom recognition, and severity assessment. Experiments on the WU3D dataset (32,570 users, 2.19 million posts) demonstrate that our model achieves 91.8% F1-score, significantly outperforming baseline methods (BERT: 85.6%, TextCNN: 78.6%, and SVM: 72.1%) and large language models (GPT-4 few-shot: 86.9%). Ablation studies confirm that each component contributes meaningfully with synergistic effects. The model provides interpretable predictions through attention visualization and outputs fine-grained symptom assessments aligned with DSM-5 criteria. With low computational cost (~50 ms inference time), local deployability, and superior privacy protection, our approach offers significant practical value for large-scale mental health screening applications. This work demonstrates that domain-specialized methods with explicit clinical knowledge integration remain highly competitive in the era of general-purpose large language models. Full article

With the continuous evolution of social media, users are increasingly inclined to express their personal emotions on digital platforms by integrating information presented in multiple modalities. Within this context, research on image–text sentiment analysis has garnered significant attention. Prior research efforts have made notable progress by leveraging shared emotional concepts across visual and textual modalities. However, existing cross-modal sentiment analysis methods face two key challenges: Previous approaches often focus excessively on fusion, resulting in learned features that may not achieve emotional alignment; traditional fusion strategies are not optimized for sentiment tasks, leading to insufficient robustness in final sentiment discrimination. To address the aforementioned issues, this paper proposes a Dual-path Interaction Network with Multi-level Consistency Learning (DINMCL). It employs a multi-level feature representation module to decouple the global and local features of both text and image. These decoupled features are then fed into the Global Congruity Learning (GCL) and Local Crossing-Congruity Learning (LCL) modules, respectively. GCL models global semantic associations using Crossing Prompter, while LCL captures local consistency in fine-grained emotional cues across modalities through cross-modal attention mechanisms and adaptive prompt injection. Finally, a CLIP-based adaptive fusion layer integrates the multi-modal representations in a sentiment-oriented manner. Experiments on the MVSA_Single, MVSA_Multiple, and TumEmo datasets with baseline models such as CTMWA and CLMLF demonstrate that DINMCL significantly outperforms mainstream models in sentiment classification accuracy and F1-score and exhibits strong robustness when handling samples containing highly noisy symbols. Full article

The task of identifying high-value projects from vast investment portfolios presents a major challenge in the construction industry, particularly within the energy sector, where decision-making carries high financial and operational stakes. This complexity is driven by both the volume and heterogeneity of project documentation, as well as the multidimensional criteria used to assess project value. Despite this, research gaps remain: large language models (LLMs) as pretrained transformer encoder models are underutilized in construction project selection, especially in domains where investment precision is paramount. Existing methodologies have largely focused on multi-criteria decision-making (MCDM) frameworks, often neglecting the potential of LLMs to automate and enhance early-phase project evaluation. However, deploying LLMs for such tasks introduces high computational demands, particularly in privacy-sensitive, enterprise-level environments. This study investigates the application of the robustly optimized BERT model (RoBERTa) for identifying high-value energy infrastructure projects. Our dual objective is to (1) leverage RoBERTa’s pre-trained language architecture to extract key information from unstructured investment texts and (2) evaluate its effectiveness in enhancing project selection accuracy. We benchmark RoBERTa against several leading LLMs: BERT, DistilBERT (a distilled variant), ALBERT (a lightweight version), and XLNet (a generalized autoregressive model). All models achieved over 98% accuracy, validating their utility in this domain. RoBERTa outperformed its counterparts with an accuracy of 99.6%. DistilBERT was fastest (1025.17 s), while RoBERTa took 2060.29 s. XLNet was slowest at 4145.49 s. In conclusion, RoBERTa can be the preferred option when maximum accuracy is required, while DistilBERT can be a viable alternative under computational or resource constraints. Full article