SN computer science最新文献

{"title":"Detecting Adverse Drug Events in Social Media: A Brief Literature Review.","authors":"Imane Guellil, Yousra Berrachedi, Nidhal Eddine Chenni, Massi-Nissa Abboud, Jinge Wu, Honghan Wu, Beatrice Alex","doi":"10.1007/s42979-026-04752-9","DOIUrl":"10.1007/s42979-026-04752-9","url":null,"abstract":"<p><p>Adverse drug events (ADEs) remain a significant burden to public health and a persistent challenge for pharmacovigilance. The proliferation of patient-generated discourse on social media offers a complementary, real-time signal for ADE surveillance. This article provides a concise yet comprehensive review of recent natural language processing (NLP) research on identifying ADEs in social media text. We systematically reviewed 100 peer-reviewed studies (2017-2025) on NLP/AI for detecting or analysing ADEs in social media. Searches in Google Scholar targeted English-language journal and conference papers; patents and protocols were excluded. Of 130 records screened, 6 were protocols and 24 were excluded because the full text could not be located or the item was a conference abstract lacking methodological detail (i.e., no description of approaches or experiments), yielding a final sample of 100 studies. One reviewer performed screening, with full-text eligibility verified by a second. We extracted objectives, data sources/languages, preprocessing and annotation practices, datasets, model families, evaluation metrics, and stated limitations. Studies were grouped into five task categories-classification, extraction, normalization, corpus creation, and broader analytical work-with evidence tables summarizing contributions, toolchains, datasets, and performance. Recurrent challenges include noisy/imbalanced data, multilingual and code-mixed content, and variability in annotation standards. Twitter remains the primary data source: 60% of studies analyse Twitter alone and a further 18% combine Twitter with other platforms (78% in total). English overwhelmingly dominates; only about 5% of studies draw on non-English sources (e.g., French, Chinese, Arabic). Standard pre-processing-URL removal, tokenisation, and lowercasing-is near-universal. Transformer-based models predominate, with BERT and its biomedical or \"tweet\" variants (e.g., RoBERTa, BioBERT, BERTweet) used in more than 60% of approaches. Persistent obstacles include severe class imbalance and ambiguous or implicit drug-event expressions. Although shared tasks such as SMM4H provide widely used benchmarks, comprehensive annotation guidelines remain uncommon (12% of papers). Recent work increasingly incorporates multimodal inputs and integrates structured biomedical knowledge, yet gaps persist in multilingual coverage, temporal/longitudinal modelling, and real-world deployment. To our knowledge, this is the first review to synthesise findings from a corpus of 100 peer-reviewed studies on ADE detection in social media using NLP. By organising the literature by task type and tracing methodological trends and limitations, it provides practical guidance for researchers and practitioners. The review also outlines actionable directions for future work, including model explainability, support for low-resource languages, and closer collaboration with regulatory authorities to enable real-world deployme","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"7 2","pages":"199"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12894197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146204506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Principles to Practice: Engineering Responsible AI for Geospatial Intelligence.","authors":"Muhammad Hassan, Bilal Sardar, Shareeful Islam, Maryam Imani, Akram Hakiri","doi":"10.1007/s42979-026-05258-0","DOIUrl":"https://doi.org/10.1007/s42979-026-05258-0","url":null,"abstract":"<p><p>AI-enabled geospatial applications increasingly inform high-stakes decisions in crop type classification, flood risk assessment, and land use monitoring; yet current practice prioritises predictive accuracy whilst leaving responsible AI (R-AI) largely unaddressed. This work aims to operationalise R-AI practice for geospatial AI by proposing a conceptual view and a four-phase methodology that embeds four distinct R-AI characteristics into the model development lifecycle. A conceptual R-AI view is proposed that situates four characteristics - Privacy, Fairness, Transparency, and Explainability - across three operational levels: data, model, and prediction. A four-phase methodology operationalises each through concrete techniques and quantifiable acceptance criteria. The threshold values and phase ordering of the methodology are governed by the proposed Pareto-Adaptive R-AI Compliance (PARC) algorithm, which derives dataset-specific thresholds and characterises the interactions between the four characteristics. Evaluation is conducted through two experiments using the Temporal-Spatial Vision Transformer (TSViT): crop type classification on the publicly available PASTIS/EuroCrops benchmark, and urban flood risk classification on a real-world UK Sustainable Drainage Systems (SuDS) pilot with six fused geospatial data sources. Across both experiments, all twelve R-AI acceptance criteria are satisfied. Privacy protection reduces membership inference attack accuracy from 0.712 to 0.503, approaching the random-guess baseline. Fairness interventions narrow the geographic performance gap from 23.1% to 3.1%. Transparency verification reduces physically inconsistent model behaviour from 24.1% to 3.8%. Explainability mechanisms improve temporal attribution consistency from 0.41 to 0.857, and the attribution methods are independently validated through deletion, insertion, and perturbation-stability analyses. All four characteristics are achieved whilst retaining over 97% of baseline predictive utility. These findings demonstrate that responsible AI and predictive accuracy are compatible objectives in geospatial deep learning. The proposed approach offers a replicable and measurable pathway for embedding R-AI practice into geospatial AI development, validated across two structurally distinct real-world tasks.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"7 6","pages":"692"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13473224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"To Signal or Not to Signal? A Non-cooperative Game-Theoretic Approach to Discretionary Communication Between Road Users.","authors":"Isam Bitar, Albert Solernou Crusat, Richard Romano, David Watling","doi":"10.1007/s42979-025-04533-w","DOIUrl":"10.1007/s42979-025-04533-w","url":null,"abstract":"<p><p>Reciprocal communication between road users is a vital element of road user interaction. Non-cooperative game theory is an effective framework for modelling and characterising communicative behaviour between road users, which enables the study of emergent benefits for both the issuer and recipient of communicative signals. In this paper, we introduce discretionary communication to gain an advantage over the other road user by masking one's intent if beneficial to do so. We conduct a series of experiments with simulated interactions and compare interaction outcomes where communication is mandatory against those where communication is discretionary. Our findings further support the premise that non-cooperative game theory is an effective paradigm for modelling and producing emergent behaviours which benefit the network. Moreover, we see that including a layer of <i>discretionary</i> communication reaps benefits in interaction outcome to the communicator. It also provides benefits in safety to all parties involved above and beyond the benefits seen from mandatory communication.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"7 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12711921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans.","authors":"Boyuan Feng, Ruting Cheng, Yijiang Zheng, Shuya Feng, Ningshuo Bai, Khashayar Vaziri, James Hahn","doi":"10.1007/s42979-026-04864-2","DOIUrl":"10.1007/s42979-026-04864-2","url":null,"abstract":"<p><p>Accurate estimation of body composition remains a critical task in personalized healthcare and clinical decision-making. However, existing measurement methods, such as Dual-Energy X-ray Absorptiometry (DXA) and Computed Tomography (CT), are often difficult to access and pose potential risks associated with ionizing exposure. In this study, we present a streamlined pipeline that replaces handcrafted feature engineering with end-to-end learning from voxel maps and demographic features. Our extended experiments demonstrate promising RMSE performance on real-world 3D scan datasets across multiple regional and total body composition values, validating the effectiveness of this voxel-demographic approach over traditional part-based feature descriptors. The proposed methodology represents a paradigm shift from manual feature extraction to automated pattern learning, demonstrating significant improvements in both accuracy and scalability for clinical body composition analysis.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"7 4","pages":"292"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13005780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147505953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TRAM: The Telecommunications-Related AcciMap Method.","authors":"Hans C A Wienen, Faiza A Bukhsh, Eelco Vriezekolk, Luís Ferreira Pires","doi":"10.1007/s42979-025-04502-3","DOIUrl":"10.1007/s42979-025-04502-3","url":null,"abstract":"<p><p>Telecommunications networks are essential to the functioning of modern society, and large-scale disruptions in these networks can significantly impact societal operations. This paper presents Tram (Telecommunications Related AcciMap), an accident analysis method developed to support the analysis of accidents in telecommunication networks. Tram aimed to allow lessons learned from an accident to be used to mitigate and/or prevent new accidents, ultimately enhancing societal resilience. Initially, we studied the state-of-the-art in accident analysis methods, concluding that we should start from the AcciMap method since this is the most popular and appropriate method for our purposes. Tram has been developed through iterative cycles in which improvements have been proposed and validated; this paper focuses on the last iteration. Tram is a validated method that supports comprehensive analyses of telecom accidents. The latest version added support to the representation and mitigation of positive feedback loops during telecom and information technology systems breakdowns, and can help prioritise the recommendations derived from an analysis. This paper demonstrates that Tram is a suitable method to analyse and learn lessons from accidents in the telecom domain. Particularly, our findings with the application of Tram to real-life accidents indicate that dividing the analysis process by participant expertise can negatively impact the efficiency of the overall process, so the partitioning of the analysis process should be carefully considered.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"7 1","pages":"103"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12799682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145992347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Combinatorial Approach to Synthetic Data Generation for Machine Learning.","authors":"Krishna Khadka, Jaganmohan Chandrasekaran, Yu Lei, Raghu Kacker, D Richard Kuhn","doi":"10.1007/s42979-025-04540-x","DOIUrl":"10.1007/s42979-025-04540-x","url":null,"abstract":"<p><p>Datasets used in machine learning often contain sensitive information, including personally identifiable health and financial details. A common challenge faced by organizations and researchers is the risk of privacy breaches when using real-world data. Synthetic data can be used as an alternative to the real-world data. In existing synthetic data generation techniques, an encoder processes the real-world data to map it into a lower-dimensional latent space. Random sampling is then performed in this latent space. Subsequently, a decoder network is utilized to generate synthetic data from these sampled points in the latent space. Such approaches typically require generating a large number of synthetic samples to approximate the performance of real-world data, subsequently slowing down downstream machine learning tasks. Addressing this, we introduce a combinatorial approach to sampling the latent space, motivated by our empirical findings within this study that most model predictions are largely influenced by interactions between a few features. In some cases, just using a small number of features produces accuracy better than using entire features. Through this approach, we generate samples that utilize t-way interactions among the t latent dimensions out of n. Our experimental results indicate that our approach requires fewer samples than traditional random sampling to achieve comparable model performance for real-world data sets. We also show that when integrated with a differentially private mechanism, our approach incurs a smaller decline in model performance than existing random sampling approach.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"7 1","pages":"59"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12779700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145954553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Cybersecurity Testing and Monitoring of IoT Ecosystems.","authors":"Steve Taylor, Martin Gilje Jaatun, Aida Omerovic, Ravi Borgaonkar, Robert Seidl, Norbert Goetze, Jens Kuhr, Dmytro Prosvirin, Manuel Leone, Andrey Kuznetsov, Anatoliy Gritskevich, Antonis Mpantis, Oscar Garcia Perales, Bernd-Ludwig Wenning, Sayon Duttagupta","doi":"10.1007/s42979-026-05048-8","DOIUrl":"10.1007/s42979-026-05048-8","url":null,"abstract":"<p><p>Internet of things (IoT) ecosystems introduce significant cybersecurity challenges due to device heterogeneity, firmware opacity, constrained resources, distributed deployment, and the integration of devices within wider socio-technical systems where they are used. Existing approaches to address IoT cybersecurity typically address isolated aspects of this problem, such as vulnerability enumeration, anomaly detection, or risk assessment; but without integrating them across the full lifecycle of devices and systems. This paper presents an extensible architecture that unifies cybersecurity testing, runtime monitoring, contextual risk modelling, secure update mechanisms, and auditable evidence management for IoT ecosystems that aims to address these challenges. The framework supports both device under test and system under test perspectives and integrates component-level techniques (such as SBOM generation, network fuzzing, machine learning-based anomaly detection, and access control risk evaluation) with system-level, knowledge-based, risk modelling to capture threat propagation across interconnected assets. A distributed ledger-backed auditable data infrastructure ensures integrity and traceability of indicators, results, and decisions. Automated workflow orchestration enables flexible tool chaining and lifecycle-aware execution aligned with established security development lifecycles. The approach is validated through three industrial use cases in aviation cargo monitoring, smart manufacturing, and telecommunication residential gateways. Results demonstrate the feasibility of combining static analysis, runtime indicators, and dynamic risk assessment to prioritise vulnerabilities contextually, detect anomalous behaviour, and support secure patch deployment in resource-constrained environments. The work advances lifecycle-integrated, system-aware cybersecurity assurance for IoT ecosystems and highlights the need for contextualised, interoperable tooling to address systemic vulnerability and risk propagation in complex systems where IoT, ICT and people interact.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"7 5","pages":"468"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13190456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast and Secure Multiparty Querying over Federated Graph Databases.","authors":"Nouf Aljuaid, Alexei Lisitsa, Sven Schewe","doi":"10.1007/s42979-025-04503-2","DOIUrl":"10.1007/s42979-025-04503-2","url":null,"abstract":"<p><p>We have developed a framework for efficient privacy preserving multi-party querying (PPMQ) over federated graph databases, leveraging Secure Multi-Party Computation (SMPC) protocols to enhance data security. The system offers two distinct security protocols: a client-based protocol and a server-based protocol. In the client-based protocol, standard SMPC techniques are employed, allowing computations to be performed on data without exposing the data itself. The server-based protocol employs SMPC to facilitate secure data processing and is further enhanced by encrypted hashing, which adds an additional layer of security to prevent data exposure. We conducted experiments comparing PPMQ with Neo4j Fabric and two previous systems, SMPQ and Conclave. The results indicate that PPMQ's execution times and overheads are comparable to those of Neo4j Fabric, while outperforming both SMPQ and Conclave, demonstrating its superior efficiency. Additionally, PPMQ, like SMPQ and Conclave, utilises an honest but curious security model. However, it enhances the security of the server protocol, making it more robust against brute force attacks and providing stronger privacy guarantees than previous solutions.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"6 8","pages":"982"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12662885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145650667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outdoor Warehouse Management: UAS-Driven Precision Tracking of Stacked Steel Bars.","authors":"Assia Belbachir, Antonio M Ortiz, Erik T Hauge, Ahmed Nabil Belbachir, Giusy Bonanno, Emanuele Ciccia, Giorgio Felline","doi":"10.1007/s42979-025-04206-8","DOIUrl":"10.1007/s42979-025-04206-8","url":null,"abstract":"<p><p>Accurately identifying the positions of products in outdoor environments-such as warehouses or industrial yards-presents unique challenges due to variable lighting, weather conditions, and the lack of fixed infrastructure. This work presents a vision-based drone system for product localization using QR code detection and relative positioning. The proposed system enables a UAV to autonomously scan an area, extract QR codes from captured video frames, and compute the spatial relationships between products using a trust-ability graph. Unlike traditional GPS- or RFID-based methods, our approach does not rely on external infrastructure, making it scalable and adaptable for outdoor and semi-structured environments. We demonstrate that the proposed algorithm achieves over 94% positioning accuracy in indoor settings and 80% in outdoor environments, even under occlusion and varying illumination. The key contributions of this work include: (1) a novel infrastructure-free method for product positioning based on relative spatial relationships, (2) the integration of trust-ability scoring to improve the reliability of detected positions, and (3) an extensive evaluation in real-world indoor and outdoor industrial scenarios. These results validate the potential of UAV-assisted inventory systems to enhance automation in logistics and warehouse management.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"6 6","pages":"701"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12304023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variable Trust Control Setting for Autonomous Vehicle Highway Navigation and Improved User Experience.","authors":"James E Pickering, Jisun Kim, Joshua D'Souza, Keith J Burnham","doi":"10.1007/s42979-025-03714-x","DOIUrl":"https://doi.org/10.1007/s42979-025-03714-x","url":null,"abstract":"<p><p>This paper addresses the development of a model-based design approach to enhance the acceptance of safe manoeuvrability of autonomous vehicles (AVs) on highways. A variable trust control setting (TCS) is introduced that empowers users to 'feel in control' of the AV, potentially increasing confidence in, and acceptance of, the technology. This setting is grounded in deontological ethics and utilises virtual boundaries (VBs) to guide driving decisions, i.e., the distance between two AVs interacting with one another. The approach is simulated using a dynamic bicycle model that represents each AV, controlled through an adaptive model-predictive control (MPC) algorithm. The paper outlines the MPC approach, the dynamic bicycle model, and the associated velocity control algorithm. Metrics are introduced to quantify safety of specific AV manoeuvres during interactions with other AVs, enabling the examination of various scenarios. A novel simulation package has been developed to investigate the impact of the proposed variable TCS, focusing on how VBs and steering limitations influence the safety and comfort of AVs during overtaking manoeuvres. The findings demonstrate the effectiveness of this approach, showing that it could potentially allow users to actively manage the safety and comfort aspects of AV operation.</p>","PeriodicalId":94207,"journal":{"name":"SN computer science","volume":"6 3","pages":"278"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11906505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}